莫烦TensorFlow_05 add_layer

import tensorflow as tf  
import numpy as np  
  
def add_layer(inputs, in_size, out_size, activation_function = None):  
  Weights = tf.Variable(tf.random_normal([in_size, out_size]))  # hang lie  
  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] # 一列；[np.newaxis,:] 一行  
noise  = np.random.normal(0, 0.05, x_data.shape)  
y_data = np.square(x_data) - 0.5 + noise  
  
#input layer 1   
#hidden layer 10  
#output layer 1  
  
xs = tf.placeholder(tf.float32, [None, 1]) # 行数不固定，列数是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}  
           )  
      )