莫烦 tensorflow 笔记 （一）搭建神经网络

#!/usr/bin/env python3# -*- coding: utf-8 -*-"""Created on Wed Aug 16 08:56:03 2017@author: xiaolian"""# add layersimport tensorflow as tfimport numpy as npimport matplotlib.pyplot as plt# inputs 输入值# in_size 输入的大小 # out_size ‘输出的大小# activation_function 激励函数def add_layer(inputs, in_size, out_size, activation_function = None):    Weights = tf.Variable(tf.random_normal([in_size, out_size]))    #tf.cast(Weights, tf.float64)      #tf.cast(inputs, tf.float64)    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)    #tf.cast(biases, tf.float64)    z =  tf.matmul(inputs, Weights) + biases    if activation_function is None:        outputs = z    else:        outputs = activation_function(z)    return outputsx_data = np.linspace(-1, 1, 300)[:,np.newaxis].astype(np.float32)noise = np.random.normal(0, 0.05, x_data.shape).astype(np.float32)y_data = (np.square(x_data) - 0.5 + noise).astype(np.float32)xs = tf.placeholder(tf.float32, [None, 1])ys = tf.placeholder(tf.float32, [None, 1])l1 = add_layer(x_data, 1, 10, activation_function = tf.nn.relu)# add output layerprediction = add_layer(l1, 10, 1, activation_function = None)# the error between prediction and real dataloss = tf.reduce_mean(tf.reduce_sum( \    tf.square(ys - prediction), reduction_indices = 1))train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)# init variableinit = tf.global_variables_initializer()sess = tf.Session()sess.run(init)# visualizationfig = plt.figure()ax = fig.add_subplot(1,1,1)ax.scatter(x_data, y_data)plt.ion()for i in range(1000):    sess.run(train_step, feed_dict = {xs:x_data, ys:y_data})    if i == 999:        print(sess.run(loss, feed_dict = {xs:x_data, ys:y_data}))        prediction_value = sess.run(prediction, feed_dict = {xs:x_data})        line = ax.plot(x_data, prediction_value, 'g', lw = 5)plt.show()

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