吴裕雄 python 神经网络——TensorFlow实现AlexNet模型处理手写数字识别MNIST数据集

import tensorflow as tf

# 输入数据

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("E:\\MNIST_data", one_hot=True)

# 定义网络的超参数

learning_rate = 0.001

training_iters = 200000

batch_size = 128

display_step = 5

# 定义网络的参数

# 输入的维度 (img shape: 28*28)

n_input = 784

# 标记的维度 (0-9 digits)

n_classes = 10

# Dropout的概率，输出的可能性

dropout = 0.75

# 输入占位符

x = tf.placeholder(tf.float32, [None, n_input])

y = tf.placeholder(tf.float32, [None, n_classes])

#dropout (keep probability)

keep_prob = tf.placeholder(tf.float32)

# 定义卷积操作

def conv2d(name,x, W, b, strides=1):

    # Conv2D wrapper, with bias and relu activation

    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')

    x = tf.nn.bias_add(x, b)

    # 使用relu激活函数

    return tf.nn.relu(x,name=name)

# 定义池化层操作

def maxpool2d(name,x, k=2):

    # MaxPool2D wrapper

    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)

# 定义所有的网络参数

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, 1024])),

    'out': tf.Variable(tf.random_normal([1024, n_classes]))

}

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([1024])),

    '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 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', norm1, 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'])

    # 规范化

    norm3 = norm('norm3', conv3, lsize=4)

    # 第四层卷积

    conv4 = conv2d('conv4', norm3, weights['wc4'], biases['bc4'])

    # 第五层卷积

    conv5 = conv2d('conv5', conv4, weights['wc5'], biases['bc5'])

    # 最大池化（向下采样）

    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2

    fc2 = tf.reshape(fc1, [-1, weights['wd2'].get_shape().as_list()[0]])

    fc2 =tf.add(tf.matmul(fc2, weights['wd2']),biases['bd2'])

    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))

# 初始化变量

init = tf.global_variables_initializer()

# 开启一个训练

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:

        #获取批量数据

        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!")

    # 计算测试集的精确度

    print ("Testing Accuracy:",sess.run(accuracy, feed_dict={x: mnist.test.images[:256],y: mnist.test.labels[:256],keep_prob: 1.}))