model_save.py

import cifar10_input
import tensorflow as tf
import numpy as np

batch_size = 128
data_dir = '/tmp/cifar10_data/cifar-10-batches-bin'
print("begin")
images_train, labels_train = cifar10_input.inputs(eval_data=False, data_dir=data_dir, batch_size=batch_size)
images_test, labels_test = cifar10_input.inputs(eval_data=True, data_dir=data_dir, batch_size=batch_size)
print("begin data")


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')


def avg_pool_6x6(x):
    return tf.nn.avg_pool(x, ksize=[1, 6, 6, 1],
                          strides=[1, 6, 6, 1], padding='SAME')


# tf Graph Input
x = tf.placeholder(tf.float32, [None, 24, 24, 3])  # cifar data image of shape 24*24*3
y = tf.placeholder(tf.float32, [None, 10])  # 0-9 数字=> 10 classes

W_conv1 = weight_variable([5, 5, 3, 64])
b_conv1 = bias_variable([64])

x_image = tf.reshape(x, [-1, 24, 24, 3])

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, 64, 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_conv3 = weight_variable([5, 5, 64, 10])
b_conv3 = bias_variable([10])
h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)

nt_hpool3 = avg_pool_6x6(h_conv3)  # 10
nt_hpool3_flat = tf.reshape(nt_hpool3, [-1, 10])
y_conv = tf.nn.softmax(nt_hpool3_flat)

cross_entropy = -tf.reduce_sum(y * tf.log(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))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

sess = tf.Session()
sess.run(tf.global_variables_initializer())
tf.train.start_queue_runners(sess=sess)
for i in range(15000):  # 20000
    image_batch, label_batch = sess.run([images_train, labels_train])
    label_b = np.eye(10, dtype=float)[label_batch]  # one hot

    train_step.run(feed_dict={x: image_batch, y: label_b}, session=sess)

    if i % 200 == 0:
        train_accuracy = accuracy.eval(feed_dict={
            x: image_batch, y: label_b}, session=sess)
        print("step %d, training accuracy %g" % (i, train_accuracy))

image_batch, label_batch = sess.run([images_test, labels_test])
label_b = np.eye(10, dtype=float)[label_batch]  # one hot
print("finished！ test accuracy %g" % accuracy.eval(feed_dict={
    x: image_batch, y: label_b}, session=sess))