• TensorFlow基于Lenet模型手写数字识别


    手写识别较为简单的版本应该是只用FC,这样参考这篇博客.
    Lenet-5模型:

    本文卷积模型:

    forward:

    #coding:utf-8
    import tensorflow as tf
    import  numpy as np
    
    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
    
    def get_weight(shape,regularizer):
        #产生截断正态分布随机数,取值范围为 [ mean - 2 * stddev, mean + 2 * stddev ]
        # (mean=0 stddev=1)。
        w = tf.Variable(tf.truncated_normal(shape,stddev=0.1))
        #tf.add_to_collection(‘list_name’, element):
        #将元素element添加到列表list_name中
        #regularizer 是L2正则化乘上的系数,加入到losses列表中
        if regularizer != None:tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(regularizer)(w))
        return w
    
    def get_bias(shape):
        b = tf.Variable(tf.zeros(shape))
        return b
    
    #x输入描述,[batch,行分辨率,列分辨率,通道数]
    #w卷积核描述,[行分辨率,列分辨率,通道数,核个数]
    #核滑动步长,左右默认填1
    def conv2d(x,w):
        return tf.nn.conv2d(x,w,strides=[1,1,1,1],padding='SAME')
    
    def max_pool_2x2(x):
        return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
    
    def forward(x,train,regularizer):
        conv1_w = get_weight([CONV1_SIZE, CONV1_SIZE, NUM_CHANNELS, CONV1_KERNEL_NUM],
                             regularizer)  # 初始化卷积核
        conv1_b = get_bias([CONV1_KERNEL_NUM])  # 初始化偏置项
        conv1 = conv2d(x, conv1_w)  # 实现卷积运算
        relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_b))
        pool1 = max_pool_2x2(relu1)  # 将激活后的输出进行最大池化
        print("pool1'size: ",pool1.get_shape())
    
        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)
    
        #a.get_shape()中a的数据类型只能是tensor,且返回的是一个元组。
        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])
        # 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)
        # 如果是训练阶段,
        # 则对该层输出使用 dropout,也就是随机的将该层输出中的一半神经元置为无效,
        # 是为了避免过拟合而设置的,一般只在全连接层中使用
        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
    

    backward:

    #coding:utf-8
    import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data
    import os
    import numpy as np
    import forward
    
    # 定义训练过程中的超参数
    BATCH_SIZE = 100 # 一个 batch 的数量
    LEARNING_RATE_BASE = 0.005 # 初始学习率
    LEARNING_RATE_DECAY = 0.99 # 学习率的衰减率
    GEGULARIZER = 0.0001 # 正则化项的权重
    STEPS = 50000 # 最大迭代次数
    MOVING_AVERAGE_DECAY = 0.99 # 滑动平均的衰减率
    MODEL_SAVE_PATH="./model/" # 保存模型的路径
    MODEL_NAME="mnist_model" # 模型命名
    
    def backward(mnist):
        #x, y_是定义的占位符,需要指定参数的类型,维度(要和网络的输入与输出维度一致),类似
        # 于函数的形参,运行时必须传入值
        x = tf.placeholder(tf.float32,[
            BATCH_SIZE,
            forward.IMAGE_SIZE,
            forward.IMAGE_SIZE,
            forward.NUM_CHANNELS
        ])
        y_ = tf.placeholder(tf.float32,[None,forward.OUTPUT_NODE])
        y = forward.forward(x,True,GEGULARIZER)
        global_step = tf.Variable(0,trainable=False)
        #logits 为神经网络最后的输出,大小为[batch_size,output]
        # 参数labels表示实际标签值,大小为[batch_size,output]
        #第一步对网络最后输出做softmax,再将概率向量与实际标签向量做交叉熵
        ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
        cem = tf.reduce_mean(ce)
        loss = cem + tf.add_n(tf.get_collection('losses'))  # 加上w的损失
    
        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)
        # 学习的滑动平均
        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')
    
        saver = tf.train.Saver()  # 实例化saver对象
        with tf.Session() as sess:
            init_op = tf.initialize_all_variables()
            sess.run(init_op)  # 执行训练过程
            ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
            if ckpt and ckpt.model_checkpoint_path:
                saver.restore(sess, ckpt.model_checkpoint_path)
            for i in range(STEPS):
                xs, ys = mnist.train.next_batch(BATCH_SIZE)
                reshaped_xs = np.reshape(xs,(
                    BATCH_SIZE,
                    forward.IMAGE_SIZE,
                    forward.IMAGE_SIZE,
                    forward.NUM_CHANNELS
                ))
                # 喂入训练图像和标签,开始训练
                _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: reshaped_xs, y_: ys})
                if i % 100 == 0:
                    print("After %d step(s),loss on all data 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)
            backward(mnist)
    
    if __name__ == '__main__':
            main()
    

    结果展示:

  • 相关阅读:
    新人数据库连接不上或数据库配置管理器里面sql服务打不开问题
    新人对区块链的理解
    数据库文件不能附加
    数据库 'C:Program FilesMicrosoft SQL ServerMSSQL10_50.MSSQLSERVERMSSQLDATA est1.mdf' 已存在。请选择其他数据库
    springboot 打包发布(war包)
    springcloud工程建立 父工程+公用子模块+微服务提供模块
    java 扫描微信公众号二维码,关注并登录逻辑
    eclipse创建springboot项目
    log4j托管tomcat日志
    随笔
  • 原文地址:https://www.cnblogs.com/gzr2018/p/12773464.html
走看看 - 开发者的网上家园