getting_started_tensorflow_4.py

import tensorflow as tf
import numpy as np

# Start by creating random values:

x_data = np.random.rand(100).astype(np.float32)
_y_data = 3 + 5 * x_data
y_data = np.vectorize(lambda y: y + np.random.normal(loc=0.0, scale=0.1))(_y_data)

# ... and then initializing the variables a and b, with any random guess,
# and then defining the linear function:
b0 = tf.Variable(0.5)
b1 = tf.Variable(1.0)
y = b0 + b1 * x_data

# Loss function
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

init = tf.global_variables_initializer()
# Note: on older tensorflow versions the function was called: initialize_all_variables()
ses = tf.Session()
ses.run(init)

train_data = []
for step in range(100):
    evals = ses.run([train, b0, b1])[1:]
    if step % 5 == 0:
        print("Step: {step}, evaluation: {evals}".format(step=step, evals=evals))
        # Step: 0, evaluation: [4.9611549, 3.5183027]
        # Step: 5, evaluation: [3.7335129, 3.6137826]
        # Step: 10, evaluation: [3.5216508, 4.0207472]
        # Step: 15, evaluation: [3.37235, 4.3058095]
        # Step: 20, evaluation: [3.2676356, 4.5057435]
        # Step: 25, evaluation: [3.1941922, 4.6459718]
        # Step: 30, evaluation: [3.1426811, 4.7443233]
        # Step: 35, evaluation: [3.1065528, 4.8133044]
        # Step: 40, evaluation: [3.0812135, 4.8616853]
        # Step: 45, evaluation: [3.0634415, 4.895618]
        # Step: 50, evaluation: [3.0509768, 4.9194169]
        # Step: 55, evaluation: [3.0422344, 4.9361095]
        # Step: 60, evaluation: [3.0361025, 4.9478168]
        # Step: 65, evaluation: [3.0318019, 4.956028]
        # Step: 70, evaluation: [3.0287859, 4.9617872]
        # Step: 75, evaluation: [3.02667, 4.965827]
        # Step: 80, evaluation: [3.0251865, 4.9686594]
        # Step: 85, evaluation: [3.0241456, 4.9706464]
        # Step: 90, evaluation: [3.0234158, 4.9720407]
        # Step: 95, evaluation: [3.0229037, 4.9730182]