def build_encoder(self): conv_filters = [32, 64, 64, 64] conv_kernel_size = [3, 3, 3, 3] conv_strides = [2, 2, 2, 2] # Number of Conv layers n_layers = len(conv_filters) # Define model input x = self.encoder_input # Add convolutional layers for i in range(n_layers): x = Conv2D(filters=conv_filters[i], kernel_size=conv_kernel_size[i], strides=conv_strides[i], padding='same', name='encoder_conv_' + str(i) )(x) if self.use_batch_norm: # True x = BatchNormalization()(x) x = LeakyReLU()(x) if self.use_dropout: # False x = Dropout(rate=0.25)(x) # Required for reshaping latent vector while building Decoder self.shape_before_flattening = K.int_shape(x)[1:] x = Flatten()(x) self.mean_layer = Dense(self.encoder_output_dim, name='mu')(x) self.sd_layer = Dense(self.encoder_output_dim, name='log_var')(x) # Defining a function for sampling def sampling(args): mean_mu, log_var = args epsilon = K.random_normal(shape=K.shape(mean_mu), mean=0., stddev=1.) return mean_mu + K.exp(log_var / 2) * epsilon # Using a Keras Lambda Layer to include the sampling function as a layer # in the model encoder_output = Lambda(sampling, name='encoder_output')([self.mean_layer, self.sd_layer]) return Model(self.encoder_input, encoder_output, name="VAE_Encoder")
