TensorFlow 学习笔记（三）回归，分类

目录：

1.非线性回归

2.手写数字训练集

3.T3-3MNIST数据集分类简单版本

1.非线性回归

代码：

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]  # 从-0.5到0.5，均匀分布的200个点，，，增加维度
noise = np.random.normal(0, 0.02, x_data.shape)
y_data = np.square(x_data) + noise

# 根据样本定义x,y
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]))  # 1行10列，1个输入，10个输出（10个神经网络）
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))

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

with tf.Session() as sess:
    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()

2.手写数字训练集

数据集下载地址：http://yann.lecun.com/exdb/mnist/

3.T3-3MNIST数据集分类简单版本

结果：

代码：

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


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

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()

#结果对比(true,false)存放在一个bool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 betch 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))