I am a freshman in Tensorflow. Recently i want to fit a non-liner function"y = 1 + sin(x * pi/4)" with a two-layer neural network model. The code of program is following:
#!/usr/bin/python
import tensorflow as tf
import numpy as np
import math
import matplotlib.pyplot as plt
def check_mode():
x_data = np.linspace(-2,2,100)
y_data = [1 + math.sin(x * math.pi/4) for x in x_data]
w_1 = tf.Variable(tf.random_uniform([1,2],0,0.5))
b_1 = tf.Variable(tf.random_uniform([1,2],0,0.5))
w_2 = tf.Variable(tf.random_uniform([2,1],0,0.5))
b_2 = tf.Variable(tf.random_uniform([1,1],0,0.5))
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess,"mode.ckpt")
print("lay1: ",sess.run(w_1),sess.run(b_1))
print("lay2: ",sess.run(w_2),sess.run(b_2))
a = []
for x_i in x_data:
w_plus_b = tf.matmul([[x_i]],w_1) + b_1
a_1 = sigma(w_plus_b)
a_2 = tf.matmul(a_1,w_2) + b_2
a.append(sess.run(a_2[0][0]))
print a
draw_point(a,x_data,y_data)
return
def draw_point(a,x_data,y_data):
fx,ax = plt.subplots()
plt.plot(x_data,y_data,'o-')
plt.plot(x_data,a,'k-')
plt.show()
def sigma(x):
return tf.div(tf.constant(1.0),tf.add(tf.constant(1.0),tf.exp(tf.negative(x))))
def first_function():
x_data = np.linspace(-2,2,100)
y_data = [1 + math.sin(x * math.pi/4) for x in x_data]
x_i = tf.placeholder(tf.float32,[1,1])
y_data_i = tf.placeholder(tf.float32,[1,1])
w_1 = tf.Variable(tf.random_uniform([1,2],0,0.5))
b_1 = tf.Variable(tf.random_uniform([1,2],0,0.5))
w_2 = tf.Variable(tf.random_uniform([2,1],0,0.5))
b_2 = tf.Variable(tf.random_uniform([1,1],0,0.5))
z_1 = tf.add(tf.matmul(x_i,w_1), b_1)
a_1 = sigma(z_1)
a_2 = tf.add(tf.matmul(a_1,w_2),b_2)
diff = tf.subtract(a_2,y_data_i)
loss = tf.multiply(diff,diff)
optimizer = tf.train.GradientDescentOptimizer(0.1)
train = optimizer.minimize(loss)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
saver = tf.train.Saver()
for step in range(100):
sess.run(train,feed_dict={x_i:[[x_data[step]]],y_data_i:[[y_data[step]]]})
print("lay1: ",sess.run(w_1),sess.run(b_1))
print("lay2: ",sess.run(w_2),sess.run(b_2))
saver.save(sess,"mode.ckpt")
return
def main():
#first_function()
check_mode()
return
if __name__ == '__main__':
main()
I not sure that whether and how do the tensorflow complete the back propagate automatically? I really do not want to implement the back propagate by myself. Is there something wrong about my code? Thank you very very much for your help!!
It certainly looks like it completes the back prop, but it doesn't look like you are training it very much. Specifically, your training loop only goes through each data point once. Try many many loops through the data.