Lec-04 Multi variable linear regression LAB

import tensorflow as tf

# data and label
x1 = [73., 93., 89., 96., 73.]
x2 = [80., 88., 91., 98., 66.]
x3 = [75., 93., 90., 100., 70.]
Y = [152., 185., 100., 196., 142.]

# random weights
w1 = tf.Variable(tf.random.normal([1]))
w2 = tf.Variable(tf.random.normal([1]))
w3 = tf.Variable(tf.random.normal([1]))
b = tf.Variable(tf.random.normal([1]))

learning_rate = 0.000001

for i in range(1000+1):	#Gradient defent
	#tf.GradientTape() to record the gradient of the cost function
    	with tf.GradientTape() as tape:
    		hypothesis = w1 * x1 + w2 * x2 + w3 * x3 + b
        	cost = tf.reduce_mean(tf.square(hypothesis-Y))
    	# calculates the gradients of the cost
    	w1_grad, w2_grad, w3_grad, b_grad = tape.gradient(cost, [w1, w2, w3, b])
    
    	#update w1, w2, w3 and b
    	w1.assign_sub(learning_rate * w1_grad)    	
    	w2.assign_sub(learning_rate * w2_grad)
    	w3.assign_sub(learning_rate * w3_grad)
    	b.assign_sub(learning_rate * b_grad)
    
    	if i % 50 == 0:
    		print("{:5} | {:12.4f}".format(i, cost.numpy()))

import tensorflow as tf
import numpy as np

data = np.array([
	# x1, x2, x3, y
    [73., 80., 75., 152. ],
    [93., 88., 93., 185. ],
    [89., 91., 90., 100., ],
    [96., 98., 100., 196. ],
    [73., 66., 70., 142. ]
    ], dtype=np.float32)
    
# slice data
X = data[:, :-1]
Y = data[:,[-1]]

W = tf.Variable(tf.random.normal([3,1]))
b = tf.Variable(tf.random.normal([1]))

learning_rate = 0.000001

# hypothesis, prediction function
def predict(x):
	return tf.matmul(X, W) + b
    
n_epochs = 2000
for i in range(n_epochs+1):
	# record the gradient of the cost function
    with tf.GradientTape() as tape:
    	cost = tf.reduce_mean((tf.square(predict(X) - Y)))
        
    # calculates the gradients of the Loss
    W_grad, b_grad = tape.gradient(cost, [W, b])
    
    #updates parameters (W and b)
    W.assign_sub(learning_rate * W_grad)
    W.assign_sub(learning_rate * b_grad)
    
    if i % 100 == 0:
    	print("{:5} | {:10.4f}".format(i, cost.numpy()))