TensorFlow: Linear Regression

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 30 09:00:48 2019 

@author: onno
"""

# Import libraries (Numpy, Tensorflow, matplotlib)
import numpy as np
import matplotlib.pyplot as plot
# Create 1000 points following a function y=0.1 * x + 0.4 (i.e. y = W * x + b) with some normal random distribution:
num_points = 1000
vectors_set = []
for i in range(num_points):
    W = 0.1 # W
    b = 0.4 # b
    x1 = np.random.normal(0.0, 1.0)
    nd = np.random.normal(0.0, 0.05)
    y1 = W * x1 + b
# Add some impurity with some normal distribution -i.e. nd:
    y1 = y1 + nd
# Append them and create a combined vector set:
    vectors_set.append([x1, y1])
# Separate the data point across axises
x_data = [v[0] for v in vectors_set]
y_data = [v[1] for v in vectors_set]
# Plot and show the data points in a 2D space
plot.plot(x_data, y_data, 'ro', label='Original data')
plot.legend()
plot.show()
import tensorflow as tf
#tf.name_scope organize things on the tensorboard graph view
with tf.name_scope("LinearRegression") as scope:
    W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name="Weights")
    b = tf.Variable(tf.zeros([1]))
    y = W * x_data + b
# Define a loss function that takes into account the distance between the prediction and our dataset
with tf.name_scope("LossFunction") as scope:
    loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.6)
train = optimizer.minimize(loss)
# Annotate loss, weights, and bias (Needed for tensorboard)
loss_summary = tf.summary.scalar("loss", loss)
w_ = tf.summary.histogram("W", W)
b_ = tf.summary.histogram("b", b)
# Merge all the summaries
merged_op = tf.summary.merge_all()
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
# Writer for TensorBoard (replace with our preferred location
writer_tensorboard = tf.summary.FileWriter('/home/onno/.config/spyder/', sess.graph_def)
for i in range(16):
    sess.run(train)
    print(i, sess.run(W), sess.run(b), sess.run(loss))
    plot.plot(x_data, y_data, 'ro', label='Original data')
    plot.plot(x_data, sess.run(W)*x_data + sess.run(b))
    plot.xlabel('X')
    plot.xlim(-2, 2)
    plot.ylim(0.1, 0.6)
    plot.ylabel('Y')
    plot.legend()
    plot.show()
# Finally, close the TensorFlow session when you're done
sess.close()

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