diff --git a/.ipynb_checkpoints/Train_CNN_Digital-Readout-Small-v2-checkpoint.ipynb b/.ipynb_checkpoints/Train_CNN_Digital-Readout-Small-v2-checkpoint.ipynb
index 9577537cb..6ff0c90bd 100644
--- a/.ipynb_checkpoints/Train_CNN_Digital-Readout-Small-v2-checkpoint.ipynb
+++ b/.ipynb_checkpoints/Train_CNN_Digital-Readout-Small-v2-checkpoint.ipynb
@@ -24,7 +24,7 @@
"source": [
"########### Basic Parameters for Running: ################################\n",
" \n",
- "TFlite_Version = \"1701\" \n",
+ "TFlite_Version = \"1800\" \n",
"TFlite_MainType = \"dig-class11\"\n",
"TFlite_Size = \"s2\"\n",
"Training_Percentage = 0.0 # 0.0 = Use all Images for Training\n",
@@ -32,6 +32,8 @@
"\n",
"##########################################################################\n",
"\n",
+ "## 2024-03-30: Code adapted to TF 2.16 ###################################\n",
+ "\n",
"\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
@@ -82,8 +84,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "(1573, 32, 20, 3)\n",
- "(1573, 11)\n"
+ "(1300, 32, 20, 3)\n",
+ "(1300, 11)\n"
]
}
],
@@ -141,45 +143,119 @@
"metadata": {},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"model\"\n",
- "_________________________________________________________________\n",
- " Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- " input_1 (InputLayer) [(None, 32, 20, 3)] 0 \n",
- " \n",
- " batch_normalization (BatchN (None, 32, 20, 3) 12 \n",
- " ormalization) \n",
- " \n",
- " conv2d (Conv2D) (None, 32, 20, 32) 896 \n",
- " \n",
- " max_pooling2d (MaxPooling2D (None, 16, 10, 32) 0 \n",
- " ) \n",
- " \n",
- " conv2d_1 (Conv2D) (None, 16, 10, 32) 9248 \n",
- " \n",
- " max_pooling2d_1 (MaxPooling (None, 8, 5, 32) 0 \n",
- " 2D) \n",
- " \n",
- " conv2d_2 (Conv2D) (None, 8, 5, 32) 9248 \n",
- " \n",
- " max_pooling2d_2 (MaxPooling (None, 4, 2, 32) 0 \n",
- " 2D) \n",
- " \n",
- " flatten (Flatten) (None, 256) 0 \n",
- " \n",
- " dense (Dense) (None, 256) 65792 \n",
- " \n",
- " dense_1 (Dense) (None, 11) 2827 \n",
- " \n",
- "=================================================================\n",
- "Total params: 88,023\n",
- "Trainable params: 88,017\n",
- "Non-trainable params: 6\n",
- "_________________________________________________________________\n"
- ]
+ "data": {
+ "text/html": [
+ "Model: \"functional_1\"\n",
+ "
\n"
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m) │ \u001b[38;5;34m2,827\u001b[0m │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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"source": [
@@ -238,1005 +314,1020 @@
"output_type": "stream",
"text": [
"Epoch 1/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 2.1516 - accuracy: 0.3223\n",
+ "\u001b[1m 51/325\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.2469 - loss: 2.3019"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\Muell\\anaconda3\\envs\\py311-tf216-opencv\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+ " self._warn_if_super_not_called()\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.2822 - loss: 2.2734\n",
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+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.3882 - loss: 1.8318\n",
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+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.5754 - loss: 1.2654\n",
"Epoch 4/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.8070 - accuracy: 0.7451\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7161 - loss: 0.8801\n",
"Epoch 5/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.6602 - accuracy: 0.7991\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7768 - loss: 0.7268\n",
"Epoch 6/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.5955 - accuracy: 0.8258\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8277 - loss: 0.6434\n",
"Epoch 7/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.5233 - accuracy: 0.8474\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8044 - loss: 0.6498\n",
"Epoch 8/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.4445 - accuracy: 0.8652\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8499 - loss: 0.4638\n",
"Epoch 9/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.4406 - accuracy: 0.8735\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8493 - loss: 0.5074\n",
"Epoch 10/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.4012 - accuracy: 0.8887\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8727 - loss: 0.4695\n",
"Epoch 11/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.3693 - accuracy: 0.8900\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8885 - loss: 0.3451\n",
"Epoch 12/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.3075 - accuracy: 0.9142\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8832 - loss: 0.4054\n",
"Epoch 13/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.3734 - accuracy: 0.8964\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8786 - loss: 0.4139\n",
"Epoch 14/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.3359 - accuracy: 0.9053\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8955 - loss: 0.4001\n",
"Epoch 15/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.3208 - accuracy: 0.9027\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8891 - loss: 0.3343\n",
"Epoch 16/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2705 - accuracy: 0.9256\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8960 - loss: 0.3246\n",
"Epoch 17/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2488 - accuracy: 0.9186\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9061 - loss: 0.2965\n",
"Epoch 18/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2749 - accuracy: 0.9199\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9290 - loss: 0.2852\n",
"Epoch 19/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2611 - accuracy: 0.9193\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9288 - loss: 0.2614\n",
"Epoch 20/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2224 - accuracy: 0.9193\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9309 - loss: 0.2214\n",
"Epoch 21/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2185 - accuracy: 0.9421\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9427 - loss: 0.2345\n",
"Epoch 22/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2420 - accuracy: 0.9224\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9179 - loss: 0.2504\n",
"Epoch 23/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2117 - accuracy: 0.9307\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9116 - loss: 0.2614\n",
"Epoch 24/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2134 - accuracy: 0.9358\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9420 - loss: 0.2213\n",
"Epoch 25/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2233 - accuracy: 0.9402\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9414 - loss: 0.2223\n",
"Epoch 26/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2151 - accuracy: 0.9307\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9513 - loss: 0.1700\n",
"Epoch 27/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2009 - accuracy: 0.9421\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9464 - loss: 0.1881\n",
"Epoch 28/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1893 - accuracy: 0.9390\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9527 - loss: 0.1516\n",
"Epoch 29/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1961 - accuracy: 0.9415\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9351 - loss: 0.2181\n",
"Epoch 30/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1643 - accuracy: 0.9555\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9263 - loss: 0.2048\n",
"Epoch 31/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1892 - accuracy: 0.9441\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9471 - loss: 0.1739\n",
"Epoch 32/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1589 - accuracy: 0.9580\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9500 - loss: 0.1479\n",
"Epoch 33/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1582 - accuracy: 0.9593\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9524 - loss: 0.1546\n",
"Epoch 34/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1444 - accuracy: 0.9574\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9458 - loss: 0.1731\n",
"Epoch 35/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1503 - accuracy: 0.9561\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9478 - loss: 0.1630\n",
"Epoch 36/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1485 - accuracy: 0.9549\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9449 - loss: 0.1956\n",
"Epoch 37/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1624 - accuracy: 0.9479\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9632 - loss: 0.1049\n",
"Epoch 38/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1483 - accuracy: 0.9606\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9583 - loss: 0.1624\n",
"Epoch 39/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1401 - accuracy: 0.9593\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9594 - loss: 0.1297\n",
"Epoch 40/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1369 - accuracy: 0.9631\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9492 - loss: 0.1808\n",
"Epoch 41/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1401 - accuracy: 0.9574\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9674 - loss: 0.1105\n",
"Epoch 42/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1526 - accuracy: 0.9574\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9570 - loss: 0.1492\n",
"Epoch 43/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1262 - accuracy: 0.9650\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9490 - loss: 0.1594\n",
"Epoch 44/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1481 - accuracy: 0.9619\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9455 - loss: 0.1943\n",
"Epoch 45/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1159 - accuracy: 0.9631\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9625 - loss: 0.1204\n",
"Epoch 46/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1202 - accuracy: 0.9682\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9726 - loss: 0.1119\n",
"Epoch 47/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1216 - accuracy: 0.9676\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9699 - loss: 0.1124\n",
"Epoch 48/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1354 - accuracy: 0.9657\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9571 - loss: 0.1533\n",
"Epoch 49/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1233 - accuracy: 0.9695\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9654 - loss: 0.1261\n",
"Epoch 50/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1466 - accuracy: 0.9631\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9631 - loss: 0.0931\n",
"Epoch 51/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1297 - accuracy: 0.9612\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9644 - loss: 0.1255\n",
"Epoch 52/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1217 - accuracy: 0.9657\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9533 - loss: 0.1915\n",
"Epoch 53/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1005 - accuracy: 0.9739\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9569 - loss: 0.1536\n",
"Epoch 54/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.1320 - accuracy: 0.9631\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9621 - loss: 0.1049\n",
"Epoch 55/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1289 - accuracy: 0.9688\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9600 - loss: 0.1669\n",
"Epoch 56/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1170 - accuracy: 0.9669\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9625 - loss: 0.1000\n",
"Epoch 57/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1026 - accuracy: 0.9771\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9649 - loss: 0.1221\n",
"Epoch 58/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0860 - accuracy: 0.9695\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9765 - loss: 0.1004\n",
"Epoch 59/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1186 - accuracy: 0.9701\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9625 - loss: 0.1121\n",
"Epoch 60/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1105 - accuracy: 0.9638\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9665 - loss: 0.1569\n",
"Epoch 61/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1149 - accuracy: 0.9733\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9573 - loss: 0.1140\n",
"Epoch 62/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1056 - accuracy: 0.9727\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9641 - loss: 0.1098\n",
"Epoch 63/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1295 - accuracy: 0.9682\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9670 - loss: 0.1083\n",
"Epoch 64/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1250 - accuracy: 0.9663\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9679 - loss: 0.1169\n",
"Epoch 65/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1071 - accuracy: 0.9688\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9679 - loss: 0.1274\n",
"Epoch 66/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1257 - accuracy: 0.9663\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9619 - loss: 0.1143\n",
"Epoch 67/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0856 - accuracy: 0.9758\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9820 - loss: 0.0762\n",
"Epoch 68/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0971 - accuracy: 0.9714\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9714 - loss: 0.0866\n",
"Epoch 69/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0976 - accuracy: 0.9720\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9667 - loss: 0.1239\n",
"Epoch 70/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0914 - accuracy: 0.9746\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9678 - loss: 0.0995\n",
"Epoch 71/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1237 - accuracy: 0.9714\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9824 - loss: 0.0672\n",
"Epoch 72/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0837 - accuracy: 0.9727\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9728 - loss: 0.0884\n",
"Epoch 73/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0857 - accuracy: 0.9720\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9747 - loss: 0.0835\n",
"Epoch 74/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0881 - accuracy: 0.9758\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9714 - loss: 0.0934\n",
"Epoch 75/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0782 - accuracy: 0.9790\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9779 - loss: 0.1212\n",
"Epoch 76/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0760 - accuracy: 0.9765\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9759 - loss: 0.0924\n",
"Epoch 77/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1479 - accuracy: 0.9631\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9824 - loss: 0.0785\n",
"Epoch 78/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0786 - accuracy: 0.9771\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9685 - loss: 0.0851\n",
"Epoch 79/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1087 - accuracy: 0.9695\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9618 - loss: 0.1137\n",
"Epoch 80/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1014 - accuracy: 0.9777\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9792 - loss: 0.1020\n",
"Epoch 81/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1142 - accuracy: 0.9663\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9775 - loss: 0.0788\n",
"Epoch 82/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0923 - accuracy: 0.9752\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9776 - loss: 0.0670\n",
"Epoch 83/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0820 - accuracy: 0.9752\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9563 - loss: 0.1152\n",
"Epoch 84/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0893 - accuracy: 0.9720\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9821 - loss: 0.0509\n",
"Epoch 85/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1339 - accuracy: 0.9676\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9719 - loss: 0.0951\n",
"Epoch 86/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0833 - accuracy: 0.9746\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9641 - loss: 0.1079\n",
"Epoch 87/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0780 - accuracy: 0.9784\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9648 - loss: 0.1229\n",
"Epoch 88/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0935 - accuracy: 0.9777\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9736 - loss: 0.0691\n",
"Epoch 89/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0898 - accuracy: 0.9758\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9742 - loss: 0.0900\n",
"Epoch 90/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0911 - accuracy: 0.9727\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9813 - loss: 0.0680\n",
"Epoch 91/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0625 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9793 - loss: 0.0672\n",
"Epoch 92/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0563 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9744 - loss: 0.0710\n",
"Epoch 93/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0951 - accuracy: 0.9765\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9779 - loss: 0.0737\n",
"Epoch 94/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0784 - accuracy: 0.9765\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9784 - loss: 0.0703\n",
"Epoch 95/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0662 - accuracy: 0.9822\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9869 - loss: 0.0424\n",
"Epoch 96/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0784 - accuracy: 0.9752\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9776 - loss: 0.0881\n",
"Epoch 97/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0724 - accuracy: 0.9803\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9713 - loss: 0.1001\n",
"Epoch 98/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0984 - accuracy: 0.9746\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.0472\n",
"Epoch 99/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0743 - accuracy: 0.9771\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9789 - loss: 0.1111\n",
"Epoch 100/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0909 - accuracy: 0.9771\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0586\n",
"Epoch 101/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0688 - accuracy: 0.9771\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9805 - loss: 0.0892\n",
"Epoch 102/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0929 - accuracy: 0.9739\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9857 - loss: 0.0545\n",
"Epoch 103/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0740 - accuracy: 0.9828\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9765 - loss: 0.0870\n",
"Epoch 104/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0888 - accuracy: 0.9777\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9747 - loss: 0.0764\n",
"Epoch 105/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0670 - accuracy: 0.9816\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9842 - loss: 0.0491\n",
"Epoch 106/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0800 - accuracy: 0.9809\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9714 - loss: 0.1157\n",
"Epoch 107/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0834 - accuracy: 0.9790\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9872 - loss: 0.0501\n",
"Epoch 108/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0586 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9736 - loss: 0.1021\n",
"Epoch 109/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0784 - accuracy: 0.9790\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9854 - loss: 0.0487\n",
"Epoch 110/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0701 - accuracy: 0.9822\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9844 - loss: 0.0568\n",
"Epoch 111/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0799 - accuracy: 0.9797\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9752 - loss: 0.0872\n",
"Epoch 112/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0934 - accuracy: 0.9733\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9765 - loss: 0.0811\n",
"Epoch 113/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0422 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9763 - loss: 0.0715\n",
"Epoch 114/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1159 - accuracy: 0.9765\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9837 - loss: 0.0581\n",
"Epoch 115/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9822\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9799 - loss: 0.0676\n",
"Epoch 116/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1053 - accuracy: 0.9752\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9662 - loss: 0.1312\n",
"Epoch 117/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0595 - accuracy: 0.9828\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9838 - loss: 0.0620\n",
"Epoch 118/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1004 - accuracy: 0.9688\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9795 - loss: 0.1159\n",
"Epoch 119/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0747 - accuracy: 0.9784\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9747 - loss: 0.0916\n",
"Epoch 120/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0942 - accuracy: 0.9809\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9832 - loss: 0.0775\n",
"Epoch 121/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0587 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9797 - loss: 0.0498\n",
"Epoch 122/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0854 - accuracy: 0.9714\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9726 - loss: 0.1010\n",
"Epoch 123/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0796 - accuracy: 0.9784\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9843 - loss: 0.0592\n",
"Epoch 124/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0968 - accuracy: 0.9746\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9820 - loss: 0.0797\n",
"Epoch 125/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0523 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9747 - loss: 0.1152\n",
"Epoch 126/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0764 - accuracy: 0.9771\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9671 - loss: 0.0974\n",
"Epoch 127/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0784 - accuracy: 0.9797\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9861 - loss: 0.0412\n",
"Epoch 128/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0448 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9824 - loss: 0.0762\n",
"Epoch 129/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0925 - accuracy: 0.9708\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9810 - loss: 0.0423\n",
"Epoch 130/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0692 - accuracy: 0.9816\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9835 - loss: 0.0628\n",
"Epoch 131/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0958 - accuracy: 0.9797\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9783 - loss: 0.0911\n",
"Epoch 132/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0657 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9754 - loss: 0.0995\n",
"Epoch 133/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0485 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9752 - loss: 0.0862\n",
"Epoch 134/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0846 - accuracy: 0.9765\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9716 - loss: 0.0907\n",
"Epoch 135/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0726 - accuracy: 0.9758\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9875 - loss: 0.0420\n",
"Epoch 136/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0643 - accuracy: 0.9816\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9873 - loss: 0.0294\n",
"Epoch 137/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0925 - accuracy: 0.9816\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9842 - loss: 0.0499\n",
"Epoch 138/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0575 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9791 - loss: 0.0646\n",
"Epoch 139/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0546 - accuracy: 0.9822\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9830 - loss: 0.1075\n",
"Epoch 140/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0565 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9739 - loss: 0.0880\n",
"Epoch 141/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1083 - accuracy: 0.9720\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9918 - loss: 0.0302\n",
"Epoch 142/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0528 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9813 - loss: 0.0571\n",
"Epoch 143/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0743 - accuracy: 0.9797\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9803 - loss: 0.0475\n",
"Epoch 144/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0753 - accuracy: 0.9816\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9745 - loss: 0.1400\n",
"Epoch 145/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0504 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9880 - loss: 0.0385\n",
"Epoch 146/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0453 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9795 - loss: 0.0687\n",
"Epoch 147/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0463 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9885 - loss: 0.0390\n",
"Epoch 148/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0796 - accuracy: 0.9803\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.0714\n",
"Epoch 149/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0552 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9798 - loss: 0.0580\n",
"Epoch 150/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0780 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9887 - loss: 0.0394\n",
"Epoch 151/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0662 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9916 - loss: 0.0343\n",
"Epoch 152/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0714 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9717 - loss: 0.0886\n",
"Epoch 153/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9818 - loss: 0.0738\n",
"Epoch 154/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0793 - accuracy: 0.9777\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9803 - loss: 0.0637\n",
"Epoch 155/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9791 - loss: 0.0567\n",
"Epoch 156/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0636 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9774 - loss: 0.0991\n",
"Epoch 157/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0745 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9786 - loss: 0.0689\n",
"Epoch 158/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0616 - accuracy: 0.9828\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9825 - loss: 0.0482\n",
"Epoch 159/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0360 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9844 - loss: 0.0529\n",
"Epoch 160/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0455 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9803 - loss: 0.0766\n",
"Epoch 161/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0543 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9808 - loss: 0.0686\n",
"Epoch 162/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0399 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9723 - loss: 0.1078\n",
"Epoch 163/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0493 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9690 - loss: 0.1042\n",
"Epoch 164/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0584 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9801 - loss: 0.0804\n",
"Epoch 165/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0670 - accuracy: 0.9816\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9921 - loss: 0.0277\n",
"Epoch 166/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0614 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9897 - loss: 0.0463\n",
"Epoch 167/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0367 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9734 - loss: 0.0863\n",
"Epoch 168/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0599 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9757 - loss: 0.0812\n",
"Epoch 169/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0718 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9788 - loss: 0.1058\n",
"Epoch 170/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0304 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9856 - loss: 0.0552\n",
"Epoch 171/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0709 - accuracy: 0.9822\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0404\n",
"Epoch 172/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0538 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9854 - loss: 0.0462\n",
"Epoch 173/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0685 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9846 - loss: 0.0502\n",
"Epoch 174/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0771 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9932 - loss: 0.0165\n",
"Epoch 175/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0528 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9690 - loss: 0.1168\n",
"Epoch 176/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0525 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9848 - loss: 0.0812\n",
"Epoch 177/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0911 - accuracy: 0.9816\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9825 - loss: 0.0938\n",
"Epoch 178/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0511 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9883 - loss: 0.0394\n",
"Epoch 179/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0489 - accuracy: 0.9809\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9825 - loss: 0.0569\n",
"Epoch 180/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0591 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0472\n",
"Epoch 181/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0528 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9779 - loss: 0.0783\n",
"Epoch 182/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0449 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9910 - loss: 0.0448\n",
"Epoch 183/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9828\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9819 - loss: 0.0579\n",
"Epoch 184/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0346 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9801 - loss: 0.0596\n",
"Epoch 185/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0654 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9856 - loss: 0.0533\n",
"Epoch 186/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0744 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9877 - loss: 0.0890\n",
"Epoch 187/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0420 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.0752\n",
"Epoch 188/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0794 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9755 - loss: 0.0908\n",
"Epoch 189/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0300 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9790 - loss: 0.1057\n",
"Epoch 190/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0822 - accuracy: 0.9784\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9906 - loss: 0.0213\n",
"Epoch 191/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0395 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9883 - loss: 0.0768\n",
"Epoch 192/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0319 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9876 - loss: 0.0569\n",
"Epoch 193/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0602 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9924 - loss: 0.0350\n",
"Epoch 194/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0627 - accuracy: 0.9822\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9900 - loss: 0.0345\n",
"Epoch 195/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0511 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9879 - loss: 0.0392\n",
"Epoch 196/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0372 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9810 - loss: 0.1056\n",
"Epoch 197/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0384 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9809 - loss: 0.0518\n",
"Epoch 198/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0575 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9833 - loss: 0.0829\n",
"Epoch 199/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0586 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9831 - loss: 0.0737\n",
"Epoch 200/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0843 - accuracy: 0.9828\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9811 - loss: 0.0602\n",
"Epoch 201/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0584 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9837 - loss: 0.0558\n",
"Epoch 202/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0537 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.1004\n",
"Epoch 203/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0420 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9880 - loss: 0.0430\n",
"Epoch 204/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0667 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9832 - loss: 0.0618\n",
"Epoch 205/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0463 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9850 - loss: 0.0480\n",
"Epoch 206/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0445 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9825 - loss: 0.0667\n",
"Epoch 207/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0741 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9865 - loss: 0.0468\n",
"Epoch 208/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0559 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9873 - loss: 0.0379\n",
"Epoch 209/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0523 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9934 - loss: 0.0237\n",
"Epoch 210/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0396 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9928 - loss: 0.0247\n",
"Epoch 211/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0409 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9803 - loss: 0.0800\n",
"Epoch 212/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0604 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9809 - loss: 0.0913\n",
"Epoch 213/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0311 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9905 - loss: 0.0268\n",
"Epoch 214/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0606 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9950 - loss: 0.0224\n",
"Epoch 215/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0586 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9842 - loss: 0.0853\n",
"Epoch 216/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0723 - accuracy: 0.9777\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0475\n",
"Epoch 217/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0359 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9818 - loss: 0.0626\n",
"Epoch 218/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0748 - accuracy: 0.9809\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0343\n",
"Epoch 219/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0530 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9887 - loss: 0.0497\n",
"Epoch 220/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0674 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9936 - loss: 0.0257\n",
"Epoch 221/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0293 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9953 - loss: 0.0221\n",
"Epoch 222/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0401 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9825 - loss: 0.0648\n",
"Epoch 223/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0420 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9904 - loss: 0.0262\n",
"Epoch 224/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0438 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9875 - loss: 0.0401\n",
"Epoch 225/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0472 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9906 - loss: 0.0523\n",
"Epoch 226/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0799 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9924 - loss: 0.0175\n",
"Epoch 227/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0532 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9790 - loss: 0.0624\n",
"Epoch 228/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0698 - accuracy: 0.9828\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9874 - loss: 0.0437\n",
"Epoch 229/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0452 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.9893 - loss: 0.0441\n",
"Epoch 230/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0527 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9898 - loss: 0.0422\n",
"Epoch 231/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0337 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9826 - loss: 0.0511\n",
"Epoch 232/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0469 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9837 - loss: 0.0645\n",
"Epoch 233/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0355 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9892 - loss: 0.0389\n",
"Epoch 234/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0281 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9828 - loss: 0.0621\n",
"Epoch 235/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0309 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9914 - loss: 0.0248\n",
"Epoch 236/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0817 - accuracy: 0.9803\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9876 - loss: 0.0304\n",
"Epoch 237/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0530 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.9879 - loss: 0.0423\n",
"Epoch 238/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0437 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9869 - loss: 0.0492\n",
"Epoch 239/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0363 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9779 - loss: 0.0580\n",
"Epoch 240/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0475 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9851 - loss: 0.0473\n",
"Epoch 241/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0234 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.0531\n",
"Epoch 242/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0483 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9885 - loss: 0.0328\n",
"Epoch 243/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0543 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9940 - loss: 0.0214\n",
"Epoch 244/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0356 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9826 - loss: 0.0636\n",
"Epoch 245/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0577 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9898 - loss: 0.0216\n",
"Epoch 246/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0643 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9793 - loss: 0.0660\n",
"Epoch 247/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0518 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0356\n",
"Epoch 248/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0553 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9913 - loss: 0.0256\n",
"Epoch 249/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0447 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0290\n",
"Epoch 250/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0438 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9849 - loss: 0.0438\n",
"Epoch 251/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0654 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9896 - loss: 0.0583\n",
"Epoch 252/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0600 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9806 - loss: 0.1203\n",
"Epoch 253/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0378 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9844 - loss: 0.0670\n",
"Epoch 254/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0407 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9936 - loss: 0.0278\n",
"Epoch 255/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0645 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9867 - loss: 0.0302\n",
"Epoch 256/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0378 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9836 - loss: 0.0429\n",
"Epoch 257/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0497 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0280\n",
"Epoch 258/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0454 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9894 - loss: 0.0426\n",
"Epoch 259/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0423 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.0653\n",
"Epoch 260/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0610 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9887 - loss: 0.0462\n",
"Epoch 261/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0366 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.0592\n",
"Epoch 262/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0396 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9807 - loss: 0.0897\n",
"Epoch 263/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0318 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9928 - loss: 0.0245\n",
"Epoch 264/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0552 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0552\n",
"Epoch 265/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0317 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9947 - loss: 0.0201\n",
"Epoch 266/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0414 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9858 - loss: 0.0557\n",
"Epoch 267/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0493 - accuracy: 0.9822\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9916 - loss: 0.0496\n",
"Epoch 268/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0410 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9894 - loss: 0.0536\n",
"Epoch 269/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0695 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9910 - loss: 0.0252\n",
"Epoch 270/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0325 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9882 - loss: 0.0514\n",
"Epoch 271/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0353 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9930 - loss: 0.0374\n",
"Epoch 272/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0782 - accuracy: 0.9797\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0146\n",
"Epoch 273/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0462 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.1504\n",
"Epoch 274/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0401 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0313\n",
"Epoch 275/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0284 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9897 - loss: 0.0372\n",
"Epoch 276/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0403 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9886 - loss: 0.0327\n",
"Epoch 277/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0545 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9949 - loss: 0.0226\n",
"Epoch 278/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0514 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9940 - loss: 0.0254\n",
"Epoch 279/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0308 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9876 - loss: 0.0423\n",
"Epoch 280/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0459 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9912 - loss: 0.0498\n",
"Epoch 281/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0490 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9861 - loss: 0.0585\n",
"Epoch 282/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0271 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9854 - loss: 0.0725\n",
"Epoch 283/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0509 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9821 - loss: 0.0624\n",
"Epoch 284/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0570 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9941 - loss: 0.0338\n",
"Epoch 285/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0388 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9914 - loss: 0.0272\n",
"Epoch 286/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0518 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9835 - loss: 0.0731\n",
"Epoch 287/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9831 - loss: 0.0728\n",
"Epoch 288/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0302 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.0281\n",
"Epoch 289/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0437 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9833 - loss: 0.0592\n",
"Epoch 290/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0414 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9940 - loss: 0.0266\n",
"Epoch 291/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0394 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9848 - loss: 0.0428\n",
"Epoch 292/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0302 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9876 - loss: 0.0495\n",
"Epoch 293/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0554 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9879 - loss: 0.0449\n",
"Epoch 294/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0884 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9849 - loss: 0.0592\n",
"Epoch 295/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0416 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9890 - loss: 0.0433\n",
"Epoch 296/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0508 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9951 - loss: 0.0233\n",
"Epoch 297/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0648 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9896 - loss: 0.0334\n",
"Epoch 298/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0336 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9915 - loss: 0.0258\n",
"Epoch 299/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0473 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9851 - loss: 0.0627\n",
"Epoch 300/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0524 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9947 - loss: 0.0190\n",
"Epoch 301/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0460 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9763 - loss: 0.0931\n",
"Epoch 302/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0441 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9829 - loss: 0.0692\n",
"Epoch 303/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0212 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9852 - loss: 0.0356\n",
"Epoch 304/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0462 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.0393\n",
"Epoch 305/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0221 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9847 - loss: 0.0548\n",
"Epoch 306/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0631 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9793 - loss: 0.0820\n",
"Epoch 307/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0362 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9917 - loss: 0.0270\n",
"Epoch 308/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0692 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9942 - loss: 0.0189\n",
"Epoch 309/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0445 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9962 - loss: 0.0088\n",
"Epoch 310/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0341 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9910 - loss: 0.0358\n",
"Epoch 311/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0399 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9855 - loss: 0.0663\n",
"Epoch 312/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0213 - accuracy: 0.9962\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9912 - loss: 0.0225\n",
"Epoch 313/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0602 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9910 - loss: 0.0406\n",
"Epoch 314/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0537 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9914 - loss: 0.0512\n",
"Epoch 315/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0383 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9893 - loss: 0.0391\n",
"Epoch 316/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0470 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9872 - loss: 0.0396\n",
"Epoch 317/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0328 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9836 - loss: 0.0535\n",
"Epoch 318/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0477 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9931 - loss: 0.0408\n",
"Epoch 319/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0467 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9823 - loss: 0.0658\n",
"Epoch 320/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0295 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9890 - loss: 0.0477\n",
"Epoch 321/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0491 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9913 - loss: 0.0209\n",
"Epoch 322/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0249 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9883 - loss: 0.0411\n",
"Epoch 323/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0489 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9855 - loss: 0.0484\n",
"Epoch 324/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0267 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0400\n",
"Epoch 325/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0558 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9891 - loss: 0.0526\n",
"Epoch 326/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0283 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9801 - loss: 0.0741\n",
"Epoch 327/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0409 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0256\n",
"Epoch 328/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0260 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9945 - loss: 0.0247\n",
"Epoch 329/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0492 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9946 - loss: 0.0142\n",
"Epoch 330/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0422 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0523\n",
"Epoch 331/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0223 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9911 - loss: 0.0438\n",
"Epoch 332/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0308 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9948 - loss: 0.0277\n",
"Epoch 333/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0409 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9970 - loss: 0.0081\n",
"Epoch 334/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0449 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9870 - loss: 0.0370\n",
"Epoch 335/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0349 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9846 - loss: 0.0421\n",
"Epoch 336/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0393 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9818 - loss: 0.0550\n",
"Epoch 337/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0338 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9904 - loss: 0.0579\n",
"Epoch 338/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0432 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0451\n",
"Epoch 339/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0527 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9934 - loss: 0.0334\n",
"Epoch 340/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0219 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9819 - loss: 0.0630\n",
"Epoch 341/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0243 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9859 - loss: 0.0384\n",
"Epoch 342/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0317 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0580\n",
"Epoch 343/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0257 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9910 - loss: 0.0355\n",
"Epoch 344/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0401 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9958 - loss: 0.0194\n",
"Epoch 345/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0331 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9874 - loss: 0.0323\n",
"Epoch 346/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0464 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9907 - loss: 0.0267\n",
"Epoch 347/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0348 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0634\n",
"Epoch 348/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0445 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9873 - loss: 0.0474\n",
"Epoch 349/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0292 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9900 - loss: 0.0492\n",
"Epoch 350/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0372 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9891 - loss: 0.0374\n",
"Epoch 351/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0346 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9838 - loss: 0.0468\n",
"Epoch 352/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0410 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9791 - loss: 0.0854\n",
"Epoch 353/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0299 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0197\n",
"Epoch 354/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0368 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9854 - loss: 0.0662\n",
"Epoch 355/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0613 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0387\n",
"Epoch 356/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0530 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9853 - loss: 0.0491\n",
"Epoch 357/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0347 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9928 - loss: 0.0334\n",
"Epoch 358/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0418 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0486\n",
"Epoch 359/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0436 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0270\n",
"Epoch 360/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0699 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9880 - loss: 0.0321\n",
"Epoch 361/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0310 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9844 - loss: 0.0577\n",
"Epoch 362/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0313 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9908 - loss: 0.0532\n",
"Epoch 363/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0318 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9816 - loss: 0.1538\n",
"Epoch 364/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0427 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9897 - loss: 0.0727\n",
"Epoch 365/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0260 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9933 - loss: 0.0249\n",
"Epoch 366/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0165 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9843 - loss: 0.0523\n",
"Epoch 367/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0359 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9969 - loss: 0.0272\n",
"Epoch 368/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0111 - accuracy: 0.9955\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9898 - loss: 0.0320\n",
"Epoch 369/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0527 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9926 - loss: 0.0565\n",
"Epoch 370/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0389 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9940 - loss: 0.0258\n",
"Epoch 371/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0410 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9949 - loss: 0.0194\n",
"Epoch 372/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0393 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9912 - loss: 0.0262\n",
"Epoch 373/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0341 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9915 - loss: 0.0287\n",
"Epoch 374/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0350 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9941 - loss: 0.0228\n",
"Epoch 375/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0594 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9952 - loss: 0.0169\n",
"Epoch 376/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0468 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0326\n",
"Epoch 377/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0485 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9905 - loss: 0.0493\n",
"Epoch 378/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0393 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9905 - loss: 0.0338\n",
"Epoch 379/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0298 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9899 - loss: 0.0261\n",
"Epoch 380/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0310 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9879 - loss: 0.0555\n",
"Epoch 381/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0349 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9908 - loss: 0.0290\n",
"Epoch 382/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0327 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9932 - loss: 0.0283\n",
"Epoch 383/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0218 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0360\n",
"Epoch 384/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0386 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9870 - loss: 0.0545\n",
"Epoch 385/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0416 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9922 - loss: 0.0397\n",
"Epoch 386/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0309 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0222\n",
"Epoch 387/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0357 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9986 - loss: 0.0194\n",
"Epoch 388/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0252 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9864 - loss: 0.0482\n",
"Epoch 389/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0409 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9894 - loss: 0.0449\n",
"Epoch 390/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0171 - accuracy: 0.9962\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9919 - loss: 0.0191\n",
"Epoch 391/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0196 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9978 - loss: 0.0086\n",
"Epoch 392/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0364 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9946 - loss: 0.0604\n",
"Epoch 393/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0273 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9826 - loss: 0.0802\n",
"Epoch 394/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0492 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9929 - loss: 0.0291\n",
"Epoch 395/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0267 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9928 - loss: 0.0257\n",
"Epoch 396/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0134 - accuracy: 0.9962\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9941 - loss: 0.0271\n",
"Epoch 397/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0630 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9897 - loss: 0.0585\n",
"Epoch 398/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0431 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9913 - loss: 0.0463\n",
"Epoch 399/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0210 - accuracy: 0.9968\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9957 - loss: 0.0137\n",
"Epoch 400/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0473 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.9982 - loss: 0.0102\n",
"Epoch 401/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0495 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.9945 - loss: 0.0195\n",
"Epoch 402/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0350 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9959 - loss: 0.0181\n",
"Epoch 403/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0232 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9890 - loss: 0.0346\n",
"Epoch 404/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0362 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9937 - loss: 0.0275\n",
"Epoch 405/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0329 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9949 - loss: 0.0469\n",
"Epoch 406/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0199 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9792 - loss: 0.0668\n",
"Epoch 407/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0474 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.9903 - loss: 0.0355\n",
"Epoch 408/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0154 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - accuracy: 0.9934 - loss: 0.0222\n",
"Epoch 409/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0448 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9951 - loss: 0.0323\n",
"Epoch 410/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0293 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9952 - loss: 0.0245\n",
"Epoch 411/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0434 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9887 - loss: 0.0478\n",
"Epoch 412/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0102 - accuracy: 0.9968\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9977 - loss: 0.0135\n",
"Epoch 413/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0151 - accuracy: 0.9968\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9914 - loss: 0.0486\n",
"Epoch 414/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0388 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9945 - loss: 0.0316\n",
"Epoch 415/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0177 - accuracy: 0.9955\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9959 - loss: 0.0134\n",
"Epoch 416/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0182 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9910 - loss: 0.0336\n",
"Epoch 417/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0548 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.9934 - loss: 0.0210\n",
"Epoch 418/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0473 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9967 - loss: 0.0182\n",
"Epoch 419/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0227 - accuracy: 0.9975\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9982 - loss: 0.0168\n",
"Epoch 420/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0466 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9976 - loss: 0.0143\n",
"Epoch 421/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0452 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9917 - loss: 0.0328\n",
"Epoch 422/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0263 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9913 - loss: 0.0214\n",
"Epoch 423/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0311 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9945 - loss: 0.0202\n",
"Epoch 424/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0385 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0375\n",
"Epoch 425/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0403 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9947 - loss: 0.0233\n",
"Epoch 426/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0312 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0286\n",
"Epoch 427/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0357 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9867 - loss: 0.0736\n",
"Epoch 428/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0513 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0462\n",
"Epoch 429/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0247 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9840 - loss: 0.0531\n",
"Epoch 430/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0550 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9919 - loss: 0.0302\n",
"Epoch 431/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0380 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9859 - loss: 0.0687\n",
"Epoch 432/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0398 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9964 - loss: 0.0111\n",
"Epoch 433/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0334 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9970 - loss: 0.0245\n",
"Epoch 434/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0249 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9824 - loss: 0.0684\n",
"Epoch 435/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0397 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9945 - loss: 0.0252\n",
"Epoch 436/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0403 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9862 - loss: 0.0671\n",
"Epoch 437/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0414 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9867 - loss: 0.0521\n",
"Epoch 438/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0263 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0235\n",
"Epoch 439/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0118 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9868 - loss: 0.0337\n",
"Epoch 440/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0543 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9894 - loss: 0.0301\n",
"Epoch 441/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0215 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9958 - loss: 0.0258\n",
"Epoch 442/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0333 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9961 - loss: 0.0189\n",
"Epoch 443/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0300 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9914 - loss: 0.0170\n",
"Epoch 444/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0611 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9947 - loss: 0.0296\n",
"Epoch 445/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0302 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9928 - loss: 0.0274\n",
"Epoch 446/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0319 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9902 - loss: 0.0442\n",
"Epoch 447/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0290 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0442\n",
"Epoch 448/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0395 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9962 - loss: 0.0290\n",
"Epoch 449/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0272 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9918 - loss: 0.0232\n",
"Epoch 450/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0285 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9957 - loss: 0.0115\n",
"Epoch 451/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0281 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9823 - loss: 0.0913\n",
"Epoch 452/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0159 - accuracy: 0.9962\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9942 - loss: 0.0357\n",
"Epoch 453/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0502 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9978 - loss: 0.0156\n",
"Epoch 454/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0357 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9870 - loss: 0.0515\n",
"Epoch 455/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0495 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9909 - loss: 0.0351\n",
"Epoch 456/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0277 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9925 - loss: 0.0302\n",
"Epoch 457/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0246 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9921 - loss: 0.0288\n",
"Epoch 458/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0316 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9945 - loss: 0.0135\n",
"Epoch 459/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0291 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9877 - loss: 0.0352\n",
"Epoch 460/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0196 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9918 - loss: 0.0390\n",
"Epoch 461/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0203 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0517\n",
"Epoch 462/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0309 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9797 - loss: 0.0902\n",
"Epoch 463/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0520 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9912 - loss: 0.0169\n",
"Epoch 464/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0219 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9933 - loss: 0.0224\n",
"Epoch 465/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0351 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9939 - loss: 0.0285\n",
"Epoch 466/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0545 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9877 - loss: 0.0489\n",
"Epoch 467/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0185 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9931 - loss: 0.0314\n",
"Epoch 468/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0237 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9967 - loss: 0.0072\n",
"Epoch 469/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0194 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9922 - loss: 0.0319\n",
"Epoch 470/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0558 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9953 - loss: 0.0118\n",
"Epoch 471/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0372 - accuracy: 0.9962\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9872 - loss: 0.0471\n",
"Epoch 472/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0345 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9967 - loss: 0.0234\n",
"Epoch 473/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0134 - accuracy: 0.9955\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9917 - loss: 0.0561\n",
"Epoch 474/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0427 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9876 - loss: 0.0377\n",
"Epoch 475/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0215 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9977 - loss: 0.0083\n",
"Epoch 476/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0157 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9960 - loss: 0.0137\n",
"Epoch 477/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0237 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9901 - loss: 0.0569\n",
"Epoch 478/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0339 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0388\n",
"Epoch 479/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0219 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9875 - loss: 0.0265\n",
"Epoch 480/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0227 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9943 - loss: 0.0349\n",
"Epoch 481/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0291 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0479\n",
"Epoch 482/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0192 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9930 - loss: 0.0214\n",
"Epoch 483/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0289 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9901 - loss: 0.0361\n",
"Epoch 484/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0395 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9903 - loss: 0.0253\n",
"Epoch 485/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0155 - accuracy: 0.9955\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0188\n",
"Epoch 486/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0290 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9971 - loss: 0.0069\n",
"Epoch 487/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0114 - accuracy: 0.9968\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9968 - loss: 0.0189\n",
"Epoch 488/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0121 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9874 - loss: 0.0624\n",
"Epoch 489/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0211 - accuracy: 0.9962\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9934 - loss: 0.0221\n",
"Epoch 490/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0374 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9964 - loss: 0.0097\n",
"Epoch 491/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0250 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9890 - loss: 0.0315\n",
"Epoch 492/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0326 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9967 - loss: 0.0207\n",
"Epoch 493/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0215 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0615\n",
"Epoch 494/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0321 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9925 - loss: 0.0141\n",
"Epoch 495/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0136 - accuracy: 0.9955\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0557\n",
"Epoch 496/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0336 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0229\n",
"Epoch 497/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0310 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9878 - loss: 0.0459\n",
"Epoch 498/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0300 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9938 - loss: 0.0249\n",
"Epoch 499/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0274 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9929 - loss: 0.0390\n",
"Epoch 500/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0494 - accuracy: 0.9892\n"
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9964 - loss: 0.0116\n"
]
}
],
@@ -1279,7 +1370,7 @@
"outputs": [
{
"data": {
- "image/png": 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cOUZERBQLDIRiRM4IAVw5RkREFCsMhGLEYbPCZvVttsqMEBERUWwwEIqhhOasEDNCREREscFAKIZcDrmpIjNCREREscBAKIbkjFCDmxkhIiKiWGAgFEPMCBEREcUWA6EYcjEjREREFFMMhGIogRkhIiKimGIgFEMurhojIiKKKQZCMSRnhNhHiIiIKDYYCMUQM0JERESxxUAohpgRIiIiii0GQjGU4GBGiIiIKJYYCMWQy96cEWIgREREFBMMhGJIzghxaoyIiCg2GAjFkJwR4tQYERFRbHT4QOjTTz/FoEGDMGDAALzyyiuxHo4KM0JERESxZY/1AKLJ7XZj9uzZWLx4MdLT0zFy5EhcdNFFyMrKivXQAIidpZkRIiIiioUOnRFatWoVhgwZgu7duyMlJQXnnnsuFixYEOthKVr6CDEjREREFAtxHQh98803mDp1KvLz82GxWDBv3jy/Y4qKitCnTx8kJCRg7NixWLVqlfLYwYMH0b17d+V29+7dceDAgbYYuikupY8QM0JERESxENeBUE1NDQoLC1FUVKT7+Ntvv43Zs2fjwQcfxLp161BYWIjJkyejtLS0jUcaHmaEiIiIYiuuA6Fzzz0Xf/rTn3DRRRfpPv7UU0/h+uuvxzXXXIOCggK89NJLSEpKwmuvvQYAyM/PV2WADhw4gPz8fMP3a2hoQGVlpeq/aEpgRoiIiCim4joQCqSxsRFr167FpEmTlPusVismTZqE5cuXAwDGjBmDTZs24cCBA6iursbnn3+OyZMnG77mnDlzkJ6ervzXs2fPqH4GZoSIiIhiq90GQkeOHIHH40Fubq7q/tzcXBQXFwMA7HY7nnzySZx++ukYPnw47rjjjoArxu655x5UVFQo/+3fvz+qn0FZNcaMEBERUUx06OXzAHD++efj/PPPN3Wsy+WCy+WK8ohapLh8p7+q3t1m70lEREQt2m1GKDs7GzabDSUlJar7S0pKkJeXF6NRhaZLshMAUF7bCEmSYjwaIiKizqfdBkJOpxMjR47EokWLlPu8Xi8WLVqEcePGxXBk5nVJcgAAmjwSaho5PUZERNTW4npqrLq6Gjt37lRu7969Gxs2bEBmZiZ69eqF2bNnY/r06Rg1ahTGjBmDp59+GjU1NbjmmmtiOGrzEh02uOxWNLi9OFbTqEyVERERUduI6yvvmjVrcPrppyu3Z8+eDQCYPn065s6di2nTpuHw4cN44IEHUFxcjOHDh+OLL77wK6COVxaLBV2SnCiurMex2kb0zEyK9ZCIiIg6lbgOhCZOnBi0dmbWrFmYNWtWRN+3qKgIRUVF8HiiP12VkeRoDoSaov5eREREpNZua4SiaebMmdiyZQtWr14d9ffqktRSME1ERERti4FQjHVJ9hVMH6thIERERNTWGAjFWEZzRohTY0RERG2PgVCMZXJqjIiIKGYYCMVYRnMvIWaEiIiI2h4DoRjrokyNMSNERETU1hgI6SgqKkJBQQFGjx4d9fdSiqUZCBEREbU5BkI62nL5vFIsXcOpMSIiorbGQCjG2EeIiIgodhgIxZi8aqym0YNGtzfGoyEiIupcGAjFWGqCHVaL72tmhYiIiNoWA6EYs1otbKpIREQUIwyE4kBLLyFmhIiIiNoSA6E4oPQS4n5jREREbYqBkI627CMEiE0VOTVGRETUlhgI6WjLPkIA0IVTY0RERDHBQCgOdElmLyEiIqJYYCAUB7jxKhERUWwwEIoDXVMTAADFFfUxHgkREVHnwkAoDvTOSgIA7CmrifFIiIiIOhcGQnFADoQOltdxmw0iIqI2xEAoDuSkuJDktMErAb8cq431cIiIiDoNBkJxwGKxoHdWMgBgbxkDISIiorbCQChO9M5knRAREVFbYyCko607SwMtdULMCBEREbUdBkI62rqzNAB075IIADhUUddm70lERNTZMRCKE3lp7CVERETU1hgIxYn8DF9G6CADISIiojbDQChO5KX7MkJHqhvYS4iIiKiNMBCKE1nJTjhtVkgSUFLJrBAREVFbYCAUJywWi5IVKmYgRERE1CYYCMURORA6WM6VY0RERG2BgVAcyU/nyjEiIqK2xEAojuSkugD4CqaJiIgo+hgIxZHsFDkQaozxSIiIiDoHBkJxJCuFGSEiIqK2xEBIRyz2GgOA7BQnAGaEiIiI2goDIR2x2GsMEKfGmBEiIiJqCwyE4ogcCB2taYTXK8V4NERERB0fA6E4kpnsmxrzeCWU1zXFeDREREQdHwOhOOK0W5Ge6AAAlHF6jIiIKOoYCMWZrOaC6cMMhIiIiKKOgVCckeuEnlu0E5LEOiEiIqJoYiAUZ84ZkgcAWL6rDN/sOBLj0RAREXVsDITizLUT+mLioBwAwNZDlTEeDRERUcfGQCgOjejVBQCwo6Q6xiMhIiLq2BgIxaH+XVMAADtLq2I8EiIioo6NgVAcGqAEQtUsmCYiIooiBkJxqHdWMuxWC2oaPThUUR/r4RAREXVYDITikNNuRZ/sZADAjlLWCREREUULAyEdsdp9XtQ/p2V6jIiIiKKDgZCOWO0+LxqQy4JpIiKiaGMgFKfklWM7SqpRVt2ABrcnxiMiIiLqeBgIxakBXVMBAGv2HsPIPy3EhUXfx3hEREREHQ8DoTjVLydZdXvroUq4Pd4YjYaIiKhjYiAUpxIcNqQnOlT3cSk9ERFRZDEQimOZyU7V7X1Ha2M0EiIioo6JgVAc65KkzgjtLWMgREREFEkMhOJYZrJLdXvv0ZoYjYSIiKhjYiAUxzKT1RmhfcwIERERRRQDoTjWRVMjtPVQJTdhJSIiiiAGQnEsM0kdCO0pq+WWG0RERBHEQCiOiRmhBIfvW7VgS0mshkNERNThMBCKY2JG6OIRPQAAq/ccjdVwiIiIOhwGQnGsi1AsfUJ+OgCgrLoxVsMhIiLqcBgIxTGHreXbc1zzlhtl1Q2xGg4REVGHY4/1AMjY8XlpOC4nGdkpLuRnJAIAymoaIUkSLBZLjEdHRETU/jEQimNOuxULbj8NVgtQ1+QBADS4vahp9CDFxW8dERFRa3FqTEdRUREKCgowevToWA8FNqsFFosFSU47Eh02AMBR1gkRERFFBAMhHTNnzsSWLVuwevXqWA9FRd6E9UgN64SIiIgigYFQO5Kd4guEmBEiIiKKDAZC7UhWim8T1jJmhIiIiCKCgVA7Ik+N/ePb3Zj4xGJsL6mK8YiIiIjaNwZC7UhW89TYztJq7Cmrxe//vS7GIyIiImrfGAi1I93SElS3uQErERFR6zAQakcG5qX63bevrDYGIyEiIuoYGAi1I4Ny/QOhHw6Ut/1AiIiIOggGQu2IvGpMdKSKK8iIiIjCxUConTvCnkJERERhYyDUzlw+uqfq9hHuRk9ERBQ2iyRJUqwHEa8qKyuRnp6OiooKpKWlxXo4AIC6Rg/W7j2GnaVVeOiTLTjj+K5ocHvgstvw6vRR3JWeiIg6vVCu39zCvJ1JdNowYUA2ahrdAIC1e4+hoq4JAHCstklpukhERETBcWqsncpuLpyWgyAAKK6oj9VwiIiI2iUGQu1Ujs4KsuLKuhiMhIiIqP1iINROZaf6T4EVV7BwmoiIKBQMhNqpJKcdSU6b6r41e49iyjPf4l8r9sZoVERERO0LA6F2rFdmkur2B+sOYMuhStw/bxPqmzwxGhUREVH7wUCoHbvvvALDx77cXNyGIyEiImqfGAi1YxMGZOOtG07CjJP7+D32xSYGQkRERMEwEGrnTuqXhd9N6KvcPr55h/qN+8tjNCIiIqL2gw0VO4CemUl48aoRSHbZMaJ3Fwx96EscrKhHaVU9uqYmxHp4REREcYsZoQ7i3KHdcOrAHKS47BjQNQUA8MP+ihiPioiIKL4xEOqACntkAADW7z8W24EQERHFOQZCOoqKilBQUIDRo0fHeihhGdm7CwCgaPHP+P2/16KqvinIM4iIiDonBkI6Zs6ciS1btmD16tWxHkpYxvTNVL7+7MdivLVqfwxHQ0REFL8YCHVAfbOTVbeX7yrDxxsPxmg0RERE8YuBUAdksVhw2sAc5fbXP5Xilv+ux7biqhiOioiIKP4wEOqg/nLpMOSmqXeo33+0NkajISIiik8MhDqo3LQE3DZpoOq+Jo83RqMhIiKKTwyEOrDMZKfqdllNY4xGQkREFJ8YCHVg2kDoKAMhIiIiFQZCHRgDISIiosDCCoT++c9/Yv78+crtP/zhD8jIyMDJJ5+MvXv3Rmxw1DpZnBojIiIKKKxA6LHHHkNiYiIAYPny5SgqKsJf/vIXZGdn4/bbb4/oACl8aQkO1e2y6oYYjYSIiCg+hbX7/P79+9G/f38AwLx583DJJZfghhtuwPjx4zFx4sRIjo9awWq1qG5zaoyIiEgtrIxQSkoKysrKAAALFizAWWedBQBISEhAXV1d5EZHreawtQRD4tTY6j1Hcfpfl+Drn0piMSwiIqK4EFYgdNZZZ+G6667Dddddh+3bt2PKlCkAgM2bN6NPnz6RHB+10pK7TscTlw4DAByualCaKt705jrsPlKDa+euieXwiIiIYiqsQKioqAjjxo3D4cOH8f777yMrKwsAsHbtWlxxxRURHSC1TveMRJw/PB9Om+9bffGL30OSJBxhvRAREVF4NUIZGRl4/vnn/e5/+OGHWz0gijyX3YZnrxiOG99ch8NVDThcpQ6CPF4JNqsFkiRBkvxri4iIiDqqsDJCX3zxBb777jvldlFREYYPH44rr7wSx44di9jgKHLOOaEb+mQlAQC+/7lM9djB8jpIkoTfvroKU579lltxEBFRpxFWIHTXXXehsrISAPDjjz/ijjvuwJQpU7B7927Mnj07ogOkyDkuJwUAcNvbG1T37yytxpHqRny38wh+Kq7CloOVcMcgGPJ4pTZ/TyIi6tzCCoR2796NgoICAMD777+PX/3qV3jsscdQVFSEzz//PKIDpMg5rmuK7v3bS6qwo6RKuX1B0TJc+Y+VqmN2llbjzRV7oxas7CipwvCHF6Bo8c6ovD4REZGesAIhp9OJ2lrf6qOFCxfi7LPPBgBkZmYqmSKKP70yk5Sv0xLsOOP4rgCA1XuOYbsQCAHAqj1HVbfP/ttS3DdvE/69Mjqdwx/5dAuqGtx44sttUXl9IiIiPWEVS0+YMAGzZ8/G+PHjsWrVKrz99tsAgO3bt6NHjx4RHSBFTkF+mvL1uvvPwuaDlfj6p1Ks3F2GnFSn3/Fer6QUTsuJoG+2H8bV4/q0xXCJiIiiLqyM0PPPPw+73Y733nsPL774Irp37w4A+Pzzz3HOOedEdIAUOSN6dcGzV5yIT2+eALvNiiH5aUh12VFV78aH6w/4HV/d6Pa7r8HNQmoiIuo4wsoI9erVC59++qnf/X/7299aPSCKrvML85Wv7TYrxh2XhQVbSlDf5B/gVNW7kZbgUK0iq2/yRGVcFguX7BMRUdsLKxACAI/Hg3nz5mHr1q0AgCFDhuD888+HzWaL2OAo+m6dNAALtuhvs1FV34SyaiteX7ZHua+mITqBEBERUSyEFQjt3LkTU6ZMwYEDBzBo0CAAwJw5c9CzZ0/Mnz8fxx13XEQHSdEzJD8dD00twKKfSlHYIwPPC6u2qurd+L/Pf8LibYeV+0oq69t8jJIkYfY7G9EnKxm3ThrQ5u9PREQdV1g1QrfccguOO+447N+/H+vWrcO6deuwb98+9O3bF7fcckukx0hRNmN8X/zrd2MxIFe9vL6qvkkVBAG+jVtrGtyY8foq3PPBj5Ck6Pf+WbfvGD5cfwB/W7g96u9FRESdS1gZoaVLl2LFihXIzMxU7svKysLjjz+O8ePHR2xw1LYyk9Urx6rq/YulAeC7nUewpDlAGt2nCy4e4b9S8NXvdqOyrgm3TRpgqv4n0BHidJwkSawnIiKiiAkrI+RyuVBVVeV3f3V1NZxO/2XY1D50SVJ/7yrqmnSPW7e3ZRuVl5fu8nu8vsmDRz/dgmcW7cC3O460elxizqnJw+7TREQUOWEFQr/61a9www03YOXKlc0bdUpYsWIFbrzxRpx//vmRHiO1kQSHutB9Z2m17nFLt7dMl+06Uu23HYcYQP3j21245MXv8eaKwI0YAyV5xOm3Ru6DRkREERRWIPTss8/iuOOOw7hx45CQkICEhAScfPLJ6N+/P55++ukID5HaSr/sZJx8XJZye/NB/S7hPxW3ZAObPBIOlNepHi+vbQmEvt1xBGv3HsN98zYp972zZj+uemWFYcZJyysEQg1RWr5PRESdU1iBUEZGBj766CNs374d7733Ht577z1s374dH374ITIyMiI8RGorVqsF/7n+JNxypm9l1lphCgwArh7XW/d5u47UqG6X1zYGfJ8/vPcDlu0sw0tLfzY1rgahxxEbOhIRUSSZLpYOtqv84sWLla+feuqp8EdEMZeWoP6x+O1JvXHqwBxMHJSDD9cdQFWDuoh69+EaTBzoywx1z0hEuclMT6XBcR6vBJu1Za6s3t2SBWIgREREkWQ6EFq/fr2p47iip/1LFQKhiYNycO+UwUh0+uqHMlOc/oHQkRo8//VOPPnVdjxx6TCYXVEvBjuiJo8XNmtLvZLY9bqRgRAREUWQ6UBIzPhQ53Hn2YOUIAgAMpKc2FtW2/y1A+W1TdhTVoN/NRdD3/XeD7j+lL6mXtsqBM1iSNTk8aoKt8VtPRrc0a0Reu273dheUoU5Fw9lUE9E1AmEVSNEHduJvboAAPLSEnBC93TVY12SHMrXg3JTAQAHNcXS//h2t+7ryisMZVaDQMPtkfDxxoP47asrUVbdoMoIRXtq7JFPt+Ct1fuxbGdZVN+HiIjiQ9h7jVHHNTA3FV/cdgq6pSf6PZYp9Brq3zUFK3cfxaGKevTJSsKe5kyRkSaPBI+3JRCyCWG42B6oyePFLf/1TcW+sORnpLhafkwbdDaHjYaqenN1TkRE1L4xECJdx+el6d6foQmEAKC2UX+6KjfNhZLKBuV2vdujqvERM0JNwv1ir6Cq+ibYbRbhsbZZPu9pg61DiIgo9jg1RiERp8ayU1zIaL6tlw3SZpTqGz2obdBfAeb2tnz9m1dWKl+nuBzq5fNtlBESM1eRVF7biH8t34NjNYFbDBARUdvoFIHQRRddhC5duuDSSy+N9VDavS7CfmQpCXbkpSWoHr98dM+WY4WgCQDqmjyoaWxZcVYnZJIahbkxMajySpKmWLptAqFoJYRufWsD7v9oM258c2103oCIiELSKQKhW2+9FW+88Uash9EhiPuRpbjs6JauDoSuHNtL+frUgTmqx+qbvKgVAiGxP5B2mw5ZZV0T6tpo1ZhYyP3op1vw2nf6Rd+tIW9PsnL30Yi/NhERha5TBEITJ05EampqrIfRIYhZnhSXHXma6a/sFBdG9MpAaoIdF5/YA4vuOA2pzcXOdU0e1U7yYqanySAQqqhrUh0XzT5C4nRYWU0jHvl0S9Tei4iI4kPMA6FvvvkGU6dORX5+PiwWC+bNm+d3TFFREfr06YOEhASMHTsWq1atavuBEgB1sbReRijJacM7/zMO3999BtKTHDguJwVd01wAfIFPjdCMsU6o93Eb7CrvC4TaZvm8W6cuSGLRNBFRhxbzQKimpgaFhYUoKirSffztt9/G7Nmz8eCDD2LdunUoLCzE5MmTUVpaqhwzfPhwnHDCCX7/HTx4sK0+RqeRltiy0DDZZUfXVJfq8SSnHXabFakJLZkjuSGjr0ZIPyNktKt8uSYjFM1ASK9A+qY31+GLTcVRe08iIoqtmC+fP/fcc3HuuecaPv7UU0/h+uuvxzXXXAMAeOmllzB//ny89tpruPvuuwEAGzZsiMhYGhoa0NDQsty7slJ/9/XOLEcIfFIT7MgUiqftVgucdv/YOsHuC4TqGz3qGqEmsUbIOCOUrOojFLxGqL7Jg3fW7MfEgV3RKysp6PEyvSXzX2wuxhebi7Hn8fNMvw4REbUfMc8IBdLY2Ii1a9di0qRJyn1WqxWTJk3C8uXLI/5+c+bMQXp6uvJfz549gz+pk3HZbVj9x0lYc98kOGxWZKW0BEJJwlYcIjkjdNO/1+GBjzYr9weqEXri0mEAfIGQGPw0GGSORC8s+RkPfLQZk5/+xsQnauExCMa0thysxBebDoX02kREFJ/iOhA6cuQIPB4PcnNzVffn5uaiuNj8dMWkSZPw61//Gp999hl69OhhGETdc889qKioUP7bv39/q8bfUeWkupCd4ssMZSa3ZIiSnPoJRnHfMFFdgEAoP8NXhN3o9qK8tqXLs14foX1ltVi9p2UV1nc7Dvu9vhl6NUJ6pjz7LW58cx1++KVcdf+6fcdw+d+XY9OBipDel4iIYieuA6FIWbhwIQ4fPoza2lr88ssvGDdunO5xLpcLaWlpqv8oMHFqzGg3eaNASCyCbtJkY3JSXcrrlVTVK/c3uD3Ydbhatdz++jfW4LKXl+NA855n4WyWKr+ukf/51xq/AEfbRPLiF77Hil1HMeN1FvMTEbUXcR0IZWdnw2azoaSkRHV/SUkJ8vLyYjQqEqUlCPU7Bj1+Eh36P2aqGiGvOtOT5LQhPdFXcC2W7ny1pQRnPLkUf3jvB+W+4sp6SBKwt6wGAKCNx95dsx9X/H0FymuNuzlPf20Vpv19heHjX24uwbSXl6uaQKa4fAFedYMbj3/+k3L/kWr1+/xrxV58/ZP6Z5iIiOJDXAdCTqcTI0eOxKJFi5T7vF4vFi1aZJjVobYlZl/qDba/MM4I+YIKSZL8MkJJTjsyEh1+z5GDjA/WH8Af3tuI0sp6pbfQsRrfFJoFLWN6c8Ve3PXeD1i+qwzPLNph+DlW7Are4LCm0YPyupYgx2nzfa5nFm7HS0t/1n3O5oMVuH/eJlw7d03Q128rH6z7BW8s3xPrYRARxYWYrxqrrq7Gzp07ldu7d+/Ghg0bkJmZiV69emH27NmYPn06Ro0ahTFjxuDpp59GTU2NsoqM4ke9QU2OS1hJdmKvDKzfVw7ANx3m9nihV5qT5LShW0YCdh2pMXy/d9b8gv5dU5Sl90fljI+QEbpv3ibl6yPVjfB4JcMpPDPkYAsAmpqzWNtKjKfUDpXXGz4WC16vhNnvbAQATBqcq9RiERF1VjEPhNasWYPTTz9duT179mwAwPTp0zF37lxMmzYNhw8fxgMPPIDi4mIMHz4cX3zxhV8BdSQVFRWhqKgInjba6byjMCo2Fnen/891J2Hf0VplRde8DQd1V5u57FZ0N3GRloMbAMpGpkZhzicbD+LHX8rx5e2nwmVveU9vCBusitNrTc2ZKJdOywDltYV5vVDeJ1qahCnIqnp3gCMpluqbPLjrvR9wVkEuzi/Mj/VwiDq0mAdCEydODNq9d9asWZg1a1YbjQiYOXMmZs6cicrKSqSnp7fZ+3ZUx4TgIdFpw8DcFOX2ne9u1H2OxWJB94zgPYDETtVH5UAoQMJnT1ktNh2owMjembrjC+aYsIJNns4LHAi1fG3UNLItiVuUhFFTTm3ktWW78cnGg/hk40EGQkRRFtc1QtQxyAGKzGKxIMGggFrUvYtxRqiwZwYAoLLePxAyas4os1nV7324usHgSH8Pf9LSB0le8q/XRFImBvnR7IptVjT3aqPIKa00/zNJRK3DQIha7Z/XjkF2iguvTh+l+/gVY3w70k/on63cZ1RYLeohBEJ9s5NVj3XP8O1xVlHXkqGRMzviVJye2ka3avn9kSrzGaHSqpYLlJzhEafZZH/6dAsmPrEYR4Qg0GhVXVsSi9KNNroN/TW9mPWfdfjXir0ReT1ST6kSUXTFfGqM2r/TBuZg9R/PNOzfc35hPvplp6B/1xTdx42INUJD8tOwWyiczm/e9V4MhOSMkLiNh56fDlVh1n/WY/KQXMy5eBgOV4dX0Nzk0a8RslqAV77bDQD4xze7lPv1mkG2NTEjpF2pZ6Sksh4ZSQ7dgA/w1V59+sMhfPrDIfz2pN4RGWdnp7fvHRFFBzNCFBGBmhhaLBYM7ZGubLUBAC9cNQL/c2o/XDKih+Hz8oSd7Yc3T4XJ5NVOlTqBUE2QjNAjn27B0ZpG/HeVr3P44arwpiGMiqWtwrn45VhL08W4qBESFgCYmSbbUVKFsY8twq+e/c7wGDEYjZVvdxzGdf9cg9LK+FqlFy5mhIjaDgMhiokpQ7vhnimD0S8n2fAYh82Ki07sjsHd0nDVWHWmQd7jTBsISZKE2obQVkNpGyCapWRUNDGgGBOKf9gHywhV1DZFbLrKSKPb/NSYJEn45Affnmo7So1bBERbaWU95v9wSDWdqfXbV1dh4dYS3PvhJsNj2hNmhIjaDqfGKKaCLZH/27Thytc5qS4leyPvayZmIxrcXuworQ6aEZLZrRbsK6tFcUV4WYRGjxelVfWo1ixD9zV09L+QBaoRKq6ox0lzFmFwtzR8fuspyv1Fi3firdX78N6NJyM3LcHw+aGMWfk6QEbI65Vw+d9XYNWe4I0mRZIkhbXFSSDnPvMtymoa8eDUAlwzvm/AY8UMXFtr8nhht1oi8vnjIHlI1GkwI6SjqKgIBQUFGD16dKyH0uEFWhmm1TuzZTm9vOpM27vo7L+Z33He7ZVw6hOL8fHGg6afI1q/7xhOnvM1/r1yn+Z19a9igVaNLdzq24Jj66FKnPXUUry/9hcAwBNfbsP+o3V4cYl+52qzJEnCsZpGVfATaKruYEVdyEEQEJ2VcWXNU57yOQrE7Ma5kVbb6MbYxxbhN6+ujMjrcWqMqO0wENIxc+ZMbNmyBatXr471UDq8QXmpqttXjOmFz245RffYh84fAqsFuHJsLyQabNvRFk4Z4Fv9tnBrqe6F1+haHChIsAvdrneUVuMOTX+lOpNZLiP3frgJJz76FVbsKlPuCzQ1ZhDL6RKv2a0dZyBmxhRo+iyalu0sw9GaRizbWRb8YBNiFdARdUYMhCim0hIcmDgoR7k95+KhKMhP0z32hO7pWH//2fjzhScY7l/WFtJ09kAzo06zmu2dNftx9/s/oMHtgVVn2w9to1GvVwrafNTIf1f5slZ/W7hduS/g1FgI7yMGeHUG26wEU1HbhIPldQGPMTMmsyvhIs0W4d+k8dCFnKizYI0Qxdwzl5+I++ZtwhnH5wQ9Nj3JF4Qk6mzL0VbSEsILhLRbWvzhvR8AAFML82HTqSsR+yF5JAm/eu47JLtseOd/xoVdh5LqsitNKI0yQn/9chv+GcKmrGLwow2EvthUjC0HK3D7WQMDjrnwkQUAgDX3TUJ2ikv3GDOxmdG0ZLSJny0SdVJi0BeNuisiasFAiGIuPdGB5644MaTnxHJqLC0xvH82NQar2Woa3LobwYodubeXVGHLoUoAQFWDO6RgTOyrlJrgUAKhRoPsyfOLd+reb0TcbFc7NXbjm2sBAMN7ZeCM4/33B5QkCdXCedl0oAITB3XVfR8zGaFYrbYSWyY0eryGPZfMEj9Hk0eC085AiChaODVG7ZKZqbFv7mrZzDfRYcOvRxr3LArk/ZvGqW6nusIMhAzqZ+rdXkg6q8zeXNnSqXlvWctqqPKawH17PF4Jj366BV9s8i19P3BMf8pJb2qsPoypLTH4MXr+LwZjuOOdjRj60ALldqCprbieGhMDoQgUjIufNVZZLqLOghkhapeCZYSenjYcvbJaVpn1yU7G1MJ8vNu8GisU2qAr3L/2jXZ7rzcIkF5e2tKVWruViPjZtD5Y9wte/W43Xv1uN/pmJ6OwR8vGweLr6E2NHQrQSsBoiibQ1JjMKDj4YP0B1e2ABdwmYpxYZYTE0xKJQEibEeoM3B4v7JEutiIygT911C4F2vH9xatG4MITu6vukyQJyWFmcrTv5bCFN01hNDVW7/agIYTVTkdrAzeA3Hm4pfnh7iM1mLehpT2AOA2ld8E+FKBg2eiCLGaEjFaNmb2YBwqENuwvx2UvLw8YaES7IaXWF5sO4c/zt6jeNxIdxMXTZXYlXHWDG0u3H27zcxAJbyzfg4IHvlStaiRqKwyEdLCPUPyzWtU72Is7wGfpFNs6bFakJrQEQtqanCvG9DR8L6dNnQFyBAjCAqk2CoSaPCFlEcqDBEJGmSetUDNCRlM0RhkhryqrYe7zBQuYVu0+iqXbDwcYY9tmT258cx3+8e1ufLLxkHJfJDJCjULzTbOf6cZ/rcX011bhaWFlYHvxwEeb0ejx4ra3NsR6KNQJMRDSwT5C7YM4PZYndF3OTHYqXz9ywRCkJzrw2EVDVRkhcWf7j2aOx4m9uhi+j9MvI6T/zyYjyYG0BOOsk3Eg5A3pr/hjOjVCB8vr8P3PRwCY3/tLL3NxqKJ1GSG5Rsjt8apeXy+roXffH97biFe+bZkS1GsXEKhWKFZTY+J5i0Qg1KDaHNfc63230/f9f3PFviBHxi+9RQNE0cZAiNotsXZH3nsMALKFr68e1wcbHjgLQ3ukI1lYci/vXg8AyS6bqqGhljYQchoEQv93yTAMDxBQGU6NhZgROqaTETr3mW9x5T9WYuWuMtX+a4Fo31OSJBwMlBEyuCDXaVaNvbf2F5zw0JeqTtB6K9QqdTJXXgn40/ytym294CvcqcloEoOzULtr6wU64r507hBrhNpzV2oGQhQLDISo3RIzQj27tBQPa5eWywW+GUlOPHLBEMy5eCgyhWAp2WUPWKRpNiPksFmQ6DB+HaOMUF0EAiE5C7RgS4lugKFHvAB/sakYo/60UNnaQ4/RFI1q+XyTF3e+uxH1TV7M+s965X69fdbMZK70slYW7S63YZIkCXe8sxHPLdqhui8c4qkJpUbo7dX7cMKDX+IbzXSfeL7qmjxYu/eo6Vqh9tyMkYEQxQIDIWq3xIxQ9y6J+Oe1Y/D+TeN0uzTLrh7XB1eM6aXKACW77HAEygjZzBVL261WZTNYwL/IOtDUmPbiecWYnrjljP7okuRAljDVB/hPjYl9girrmlBlMiPUJOxEf+Oba1FW0xgwm2E0RSM2fjRaNaaXDTMVCOmMJ9z9zFbvOYqb3lyrdLDeVlKF99f9gie/2o76Jg++2X4YJz76Fb7YVBzya4vBRyhB7f++/yMa3F7M/Pc61f3iz8PdH/yIS15cjicWbDP1mh5mhIhCwkCI2i2xu7TTZsVpA3MwsnemqeeKtSRJDpvqF/DFI7rjwakFym1t4GNULG23WZAkjKlfTorqcaOpsQadjNCMk/ti9tmDsO7+s/DkZYWqx7QZoWJhOutQRT0q68OvEQrE7fFt8aENYMTgx6iPUE2D//2Bir7lzIx+IBTeNh6/fmk5Pt9UjEc+2dL8Hi2P7Tpcg6tfW4Xy2ialCWQwYvAjTkeFUyPk0rRoEKfGNu4vB6BupyCqbnDjpaUtm/K244SQbof1tiJJEv69ci9Wh7HZMLVvDISo3RKnxrTTV8GIF0G7zaqa7rJb1ZMv2t45RjVCDptVVZCdmmDHynvPxMzTjwMAVBv1EXJ7/IISOaCyWCw4bWAOphbmK4+JHacBoLiyJRDaUVqFyjpzU2MhB0JeL55dtBOFDy/AdzuOtIzfxPJ5vWxYoIyQHKjqBkJNrStGPtCcERKDth2lVSG/jnj+xDKecAKhJM2WMXpZL6Ofuz/P34LHP/9Jud2ep8YCZXOjbcWuo/jjh5vw65eWx2wMFBsMhKjdkvcdAwL3FdKjLSi1C1kfm9Wq0+e5hVGNkN2qzgi57FbkpiWgV6avfsmws3ST1+/iKU77WSwWPHfFifh41ngAQHmtOoAoEQKhksoG0wFOqBfs+iavsmnr3O93K/ebaagY6tSYXI/U6PF/vXAyQmLtT3rzprn1QkC1o6Ta7zkAsGJXGRb/VKr7mBisiK8fTh8h/0DI/zO6DOrPVuxSZzDifWpMkiTc88EPeO273X6PxbKf4i/HaoMfFCd+PlyNuct2R2SFIrGzNLVjvTNbCqRDzQhpAyFxasxutRhu8GmzWgxrhBw2K1KEjJAcMAXrRF3X6EGjSz8jJJKzTbWaXeyLKxoCvr6RUBvvLdnWEhB0a1511+TxqoqojQIhOSNUWlWPO9/9AbmproBdvuWMkF5mpD5IRsjjlfxqTY5Ut2TR5O+RmBHaXuKfEfJ6JVz+9xUA9DeDbVQtcW/d1Jg4zStJku7nNtpWRptEEX92n/96B7JTXLh8TK+QxxQt6/Ydw39X7QcAXDO+jyrjarPGLhIK9XdILJ355FIAvj+uZp7eP8ajaf8YCFG71VvYZsJo2sCINgYQszyBCjYtCJARsllUxdLymIL9gq13+9cI6V305NfTZhzEjFAoGt1efLThAIZ2Tw9+MIBvhekwObDRBj5G24XIxz/yyRa/FVJ65CXj4dQINbq9qsACAPYdbflrv7Z5zGIgtOtIjc77tLx3eW2jfyAkfB8ahNdq7dRYk0fSDcSNtpUx+nndf7QWf13gy+BdMrKH4c9tWxM/m3YD4VA7Izz08WakJTow+6yBrR6XmFU22k4m3qxhPVNEMBDSUVRUhKKiInh00vIUP3plJitfh14jpJkas4p/lVpwwfB8/OXLn3DqgBzVcU671fC97FYrkl3+dUvBpu30Vo3pXdzk19FeaMMNhL7/uQzf/2x+SwOxY7Vc6KzdADbY1FigztUiuYu1/sawgQON+iaPTiDUEujIU3L1QkClN3Unfha9i6I4NjGoMrtdiljLk+ho+VVsFOgZBUJWExfs4op69Mw03p+uLYl1QKWVDepAKIQaoV+O1WLu93sAALNO79/qjI6YuW1we01t7Bxr8T0J2n7Ex58IcYadpdsHMSMU6rLb/z33eCQ6bEohs92qLpbOSnFh44Nn4+XfjgQAPHvFieiS5MCr00cH7CMkZoTMTo01NHlMTVPJr+eV1M0NS6vCmxoLlRgYlDcHE3/+bIvqGG0ht0xeNWZ2u4iWGqHgGSFtUKs3rbSvrKXzs9xwUgyo9J4jfl69poaqQEj42kxG6JdjtarVf2LgZtQeIMGgRsjoZ1+c/t0fR/Uv4vkpraxXff/MBHUyVWbJ5ErJQMRAyqjonzomZoSo3RK31TgcYjAwMDcVGx88W/nlJxZLy3+xigHM+YX5mDqsGywWi6qo0mJp+YVst1lV3avl185IUjd41Kpv8pjqjSP+om4Uduouq26bQEisTaqobUKD24MvN/u6R7941Qjc9O912HXYf4oJ8I230e31W9GU6rKjSicb4w6wakybEdJuq6G3hF8MOpSMkJjF0XmOeDHUG4d4X7BjRWv2HMWlLy3H8Xmpyn3ilJBxIKQfUBt1RReDzl+OGW+d0tbEz1da1aAaZyh/0IjPq6x36+4xGArxnevDbNHQ1uJ/8q59YEaI2i0xxS6vBAqFGFjYNcXSeuTpEbEeSUzrO2wW1fJ5eSrr+LzUgOMz21laFQgJx5c1FwKnugL/XRPqyjot8WJ/rLYR9Y0tYzhzcC6SnbaAK6aqG9x+GSFxaxSRnPEyUyOkfU29QEK872hNI34qrgyaERIDJXn12r6y2paxCVPndSHUCL3U3A/op+KWAu0mEw0ZjaZ+jJace0MIhCrrm3D+89+p+hFFi/j5SirrVec+lEBIfJ1IZITEH6O2zgiF29E8GuqbPLj6tVW6q/o6KgZC1K69+bux+J/T+uGC4d1b9Tp2k8XSgLpYWswAOTQ1QvLqMrvNiuE9MwxfT2/5vO4YrRbIMweNHi8OlNehvsmjZFS6ZSQEeLZxRsEs8WJfUdeE2ibf+9qsFjjtVgwUMhx6dh+phkezg73RmAJNjYWaESqrbvDL+Jzz9LfYdKDC7/1E4udtcHvxxaZDOPWJxUrDRaPMjRwgVdY3YcHmYr9pzyM6GTxxqtOoRshoDzGjJoTqjFDgqbG5y/bgh18qVP2IoqVRkxESb4cyNSY+z2zvrEDEtgNGtW5mSJKERz7ZYjqQ2FlajZPmLMIby/cEPK68ttEvYIpU+FRSWY+1e32F1++u/QXfbD+MRz7dEuRZHQcDIWrXJgzIxj3nDm51oaSqWDrIL2Oxs7QYQPmtGhOOO+eEPN99OvVFYkPFVJcdz1w+XPd9LRaL8vz/rtyP8Y9/jTvf3aiMX7uqScuo2NYscYm4V2qZjkxo/pyDcgMHQuv3lfsFHHptAoDAy+e1gYK2b474nBW7yjDyTwvxwfoDfq/z7Y7Aq9e0011//8aXyVm4tVS5T498/30fbsIN/1qLv36p3hpDPxBq+Qzi1icivQ1oAeOMkCeEjFBrLvyhEjNpvoxQy+1QNowVXycyGaGW9zbqkG7G9pJqvLZsNx75dIup5pYPf7IZJZUNeOCjzYbHrN17FMMf+Qq3vLUh7HEFMus/63DJi8ux63C17jRxR8dAiAiahopB1vCKfYTUX1uRLARCYn/qy0b1xMPnD8Fnt57i93qSBNQ2Z3XmXjs6YHZLDq7kxoaf/nAIgG+KKdDU15i+mX4rqVpL3tpDzur075oS6HBs2F/ul70RA0dRU4CpsW3FVaptRTwe44zQnAAZjmCXKG1GSCtYIPTxxoMAgJe/UW+NoRsIiVNjBtOLRgX1RoG7eK7l/dWMhFNr0uj24sdfKkLuZK3NCImdwvWK0n33e7F27zFV0CR+T8xuKxOIpAqEwm9UKMalehska5kJ/uTp1E+af6Yi7WC5799TaVVD3LRZaEud7xMT6RBXjQXNCAnHarfmSBKmxpq86tqH6Sf3Qf+uKUpmZnz/LOVxuYDXaQscrBj1S8pMdhlmxcb0ycRLvxmpavYYCfKyfTkQMspIyav7Nuwv97vQGQVngbbY2FNWi5PmLFJua7NMYlF3oELyKoMtT2T1mrof7RJ6o4BFzO7JxGBB7yLrFn5WjAKsJo8Xj3/+Eyb/7RvVhV9vKtfjlVTnxWifu9a4672NmPr8d3g1xFqSBlV9W4PqtjZQlj29cAcuefF7/O97Pyj3qWuEIjA1Jpz21tQIiT8nZlZ0mgk8jH4jtaZYury2EW+v3ofK+iYlwPR6JdUfhZ0FAyEiqDM7wcoUxKmIPlktvYx8Xadb/kkZ/XX78azxuHZ8Xzxz+YnKX4/y9hsOe+A3Nwp2slOchsv0p5/cB5nJTtx65gD0y0kO+vnMkvc4k7d+SE3QD7QKe2QA8E3PaDMr4nRdjy6JytfyNJAcVFx0Ynf89dfqzWflv+C1F09xCxK97ItZ2qkxbX2GUcAif8a+OS0/G9uaO1dru4LLxGkvo8yP2yPhpaU/Y1tJFeYJU316U2MNbo8q01Ab5MIezs/ERxv0M17BqPsveVVZniav/meXi7jnbWjJiKhqhCIQCHkjVCMk/jwGCoQkScJTX23Hkm3BG4yGUjtl1o1vrsX/vv8j7nxno5KV80iS6ndYe963LhQMhIig/qvaTJnCynvPxNK7JqJLcstqMG3GwOiCNiA3FQ9MLUB2isuvWDhYh2yjQCgr2Wn4mPzZJhXk4us7JmLVvZMCvodZ8tYecjCTmqC/Mk5cGaadwhBrhIZ2T0e/bF/woM0IJTis6JqqzjjJAYe2RuioMB2hzb6ccXzXQB9JRbUSTOd7afT9lccsXqi3Na8QO2BQqyMWSxu9rhjUiYGT3h/wDU1eVSDe4PYaZltayygANiIGww1u9UIBozHq1beJ35PKAPvWmaXO2kUmEArU1mPJ9sN4dtEOU69ptPOI0XfU7fFi88HA05byHnULtpQo3xO3V1L9UWimrUdHwECICOr0tJnLRW5aAnpnJSNQctqouFWkrZEJVvRtFChlpRhPjWnbAWSnOJFtsGzdDDmwKqtpLpZWAiH9C2Ky066MTZtFEafG7DarkpbXLp932qx+NVDKX7Ee44yQ1tXjeuPeKccbPi7765fbcExoDtnQ5FEFupIkGe7xJo+5WpiOkrMeRrUs4sXTOBASeiEJwZ7e1FiD2+tXexJqlsPsku5QAyHxZ6BB00PL6N9Mgs4UaiSnxiRJUi2f9/X28mDxttKQt0wRz3tplXEn9WB1WyJLiJNg93+0Cec9+x0e+XQLVu4qC/q9lINKr1dSlQm0JiA0o8HtwardR7Fhf3lU3ycYBkI6ioqKUFBQgNGjR8d6KNRGxGAhlJUrgVbam+kW3V2YDgJMBEIGj2cmOw2DJO2cv8Viwdi+WbrHmiH3RJK7SCcEmRpz2q2GPY6SVO0HLMqmm9rl80671S97JgcXbs10ijwuvd3tXXabqZ5Tzy/eiWe/3qncbvSop8bmfP6TUrCuJY+5VjO19szCHfjT/K26z1Ft2moigBa3KtELhOqbPAFrpwCgaPFOXPXKCvxn5T6l6F72zpr9GPHoV1i/71jQsYRaeyYGPvVuj2pqTNteQaabERJep6IueFGykdvf3oCJf12CGuH81DV5cMMba3HN66vx5oq9Ib2eeN5LK40zQkZT53rEZLOZAFXe1Hbu93sw7e8rsMTE/n6Ab+zie0W7seThqgZc9vJyTHt5eVTfJxgGQjq4xUbnE+oWHbJAU/dmXrKXZv8nV7Bi6QCBkNGqMbtOXv3qcb0BGBc4B+IXCNkDT4257FZVo0mReIGz2yxKWl6715jTblVqkWTK1Jjmgi9ncvSmoZx2a1jNN7VZgb8HqIuRjxULlPcfq8PfFm7H+n3lus8Rg7kmExmIYIFQg04Xb7kBpnwhfeLLbVi2swz3fvgj9pa19BmSJAl/eO8HHKttwk1vrgs6llADoUZNBkgMGI2Cg2BTYwu3luKhj42Xnwfy4foD2FtWi6+2lCj31TR4sLQ5eHhv7S8hvZ7ZqTG9P5SMghwxGym+vtnfWktN1CEBvoyQJ0hhfySJGd9YYiBEBGimPcw/T6+I8d4px6NXZhJumxR8R+xemeqMULBiaaMVJumJjqA1QqKx/bLw7o3j8N6N44KOUe+9ADEj5LtIGV0QnYECIWFq0G6zKmOVL4gNyi9KmxJwyeS0vTbzcay2EXM+34oHP97k934uuxXpiaFPC4YyPdLo9sLt8aoyH6t2Hw34HLeJqTHRoYrA0yoNbv+M0OHqenyxqRhjHluELzYVGz5XzE4dNig2F6dMjAJgI42azazFaS2jveiCTY0BUDZgDZcYOK7c3bIZcaGmGercZbuxeFup8euYnBrTmwY0qskR/wmLzzP7q8qoW76W2y8Qim5GSP4sjlb2gWst7jVGpBFKu3u9QOiGU4/DDaceZ+r52oxQsL+MjLI+6YkOw+c6DJbDju6TaWKE+u8FtEz9yJkao6ya025Fiks/06WdGpN/Ycu/jOuapytcDv+M0Fl/+wZ3TR6E0wbmqO7fdLAS6wwyLwkOK6wW9YXbalFvr6AnlKLRRo8XtZoLSLAaCLeJVWOiQxX1Sl2LXoFxfZPXb5rpkhdbph/k7ti6YxGeZ1S8LE47Jjr9f+72ltUgNy1Bt3O4NoARX8tt8NkTdH7uI13IKwYwciGxdkxr9x7DQ5/4Oi7vefw83dcRv5cHjtXhte92Y8rQbshLT9Acp7+ti945E/9lGa2sC8RuMuPildRtF6IfCDEjRNTp9RQCIasl+C8so18Y6YkOv0BBFuq0X7AO1NpNZINt3eG0GWeEkrTF0s3TePLeW9tKqgEAfbOT/TJCgG96R3uxDpS9cdpsSNeM38xUWUiBkNsbct8e8aJopkaottGDPWW1GPPnhcrGt6IGtwcm4ildRp2tRWKjQO37rN93DKc9sQQXvfC97nO13x9xxZdRRkiv35T2dayW0PfsEo83Ou1ikXmwTBygDqgOVtTjkU+34KQ5i/zG1qTzWY0K2sU/uEKpLZIZ/TGk5fFKQXteRZI8vRksEx5tDISINEL5Xdra9h69hT5EI3t3CXq80fRXoIyQXo1QIAkGAZX4XqrjDfoXyVwOm+G0WYKmRkgu7PZ4ff1ldjT33xmSn2YY6MkXz2QTnbNdDisyEkMPhAJtJqtVUdeEmobQ/pJuCnFqDADuencjymr0i4Qbmrx+ReTmx+LVnUo5WtOoBHjiyjxtZkPuL7T1UKX+2AJlhIymxoSfMa+mkP68od1890vqlXpmqKaZDP7hi/2kxEOMlqYbfYblu8rUx+l8n40aOVpUgVDwjJ2W2d8Bbm/bZoTkYDbW3awZCBFphPL3ltm5dyN5aQnIT09AosOGORcPC3q8YSCU5IDTICAJ1ik2X5OyD7ZyzS8QChI41Ta4DQOhJM2mtfL5bPJI2FFSDbdXQnqiA90zEg0DLvlikGWi8NtpsyLJaVN939KTgtcMhVIjVFrVEPIFWbV8Psh7ndgrAwCwZq/xiq56TUPFULg9EjKEcyJJEo7WNGLEo1/hvGe/BeDrSizTXozFPw7u+eAHpYeSLJypMTEjJK9kkl+nR2aiMmUcqHWCHjHoNDpfYjG3eMSJj36FosU7/Y43CpAWblHXFQXb6FcknlMxaDabHTLbLdrrlSK255oZnBojilOhXD+uP7Ufuqa6cONp5mqCtGxWCz65eQKW3DUx6F5dgPEvjFSX3XQfIa1Xpo/G4G5pyu1gf535B0L6AYp8cTqhe7qpqTGHzaosn/d4JWw+6NsdvqBbGiwWC6xWC96/yb+4W858uOxWpAXpaeNyWGGxWFSfN9JTYx6vhP1HA+/2rnW0phFvrtgLr1cKmhH69cieQV+vrtET1hQK4Ls4pSe2nMeaRg+W7TwCwLe9idcrqTNCmgu6OI3z31X7cfELy1SPa7NrZjJC4s+9nG2TAyGXrWUloF7LhEBUgZDBaReDEzFrVFHXhCe+3ObXvdwoS1PXpA6O9YJro8BD/Cdspvmm9neB2c7Ubq+k+rmJ9vL5JqE9RiwxECLSkELICXVNTcDKe8/E3ecGb9JnJCvFhdy0hOAHwvgXhsViCWnVmKggPw3zb54Q9D1k2sDBqKbo+7vPwHs3jgsYCCU6xFVjLcXSbo8XK5tXWg3rka4cM7J3Jk4ZkK16DfmCYrNa0CU5cHZHvqD++aITAPgCyOwgz/G9R2gXhN1HakI6HgDum7cJr3+/J2iN0En9ghe51zS4w84INXm8qmD4cFWDaquI2iYPjgmBkPbCr/1xq9FM92iDygNCY0E5EPJ6JdUFX/w3KfdDElsryHVroQZCYlBmNJUYbN+xUX9aiNeX7RZeR/+8N2pqr/SmW+sa9ccgNlQUgx+9OiPAf1GF2elWr6TNCHmb/x+dgEg+J5waI4ozoV4/tFtrRFOgXxjGq8aC/zMX96tyBKknyNBMJRlNjWWluDCqeWWaqVVjQo1QXZMHC5v7upw5OFf1HO0vefniZ7dZ0CXINJdcjD6sRwY+v/UUvP0/40x1Rm50e011CpftOlxt+ljRG8v3BL1omVmuXt3gNrwgB9PkUWelDlc1qLogV9U3abI42qLlwP8etJmQXYdbgkZ3c+PKqc9/hynPfqsEWeJHUTJCQjZBDs5DnRrT7numR50R0n+dh5tXkvnGahAIab6v9ToBlmGxtPAjL/4cGk0lavcdNBsIaTfrrW/y4O/f/Izj7/9C+fcYSfK4zBZzRwsDISKN1mw/EW1Gy+e1j4l/lYe6aixYXKfNCLnEgmeD90px6V+8xdoPC1oyQt/uOILKejeyU1x+ReTaX/LyRdlmsaBLkvmeNoO7paEgP001fiONHq+pi4mcmdgVRkYIAPaW1QatRzITuFU3eMLeMLPJow76jlQ3qAK76nq3aquQUKfgAk0zeiXfBqqbD1Zie0m1srGvGFzUaDNCtpbeUHoZoRW7yrD85zK/+wF1UNFgkPVT1wgF/6xGU2ParKJe0KPNvMiBobiA3q0KhMxlhMwW+3t0Gio+9tlPAIA73t1o6jVCwWJpojjz/JUn4tcje2Da6F6xHoqhQNNW4i+/ZLFRYRgF3ZeM6GH4WKAaoTevG4tu6Qn4x9WjVMckG2SEtMui5YzNtzt8NSmTh+T6BXLaX/Ly8mszU2N6jILLN64dg1HNQVijZnNQI/npvgaZuw+HFwgBwJbm1VZG43LZrUH/gq5uaAo7I7Rq91HV8v8Gtwc/C5+nst4dsAmiUVbj+5+P4OWlPysBh/h9PX1QSy+oKiHIko8Qg7pnF+1Ao7ulYaVT2DalXLPVRkVtEy7/+wpc8Y8VaHB7UN/kwbz1B5S6HjG4NQrQ6oStN8xki40DIfXr650n8b6q+iac+8y3uLBIXWMl9hEy6imk/T1httjfPxBqGU80dqKXz3+gP/DaAhsqEjX71bB8/GpYfqyHEVCg1RXiL79Epw1VzRczs83URE9eVojB3VJ198ZKS1T/2hCb3Z3ULwvL7znT7zlGq8a0vwC1QduU5qXRquc49DNCdqs16NSYmTHITh2YgyaPF7/75xo0uL2mCqZ7dEnElkOVyrlPT3SEXLeytnk12O8n9keS04bTj++KSU8tVR63WCywW61o8hjXbVTXu8Pebf6RT7eobtc2evDLsZbi76r6JlWwIr5PbaPbcHrqyn+sVN1OS7ArtUbinnvi8+ULuPhRvt1xBE99tV01NWZUI7RFWMJfVe/GP77ZhZe/2YV+Ocn4+o6Jmqkx/fNZ1+SBJEmwWCyGgZD4b88wEGoe776yWhyublBlmvTG8NRX27Gj1JeJO05YSNEgTOEZZSnDrRHy6ywtZLHCrTkLpGVqjIEQEZkUKCMkPiZ+He4+anrP8y0/1wRCJqaWjIqltfVI4nt2SXJgbF//wmCjGiGb1YLMsDJCxuOXz+Pmg5V++zpdNqoHymubsECondBuotslKfRASJaaYMe1E/rqPma3WYAALztvw0HMa+7n01rHahpVgUh1gzYj1FJQO/LRhaZ3uU9LdCiBUE5Ky2IBscGisiu65iL85oq9OKG7b+Wf097SG6qi1jgQqm3wKJvLynVJjSYyQl7J91iCw2Y4MSb+geIxCBia3L5mhac+sRgAdH9WH/hoMyYNzsXG/eV4fdke5X6jgE2eGjtW0wiXo+Xfpvb3hLZJplF2R7vXmBh0GX0u2dGaxpD//ckLA2IdCHFqTAd3n6d4Jf6CO7V5a4lbzujv95gYLITb60gvEHLYLKoCZ8BcIGSUERKLtCVIql+IJ3RP181maRsrioGQtuu1GUaNGoGWC5y2iBQAxvbNwqzmcy/L0lwIzPQoMhJo/6W2vHAc0wQXs/6zXslaAS0X4/1Haw2DoNpG/75KaULRd05qSw8oMXCUL8TaQKi6wY2dpb5gxmmzKt3CtdkouQWD/BxttqbJREYIaFk5ZtR00Wm3QpKk5o7e+sc0eLxYu6/lvB01aIb54Meb8beF21X3GQVCTR4JlfVNOPHRrzD6TwuV+7ULOLQ1QkY1Q26vpAp4xBVzgRKMzy3agRGPfoV31uw3PkgHa4TiGHefp3gl/sJ4aGoBFtx+qrK5q/hXqZjlMNtMTUsvELLbrHDYrKr3CtZQEfB1hr52fF/8+aITAhb7iu+Z7DSaTjMolrZakBlG4GF2ulEr2WX369ibpqmf0naxDkYMWp0Bvm+tbeQZCnE7DT1ygBgo81hW7f8a4hRrVopTKdJXBULNUzN6pTBynY+vf5TvPItF3ACw5aCQEWp0+2U11MXSxtNHcoAXKCN0y1sbMOSBL3GoXH8bjka3N+Bmt7IDx+r8soji1JY4XeX2evHTIV/DyppGjxKoaTM+2sDHOPtlPDUWqE78ya98gdu9H/xofJAO9hEiopCJF8CMJCcG5qYqWRWjqbFQt9jQey+Z3l9u2qkyPRaLBQ9MLcBVY3sHfk/h4p9kUGCtDbwq6tzKeLVL+80Qe+TI5OAo0LRZaoLdr2hZG+QZNWu8+MTu6JudjCd/Xai6Xxx/oL+S2/Iv6GBL0uULp9HF1Wa16G4FkiqsJOyS5FSmSdWBkO81xQCmT5Zmo2J7y1522j3e9gmNLWsa/bM1YoARaOanNkhGyGG34JONB+H2SvjPqn26xzS6PVixS3/1mqhbeoLfuRQDGXGZf5Pbq57Kan6eNnupLZY2Kp4OVCwdbGoMCL3ouaWzNJfPE5FJ4i9I7UVXFQgJF8rwa4T8fz3Iv7DEX8x9s5P9jguXGHxpp+Bk2uCkspU1QtnC1hxyc8jhPTMABP5LNcVl95u6S9W0CTCaqivIT8PiOyfikpHq1Xni9zRQsBNKlu/C4fkY3Sf4PnZGyk1mhIwCIY9Xwn9X+gcHYkYoM9mh/JyKgZB8wZYDkMcvHoozjlf3lXLYrMrUq3ZrE/GCX6MzNWa2Y7gcEBjVHIv/3gwbKnq8OGYwHSY6Wtuoqs0B1LU6qqkxgy0xtBkhbbG00dSY3vJ5mZliaXEhg8cr4aY31+K5RTsMj29ksTQRhUr8Jaj95SH+Mg4zCaSilxGyNV+Ak5w21DZ68PuJx4UdaGlJkjp7ZTw1FqihYug1QheP6I6SynqcfnxXJDiseGP5XtxyxgDd9xL5psYCZ4SMpsaMfvGLtVR6QZj8fqFMjWUmu/D05Seiz93zTT9HFDwj5LuYNQSosXlbp3ZELKDvkuRUCsB1p8aar8FWqwW5aeo95Zx2K2zN82riZrdeTV2XXiCkzSAZkTNCRt2nxX3+jHr7NLkl1Wo7rXH9srB8VxmO1jQqnzvVZUdVg1tVeyUGJ26Puq1DXZMHGfDP3vgFQiYzQmJgaWbRmPjv5dsdh/H5pmJ8vqkYN585QPd4sTt4LDEQImpHjLrfAuqiZaMgIhRWnYut/Ivrn9eOwfp9x3DteP1VTeFSZ4SC71gPtPyytlrCmxpLcNhw+1kDlduPXTRU+TpQIXVqgt3vr2RtjZBRsbSpQEjnGDkTFMpf0K39YztojVDzhb8+hP3YAPWFNT3RoXwm7dTY3rIapWjXarH4bUfjtFmV6VLxwq3NemgDoacWbMOzX/tvmqrn0x8OYtnOI37fX2UMdjEjpH8eahvdftuNiKYW5mP5rjKUVjYogV9aosMXCAnPE4MiXwPKlvMlH+dXI2RyakxbLL29pEr3OCNiIGQm28bl80QUsqwAXa8THDa8/NuRkCQJn5soygxGL+sg/2U8uk8mRvcJvudVKBw2q+qXsFETRqPfmXarb7+1MX0zsap5n7LW6pqagIemFuChT7b4PZbisvv1gjGfETLowB1kaky+z2hq7Jwhefhis/p7L09xWiyhbx8D+K8a01KmxkLYjyrZaVNlKew2q+7U2NJth3HrWxuU21YL0FUnIySft5pGt9LzR5sF0X4PzQZBAPDG8r0A1PveiVzi8nmDqbHK+sDZJznTJQY68s+TeJ/2PIvtBuTjtBkh7f51Rl20vZIEj3BsVZAxa4nT1jZh5ZrXK+n+YSUv6491Rog1QkTtyGWjeuKKMb3w0m9G6j4+eUgezjnBvwlhOPT2jNJrAtdat545AEO7p2Pa6J6qvkLartMyozoN+YL/1vUn4bSBOX6PD+2ufxELZsb4vn4FuoBvetC/WFqTETKRQZDZrRakusRAyLhYXa8A/t4px+OpaYV+98vX6NZOYBrNxgUrltaTkeT028zU0fwG4oX9g/UHVMfYrP4ZIZe9pUZIknzbm/xrxd6QL+Jm/PBLhe796oxQeI0HM5OdftPMciAk/rvTLvMXA0f5Me30XFMoxdKtaJwoZlDFH1Gjn4142WuMGSGidsRpt2LOxUODHxgBenuOhdutOJDbzxqoTE2ZWT5vVLQpZ7CsVotqZdkfpwxG1zQXTh3gHxyZpdcryWKxqIqlExxWpCXYYbW01LQYFUvrBTJ2m0WdEdIJluTzo3fhOKsgT3c6Ua/oPRzd0hNVO8XLxIaKZqUnOvwuuHL9WaAGlBadqbHUBDsSHTblvF/96iocKK/DymGt/4NArtEJRvx+hBtHOGxWdEly4EhzqwHflJ/v586oRgjQBkLqvks3n9Efz32907+PkMkaoVCJU2NiL6O6Jo/uHzYNyqoxZoSIKA5cOda3x9odZw8CEP4v9NYQLyhGq8aMuuLahOeKAUqyy44LhncPax+yYGMRpw/7ZafAYrGoghGjQEg322O1qgqI9S4OzgAZIaMCaluwXXRNuujE7rh6nH/7Azn7oP2rf0L/bMPXSk904LZJA5Gd4sQfzvH9vOktn9eyWtR1VCN6ZSAjyQmLxaIEznKwJneRbo0UExvcApGpcXHYrKpVjy67VZlqUjVUdBtnhOQsmxzMdG1uVKmdJmwwsWosPz1B95jFP5Xisx9bzq3YUkCcGhOn2LRBsvwecqYqUPPQtsBAiIgAAH++8ASsuW8SzirIDX5wlIjZC6NtOYwuTuIFXwwiwm0oKTKaphMvgPJ+UGL2KC3BIBDSmxqzWVQXeb2Lq1w3pfeZ5BqM356kDlYi8fkB33L3Ry44AVeNVW9KbLR8/s3rxqpW8d01eZDydUaSA32zk7H6j5Pw+4m+7tx6NUJa8vf40pE90KNLIp6/coTymNmgJRRGP4NakfibQdv+weWw6jYr/XijeuuUCp0aITkjJP8shjQ11vz91PvsHq+Ea+auxu//vU5paCl+38WMkKoJpBAIPb1wO4Y/vAA7S6tYLE1E8cVisah66kQokRASMathFHycMyQPFwzPR5ckJ+Z+v0e5X5xWE18nEvUHiQbbiIhJmH7N/ZTE3+lJwsXEbrUoQYNetsdhs2r6CLW8+JO/LsRTX23H36YNV47Vkj/zoxeegLKaBnz2Y3HzeCLzjZQvjPdMGYxu6QkYkp+Oa+auVi6celNjYq1KQX6a8rWcKROnT+QsXqCZGfn4v/66UCmK1o4vkoy2htEKZWf2rqku3SaeDpsmELLbdBt6ajO1YnsDpVjaqw6ETE+NCZ2l9erYxCm6yromZKe4VPVL4nPE9zxa04i+2b7v19MLfX2F7nz3B+X8cmqMiOJeWxUzitkLoxohu82KZy4/ETNPV+/zJQY/4tRYuJ21RYkGYxEvxP1y/BtLigGU+Ney0Yowo4zQJSN7YNndZ2BIvq/gW28aTCxuF7NSchZl4qCuAPw7M5sljy3FZcesMwagf3MGTK4REjMDtzdv+yJOwYiF4OmJ/tOUZnojiUGddj+taARCgbaDEYVSYKzdmFdmt1lVP/MuuzVg+waZuBpNKZZuDmbkn79QOku7AwVCOoslagx6DYnvcelLy/HwJ1tQUlmv3LeztDpu+ggxECKioCJ5kQl0uRMvdEZ1OTLtRUqsEXIYfB2uxAAXpNF9uiA7xYUzB/tPKYqfR+y6qzcm7dRYoItDoIwQoN9Z/K+/LsQdZw3Ev68/yfB1A9FmR+QxeLwSdh+pwZJtpQCA6yb0xa2TfA30xIuhWDult5rOzBReoFgpNQqBkNl+XNoanEB6dGkJRMWfYbvVoq4Rs1tNbVmx9VDLfmraPkJiRqiu0aN8PwJ1lpan1fTeW8z6ya8hZoTEFXPa95j7/R6MfWyRcru6wR03naUZCBFRULc2d4a9cHh+VN/HGsJUR4LDpgoWxBohMQsUiYzQhAArzv57/Un4/u4zgk6jBMsIaS+EoW6xYVVlxFq+lgOhzGQnbj5zALpn6GckgtF+P+TXbfJIOP2vS5Sl5Xor7HzPb7lfr4jcTEZIr6WD3utHitm6I6MMix7x/MvFzID8/VdnEAPtdadHXjUmZ6gSnVbl/jOfXIJznv4GXq9kMiPk/97qZfy+16hpbMkIeYRmktq6JP3x+l6Py+eJKO7NOLkPxvbNwoDclFa/Vq+sJGw6UKn7mDqDEPwikJbgUIo2VXVBdvHr1gdCU5uXYmcmOfHu2v24/pR+ymPa/caMiAGCXrbHYbNqgqVAu88Hzgg5dDJCraUN9IwCF6MshljzpZcREqfWEh02VT2KTK8pnyyWNULahoWBiFNjuWkJ+PlwDQDf9zRJNTVmC3kTU6VYuvlUij9zByt801IHyusMGyp6vJKSTdJ7b73GjrUN5jJCerY1d66OdY0QAyGiDijSS98tFouq2LU1iq4cgUc/3YKbJh7n95j4y9PMRSAt0a4EQgnChVZszOiIQCBgsVhwfqEvGzZhgPGy8ECG98zA7iO+i55RjZCYAQi8+7zOPnBBpsZaSxuY2gwCNaO6FnGaSW811OmDumJ7SRVuOXMA0hIcuG/eJr9jAn0Uo6Clb3ayct5DZTYQMpP9kOWkOPHni05AZZ0b+4/V4vuffTvSa6dGXQ5zNUKilhoh33j0snNbD1UGLJaWn6sXrNcK2R85cFVnhFp+8TSZCA7l31OsESKiiLt4RHcAwPF5qWG/RrSS1b2zkvHK9NEY2dt/iw7xF7S2GFaPuDy9b1ZLsbLdoKdQLMy/ZQIeu2gopha2NPjTy6b0ykxSTRmFOjVmi3JGSJtxMcoIGU2NiYXjehmt288aiE0PTcbvJ/Y3fI1AU2NiIe65J+QpX+ekurDy3jPRMzP0KUGzWaZgNUJiYJqV4sJVY3vjponHqf6N2W0WVbDpsluREOLUWF2jB5IkKSvv9M7jT8VVhnsW+jJCLe+vVd/k3+FaDI7EjFAoncZZIxSHioqKUFBQgNGjR8d6KERhmTioK766/VTMmzk+7NcY1byXWFYrGhGGyihlb0QsNpX7+ADqX6yR6qNjlkUTQg7JT8eVY3vBadOfGnvj2jGYPCQXD50/BD0zk/CHcwbh0QuGBAxg9AIJsUZK9fkjFAhp2xkY1V4ZZfLEaS29FXZAS9Bq+BoBAqFBeb6MZaLDpqrDcdmtyE1LQJcwNuQ1XSMUJBBKEmp/xH9P4sexW9WrBl12W8CM0CUjevg1Paxr8qjaDyTonMdtxVWGXcB9NULe5vfXWzXW8jkb3F5IkoQaYWpMnRFqP4EQp8Z0zJw5EzNnzkRlZSXS08Pbn4go1gbkhp8NAnzFtevvP8uwn0805KWH9le7mEHqLSwLV9XLRGiLidYyyticOjAHpwp7o8kNBgPRmxqzGtRI6QVUM07uo+rBZEaSQxsIGdUI+f+8yAHrF7edgvLaJtXKKf3XMAqEjJ9zw6n9kOS04eIR3fHG93uV++VzbSbDqJVisgA72NSYeK6yUly6x9isFlXfqUDF0n+5dBguG9UTe8tqcNoTS5T765s8qmDEbrPCYbOopqm2Fleq+hWJfHuNye+vVyzdkv3Zd7QW4x//WrV8f+3eY7igaBlenzE6aAG5uH1JrKfGGAgRkaHWbEsRjl8N7YYdJVVKNiqYw9UtjemM6mvaOiNkRLwOt3aVTLDpvmA1Qg+dPwSXjuyBXz33nan3c9qtfu9ptVp0d7QX63/+c91Y/PmzrfjzRb798Y7PM1dnZnRhDFQsneKy48bTjvMbg3yuw8mMpbj0O4NrBcsIiVNRaQGyTGLg5XIYL5/v0Vxw3T0jEU67VQk6FmwpwT++3aUcZ7Na4LBZ0eRpydqUVTcGzAh5AtQIic/764JturWIG/eX4/mvdyqZJSOD8lKxZu8x33txaoyIyMdqteCOswfp7h6vp6x5g0qtSPcRCsU5zfUpx2mmf8RRtHYqIFgBuKpGyCATEkqmz8xeazIxID25fzbm33IKhvfMMP1e2tcQBZoaE6lX6Pm+DmfPtWBL8i8Z0QNA8OXzYgAhZqa006hmV43J58dus2LtfZPwuLAR8xNfblO+tlksfj9r9U0e1AfsLO37Wi84EZfPB1qQUdfkCXpOju/WkrEWM5ixwECIiNqtx5ozDfLGnbJId5YOxf+eczz+cukw/PcG48aFrQ2EgmWEzBRL613ozji+q+6xRo0F9V471CXfeoxqY8wmdfSaV4bzY5AaJCPkbL6AB8sIuU1uwaFtqOkyKBoXM16pCQ7kGmyQarX6Z3Ya3F7dDtFA8IxQlTANFojVEvycDBKyg6wRIiIK03nDumHccWepNvcEtNtttO1fm4lOGy4b1TPgMa1dyRXsM+k1VDTzGq/NGI0+d8/3u99w01mrFfVQX/CMLt6hMAqmzJ43sUhYfq1wznmwrJl8ATezVFyPNkklZt7sVkvQjJBMMkjP2K1WpCXYcVizt1llvf7GtuKmq3qBkNHztKwWS9CM0EBhcQNrhIiIWkGv8FP8CzPWf23K8sPs6KwnWAG4mT5CoZyXZIOAQK+XUEQyQhGcGpM/p9FzbVaLqsBY/TrmMm9Gzw+VuFzf45UM2whoz3GfLP1VeFaL7+dObtooq6gNHgjpfR8r68wGQsFXjeVnJGLm6cehySOp2mDEAgMhIupwjFZoxVLPzCS8NmOU7oajoQqWETIzNRbKajqjzIhejVCw4MEMo2DKbJmPqkbIZpwRykp2YtndZ+D4+78I+jp6Iv2zJX5ut1cyPA/acfXLScF7N47Dhv3l+NP8rQB8wYjFYtHdUqW8Tr+2zrdqLFBGyNzUmMVERshus+Cuyceber1oYyBERB2Oqj9LnKwaA4AzjvffmDUcwS7ADhNTY2KB6rXj+6oaPmoZZWj0XjsSwUGrp8bEVWPy1JhOFOW0W5HgsMFqAfSSOsECIWeAny1xRd05Q/LwxeZi/M9p/dTH+D2n5R63x2s4ZaRXQzWqTyYqhIyNfK50AyGjjJAkweORM0L+n73K5NSYxRJ8ujBSjT4jIT7+VCIiipJ46SMUScFWwon7qxkVi4v3nzesG07s1QUA8MSlwwAAf77oBOH9gr+GzOz0VSBRmRoLELQZfT69hoR6z9cjBl5PTSvEG9eOwZ1nDzI8XsvtlQx/do06Tovjkc+V3pSstuuznNkLmhGqM1ssbSIjFEf/LuNnJEREURBPGaFICXYRUdcI6R+jDqZa/nr/9aie+OnRc5Sl4UDL6ii/cQivkeqy4+pxvdEzM3CzRDNau2pM3M5Dzi4l6mR35HNgGOgFy7wFCJTEwCvJacepA3P83idQk0e3RzLcz80oEHaqAmDjQEhLnvr01Qj57tOtETJdLB181RgzQkREbSRSW0zEk9BqhIzqbdQXalGCw6bZpiP4VNX9vyrAIxecoHtcqIwa7JnPCPk3VPzDOYP8ponkzxhusBxwP7hW/tylJNgN+0UZBVCqjFCAqTGZfJ7k9gjeIMvnTRdLW81khOLn3yUDISLq0MLZWiHehVQjFODz33feYFx/Sl8M7ubf8dlMwbl4MTO7QakZVqtFfxsRk99LcWpNDqp6dEnCsrvPQLawxUWwqbFgAtUImWngOLUwH4B677W/XDoMY/pmYubp/UPOmohZHPm5eUKPofRE9eosuW+RvGzfHWTVWI1B/yEtq8WirBqbNFi/Li6eMkIsliaiDkfbsbejCfbXtNnd5687pZ/hY+rXM1p63vI+aYmRvZy47DY0edQ1KeHUCGmnt1T70MlTY2FelAMFUIG2A5GN7N0Fi++ciG5CsHLZqJ5KH6oKkxkYvfHIgZjTbsWjFwzBkepGLP+5DKv2HFWOSXbZcaS6UZkaq2v0oL554+PWtEEQp8ZumtgPP/xSjlJNL6N4yggxECIiameC/TVtNhAyy+iCL97dKwK1QSKX3Ypq9bXTdHfoQEv47Tb/TFewWiAjAYulTZ73vtn6PYB8rx/a986pkxECgN+O6wMAWLdvpep4eUpM/r+8CSpgXLBuhgUWZSNap838isNY4dQYEXU42Sltu1lsWwuWGDGzfD4URhf8I1Ut/WgC1aKEQ69GJZyMkFfTdVnMRMjvYSbg0KtbClgsHYEp2VC/d8G+79p2APLUmF6fqNZ0e7YIGSG9fcRsVktcTVkzECKiDmdM30zcPmkgiq4cEeuhxIS60DkSgZD+axRX1re8TxSbC8rMBhfi59d2fdarfQqU2Zky1LeJ7g2n+k8jBqoRish5D3GJuRi86J0rceWcxQIkNAdAepvLtmZHeI9XUoql9V4nnrJBAKfGiKgDslgsuHXSgFgPI2qC1UAZTZGEKxYtCPSmZsJpPaMNhMQVcHKAF+jzFV05ApX1bqzYVeb3WKAAamy/THy04WCow1WxWi2qxozBBAtexCnDBLtNqY3SrhoEWpcR8nilloyQXiaNgRAREUWTePGJxAyEdrWRlnbT20jQ6yUUznSTNogINSNksViQnujQzVAFet5vTuqNk/plYWzfzBBHrHkPqzVoTx6ZGLzo7X8mTo0lOKzKuUjSmRprTbG02yspnaVddiu0I2FGiIiIIuq4HHXBrTiVZTaboOe+8wZj4dYS/Oak3gGPG9WndRd7PbpTKmEEQh7NCRDPjfweZqag9DIkgQIhp82KK8b0MjtMQ3abBSZXraunBHW+8epGkzYlExbpGqFGt1cJxBw2q9/PYKSnUVuLgRARUXsjxAMfzxqPfjkpqofFC6K2WDgU153SL+AS+7nXjMZ/Vu7DnIuHhv0eRlqbEeqWnoBDFfU48/iuqvv1MkJmpv70MiRGHbe179MaobyOWJfk1ckIuTQZobML8rDpQCVOG5CDl5fu0ryWcXfvUb0zkZpgx5HqBmz8pcLvmLqmlsjNabf61ZgxI0RERK0iZkaG9cjwe1wMhNISIj9tJZs4qCsmDuoa/MAw6NUIWUJIJCy64zSUVTf6bfmhqhGyB95iQ6S3DDxgH6EIrYrSvsejFwwxPFa1aavu1JhQI+Sw4cITu+PCE7ujQmcTVqMkWbLTjnduHAcAuKBome4xdUIKy2Gz4vkrT8QlLy5X7ounHkIAAyEionZn3HFZKOyRjoG5qbqP26wWvHXDSahr8qBLcvtsJaCXgQllaizJaUdSpv8lTr9GqOW+cf2ysHxXGTI0dU96WaOAW2xEqMBcHO+nN0/ACd3TTT1PLyOktwcboB/0GGWEXDp7tmmJGSGHzYKRvTOx/J4zMG7O1wCYESIiolZy2Kz4aNaEgMec1C+rjUYTHa3pIxSIXadGSLzo33xGf1w0ojtOGZCtep7exbtNMkI6fY/M0KsREoulxYBGL+gxilUCNauU1Tb6GjM6bVYlSxXpJp+RxECIiIjijl5GKBKxhV0vIyS8l8thU7a4EOkFNoGWq0fqYi8WFocSXOmvGmt5LXGlmF7yx6jhobYpox55akwM3MTzHqkgMVLiq3SbiIgI+jVCkQgubKo+QvKqMfEirf88/c7S0W2oqH2dUF5SNxASzumpA3KUr0OZclQFqAaF+PLUmFGna6k1SxmjgIGQjqKiIhQUFGD06NGxHgoRUafUms7SgYiBhV2noaLRe/TMTMTZBeqd1Fu76aoZ9jC3S9GbGhPvuWREj7Be11RGqMk/I2SmID1W4ndkMTRz5kxs2bIFq1evjvVQiIg6Jf0aoda/rk0ILOSCaDP1KxaLBX+/ehRuPqO/cl/ATVcjNP0jZrBCCQT1ki6nD+qKM47vikcvPAHpQjF4KPt+makRqmv07yodb3VBIgZCREQUd3SXz0cguBCnwfLSEnz3hdmJO9BmrZG68Ivv0dqPn+i04bUZo/HbAA0yx/bNxNxrjGdDxOm1ayf0BQCcNjBHdUydXCwtbvUSZ3VBIhZLExFR3NFrqBgJYoYltzkQEqfLQglgAh0bsWLpMMcWrmsn9A3YG0qcGju/MB+Du6WhT1YyBt73uXK/MjUmFnrHcUaIgRAREcWd1ux1FYgkVMrIgZAjyK7tRgIdG6kMiD3MqbFwDc5LC/i4GKBaLBbdXlZynXZrtuloSwyEiIgo7kTrIloudFHOam42aWbVmEysvQkU7CQ4IzN+M4XckfDV7afiWG0TemUlBTzOTLG0LJ4LpEUMhIiIKO7o1QhFwpHqBuVrebom3F49Roee1C8zYuO3hbl8PlQDDLqUa4WSqTPqsxRfi+dZLE1ERHEoWlNjR6oa/O5zhB0IWXSDkz9OKQhrbHpscdaIMKSMEKfGiIiIwhO1QKim0e8+Ryumn6wWC7zN82XTx/XGb8f1Qf+uKa0bpOb1la/joOA4IYRMV6DO2/GkfYySiIg6lWjVCJ3S37eH2OBuLUXBqu7NQd5W0kzsiMFJfkZiRIMgQD0dFgdxUEhjiFYwG2nMCBERUdyJVo3Q/106DO+u+QWXjOyu3BfuqjHf8S1fR2N5u9g7KR6aEurs3GHIqM9SnO2wwUCIiIjiT4orOpen7BQXbpp4nOo+h9V8B2QL1I/bohyoqDNCsQ+E9LbuMNJels+3j1ESEVGnMiQ/Ddc1dy6ONnsI3ZsDTY1FaqNV1etbzI+tLXhDSAm1l+Xz7WOURETUqVitFtz3qwJ0S0+I+nuFu3xee7wtWIFRGMRAy0yTxq6proiPQRSJjJA2mIw1BkJERBS32iIJYg8x2BDZ2jAjZCZI++e1Y3BSv0y8d+O4iI8FiEwfoXjDGiEiIopbkdhoNZhQgo30RIfqdrSLpVU1QiZef3C3NLx1Q+SDoHvOPR6f/nAI14w3P11plBHS1lnFWvsI14iIiKJEDGAsQa6Kvz2pDyYN7or/u2QoAHXgZA+wG3244qFAGgD+57Tj8MnNE/wCQdlbN5yE4/PU3alZI0RERNQOiNfrYFNjiU4bXpk+GtNG9wKgrRGKxvL5iL9kVJzULwsv/mak6j7WCBEREbVSWwQCodbhiMTgJ5Suy6Zfv71EQvCvkWJGiIiIqB1Qb2MR2nPFOCXRGflAKF6mxszQZsTYR4iIiKiV2iIOsLdiY1NVRsgRjeXzEX/JqNFmhJztpLN0OzrFRETU2bTFCiNrKwIh8fhQdmY3qy1WzUUKM0JEREQR1vY1QqE+t+XraARCcbC9mGl2TfqKNUJERESt1BZxgGr5fCsyQolRCITaU7G0zaadGtMPMaLd/TpUDISIiKhTC6VbspYlyhmhWE6NndQvEwAwrEe6qeP9Vo1pzuubvxuL8f2z8LdpwyMyvkhhZ2kiIopbbREIDMlPw5nHd0VeGPuaNbq9ytdRKZaOYSBUdOUIvLv2F1w8orup47U1Qi5NRmjCgGxMGJAdsfFFCgMhIiKKW20RBlgsFrw6Y3RYz23ytCyBikYfoVjWCGWluHDjaceZPl47jafNCMWr9jFKIiLqnOK8RKZByAiZ2QssVNF4zWixWi2qwK29bLraPkZJRESdUryHAY1uT1Rfvz01VATUK8e4aoyIiKiV4r2PTqPHG/ygVmhHCSEA6gaQ7CNERETUwYnF0tHQnjNCnBojIiJqpXgPA7xR3i6ivWWEqhvcytfpiY4YjsQ8BkJERERxKislvpoPhiI9qX0EQlw+T0REcaudzQxF3OVjemLN3mOYODAn1kMJSaHJJozxgIEQERHFrbbYdDWeuew2PHfFibEeRsiG98yI9RBM49QYERHFrc6eEWpvzhmShxSXHTdONN+IMdaYESIiorh1Qvd0/FRcFethkEkvXDUCjR5vVPZdi5YOnxHav38/Jk6ciIKCAgwbNgzvvvturIdEREQm3f+rAtxwaj/Mv2VCrIcSEDNXPlarpV0FQUAnyAjZ7XY8/fTTGD58OIqLizFy5EhMmTIFycnJsR4aEREFkZ7owL1TBsd6GEG1l5455K/DB0LdunVDt27dAAB5eXnIzs7G0aNHGQgREVHEtJcuyuQv5t+5b775BlOnTkV+fj4sFgvmzZvnd0xRURH69OmDhIQEjB07FqtWrQrrvdauXQuPx4OePXu2ctREREQtmBFqv2L+naupqUFhYSGKiop0H3/77bcxe/ZsPPjgg1i3bh0KCwsxefJklJaWKscMHz4cJ5xwgt9/Bw8eVI45evQorr76avz973+P+mciIqLOpb10USZ/FkmSotwg3DyLxYIPP/wQF154oXLf2LFjMXr0aDz//PMAAK/Xi549e+Lmm2/G3Xffbep1GxoacNZZZ+H666/Hb3/724DHNTQ0KLcrKyvRs2dPVFRUIC0tLbwPRUREHdYnGw/irwu24cWrRqIgn9eJeFFZWYn09HRT1++YZ4QCaWxsxNq1azFp0iTlPqvVikmTJmH58uWmXkOSJMyYMQNnnHFGwCAIAObMmYP09HTlP06hERFRIFML87H0rtMZBLVjcR0IHTlyBB6PB7m5uar7c3NzUVxcbOo1li1bhrfffhvz5s3D8OHDMXz4cPz444+6x95zzz2oqKhQ/tu/f3+rPwMRERHFrw6/amzChAnwer2mjnW5XHC52u8Gd0RERBSauM4IZWdnw2azoaSkRHV/SUkJ8vLyYjQqIiIi6ijiOhByOp0YOXIkFi1apNzn9XqxaNEijBs3LoYjIyIioo4g5lNj1dXV2Llzp3J79+7d2LBhAzIzM9GrVy/Mnj0b06dPx6hRozBmzBg8/fTTqKmpwTXXXBPDURMREVFHEPNAaM2aNTj99NOV27NnzwYATJ8+HXPnzsW0adNw+PBhPPDAAyguLsbw4cPxxRdf+BVQExEREYUqrvoIxYuioiIUFRXB4/Fg+/bt7CNERETUjoTSR4iBUAChnEgiIiKKDx2moSIRERFRNDEQIiIiok6LgRARERF1WgyEiIiIqNNiIERERESdFgMhHUVFRSgoKMDo0aNjPRQiIiKKIi6fD6CiogIZGRnYv38/l88TERG1E5WVlejZsyfKy8uRnp4e8NiYd5aOZ1VVVQCAnj17xngkREREFKqqqqqggRAzQgF4vV4cPHgQqampsFgsEX1tOVpltim6eJ7bDs912+B5bhs8z20nGudakiRUVVUhPz8fVmvgKiBmhAKwWq3o0aNHVN8jLS2N/8jaAM9z2+G5bhs8z22D57ntRPpcB8sEyVgsTURERJ0WAyEiIiLqtBgIxYjL5cKDDz4Il8sV66F0aDzPbYfnum3wPLcNnue2E+tzzWJpIiIi6rSYESIiIqJOi4EQERERdVoMhIiIiKjTYiBEREREnRYDoRgpKipCnz59kJCQgLFjx2LVqlWxHlK78s0332Dq1KnIz8+HxWLBvHnzVI9LkoQHHngA3bp1Q2JiIiZNmoQdO3aojjl69CiuuuoqpKWlISMjA7/73e9QXV3dhp8i/s2ZMwejR49GamoqunbtigsvvBDbtm1THVNfX4+ZM2ciKysLKSkpuOSSS1BSUqI6Zt++fTjvvPOQlJSErl274q677oLb7W7LjxLXXnzxRQwbNkxpKDdu3Dh8/vnnyuM8x9Hx+OOPw2Kx4LbbblPu47mOjIceeggWi0X13/HHH688HlfnWaI299Zbb0lOp1N67bXXpM2bN0vXX3+9lJGRIZWUlMR6aO3GZ599Jv3xj3+UPvjgAwmA9OGHH6oef/zxx6X09HRp3rx50saNG6Xzzz9f6tu3r1RXV6ccc84550iFhYXSihUrpG+//Vbq37+/dMUVV7TxJ4lvkydPll5//XVp06ZN0oYNG6QpU6ZIvXr1kqqrq5VjbrzxRqlnz57SokWLpDVr1kgnnXSSdPLJJyuPu91u6YQTTpAmTZokrV+/Xvrss8+k7Oxs6Z577onFR4pLH3/8sTR//nxp+/bt0rZt26R7771Xcjgc0qZNmyRJ4jmOhlWrVkl9+vSRhg0bJt16663K/TzXkfHggw9KQ4YMkQ4dOqT8d/jwYeXxeDrPDIRiYMyYMdLMmTOV2x6PR8rPz5fmzJkTw1G1X9pAyOv1Snl5edITTzyh3FdeXi65XC7pv//9ryRJkrRlyxYJgLR69WrlmM8//1yyWCzSgQMH2mzs7U1paakEQFq6dKkkSb7z6nA4pHfffVc5ZuvWrRIAafny5ZIk+YJWq9UqFRcXK8e8+OKLUlpamtTQ0NC2H6Ad6dKli/TKK6/wHEdBVVWVNGDAAOmrr76STjvtNCUQ4rmOnAcffFAqLCzUfSzezjOnxtpYY2Mj1q5di0mTJin3Wa1WTJo0CcuXL4/hyDqO3bt3o7i4WHWO09PTMXbsWOUcL1++HBkZGRg1apRyzKRJk2C1WrFy5co2H3N7UVFRAQDIzMwEAKxduxZNTU2qc3388cejV69eqnM9dOhQ5ObmKsdMnjwZlZWV2Lx5cxuOvn3weDx46623UFNTg3HjxvEcR8HMmTNx3nnnqc4pwJ/nSNuxYwfy8/PRr18/XHXVVdi3bx+A+DvP3HS1jR05cgQej0f1zQWA3Nxc/PTTTzEaVcdSXFwMALrnWH6suLgYXbt2VT1ut9uRmZmpHENqXq8Xt912G8aPH48TTjgBgO88Op1OZGRkqI7Vnmu974X8GPn8+OOPGDduHOrr65GSkoIPP/wQBQUF2LBhA89xBL311ltYt24dVq9e7fcYf54jZ+zYsZg7dy4GDRqEQ4cO4eGHH8Ypp5yCTZs2xd15ZiBERKbMnDkTmzZtwnfffRfroXRIgwYNwoYNG1BRUYH33nsP06dPx9KlS2M9rA5l//79uPXWW/HVV18hISEh1sPp0M4991zl62HDhmHs2LHo3bs33nnnHSQmJsZwZP44NdbGsrOzYbPZ/KrjS0pKkJeXF6NRdSzyeQx0jvPy8lBaWqp63O124+jRo/w+6Jg1axY+/fRTLF68GD169FDuz8vLQ2NjI8rLy1XHa8+13vdCfox8nE4n+vfvj5EjR2LOnDkoLCzEM888w3McQWvXrkVpaSlGjBgBu90Ou92OpUuX4tlnn4Xdbkdubi7PdZRkZGRg4MCB2LlzZ9z9TDMQamNOpxMjR47EokWLlPu8Xi8WLVqEcePGxXBkHUffvn2Rl5enOseVlZVYuXKlco7HjRuH8vJyrF27Vjnm66+/htfrxdixY9t8zPFKkiTMmjULH374Ib7++mv07dtX9fjIkSPhcDhU53rbtm3Yt2+f6lz/+OOPqsDzq6++QlpaGgoKCtrmg7RDXq8XDQ0NPMcRdOaZZ+LHH3/Ehg0blP9GjRqFq666Svma5zo6qqur8fPPP6Nbt27x9zMd0dJrMuWtt96SXC6XNHfuXGnLli3SDTfcIGVkZKiq4ymwqqoqaf369dL69eslANJTTz0lrV+/Xtq7d68kSb7l8xkZGdJHH30k/fDDD9IFF1ygu3z+xBNPlFauXCl999130oABA7h8XuOmm26S0tPTpSVLlqiWwdbW1irH3HjjjVKvXr2kr7/+WlqzZo00btw4ady4ccrj8jLYs88+W9qwYYP0xRdfSDk5OVxuLLj77rulpUuXSrt375Z++OEH6e6775YsFou0YMECSZJ4jqNJXDUmSTzXkXLHHXdIS5YskXbv3i0tW7ZMmjRpkpSdnS2VlpZKkhRf55mBUIw899xzUq9evSSn0ymNGTNGWrFiRayH1K4sXrxYAuD33/Tp0yVJ8i2hv//++6Xc3FzJ5XJJZ555prRt2zbVa5SVlUlXXHGFlJKSIqWlpUnXXHONVFVVFYNPE7/0zjEA6fXXX1eOqaurk37/+99LXbp0kZKSkqSLLrpIOnTokOp19uzZI5177rlSYmKilJ2dLd1xxx1SU1NTG3+a+HXttddKvXv3lpxOp5STkyOdeeaZShAkSTzH0aQNhHiuI2PatGlSt27dJKfTKXXv3l2aNm2atHPnTuXxeDrPFkmSpMjmmIiIiIjaB9YIERERUafFQIiIiIg6LQZCRERE1GkxECIiIqJOi4EQERERdVoMhIiIiKjTYiBEREREnRYDISKiECxZsgQWi8VvnyQiap8YCBEREVGnxUCIiIiIOi0GQkTUrni9XsyZMwd9+/ZFYmIiCgsL8d577wFombaaP38+hg0bhoSEBJx00knYtGmT6jXef/99DBkyBC6XC3369MGTTz6peryhoQH/+7//i549e8LlcqF///549dVXVcesXbsWo0aNQlJSEk4++WRs27Ytuh+ciKKCgRARtStz5szBG2+8gZdeegmbN2/G7bffjt/85jdYunSpcsxdd92FJ598EqtXr0ZOTg6mTp2KpqYmAL4A5rLLLsPll1+OH3/8EQ899BDuv/9+zJ07V3n+1Vdfjf/+97949tlnsXXrVrz88stISUlRjeOPf/wjnnzySaxZswZ2ux3XXnttm3x+IoosbrpKRO1GQ0MDMjMzsXDhQowbN065/7rrrkNtbS1uuOEGnH766Xjrrbcwbdo0AMDRo0fRo0cPzJ07F5dddhmuuuoqHD58GAsWLFCe/4c//AHz58/H5s2bsX37dgwaNAhfffUVJk2a5DeGJUuW4PTTT8fChQtx5plnAgA+++wznHfeeairq0NCQkKUzwIRRRIzQkTUbuzcuRO1tbU466yzkJKSovz3xhtv4Oeff1aOE4OkzMxMDBo0CFu3bgUAbN26FePHj1e97vjx47Fjxw54PB5s2LABNpsNp512WsCxDBs2TPm6W7duAIDS0tJWf0Yialv2WA+AiMis6upqAMD8+fPRvXt31WMul0sVDIUrMTHR1HEOh0P52mKxAPDVLxFR+8KMEBG1GwUFBXC5XNi3bx/69++v+q9nz57KcStWrFC+PnbsGLZv347BgwcDAAYPHoxly5apXnfZsmUYOHAgbDYbhg4dCq/Xq6o5IqKOixkhImo3UlNTceedd+L222+H1+vFhAkTUFFRgWXLliEtLQ29e/cGADzyyCPIyspCbm4u/vjHPyI7OxsXXnghAOCOO+7A6NGj8eijj2LatGlYvnw5nn/+ebzwwgsAgD59+mD69Om49tpr8eyzz6KwsBB79+5FaWkpLrvsslh9dCKKEgZCRNSuPProo8jJycGcOXOwa9cuZGRkYMSIEbj33nuVqanHH38ct956K3bs2IHhw4fjk08+gdPpBACMGDEC77zzDh544AE8+uij6NatGx555BHMmDFDeY8XX3wR9957L37/+9+jrKwMvXr1wr333huLj0tEUcZVY0TUYcgruo4dO4aMjIxYD4eI2gHWCBEREVGnxUCIiIiIOi1OjREREVGnxYwQERERdVoMhIiIiKjTYiBEREREnRYDISIiIuq0GAgRERFRp8VAiIiIiDotBkJERETUaTEQIiIiok6LgRARERF1Wv8PrQdh01wygj4AAAAASUVORK5CYII=",
+ "image/png": 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",
"text/plain": [
""
]
@@ -1336,12 +1427,49 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "ziffer_sortiert_resize\\NaN_D8_2020-08-14_19-11-52.jpg -1 6\n"
+ "ziffer_sortiert_resize\\0_litre3_20220312-060001.jpg 0 8\n"
+ ]
+ },
+ {
+ "data": {
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+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
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+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ziffer_sortiert_resize\\9_digit4_2020-03-23_20-31-53.jpg 9 5\n"
]
},
{
"data": {
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""
]
@@ -1351,7 +1479,7 @@
},
{
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",
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",
"text/plain": [
""
]
@@ -1421,89 +1549,104 @@
"execution_count": 7,
"metadata": {},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: test\\assets\n"
+ ]
+ },
{
"name": "stderr",
"output_type": "stream",
"text": [
- "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
+ "INFO:tensorflow:Assets written to: test\\assets\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmp6_pi19yr\\assets\n"
+ "Saved artifact at 'test'. The following endpoints are available:\n",
+ "\n",
+ "* Endpoint 'serve'\n",
+ " args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 32, 20, 3), dtype=tf.float32, name='keras_tensor')\n",
+ "Output Type:\n",
+ " TensorSpec(shape=(None, 11), dtype=tf.float32, name=None)\n",
+ "Captures:\n",
+ " 1786040602288: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040602464: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040601408: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040602112: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040602992: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040603696: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040603872: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040604224: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040604048: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040604752: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040600528: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040603344: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040605104: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040604928: TensorSpec(shape=(), dtype=tf.resource, name=None)\n"
]
- },
+ }
+ ],
+ "source": [
+ "model.export(\"test\", \"tf_saved_model\") # replace tf.saved_model.save with this line"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
{
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmp6_pi19yr\\assets\n"
+ "dig-class11_1800_s2.tflite\n"
]
},
{
"data": {
"text/plain": [
- "356488"
+ "356676"
]
},
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size\n",
- "\n",
+ "FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size + \".tflite\"\n",
"\n",
- "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
+ "converter = tf.lite.TFLiteConverter.from_saved_model(\"test\")\n",
"tflite_model = converter.convert()\n",
- "open(FileName + \".tflite\", \"wb\").write(tflite_model)"
+ "open(FileName, \"wb\").write(tflite_model)\n",
+ "print(FileName)\n",
+ "Path(FileName).stat().st_size"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpenlq9tjf\\assets\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpenlq9tjf\\assets\n",
- "C:\\Users\\Muell\\anaconda3\\envs\\py39-td-opencv\\lib\\site-packages\\tensorflow\\lite\\python\\convert.py:766: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway.\n",
- " warnings.warn(\"Statistics for quantized inputs were expected, but not \"\n"
- ]
- },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "dig-class11_1701_s2_q.tflite\n"
+ "dig-class11_1800_s2_q.tflite\n"
]
},
{
"data": {
"text/plain": [
- "96984"
+ "97240"
]
},
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -1518,7 +1661,7 @@
" data = np.expand_dims(x_data[5], axis=0)\n",
" yield [data.astype(np.float32)]\n",
" \n",
- "converter2 = tf.lite.TFLiteConverter.from_keras_model(model)\n",
+ "converter2 = tf.lite.TFLiteConverter.from_saved_model(\"test\")\n",
"converter2.representative_dataset = representative_dataset\n",
"converter2.optimizations = [tf.lite.Optimize.DEFAULT]\n",
"converter2.representative_dataset = representative_dataset\n",
@@ -1553,7 +1696,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.18"
+ "version": "3.11.8"
}
},
"nbformat": 4,
diff --git a/01 - Clean Images.ipynb b/01 - Clean Images.ipynb
new file mode 100644
index 000000000..882f0ce93
--- /dev/null
+++ b/01 - Clean Images.ipynb
@@ -0,0 +1,82 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "80f3d28e-8319-4249-8fcd-cd00a1ca3cad",
+ "metadata": {},
+ "source": [
+ "## Remove Duplicates"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "9fe06b5a-33d3-45ce-9caa-51c8d98c84d8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from collectmeterdigits.collect import ziffer_data_files, remove_similar_images"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "edc70529-736c-496e-95e4-48b58c30425d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "target_raw_path = \"./ziffer_sortiert_raw\" # here all raw images are stored"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "3c680b18-231e-4892-8698-9f0cb83e408b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Find similar images now in 1519 images ...\n",
+ "...1000\n",
+ "273 duplicates will be removed.\n"
+ ]
+ }
+ ],
+ "source": [
+ "files = ziffer_data_files(target_raw_path)\n",
+ "remove_similar_images(files, \"abc\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "22b0a110-7f4e-4e1f-a6cc-97078cb18adf",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/Image_Preparation-checkpoint.ipynb b/02 - Image_Preparation.ipynb
similarity index 84%
rename from .ipynb_checkpoints/Image_Preparation-checkpoint.ipynb
rename to 02 - Image_Preparation.ipynb
index dc53e5aaa..4f9797ce3 100644
--- a/.ipynb_checkpoints/Image_Preparation-checkpoint.ipynb
+++ b/02 - Image_Preparation.ipynb
@@ -41,7 +41,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "1554 files have been deleted.\n"
+ "1573 files have been deleted.\n"
]
}
],
@@ -59,14 +59,6 @@
"execution_count": 3,
"metadata": {},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\Muell\\AppData\\Local\\Temp\\ipykernel_17672\\2984607959.py:11: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.\n",
- " test_image = test_image.resize((target_size_x, target_size_y), Image.NEAREST)\n"
- ]
- },
{
"name": "stdout",
"output_type": "stream",
@@ -76,8 +68,7 @@
"750 ...\n",
"1000 ...\n",
"1250 ...\n",
- "1500 ...\n",
- "1573\n"
+ "1300\n"
]
}
],
@@ -97,7 +88,7 @@
" base = os.path.splitext(base)[0] + \".jpg\"\n",
" save_name = Output_dir + '/' + base\n",
"# print(\"in: \" + aktfile + \" - out: \" + save_name)\n",
- " test_image.save(save_name, \"JPEG\")\n",
+ " test_image.save(save_name, \"JPEG\", quality=100)\n",
"print(count)"
]
},
@@ -125,7 +116,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.18"
+ "version": "3.11.8"
}
},
"nbformat": 4,
diff --git a/Image_Preparation.ipynb b/Image_Preparation.ipynb
deleted file mode 100644
index dc53e5aaa..000000000
--- a/Image_Preparation.ipynb
+++ /dev/null
@@ -1,133 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Image preparation\n",
- "\n",
- "The original image size is 55x90 pixels with a color depth of 3 (RGB).\n",
- "The below code can be used to transform the images in an input directory (Input_dir) to the right size (20x32 pixels) into an output directory (Output_dir). Inside the directory the pictures are stored in subdirectories according their labeling (0 ... 9 + NaN).\n",
- "Any other image converter can be used as well.\n",
- "\n",
- "### Prerequisite\n",
- "Installed OpenCV libary within python (opencv)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import glob\n",
- "import os\n",
- "from PIL import Image \n",
- "\n",
- "Input_dir = 'ziffer_sortiert_raw'\n",
- "Output_dir= 'ziffer_sortiert_resize'\n",
- "\n",
- "target_size_x = 20\n",
- "target_size_y = 32\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1554 files have been deleted.\n"
- ]
- }
- ],
- "source": [
- "files = glob.glob(Output_dir + '/*.jpg')\n",
- "i = 0\n",
- "for f in files:\n",
- " os.remove(f)\n",
- " i=i+1\n",
- "print(str(i) + \" files have been deleted.\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\Muell\\AppData\\Local\\Temp\\ipykernel_17672\\2984607959.py:11: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.\n",
- " test_image = test_image.resize((target_size_x, target_size_y), Image.NEAREST)\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "250 ...\n",
- "500 ...\n",
- "750 ...\n",
- "1000 ...\n",
- "1250 ...\n",
- "1500 ...\n",
- "1573\n"
- ]
- }
- ],
- "source": [
- "files = glob.glob(Input_dir + '/*.jpg')\n",
- "files = files + glob.glob(Input_dir + '/*.png')\n",
- "files = files + glob.glob(Input_dir + '/*.bmp')\n",
- "count = 0\n",
- "for aktfile in files:\n",
- " count = count + 1\n",
- " if not count % 250:\n",
- " print(str(count) + \" ...\")\n",
- " test_image = Image.open(aktfile)\n",
- " test_image = test_image.convert('RGB')\n",
- " test_image = test_image.resize((target_size_x, target_size_y), Image.NEAREST)\n",
- " base=os.path.basename(aktfile)\n",
- " base = os.path.splitext(base)[0] + \".jpg\"\n",
- " save_name = Output_dir + '/' + base\n",
- "# print(\"in: \" + aktfile + \" - out: \" + save_name)\n",
- " test_image.save(save_name, \"JPEG\")\n",
- "print(count)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.18"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Train_CNN_Digital-Readout-Small-v2.ipynb b/Train_CNN_Digital-Readout-Small-v2.ipynb
index 9577537cb..6ff0c90bd 100644
--- a/Train_CNN_Digital-Readout-Small-v2.ipynb
+++ b/Train_CNN_Digital-Readout-Small-v2.ipynb
@@ -24,7 +24,7 @@
"source": [
"########### Basic Parameters for Running: ################################\n",
" \n",
- "TFlite_Version = \"1701\" \n",
+ "TFlite_Version = \"1800\" \n",
"TFlite_MainType = \"dig-class11\"\n",
"TFlite_Size = \"s2\"\n",
"Training_Percentage = 0.0 # 0.0 = Use all Images for Training\n",
@@ -32,6 +32,8 @@
"\n",
"##########################################################################\n",
"\n",
+ "## 2024-03-30: Code adapted to TF 2.16 ###################################\n",
+ "\n",
"\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
@@ -82,8 +84,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "(1573, 32, 20, 3)\n",
- "(1573, 11)\n"
+ "(1300, 32, 20, 3)\n",
+ "(1300, 11)\n"
]
}
],
@@ -141,45 +143,119 @@
"metadata": {},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"model\"\n",
- "_________________________________________________________________\n",
- " Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- " input_1 (InputLayer) [(None, 32, 20, 3)] 0 \n",
- " \n",
- " batch_normalization (BatchN (None, 32, 20, 3) 12 \n",
- " ormalization) \n",
- " \n",
- " conv2d (Conv2D) (None, 32, 20, 32) 896 \n",
- " \n",
- " max_pooling2d (MaxPooling2D (None, 16, 10, 32) 0 \n",
- " ) \n",
- " \n",
- " conv2d_1 (Conv2D) (None, 16, 10, 32) 9248 \n",
- " \n",
- " max_pooling2d_1 (MaxPooling (None, 8, 5, 32) 0 \n",
- " 2D) \n",
- " \n",
- " conv2d_2 (Conv2D) (None, 8, 5, 32) 9248 \n",
- " \n",
- " max_pooling2d_2 (MaxPooling (None, 4, 2, 32) 0 \n",
- " 2D) \n",
- " \n",
- " flatten (Flatten) (None, 256) 0 \n",
- " \n",
- " dense (Dense) (None, 256) 65792 \n",
- " \n",
- " dense_1 (Dense) (None, 11) 2827 \n",
- " \n",
- "=================================================================\n",
- "Total params: 88,023\n",
- "Trainable params: 88,017\n",
- "Non-trainable params: 6\n",
- "_________________________________________________________________\n"
- ]
+ "data": {
+ "text/html": [
+ "Model: \"functional_1\"\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"functional_1\"\u001b[0m\n"
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+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Total params: 88,023 (343.84 KB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m88,023\u001b[0m (343.84 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 88,017 (343.82 KB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m88,017\u001b[0m (343.82 KB)\n"
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"source": [
@@ -238,1005 +314,1020 @@
"output_type": "stream",
"text": [
"Epoch 1/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 2.1516 - accuracy: 0.3223\n",
+ "\u001b[1m 51/325\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.2469 - loss: 2.3019"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\Muell\\anaconda3\\envs\\py311-tf216-opencv\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+ " self._warn_if_super_not_called()\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.2822 - loss: 2.2734\n",
"Epoch 2/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 1.4881 - accuracy: 0.5226\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.3882 - loss: 1.8318\n",
"Epoch 3/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.9700 - accuracy: 0.6910\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.5754 - loss: 1.2654\n",
"Epoch 4/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.8070 - accuracy: 0.7451\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7161 - loss: 0.8801\n",
"Epoch 5/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.6602 - accuracy: 0.7991\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7768 - loss: 0.7268\n",
"Epoch 6/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.5955 - accuracy: 0.8258\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8277 - loss: 0.6434\n",
"Epoch 7/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.5233 - accuracy: 0.8474\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8044 - loss: 0.6498\n",
"Epoch 8/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.4445 - accuracy: 0.8652\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8499 - loss: 0.4638\n",
"Epoch 9/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.4406 - accuracy: 0.8735\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8493 - loss: 0.5074\n",
"Epoch 10/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.4012 - accuracy: 0.8887\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8727 - loss: 0.4695\n",
"Epoch 11/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.3693 - accuracy: 0.8900\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8885 - loss: 0.3451\n",
"Epoch 12/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.3075 - accuracy: 0.9142\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8832 - loss: 0.4054\n",
"Epoch 13/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.3734 - accuracy: 0.8964\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8786 - loss: 0.4139\n",
"Epoch 14/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.3359 - accuracy: 0.9053\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8955 - loss: 0.4001\n",
"Epoch 15/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.3208 - accuracy: 0.9027\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8891 - loss: 0.3343\n",
"Epoch 16/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2705 - accuracy: 0.9256\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8960 - loss: 0.3246\n",
"Epoch 17/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2488 - accuracy: 0.9186\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9061 - loss: 0.2965\n",
"Epoch 18/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2749 - accuracy: 0.9199\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9290 - loss: 0.2852\n",
"Epoch 19/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2611 - accuracy: 0.9193\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9288 - loss: 0.2614\n",
"Epoch 20/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2224 - accuracy: 0.9193\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9309 - loss: 0.2214\n",
"Epoch 21/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2185 - accuracy: 0.9421\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9427 - loss: 0.2345\n",
"Epoch 22/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2420 - accuracy: 0.9224\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9179 - loss: 0.2504\n",
"Epoch 23/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2117 - accuracy: 0.9307\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9116 - loss: 0.2614\n",
"Epoch 24/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2134 - accuracy: 0.9358\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9420 - loss: 0.2213\n",
"Epoch 25/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2233 - accuracy: 0.9402\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9414 - loss: 0.2223\n",
"Epoch 26/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2151 - accuracy: 0.9307\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9513 - loss: 0.1700\n",
"Epoch 27/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.2009 - accuracy: 0.9421\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9464 - loss: 0.1881\n",
"Epoch 28/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1893 - accuracy: 0.9390\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9527 - loss: 0.1516\n",
"Epoch 29/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1961 - accuracy: 0.9415\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9351 - loss: 0.2181\n",
"Epoch 30/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1643 - accuracy: 0.9555\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9263 - loss: 0.2048\n",
"Epoch 31/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1892 - accuracy: 0.9441\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9471 - loss: 0.1739\n",
"Epoch 32/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1589 - accuracy: 0.9580\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9500 - loss: 0.1479\n",
"Epoch 33/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1582 - accuracy: 0.9593\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9524 - loss: 0.1546\n",
"Epoch 34/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1444 - accuracy: 0.9574\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9458 - loss: 0.1731\n",
"Epoch 35/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1503 - accuracy: 0.9561\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9478 - loss: 0.1630\n",
"Epoch 36/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1485 - accuracy: 0.9549\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9449 - loss: 0.1956\n",
"Epoch 37/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1624 - accuracy: 0.9479\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9632 - loss: 0.1049\n",
"Epoch 38/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1483 - accuracy: 0.9606\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9583 - loss: 0.1624\n",
"Epoch 39/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1401 - accuracy: 0.9593\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9594 - loss: 0.1297\n",
"Epoch 40/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1369 - accuracy: 0.9631\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9492 - loss: 0.1808\n",
"Epoch 41/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1401 - accuracy: 0.9574\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9674 - loss: 0.1105\n",
"Epoch 42/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1526 - accuracy: 0.9574\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9570 - loss: 0.1492\n",
"Epoch 43/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1262 - accuracy: 0.9650\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9490 - loss: 0.1594\n",
"Epoch 44/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1481 - accuracy: 0.9619\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9455 - loss: 0.1943\n",
"Epoch 45/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1159 - accuracy: 0.9631\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9625 - loss: 0.1204\n",
"Epoch 46/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1202 - accuracy: 0.9682\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9726 - loss: 0.1119\n",
"Epoch 47/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1216 - accuracy: 0.9676\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9699 - loss: 0.1124\n",
"Epoch 48/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1354 - accuracy: 0.9657\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9571 - loss: 0.1533\n",
"Epoch 49/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1233 - accuracy: 0.9695\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9654 - loss: 0.1261\n",
"Epoch 50/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1466 - accuracy: 0.9631\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9631 - loss: 0.0931\n",
"Epoch 51/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1297 - accuracy: 0.9612\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9644 - loss: 0.1255\n",
"Epoch 52/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1217 - accuracy: 0.9657\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9533 - loss: 0.1915\n",
"Epoch 53/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1005 - accuracy: 0.9739\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9569 - loss: 0.1536\n",
"Epoch 54/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.1320 - accuracy: 0.9631\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9621 - loss: 0.1049\n",
"Epoch 55/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1289 - accuracy: 0.9688\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9600 - loss: 0.1669\n",
"Epoch 56/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1170 - accuracy: 0.9669\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9625 - loss: 0.1000\n",
"Epoch 57/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1026 - accuracy: 0.9771\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9649 - loss: 0.1221\n",
"Epoch 58/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0860 - accuracy: 0.9695\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9765 - loss: 0.1004\n",
"Epoch 59/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1186 - accuracy: 0.9701\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9625 - loss: 0.1121\n",
"Epoch 60/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1105 - accuracy: 0.9638\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9665 - loss: 0.1569\n",
"Epoch 61/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1149 - accuracy: 0.9733\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9573 - loss: 0.1140\n",
"Epoch 62/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1056 - accuracy: 0.9727\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9641 - loss: 0.1098\n",
"Epoch 63/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1295 - accuracy: 0.9682\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9670 - loss: 0.1083\n",
"Epoch 64/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1250 - accuracy: 0.9663\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9679 - loss: 0.1169\n",
"Epoch 65/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1071 - accuracy: 0.9688\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9679 - loss: 0.1274\n",
"Epoch 66/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1257 - accuracy: 0.9663\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9619 - loss: 0.1143\n",
"Epoch 67/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0856 - accuracy: 0.9758\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9820 - loss: 0.0762\n",
"Epoch 68/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0971 - accuracy: 0.9714\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9714 - loss: 0.0866\n",
"Epoch 69/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0976 - accuracy: 0.9720\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9667 - loss: 0.1239\n",
"Epoch 70/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0914 - accuracy: 0.9746\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9678 - loss: 0.0995\n",
"Epoch 71/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1237 - accuracy: 0.9714\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9824 - loss: 0.0672\n",
"Epoch 72/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0837 - accuracy: 0.9727\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9728 - loss: 0.0884\n",
"Epoch 73/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0857 - accuracy: 0.9720\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9747 - loss: 0.0835\n",
"Epoch 74/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0881 - accuracy: 0.9758\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9714 - loss: 0.0934\n",
"Epoch 75/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0782 - accuracy: 0.9790\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9779 - loss: 0.1212\n",
"Epoch 76/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0760 - accuracy: 0.9765\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9759 - loss: 0.0924\n",
"Epoch 77/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1479 - accuracy: 0.9631\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9824 - loss: 0.0785\n",
"Epoch 78/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0786 - accuracy: 0.9771\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9685 - loss: 0.0851\n",
"Epoch 79/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1087 - accuracy: 0.9695\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9618 - loss: 0.1137\n",
"Epoch 80/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1014 - accuracy: 0.9777\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9792 - loss: 0.1020\n",
"Epoch 81/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1142 - accuracy: 0.9663\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9775 - loss: 0.0788\n",
"Epoch 82/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0923 - accuracy: 0.9752\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9776 - loss: 0.0670\n",
"Epoch 83/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0820 - accuracy: 0.9752\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9563 - loss: 0.1152\n",
"Epoch 84/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0893 - accuracy: 0.9720\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9821 - loss: 0.0509\n",
"Epoch 85/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1339 - accuracy: 0.9676\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9719 - loss: 0.0951\n",
"Epoch 86/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0833 - accuracy: 0.9746\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9641 - loss: 0.1079\n",
"Epoch 87/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0780 - accuracy: 0.9784\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9648 - loss: 0.1229\n",
"Epoch 88/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0935 - accuracy: 0.9777\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9736 - loss: 0.0691\n",
"Epoch 89/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0898 - accuracy: 0.9758\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9742 - loss: 0.0900\n",
"Epoch 90/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0911 - accuracy: 0.9727\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9813 - loss: 0.0680\n",
"Epoch 91/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0625 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9793 - loss: 0.0672\n",
"Epoch 92/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0563 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9744 - loss: 0.0710\n",
"Epoch 93/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0951 - accuracy: 0.9765\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9779 - loss: 0.0737\n",
"Epoch 94/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0784 - accuracy: 0.9765\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9784 - loss: 0.0703\n",
"Epoch 95/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0662 - accuracy: 0.9822\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9869 - loss: 0.0424\n",
"Epoch 96/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0784 - accuracy: 0.9752\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9776 - loss: 0.0881\n",
"Epoch 97/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0724 - accuracy: 0.9803\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9713 - loss: 0.1001\n",
"Epoch 98/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0984 - accuracy: 0.9746\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.0472\n",
"Epoch 99/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0743 - accuracy: 0.9771\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9789 - loss: 0.1111\n",
"Epoch 100/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0909 - accuracy: 0.9771\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0586\n",
"Epoch 101/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0688 - accuracy: 0.9771\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9805 - loss: 0.0892\n",
"Epoch 102/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0929 - accuracy: 0.9739\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9857 - loss: 0.0545\n",
"Epoch 103/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0740 - accuracy: 0.9828\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9765 - loss: 0.0870\n",
"Epoch 104/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0888 - accuracy: 0.9777\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9747 - loss: 0.0764\n",
"Epoch 105/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0670 - accuracy: 0.9816\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9842 - loss: 0.0491\n",
"Epoch 106/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0800 - accuracy: 0.9809\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9714 - loss: 0.1157\n",
"Epoch 107/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0834 - accuracy: 0.9790\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9872 - loss: 0.0501\n",
"Epoch 108/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0586 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9736 - loss: 0.1021\n",
"Epoch 109/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0784 - accuracy: 0.9790\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9854 - loss: 0.0487\n",
"Epoch 110/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0701 - accuracy: 0.9822\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9844 - loss: 0.0568\n",
"Epoch 111/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0799 - accuracy: 0.9797\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9752 - loss: 0.0872\n",
"Epoch 112/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0934 - accuracy: 0.9733\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9765 - loss: 0.0811\n",
"Epoch 113/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0422 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9763 - loss: 0.0715\n",
"Epoch 114/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1159 - accuracy: 0.9765\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9837 - loss: 0.0581\n",
"Epoch 115/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9822\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9799 - loss: 0.0676\n",
"Epoch 116/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1053 - accuracy: 0.9752\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9662 - loss: 0.1312\n",
"Epoch 117/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0595 - accuracy: 0.9828\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9838 - loss: 0.0620\n",
"Epoch 118/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1004 - accuracy: 0.9688\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9795 - loss: 0.1159\n",
"Epoch 119/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0747 - accuracy: 0.9784\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9747 - loss: 0.0916\n",
"Epoch 120/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0942 - accuracy: 0.9809\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9832 - loss: 0.0775\n",
"Epoch 121/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0587 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9797 - loss: 0.0498\n",
"Epoch 122/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0854 - accuracy: 0.9714\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9726 - loss: 0.1010\n",
"Epoch 123/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0796 - accuracy: 0.9784\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9843 - loss: 0.0592\n",
"Epoch 124/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0968 - accuracy: 0.9746\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9820 - loss: 0.0797\n",
"Epoch 125/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0523 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9747 - loss: 0.1152\n",
"Epoch 126/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0764 - accuracy: 0.9771\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9671 - loss: 0.0974\n",
"Epoch 127/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0784 - accuracy: 0.9797\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9861 - loss: 0.0412\n",
"Epoch 128/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0448 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9824 - loss: 0.0762\n",
"Epoch 129/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0925 - accuracy: 0.9708\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9810 - loss: 0.0423\n",
"Epoch 130/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0692 - accuracy: 0.9816\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9835 - loss: 0.0628\n",
"Epoch 131/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0958 - accuracy: 0.9797\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9783 - loss: 0.0911\n",
"Epoch 132/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0657 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9754 - loss: 0.0995\n",
"Epoch 133/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0485 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9752 - loss: 0.0862\n",
"Epoch 134/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0846 - accuracy: 0.9765\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9716 - loss: 0.0907\n",
"Epoch 135/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0726 - accuracy: 0.9758\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9875 - loss: 0.0420\n",
"Epoch 136/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0643 - accuracy: 0.9816\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9873 - loss: 0.0294\n",
"Epoch 137/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0925 - accuracy: 0.9816\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9842 - loss: 0.0499\n",
"Epoch 138/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0575 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9791 - loss: 0.0646\n",
"Epoch 139/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0546 - accuracy: 0.9822\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9830 - loss: 0.1075\n",
"Epoch 140/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0565 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9739 - loss: 0.0880\n",
"Epoch 141/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.1083 - accuracy: 0.9720\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9918 - loss: 0.0302\n",
"Epoch 142/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0528 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9813 - loss: 0.0571\n",
"Epoch 143/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0743 - accuracy: 0.9797\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9803 - loss: 0.0475\n",
"Epoch 144/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0753 - accuracy: 0.9816\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9745 - loss: 0.1400\n",
"Epoch 145/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0504 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9880 - loss: 0.0385\n",
"Epoch 146/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0453 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9795 - loss: 0.0687\n",
"Epoch 147/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0463 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9885 - loss: 0.0390\n",
"Epoch 148/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0796 - accuracy: 0.9803\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.0714\n",
"Epoch 149/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0552 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9798 - loss: 0.0580\n",
"Epoch 150/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0780 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9887 - loss: 0.0394\n",
"Epoch 151/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0662 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9916 - loss: 0.0343\n",
"Epoch 152/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0714 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9717 - loss: 0.0886\n",
"Epoch 153/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9818 - loss: 0.0738\n",
"Epoch 154/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0793 - accuracy: 0.9777\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9803 - loss: 0.0637\n",
"Epoch 155/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9791 - loss: 0.0567\n",
"Epoch 156/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0636 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9774 - loss: 0.0991\n",
"Epoch 157/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0745 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9786 - loss: 0.0689\n",
"Epoch 158/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0616 - accuracy: 0.9828\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9825 - loss: 0.0482\n",
"Epoch 159/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0360 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9844 - loss: 0.0529\n",
"Epoch 160/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0455 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9803 - loss: 0.0766\n",
"Epoch 161/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0543 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9808 - loss: 0.0686\n",
"Epoch 162/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0399 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9723 - loss: 0.1078\n",
"Epoch 163/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0493 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9690 - loss: 0.1042\n",
"Epoch 164/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0584 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9801 - loss: 0.0804\n",
"Epoch 165/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0670 - accuracy: 0.9816\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9921 - loss: 0.0277\n",
"Epoch 166/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0614 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9897 - loss: 0.0463\n",
"Epoch 167/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0367 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9734 - loss: 0.0863\n",
"Epoch 168/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0599 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9757 - loss: 0.0812\n",
"Epoch 169/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0718 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9788 - loss: 0.1058\n",
"Epoch 170/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0304 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9856 - loss: 0.0552\n",
"Epoch 171/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0709 - accuracy: 0.9822\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0404\n",
"Epoch 172/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0538 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9854 - loss: 0.0462\n",
"Epoch 173/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0685 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9846 - loss: 0.0502\n",
"Epoch 174/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0771 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9932 - loss: 0.0165\n",
"Epoch 175/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0528 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9690 - loss: 0.1168\n",
"Epoch 176/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0525 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9848 - loss: 0.0812\n",
"Epoch 177/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0911 - accuracy: 0.9816\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9825 - loss: 0.0938\n",
"Epoch 178/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0511 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9883 - loss: 0.0394\n",
"Epoch 179/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0489 - accuracy: 0.9809\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9825 - loss: 0.0569\n",
"Epoch 180/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0591 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0472\n",
"Epoch 181/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0528 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9779 - loss: 0.0783\n",
"Epoch 182/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0449 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9910 - loss: 0.0448\n",
"Epoch 183/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9828\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9819 - loss: 0.0579\n",
"Epoch 184/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0346 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9801 - loss: 0.0596\n",
"Epoch 185/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0654 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9856 - loss: 0.0533\n",
"Epoch 186/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0744 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9877 - loss: 0.0890\n",
"Epoch 187/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0420 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.0752\n",
"Epoch 188/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0794 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9755 - loss: 0.0908\n",
"Epoch 189/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0300 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9790 - loss: 0.1057\n",
"Epoch 190/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0822 - accuracy: 0.9784\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9906 - loss: 0.0213\n",
"Epoch 191/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0395 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9883 - loss: 0.0768\n",
"Epoch 192/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0319 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9876 - loss: 0.0569\n",
"Epoch 193/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0602 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9924 - loss: 0.0350\n",
"Epoch 194/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0627 - accuracy: 0.9822\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9900 - loss: 0.0345\n",
"Epoch 195/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0511 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9879 - loss: 0.0392\n",
"Epoch 196/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0372 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9810 - loss: 0.1056\n",
"Epoch 197/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0384 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9809 - loss: 0.0518\n",
"Epoch 198/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0575 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9833 - loss: 0.0829\n",
"Epoch 199/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0586 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9831 - loss: 0.0737\n",
"Epoch 200/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0843 - accuracy: 0.9828\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9811 - loss: 0.0602\n",
"Epoch 201/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0584 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9837 - loss: 0.0558\n",
"Epoch 202/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0537 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.1004\n",
"Epoch 203/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0420 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9880 - loss: 0.0430\n",
"Epoch 204/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0667 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9832 - loss: 0.0618\n",
"Epoch 205/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0463 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9850 - loss: 0.0480\n",
"Epoch 206/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0445 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9825 - loss: 0.0667\n",
"Epoch 207/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0741 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9865 - loss: 0.0468\n",
"Epoch 208/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0559 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9873 - loss: 0.0379\n",
"Epoch 209/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0523 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9934 - loss: 0.0237\n",
"Epoch 210/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0396 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9928 - loss: 0.0247\n",
"Epoch 211/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0409 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9803 - loss: 0.0800\n",
"Epoch 212/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0604 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9809 - loss: 0.0913\n",
"Epoch 213/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0311 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9905 - loss: 0.0268\n",
"Epoch 214/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0606 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9950 - loss: 0.0224\n",
"Epoch 215/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0586 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9842 - loss: 0.0853\n",
"Epoch 216/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0723 - accuracy: 0.9777\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0475\n",
"Epoch 217/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0359 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9818 - loss: 0.0626\n",
"Epoch 218/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0748 - accuracy: 0.9809\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0343\n",
"Epoch 219/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0530 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9887 - loss: 0.0497\n",
"Epoch 220/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0674 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9936 - loss: 0.0257\n",
"Epoch 221/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0293 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9953 - loss: 0.0221\n",
"Epoch 222/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0401 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9825 - loss: 0.0648\n",
"Epoch 223/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0420 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9904 - loss: 0.0262\n",
"Epoch 224/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0438 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9875 - loss: 0.0401\n",
"Epoch 225/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0472 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9906 - loss: 0.0523\n",
"Epoch 226/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0799 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9924 - loss: 0.0175\n",
"Epoch 227/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0532 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9790 - loss: 0.0624\n",
"Epoch 228/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0698 - accuracy: 0.9828\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9874 - loss: 0.0437\n",
"Epoch 229/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0452 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.9893 - loss: 0.0441\n",
"Epoch 230/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0527 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9898 - loss: 0.0422\n",
"Epoch 231/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0337 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9826 - loss: 0.0511\n",
"Epoch 232/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0469 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9837 - loss: 0.0645\n",
"Epoch 233/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0355 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9892 - loss: 0.0389\n",
"Epoch 234/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0281 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9828 - loss: 0.0621\n",
"Epoch 235/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0309 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9914 - loss: 0.0248\n",
"Epoch 236/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0817 - accuracy: 0.9803\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9876 - loss: 0.0304\n",
"Epoch 237/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0530 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.9879 - loss: 0.0423\n",
"Epoch 238/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0437 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9869 - loss: 0.0492\n",
"Epoch 239/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0363 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9779 - loss: 0.0580\n",
"Epoch 240/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0475 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9851 - loss: 0.0473\n",
"Epoch 241/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0234 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.0531\n",
"Epoch 242/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0483 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9885 - loss: 0.0328\n",
"Epoch 243/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0543 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9940 - loss: 0.0214\n",
"Epoch 244/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0356 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9826 - loss: 0.0636\n",
"Epoch 245/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0577 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9898 - loss: 0.0216\n",
"Epoch 246/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0643 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9793 - loss: 0.0660\n",
"Epoch 247/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0518 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0356\n",
"Epoch 248/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0553 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9913 - loss: 0.0256\n",
"Epoch 249/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0447 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0290\n",
"Epoch 250/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0438 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9849 - loss: 0.0438\n",
"Epoch 251/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0654 - accuracy: 0.9847\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9896 - loss: 0.0583\n",
"Epoch 252/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0600 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9806 - loss: 0.1203\n",
"Epoch 253/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0378 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9844 - loss: 0.0670\n",
"Epoch 254/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0407 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9936 - loss: 0.0278\n",
"Epoch 255/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0645 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9867 - loss: 0.0302\n",
"Epoch 256/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0378 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9836 - loss: 0.0429\n",
"Epoch 257/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0497 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0280\n",
"Epoch 258/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0454 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9894 - loss: 0.0426\n",
"Epoch 259/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0423 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.0653\n",
"Epoch 260/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0610 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9887 - loss: 0.0462\n",
"Epoch 261/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0366 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9845 - loss: 0.0592\n",
"Epoch 262/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0396 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9807 - loss: 0.0897\n",
"Epoch 263/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0318 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9928 - loss: 0.0245\n",
"Epoch 264/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0552 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0552\n",
"Epoch 265/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0317 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9947 - loss: 0.0201\n",
"Epoch 266/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0414 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9858 - loss: 0.0557\n",
"Epoch 267/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0493 - accuracy: 0.9822\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9916 - loss: 0.0496\n",
"Epoch 268/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0410 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9894 - loss: 0.0536\n",
"Epoch 269/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0695 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9910 - loss: 0.0252\n",
"Epoch 270/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0325 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9882 - loss: 0.0514\n",
"Epoch 271/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0353 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9930 - loss: 0.0374\n",
"Epoch 272/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0782 - accuracy: 0.9797\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0146\n",
"Epoch 273/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0462 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.1504\n",
"Epoch 274/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0401 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0313\n",
"Epoch 275/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0284 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9897 - loss: 0.0372\n",
"Epoch 276/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0403 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9886 - loss: 0.0327\n",
"Epoch 277/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0545 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9949 - loss: 0.0226\n",
"Epoch 278/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0514 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9940 - loss: 0.0254\n",
"Epoch 279/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0308 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9876 - loss: 0.0423\n",
"Epoch 280/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0459 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9912 - loss: 0.0498\n",
"Epoch 281/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0490 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9861 - loss: 0.0585\n",
"Epoch 282/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0271 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9854 - loss: 0.0725\n",
"Epoch 283/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0509 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9821 - loss: 0.0624\n",
"Epoch 284/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0570 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9941 - loss: 0.0338\n",
"Epoch 285/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0388 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9914 - loss: 0.0272\n",
"Epoch 286/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0518 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9835 - loss: 0.0731\n",
"Epoch 287/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0656 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9831 - loss: 0.0728\n",
"Epoch 288/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0302 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.0281\n",
"Epoch 289/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0437 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9833 - loss: 0.0592\n",
"Epoch 290/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0414 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9940 - loss: 0.0266\n",
"Epoch 291/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0394 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9848 - loss: 0.0428\n",
"Epoch 292/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0302 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9876 - loss: 0.0495\n",
"Epoch 293/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0554 - accuracy: 0.9860\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9879 - loss: 0.0449\n",
"Epoch 294/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0884 - accuracy: 0.9835\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9849 - loss: 0.0592\n",
"Epoch 295/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0416 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9890 - loss: 0.0433\n",
"Epoch 296/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0508 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9951 - loss: 0.0233\n",
"Epoch 297/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0648 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9896 - loss: 0.0334\n",
"Epoch 298/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0336 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9915 - loss: 0.0258\n",
"Epoch 299/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0473 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9851 - loss: 0.0627\n",
"Epoch 300/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0524 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9947 - loss: 0.0190\n",
"Epoch 301/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0460 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9763 - loss: 0.0931\n",
"Epoch 302/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0441 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9829 - loss: 0.0692\n",
"Epoch 303/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0212 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9852 - loss: 0.0356\n",
"Epoch 304/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0462 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.0393\n",
"Epoch 305/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0221 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9847 - loss: 0.0548\n",
"Epoch 306/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0631 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9793 - loss: 0.0820\n",
"Epoch 307/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0362 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9917 - loss: 0.0270\n",
"Epoch 308/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0692 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9942 - loss: 0.0189\n",
"Epoch 309/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0445 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9962 - loss: 0.0088\n",
"Epoch 310/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0341 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9910 - loss: 0.0358\n",
"Epoch 311/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0399 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9855 - loss: 0.0663\n",
"Epoch 312/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0213 - accuracy: 0.9962\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9912 - loss: 0.0225\n",
"Epoch 313/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0602 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9910 - loss: 0.0406\n",
"Epoch 314/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0537 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9914 - loss: 0.0512\n",
"Epoch 315/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0383 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9893 - loss: 0.0391\n",
"Epoch 316/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0470 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9872 - loss: 0.0396\n",
"Epoch 317/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0328 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9836 - loss: 0.0535\n",
"Epoch 318/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0477 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9931 - loss: 0.0408\n",
"Epoch 319/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0467 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9823 - loss: 0.0658\n",
"Epoch 320/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0295 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9890 - loss: 0.0477\n",
"Epoch 321/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0491 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9913 - loss: 0.0209\n",
"Epoch 322/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0249 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9883 - loss: 0.0411\n",
"Epoch 323/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0489 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9855 - loss: 0.0484\n",
"Epoch 324/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0267 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0400\n",
"Epoch 325/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0558 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9891 - loss: 0.0526\n",
"Epoch 326/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0283 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9801 - loss: 0.0741\n",
"Epoch 327/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0409 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0256\n",
"Epoch 328/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0260 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9945 - loss: 0.0247\n",
"Epoch 329/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0492 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9946 - loss: 0.0142\n",
"Epoch 330/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0422 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0523\n",
"Epoch 331/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0223 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9911 - loss: 0.0438\n",
"Epoch 332/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0308 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9948 - loss: 0.0277\n",
"Epoch 333/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0409 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9970 - loss: 0.0081\n",
"Epoch 334/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0449 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9870 - loss: 0.0370\n",
"Epoch 335/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0349 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9846 - loss: 0.0421\n",
"Epoch 336/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0393 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9818 - loss: 0.0550\n",
"Epoch 337/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0338 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9904 - loss: 0.0579\n",
"Epoch 338/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0432 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0451\n",
"Epoch 339/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0527 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9934 - loss: 0.0334\n",
"Epoch 340/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0219 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9819 - loss: 0.0630\n",
"Epoch 341/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0243 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9859 - loss: 0.0384\n",
"Epoch 342/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0317 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9863 - loss: 0.0580\n",
"Epoch 343/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0257 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9910 - loss: 0.0355\n",
"Epoch 344/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0401 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9958 - loss: 0.0194\n",
"Epoch 345/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0331 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9874 - loss: 0.0323\n",
"Epoch 346/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0464 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9907 - loss: 0.0267\n",
"Epoch 347/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0348 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0634\n",
"Epoch 348/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0445 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9873 - loss: 0.0474\n",
"Epoch 349/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0292 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9900 - loss: 0.0492\n",
"Epoch 350/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0372 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9891 - loss: 0.0374\n",
"Epoch 351/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0346 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9838 - loss: 0.0468\n",
"Epoch 352/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0410 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9791 - loss: 0.0854\n",
"Epoch 353/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0299 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0197\n",
"Epoch 354/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0368 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9854 - loss: 0.0662\n",
"Epoch 355/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0613 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0387\n",
"Epoch 356/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0530 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9853 - loss: 0.0491\n",
"Epoch 357/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0347 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9928 - loss: 0.0334\n",
"Epoch 358/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0418 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0486\n",
"Epoch 359/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0436 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0270\n",
"Epoch 360/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0699 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9880 - loss: 0.0321\n",
"Epoch 361/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0310 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9844 - loss: 0.0577\n",
"Epoch 362/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0313 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9908 - loss: 0.0532\n",
"Epoch 363/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0318 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9816 - loss: 0.1538\n",
"Epoch 364/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0427 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9897 - loss: 0.0727\n",
"Epoch 365/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0260 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9933 - loss: 0.0249\n",
"Epoch 366/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0165 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9843 - loss: 0.0523\n",
"Epoch 367/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0359 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9969 - loss: 0.0272\n",
"Epoch 368/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0111 - accuracy: 0.9955\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9898 - loss: 0.0320\n",
"Epoch 369/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0527 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9926 - loss: 0.0565\n",
"Epoch 370/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0389 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9940 - loss: 0.0258\n",
"Epoch 371/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0410 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9949 - loss: 0.0194\n",
"Epoch 372/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0393 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9912 - loss: 0.0262\n",
"Epoch 373/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0341 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9915 - loss: 0.0287\n",
"Epoch 374/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0350 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9941 - loss: 0.0228\n",
"Epoch 375/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0594 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9952 - loss: 0.0169\n",
"Epoch 376/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0468 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0326\n",
"Epoch 377/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0485 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9905 - loss: 0.0493\n",
"Epoch 378/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0393 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9905 - loss: 0.0338\n",
"Epoch 379/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0298 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9899 - loss: 0.0261\n",
"Epoch 380/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0310 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9879 - loss: 0.0555\n",
"Epoch 381/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0349 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9908 - loss: 0.0290\n",
"Epoch 382/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0327 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9932 - loss: 0.0283\n",
"Epoch 383/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0218 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0360\n",
"Epoch 384/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0386 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9870 - loss: 0.0545\n",
"Epoch 385/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0416 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9922 - loss: 0.0397\n",
"Epoch 386/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0309 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0222\n",
"Epoch 387/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0357 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9986 - loss: 0.0194\n",
"Epoch 388/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0252 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9864 - loss: 0.0482\n",
"Epoch 389/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0409 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9894 - loss: 0.0449\n",
"Epoch 390/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0171 - accuracy: 0.9962\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9919 - loss: 0.0191\n",
"Epoch 391/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0196 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9978 - loss: 0.0086\n",
"Epoch 392/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0364 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9946 - loss: 0.0604\n",
"Epoch 393/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0273 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9826 - loss: 0.0802\n",
"Epoch 394/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0492 - accuracy: 0.9873\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9929 - loss: 0.0291\n",
"Epoch 395/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0267 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9928 - loss: 0.0257\n",
"Epoch 396/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0134 - accuracy: 0.9962\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9941 - loss: 0.0271\n",
"Epoch 397/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0630 - accuracy: 0.9854\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9897 - loss: 0.0585\n",
"Epoch 398/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0431 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9913 - loss: 0.0463\n",
"Epoch 399/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0210 - accuracy: 0.9968\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9957 - loss: 0.0137\n",
"Epoch 400/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0473 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.9982 - loss: 0.0102\n",
"Epoch 401/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0495 - accuracy: 0.9866\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.9945 - loss: 0.0195\n",
"Epoch 402/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0350 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9959 - loss: 0.0181\n",
"Epoch 403/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0232 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9890 - loss: 0.0346\n",
"Epoch 404/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0362 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9937 - loss: 0.0275\n",
"Epoch 405/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0329 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9949 - loss: 0.0469\n",
"Epoch 406/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0199 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9792 - loss: 0.0668\n",
"Epoch 407/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0474 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.9903 - loss: 0.0355\n",
"Epoch 408/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0154 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - accuracy: 0.9934 - loss: 0.0222\n",
"Epoch 409/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0448 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9951 - loss: 0.0323\n",
"Epoch 410/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0293 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9952 - loss: 0.0245\n",
"Epoch 411/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0434 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9887 - loss: 0.0478\n",
"Epoch 412/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0102 - accuracy: 0.9968\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9977 - loss: 0.0135\n",
"Epoch 413/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0151 - accuracy: 0.9968\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9914 - loss: 0.0486\n",
"Epoch 414/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0388 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9945 - loss: 0.0316\n",
"Epoch 415/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0177 - accuracy: 0.9955\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.9959 - loss: 0.0134\n",
"Epoch 416/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0182 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.9910 - loss: 0.0336\n",
"Epoch 417/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0548 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.9934 - loss: 0.0210\n",
"Epoch 418/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0473 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9967 - loss: 0.0182\n",
"Epoch 419/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0227 - accuracy: 0.9975\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9982 - loss: 0.0168\n",
"Epoch 420/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0466 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9976 - loss: 0.0143\n",
"Epoch 421/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0452 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9917 - loss: 0.0328\n",
"Epoch 422/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0263 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9913 - loss: 0.0214\n",
"Epoch 423/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0311 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9945 - loss: 0.0202\n",
"Epoch 424/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0385 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0375\n",
"Epoch 425/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0403 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9947 - loss: 0.0233\n",
"Epoch 426/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0312 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0286\n",
"Epoch 427/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0357 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9867 - loss: 0.0736\n",
"Epoch 428/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0513 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0462\n",
"Epoch 429/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0247 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9840 - loss: 0.0531\n",
"Epoch 430/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0550 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9919 - loss: 0.0302\n",
"Epoch 431/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0380 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9859 - loss: 0.0687\n",
"Epoch 432/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0398 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9964 - loss: 0.0111\n",
"Epoch 433/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0334 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9970 - loss: 0.0245\n",
"Epoch 434/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0249 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9824 - loss: 0.0684\n",
"Epoch 435/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0397 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9945 - loss: 0.0252\n",
"Epoch 436/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0403 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9862 - loss: 0.0671\n",
"Epoch 437/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0414 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9867 - loss: 0.0521\n",
"Epoch 438/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0263 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0235\n",
"Epoch 439/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0118 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9868 - loss: 0.0337\n",
"Epoch 440/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0543 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9894 - loss: 0.0301\n",
"Epoch 441/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0215 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9958 - loss: 0.0258\n",
"Epoch 442/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0333 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9961 - loss: 0.0189\n",
"Epoch 443/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0300 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9914 - loss: 0.0170\n",
"Epoch 444/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0611 - accuracy: 0.9841\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9947 - loss: 0.0296\n",
"Epoch 445/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0302 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9928 - loss: 0.0274\n",
"Epoch 446/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0319 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9902 - loss: 0.0442\n",
"Epoch 447/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0290 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9871 - loss: 0.0442\n",
"Epoch 448/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0395 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9962 - loss: 0.0290\n",
"Epoch 449/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0272 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9918 - loss: 0.0232\n",
"Epoch 450/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0285 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9957 - loss: 0.0115\n",
"Epoch 451/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0281 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9823 - loss: 0.0913\n",
"Epoch 452/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0159 - accuracy: 0.9962\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9942 - loss: 0.0357\n",
"Epoch 453/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0502 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9978 - loss: 0.0156\n",
"Epoch 454/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0357 - accuracy: 0.9917\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9870 - loss: 0.0515\n",
"Epoch 455/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0495 - accuracy: 0.9879\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9909 - loss: 0.0351\n",
"Epoch 456/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0277 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9925 - loss: 0.0302\n",
"Epoch 457/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0246 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9921 - loss: 0.0288\n",
"Epoch 458/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0316 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9945 - loss: 0.0135\n",
"Epoch 459/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0291 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9877 - loss: 0.0352\n",
"Epoch 460/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0196 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9918 - loss: 0.0390\n",
"Epoch 461/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0203 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9895 - loss: 0.0517\n",
"Epoch 462/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0309 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9797 - loss: 0.0902\n",
"Epoch 463/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0520 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9912 - loss: 0.0169\n",
"Epoch 464/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0219 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9933 - loss: 0.0224\n",
"Epoch 465/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0351 - accuracy: 0.9905\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9939 - loss: 0.0285\n",
"Epoch 466/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0545 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9877 - loss: 0.0489\n",
"Epoch 467/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0185 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9931 - loss: 0.0314\n",
"Epoch 468/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0237 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9967 - loss: 0.0072\n",
"Epoch 469/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0194 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9922 - loss: 0.0319\n",
"Epoch 470/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0558 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9953 - loss: 0.0118\n",
"Epoch 471/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0372 - accuracy: 0.9962\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9872 - loss: 0.0471\n",
"Epoch 472/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0345 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9967 - loss: 0.0234\n",
"Epoch 473/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0134 - accuracy: 0.9955\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9917 - loss: 0.0561\n",
"Epoch 474/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0427 - accuracy: 0.9892\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9876 - loss: 0.0377\n",
"Epoch 475/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0215 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9977 - loss: 0.0083\n",
"Epoch 476/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0157 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9960 - loss: 0.0137\n",
"Epoch 477/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0237 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9901 - loss: 0.0569\n",
"Epoch 478/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0339 - accuracy: 0.9924\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0388\n",
"Epoch 479/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0219 - accuracy: 0.9936\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9875 - loss: 0.0265\n",
"Epoch 480/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0227 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9943 - loss: 0.0349\n",
"Epoch 481/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0291 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9898 - loss: 0.0479\n",
"Epoch 482/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0192 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9930 - loss: 0.0214\n",
"Epoch 483/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0289 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9901 - loss: 0.0361\n",
"Epoch 484/500\n",
- "394/394 [==============================] - 2s 4ms/step - loss: 0.0395 - accuracy: 0.9886\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9903 - loss: 0.0253\n",
"Epoch 485/500\n",
- "394/394 [==============================] - 1s 4ms/step - loss: 0.0155 - accuracy: 0.9955\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9927 - loss: 0.0188\n",
"Epoch 486/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0290 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9971 - loss: 0.0069\n",
"Epoch 487/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0114 - accuracy: 0.9968\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9968 - loss: 0.0189\n",
"Epoch 488/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0121 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9874 - loss: 0.0624\n",
"Epoch 489/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0211 - accuracy: 0.9962\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9934 - loss: 0.0221\n",
"Epoch 490/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0374 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9964 - loss: 0.0097\n",
"Epoch 491/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0250 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9890 - loss: 0.0315\n",
"Epoch 492/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0326 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9967 - loss: 0.0207\n",
"Epoch 493/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0215 - accuracy: 0.9943\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0615\n",
"Epoch 494/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0321 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9925 - loss: 0.0141\n",
"Epoch 495/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0136 - accuracy: 0.9955\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0557\n",
"Epoch 496/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0336 - accuracy: 0.9930\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0229\n",
"Epoch 497/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0310 - accuracy: 0.9949\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9878 - loss: 0.0459\n",
"Epoch 498/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0300 - accuracy: 0.9898\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9938 - loss: 0.0249\n",
"Epoch 499/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0274 - accuracy: 0.9911\n",
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9929 - loss: 0.0390\n",
"Epoch 500/500\n",
- "394/394 [==============================] - 1s 3ms/step - loss: 0.0494 - accuracy: 0.9892\n"
+ "\u001b[1m325/325\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9964 - loss: 0.0116\n"
]
}
],
@@ -1279,7 +1370,7 @@
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -1336,12 +1427,49 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "ziffer_sortiert_resize\\NaN_D8_2020-08-14_19-11-52.jpg -1 6\n"
+ "ziffer_sortiert_resize\\0_litre3_20220312-060001.jpg 0 8\n"
+ ]
+ },
+ {
+ "data": {
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+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
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+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ziffer_sortiert_resize\\9_digit4_2020-03-23_20-31-53.jpg 9 5\n"
]
},
{
"data": {
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""
]
@@ -1351,7 +1479,7 @@
},
{
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",
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",
"text/plain": [
""
]
@@ -1421,89 +1549,104 @@
"execution_count": 7,
"metadata": {},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: test\\assets\n"
+ ]
+ },
{
"name": "stderr",
"output_type": "stream",
"text": [
- "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
+ "INFO:tensorflow:Assets written to: test\\assets\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmp6_pi19yr\\assets\n"
+ "Saved artifact at 'test'. The following endpoints are available:\n",
+ "\n",
+ "* Endpoint 'serve'\n",
+ " args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 32, 20, 3), dtype=tf.float32, name='keras_tensor')\n",
+ "Output Type:\n",
+ " TensorSpec(shape=(None, 11), dtype=tf.float32, name=None)\n",
+ "Captures:\n",
+ " 1786040602288: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040602464: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040601408: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040602112: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040602992: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040603696: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040603872: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040604224: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040604048: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040604752: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040600528: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040603344: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040605104: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+ " 1786040604928: TensorSpec(shape=(), dtype=tf.resource, name=None)\n"
]
- },
+ }
+ ],
+ "source": [
+ "model.export(\"test\", \"tf_saved_model\") # replace tf.saved_model.save with this line"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
{
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmp6_pi19yr\\assets\n"
+ "dig-class11_1800_s2.tflite\n"
]
},
{
"data": {
"text/plain": [
- "356488"
+ "356676"
]
},
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size\n",
- "\n",
+ "FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size + \".tflite\"\n",
"\n",
- "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
+ "converter = tf.lite.TFLiteConverter.from_saved_model(\"test\")\n",
"tflite_model = converter.convert()\n",
- "open(FileName + \".tflite\", \"wb\").write(tflite_model)"
+ "open(FileName, \"wb\").write(tflite_model)\n",
+ "print(FileName)\n",
+ "Path(FileName).stat().st_size"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpenlq9tjf\\assets\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpenlq9tjf\\assets\n",
- "C:\\Users\\Muell\\anaconda3\\envs\\py39-td-opencv\\lib\\site-packages\\tensorflow\\lite\\python\\convert.py:766: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway.\n",
- " warnings.warn(\"Statistics for quantized inputs were expected, but not \"\n"
- ]
- },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "dig-class11_1701_s2_q.tflite\n"
+ "dig-class11_1800_s2_q.tflite\n"
]
},
{
"data": {
"text/plain": [
- "96984"
+ "97240"
]
},
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -1518,7 +1661,7 @@
" data = np.expand_dims(x_data[5], axis=0)\n",
" yield [data.astype(np.float32)]\n",
" \n",
- "converter2 = tf.lite.TFLiteConverter.from_keras_model(model)\n",
+ "converter2 = tf.lite.TFLiteConverter.from_saved_model(\"test\")\n",
"converter2.representative_dataset = representative_dataset\n",
"converter2.optimizations = [tf.lite.Optimize.DEFAULT]\n",
"converter2.representative_dataset = representative_dataset\n",
@@ -1553,7 +1696,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.18"
+ "version": "3.11.8"
}
},
"nbformat": 4,
diff --git a/collectmeterdigits/__init__.py b/collectmeterdigits/__init__.py
new file mode 100644
index 000000000..734985887
--- /dev/null
+++ b/collectmeterdigits/__init__.py
@@ -0,0 +1 @@
+from collectmeterdigits import collect
\ No newline at end of file
diff --git a/collectmeterdigits/__main__.py b/collectmeterdigits/__main__.py
new file mode 100644
index 000000000..a8bc587c7
--- /dev/null
+++ b/collectmeterdigits/__main__.py
@@ -0,0 +1,46 @@
+import argparse
+import sys
+from collectmeterdigits.collect import collect
+from collectmeterdigits.labeling import label
+from collectmeterdigits import glob
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--collect',
+ help='collect all images. The edgeAI meter server name must be set')
+ parser.add_argument('--days', type=int, default=3, help='count of days back to read. (default: 3)')
+ parser.add_argument('--labeling', default='', help='labelpath if you want label the images')
+ parser.add_argument('--keepdownloads', action='store_true',
+ help='Normally all downloaded data will be deleted. If set it keeps the images.')
+ parser.add_argument('--nodownload', action='store_true',
+ help='Do not download pictures. Only remove duplicates and labeling.')
+ parser.add_argument('--startlabel', type=float, default=0.0, help='only images >= startlabel. (default: all)')
+ parser.add_argument('--savedublicates', action='store_true',
+ help='Save the dublicates in an intermediate subdirectory in raw_images.')
+ parser.add_argument('--labelfile', default=None,
+ help='file with list of image urls if you want label specific images.')
+ parser.add_argument('--model', default=None, help='model file path if a external model should be used')
+
+ # print help message if no argument is given
+ if len(sys.argv) == 1:
+ parser.print_help(sys.stderr)
+ sys.exit(1)
+
+ args = parser.parse_args()
+
+ if args.model is not None:
+ glob.model_path = args.model
+
+ if args.labeling == '':
+ if args.labelfile is not None:
+ label(args.labeling, args.startlabel, args.labelfile)
+ else:
+ collect(args.collect, args.days, keepolddata=args.keepdownloads, download=not args.nodownload,
+ startlabel=args.startlabel)
+ else:
+ label(args.labeling, args.startlabel)
+
+
+if __name__ == '__main__':
+ main()
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diff --git a/collectmeterdigits/collect.py b/collectmeterdigits/collect.py
new file mode 100644
index 000000000..c82f202cc
--- /dev/null
+++ b/collectmeterdigits/collect.py
@@ -0,0 +1,229 @@
+from urllib.error import HTTPError, URLError
+import urllib.request
+import re
+import requests
+import os
+from PIL import Image
+from datetime import date, timedelta
+import imagehash
+import secrets
+import shutil
+from collectmeterdigits.labeling import label
+import time
+import numpy as np
+
+
+
+target_path = "./data" # root data path
+target_raw_path = "./data/raw_images" # here all raw images will be stored
+target_label_path = "./data/labeled"
+target_store_dublicates = "./data/raw_images/dublicates"
+
+
+def yesterday(daysbefore=1):
+ ''' return the date of yesterday as string in format yyyymmdd'''
+ yesterday = date.today() - timedelta(days=daysbefore)
+ return yesterday.strftime("%Y%m%d")
+
+
+def readimages(servername, output_dir, daysback=15):
+ '''get all images taken yesterday and store it in target path'''
+ serverurl = "http://" + servername
+ count = 0
+ print(f"Loading data from {servername} ...")
+ for datesbefore in range(0, daysback):
+ picturedate = yesterday(daysbefore=datesbefore)
+ # only if not exists already
+ for i in range(24):
+ hour = f'{i:02d}'
+ if not os.path.exists(path = output_dir + "/" + servername + "/" + picturedate + "/" + hour):
+ try:
+ fp = urllib.request.urlopen(serverurl + "/fileserver/log/digit/" + picturedate + "/" + hour + "/")
+ except HTTPError as h:
+ print( serverurl + "/fileserver/log/digit/" + picturedate + "/" + hour + "/ not found." )
+ continue
+ except URLError as ue:
+ print("URL-Error! Server not available? Requested URL was: ", serverurl + "/fileserver/log/digit/" + picturedate + "/" + hour + "/" )
+ exit(1)
+ print("Loading ... ", servername + "/" + picturedate + "/" + hour)
+
+ mybytes = fp.read()
+
+ mystr = mybytes.decode("utf8")
+ fp.close()
+
+ urls = re.findall(r'href=[\'"]?([^\'" >]+)', mystr)
+ path = output_dir + "/" + servername + "/" + picturedate + "/" + hour
+ os.makedirs(path, exist_ok=True)
+ for url in urls:
+ if (url[-3:] != 'jpg'):
+ continue
+ prefix = os.path.basename(url).split('_', 1)[0]
+ if (prefix == os.path.basename(url)):
+ prefix = ''
+ else:
+ prefix = prefix + '_'
+ filename = secrets.token_hex(nbytes=16) + ".jpg"
+ countrepeat = 10
+ while countrepeat > 0:
+ if (not os.path.exists(path + "/" + prefix + filename)):
+ try:
+ print(serverurl + url)
+ image = requests.get(serverurl + url, stream=True)
+ count = count + 1
+ countrepeat = 0
+ except ConnectionError as h:
+ print(
+ path + "/" + prefix + filename + " could not be loaded - Retry in 10 s ... " + str(
+ countrepeat))
+ time.sleep(10)
+ countrepeat = countrepeat - 1
+ continue
+ except TimeoutError as h:
+ print(
+ path + "/" + prefix + filename + " could not be loaded - Retry in 10 s ... " + str(
+ countrepeat))
+ time.sleep(10)
+ countrepeat = countrepeat - 1
+ continue
+
+ try:
+ img = Image.open(image.raw)
+ img.save(path + "/" + prefix + filename)
+ except Exception as e:
+ print(path + "/" + prefix + filename + " could not be opened as an image: %r!" % e)
+
+ count = count + 1
+ countrepeat = 0
+ print(f"{count} images are loaded from meter: {servername}")
+
+def save_hash_file(images, hashfilename):
+ f = open(hashfilename, 'w', encoding='utf-8')
+ for hash, img, meter, datum in images:
+ f.write(datum + "\t" + meter+ "\t" + img + "\t" + str(hash)+'\n');
+ f.close
+
+def load_hash_file(hashfilename):
+ images = []
+
+ try:
+ file1 = open(hashfilename, 'r')
+ Lines = file1.readlines()
+ file1.close
+ except Exception as e:
+ print('No historic Hashdata could be loaded (' + hashfilename + ')')
+ return images
+
+ for line in Lines:
+ cut = line.strip('\n').split(sep="\t")
+ datum = cut[0]
+ meter = cut[1]
+ _hash = imagehash.hex_to_hash(cut[3])
+ images.append([_hash, cut[2], meter, datum])
+ return images
+
+
+def ziffer_data_files(input_dir):
+ '''return a list of all images in given input dir in all subdirectories'''
+ imgfiles = []
+ for root, dirs, files in os.walk(input_dir):
+ for file in files:
+ if (file.endswith(".jpg")):
+ imgfiles.append(root + "/" + file)
+ return imgfiles
+
+def remove_similar_images(image_filenames, meter, hashfunc = imagehash.average_hash, savedublicates=False):
+ '''removes similar images.
+
+ '''
+ images = []
+ count = 0
+ cutoff = 3 # maximum bits that could be different between the hashes.
+ print(f"Find similar images now in {len(image_filenames)} images ..." )
+
+ datum = date.today().strftime("%Y-%m-%d")
+
+ for img in sorted(image_filenames):
+ try:
+ hash = hashfunc(Image.open(img).convert('L').resize((32,20)))
+ except Exception as e:
+ print('Problem: ', e, ' with ', img)
+ continue
+ images.append([hash, img, meter, datum])
+
+ if (os.path.exists('./data/HistoricHashData.txt')):
+ HistoricHashData = load_hash_file('./data/HistoricHashData.txt')
+ else:
+ HistoricHashData = []
+
+ count = 0
+
+ duplicates = {}
+ for hash in images:
+ if (hash[1] not in duplicates):
+ similarimgs = [i for i in HistoricHashData if abs(i[0]-hash[0]) < cutoff and i[1]!=hash[1]]
+ if len(similarimgs) > 0: # es wurden in den alten hashes schon vergleichbare bilder gefunden
+ if (duplicates == {}):
+ duplicates = set([hash[1]])
+ else:
+ duplicates |= set([hash[1]])
+ else: # es wird in den neuen Biler gesucht gefunden
+ similarimgs = [i for i in images if abs(i[0]-hash[0]) < cutoff and i[1]!=hash[1]]
+ # add duplicates
+ if (duplicates == {}):
+ duplicates = set([row[1] for row in similarimgs])
+ else:
+ duplicates |= set([row[1] for row in similarimgs])
+ count = count + 1
+ if not count % 1000:
+ print("..." + str(count))
+
+ # extend Historic Hash Data
+ for _image in images:
+ if not _image[1] in duplicates:
+ HistoricHashData.append(_image)
+ save_hash_file(HistoricHashData, './data/HistoricHashData.txt')
+
+ # remove now all duplicates
+ if savedublicates:
+ print(f"{len(duplicates)} duplicates will moved to .data/raw_images/dublicates.")
+ os.makedirs(target_store_dublicates, exist_ok=True)
+ for image in duplicates:
+ os.replace(image, os.path.join(target_store_dublicates, os.path.basename(image)))
+ else:
+ print(f"{len(duplicates)} duplicates will be removed.")
+ for image in duplicates:
+ os.remove(image)
+
+def move_to_label(files, meter):
+ print("Move to label")
+ os.makedirs(target_label_path, exist_ok=True)
+ for file in files:
+ os.replace(file, os.path.join(target_label_path, os.path.basename(file)))
+
+
+
+
+def collect(meter, days, keepolddata=False, download=True, startlabel=0, savedublicates=False):
+ # ensure the target path exists
+ os.makedirs(target_raw_path, exist_ok=True)
+ print("Startlabel", startlabel)
+ # read all images from meters
+ if download:
+ print("retrieve images")
+ readimages(meter, target_raw_path, days)
+
+ # remove all same or similar images and remove the empty folders
+ remove_similar_images(ziffer_data_files(os.path.join(target_raw_path, meter)), meter, savedublicates=savedublicates)
+
+ # move the files in one zip without directory structure
+ move_to_label(ziffer_data_files(os.path.join(target_raw_path, meter)), meter)
+
+ # cleanup
+ if not keepolddata:
+ shutil.rmtree(target_raw_path)
+
+ # label now
+ label(target_label_path, startlabel=startlabel)
+
+
diff --git a/collectmeterdigits/glob.py b/collectmeterdigits/glob.py
new file mode 100644
index 000000000..5aee51e95
--- /dev/null
+++ b/collectmeterdigits/glob.py
@@ -0,0 +1,2 @@
+interpreter=None
+model_path: None = None
\ No newline at end of file
diff --git a/collectmeterdigits/hash_manual.py b/collectmeterdigits/hash_manual.py
new file mode 100644
index 000000000..b23b15ce6
--- /dev/null
+++ b/collectmeterdigits/hash_manual.py
@@ -0,0 +1,29 @@
+from PIL import Image
+from datetime import date
+import imagehash
+from collectmeterdigits.collect import save_hash_file, ziffer_data_files
+
+def calculate_hash(image_filenames, meter, hashfunc = imagehash.average_hash):
+ images = []
+ print(f"Calculate hashes for {len(image_filenames)} images ..." )
+
+ datum = date.today().strftime("%Y-%m-%d")
+
+ for img in sorted(image_filenames):
+ try:
+ hash = hashfunc(Image.open(img).convert('L').resize((32,20)))
+ except Exception as e:
+ print('Problem: ', e, ' with ', img)
+ continue
+ images.append([hash, img, meter, datum])
+ return images;
+
+
+input_dir = "./data/labeled_20220606"
+hashfilename = "./data/HistoricHashData.txt"
+
+image_filenames = ziffer_data_files(input_dir)
+hash = calculate_hash(image_filenames, "hash_manual")
+save_hash_file(hash, hashfilename)
+
+
diff --git a/collectmeterdigits/labeling.py b/collectmeterdigits/labeling.py
new file mode 100644
index 000000000..e1f1d2e69
--- /dev/null
+++ b/collectmeterdigits/labeling.py
@@ -0,0 +1,220 @@
+import os
+from PIL import Image
+import matplotlib
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.figure as fig
+from matplotlib.widgets import Slider, Button, RadioButtons
+import shutil
+import pandas as pd
+from collectmeterdigits.predict import predict
+
+def ziffer_data_files(input_dir):
+ '''return a list of all images in given input dir in all subdirectories'''
+ imgfiles = []
+ for root, dirs, files in os.walk(input_dir):
+ for file in files:
+ if (file.endswith(".jpg")):
+ imgfiles.append(root + "/" + file)
+
+ imgfiles = sorted(imgfiles, key=lambda x : os.path.basename(x))
+ return imgfiles
+
+
+def label(path, startlabel=0, imageurlsfile=None):
+ global filename
+ global i
+ global im
+ global filelabel
+ global title
+ global ax
+ global slabel
+ global files
+ global predbox
+
+ print(f"Startlabel", startlabel)
+
+ if (imageurlsfile!=None):
+ files = pd.read_csv(imageurlsfile, index_col=0).to_numpy().reshape(-1)
+
+ # remove files not exists
+ for file in files:
+ if (not os.path.exists(file)):
+ files = files[~np.isin(files, file)]
+ else:
+ files = ziffer_data_files(path)
+
+ if (len(files)==0):
+ print("No images found in path")
+ exit(1)
+
+ i = 0
+ img, img_2032, filelabel, filename, i = load_image(files, i, startlabel)
+
+ # disable toolbar
+ matplotlib.rcParams['toolbar'] = 'None'
+
+ # set window title
+ fig = plt.gcf()
+ fig.canvas.manager.set_window_title('1 of ' + str(len(files)) + ' images')
+
+ title = plt.title(filelabel) # set title
+ plt.yticks(np.arange(0, 1, step=0.1))
+ im = plt.imshow(img, aspect='1.6', extent=[0, 1, 0, 1])
+ for y in np.arange(0.1, 0.91, 0.1):
+ if (int(y*10)%2==0):
+ color='yellow'
+ else:
+ color='blue'
+ plt.axhline(y=y, xmin=0, xmax=0.2, color=color)
+ plt.axhline(y=y, xmin=0.8, xmax=1, color=color)
+ plt.axhline(y=y, xmin=0.2, xmax=0.8, color=color, linestyle="--")
+
+ plt.axvline(x=0.2, ymin=0.0, ymax=1, linewidth=3, color='red', linestyle=":")
+ plt.axvline(x=0.8, ymin=0.0, ymax=1, linewidth=3, color='red', linestyle=":")
+
+ plt.text(1.1, 0.9, "You can use cursor key controll also:\n\nleft/right = prev/next\nup/down=in/decrease value\ndelete=remove.", fontsize=6)
+ prediction = predict(img_2032)
+ plt.text(-0.6, 0.7, "Prediction:", fontsize=8)
+ predbox = plt.text(-0.6, 0.6, "{:.1f}".format(prediction), fontsize=24, bbox=dict(facecolor='none', edgecolor='black', boxstyle='round,pad=1'))
+ ax=plt.gca()
+ ax.get_xaxis().set_visible(False)
+ axlabel = plt.axes([0.1, 0.025, 0.7, 0.04])
+ slabel = Slider(axlabel, label='Label',valmin= 0.0, valmax=9.9, valstep=0.1,
+ valinit=filelabel,
+ orientation='horizontal')
+ previousax = plt.axes([0.87, 0.225, 0.1, 0.04])
+ bprevious = Button(previousax, 'previous', hovercolor='0.975')
+ nextax = plt.axes([0.87, 0.025, 0.1, 0.04])
+ bnext = Button(nextax, 'update', hovercolor='0.975')
+ removeax = plt.axes([0.87, 0.4, 0.1, 0.04])
+ bremove = Button(removeax, 'delete', hovercolor='0.975')
+
+ increaselabel = plt.axes([0.93, 0.1, 0.05, 0.04])
+ bincreaselabel = Button(increaselabel, '+0.1', hovercolor='0.975')
+ decreaselabel = plt.axes([0.87, 0.1, 0.05, 0.04])
+ bdecreaselabel = Button(decreaselabel, '-0.1', hovercolor='0.975')
+
+ def load_previous():
+ global im
+ global title
+ global slabel
+ global i
+ global filelabel
+ global filename
+ global predbox
+
+ i = (i - 1) % len(files)
+ img, img_2032, filelabel, filename, i = load_image(files, i)
+ im.set_data(img)
+ title.set_text(filelabel)
+ slabel.set_val(filelabel)
+ fig = plt.gcf()
+ fig.canvas.manager.set_window_title(str(i) + ' of ' + str(len(files)) + ' images')
+ predbox.set_text("{:.1f}".format(predict(img_2032)))
+ plt.draw()
+
+
+ def load_next(increaseindex = True):
+ global im
+ global title
+ global slabel
+ global i
+ global filelabel
+ global filename
+ global predbox
+
+ if increaseindex:
+ i = (i + 1) % len(files)
+ img, img_2032, filelabel, filename, i = load_image(files, i)
+ im.set_data(img)
+ title.set_text(filelabel)
+ slabel.set_val(filelabel)
+ fig = plt.gcf()
+ fig.canvas.manager.set_window_title(str(i) + ' of ' + str(len(files)) + ' images')
+ predbox.set_text("{:.1f}".format(predict(img_2032)))
+
+ plt.draw()
+
+ def increase_label(event):
+ slabel.set_val((slabel.val + 0.1) % 10)
+
+ def decrease_label(event):
+ slabel.set_val((slabel.val - 0.1) % 10)
+
+ def remove(event):
+ global filename
+ global files
+ global i
+ os.remove(filename)
+ files = np.delete(files,i, 0)
+ load_next(False)
+
+ def previous(event):
+ load_previous()
+
+ def next(event):
+ global filelabel
+ global filename
+
+ basename = os.path.basename(filename).split('_', 1)
+ basename = basename[-1]
+ if (filelabel != slabel.val):
+ _zw = os.path.join(os.path.dirname(filename), "{:.1f}".format(slabel.val) + "_" + basename)
+ files[i] = _zw
+ shutil.move(filename, _zw)
+ load_next()
+
+ def on_press(event):
+# print('press', event.key)
+ if event.key == 'right':
+ next(event)
+ if event.key == 'left':
+ previous(event)
+ if event.key == 'up':
+ increase_label(event);
+ if event.key == 'down':
+ decrease_label(event)
+ if event.key == 'enter':
+ next(event)
+ if event.key == 'delete':
+ remove(event)
+
+ fig.canvas.mpl_connect('key_press_event', on_press)
+
+
+ bnext.on_clicked(next)
+ bprevious.on_clicked(previous)
+ bremove.on_clicked(remove)
+ bincreaselabel.on_clicked(increase_label)
+ bdecreaselabel.on_clicked(decrease_label)
+
+ plt.show()
+
+
+
+
+
+def load_image(files, i, startlabel = -1):
+
+ while True:
+ base = os.path.basename(files[i])
+ # get label from filename (1.2_ new or 1_ old),
+ if (base[1]=="."):
+ target = base[0:3]
+ else:
+ target = base[0:1]
+ try:
+ category = float(target)
+ except ValueError:
+ category = 0.0
+ if category >= startlabel:
+ break
+ else:
+ i = (i + 1)
+
+ filename = files[i]
+ test_image = Image.open(filename)
+ test_image_resized = test_image.resize((20, 32), Image.NEAREST)
+ return test_image, test_image_resized, category, filename, i
+
diff --git a/collectmeterdigits/models/dig-class100-0160_s2_q.tflite b/collectmeterdigits/models/dig-class100-0160_s2_q.tflite
new file mode 100644
index 000000000..a1769322b
Binary files /dev/null and b/collectmeterdigits/models/dig-class100-0160_s2_q.tflite differ
diff --git a/collectmeterdigits/predict.py b/collectmeterdigits/predict.py
new file mode 100644
index 000000000..249dfec50
--- /dev/null
+++ b/collectmeterdigits/predict.py
@@ -0,0 +1,57 @@
+try:
+ import tflite_runtime.interpreter as tflite
+ has_tflite_runtime = True
+except ImportError:
+ try:
+ import tensorflow.lite as tflite
+ has_tflite_runtime = True
+ except ImportError:
+ has_tflite_runtime = False
+import numpy as np
+import pkg_resources
+from collectmeterdigits import glob
+
+interpreter=None
+internal_model_path = pkg_resources.resource_filename('collectmeterdigits', 'models/dig-class100-0160_s2_q.tflite')
+
+def load_interpreter(model_path):
+ global interpreter
+
+ print('modelpath=', model_path)
+
+ if not has_tflite_runtime:
+ print('no tflite')
+ return
+ print("Use model: " + model_path)
+ if "off" == glob.model_path:
+ print('model_path=off')
+ return
+ interpreter = tflite.Interpreter(model_path=model_path)
+ return interpreter
+
+
+
+def predict( image):
+ global interpreter
+
+ if has_tflite_runtime and not glob.model_path == "off":
+ if interpreter is None:
+ if glob.model_path is None:
+ glob.model_path = internal_model_path
+ load_interpreter(glob.model_path)
+ # if tflite can not be loaded
+ if interpreter == None:
+ return -1
+ interpreter.allocate_tensors()
+ input_index = interpreter.get_input_details()[0]["index"]
+ output_index = interpreter.get_output_details()[0]["index"]
+
+ interpreter.set_tensor(input_index, np.expand_dims(np.array(image).astype(np.float32), axis=0))
+ # Run inference.
+ interpreter.invoke()
+ output = interpreter.get_tensor(output_index)
+ return np.argmax((output)[0])/10
+ else:
+ return -1
+
+
\ No newline at end of file
diff --git a/data/HistoricHashData.txt b/data/HistoricHashData.txt
new file mode 100644
index 000000000..98b0cac1d
--- /dev/null
+++ b/data/HistoricHashData.txt
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