TensorFlow（一）

import tensorflow as tfimport numpy as np

def 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)    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

x_data=np.linspace(-1,1,300,tf.float32)[:,np.newaxis]noise=np.random.normal(0,0.05,x_data.shape)y_data=np.square(x_data)-0.5+noise

xs=tf.placeholder(tf.float32,[None,1])ys=tf.placeholder(tf.float32,[None,1])

l1=add_layer(xs,1,10,activation_function=tf.nn.relu)predition=add_layer(l1,10,1,activation_function=None)   loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-predition),reduction_indices=[1]))      train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init=tf.initialize_all_variables()sess=tf.Session()sess.run(init)for i in range(1000):  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}))