Tensorflow学习：单层神经网络的建立

本文内容：简单的建立一个神经网络
注：由于前博客中已经写过了线性回归以及深度神经网络（cnn）的博客，这篇看起来更简单些。

# -*- coding: utf-8 -*-"""Created on Wed May  3 11:19:01 2017E-mail: [e-mail]@author: DidiLv"""import tensorflow as tfimport numpy as np# define a layerdef add_layer(inputs, in_size, out_size, activation_function = None):    Weights = tf.Variable(tf.random_normal([in_size,out_size]))    biases = tf.Variable(tf.zeros([1,out_size]) + 0.1) # better not all zeros    Wx_plus_b = tf.matmul(inputs,Weights) + biases    if activation_function is None:        outputs = Wx_plus_b    else:        outputs = activation_function(Wx_plus_b)    return outputs# create data pointsx_data = np.linspace(-1,1,300)[:, np.newaxis]noise = np.random.normal(0,0.05,x_data.shape)y_data = np.square(x_data) - 0.5 + noise# placeholderxs = tf.placeholder(tf.float32, [None, 1])ys = tf.placeholder(tf.float32, [None, 1])# structurel1 = add_layer(xs, 1, 10, activation_function = tf.nn.relu)prediction = add_layer(l1, 10, 1, activation_function = None)loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices= [1]))# triantrain_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)# initializationinit = tf.global_variables_initializer()with tf.Session() as sess:    sess.run(init)    for i in range(10000):                sess.run(train_step, feed_dict = {xs: x_data, ys: y_data})        if i % 50 == 0:            print(sess.run(loss, feed_dict = {xs: x_data, ys: y_data}))