mnist_test.py

# coding=utf-8
from __future__ import print_function

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

import tensorflow as tf

# 定义网络超参数
learning_rate = 0.001
training_iters = 200000
batch_size = 64
display_step = 20

# 定义网络参数
n_input = 784  # 输入的维度
n_classes = 10  # 标签的维度
dropout = 0.75  # Dropout 的概率

# 占位符输入
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32)


# 卷积操作
def conv2d(name, l_input, w, b):
    return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], padding='SAME'), b), name=name)


# 最大下采样操作
def max_pool(name, l_input, k):
    return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME', name=name)


# 归一化操作
def norm(name, l_input, lsize=4):
    return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name)


# 存储所有的网络参数
weights = {
    'wc1': tf.Variable(tf.random_normal([11, 11, 1, 64])),
    'wc2': tf.Variable(tf.random_normal([5, 5, 64, 192])),
    'wc3': tf.Variable(tf.random_normal([3, 3, 192, 384])),
    'wc4': tf.Variable(tf.random_normal([3, 3, 384, 384])),
    'wc5': tf.Variable(tf.random_normal([3, 3, 384, 256])),
    'wd1': tf.Variable(tf.random_normal([4 * 4 * 256, 4096])),
    'wd2': tf.Variable(tf.random_normal([4096, 4096])),
    'out': tf.Variable(tf.random_normal([4096, 10]))
}
biases = {
    'bc1': tf.Variable(tf.random_normal([64])),
    'bc2': tf.Variable(tf.random_normal([192])),
    'bc3': tf.Variable(tf.random_normal([384])),
    'bc4': tf.Variable(tf.random_normal([384])),
    'bc5': tf.Variable(tf.random_normal([256])),
    'bd1': tf.Variable(tf.random_normal([4096])),
    'bd2': tf.Variable(tf.random_normal([4096])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}


# 定义整个网络
def alex_net(_X, _weights, _biases, _dropout):
    # 向量转为矩阵
    _X = tf.reshape(_X, shape=[-1, 28, 28, 1])

    # 第一层卷积
    # 卷积
    conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'])
    # 下采样
    pool1 = max_pool('pool1', conv1, k=2)
    # 归一化
    norm1 = norm('norm1', pool1, lsize=4)

    # 第二层卷积
    # 卷积
    conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'])
    # 下采样
    pool2 = max_pool('pool2', conv2, k=2)
    # 归一化
    norm2 = norm('norm2', pool2, lsize=4)

    # 第三层卷积
    # 卷积
    conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'])
    # 归一化
    norm3 = norm('norm3', conv3, lsize=4)

    # 第四层卷积
    # 卷积
    conv4 = conv2d('conv4', norm3, _weights['wc4'], _biases['bc4'])
    # 归一化
    norm4 = norm('norm4', conv4, lsize=4)

    # 第五层卷积
    # 卷积
    conv5 = conv2d('conv5', norm4, _weights['wc5'], _biases['bc5'])
    # 下采样
    pool5 = max_pool('pool5', conv5, k=2)
    # 归一化
    norm5 = norm('norm5', pool5, lsize=4)

    # 全连接层1，先把特征图转为向量
    dense1 = tf.reshape(norm5, [-1, _weights['wd1'].get_shape().as_list()[0]])
    dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1')
    dense1 = tf.nn.dropout(dense1, _dropout)

    # 全连接层2
    dense2 = tf.reshape(dense1, [-1, _weights['wd2'].get_shape().as_list()[0]])
    dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2')  # Relu activation
    dense2 = tf.nn.dropout(dense2, _dropout)

    # 网络输出层
    out = tf.matmul(dense2, _weights['out']) + _biases['out']
    return out


# 构建模型
pred = alex_net(x, weights, biases, keep_prob)

# 定义损失函数和学习步骤
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# 测试网络
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# 初始化所有的共享变量
init = tf.initialize_all_variables()

# 开启一个训练
with tf.Session() as sess:
    sess.run(init)
    step = 1
    # Keep training until reach max iterations
    while step * batch_size < training_iters:
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        # 获取批数据
        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})
        if step % display_step == 0:
            # 计算精度
            acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
            # 计算损失值
            loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
            print("Iter " + str(step * batch_size) + ", Minibatch Loss = " + "{:.6f}".format(
                loss) + ", Training Accuracy = " + "{:.5f}".format(acc))
        step += 1
    print("Optimization Finished!")
    # 计算测试精度
    print("Testing Accuracy:",
          sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.}))