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  • 10.优化器

    import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data
    #载入数据集
    mnist = input_data.read_data_sets("MNIST_data",one_hot=True)
    
    #每个批次的大小
    batch_size = 64
    #计算一共有多少个批次
    n_batch = mnist.train.num_examples // batch_size
    
    #定义两个placeholder
    x = tf.placeholder(tf.float32,[None,784])
    y = tf.placeholder(tf.float32,[None,10])
    
    #创建一个简单的神经网络
    W = tf.Variable(tf.zeros([784,10]))
    b = tf.Variable(tf.zeros([10]))
    prediction = tf.nn.softmax(tf.matmul(x,W)+b)
    
    #交叉熵代价函数
    # loss = tf.losses.softmax_cross_entropy(y,prediction)
    loss = tf.losses.mean_squared_error(y,prediction)
    #使用梯度下降法
    # train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
    train_step = tf.train.AdamOptimizer(0.001).minimize(loss)
    
    #初始化变量
    init = tf.global_variables_initializer()
    
    #结果存放在一个布尔型列表中
    correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大的值所在的位置
    #求准确率
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    
    with tf.Session() as sess:
        sess.run(init)
        for epoch in range(21):
            for batch in range(n_batch):
                batch_xs,batch_ys =  mnist.train.next_batch(batch_size)
                sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
            
            acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
            print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))
    Iter 0,Testing Accuracy 0.9106
    Iter 1,Testing Accuracy 0.921
    Iter 2,Testing Accuracy 0.9261
    Iter 3,Testing Accuracy 0.9277
    Iter 4,Testing Accuracy 0.9291
    Iter 5,Testing Accuracy 0.9315
    Iter 6,Testing Accuracy 0.9293
    Iter 7,Testing Accuracy 0.9299
    Iter 8,Testing Accuracy 0.9298
    Iter 9,Testing Accuracy 0.9315
    Iter 10,Testing Accuracy 0.9317
    Iter 11,Testing Accuracy 0.9329
    Iter 12,Testing Accuracy 0.9324
    Iter 13,Testing Accuracy 0.9339
    Iter 14,Testing Accuracy 0.9321
    Iter 15,Testing Accuracy 0.9322
    Iter 16,Testing Accuracy 0.934
    Iter 17,Testing Accuracy 0.9326
    Iter 18,Testing Accuracy 0.9331
    Iter 19,Testing Accuracy 0.9334
    Iter 20,Testing Accuracy 0.9334
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  • 原文地址:https://www.cnblogs.com/liuwenhua/p/11605491.html
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