plot.py

# -*- coding: utf-8 -*-
"""
Created on Wed Sep 20 15:05:00 2017

@author: ESTERIFIED

"""
import math
##########excess functions

x1 = tf.reshape(x, shape=[-1, 28, 28, 1])
conv1 = conv2d(x1, weights['wc1'], biases['bc1'])
def plot_conv_layer(layer, image):
    # Assume layer is a TensorFlow op that outputs a 4-dim tensor
    # which is the output of a convolutional layer,
    # e.g. layer_conv1 or layer_conv2.

    # Create a feed-dict containing just one image.
    # Note that we don't need to feed y_true because it is
    # not used in this calculation.
    feed_dict = {x: image,keep_prob:1.}
    
    # Calculate and retrieve the output values of the layer
    # when inputting that image.
    session=tf.Session()
    session.run(tf.global_variables_initializer())
    values = session.run(conv1, feed_dict=feed_dict)

    # Number of filters used in the conv. layer.
    num_filters = values.shape[3]

    # Number of grids to plot.
    # Rounded-up, square-root of the number of filters.
    num_grids = math.ceil(math.sqrt(num_filters))
    
    # Create figure with a grid of sub-plots.
    fig, axes = plt.subplots(num_grids, num_grids)

    # Plot the output images of all the filters.
    for i, ax in enumerate(axes.flat):
        # Only plot the images for valid filters.
        if i<num_filters:
            # Get the output image of using the i'th filter.
            # See new_conv_layer() for details on the format
            # of this 4-dim tensor.
            img = values[0, :, :, i]

            # Plot image.
            ax.imshow(img, interpolation='nearest', cmap='binary')
        
        # Remove ticks from the plot.
        ax.set_xticks([])
        ax.set_yticks([])
    session.close()
    # Ensure the plot is shown correctly with multiple plots
    # in a single Notebook cell.
    plt.show()