tf

参考：https://www.jianshu.com/p/e2f62043d02b

利用tensorflow框架和Python语言编写一个简单的卷积神经网络结构CNN来识别手写数字（mnist数据集方便调用）

网络一共包括4层，分别是

import tensorflow as tf

# 导入tensorflow自带的mnist数据集

from tensorflow.examples.tutorials.mnist import input_data

# 读入

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

def compute_accuracy(v_xs, v_ys):

global prediction

y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})

correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})

return result

# 产生随机变量，符合 normal 分布

# 传递 shape 就可以返回weight和bias的变量

def weight_variable(shape):

initial = tf.truncated_normal(shape, stddev=0.1)

return tf.Variable(initial)

def bias_variable(shape):

initial = tf.constant(0.1, shape=shape)

return tf.Variable(initial)

# 定义2维的 convolutional 图层

def conv2d(x, W):

# stride [1, x_movement, y_movement, 1]

# Must have strides[0] = strides[3] = 1

# strides 就是跨多大步抽取信息

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

# 定义 pooling 图层

def max_pool_2x2(x):

# stride [1, x_movement, y_movement, 1]

# 用pooling对付跨步大丢失信息问题

return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')

# define placeholder for inputs to network

xs = tf.placeholder(tf.float32, [None, 784]) # 784＝28x28

ys = tf.placeholder(tf.float32, [None, 10])

keep_prob = tf.placeholder(tf.float32)

x_image = tf.reshape(xs, [-1, 28, 28, 1]) # 最后一个1表示数据是黑白的，彩色是3

# print(x_image.shape) # [n_samples, 28,28,1]

## 1.第1个卷积层

# 把x_image的厚度1加厚变成了32

W_conv1 = weight_variable([5, 5, 1, 32]) # patch 5x5, in size 1, out size 32

b_conv1 = bias_variable([32])

# 构建第一个convolutional层，外面再加一个非线性化的处理relu

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 28x28x32

# 经过pooling后，长宽缩小为14x14

h_pool1 = max_pool_2x2(h_conv1) # output size 14x14x32

## 2. 第2个卷积层

# 把厚度32加厚变成了64

W_conv2 = weight_variable([5,5, 32, 64]) # patch 5x5, in size 32, out size 64

b_conv2 = bias_variable([64])

# 构建第二个convolutional层

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 14x14x64

# 经过pooling后，长宽缩小为7x7

h_pool2 = max_pool_2x2(h_conv2) # output size 7x7x64

## 3. 第1个全连接层

W_fc1 = weight_variable([7*7*64, 1024])

b_fc1 = bias_variable([1024])

# [n_samples, 7, 7, 64] ->> [n_samples, 7*7*64]

# 把pooling后的结果变平

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])

h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

## 4. 第2个全连接层

# 最后一层，输入1024，输出size 10，用 softmax 计算概率进行分类的处理

W_fc2 = weight_variable([1024, 10])

b_fc2 = bias_variable([10])

prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# 计算损失函数

cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),

reduction_indices=[1]))

train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

sess = tf.Session()

# important step

sess.run(tf.initialize_all_variables())

for i in range(1000):

batch_xs, batch_ys = mnist.train.next_batch(100) #batch_size

sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5})

if i % 50 == 0:

print(compute_accuracy(

mnist.test.images, mnist.test.labels))

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