【Python】scikit-learn机器学习（二）——BP神经网络

环境

Mac os

python3.6

代码

# coding:utf-8"""Author: roguesirDate: 2017/8/30GitHub: https://roguesir.github.comBlog: http://blog.csdn.net/roguesir"""from __future__ import print_functionimport tensorflow as tfimport numpy as npdef add_layer(inputs, in_size, out_size, activation_function=None):    # add one more layer and return the output of this layer    Weights = tf.Variable(tf.random_normal([in_size, out_size]))    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)    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# Make up some real datax_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# define placeholder for inputs to networkxs = tf.placeholder(tf.float32, [None, 1])ys = tf.placeholder(tf.float32, [None, 1])# add hidden layerl1 = add_layer(xs, 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)# important stepif int((tf.__version__).split('.')[1]) < 12:    init = tf.initialize_all_variables()else:    init = tf.global_variables_initializer()sess = tf.Session()sess.run(init)for i in range(1000):    # training    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})    if i % 50 == 0:        # to see the step improvement        print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))