Tensorflow卷积神经网络lenet 5代码

设置神经网络结构（向前传播）

#coding:utf-8
import tensorflow as tf
IMAGE_SIZE = 28
NUM_CHANNELS = 1
CONV1_SIZE = 5
CONV1_KERNEL_NUM = 32
CONV2_SIZE = 5
CONV2_KERNEL_NUM = 64
FC_SIZE = 512
OUTPUT_NODE = 10

# 初始化神经元
def get_weight(shape, regularizer):
    """

    :param shape: 输入形状
    :param regularizer: 正则化
    :return:
    """
	w = tf.Variable(tf.truncated_normal(shape,stddev=0.1))
	if regularizer != None: tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w))
	return w


# 添加偏置
def get_bias(shape):
	b = tf.Variable(tf.zeros(shape))
	return b

# 卷积操作
def conv2d(x,w):
    # 全零填充，步长为1，第一个第四个参数为1，第二第三为步长
	return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    # 池化ksize表示池化尺寸2*2，步长为2
	return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

# 搭建卷积神经网络体系
def forward(x, train, regularizer):
    # 创建第一层卷积层
    conv1_w = get_weight([CONV1_SIZE, CONV1_SIZE, NUM_CHANNELS, CONV1_KERNEL_NUM], regularizer)
    # 创建第一次偏置层
    conv1_b = get_bias([CONV1_KERNEL_NUM])
    # 卷积操作
    conv1 = conv2d(x, conv1_w)
    # 偏置操作，并去relu
    relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_b))
    # 池化操作
    pool1 = max_pool_2x2(relu1)

    conv2_w = get_weight([CONV2_SIZE, CONV2_SIZE, CONV1_KERNEL_NUM, CONV2_KERNEL_NUM],regularizer)
    conv2_b = get_bias([CONV2_KERNEL_NUM])
    conv2 = conv2d(pool1, conv2_w)
    relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_b))
    pool2 = max_pool_2x2(relu2)

    # 获取经过第二层池化后的张量形状
    pool_shape = pool2.get_shape().as_list()
    nodes = pool_shape[1] * pool_shape[2] * pool_shape[3]
    # 改变张量形状，变为一维张量
    reshaped = tf.reshape(pool2, [pool_shape[0], nodes])

    # 经过第一层全连接层，正则化
    fc1_w = get_weight([nodes, FC_SIZE], regularizer)
    fc1_b = get_bias([FC_SIZE])
    fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b)
    # 经过第二层全连接层使用0.5的dropout避免过拟合
    if train: fc1 = tf.nn.dropout(fc1, 0.5)

    # 经过
    fc2_w = get_weight([FC_SIZE, OUTPUT_NODE], regularizer)
    fc2_b = get_bias([OUTPUT_NODE])
    y = tf.matmul(fc1, fc2_w) + fc2_b
    return y

训练（向后传播）

# coding:utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_lenet5_forward
import os
import numpy as np

BATCH_SIZE = 100              # 100张图片为一批次
LEARNING_RATE_BASE = 0.005    # 学习率
LEARNING_RATE_DECAY = 0.99    # 学习衰减率
REGULARIZER = 0.0001          # 正则化参数
STEPS = 50000                 # 训练步长
MOVING_AVERAGE_DECAY = 0.99   # 滑动平均衰减率
MODEL_SAVE_PATH = "./model/"  # 模型保存路径
MODEL_NAME = "mnist_model"     # 模型保存名字


def backward(mnist):
    # 创建输入数据占位符
    x = tf.placeholder(tf.float32, [
        BATCH_SIZE,
        mnist_lenet5_forward.IMAGE_SIZE,
        mnist_lenet5_forward.IMAGE_SIZE,
        mnist_lenet5_forward.NUM_CHANNELS])
    # 输出数据占位符
    y_ = tf.placeholder(tf.float32, [None, mnist_lenet5_forward.OUTPUT_NODE])
    # 用向前传播，计算输出数据
    y = mnist_lenet5_forward.forward(x, True, REGULARIZER)
    # 记录步长
    global_step = tf.Variable(0, trainable=False)
    # 使用交叉熵代价函数
    ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))    #注意ce为向量
    # 取平均得到数
    cem = tf.reduce_mean(ce)
    # 加入正则化系数
    loss = cem + tf.add_n(tf.get_collection('losses'))

    # 指数衰减学习率,计算公式为 learning_rate = learning_rate * learning_rate_decay^(global_step/decay_step)
    learning_rate = tf.train.exponential_decay(
        LEARNING_RATE_BASE,
        global_step,
        mnist.train.num_examples / BATCH_SIZE,
        LEARNING_RATE_DECAY,
        staircase=True)

    # 梯度下降优化
    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
    # 下面是一个滑动平均模型
    ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
    ema_op = ema.apply(tf.trainable_variables())
    with tf.control_dependencies([train_step, ema_op]):
        train_op = tf.no_op(name='train')
    # 保存模型
    saver = tf.train.Saver()
    # 启动会话
    with tf.Session() as sess:
        # 初始化
        init_op = tf.global_variables_initializer()
        sess.run(init_op)
        # 通过checkpoint来定位模型
        ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
        # 如果已经训练过模型，加载训练好的模型
        if ckpt and ckpt.model_checkpoint_path:
            saver.restore(sess, ckpt.model_checkpoint_path)
        # 开始训练
        for i in range(STEPS):
            # 每次取一批次的数据和标注
            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            reshaped_xs = np.reshape(xs, (
                BATCH_SIZE,
                mnist_lenet5_forward.IMAGE_SIZE,
                mnist_lenet5_forward.IMAGE_SIZE,
                mnist_lenet5_forward.NUM_CHANNELS))
            _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: reshaped_xs, y_: ys})
            # 每100次输出一次，并保存模型
            if i % 100 == 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():
    # 读取模型，one_hot代表向量话
    mnist = input_data.read_data_sets("./data/", one_hot=True)
    backward(mnist)


if __name__ == '__main__':
    main()

预测

import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_lenet5_forward
import mnist_lenet5_backward
import numpy as np

TEST_INTERVAL_SECS = 5


# 测试函数
def test(mnist):
    # 创建一个图
    with tf.Graph().as_default() as g:
        x = tf.placeholder(tf.float32,
                           [mnist.test.num_examples,
                            mnist_lenet5_forward.IMAGE_SIZE,
                            mnist_lenet5_forward.IMAGE_SIZE,
                            mnist_lenet5_forward.NUM_CHANNELS,
                           ]

        )
        y_ = tf.placeholder(tf.float32, [None, mnist_lenet5_forward.OUTPUT_NODE])
        # 使用向前传播计算结果，第二参数代表训练过程，第三个参数代表不使用正则化
        y = mnist_lenet5_forward.forward(x, False, None)

        # 使用滑动平均模型
        ema = tf.train.ExponentialMovingAverage(mnist_lenet5_backward.MOVING_AVERAGE_DECAY)
        ema_restore = ema.variables_to_restore()

        # 保存模型
        saver = tf.train.Saver(ema_restore)
        # 计算训练率
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))    # 比较预测值与实际值是否相等，为逻辑向量
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))  # 求和取平均得到最终结果

        while True:
            with tf.Session() as sess:
                # 查找check_point
                ckpt = tf.train.get_checkpoint_state(mnist_lenet5_backward.MODEL_SAVE_PATH)
                # 找到了，加载这个模型
                if ckpt and ckpt.model_checkpoint_path:
                    saver.restore(sess, ckpt.model_checkpoint_path)
                    # 加载对应的迭代步长
                    global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
                    # 将图片用四维张量的形式传入
                    reshaped_x = np.reshape(mnist.test.images, (
                        mnist.test.num_examples,
                        mnist_lenet5_forward.IMAGE_SIZE,
                        mnist_lenet5_forward.IMAGE_SIZE,
                        mnist_lenet5_forward.NUM_CHANNELS))
                    # 计算准确率
                    accuracy_score = sess.run(accuracy, feed_dict={x: reshaped_x, y_: mnist.test.labels})
                    
                    print("After %s training step(s), test accuracy = %g" % (global_step, accuracy_score))
                else:
                    print('No checkpoint file found')
                    return
            # 每个5秒搜索新的模型
            time.sleep(TEST_INTERVAL_SECS)


 def main():
            mnist = input_data.read_data_sets("./data/", one_hot=True)
            test(mnist)

 if __name__ == '__main__':
            main()