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使用卷积层+池化层+全连接层 训练Mnist数据集
# coding:utf-8
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
'''
step：
1 获取MNIST数据集
输入大小:None*28*28
2 第一层卷积层
输入：占位符 [None, 28*28] 卷积核大小：5×5 步长为1 输入图像的厚度：1 卷积核个数：32
池化层池化大小:2×2 池化方式：最大池化
3 第二层卷积层
输入：None*14*14*32（上一层卷积层的卷积核个数为32 && 池化后图像大小变为14*14）
卷积核大小：5×5 步长为1 输入图像的厚度：32 卷积核个数：64
池化层池化大小：2×2 池化方式：最大池化
4 第三层全连接层
输入：None*7×7*64
权值向量大小：[7*7*64, 1024] (1024为自定义）
输出:[1034,10]
5 softmax层
输入:None*1024
权值向量大小:1024*10
输出:10
6 训练数据 && 验证数据
'''
'''
封装函数
'''
# 权值初始化
def weight_variable(shape):
return tf.Variable(tf.truncated_normal(shape=shape, stddev=0.1))
# 偏置项初始化
def bias_variable(shape):
return tf.Variable(tf.constant(0.1, shape=shape))
# 卷积操作 图像与卷积核进行卷积操作
def conv2d(img_array, W):
# stride 【batch，height， width， channels】
# 第一个1表示batch维度上滑动步长为1，不跳过任何一个样本
# height 表示卷积核的垂直滑动步长
# width 表示卷积核的水平滑动步长
# 1 channel 表示通道维度上的滑动步长为1，不跳过任何一个颜色通道
return tf.nn.conv2d(img_array, W, strides=[1, 1, 1, 1], padding='SAME')
# 池化操作
def max_pool_2x2(conv):
# ksize 池化层kernel大小
# 分别对应 batch height width channels
return tf.nn.max_pool(conv, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
if __name__ == '__main__':
'''
1 输入Mnist数据集
'''
mnist_data = input_data.read_data_sets("MNIST_data", one_hot=True)
'''
2 第一层卷积层
输入：占位符 [None, 28*28] 卷积核大小：5×5 步长为1 输入图像的厚度：1 卷积核个数：32
池化层池化大小:2×2 池化方式：最大池化
'''
x = tf.placeholder(tf.float32, shape=[None, 28*28])
y_= tf.placeholder(tf.float32, shape=[None, 10])
# 将输入的图片转换为28*28的大小
img_array = tf.reshape(x, [-1, 28, 28, 1]) # -1表示形状第一维的大小是根据x自动确定的
# 5*5 patch 卷积核的大小
# 1 in_size 图像的厚度
# 32 out_size 图像的厚度，或者卷积核的个数
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(img_array, W_conv1)+b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
'''
3 第二层卷积层
输入：None*14*14*32（上一层卷积层的卷积核个数为32 && 池化后图像大小变为14*14）
卷积核大小：5×5 步长为1 输入图像的厚度：32 卷积核个数：64
池化层池化大小：2×2 池化方式：最大池化
'''
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2)+b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
'''
4 第三层全连接层
输入：None*7*7*64
权值向量大小：[7*7*64, 1024](1024为自定义）
输出: [1034, 10]
'''
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
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)
# 使用dropout
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
'''
5 softmax层
输入: None*1024
权值向量大小: 1024*10
输出: 10
'''
# 将输出的1024维的向量转换为10个类别,对应于10个数字
W_fc2= weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop,W_fc2)+b_fc2
'''
6 训练数据 && 验证数据
'''
cross_entry = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.GradientDescentOptimizer(0.0001).minimize(cross_entry)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
correction_prediction = tf.equal(tf.argmax(y_, 1), tf.argmax(y_conv, 1))
accuracy = tf.reduce_mean(tf.cast(correction_prediction, tf.float32))
for i in range(20000):
batch_xs, batch_ys = mnist_data.train.next_batch(100)
sess.run(train_step, feed_dict={x:batch_xs, y_:batch_ys, keep_prob:0.5})
if i%100 == 0:
train_accuracy = accuracy.eval(
feed_dict={x:mnist_data.test.images[0:100], y_:mnist_data.test.labels[0:100], keep_prob:1.0})
print("step: %d, accuracy:%g" %(i, train_accuracy))
print(sess.run(accuracy, feed_dict={x:mnist_data.test.images[0:100], y_:mnist_data.test.labels[0:100], keep_prob:1.0}))
'''
step: 0, accuracy:0.16
step: 100, accuracy:0.22
step: 200, accuracy:0.27
step: 300, accuracy:0.32
step: 400, accuracy:0.34
step: 500, accuracy:0.36
step: 600, accuracy:0.39
step: 700, accuracy:0.42
step: 800, accuracy:0.47
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step: 1000, accuracy:0.49
step: 1100, accuracy:0.52
step: 1200, accuracy:0.52
step: 1300, accuracy:0.54
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step: 1600, accuracy:0.58
step: 1700, accuracy:0.6
step: 1800, accuracy:0.62
step: 1900, accuracy:0.61
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step: 2100, accuracy:0.63
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step: 2500, accuracy:0.7
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step: 2700, accuracy:0.72
step: 2800, accuracy:0.73
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step: 3000, accuracy:0.76
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step: 5000, accuracy:0.82
step: 5100, accuracy:0.84
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step: 8000, accuracy:0.88
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...
'''
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