# getting started with tensorflow
import numpy as np
import tensorflow as tf

node1 = tf.constant(3.0, dtype=tf.float32)
node2 = tf.constant(4.0)
print(node1, node2)

sess = tf.Session()
print(sess.run([node1,node2]))

node3 = tf.add(node1,node2)
print("node3:",node3)
print("sess.run(node3):", sess.run(node3))


a=tf.placeholder(tf.float32)
b=tf.placeholder(tf.float32)
adder_node = a+b

print(sess.run(adder_node, {a:3, b:4.5}))
print(sess.run(adder_node, {a:[1,3], b:[2,4]}))

add_and_triple = adder_node * 3.
print(sess.run(add_and_triple,{a:3, b:4.5}))

W=tf.Variable([.3], dtype=tf.float32)
b=tf.Variable([-.3], dtype=tf.float32)
x=tf.placeholder(tf.float32)
linear_model = W*x+b

init = tf.global_variables_initializer()
sess.run(init) # until call sess.run, the variables are uninitialized

print(sess.run(linear_model, {x:[1,2,3,4]}))
y= tf.placeholder(tf.float32)
squared_deltas = tf.square(linear_model - y)
loss = tf.reduce_sum(squared_deltas)
print(sess.run(loss, {x:[1,2,3,4], y:[0,-1,-2,-3]}))

 # improve this manually by reassigning the values of W and b
 # to the perfect values of -1 and 1.
 # A variable is initialized to the value provided to tf.Variable but can be changed
 # using operations like tf.assign. For example, W=-1 and b=1 are the optimal paraeters
 # for our model. We can change W and b accordingly.

fixW = tf.assign(W, [-1.])
fixb = tf.assign(b, [1.])
sess.run([fixW,fixb])
print(sess.run(loss, {x:[1,2,3,4], y:[0,-1,-2,-3]}))

## tf.train API

optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)

sess.run(init) # reset values to incorrect defaults.
for i in range(1000):
    sess.run(train, {x:[1,2,3,4], y:[0,-1,-2,-3]})

print(sess.run([W,b]))
# It was actual machine learning!
