activation.py

#coding=utf-8
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
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:
		ot = Wx_plus_b
	else:
		ot = activation_function(Wx_plus_b)


	return ot

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])
## hidden layer
l1 = add_layer(xs , 1 , 10 , activation_function = tf.nn.relu)

## outpur layer
predition = add_layer(l1 , 10 , 1 , activation_function =None)

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - predition) ,  reduction_indices =[1])) #reduction_indices代表取平均值的维度

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

fig = plt.figure()
ax = fig.add_subplot(1 , 1 , 1)
ax.scatter(x_data , y_data)
#plt.ion()
plt.show(block = False)


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})
		try:
			ax.lines.remove(lines[0])
		except Exception:
			pass
		predition_value = sess.run(predition , feed_dict = {xs:x_data , ys:y_data})
		lines = ax.plot(x_data , predition_value , 'r-' , lw = 5)		
		plt.pause(0.1)