in [ ]first_conv_layer function([model layers[ inputk learning_phase()][model layers[ output]second_conv_layer function([model layers[ inputk learning_phase()][model layers[ output]let' now use these functions to extract the feature representations learned in the convolutional layers and visualize these features to see what the network is trying to learn from the images in [ ]catr cat reshape( , extract features first_conv_features first_conv_layer([catr])[ ][ second_conv_features second_conv_layer([catr])[ ][ view feature representations fig plt figure(figsize ( , )ax fig add_subplot( , ax imshow(first_conv_features[:,:, ]ax fig add_subplot( , ax imshow(first_conv_features[:,:, ]ax fig add_subplot( , ax imshow(first_conv_features[:,:, ]ax fig add_subplot( , ax imshow(first_conv_features[:,:, ]ax fig add_subplot( , ax imshow(second_conv_features[:,:, ]ax fig add_subplot( , ax imshow(second_conv_features[:,:, ]ax fig add_subplot( , ax imshow(second_conv_features[:,:, ]ax fig add_subplot( , ax imshow(second_conv_features[:,:, ]figure - intermediate feature maps obtained after passing though convolutional layers