Now we have the input; that is, x_image is ready to feed to the convolutional layer. We formally create a convolutional layer, followed by the max pooling:
layer_conv1, weights_conv1 = LayersConstructor.new_conv_layer(input=x_image, num_input_channels=num_channels, filter_size=filter_size1, num_filters=num_filters1, use_pooling=True)
We must have the second convolutional layer, where the input is the first convolutional layer, layer_conv1, followed by the max pooling:
layer_conv2, weights_conv2 = LayersConstructor.new_conv_layer(input=layer_conv1, num_input_channels=num_filters1, filter_size=filter_size2, num_filters=num_filters2, use_pooling=True)
We now have the third convolutional layer where the input is ...