Tensorflow学习1-简单搭建神经网络

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
if __name__=='__main__':
    x_data=np.linspace(-1,1,200)[:,np.newaxis] #构建等差数列,:表示构建的等差数列,newaxis,创建新列,及把一维变2维
    noise=np.random.normal(0,0.2,x_data.shape)
    y_data=np.square(x_data)+noise
    f1=plt.figure(1)
    plt.subplot(211)
    plt.scatter(x_data,y_data,marker='o',color='r',label='1',s=5)
    plt.show()
    x=tf.placeholder(tf.float32,[None,1])
    y = tf.placeholder (tf.float32, [None, 1])
    Weight_L1=tf.Variable(tf.random_normal([1,10]))
    biases_L1=tf.Variable(tf.zeros([1,10]))
    Wx_plus_L1=tf.matmul(x,Weight_L1)+biases_L1
    L1=tf.nn.tanh(Wx_plus_L1)

    Weight_L2=tf.Variable(tf.random_normal([10,1]))
    biases_L2=tf.Variable(tf.zeros([1,1]))
    Wx_plus_L2=tf.matmul(L1,Weight_L2)+biases_L2
    predication=tf.nn.tanh(Wx_plus_L2)
    #二次代价函数
    loss=tf.reduce_mean(tf.square(y-predication))
    #梯度下降法
    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})
        #获取测试值
        pre_val=sess.run(predication,feed_dict={x:x_data})
        #画图
        plt.figure(1)
        plt.scatter(x_data,y_data)
        plt.plot(x_data,pre_val,'b-',lw=2)
        plt.show()
基础功能,搭建一个神经网络并画图,作为对tensorflow的了解

格式转换:jupyter nbconvert --to script test.ipynb 将ipynb转py

Tensorflow学习1-简单搭建神经网络

;

Popular Programs