Convolution visualizer

conv_visual

# Extracting the trained layers from the model 
successive_outputs = [layer.output for layer in model.layers[1:]]
visualization_model = tf.keras.models.Model(inputs = model.input, outputs = successive_outputs)

# Getting an image from the training set
image = training_images[1]

# Intermediate representations for this image
successive_feature_maps = visualization_model.predict(image.reshape(1, 28, 28, 1))

# Getting the names of the layers
layer_names = [layer.name for layer in model.layers]

plt.figure(figsize=(22, 3))

# Visualizing the convoluted layer
for layer_name, feature_map in zip(layer_names, successive_feature_maps):
    if layer_name in ['conv2d']:
        n_features = feature_map.shape[-1] 
        # The feature map has shape (1, size, size, n_features)
        size = feature_map.shape[1]
        # We will tile our images in this matrix
        display_grid = np.zeros((size, size * n_features))
        for i in range(n_features):
            # Postprocess the feature to make it visually palatable
            x = feature_map[0, :, :, i]
            x -= x.mean()
            x /= x.std()
            x *= 64
            x += 128
            x = np.clip(x, 0, 255).astype('uint8')
            # We'll tile each filter into this big horizontal grid
            display_grid[:, i * size : (i + 1) * size] = x
        # Display the grid
        scale = 20. / n_features
        plt.title(layer_name)
        plt.grid(False)
        plt.imshow(display_grid, aspect='auto', cmap='viridis')