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))

运行结果:

《用Tensorflow实现卷积神经网络CNN》 result.png

三、Github代码下载

下载

四、参考

1 http://v.youku.com/v_show/id_XMTYyMTUyMjc0OA==.html?spm=a2hzp.8253869.0.0
2 https://github.com/MorvanZhou/tutorials/tree/master/tensorflowTUT/tf18_CNN3
3 https://www.jianshu.com/p/e2f62043d02b
4 https://blog.csdn.net/i8088/article/details/79126150

了解小朋友学编程请加QQ307591841(微信与QQ同号),或QQ群581357582。
关注公众号请扫描二维码

《用Tensorflow实现卷积神经网络CNN》 qrcode_for_kidscode_258.jpg

点赞

发表评论

电子邮件地址不会被公开。 必填项已用*标注