mnist_2.py

# Source: https://www.tensorflow.org/get_started/mnist/pros
# Modified to reduce memory usage by removing a hidden layer.
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data
# input
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

#helper
def weight_variable(shape):
	initial = tf.truncated_normal(shape, stddev=0.1)
	return tf.Variable(initial)

def bias_variable(shape):
	initial = tf.constant(0.1, shape=shape)
	return tf.Variable(initial)

def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')

# the graph (neural network)
x = tf.placeholder(tf.float32, [None, 784])

W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

x_image = tf.reshape(x, [-1, 28, 28, 1])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_fc1 = weight_variable([14 * 14 * 32, 256])
b_fc1 = bias_variable([256])

h_pool2_flat = tf.reshape(h_pool1, [-1, 14 * 14 * 32])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([256, 10])
b_fc2 = bias_variable([10])

y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

#y = tf.nn.softmax(tf.matmul(x, W) + b)

y_ = tf.placeholder(tf.float32, [None, 10])

# train
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))

train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

sess = tf.InteractiveSession()
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.global_variables_initializer())

for i in range(20000):
	batch = mnist.train.next_batch(50)
	if i % 100 == 0:
		train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
		print("step %d, training accuracy %g" % (i, train_accuracy))
	train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

# test
print("test accuracy %g" % accuracy.eval(feed_dict={
	x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))