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Again thanks for sharing your code. You linked to the keras version of this implementation. Are you sure that version is valid?
I went through it and I dont know why the way he/she calculated mean and sigma is the same. Could you please comment on this?
The Keras repository is read only thats why I could not open an issue there. I just want to know if you think the same as me that the implementation is not correct or am I missing somethin.
x = Input(batch_shape=(batch_size, V)) h = Dense(num_hidden, activation='softplus')(x) h = Dense(num_hidden, activation='softplus')(h) z_mean = BatchNormalization()(Dense(num_topic)(h)) z_log_var = BatchNormalization()(Dense(num_topic)(h))
Thanks for taking the time.
The text was updated successfully, but these errors were encountered:
Your mentioned part in my old keras code is almost the same as the original implementation that is defined in these lines. I believe that the difference is only the existence of dropout, which I have missed.
If you have a question for my repo, please send e-mail to me.
Hi,
Again thanks for sharing your code. You linked to the keras version of this implementation. Are you sure that version is valid?
I went through it and I dont know why the way he/she calculated
mean
andsigma
is the same. Could you please comment on this?The Keras repository is read only thats why I could not open an issue there. I just want to know if you think the same as me that the implementation is not correct or am I missing somethin.
x = Input(batch_shape=(batch_size, V)) h = Dense(num_hidden, activation='softplus')(x) h = Dense(num_hidden, activation='softplus')(h) z_mean = BatchNormalization()(Dense(num_topic)(h)) z_log_var = BatchNormalization()(Dense(num_topic)(h))
Thanks for taking the time.
The text was updated successfully, but these errors were encountered: