p220_LeNetPP.py

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import time
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

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

sess = tf.InteractiveSession()

#训练数据
x = tf.placeholder('float', [None, 784])
#训练标签数据
y_ = tf.placeholder('float', [None, 10])
#把x更改为4维张量，第1维代表样本数量，第2维和第3维代表图像长宽，第4代表图像通道数，1表示黑白
x_image = tf.reshape(x, [-1, 28, 28, 1])

#第一层：卷积层
filter1 = tf.get_variable("filter1", [5, 5, 1, 32], initializer=tf.truncated_normal_initializer(stddev=0.1))
bias1 = tf.get_variable("bias1", [32], initializer=tf.constant_initializer(0.0))
conv1 = tf.nn.conv2d(x_image, filter1, strides=[1, 1, 1, 1], padding="SAME")
h_conv1 = tf.nn.relu(tf.nn.bias_add(conv1, bias1))

# 第二层：最大池化层
# 池化层过滤器的大小为2*2, 移动步长为2，使用全0填充
maxPool2 = tf.nn.max_pool(h_conv1, ksize=[1, 3, 3, 1], strides=[1, 3, 3, 1], padding="SAME")

# 第三层：全连接层
h_pool2_flat = tf.reshape(maxPool2, [-1, 10 * 10 * 32])
W_fc2 = tf.get_variable("W_fc2", [10 * 10 * 32, 10], initializer=tf.truncated_normal_initializer(stddev=0.1))
b_fc2 = tf.get_variable("b_fc2", [10], initializer=tf.constant_initializer(0.1))
fc2 = tf.matmul(h_pool2_flat, W_fc2) + b_fc2

# 第四层：输出层
y_conv = tf.nn.softmax(fc2)

# 定义交叉熵损失函数
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))

# 选择优化器，并让优化器最小化损失函数/收敛, 反向传播
#train_step = tf.train.GradientDescentOptimizer(0.0001).minimize(cross_entropy)
train_step = tf.train.AdamOptimizer(0.0001).minimize(cross_entropy)

# tf.argmax()返回的是某一维度上其数据最大所在的索引值，在这里即代表预测值和真实值
# 判断预测值y和真实值y_中最大数的索引是否一致，y的值为1-10概率
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
# 用平均值来统计测试准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 开始训练
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(10000):
        batch_xs, batch_ys = mnist.train.next_batch(100)
        if i % 100 == 0:
            train_accuracy = accuracy.eval(feed_dict={x: batch_xs, y_: batch_ys})
            print('step %d, training accuracy %g' % (i, train_accuracy))
            print("test accuracy %g" % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

        train_step.run(feed_dict={x: batch_xs, y_: batch_ys})