用Tensorflow实现卷积神经网络CNN

一、数据准备

实验数据使用MNIST数据集。
MNIST 数据集已经是一个被”嚼烂”了的数据集, 很多教程都会对它”下手”, 几乎成为一个 “典范”。

在很多tensorflow教程中，用下面这一句下载mnist数据集：

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

但实际运行时根本无法通过网络下载，解决方案就是手工下载数据，然后直接导入使用。

下载地址：http://yann.lecun.com/exdb/mnist/
4个文件，注意下载后不需要解压。

如果把上述下载的文件放在与运行的.py文件同一个目录下，那么导入数据的代码是这样的：

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

二、代码

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# number 1 to 10 data
mnist = input_data.read_data_sets('./', 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
# print(x_image.shape) # [n_samples, 28,28,1]
## 1. conv1 layer ##
# 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. conv2 layer ##
# 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. func1 layer ##
# 飞的更高变成1024
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. func2 layer ##
# 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)
# the error between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
                                              reduction_indices=[1]))       # loss
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess = tf.Session()
# important step
sess.run(tf.global_variables_initializer())
for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    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))