"""
Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
"""
#tensorboard --logdir="./"
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
def add_layer(inputs, in_size, out_size, activation_function=None):
# add one more layer and return the output of this layer
with tf.name_scope("layer"):
with tf.name_scope("weights"):
Weights = tf.Variable(tf.random_normal([in_size, out_size]),name="W")
with tf.name_scope("biases"):
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
with tf.name_scope("Wx_plus_b"):
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
# Make up some real data
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
# define placeholder for inputs to network
with tf.name_scope("inputs"):
xs = tf.placeholder(tf.float32, [None, 1],name="x_input")
ys = tf.placeholder(tf.float32, [None, 1],name="y_input")
# add hidden layer
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.tanh)
# add output layer
prediction = add_layer(l1, 10, 1, activation_function=None)
# the error between prediciton and real data
with tf.name_scope("loss"):
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
reduction_indices=[1]))
with tf.name_scope("train"):
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
# important step
init = tf.initialize_all_variables()
sess = tf.Session()
writer = tf.summary.FileWriter("./",sess.graph)
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):
# training
sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
if i % 50 == 0:
# to see the step improvement
print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))
try:
ax.lines.remove(lines[0])
except Exception:
prediction_value = sess.run(prediction,feed_dict={xs: x_data, ys: y_data})
lines = ax.plot(x_data,prediction_value,"r-",lw = 5)
plt.pause(0.1)
