conv_visuals.py

import numpy as np
import matplotlib.pyplot as plt
import cifar_tools
import tensorflow as tf

names, data, labels = \
    cifar_tools.read_data('/home/binroot/res/cifar-10-batches-py')


def show_conv_results(data, filename=None):
    plt.figure()
    rows, cols = 4, 8
    for i in range(np.shape(data)[3]):
        img = data[0, :, :, i]
        plt.subplot(rows, cols, i + 1)
        plt.imshow(img, cmap='Greys_r', interpolation='none')
        plt.axis('off')
    if filename:
        plt.savefig(filename)
    else:
        plt.show()


def show_weights(W, filename=None):
    plt.figure()
    rows, cols = 4, 8
    for i in range(np.shape(W)[3]):
        img = W[:, :, 0, i]
        plt.subplot(rows, cols, i + 1)
        plt.imshow(img, cmap='Greys_r', interpolation='none')
        plt.axis('off')
    if filename:
        plt.savefig(filename)
    else:
        plt.show()

raw_data = data[4, :]
raw_img = np.reshape(raw_data, (24, 24))
plt.figure()
plt.imshow(raw_img, cmap='Greys_r')
plt.savefig('input_image.png')

x = tf.reshape(raw_data, shape=[-1, 24, 24, 1])
W = tf.Variable(tf.random_normal([5, 5, 1, 32]))
b = tf.Variable(tf.random_normal([32]))

conv = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
conv_with_b = tf.nn.bias_add(conv, b)
conv_out = tf.nn.relu(conv_with_b)

k = 2
maxpool = tf.nn.max_pool(conv_out, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())

    W_val = sess.run(W)
    show_weights(W_val, 'step0_weights.png')

    conv_val = sess.run(conv)
    show_conv_results(conv_val, 'step1_convs.png')
    print(np.shape(conv_val))

    conv_out_val = sess.run(conv_out)
    show_conv_results(conv_out_val, 'step2_conv_outs.png')
    print(np.shape(conv_out_val))

    maxpool_val = sess.run(maxpool)
    show_conv_results(maxpool_val, 'step3_maxpool.png')
    print(np.shape(maxpool_val))