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}
           )
      )