import tensorflow as tf

# Model Perimeters
W = tf.Variable([0.3],tf.float32)
b = tf.Variable([0.3],tf.float32)

# Input and Output
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)

# Model Output
linear_model = tf.add(tf.multiply(W , x) , b)

# Loss
squared_delta = tf.square(linear_model - y)
loss = tf.reduce_sum(squared_delta)

# Optimize
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
train = optimizer.minimize(loss)
# initialize variables
init = tf.global_variables_initializer()

# start session
sess = tf.Session()
sess.run(init)

print( sess.run(loss,{x:[1,2,3,4],y:[0,-1,-2,-3]}))

# Traning
for i in range(1000):
    sess.run(train,{x:[1,2,3,4],y:[0,-1,-2,-3]})

print(sess.run([W , b]))



File_Writer = tf.summary.FileWriter('./graph',sess.graph)
sess.run(File_Writer)
# run following command in Terminal to run the Tensorboard
# tensorboard --logdir graph
# graph is the path of folder in which graph summary is saved