from __future__ import absolute_import, division, print_function

import tensorflow as tf

import numpy as np

from datetime import datetime

start = datetime.now()

# Parameters.

learning_rate = 0.01

training_steps = 1000

display_step = 100

# Training Data.

X = np.array([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,

7.042,10.791,5.313,7.997,5.654,9.27,3.1])

Y = np.array([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,

2.827,3.465,1.65,2.904,2.42,2.94,1.3])

n_samples = X.shape[0]

# Weight and Bias, initialized randomly.

W = tf.Variable(-0.06, name="weight")

b = tf.Variable(-0.73, name="bias")

# Linear regression (Wx + b).

def linear_regression(x):

return W * x + b

# Mean square error.

def mean_square(y_pred, y_true):

pow = tf.pow(y_pred-y_true, 2)

denominator = 2 * n_samples

sum = tf.reduce_sum(pow)

return sum / denominator

# Stochastic Gradient Descent Optimizer.

optimizer = tf.optimizers.SGD(learning_rate)

# Optimization process.

def run_optimization():

# Wrap computation inside a GradientTape for automatic differentiation.

with tf.GradientTape() as g:

pred = linear_regression(X)

loss = mean_square(pred, Y)

# Compute gradients.

gradients = g.gradient(loss, [W, b])

# Update W and b following gradients.

optimizer.apply_gradients(zip(gradients, [W, b]))

# Run training for the given number of steps.

for step in range(1, training_steps + 1):

# Run the optimization to update W and b values.

run_optimization()

if step % display_step == 0:

pred = linear_regression(X)

loss = mean_square(pred, Y)

print("step: %i, loss: %f, W: %f, b: %f" % (step, loss, W.numpy(), b.numpy()))

print(datetime.now() - start)

print("finished")