AlexNet(MINIST).py

#1加载数据
#2定义网络模型
#3训练模型
#4评估模型

#加载数据：还要定义模型的超参数,模型所用的网络的参数以及数据的输入
import tensorflow as tf

#输入数据
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('data/', one_hot=True)

#定义网络的超参数
learning_rate = 0.001#学习率
training_iters = 200000
batch_size = 128
display_step = 10

#定义网络的参数
n_input = 784 #输入的纬度（img shape:28x28）
n_classes = 10 #标记的纬度（0-9 digits）
dropout = 0.75 #Dropout的概率，输出的可能性

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

#构建网络模型
#定义卷积操作
def conv2d(name, x, W, b, strides=1):
    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
    x = tf.nn.bias_add(x, b)
    return tf.nn.relu(x, name=name) #使用relu激活函数

#定义池化层操作
def maxpool2d(name, x, k=2):
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
                          padding='SAME', name=name)

#规范化操作
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)

#定义所有网络参数
weights = {
    'wc1': tf.Variable(tf.random_normal([11, 11, 1, 96])),
    'wc2': tf.Variable(tf.random_normal([5, 5, 96, 256])),
    'wc3': tf.Variable(tf.random_normal([3, 3, 256, 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([96])),
    'bc2': tf.Variable(tf.random_normal([256])),
    '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):
    #Reshape input picture
    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 = maxpool2d('pool1', conv1, k=2)
    #规范化
    norm1 = norm('norm1', pool1, lsize=4)

    #第二层卷积
    #卷积
    conv2 = conv2d('conv2', conv1, weights['wc2'], biases['bc2'])
    #最大池化（向下采样）
    pool2 = maxpool2d('pool2', conv2, k=2)
    #规范化
    norm2 = norm('norm2', pool2, lsize=4)

    #第三层卷积
    #卷积
    conv3 = conv2d('conv3', norm2, weights['wc3'], biases['bc3'])
    #下采样
    pool3 = maxpool2d('pool3', conv3, k=2)
    #规范化
    norm3 = norm('norm3', pool3, lsize=4)

    #第四层卷积
    conv4 = conv2d('conv4', norm3, weights['wc4'], biases['bc4'])
    #第五层卷积
    conv5 = conv2d('conv5', norm3, weights['wc5'], biases['bc5'])
    #下采样
    pool5 = maxpool2d('pool5', conv5, k=2)
    #规范化
    norm5 = norm('norm5', pool5, lsize=4)

    #全连接层1
    fc1 = tf.reshape(norm5, [-1, weights['wd1'].get_shape().as_list()[0]])
    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
    fc1 = tf.nn.relu(fc1)
    #dropout
    fc1 = tf.nn.dropout(fc1, dropout)

    #全连接层
    fc2 = tf.reshape(fc1, [-1, weights['wd1'].get_shape().as_list()[0]])
    fc2 = tf.add(tf.matmul(fc2, weights['wd1']), biases['bd1'])
    fc2 = tf.nn.relu(fc2)
    #dropout
    fc2 = tf.nn.dropout(fc2, dropout)

    #输出层
    out = tf.add(tf.matmul(fc2, weights['out']), biases['out'])
    return out

#构建模型
pred = alex_net(x, weights, biases, keep_prob)
#定义损失函数和优化器
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)

#评估函数
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

#训练和评估模型
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    step = 1
    #开始训练，直到达到training_iters,即200000
    while step * batch_size < training_iters:
        batch_x, batch_y = mnist.train.next_batch(batch_size)
        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})
        if step % display_step == 0:
            #计算损失值和准确度，输出
            loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,
                                                              y: batch_y,
                                                              keep_prob: 1.})
            print("Iter" + str(step*batch_size) + ", Minibatch Loss=" +
                  "{:.6f}".format(loss) + ", Training Accuracy= " +
                  "{:.5f}".format(acc)) 
        step += 1
        print("Optimization Finished!")

        #计算测试集的准确度
        print("Testing Accuracy:",
              sess.run(accuracy, feed_dict={x: mnist.test.images[:256],
                                            y: mnist.test.labels[:256],
                                            keep_prob: 1.}))