Tensor flow basic

tensorflow001.py

import tensorflow as tf #importing the tensorflow library

#Input node for computational graph as input
data = [
    [1, 0, 0],
    [1, 0, 1],
    [1, 1, 1],
    [0, 1, 1],
]
#Input node for computational graph as output
label = [
    [4],
    [5],
    [2],
    [1],
]


w = tf.Variable(tf.random_normal([3, 1]), dtype=tf.float32)

#Machine Learning Model
predication = tf.add(tf.matmul(data, w))

#Learner
error = tf.subtract(label, predication)
mse = tf.reduce_mean(tf.square(error)) # calculate root mean square 
delta = tf.matmul(data, error, transpose_a=True)
train = tf.assign(w, tf.add(w, delta))

#Session 
sess = tf.Session()
sess.run(tf.global_variables_initializer())

epoch, max_epochs = 0, 10

while  epoch < max_epochs:
    epoch += 1
    err, _ = sess.run([mse, train])
print('epoch:', epoch, 'mse:', err)

print(sess.run(w))