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  • mnist 简单模型


    # coding: utf-8

    # In[2]:

    import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data


    # In[3]:

    #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 = 100
    #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)

    #二次代价函数
    #loss = tf.reduce_mean(tf.square(y-prediction))
    #使用交叉熵,梯度下降更有效
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))

    #使用梯度下降法
    train_step = tf.train.GradientDescentOptimizer(0.2).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))


    # In[ ]:
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  • 原文地址:https://www.cnblogs.com/rongye/p/10002522.html
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