import tensorflow as tf
import numpy as np
# 1 collect data
x_data = np.float32(np.random.rand(200,2));
y_data = np.matmul(x_data,[[5.2],[9.6]])+3.4;
# 2 Ceate model
W = tf.Variable(tf.random_uniform([2,1],-1,1));
b = tf.Variable(tf.zeros([1]));
y_ = tf.matmul(x_data,W)+b;
# 3 loss function
loss = tf.reduce_mean(tf.square(y_-y_data));
optimizer = tf.train.GradientDescentOptimizer(0.5);
train = optimizer.minimize(loss);
# 4 Initialzer
init = tf.initialize_all_variables();
sess = tf.Session(config = tf.ConfigProto(allow_soft_placement=True,log_device_placement=True));
sess.run(init);
# 5 Train
for step in range(0,201):
sess.run(train);
if step%10 == 0:
print(step,np.transpose(sess.run(W)),sess.run(b));
# 6 Output
sess.close();
