From 1da71c8a9805f2f5834fcfbc692f70c79ed76d53 Mon Sep 17 00:00:00 2001
From: leondavi <ddleon@outlook.co.il>
Date: Sat, 8 Jun 2024 03:21:20 +0300
Subject: [PATCH 1/7] Remove Redundant files

---
 src_py/apiServer/NerlDB.py        | 110 -----------------
 src_py/apiServer/NerlDatasetDB.py | 195 ------------------------------
 src_py/apiServer/test.py          |   7 --
 3 files changed, 312 deletions(-)
 delete mode 100644 src_py/apiServer/NerlDB.py
 delete mode 100644 src_py/apiServer/NerlDatasetDB.py
 delete mode 100644 src_py/apiServer/test.py

diff --git a/src_py/apiServer/NerlDB.py b/src_py/apiServer/NerlDB.py
deleted file mode 100644
index 0ae45767f..000000000
--- a/src_py/apiServer/NerlDB.py
+++ /dev/null
@@ -1,110 +0,0 @@
-   
-
-class BatchTrainDB:
-    def __init__(self, worker_name, source_name, batch_id, batch_size, duration = None):
-        self.worker_name = worker_name
-        self.source_name = source_name
-        self.batch_id = batch_id
-        #self.batch_size = batch_size
-        self.loss_value = None
-        self.duration = duration
-
-    def get_as_triplet(self):
-        return self.worker_name, self.batch_id, self.loss_value
-    
-    def get_source_name(self):
-        return self.source_name
-    
-class BatchPredictDB:
-    def __init__(self, worker_name, source_name, batch_id, batch_size, label_dim = 1, duration = None):
-        self.worker_name = worker_name
-        self.source_name = source_name
-        self.batch_id = batch_id
-        self.duration = duration
-        #self.label_dim = label_dim
-        self.source_name = None
-        #self.labels = [ [ None ] * label_dim ] * batch_size
-
-    def set_label_dim(self, dim):
-        self.label_dim = dim
-
-    def get_as_triplet(self):
-        return self.worker_name, self.batch_id, self.labels
-    
-    def add_label(self, sample_index, label):
-        if len(label) != self.label_dim:
-            raise Exception("label dim is not as expected")
-        if sample_index >= self.batch_size:
-            raise Exception("sample_index exceeds batch_size")
-        self.labels[sample_index] = label
-    
-    def add_value(self, sample_index, label_index, value):
-        if label_index >= self.label_dim:
-            raise Exception("label_index is not as expected")
-        if sample_index >= self.batch_size:
-            raise Exception("sample_index exceeds batch_size")
-        self.labels[sample_index][label_index] = value
-    
-
-class WorkerDB:
-    PHASE_TRAINING = 0
-    PHASE_PREDICTION = 1
-
-    def __init__(self, worker_name, batch_size):
-        self.worker_name = worker_name
-        self.train_dict = {} # dictionary of batch_id -> BatchTrainDB of this worker
-        self.predict_dict = {} # dictionary of batch_id -> BatchPredictDB of this worker
-        self.batch_size = batch_size
-
-    def add_train_batch(self, batch_id, batch_size, source_name):
-        self.train_dict[batch_id] = BatchTrainDB(self.worker_name, source_name, batch_id, batch_size)
-    
-    def add_predict_batch(self, batch_id, batch_size):
-        self.predict_dict[batch_id] = BatchPredictDB(self.worker_name, batch_id, batch_size)
-
-    def get_as_sorted_by_batch_ids(self, phase):
-        batches_list = list(range(0, self.batch_size))
-        res_list = [None] * self.batch_size
-        for key in batches_list:
-            if key in self.train_dict:
-                if phase == WorkerDB.PHASE_TRAINING:
-                    res_list[key] = self.train_dict[key]
-                elif phase == WorkerDB.PHASE_PREDICTION:
-                    res_list[key] = self.predict_dict[key]
-                else:
-                    raise Exception("phase is not as expected")
-        return res_list
-
-class ClientDB:
-    def __init__(self):
-        self.worerksDBs = {} # dictionary of worker_name -> WorkerDB
-    
-    def add_worker(self, worker_name, batch_size):
-        self.worerksDBs[worker_name] = WorkerDB(worker_name, batch_size)
-    
-class NerlDB:
-    def __init__(self) -> None:
-        self.clientsDBs = {} # dictionary of client_name -> ClientDB
-    
-    def add_client(self, client_name):
-        self.clientsDBs[client_name] = ClientDB()
-    
-    def get_client(self, client_name):
-        if client_name not in self.clientsDBs:
-            raise Exception(f"client {client_name} not found in DB")
-        return self.clientsDBs[client_name]
-    
-    def get_total_workers_count(self):
-        res = 0
-        for client_name, clientDB in self.clientsDBs.items():
-            res += len(clientDB.worerksDBs)
-        return res
-
-    def get_workers_brain_data(self, phase = None, filter_by_client = [], filter_by_worker = []):
-        res = []
-        for client_name, clientDB in self.clientsDBs.items():
-            if not filter_by_client or (client_name in filter_by_client):
-                for worker_name, workerDB in clientDB.worerksDBs.items():
-                    if not filter_by_worker or (worker_name in filter_by_worker):
-                        res.append(workerDB.get_as_sorted_by_batch_ids(phase))     
-        return res   
\ No newline at end of file
diff --git a/src_py/apiServer/NerlDatasetDB.py b/src_py/apiServer/NerlDatasetDB.py
deleted file mode 100644
index 2bf45ea60..000000000
--- a/src_py/apiServer/NerlDatasetDB.py
+++ /dev/null
@@ -1,195 +0,0 @@
-
-from abc import ABC, abstractmethod
-import pandas as pd
-import math
-import hashlib
-
-
-class DataSetDB(ABC): # Abstract Class
-    PHASE_TRAINING = 0
-    PHASE_PREDICTION = 1
-
-    def __init__(self, data_set_name, batch_size):
-        self.batch_size = batch_size
-        self.data_set_name = data_set_name
-
-    def numof_samples_to_batches(self, numof_samples : int):
-        return math.floor(numof_samples / self.batch_size)
-# example csv_db = DatasetCsvDB() 
-# csv_db.sources_to_sources_db_dict['s1'] = DataSetSourceDB() from init
-# csv_db.sources_to_sources_db_dict['s1'].get_phase()
-class DataSetSourceDB(DataSetDB):
-    def __init__(self, data_set_name, batch_size, numof_batches, phase):
-        super().__init__(data_set_name, batch_size)
-        self.source_name = None # ties this sourceDB to a specific source from DC
-        self.batch_iterator = 0 # batch iterator allows to follow sent/received batches status
-        self.batch_stop_index = numof_batches # marks batch where source should stop sending batches
-        self.batch_stop_original = numof_batches
-        self.phase = phase
-
-        self.validate_batch_iterators()
-
-    def get_phase(self):
-        return self.phase
-
-    def set_source(self, source_name):
-        self.source_name = source_name
-    
-    def get_source(self):
-        return self.source_name
-
-    def validate_batch_iterators(self):
-        assert self.batch_stop_index >= self.batch_iterator
-        assert self.batch_iterator >= 0
-        return True
-
-    # use the iterator to control multiple phases of source that sends batches
-    # E.g., source sends x/n batches for training, then sends y/n batches for prediction
-    # then source sends (n-x)/n batches for training, then sends (n-y)/n batches for prediction
-    # returns False if iterator is out of bounds
-    def increment_batch_iterator(self, val = 1):
-        if self.batch_iterator + val > self.batch_stop_idx:
-            return False
-        self.batch_iterator += val
-        return True
-    
-    def reset_batch_iterator(self, phase):
-        self.batch_iterator = 0
-        self.validate_batch_iterators()
-    
-    def get_batch_iterator(self):
-        return self.batch_iterator
-    
-    def set_batch_stop_idx(self, new_value, phase):
-        self.batch_stop_idx = new_value
-        self.validate_batch_iterators()
-    
-    # def set_batch_stop_original_value(self, new_value, phase):
-    #     if phase == DataSetDB.PHASE_TRAINING:
-    #         self.batch_stop_original_value = new_value
-    #     elif phase == DataSetDB.PHASE_PREDICTION:
-    #         self.batch_stop_original_value = new_value
-    #     self.validate_batch_iterators()
-    
-    # def get_batch_stop_idx(self, phase):
-    #     if phase == DataSetDB.PHASE_TRAINING:
-    #         return self.batch_stop_idx_train
-    #     elif phase == DataSetDB.PHASE_PREDICTION:
-    #         return self.batch_stop_idx_predict
-        
-    # def get_batch_stop_original_value(self, phase):
-    #     if phase == DataSetDB.PHASE_TRAINING:
-    #         return self.batch_stop_original_value
-    #     elif phase == DataSetDB.PHASE_PREDICTION:
-    #         return self.batch_stop_original_value
-
-
-class DataSetCsvDB(DataSetDB):
-    def __init__(self, csv_dataset_path, csv_temp_path, batch_size):
-        super().__init__(csv_dataset_path, batch_size)
-        
-        self.sources_to_sources_db_dict = {} # dictionary of source_name -> DataSetSourceDB Instance
-        self.source_db_name_to_source_db_inst = {} # dictionary of source_db_name -> DataSetSourceDB Instance
-        self.sources_dbs_name_to_offset_indexes_hash = {} # dictionary of source_db_name -> offset to sample index
-        
-        self.csv_dataset_path = csv_dataset_path
-        self.csv_temp_path = csv_temp_path # where to save to and where to send from the train and predict csv data
-        self.sha_to_name_and_path = {} # dictionary of sha256 hash -> (source_name, csv_dataset_path)
-        self.list_dbs_to_send = []
-
-    def add_source_db(self, source_name, source_db_name, target_workers_names_list, starting_offset, numof_batches, phase):
-        assert source_db_name not in self.source_db_name_to_source_db_inst
-        assert source_db_name not in self.sources_dbs_name_to_offset_indexes_hash
-        assert source_db_name not in self.sha_to_name_and_path
-
-        self.sources_to_sources_db_dict[source_name] = DataSetSourceDB(source_db_name, self.batch_size, numof_batches, phase)
-        self.source_db_name_to_source_db_inst[source_db_name] = self.sources_to_sources_db_dict[source_name]
-        self.sources_dbs_name_to_offset_indexes_hash[source_db_name] = starting_offset
-        # self.generate_hash_source_csv_db_sha(source_db_name)
-
-
-    def get_source_db_name_and_path_by_sha(self, sha):
-        return self.sha_to_name_and_path[sha]
-
-    def generate_hash_source_csv_db_sha(self, source_db_name): # will be useful in future if source using more than a single csv file
-        sha256_hash = hashlib.sha256()
-        with open(f"{self.csv_dataset_path}_{source_db_name}","rb") as f:
-            # Read and update hash string value in blocks of 4K
-            for byte_block in iter(lambda: f.read(4096),b""):
-                sha256_hash.update(byte_block)
-            sha_res = sha256_hash.hexdigest()     
-            self.sha_to_name_and_path[sha_res] = (source_db_name, self.csv_dataset_path)
-            return sha_res
-
-    def calculate_total_possible_numof_batches(self):
-        df = pd.df.read_csv(self.csv_dataset_path)
-        total_num_of_batches = len(df) / self.batch_size
-        return math.floor(total_num_of_batches)
-
-    
-
-    def get_source_db(self, source_db_name):
-        assert source_db_name in self.sources_dict
-        return self.sources_dict[source_db_name]
-
-    def get_global_sample_index(self, source_db_name, batch_idx, sample_within_batch_idx, phase):
-        assert sample_within_batch_idx < self.batch_size
-        if phase == DataSetCsvDB.PHASE_TRAINING:
-            return self.sources_dbs_to_starting_indexes_train_hash[source_db_name] + batch_idx * self.batch_size + sample_within_batch_idx
-        elif phase == DataSetCsvDB.PHASE_PREDICTION:
-            return self.sources_dbs_to_starting_indexes_predict_hash[source_db_name] + batch_idx * self.batch_size + sample_within_batch_idx
-
-    def get_batch_samples_data(self, source_db_name, batches_index_list, phase):
-        starting_offset_index = None
-        if phase == DataSetCsvDB.PHASE_TRAINING:
-            starting_offset_index =  self.sources_dbs_to_starting_indexes_train_hash[source_db_name]
-        else:
-            starting_offset_index = self.sources_dbs_to_starting_indexes_predict_hash[source_db_name]
-        
-        df = pd.df.read_csv(self.csv_dataset_path, skiprows=starting_offset_index, nrows=self.batch_size * len(batches_index_list))
-        return df
-
-        
-    def generate_data_for_source(self, source_db_name, phase): # TODO change this code to be based on phase
-        """
-        Generated files for each source:
-        1. training csv file:   <source_db_name>_<data_set_name>_training.csv
-        2. prediction csv file: <source_db_name>_<data_set_name>_predict.csv
-
-        return
-        - filepath_train
-        - filepath_predict
-        - filename_train
-        - filename_predict
-        - number_of_samples_train
-        - number_of_samples_predict
-        """
-
-        # assert source_db_name in self.sources_dict
-        # source_db_inst = self.source_db_name[source_db_name]
-
-        # starting_offset_index = self.sources_dbs_to_offset_indexes_train_hash[source_db_name]
-        # starting_offset_index_predict = self.sources_dbs_to_offset_indexes_predict_hash[source_db_name]
-
-        # number_of_samples_train = source_db_inst.get_batch_stop_idx(DataSetSourceDB.PHASE_TRAINING) * self.batch_size
-        # number_of_samples_predict = source_db_inst.get_batch_stop_idx(DataSetSourceDB.PHASE_PREDICTION) * self.batch_size
-
-        # df_train = pd.df.read_csv(self.csv_dataset_path, skiprows=starting_offset_index_train, nrows=number_of_samples_train)
-        # df_predict = pd.df.read_csv(self.csv_dataset_path, skiprows=starting_offset_index_predict, nrows=number_of_samples_predict)
-
-        # filename_train = f"{source_db_name}_{super().data_set_name}_training.csv"
-        # filepath_train = f"{super().temp_path}/{filename_train}"
-        # df_train.to_csv(filepath_train , index=False)
-       
-        # filename_predict = f"{source_db_name}_{super().data_set_name}_predict.csv"
-        # filepath_predict = f"{super().temp_path}/{filename_predict}"
-        # df_predict.to_csv(filepath_predict , index=False)
-
-        # if phase == DataSetCsvDB.PHASE_PREDICTION:
-        #     pass # generate data without labels
-
-
-        # return filepath_train, filepath_predict,\
-        #        filename_train, filename_predict,\
-        #        number_of_samples_train, number_of_samples_predict
-        pass
\ No newline at end of file
diff --git a/src_py/apiServer/test.py b/src_py/apiServer/test.py
deleted file mode 100644
index c676515e0..000000000
--- a/src_py/apiServer/test.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from NerlDatasetDB import *
-
-nerlnet_csv_db = DataSetCsvDB("/home/nerlnet/nerlnet/nerlnet/src_py/apiServer/nerlnet_csv_db.csv", "/home/nerlnet/nerlnet/nerlnet/src_py/apiServer/nerlnet_csv_db_temp", 50)
-
-nerlnet_csv_db.add_source_db("source1","source1_db", "w1,w2,worker3", 0, 100, DataSetCsvDB.PHASE_TRAINING)
-nerlnet_csv_db.add_source_db("source2","source_db2" "w1,w2,worker3", 0, 100, DataSetCsvDB.PHASE_TRAINING)
-nerlnet_csv_db.generate_data_for_source("source1")
\ No newline at end of file

From 3632843d73468fe2c903291328b244a1ada080a1 Mon Sep 17 00:00:00 2001
From: leondavi <ddleon@outlook.co.il>
Date: Thu, 27 Jun 2024 18:18:37 +0300
Subject: [PATCH 2/7] [ONN] Add batch norm layer to nerlplanner

---
 src_cpp/common/worker_definitions_ag.h              |  4 ++--
 src_erl/NerlnetApp/src/Bridge/layers_types_ag.hrl   | 13 +++++++------
 src_erl/NerlnetApp/src/Bridge/models_types_ag.hrl   |  2 +-
 .../src/Bridge/neural_networks_testing_models.hrl   |  6 +++---
 src_erl/NerlnetApp/src/dc_definitions_ag.hrl        |  2 +-
 src_erl/NerlnetApp/src/nerlnetApp.app.src           |  2 +-
 src_erl/NerlnetApp/src/nerlnetApp_app.erl           |  4 ++--
 src_erl/NerlnetApp/src/router_definitions_ag.hrl    |  2 +-
 src_erl/NerlnetApp/src/source_definitions_ag.hrl    |  2 +-
 src_erl/NerlnetApp/src/worker_definitions_ag.hrl    |  2 +-
 src_py/nerlPlanner/Definitions.py                   |  4 ++--
 src_py/nerlPlanner/JsonElementWorkerDefinitions.py  | 11 ++++++-----
 12 files changed, 28 insertions(+), 26 deletions(-)

diff --git a/src_cpp/common/worker_definitions_ag.h b/src_cpp/common/worker_definitions_ag.h
index 6de8c4fc3..17ed216e4 100644
--- a/src_cpp/common/worker_definitions_ag.h
+++ b/src_cpp/common/worker_definitions_ag.h
@@ -1,11 +1,11 @@
 #pragma once
 
 // This file was auto generated
-// Generated by Nerlplanner version: 1.0.2
+// Generated by Nerlplanner version: 1.0.3
 
 namespace nerlnet {
 
-enum LayerTypeEnum{LAYER_TYPE_DEFAULT=0,LAYER_TYPE_SCALING=1,LAYER_TYPE_CONV=2,LAYER_TYPE_PERCEPTRON=3,LAYER_TYPE_POOLING=4,LAYER_TYPE_PROBABILISTIC=5,LAYER_TYPE_LSTM=6,LAYER_TYPE_RECCURRENT=7,LAYER_TYPE_UNSCALING=8,LAYER_TYPE_FLATTEN=9,LAYER_TYPE_BOUNDING=10};
+enum LayerTypeEnum{LAYER_TYPE_DEFAULT=0,LAYER_TYPE_SCALING=1,LAYER_TYPE_CONV=2,LAYER_TYPE_PERCEPTRON=3,LAYER_TYPE_POOLING=4,LAYER_TYPE_PROBABILISTIC=5,LAYER_TYPE_BATCHNORMALIZATION=6,LAYER_TYPE_LSTM=7,LAYER_TYPE_RECCURRENT=8,LAYER_TYPE_UNSCALING=9,LAYER_TYPE_FLATTEN=10,LAYER_TYPE_BOUNDING=11};
 enum ProbabilisticActivationEnum{PROBABILISTIC_ACTIVATION_BINARY=1,PROBABILISTIC_ACTIVATION_LOGISTIC=2,PROBABILISTIC_ACTIVATION_COMPETITIVE=3,PROBABILISTIC_ACTIVATION_SOFTMAX=4};
 enum ScalingEnum{SCALING_NONE=1,SCALING_MINMAX=2,SCALING_MEANSTD=3,SCALING_STD=4,SCALING_LOG=5};
 enum BoundingEnum{BOUNDING_NONE=1,BOUNDING_BOUNDING=2};
diff --git a/src_erl/NerlnetApp/src/Bridge/layers_types_ag.hrl b/src_erl/NerlnetApp/src/Bridge/layers_types_ag.hrl
index 0bc61793b..341ee11b8 100644
--- a/src_erl/NerlnetApp/src/Bridge/layers_types_ag.hrl
+++ b/src_erl/NerlnetApp/src/Bridge/layers_types_ag.hrl
@@ -1,5 +1,5 @@
 % This is an auto generated .hrl file
-% DC Fields Generated by Nerlplanner version: 1.0.2
+% DC Fields Generated by Nerlplanner version: 1.0.3
 
 -define(LAYERS_TYPE_DEFAULT_IDX,"0").
 -define(LAYERS_TYPE_SCALING_IDX,"1").
@@ -7,8 +7,9 @@
 -define(LAYERS_TYPE_PERCEPTRON_IDX,"3").
 -define(LAYERS_TYPE_POOLING_IDX,"4").
 -define(LAYERS_TYPE_PROBABILISTIC_IDX,"5").
--define(LAYERS_TYPE_LSTM_IDX,"6").
--define(LAYERS_TYPE_RECCURRENT_IDX,"7").
--define(LAYERS_TYPE_UNSCALING_IDX,"8").
--define(LAYERS_TYPE_FLATTEN_IDX,"9").
--define(LAYERS_TYPE_BOUNDING_IDX,"10").
+-define(LAYERS_TYPE_BATCHNORMALIZATION_IDX,"6").
+-define(LAYERS_TYPE_LSTM_IDX,"7").
+-define(LAYERS_TYPE_RECCURRENT_IDX,"8").
+-define(LAYERS_TYPE_UNSCALING_IDX,"9").
+-define(LAYERS_TYPE_FLATTEN_IDX,"10").
+-define(LAYERS_TYPE_BOUNDING_IDX,"11").
diff --git a/src_erl/NerlnetApp/src/Bridge/models_types_ag.hrl b/src_erl/NerlnetApp/src/Bridge/models_types_ag.hrl
index 557fce500..8aa8d264e 100644
--- a/src_erl/NerlnetApp/src/Bridge/models_types_ag.hrl
+++ b/src_erl/NerlnetApp/src/Bridge/models_types_ag.hrl
@@ -1,5 +1,5 @@
 % This is an auto generated .hrl file
-% DC Fields Generated by Nerlplanner version: 1.0.2
+% DC Fields Generated by Nerlplanner version: 1.0.3
 
 -define(MODEL_TYPE_NN_IDX,"0").
 -define(MODEL_TYPE_APPROXIMATION_IDX,"1").
diff --git a/src_erl/NerlnetApp/src/Bridge/neural_networks_testing_models.hrl b/src_erl/NerlnetApp/src/Bridge/neural_networks_testing_models.hrl
index 80f70dfac..b9a89cf1f 100644
--- a/src_erl/NerlnetApp/src/Bridge/neural_networks_testing_models.hrl
+++ b/src_erl/NerlnetApp/src/Bridge/neural_networks_testing_models.hrl
@@ -19,7 +19,7 @@
                                 _ModelTypeCNN = "0",
                                 _ModelArgsCNN = "",
                                 _LayersSizesCNN = "28x28x1k5x5x1x6p0s1t1,28x28x6k2x2p0s2,14x14x6k4x4x6x12p0s1t0,1,32,10",
-                                _LayersTypesCNN = "2,4,2,9,3,5",
+                                _LayersTypesCNN = "2,4,2,10,3,5",
                                 _LayersFunctionalityCodesCNN = "6,2,6,6,6,4", % change scaler functionality to 6 to check exception handling
                                 _LearningRateCNN = "0.01",
                                 _EpochsCNN = "50",
@@ -33,7 +33,7 @@
                                 _ModelTypeAEC = "9",
                                 _ModelArgsAEC = "",
                                 _LayersSizesAEC = "32,16,8,4,8,16,32,32", % last layer (perceptron) should be the same as the input layer , followed by bounding layer
-                                _LayersTypesAEC = "1,3,3,3,3,3,3,10",
+                                _LayersTypesAEC = "1,3,3,3,3,3,3,11",
                                 _LayersFunctionalityCodesAEC = "1,11,11,11,11,11,11,1", 
                                 _LearningRateAEC = "0.01",
                                 _EpochsAEC = "50",
@@ -47,7 +47,7 @@
                                 _ModelTypeAE = "8",
                                 _ModelArgsAE = "",
                                 _LayersSizesAE = "32,16,8,4,8,16,32,32", % last layer (perceptron) should be the same as the input layer , followed by bounding layer
-                                _LayersTypesAE = "1,3,3,3,3,3,3,10",
+                                _LayersTypesAE = "1,3,3,3,3,3,3,11",
                                 _LayersFunctionalityCodesAE = "1,11,11,11,11,11,11,1", 
                                 _LearningRateAE = "0.01",
                                 _EpochsAE = "50",
diff --git a/src_erl/NerlnetApp/src/dc_definitions_ag.hrl b/src_erl/NerlnetApp/src/dc_definitions_ag.hrl
index 73ab3cc53..ddb8eed78 100644
--- a/src_erl/NerlnetApp/src/dc_definitions_ag.hrl
+++ b/src_erl/NerlnetApp/src/dc_definitions_ag.hrl
@@ -1,5 +1,5 @@
 % This is an auto generated .hrl file
-% DC Fields Generated by Nerlplanner version: 1.0.2
+% DC Fields Generated by Nerlplanner version: 1.0.3
 
 -define(DC_KEY_NERLNET_SETTINGS_ATOM,nerlnetSettings).
 -define(DC_KEY_FREQUENCY_ATOM,frequency).
diff --git a/src_erl/NerlnetApp/src/nerlnetApp.app.src b/src_erl/NerlnetApp/src/nerlnetApp.app.src
index 20c69a4ca..062f5a3cc 100644
--- a/src_erl/NerlnetApp/src/nerlnetApp.app.src
+++ b/src_erl/NerlnetApp/src/nerlnetApp.app.src
@@ -1,6 +1,6 @@
 {application, nerlnetApp,
  [{description, "Nerlnet OTP Application"},
-  {vsn, "1.4.0"},
+  {vsn, "1.5.1"},
   {registered, []},
   {mod, {nerlnetApp_app, []}},
   {applications,
diff --git a/src_erl/NerlnetApp/src/nerlnetApp_app.erl b/src_erl/NerlnetApp/src/nerlnetApp_app.erl
index 2f228d785..acd389431 100644
--- a/src_erl/NerlnetApp/src/nerlnetApp_app.erl
+++ b/src_erl/NerlnetApp/src/nerlnetApp_app.erl
@@ -20,8 +20,8 @@
 -behaviour(application).
 -include("nerl_tools.hrl").
 
--define(NERLNET_APP_VERSION, "1.5.0").
--define(NERLPLANNER_TESTED_VERSION,"1.0.2").
+-define(NERLNET_APP_VERSION, "1.5.1").
+-define(NERLPLANNER_TESTED_VERSION,"1.0.3").
 
 -export([start/2, stop/1]).
 
diff --git a/src_erl/NerlnetApp/src/router_definitions_ag.hrl b/src_erl/NerlnetApp/src/router_definitions_ag.hrl
index 98dc37206..fa1d89851 100644
--- a/src_erl/NerlnetApp/src/router_definitions_ag.hrl
+++ b/src_erl/NerlnetApp/src/router_definitions_ag.hrl
@@ -1,5 +1,5 @@
 % This is an auto generated .hrl file
-% Source Fields Generated by Nerlplanner version: 1.0.2
+% Source Fields Generated by Nerlplanner version: 1.0.3
 
 -define(ROUTER_POLICY_ROUTINGTABLE_IDX,"0").
 
diff --git a/src_erl/NerlnetApp/src/source_definitions_ag.hrl b/src_erl/NerlnetApp/src/source_definitions_ag.hrl
index cac5e234a..7005e2ecd 100644
--- a/src_erl/NerlnetApp/src/source_definitions_ag.hrl
+++ b/src_erl/NerlnetApp/src/source_definitions_ag.hrl
@@ -1,5 +1,5 @@
 % This is an auto generated .hrl file
-% Source Fields Generated by Nerlplanner version: 1.0.2
+% Source Fields Generated by Nerlplanner version: 1.0.3
 
 -define(SOURCE_POLICY_CASTING_IDX,"0").
 -define(SOURCE_POLICY_ROUNDROBIN_IDX,"1").
diff --git a/src_erl/NerlnetApp/src/worker_definitions_ag.hrl b/src_erl/NerlnetApp/src/worker_definitions_ag.hrl
index 57bf2761d..08fb097f1 100644
--- a/src_erl/NerlnetApp/src/worker_definitions_ag.hrl
+++ b/src_erl/NerlnetApp/src/worker_definitions_ag.hrl
@@ -1,5 +1,5 @@
 % This is an auto generated .hrl file
-% Worker Fields Generated by Nerlplanner version: 1.0.2
+% Worker Fields Generated by Nerlplanner version: 1.0.3
 
 -define(WORKER_FIELD_KEY_MODEL_TYPE,modelType).
 -define(WORKER_FIELD_KEY_MODEL_ARGS,modelArgs).
diff --git a/src_py/nerlPlanner/Definitions.py b/src_py/nerlPlanner/Definitions.py
index d70512d36..277898a2a 100644
--- a/src_py/nerlPlanner/Definitions.py
+++ b/src_py/nerlPlanner/Definitions.py
@@ -1,8 +1,8 @@
 import subprocess
 from logger import *
 
-VERSION = "1.0.2"
-NERLNET_VERSION_TESTED_WITH = "1.5.0"
+VERSION = "1.0.3"
+NERLNET_VERSION_TESTED_WITH = "1.5.1"
 NERLNET_TMP_PATH = "/tmp/nerlnet"
 NERLNET_GRAPHVIZ_OUTPUT_DIR = f"{NERLNET_TMP_PATH}/nerlplanner"
 NERLNET_GLOBAL_PATH = "/usr/local/lib/nerlnet-lib/NErlNet"
diff --git a/src_py/nerlPlanner/JsonElementWorkerDefinitions.py b/src_py/nerlPlanner/JsonElementWorkerDefinitions.py
index c5fecfe0f..6bd51bfba 100644
--- a/src_py/nerlPlanner/JsonElementWorkerDefinitions.py
+++ b/src_py/nerlPlanner/JsonElementWorkerDefinitions.py
@@ -12,11 +12,12 @@
     ("Perceptron" , "3"),
     ("Pooling" , "4"),
     ("Probabilistic" , "5"),
-    ("LSTM" , "6"),
-    ("Reccurrent" , "7"),
-    ("Unscaling" , "8"),
-    ("Flatten" , "9"),
-    ("Bounding" , "10"),
+    ("BatchNormalization" , "6"),
+    ("LSTM" , "7"),
+    ("Reccurrent" , "8"),
+    ("Unscaling" , "9"),
+    ("Flatten" , "10"),
+    ("Bounding" , "11"),
     ]
 )
 

From 1fe53ed207bbfc45c1d3bb006c7f4b27bbe963b2 Mon Sep 17 00:00:00 2001
From: leondavi <ddleon@outlook.co.il>
Date: Fri, 28 Jun 2024 19:30:52 +0300
Subject: [PATCH 3/7] [NERLPLANNER] Fix missing bounding layer doc

---
 src_py/nerlPlanner/JsonElementWorker.py            | 1 +
 src_py/nerlPlanner/JsonElementWorkerDefinitions.py | 7 +++++++
 src_py/nerlPlanner/WinWorkerDialog.py              | 3 ++-
 3 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/src_py/nerlPlanner/JsonElementWorker.py b/src_py/nerlPlanner/JsonElementWorker.py
index 1eb033cd2..729e27655 100644
--- a/src_py/nerlPlanner/JsonElementWorker.py
+++ b/src_py/nerlPlanner/JsonElementWorker.py
@@ -206,6 +206,7 @@ def get_as_dict(self, documentation = True):
             (KEY_LAYERS_FUNCTIONS_POOLING_DOC, VAL_LAYERS_FUNCTIONS_POOLING_DOC),
             (KEY_LAYERS_FUNCTIONS_PROBABILISTIC_DOC, VAL_LAYERS_FUNCTIONS_PROBABILISTIC_DOC),
             (KEY_LAYERS_FUNCTIONS_SCALER_DOC, VAL_LAYERS_FUNCTIONS_SCALER_DOC),
+            (KEY_LAYERS_FUNCTIONS_BOUNDING_DOC, VAL_LAYERS_FUNCTIONS_BOUNDING_DOC),
             (KEY_LOSS_METHOD, self.LossMethod),
             (KEY_LOSS_METHOD_DOC, VAL_LOSS_METHOD_DOC),
             (KEY_LEARNING_RATE, self.LearningRate),
diff --git a/src_py/nerlPlanner/JsonElementWorkerDefinitions.py b/src_py/nerlPlanner/JsonElementWorkerDefinitions.py
index 6bd51bfba..629f11139 100644
--- a/src_py/nerlPlanner/JsonElementWorkerDefinitions.py
+++ b/src_py/nerlPlanner/JsonElementWorkerDefinitions.py
@@ -59,6 +59,11 @@
     ("Avg" , "3")]
 )
 
+BatchNomalizationMap = OrderedDict(
+    [("none" , "1"),
+    ]
+)
+
 ActivationFunctionsMap = OrderedDict(
     [("Threshold" , "1"),
     ("Sign" , "2"),
@@ -155,6 +160,7 @@ def doc_print_dict(d):#define d
 KEY_LAYERS_FUNCTIONS_SCALER_DOC = "_doc_layer_functions_scaler"
 KEY_LAYERS_FUNCTIONS_POOLING_DOC = "_doc_layer_functions_pooling"
 KEY_LAYERS_FUNCTIONS_PROBABILISTIC_DOC = "_doc_layer_functions_probabilistic"
+KEY_LAYERS_FUNCTIONS_BOUNDING_DOC = "_doc_layer_functions_bounding"
 KEY_LOSS_METHOD = "lossMethod"
 KEY_LOSS_METHOD_DOC = "_doc_lossMethod"
 KEY_EPOCHS = "epochs"
@@ -183,6 +189,7 @@ def doc_print_dict(d):#define d
 VAL_LAYERS_FUNCTIONS_POOLING_DOC = f"{doc_print_dict(PoolingMethodMap)}"
 VAL_LAYERS_FUNCTIONS_PROBABILISTIC_DOC = f"{doc_print_dict(ProbabilisticActivationFunctionMap)}"
 VAL_LAYERS_FUNCTIONS_ACTIVATION_DOC = f"{doc_print_dict(ActivationFunctionsMap)}"
+VAL_LAYERS_FUNCTIONS_BOUNDING_DOC = f"{doc_print_dict(BoundingMethodMap)}"
 VAL_LOSS_METHOD_DOC = f"{doc_print_dict(LossMethodMapping)}"
 VAL_EPOCHS_DOC = "Positve Integer"
 VAL_LEARNING_RATE_DOC = "Positve float"
diff --git a/src_py/nerlPlanner/WinWorkerDialog.py b/src_py/nerlPlanner/WinWorkerDialog.py
index 149492191..6502c6482 100644
--- a/src_py/nerlPlanner/WinWorkerDialog.py
+++ b/src_py/nerlPlanner/WinWorkerDialog.py
@@ -166,7 +166,8 @@ def ui_update_all_values(WorkerWindow):
             FlattenDictStr = f'Flatten:\n{pretty_print_dict(FlattenMethodMap)}'
             BoundingDictStr = f'Bounding:\n{pretty_print_dict(BoundingMethodMap)}'
             ProbabilisticDictStr = f'Probabilistic:\n{pretty_print_dict(ProbabilisticActivationFunctionMap)}'
-            sg.popup_ok(f"Layer Functions Codes:\n{ActivationDictStr}\n{PoolingDictStr}\n{ScalerDictStr}\n{FlattenDictStr}\n{BoundingDictStr}\n{ProbabilisticDictStr}", keep_on_top=True, title="Layer Type Codes")
+            BatchNormalizationDictStr = f'Batch Normalization:\n{pretty_print_dict(BatchNomalizationMap)}'
+            sg.popup_ok(f"Layer Functions Codes:\n{ActivationDictStr}\n{PoolingDictStr}\n{ScalerDictStr}\n{FlattenDictStr}\n{BoundingDictStr}\n{ProbabilisticDictStr}\n{BatchNormalizationDictStr}", keep_on_top=True, title="Layer Type Codes")
 
         if event == KEY_LEARNING_RATE_INPUT:
             LearningRate = values[event]

From 36212816f336bc8a62aaaadfc6976da99bc105eb Mon Sep 17 00:00:00 2001
From: leondavi <ddleon@outlook.co.il>
Date: Sun, 30 Jun 2024 22:49:11 +0300
Subject: [PATCH 4/7] [NERLPLANNER] Add BatchNorm to selection

---
 src_py/nerlPlanner/WinWorkerDialog.py           | 17 ++++++++++++++---
 src_py/nerlPlanner/WinWorkerDialogDefnitions.py |  1 +
 2 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/src_py/nerlPlanner/WinWorkerDialog.py b/src_py/nerlPlanner/WinWorkerDialog.py
index 6502c6482..3c22fe012 100644
--- a/src_py/nerlPlanner/WinWorkerDialog.py
+++ b/src_py/nerlPlanner/WinWorkerDialog.py
@@ -267,9 +267,15 @@ def LayerMethodSelection():
                  sg.Listbox(list(ScalingMethodMap.keys()),size=(20,15), enable_events=True, key=KEY_LAYER_METHOD_SELECTION_DIALOG_LISTBOX_SCALER)],
                  [ sg.Text("Bounding",expand_x=True), sg.Text('Flatten', expand_x=True), sg.Text('Probabilistic', expand_x=True)],
                  [
-                 sg.Listbox(list(BoundingMethodMap.keys()),size=(20,15), enable_events=True, key=KEY_LAYER_METHOD_SELECTION_DIALOG_LISTBOX_BOUNDING),
-                 sg.Listbox(list(FlattenMethodMap.keys()),size=(20,15), enable_events=True, key=KEY_LAYER_METHOD_SELECTION_DIALOG_LISTBOX_FLATTEN),
-                 sg.Listbox(list(ProbabilisticActivationFunctionMap.keys()),size=(20,15), enable_events=True, key=KEY_LAYER_METHOD_SELECTION_DIALOG_LISTBOX_PROBABILISTIC)
+                 sg.Listbox(list(BoundingMethodMap.keys()),size=(20,5), enable_events=True, key=KEY_LAYER_METHOD_SELECTION_DIALOG_LISTBOX_BOUNDING),
+                 sg.Listbox(list(FlattenMethodMap.keys()),size=(20,5), enable_events=True, key=KEY_LAYER_METHOD_SELECTION_DIALOG_LISTBOX_FLATTEN),
+                 sg.Listbox(list(ProbabilisticActivationFunctionMap.keys()),size=(20,5), enable_events=True, key=KEY_LAYER_METHOD_SELECTION_DIALOG_LISTBOX_PROBABILISTIC)
+                 ],
+                 [
+                    sg.Text("BatchNorm",expand_x=True), sg.Text(' ', expand_x=True), sg.Text(' ', expand_x=True)
+                 ],
+                 [
+                    sg.Listbox(list(BatchNomalizationMap.keys()),size=(20,5), enable_events=True, key=KEY_LAYER_METHOD_SELECTION_DIALOG_LISTBOX_BATCH_NORMALIZATION),
                  ],
                  [sg.Text('Selection', expand_x=True, enable_events=True, key=KEY_LAYER_METHOD_SELECTION_TEXT),sg.Button('Select', expand_x=True, key=KEY_LAYER_METHOD_SELECTION_BUTTON)]]
     
@@ -309,6 +315,11 @@ def LayerMethodSelection():
             global_layer_method_selection_code = FlattenMethodMap[layer_method_selection]
             layer_selection_win[KEY_LAYER_METHOD_SELECTION_TEXT].update(f'Selected {layer_method_selection} code: {global_layer_method_selection_code}')
 
+        if event == KEY_LAYER_METHOD_SELECTION_DIALOG_LISTBOX_BATCH_NORMALIZATION:
+            layer_method_selection = values[KEY_LAYER_METHOD_SELECTION_DIALOG_LISTBOX_BATCH_NORMALIZATION][0]
+            global_layer_method_selection_code = BatchNomalizationMap[layer_method_selection]
+            layer_selection_win[KEY_LAYER_METHOD_SELECTION_TEXT].update(f'Selected {layer_method_selection} code: {global_layer_method_selection_code}')
+
         if event == KEY_LAYER_METHOD_SELECTION_BUTTON:
             break
         
diff --git a/src_py/nerlPlanner/WinWorkerDialogDefnitions.py b/src_py/nerlPlanner/WinWorkerDialogDefnitions.py
index cabef3a6e..9ed41c320 100644
--- a/src_py/nerlPlanner/WinWorkerDialogDefnitions.py
+++ b/src_py/nerlPlanner/WinWorkerDialogDefnitions.py
@@ -55,6 +55,7 @@
 KEY_LAYER_METHOD_SELECTION_DIALOG_LISTBOX_SCALER = '-LAYER-METHOD-SELECTION-DIALOG-LISTBOX-SCALER-'
 KEY_LAYER_METHOD_SELECTION_DIALOG_LISTBOX_FLATTEN = '-LAYER-METHOD-SELECTION-DIALOG-LISTBOX-FLATTEN-'
 KEY_LAYER_METHOD_SELECTION_DIALOG_LISTBOX_BOUNDING = '-LAYER-METHOD-SELECTION-DIALOG-LISTBOX-BOUNDING-'
+KEY_LAYER_METHOD_SELECTION_DIALOG_LISTBOX_BATCH_NORMALIZATION = '-LAYER-METHOD-SELECTION-DIALOG-LISTBOX-BATCH-NORMALIZATION-'
 KEY_LAYER_METHOD_SELECTION_DIALOG_LISTBOX_PROBABILISTIC = '-LAYER-METHOD-SELECTION-DIALOG-LISTBOX-PROBABILISTIC-'
 KEY_LAYER_METHOD_SELECTION_TEXT = '-LAYER-METHOD-SELECTION-TEXT-'
 KEY_LAYER_METHOD_SELECTION_BUTTON = '-LAYER-METHOD-SELECTION-BUTTON-'

From 993ea68b3a54af38230f5b172be266d487b585e9 Mon Sep 17 00:00:00 2001
From: Ubuntu
 <nerlnet@Nerlnet-C0VM9.pc5jfx5leprevd3khbqxscqqff.oslx.internal.cloudapp.net>
Date: Tue, 2 Jul 2024 18:35:53 +0000
Subject: [PATCH 5/7] insert model and batch layer

---
 src_cpp/opennnBridge/nerlWorkerOpenNN.cpp     |  9 ++++++++
 .../Bridge/neural_networks_testing_models.hrl | 21 ++++++++++++++++---
 2 files changed, 27 insertions(+), 3 deletions(-)

diff --git a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
index 031d93075..205584cce 100644
--- a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
+++ b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
@@ -486,6 +486,15 @@ namespace nerlnet
                           }
                     break;
                 }
+                case LAYER_TYPE_BATCHNORMALIZATION: 
+                {
+                    int layer_size  = curr_layer->get_dim_size(DIM_X_IDX);
+                    BatchNormalizationLayer* newLayer;
+                    newLayer->set(layer_size);
+                    neural_network_ptr->add_layer(newLayer);
+
+                    break;
+                }
             }  
             curr_layer = curr_layer->get_next_layer_ptr();
         }
diff --git a/src_erl/NerlnetApp/src/Bridge/neural_networks_testing_models.hrl b/src_erl/NerlnetApp/src/Bridge/neural_networks_testing_models.hrl
index b9a89cf1f..ff5e7d36b 100644
--- a/src_erl/NerlnetApp/src/Bridge/neural_networks_testing_models.hrl
+++ b/src_erl/NerlnetApp/src/Bridge/neural_networks_testing_models.hrl
@@ -19,7 +19,7 @@
                                 _ModelTypeCNN = "0",
                                 _ModelArgsCNN = "",
                                 _LayersSizesCNN = "28x28x1k5x5x1x6p0s1t1,28x28x6k2x2p0s2,14x14x6k4x4x6x12p0s1t0,1,32,10",
-                                _LayersTypesCNN = "2,4,2,10,3,5",
+                                _LayersTypesCNN = "2,4,2,9,3,5",
                                 _LayersFunctionalityCodesCNN = "6,2,6,6,6,4", % change scaler functionality to 6 to check exception handling
                                 _LearningRateCNN = "0.01",
                                 _EpochsCNN = "50",
@@ -29,11 +29,26 @@
                                 _DistributedSystemTypeCNN = "0",
                                 _DistributedSystemArgCNN = ""} ).
 
+-define(CNN_1D_TESTING_NN,{     _ModelIdCNN_1D  = erlang:unique_integer([positive]),
+                                _ModelTypeCNN_1D = "0",
+                                _ModelArgsCNN_1D = "",
+                                _LayersSizesCNN_1D = "70x1x1k5x1x1x128p0s1t1 cnn ,66x1x128k2x1p0s1 pool,65x1x128k5x1x128x128p0s1t1 cnn,61x1x128k2x1p0s1 pool,
+                                60x1x128k5x1x128x64p0s1t0 cnn,1 flat,64 p, 32 p, 16 p,9 softmax",
+                                _LayersTypesCNN_1D = "2,4,2,4,2,10,3,3,3,5",
+                                _LayersFunctionalityCodesCNN = "", % change scaler functionality to 6 to check exception handling
+                                _LearningRateCNN_1D = "0.01",
+                                _EpochsCNN_1D = "50",
+                                _OptimizerTypeCNN_1D = "5",
+                                _OptimizerArgsCNN_1D = "",
+                                _LossMethodCNN_1D = "2",
+                                _DistributedSystemTypeCNN_1D = "0",
+                                _DistributedSystemArgCNN_1D = ""} ).
+
 -define(AEC_TESTING_NN,{        _ModelIdAEC  = erlang:unique_integer([positive]),
                                 _ModelTypeAEC = "9",
                                 _ModelArgsAEC = "",
                                 _LayersSizesAEC = "32,16,8,4,8,16,32,32", % last layer (perceptron) should be the same as the input layer , followed by bounding layer
-                                _LayersTypesAEC = "1,3,3,3,3,3,3,11",
+                                _LayersTypesAEC = "1,3,3,3,3,3,3,10",
                                 _LayersFunctionalityCodesAEC = "1,11,11,11,11,11,11,1", 
                                 _LearningRateAEC = "0.01",
                                 _EpochsAEC = "50",
@@ -47,7 +62,7 @@
                                 _ModelTypeAE = "8",
                                 _ModelArgsAE = "",
                                 _LayersSizesAE = "32,16,8,4,8,16,32,32", % last layer (perceptron) should be the same as the input layer , followed by bounding layer
-                                _LayersTypesAE = "1,3,3,3,3,3,3,11",
+                                _LayersTypesAE = "1,3,3,3,3,3,3,10",
                                 _LayersFunctionalityCodesAE = "1,11,11,11,11,11,11,1", 
                                 _LearningRateAE = "0.01",
                                 _EpochsAE = "50",

From 752884a2cce0a2ac603c62387444cc6b8e44586d Mon Sep 17 00:00:00 2001
From: Ubuntu
 <nerlnet@Nerlnet-C0VM9.pc5jfx5leprevd3khbqxscqqff.oslx.internal.cloudapp.net>
Date: Thu, 4 Jul 2024 20:42:15 +0000
Subject: [PATCH 6/7] 1d conv worker

---
 .../src/Bridge/neural_networks_testing_models.hrl | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/src_erl/NerlnetApp/src/Bridge/neural_networks_testing_models.hrl b/src_erl/NerlnetApp/src/Bridge/neural_networks_testing_models.hrl
index ff5e7d36b..ddf7f6864 100644
--- a/src_erl/NerlnetApp/src/Bridge/neural_networks_testing_models.hrl
+++ b/src_erl/NerlnetApp/src/Bridge/neural_networks_testing_models.hrl
@@ -19,7 +19,7 @@
                                 _ModelTypeCNN = "0",
                                 _ModelArgsCNN = "",
                                 _LayersSizesCNN = "28x28x1k5x5x1x6p0s1t1,28x28x6k2x2p0s2,14x14x6k4x4x6x12p0s1t0,1,32,10",
-                                _LayersTypesCNN = "2,4,2,9,3,5",
+                                _LayersTypesCNN = "2,4,2,10,3,5",
                                 _LayersFunctionalityCodesCNN = "6,2,6,6,6,4", % change scaler functionality to 6 to check exception handling
                                 _LearningRateCNN = "0.01",
                                 _EpochsCNN = "50",
@@ -32,10 +32,9 @@
 -define(CNN_1D_TESTING_NN,{     _ModelIdCNN_1D  = erlang:unique_integer([positive]),
                                 _ModelTypeCNN_1D = "0",
                                 _ModelArgsCNN_1D = "",
-                                _LayersSizesCNN_1D = "70x1x1k5x1x1x128p0s1t1 cnn ,66x1x128k2x1p0s1 pool,65x1x128k5x1x128x128p0s1t1 cnn,61x1x128k2x1p0s1 pool,
-                                60x1x128k5x1x128x64p0s1t0 cnn,1 flat,64 p, 32 p, 16 p,9 softmax",
+                                _LayersSizesCNN_1D = "70x1x1k5x1x1x128p0s1t0,66x1x128k2x1p0s1,65x1x128k5x1x128x128p0s1t0,61x1x128k2x1p0s1,60x1x128k5x1x128x64p0s1t0,1,64,32,16,9",
                                 _LayersTypesCNN_1D = "2,4,2,4,2,10,3,3,3,5",
-                                _LayersFunctionalityCodesCNN = "", % change scaler functionality to 6 to check exception handling
+                                _LayersFunctionalityCodesCNN_1D = "6,2,6,2,6,1,6,6,6,4", % change scaler functionality to 6 to check exception handling
                                 _LearningRateCNN_1D = "0.01",
                                 _EpochsCNN_1D = "50",
                                 _OptimizerTypeCNN_1D = "5",
@@ -48,7 +47,7 @@
                                 _ModelTypeAEC = "9",
                                 _ModelArgsAEC = "",
                                 _LayersSizesAEC = "32,16,8,4,8,16,32,32", % last layer (perceptron) should be the same as the input layer , followed by bounding layer
-                                _LayersTypesAEC = "1,3,3,3,3,3,3,10",
+                                _LayersTypesAEC = "1,3,3,3,3,3,3,11",
                                 _LayersFunctionalityCodesAEC = "1,11,11,11,11,11,11,1", 
                                 _LearningRateAEC = "0.01",
                                 _EpochsAEC = "50",
@@ -62,7 +61,7 @@
                                 _ModelTypeAE = "8",
                                 _ModelArgsAE = "",
                                 _LayersSizesAE = "32,16,8,4,8,16,32,32", % last layer (perceptron) should be the same as the input layer , followed by bounding layer
-                                _LayersTypesAE = "1,3,3,3,3,3,3,10",
+                                _LayersTypesAE = "1,3,3,3,3,3,3,11",
                                 _LayersFunctionalityCodesAE = "1,11,11,11,11,11,11,1", 
                                 _LearningRateAE = "0.01",
                                 _EpochsAE = "50",
@@ -72,5 +71,5 @@
                                 _DistributedSystemTypeAE = "0",
                                 _DistributedSystemArgAE = ""} ).
 
--define(NEURAL_NETWORK_TESTING_MODELS_LIST, [?PERCEPTRON_TESTING_NN ,?AEC_TESTING_NN , ?CNN_TESTING_NN]).
--define(NEURAL_NETWORK_TESTING_MODELS_LIST_NAMES, ["Perceptron" ,"AEC" ,"CNN"]).
\ No newline at end of file
+-define(NEURAL_NETWORK_TESTING_MODELS_LIST, [?PERCEPTRON_TESTING_NN ,?AEC_TESTING_NN , ?CNN_TESTING_NN,?CNN_1D_TESTING_NN]).
+-define(NEURAL_NETWORK_TESTING_MODELS_LIST_NAMES, ["Perceptron" ,"AEC" ,"CNN","CNN_1D"]).
\ No newline at end of file

From cc6afabb1fe5bfa56e86ea6b58fdd5777e6dc630 Mon Sep 17 00:00:00 2001
From: leondavi <ddleon@outlook.co.il>
Date: Sat, 6 Jul 2024 02:46:42 +0300
Subject: [PATCH 7/7] Version Update of Nerlplanner

---
 src_cpp/common/worker_definitions_ag.h            | 2 +-
 src_erl/NerlnetApp/src/Bridge/layers_types_ag.hrl | 2 +-
 src_erl/NerlnetApp/src/Bridge/models_types_ag.hrl | 2 +-
 src_erl/NerlnetApp/src/dc_definitions_ag.hrl      | 2 +-
 src_erl/NerlnetApp/src/router_definitions_ag.hrl  | 2 +-
 src_erl/NerlnetApp/src/source_definitions_ag.hrl  | 2 +-
 src_erl/NerlnetApp/src/worker_definitions_ag.hrl  | 2 +-
 7 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/src_cpp/common/worker_definitions_ag.h b/src_cpp/common/worker_definitions_ag.h
index 6de8c4fc3..2e4ea2f5c 100644
--- a/src_cpp/common/worker_definitions_ag.h
+++ b/src_cpp/common/worker_definitions_ag.h
@@ -1,7 +1,7 @@
 #pragma once
 
 // This file was auto generated
-// Generated by Nerlplanner version: 1.0.2
+// Generated by Nerlplanner version: 1.0.3
 
 namespace nerlnet {
 
diff --git a/src_erl/NerlnetApp/src/Bridge/layers_types_ag.hrl b/src_erl/NerlnetApp/src/Bridge/layers_types_ag.hrl
index 0bc61793b..2f1af62cf 100644
--- a/src_erl/NerlnetApp/src/Bridge/layers_types_ag.hrl
+++ b/src_erl/NerlnetApp/src/Bridge/layers_types_ag.hrl
@@ -1,5 +1,5 @@
 % This is an auto generated .hrl file
-% DC Fields Generated by Nerlplanner version: 1.0.2
+% DC Fields Generated by Nerlplanner version: 1.0.3
 
 -define(LAYERS_TYPE_DEFAULT_IDX,"0").
 -define(LAYERS_TYPE_SCALING_IDX,"1").
diff --git a/src_erl/NerlnetApp/src/Bridge/models_types_ag.hrl b/src_erl/NerlnetApp/src/Bridge/models_types_ag.hrl
index 557fce500..8aa8d264e 100644
--- a/src_erl/NerlnetApp/src/Bridge/models_types_ag.hrl
+++ b/src_erl/NerlnetApp/src/Bridge/models_types_ag.hrl
@@ -1,5 +1,5 @@
 % This is an auto generated .hrl file
-% DC Fields Generated by Nerlplanner version: 1.0.2
+% DC Fields Generated by Nerlplanner version: 1.0.3
 
 -define(MODEL_TYPE_NN_IDX,"0").
 -define(MODEL_TYPE_APPROXIMATION_IDX,"1").
diff --git a/src_erl/NerlnetApp/src/dc_definitions_ag.hrl b/src_erl/NerlnetApp/src/dc_definitions_ag.hrl
index 73ab3cc53..ddb8eed78 100644
--- a/src_erl/NerlnetApp/src/dc_definitions_ag.hrl
+++ b/src_erl/NerlnetApp/src/dc_definitions_ag.hrl
@@ -1,5 +1,5 @@
 % This is an auto generated .hrl file
-% DC Fields Generated by Nerlplanner version: 1.0.2
+% DC Fields Generated by Nerlplanner version: 1.0.3
 
 -define(DC_KEY_NERLNET_SETTINGS_ATOM,nerlnetSettings).
 -define(DC_KEY_FREQUENCY_ATOM,frequency).
diff --git a/src_erl/NerlnetApp/src/router_definitions_ag.hrl b/src_erl/NerlnetApp/src/router_definitions_ag.hrl
index 98dc37206..fa1d89851 100644
--- a/src_erl/NerlnetApp/src/router_definitions_ag.hrl
+++ b/src_erl/NerlnetApp/src/router_definitions_ag.hrl
@@ -1,5 +1,5 @@
 % This is an auto generated .hrl file
-% Source Fields Generated by Nerlplanner version: 1.0.2
+% Source Fields Generated by Nerlplanner version: 1.0.3
 
 -define(ROUTER_POLICY_ROUTINGTABLE_IDX,"0").
 
diff --git a/src_erl/NerlnetApp/src/source_definitions_ag.hrl b/src_erl/NerlnetApp/src/source_definitions_ag.hrl
index cac5e234a..7005e2ecd 100644
--- a/src_erl/NerlnetApp/src/source_definitions_ag.hrl
+++ b/src_erl/NerlnetApp/src/source_definitions_ag.hrl
@@ -1,5 +1,5 @@
 % This is an auto generated .hrl file
-% Source Fields Generated by Nerlplanner version: 1.0.2
+% Source Fields Generated by Nerlplanner version: 1.0.3
 
 -define(SOURCE_POLICY_CASTING_IDX,"0").
 -define(SOURCE_POLICY_ROUNDROBIN_IDX,"1").
diff --git a/src_erl/NerlnetApp/src/worker_definitions_ag.hrl b/src_erl/NerlnetApp/src/worker_definitions_ag.hrl
index 57bf2761d..08fb097f1 100644
--- a/src_erl/NerlnetApp/src/worker_definitions_ag.hrl
+++ b/src_erl/NerlnetApp/src/worker_definitions_ag.hrl
@@ -1,5 +1,5 @@
 % This is an auto generated .hrl file
-% Worker Fields Generated by Nerlplanner version: 1.0.2
+% Worker Fields Generated by Nerlplanner version: 1.0.3
 
 -define(WORKER_FIELD_KEY_MODEL_TYPE,modelType).
 -define(WORKER_FIELD_KEY_MODEL_ARGS,modelArgs).