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  • day-19 多种优化模型下的简单神经网络tensorflow示例

            如下样例基于tensorflow实现了一个简单的3层深度学习入门框架程序,程序主要有如下特性:

            1、  基于著名的MNIST手写数字集样例数据:http://yann.lecun.com/exdb/mnist/

            2、  加入衰减学习率优化,使得学习率可以根据训练步数指数级减少,在训练后期增加模型稳定性

            3、  加入L2正则化,减少各个权重值大小,避免过拟合问题

            4、  加入滑动平均模型,提高模型在验证数据上的准确性

            网络一共3层,第一层输入层784个节点的输入层,第二层隐藏层有500个节点,第三层输出层有10个节点。

    # 导入模块库
    import tensorflow as tf
    import datetime
    import numpy as np
    
    # 已经被废弃掉了
    #from tensorflow.examples.tutorials.mnist import input_data
    from tensorflow.contrib.learn.python.learn.datasets import mnist
    from tensorflow.contrib.layers import l2_regularizer
    
    # 屏蔽AVX2特性告警信息
    import os
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    # 屏蔽mnist.read_data_sets被弃用告警
    import logging
    class WarningFilter(logging.Filter):
        def filter(self, record):
            msg = record.getMessage()
            tf_warning = 'datasets' in msg
            return not tf_warning
    logger = logging.getLogger('tensorflow')
    logger.addFilter(WarningFilter())
    
    
    # 神经网络结构定义:输入784个特征值,包含一个500个节点的隐藏层,10个节点的输出层
    INPUT_NODE = 784
    OUTPUT_NODE = 10
    LAYER1_NODE = 500
    
    # 随机梯度下降法数据集大小为100,训练步骤为30000
    BATCH_SIZE = 100
    TRAINING_STEPS = 30000
    
    # 衰减学习率
    LEARNING_RATE_BASE = 0.8
    LEARNING_RATE_DECAY = 0.99
    
    # L2正则化
    REGULARIZATION_RATE = 0.0001
    MOVING_AVERAGE_DECAY = 0.99
    
    validation_accuracy_rate_list = []
    test_accuracy_rate_list = []
    
    
    # 定义前向更新过程
    def inference(input_tensor,avg_class,weights1,biase1,weights2,biase2):
        if avg_class == None:
            layer1 = tf.nn.relu(tf.matmul(input_tensor,weights1) + biase1)
            return tf.matmul(layer1,weights2) + biase2
        else:
            layer1 = tf.nn.relu(tf.matmul(input_tensor,avg_class.average(weights1)) + avg_class.average(biase1))
            return tf.matmul(layer1,avg_class.average(weights2)) + avg_class.average(biase2)
    
    # 定义训练过程
    def train(mnist_datasets):
        # 定义输入
        x = tf.placeholder(dtype=tf.float32,shape=[None,784])
        y_ = tf.placeholder(dtype=tf.float32,shape=[None,10])
    
        # 定义训练参数
        weights1 = tf.Variable(tf.truncated_normal(shape=[INPUT_NODE,LAYER1_NODE],mean=0.0,stddev=0.1))
        biase1 = tf.Variable(tf.constant(value=0.1,dtype=tf.float32,shape=[LAYER1_NODE]))
        weights2 = tf.Variable(tf.truncated_normal(shape=[LAYER1_NODE,OUTPUT_NODE],mean=0.0,stddev=0.1))
        biase2 = tf.Variable(tf.constant(value=0.1,dtype=tf.float32,shape=[OUTPUT_NODE]))
    
        # 前向更新
        # 训练数据时,不需要使用滑动平均模型,所以avg_class输入为空
        y = inference(x,None,weights1,biase1,weights2,biase2)
    
        # 该变量记录训练次数,训练模型时常常需要设置为不可训练的变量,即trainable=False
        global_step = tf.Variable(initial_value=0,trainable=False)
    
        # 生成滑动平均模型,用于验证
        variable_averages = tf.train.ExponentialMovingAverage(decay=MOVING_AVERAGE_DECAY,num_updates=global_step)
        # 在所有代表神经网络的可训练变量上,应用滑动模型,即所有的可训练变量都有一个影子变量
        variable_averages_ops = variable_averages.apply(tf.trainable_variables())
    
        # 定义数据验证时,前向更新结果
        average_y = inference(x,variable_averages,weights1,biase1,weights2,biase2)
    
        # 计算交叉熵
        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.argmax(y_,1),logits=y)
        cross_entropy_mean = tf.reduce_mean(cross_entropy)
    
        # 计算L2正则化损失
        regularizer = l2_regularizer(REGULARIZATION_RATE)
        regularization = regularizer(weights1) + regularizer(weights2)
    
        # 计算总损失Loss
        loss = cross_entropy_mean + regularization
    
        # 定义指数衰减的学习率
        learning_rate = tf.train.exponential_decay(learning_rate=LEARNING_RATE_BASE,global_step=global_step,
                                                   decay_steps=mnist_datasets.train.num_examples / BATCH_SIZE,
                                                   decay_rate=LEARNING_RATE_DECAY)
    
        # 定义随机梯度下降算法来优化损失函数
        train_step = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
            .minimize(loss = loss,global_step = global_step)
    
        # 每次前向更新完以后,既需要反向更新参数值,又需要对滑动平均模型中影子变量进行更新
        # 和train_op = tf.group(train_step,variable_averages_ops)是等价的
        with tf.control_dependencies([train_step,variable_averages_ops]):
            train_op = tf.no_op(name='train')
    
        # 定义验证运算,计算准确率
        correct_prediction = tf.equal(tf.argmax(average_y,1),tf.argmax(y_,1))
        accuracy = tf.reduce_mean(tf.cast(x=correct_prediction,dtype=tf.float32))
    
        with tf.Session() as sess:
            init = tf.global_variables_initializer()
            sess.run(init)
    
            validate_feed = {x:mnist_datasets.validation.images,
                             y_:mnist_datasets.validation.labels}
            test_feed = {x:mnist_datasets.test.images,
                         y_:mnist_datasets.test.labels}
    
            for i in range(TRAINING_STEPS):
                # 每1000轮,用测试和验证数据分别对模型进行评估
                if i % 1000 == 0:
                    validate_accuracy_rate = sess.run(accuracy,validate_feed)
                    print("%s: After %d training steps(s),validation accuracy"
                          "using average model is %g "%(datetime.datetime.now(),i,validate_accuracy_rate))
    
                    test_accuracy_rate = sess.run(accuracy, test_feed)
                    print("%s: After %d training steps(s),test accuracy"
                          "using average model is %g " % (datetime.datetime.now(),i, test_accuracy_rate))
    
                    validation_accuracy_rate_list.append(validate_accuracy_rate)
                    test_accuracy_rate_list.append(test_accuracy_rate)
    
                # 获得训练数据
                xs,ys = mnist_datasets.train.next_batch(BATCH_SIZE)
                sess.run(train_op,feed_dict={x:xs,y_:ys})
    
    
    # 主程序入口
    def main(argv=None):
        mnist_datasets = mnist.read_data_sets(train_dir='MNIST_data/',one_hot=True)
        train(mnist_datasets)
        print("validation accuracy rate list:",validation_accuracy_rate_list)
        print("test accuracy rate list:",test_accuracy_rate_list)
    
    # 模块入口
    if __name__ ==  '__main__':
        tf.app.run()

           每1000轮,使用测试和验证数据分别对模型进行评估,绘制出如下准确率曲线图,其中蓝色曲线表示验证数据准确率,深红色曲线表示测试数据准确率,不难发现,通过引入滑动平均模型,模型在验证数据上有更好的准确率。

           

           进一步,通过如下代码,我们对两个准确率求解相关系数:

    import numpy as np
    import math
    
    x = np.array([0.1748, 0.9764, 0.9816, 0.9834, 0.982, 0.984, 0.9838, 0.9842, 0.9846, 0.985, 0.9848, 0.9854, 0.9854, 0.9838, 0.9846, 0.9838, 0.9848, 0.9844, 0.9846, 0.9858, 0.9846, 0.9848, 0.9852, 0.9844, 0.9846, 0.9848, 0.9852, 0.9846, 0.9852, 0.9854])
    y = np.array([0.1839, 0.9751, 0.9796, 0.9807, 0.9813, 0.9825, 0.983, 0.983, 0.983, 0.9829, 0.9836, 0.9831, 0.9828, 0.9832, 0.9828, 0.9829, 0.9836, 0.9835, 0.9838, 0.9833, 0.9833, 0.9833, 0.9833, 0.9838, 0.9835, 0.9838, 0.9829, 0.9836, 0.9834, 0.984])
    
    # 计算相关度
    def computeCorrelation(x,y):
        xBar = np.mean(x)
        yBar = np.mean(y)
        SSR = 0.0
        varX = 0.0
        varY = 0.0
        for i in range(0,len(x)):
            diffXXbar = x[i] - xBar
            difYYbar = y[i] - yBar
            SSR += (diffXXbar * difYYbar)
            varX += diffXXbar**2
            varY += difYYbar**2
        SST = math.sqrt(varX * varY)
        return SSR/SST
    
    # 计算R平方
    def polyfit(x,y,degree):
        results = {}
        coeffs = np.polyfit(x,y,degree)
        results['polynomial'] = coeffs.tolist()
        p = np.poly1d(coeffs)
        yhat = p(x)
        ybar = np.sum(y)/len(y)
        ssreg = np.sum((yhat - ybar)**2)
        sstot = np.sum((y - ybar)**2)
        results['determination'] = ssreg/sstot
        return results
    
    result = computeCorrelation(x,y)
    r = result
    r_2 = result**2
    print("r:",r)
    print("r^2:",r*r)
    print(polyfit(x,y,1)['determination'])

            结果显示,二者相关系数大于0.9999,这意味着在MNIST问题上,完全可以模型在验证数据上的表现来判断模型的优劣。当然,这个仅仅是MNIST数据集上,在其它问题上,还需要具体分析。

    C:UsersAdministratorAnaconda3python.exe D:/tensorflow-study/sample.py
    r: 0.9999913306679183
    r^2: 0.999982661410994
    0.9999826614109977
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  • 原文地址:https://www.cnblogs.com/python-frog/p/9420800.html
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