利用TensorFlow搭建CNN

通过相关资料，学习使用TensorFlow搭建CNN的流程。整的提出分为以下几个步骤:设置网络参数—设置输入占位符变量—设置网络结构—优化损失函数—设置训练参数—训练网络—输出准确度等

#!/usr/bin/env python
# encoding: utf-8
'''
@author: Great
@file: CNN_practice.py
@time: 2018/11/26 17:50
@desc: 在此写上代码文件的功能描述
'''
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

#网络超参数
learning_rate = 0.001
training_epochs = 20000
batch_size = 128
display = 100

#网络参数
n_input = 784
n_classes = 10
dropout = 0.75

#占位符
x = tf.placeholder(tf.float32, shape = [None, 784], name = "input")
y = tf.placeholder(tf.float32, shape=[None, 10], name="output")
keep_prob = tf.placeholder(tf.float32)

#网络模型
#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)

#池化操作
def max_pool2d(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AlexNet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 = max_pool2d('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 = max_pool2d('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 = max_pool2d('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 = max_pool2d('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['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)
#loss and opt
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
#评估
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

#train
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_epochs:
        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 == 0:
    # 计算损失值和准确度，输出
        loss, acc = sess.run([loss, 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.}))

参考：TensorFlow技术解析与实战/  TensorFlow 官方文档中文版