LeNet-5训练minist手写体

1. 新建minist_inference.py文件，完成网络搭建

import tensorflow as tf

INPUT_NODE = 784
OUTPUT_NODE = 10

IMAGE_SIZE=28
NUM_CHANNELS=1
NUM_LABELS=10

CONV1_DEEP=32
CONV1_SIZE=5

CONV2_DEEP=64
CONV2_SIZE=5

FC_SIZE=512


def inference(input_tensor,train, regularizer):
    with tf.variable_scope("layer1-conv1"):
        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))

    with tf.name_scope("layer2_pool1"):
        pool1=tf.nn.max_pool(relu1,ksize=[1,2,2,1],strides=[1,2,2,1],padding="SAME")

    with tf.variable_scope("layer3_conv2"):
        conv2_weight=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_weight,strides=[1,1,1,1],padding='SAME')
        relu2=tf.nn.relu(tf.nn.bias_add(conv2,conv2_biases))

    with 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()
        nodes=pool_shape[1]*pool_shape[2]*pool_shape[3]
        reshaped=tf.reshape(pool2,[pool_shape[0],nodes])

    with tf.variable_scope('layer5-fc1'):
        fc1_weights=tf.get_variable("weight",[nodes,FC_SIZE],initializer=tf.truncated_normal_initializer(stddev=0.1))
        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)
        if train:
            fc1=tf.nn.dropout(fc1,0.5)

    with tf.variable_scope('layer6-fc2'):
        fc2_weights=tf.get_variable("weight",[FC_SIZE,NUM_CHANNELS],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)+fc1_biases

    return logit

2.训练代码minist_train.py

import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
import minist_inference
import os

BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.01
LEARNING_RATE_DECAY = 0.99
REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 30000
MOVING_AVERAGE_DECAY = 0.99
MODEL_SAVE_PATH="D:/python-project/MINIST/model/"
MODEL_NAME="mnist_model"


def train(mnist):

    x = tf.placeholder(tf.float32,[BATCH_SIZE,minist_inference.IMAGE_SIZE,minist_inference.IMAGE_SIZE,minist_inference.NUM_CHANNELS], name='x-input')
    y_ = tf.placeholder(tf.float32,[None, minist_inference.OUTPUT_NODE], name='y-input')

    regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
    y = minist_inference.inference(x,True,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')


    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,
                minist_inference.IMAGE_SIZE,
                minist_inference.IMAGE_SIZE,
                minist_inference.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))
                saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step)

def main(argv=None):
    mnist = input_data.read_data_sets("D:/MNIST_data/", one_hot=True)
    train(mnist)

if __name__ == '__main__':
    tf.app.run()

注：tensorflow1.4.0版本代码