如何用Tensorflow 快速搭建神经网络|莫烦

import tensorflow as tf

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)[:,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)
prediction = add_layer(l1,10,1,activation_function=None)

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),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}))