莫烦tensorflow教程笔记（四）

import tensorflow as tfimport numpy as np

def add_layer(inputs,in_size,out_size,activation=None):    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 is None:        output = Wx_plus_b    else:        output = activation(Wx_plus_b)    return output

x_data = np.linspace(-1,1,300)[:,np.newaxis].astype(np.float32)         noise = np.random.normal(0,0.05,x_data.shape)y_data = np.square(x_data) - 0.5 + noisexs = tf.placeholder(tf.float32,[None,1])        ##None的意思是xs可以是任意行的数据ys = tf.placeholder(tf.float32,[None],1)L1 = add_layer(xs,in_size=1,out_size=10,activation=tf.nn.relu)prediction = add_layer(L1,in_size=10,out_size=1,activation=None)loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),reduction_indices=[1])) #reduction_indices=[1]表示按行求和，在本例中x只有一维，不求和也一样train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)init = tf.global_variables_initializer()session = tf.Session()session.run(init)for i in range(1000):    session.run(train_step,feed_dict={xs:x_data,ys:y_data})    if i % 50 == 0:        print(i,session.run(loss,feed_dict={xs:x_data,ys:y_data}))