TensorFlow神经网络(八)卷积神经网络之Lenet-5

一、Lenet神经网络基本结构

【注】内容来自MOOC人工智能实践TensorFlow笔记课程第七讲第2课

TensorFlow神经网络(八)卷积神经网络之Lenet-5_第1张图片
注意,最后将第二池化层后的输出拉直送入全连接层。

  • Lenet神经网络特点:
    ① 卷积、池化、非线性激活函数相互交替;
    ② 层与层之间稀疏连接,减少计算复杂度。

  • 对Lenet微调,使其适应Mnist数据集:
    TensorFlow神经网络(八)卷积神经网络之Lenet-5_第2张图片

二、Lenet神经网络在Mnist数据集上的实现

1.前向传播mnist_lenet5_forward.py

# mnist_lenet5_forward.py
# 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

# 给w赋初值,并把w的正则化损失加到总损失中
def get_weight(shape, regularizer):
    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

# 给b赋初值
def get_bias(shape):
    b = tf.Variable(tf.zeros(shape))
    return b

# 计算卷积,使用padding尺寸不变
def conv2d(x, w):
    return tf.nn.conv2d(x, w, strides = [1, 1, 1, 1], padding = 'SAME')

# 池化,使用padding尺寸不变
def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')

# 前传
def forward(x, train, regularizer):
    conv1_w = get_weight([CONV1_SIZE, CONV1_SIZE, NUM_CHANNELS, CONV1_KERNEL_NUM], regularizer)
    conv1_b = get_bias([CONV1_KERNEL_NUM])
    conv1 = conv2d(x, conv1_w)
    relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_b))
    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)

    # 得到pool2输出矩阵的维度,存入list中
    # 4个值分别是一个batch的值,提取特征的长度、宽度、深度
    pool_shape = pool2.get_shape().as_list()
    # 所有特征点的个数
    nodes = pool_shape[1] * pool_shape[2] * pool_shape[3]
    # 将结果拉成二维
    reshaped = tf.reshape(pool2, [pool_shape[0], nodes])

    # 全连接层
    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)
    # 如果是训练集,则对该层输出使用50%的dropout
    if train: fc1 = tf.nn.dropout(fc1, 0.5)

    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

2.反向传播mnist_lenet5_backward.py

# mnist_lenet5_backward.py
# coding: utf-8
import tensorflow as tf
# 导入imput_data模块
from tensorflow.examples.tutorials.mnist import input_data
import mnist_lenet5_forward
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  #hide warnings

# 定义超参数
BATCH_SIZE = 100 
LEARNING_RATE_BASE = 0.005 #初始学习率
LEARNING_RATE_DECAY = 0.99 # 学习率衰减率
REGULARIZER = 0.0001 # 正则化参数
STEPS = 50000 #训练轮数
MOVING_AVERAGE_DECAY = 0.99
MODEL_SAVE_PATH = "./model/"
MODEL_NAME = "mnist_model"

def backward(mnist):
    # placeholder占位
    x = tf.placeholder(tf.float32, [
    BATCH_SIZE,
    mnist_lenet5_forward.IMAGE_SIZE,
    mnist_lenet5_forward.IMAGE_SIZE,
    mnist_lenet5_forward.NUM_CHANNELS])
    y_ = tf.placeholder(tf.float32, shape = (None, mnist_lenet5_forward.OUTPUT_NODE))

    # 前向传播推测输出y
    y = mnist_lenet5_forward.forward(x, True, REGULARIZER) ## True表示训练参数时使用dropout

    # 定义global_step轮数计数器,定义为不可训练
    global_step = tf.Variable(0, trainable = False)

    # 包含正则化的损失函数
    # 交叉熵
    ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits = y, labels = tf.argmax(y_, 1))
    cem = tf.reduce_mean(ce)
    # 使用正则化时的损失函数
    loss = cem + tf.add_n(tf.get_collection('losses'))

    # 定义指数衰减学习率
    learning_rate = tf.train.exponential_decay(
        LEARNING_RATE_BASE,
        global_step, 
        mnist.train.num_examples / BATCH_SIZE,
        LEARNING_RATE_DECAY,
        staircase = True)

    # 定义反向传播方法:包含正则化
    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step = global_step)

    # 定义滑动平均时,加上:
    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')
    
    # 实例化saver
    saver = tf.train.Saver()

    # 训练过程
    with tf.Session() as sess:
        # 初始化所有参数
        init_op = tf.global_variables_initializer()
        sess.run(init_op)

        # 断点续训 breakpoint_continue.py
        ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
        if ckpt and ckpt.model_checkpoint_path: 
        # 恢复当前会话,将ckpt中的值赋给 w 和 b       
            saver.restore(sess, ckpt.model_checkpoint_path) 

        # 循环迭代
        for i in range(STEPS):
            # 将训练集中一定batchsize的数据和标签赋给左边的变量
            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            # 修改喂入神经网络的参数
            reshaped_xs = np.reshape(xs, (
            BATCH_SIZE,
            mnist_lenet5_forward.IMAGE_SIZE,
            mnist_lenet5_forward.IMAGE_SIZE,
            mnist_lenet5_forward.NUM_CHANNELS))
            # 喂入神经网络,执行训练过程train_step
            _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict = {x: reshaped_xs, y_: ys})

            if i % 100 == 0: # 拼接成./MODEL_SAVE_PATH/MODEL_NAME-global_step路径
            # 打印提示
                print("after %d steps, loss on traing batch is %g" %(step, loss_value))
                saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step = global_step)
            

def main():
    mnist = input_data.read_data_sets('./data/', one_hot = True)
    # 调用定义好的测试函数
    backward(mnist)
# 判断python运行文件是否为主文件,如果是,则执行
if __name__ == '__main__':
    main()
  • 运行结果:
    TensorFlow神经网络(八)卷积神经网络之Lenet-5_第3张图片

3.测试mnist_lenet5_test.py

# mnist_lenet5_test.py
# coding:utf-8
import time
import tensorflow as tf
# 导入imput_data模块
from tensorflow.examples.tutorials.mnist import input_data
import mnist_lenet5_forward
import mnist_lenet5_backward
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  #hide warnings

# 程序循环间隔时间5秒
TEST_INTERVAL_SECS = 5


def test(mnist):
    # 用于复现已经定义好了的神经网络
    with tf.Graph().as_default() as g: # 其内定义的节点在计算图g中
        # placeholder占位
        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, shape=(None, mnist_lenet5_forward.OUTPUT_NODE))

        # 前向传播推测输出y
        y = mnist_lenet5_forward.forward(x, False, None) # False表示不使用dropout

        # 实例化带滑动平均的saver对象
        # 这样,所有参数在会话中被加载时,会被复制为各自的滑动平均值
        ema = tf.train.ExponentialMovingAverage(mnist_lenet5_backward.MOVING_AVERAGE_DECAY)
        ema_restore = ema.variables_to_restore()
        saver = tf.train.Saver(ema_restore)

        # 计算正确率
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

        while True:
            with tf.Session() as sess:
                # 加载训练好的模型,也即把滑动平均值赋给各个参数
                ckpt = tf.train.get_checkpoint_state(mnist_lenet5_backward.MODEL_SAVE_PATH)
                #若ckpt和保存的模型在指定路径中存在
                if ckpt and ckpt.model_checkpoint_path: 
                    # 恢复会话
                    saver.restore(sess, ckpt.model_checkpoint_path) 
                    # 恢复轮数
                    global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
                    # 修改喂入神经网络的参数
                    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})
                    # 打印提示
                    print("after %s training steps, test accuracy = %g" % (global_step, accuracy_score))
                #如果没有模型
                else:
                    print("no checkpoint file found")
                    return
            time.sleep(TEST_INTERVAL_SECS)


def main():
    mnist = input_data.read_data_sets('./data/', one_hot=True)
    # 调用定义好的测试函数
    test(mnist)


if __name__ == '__main__':
    main()



很遗憾每次跑test程序电脑都死机,不知道为什么,就很尴尬了,下载了老师的代码也还是死机。

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