Tensorflow(7) tensorflow安装

卷积神经网络

1 传统神经网络存在的问题

1. 权值太多，计算量太大

2. 需要大量样本进行训练（样本的大小，最好是权值的5-30倍）

2 引入卷积神经网络

2.1卷积层

image.png

卷积.gif

2.2 池化

image.png

2.3 SAME PADDING&VALID PADDING

• 对于卷积

  image.png

• 对于池化

  image.png

3 代码实现

# coding: utf-8

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


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

# 每个批次的大小
batch_size = 100
# 计算一共有多少个批次
n_batch = mnist.train.num_examples // batch_size


# 初始化权值
def weight_variable(shape):
 initial = tf.truncated_normal(shape, stddev=0.1) # 生成一个截断的正态分布
 return tf.Variable(initial)


# 初始化偏置
def bias_variable(shape):
 initial = tf.constant(0.1, shape=shape)
 return tf.Variable(initial)


# 卷积层
def conv2d(x, W):
 # x input tensor of shape `[batch, in_height, in_width, in_channels]` 通道数，黑白为1，彩色为3
 # W filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels]
 # `strides[0] = strides[3] = 1`. strides[1]代表x方向的步长，strides[2]代表y方向的步长
 # padding: A `string` from: `"SAME", "VALID"` same会补0，valid不会补0
 return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


# 池化层
def max_pool_2x2(x):
 # ksize [1,x,y,1] 窗口大小
 return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


# 定义两个placeholder
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
# 改变x的格式转为4D的向量[batch, in_height, in_width, in_channels]`
x_image = tf.reshape(x, [-1, 28, 28, 1])

# 初始化第一个卷积层的权值和偏置
W_conv1 = weight_variable([5, 5, 1, 32]) # 5*5的采样窗口，32个卷积核从1个平面抽取特征
b_conv1 = bias_variable([32]) # 每一个卷积核一个偏置值

# 把x_image和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1) # 进行max-pooling

# 初始化第二个卷积层的权值和偏置
W_conv2 = weight_variable([5, 5, 32, 64]) # 5*5的采样窗口，64个卷积核从32个平面抽取特征
b_conv2 = bias_variable([64]) # 每一个卷积核一个偏置值

# 把h_pool1和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2) # 进行max-pooling

# 28*28的图片第一次卷积后还是28*28，第一次池化后变为14*14
# 第二次卷积后为14*14，第二次池化后变为了7*7
# 进过上面操作后得到64张7*7的平面

# 初始化第一个全连接层的权值
W_fc1 = weight_variable([7 * 7 * 64, 1024]) # 上一层有7*7*64个神经元，全连接层有1024个神经元
b_fc1 = bias_variable([1024]) # 1024个节点

# 把池化层2的输出扁平化为1维
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
# 求第一个全连接层的输出
wx_plus_b1 = tf.matmul(h_pool2_flat, W_fc1) + b_fc1
h_fc1 = tf.nn.relu(wx_plus_b1)

# keep_prob用来表示神经元的输出概率
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# 初始化第二个全连接层
W_fc2 = weight_variable([1024, 10]) # 10代表有10个分类
b_fc2 = bias_variable([10])

# 计算输出
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2) + b_fc2)

# 交叉熵代价函数
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))

# 使用AdamOptimizer进行优化
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

# 结果存放在一个布尔列表中
correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1)) # argmax返回一维张量中最大的值所在的位置
# 求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 合并所有的summary
merged = tf.summary.merge_all()

with tf.Session() as sess:
 sess.run(tf.global_variables_initializer())
 for epoch in range(21):
 for batch in range(n_batch):
 batch_xs, batch_ys = mnist.train.next_batch(batch_size)
 sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 0.7})

 acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels, keep_prob: 1.0})
 print("Iter " + str(epoch) + ", Testing Accuracy= " + str(acc))


Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
Iter 0, Testing Accuracy= 0.8593
Iter 1, Testing Accuracy= 0.9596
Iter 2, Testing Accuracy= 0.9733
Iter 3, Testing Accuracy= 0.9801
Iter 4, Testing Accuracy= 0.9828
Iter 5, Testing Accuracy= 0.9846
Iter 6, Testing Accuracy= 0.9868
Iter 7, Testing Accuracy= 0.9875
Iter 8, Testing Accuracy= 0.9882
Iter 9, Testing Accuracy= 0.9903
Iter 10, Testing Accuracy= 0.9885
Iter 11, Testing Accuracy= 0.9892
Iter 12, Testing Accuracy= 0.9911
Iter 13, Testing Accuracy= 0.991
Iter 14, Testing Accuracy= 0.9902
Iter 15, Testing Accuracy= 0.9918
Iter 16, Testing Accuracy= 0.9925
Iter 17, Testing Accuracy= 0.991
Iter 18, Testing Accuracy= 0.9904
Iter 19, Testing Accuracy= 0.9916
Iter 20, Testing Accuracy= 0.9907