02tensorflow非线性回归以及分类的简单实用，softmax介绍

Bertha 。 2022-01-09 12:53 213阅读 0赞

import tensorflow as tf
    import numpy as np
    import matplotlib.pyplot as plt
    
    # 使用numpy生成200个随机点
    x_data = np.linspace(-0.5, 0.5, 200)[:, np.newaxis]
    noise = np.random.normal(0, 0.02, x_data.shape)
    y_data = np.square(x_data) + noise
    
    # 定义两个placeholder
    x = tf.placeholder(tf.float32, [None, 1])
    y = tf.placeholder(tf.float32, [None, 1])
    
    # 定义神经网络中间层
    Weights_L1 = tf.Variable(tf.random_normal([1, 10]))
    biases_L1 = tf.Variable(tf.zeros([1, 10]))
    Wx_plus_b_L1 = tf.matmul(x, Weights_L1) + biases_L1
    L1 = tf.nn.tanh(Wx_plus_b_L1)
    
    # 定义神经网络输出层
    Weights_L2 = tf.Variable(tf.random_normal([10, 1]))
    biases_L2 = tf.Variable(tf.zeros([1, 1]))
    Wx_plus_b_L2 = tf.matmul(L1, Weights_L2) + biases_L2
    prediction = tf.nn.tanh(Wx_plus_b_L2)
    
    # 二次代价函数
    loss = tf.reduce_mean(tf.square(y - prediction))
    # 使用梯度下降法训练
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
    
    with tf.Session() as sess:
        # 变量初始化
        sess.run(tf.global_variables_initializer())
        for _ in range(2000):
            sess.run(train_step, feed_dict={x: x_data, y: y_data})
    
        # 获得预测值
        prediction_value = sess.run(prediction, feed_dict={x: x_data})
        # 画图
        plt.figure()
        plt.scatter(x_data, y_data)
        plt.plot(x_data, prediction_value, 'r-', lw=5)
        plt.show()

![1542450-20190217105821068-1808234730.png][]

### MNIST数据集分类简单版本（神经网络：一个输入层，一个输出层） ###

### ![1542450-20190217110747208-1241567873.png][] ###

![1542450-20190217110809507-602831238.png][]

![1542450-20190217110833948-318329374.png][]

![1542450-20190217110851374-1088590091.png][]

![1542450-20190217172406838-963276764.png][]

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 = 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))

![1542450-20190217173110914-1707602872.png][]

转载于:https://www.cnblogs.com/xinmomoyan/p/10390417.html

[1542450-20190217105821068-1808234730.png]: /images/20220109/aaa2993c495f46789044858f080f5ccc.png
[1542450-20190217110747208-1241567873.png]: /images/20220109/4625a6193695422d81ceb606653bd862.png
[1542450-20190217110809507-602831238.png]: /images/20220109/93c70bd4ed514442800ce8ea25c5be79.png
[1542450-20190217110833948-318329374.png]: /images/20220109/ef7b01833f354fa0b7da1c28449a0118.png
[1542450-20190217110851374-1088590091.png]: /images/20220109/60eaf423f4804bc0aae2c5525974aae0.png
[1542450-20190217172406838-963276764.png]: /images/20220109/5375205a50a948cea0730ecd7ac0c5dc.png
[1542450-20190217173110914-1707602872.png]: /images/20220109/16c77e4fd4a748f285747f6ae010f4eb.png