Deep MNIST_for_Experts.py

'''
This demo is for "Deep MNIST for Experts"

https://www.tensorflow.org/get_started/mnist/pros

'''

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST-data', one_hot=True)

import tensorflow as tf
sess = tf.InteractiveSession()

x = tf.placeholder(tf.float32, shape = [None, 784])
y_ = tf.placeholder(tf.float32, shape = [None, 10])

# W = tf.Variable(tf.zeros([784, 10]))
# b = tf.Variable(tf.zeros([10]))
#
# sess.run(tf.global_variables_initializer())
#
# y = tf.matmul(x, W) + b
#
# cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_, logits = y))
#
# train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
#
# for _ in range(1000):
#     batch = mnist.train.next_batch(100)
#     train_step.run(feed_dict = {x: batch[0], y_: batch[1]})
#
# correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
# accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# print (accuracy.eval(feed_dict = {x: mnist.test.images, y_: mnist.test.labels}))


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")

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_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
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([1024,10])
b_fc2 = bias_variable([10])

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

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)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accurary = 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_accurary = accurary.eval(feed_dict = {x: batch[0], y_: batch[1], keep_prob: 1.0})
        print ("step %d, training accuracy %g" %(i, train_accurary))
    train_step.run(feed_dict = {x: batch[0], y_: batch[1], keep_prob: 0.5})

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