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()

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))