程序的重构--模块化设计

#encoding:utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import time
import matplotlib.pyplot as plt
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)
#输入单个卷积核的偏置值
def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

#输入特征x,用卷积核w进行卷积运算，strides为卷积核移动步长
#padding表示是否需要补齐边缘像素使输出图像大小不变
def conv2d(x,W):
    return tf.nn.conv2d(x,W,strides=[1,1,1,1,],padding='SAME')

#对x进行最大池化操作，ksize进行池化的范围
def max_pool_2x2(x):
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

sess = tf.InteractiveSession()
#声明输入的图片数据、类别
x = tf.placeholder('float',[None,784])
y_ = tf.placeholder('float',[None, 10])
#输入图片数据转化
x_image = tf.reshape(x,[-1, 28, 28, 1])
W_conv1 = weight_variable([5,5,1,6])
b_conv1 = bias_variable([6])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5,5,6,16])
b_conv2 = bias_variable([16])
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*16, 120])
#偏置项
b_fc1 = bias_variable(([120]))
#将卷积的输出项展开
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 16])
#神经网络计算,并添加relu激活函数
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

W_fc2 = weight_variable([120, 10])
b_fc2 = bias_variable([10])
y_conv = tf.nn.softmax(tf.matmul(h_fc1,W_fc2) + b_fc2)

#代价函数
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
#使用Adam优化算法来调整参数
train_step = tf.train.GradientDescentOptimizer(1e-04).minimize(cross_entropy)

#测试正确率
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,'float32'))

#所有的变量进行初始化
sess.run(tf.initialize_all_variables())

#获取mnist数据u
mnist_data_set = input_data.read_data_sets('MNIST_data',one_hot=True)
c = []

#进行训练
start_time = time.time()
for i in range(1000):
    batch_xs, batch_ys = mnist_data_set.train.next_batch(200)
    #每迭代10个batch，对当前训练数据进行测试,输出当前预测准确率
    if i % 2 == 0:
        train_accuracy = accuracy.eval(feed_dict = {x:batch_xs, y_:batch_ys})
        c.append(train_accuracy)
        print("step %d, train accuracy %g" %(i, train_accuracy))
        #计算间隔时间
        end_time = time.time()
        print("time",(end_time - start_time))
        start_time = end_time
    #训练数据
    train_step.run(feed_dict={x:batch_xs,y_:batch_ys})

sess.close()
plt.plot(c)
plt.tight_layout()
plt.savefig('cnn-tf-cifar10-2.png',dpi=200)