Load the CIFAR-10 images, and define helper functions to visualize the convolutions and weights:
In [1]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import cifar_tools
import tensorflow as tf
names, data, labels = \
cifar_tools.read_data('./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()
Let's start with a an example image:
In [2]:
raw_data = data[4, :]
raw_img = np.reshape(raw_data, (24, 24))
plt.figure()
plt.imshow(raw_img, cmap='Greys_r')
plt.show()
Define the TensorFlow ops:
In [3]:
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')
Visualize the effects of running the convolution and maxpool ops:
In [4]:
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
W_val = sess.run(W)
print('weights:')
show_weights(W_val)
conv_val = sess.run(conv)
print('convolution results:')
print(np.shape(conv_val))
show_conv_results(conv_val)
conv_out_val = sess.run(conv_out)
print('convolution with bias and relu:')
print(np.shape(conv_out_val))
show_conv_results(conv_out_val)
maxpool_val = sess.run(maxpool)
print('maxpool after all the convolutions:')
print(np.shape(maxpool_val))
show_conv_results(maxpool_val)