Tensotflow1.0入门(三)-tensorflow的小例子

朴灿烈づ我的快乐病毒、 2022-02-23 04:35 179阅读 0赞

[Tensotflow1.0入门(一)-tensorflow的设计理念和基本概念][Tensotflow1.0_-tensorflow]  
[Tensotflow1.0入门(二)-tensorflow的各种操作][Tensotflow1.0_-tensorflow 1]  
[Tensotflow1.0入门(三)-tensorflow的小例子][Tensotflow1.0_-tensorflow 2]  
[Tensotflow1.0入门(四)-tensorflow的卷积函数][Tensotflow1.0_-tensorflow 3]  
[Tensotflow1.0入门(五)-tensorflow的池化函数][Tensotflow1.0_-tensorflow 4]  
[Tensotflow1.0入门(六)-tensorflow的激活函数][Tensotflow1.0_-tensorflow 5]

# coding=utf-8
    ''' Created on 2019年4月5日 @author: admin '''
    
    import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data
    import os
    
    mnist = input_data.read_data_sets('mnist', one_hot=True)
    
    trX,trY,teX,teY = mnist.train.images,mnist.train.labels,mnist.test.images,mnist.test.labels
    
    x = tf.placeholder("float",[None,784])
    y = tf.placeholder("float",[None,10])
    #第一层隐藏层
    w1 = tf.Variable(tf.random_normal([784,625]))
    #第二层隐藏层
    w2 = tf.Variable(tf.random_normal([625,625]))
    #输出层
    o = tf.Variable(tf.random_normal([625,10]))
    #各层dropout系数
    keep_prob1 = tf.placeholder("float")
    keep_prob2 = tf.placeholder("float")
    
    def model(x,w1,w2,o):
        
        x = tf.nn.relu(tf.matmul(x,w1))
        x = tf.nn.dropout(x,keep_prob1)
        
        
        x = tf.nn.relu(tf.matmul(x,w2))
        x = tf.nn.dropout(x,keep_prob2)
    
        return tf.matmul(x,o)
    pred = model(x,w1,w2,o)
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y),name="cost")
    train_op = tf.train.RMSPropOptimizer(0.001,0.9,name="train_op").minimize(cost)
    #虽然前面命名为cost，但是最好还是打印出来看一下名称
    print(cost)
    # Tensor("cost:0", shape=(), dtype=float32)
    print(train_op)
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    
    with tf.Session() as sess:
        tf.initialize_all_variables().run()
        for step in range(1):
            for start,end in zip(range(0,len(trX),128),range(128,len(trX)+1,128)):
                loss,_,accuracy1 = sess.run([cost,train_op,accuracy],feed_dict={ x:trX[start:end],y:trY[start:end],keep_prob1:0.8,keep_prob2:0.8})
                print(accuracy1)
            print("step",step)
            #保存模型

[Tensotflow1.0_-tensorflow]: https://blog.csdn.net/u011311291/article/details/88959590
[Tensotflow1.0_-tensorflow 1]: https://blog.csdn.net/u011311291/article/details/88959599
[Tensotflow1.0_-tensorflow 2]: https://blog.csdn.net/u011311291/article/details/89057972
[Tensotflow1.0_-tensorflow 3]: https://blog.csdn.net/u011311291/article/details/89000644
[Tensotflow1.0_-tensorflow 4]: https://blog.csdn.net/u011311291/article/details/89003525
[Tensotflow1.0_-tensorflow 5]: https://blog.csdn.net/u011311291/article/details/89000263