初涉LeNet5处理mnist (CNN卷积神经网络)

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import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os
import numpy as np
#输入节点个数
INPUT_NODE = 784
#输出节点个数
OUTPUT_NODE = 10
#图片的尺寸
IMAGE_SIZE = 28
#通道数
NUM_CHANNELS = 1
#输出节点的个数 
NUM_LABELS = 10
#第一层卷积层的过滤器深度及其尺寸
CONV1_DEEP = 32
CONV1_SIZE = 5
#第二层卷积层的过滤器深度及其尺寸
CONV2_DEEP = 64
CONV2_SIZE = 5
#全连接层的节点个数
FC_SIZE = 512def inference(input_tensor, train, regularizer):#第一层卷积层输入大小是28*28*1=784=INPUT_NODE  #卷积层参数个数计算： CONV1_SIZE*CONV1_SIZE*NUM_CHANNELS*CONV1_DEEP+bias =5*5*1*32+32  过滤器的长*宽*过滤器深度*当前层深度+biases(个数为过滤器深度)#过滤器尺寸5*5深度为32     从strides=[1, 1, 1, 1]可得  步长的长宽方向分别为1  第二维度跟第三维度表示分别为长宽方向步长#输出的深度为CONV1_DEEP=32  由于SAME是全0填充，因此输出的尺寸为当前输入矩阵的长宽分别除以对应的步长 28*28   bias与输出深度个数一致with tf.variable_scope('layer1-conv1'):#weight前两个维度过滤器的尺寸  第三个维度当前层的深度 第四个是过滤器的维度conv1_weights = tf.get_variable("weight", [CONV1_SIZE, CONV1_SIZE, NUM_CHANNELS, CONV1_DEEP],initializer=tf.truncated_normal_initializer(stddev=0.1))conv1_biases = tf.get_variable("bias", [CONV1_DEEP], initializer=tf.constant_initializer(0.0))conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))#给convert卷积的这个结果加上偏置  然后利用激活函数ReLu#第二层 池化层   输入矩阵为第一层的输出  28*28*32    池化层输出与当前输入的深度一致32    池化层采用了2*2的过滤器尺寸  并且SAME方法全0填充#步长的长宽方向分别为2 所以输出尺寸为28/2=14  输出14*14*32的矩阵   池化层可以改变输入的尺寸但是不改变深度with tf.name_scope("layer2-pool1"):#其中relu1是激活函数  ksize是过滤器尺寸  strides是步长 SAME是全0填充  VALID是不适用全0   SAME方法得到的尺寸是输入的尺寸/步长#  VALID方法输出的尺寸是 ( 输入尺寸-过滤器尺寸+1)/2取得上限值pool1 = tf.nn.max_pool(relu1, ksize = [1,2,2,1],strides=[1,2,2,1],padding="SAME")#第三层 卷积层  输入矩阵为14*14*32     本层步长为1  所以输出尺寸为14/1=14    输出的矩阵为14*14*64    with tf.variable_scope("layer3-conv2"):#weight前两个维度过滤器的尺寸  第三个维度当前层的深度 第四个是过滤器的维度 :尺寸为5*5 深度为64的过滤器，  当前层深度为32  输出深度为64conv2_weights = tf.get_variable("weight", [CONV2_SIZE, CONV2_SIZE, CONV1_DEEP, CONV2_DEEP],initializer=tf.truncated_normal_initializer(stddev=0.1))conv2_biases = tf.get_variable("bias", [CONV2_DEEP], initializer=tf.constant_initializer(0.0))conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))#第四层  池化层输入矩阵为上一层输出 14*14*64 过滤器尺寸为2*2 深度为64  池化层的输出深度同输入深度  步长分别为2#所以输出尺寸是14/2=7   pool2的输出矩阵7*7*64with tf.name_scope("layer4-pool2"):pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')pool_shape = pool2.get_shape().as_list()#pool2.get_shape()获得第四层输出矩阵的维度   #每一层神经网络的输入输出都为一个batch的矩阵 所以这里面的维度也包含了一个batch中数据的个数pool_shape[0]nodes = pool_shape[1] * pool_shape[2] * pool_shape[3]reshaped = tf.reshape(pool2, [pool_shape[0], nodes])#把第四层的输出变为一个batch的向量#  第五层全连接层  输入为一组向量  向量长度为7*7*64=3136=nodes   输出一组长度为FC_SIZE=512的向量with tf.variable_scope('layer5-fc1'):fc1_weights = tf.get_variable("weight", [nodes, FC_SIZE],initializer=tf.truncated_normal_initializer(stddev=0.1))#只有全连接的权重需要加入正则化if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights))fc1_biases = tf.get_variable("bias", [FC_SIZE], initializer=tf.constant_initializer(0.1))fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)#dropout在训练时会随机将部分的节点的输出改为0 避免过拟合问题  一般只在全连接层使用if train: fc1 = tf.nn.dropout(fc1, 0.5)#第六层  全连接层  也是输出层 输入为一组长度为512的向量 输出为一组长度为10的向量  这一次输出后会通过softmax得到分类结果      with tf.variable_scope('layer6-fc2'):fc2_weights = tf.get_variable("weight", [FC_SIZE, NUM_LABELS],initializer=tf.truncated_normal_initializer(stddev=0.1))if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights))fc2_biases = tf.get_variable("bias", [NUM_LABELS], initializer=tf.constant_initializer(0.1))logit = tf.matmul(fc1, fc2_weights) + fc2_biasesreturn logitBATCH_SIZE = 100
LEARNING_RATE_BASE = 0.01
LEARNING_RATE_DECAY = 0.99
REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 6000
MOVING_AVERAGE_DECAY = 0.99#定义训练过程
def train(mnist):# 定义输出为4维矩阵的placeholderx = tf.placeholder(tf.float32, [BATCH_SIZE,IMAGE_SIZE,IMAGE_SIZE,NUM_CHANNELS],name='x-input')y_ = tf.placeholder(tf.float32, [None, LeNet5_infernece.OUTPUT_NODE], name='y-input')regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)y = inference(x,False,regularizer)global_step = tf.Variable(0, trainable=False)# 定义损失函数、学习率、滑动平均操作以及训练过程。variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)variables_averages_op = variable_averages.apply(tf.trainable_variables())cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))cross_entropy_mean = tf.reduce_mean(cross_entropy)loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,global_step,mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY,staircase=True)train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)with tf.control_dependencies([train_step, variables_averages_op]):train_op = tf.no_op(name='train')# 初始化TensorFlow持久化类。saver = tf.train.Saver()with tf.Session() as sess:tf.global_variables_initializer().run()for i in range(TRAINING_STEPS):xs, ys = mnist.train.next_batch(BATCH_SIZE)reshaped_xs = np.reshape(xs, (BATCH_SIZE,IMAGE_SIZE,IMAGE_SIZE,NUM_CHANNELS))_, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: reshaped_xs, y_: ys})if i % 1000 == 0:print("After %d training step(s), loss on training batch is %g." % (step, loss_value))def main(argv=None):mnist = input_data.read_data_sets("datasets/MNIST_data", one_hot=True)train(mnist)if __name__ == '__main__':main()

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