TensorFlow基于Lenet模型手写数字识别

标签：batch   迭代   默认   ted   png   nod   返回   oss   add   

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：

TensorFlow基于Lenet模型手写数字识别

标签：batch   迭代   默认   ted   png   nod   返回   oss   add   

原文地址：https://www.cnblogs.com/gzr2018/p/12773464.html