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:
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)
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.scatter(x_data,y_data)
plt.ion()
plt.show()
for i in range(1000):
sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
if i % 0.5 == 0:
#print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
try:
ax.lines.remove(lines[0])
except Exception:
pass
prediction_value = sess.run(prediction, feed_dict={xs: x_data})
# plot the prediction
lines = ax.plot(x_data, prediction_value, 'r-', lw=5)
plt.pause(10)
