mnist-expert.py

import tensorflow as tf
import numpy as np

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('/tmp/mnist', one_hot=True)

sess = tf.Session(
    config=tf.ConfigProto(
        gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.3)   
    )
)

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

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')

def bias_variable(shape):
    return tf.Variable(tf.constant(.1, shape=shape))

def weight_variable(shape):
    return tf.Variable(tf.truncated_normal(shape, stddev=1.0))

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

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

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

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
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.initialize_all_variables())
for i in range(20000):
    batch = mnist.train.next_batch(50)

    if i % 1000 == 0:
        train_accuracy = accuracy.eval(session=sess, feed_dict={x: batch[0], y_: batch[1], keep_prop: 1.0})
        print("step %d, training accuracy %g" % (i, train_accuracy))

    train_step.run(session=sess, feed_dict={x: batch[0], y_: batch[1], keep_prop: 0.5})

for i in range(200):
    batch = mnist.test.next_batch(50)

    print("test accuracy %g" % accuracy.eval(session=sess, feed_dict={
        x: batch[0], y_: batch[1], keep_prop: 1.0
    }))