# Linear Regression: TensorFlow Way
#----------------------------------
#
# This function shows how to use TensorFlow to
# solve linear regression.
# y = Ax + b
#
# We will use the iris data, specifically:
#  y = Sepal Length
#  x = Petal Widthimport matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from sklearn import datasets
from tensorflow.python.framework import ops
ops.reset_default_graph()# Create graph
sess = tf.Session()# Load the data
# iris.data = [(Sepal Length, Sepal Width, Petal Length, Petal Width)]
iris = datasets.load_iris()
x_vals = np.array([x[3] for x in iris.data])
y_vals = np.array([y[0] for y in iris.data])# Declare batch size
batch_size = 25# Initialize placeholders
x_data = tf.placeholder(shape=[None, 1], dtype=tf.float32)
y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)# Create variables for linear regression
A = tf.Variable(tf.random_normal(shape=[1,1]))
b = tf.Variable(tf.random_normal(shape=[1,1]))# Declare model operations
model_output = tf.add(tf.matmul(x_data, A), b)# Declare loss function (L2 loss)
loss = tf.reduce_mean(tf.square(y_target - model_output))# Declare optimizer
my_opt = tf.train.GradientDescentOptimizer(0.05)
train_step = my_opt.minimize(loss)# Initialize variables
init = tf.global_variables_initializer()
sess.run(init)# Training loop
loss_vec = []
for i in range(100):rand_index = np.random.choice(len(x_vals), size=batch_size)rand_x = np.transpose([x_vals[rand_index]])rand_y = np.transpose([y_vals[rand_index]])sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})loss_vec.append(temp_loss)if (i+1)%25==0:print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)) + ' b = ' + str(sess.run(b)))print('Loss = ' + str(temp_loss))# Get the optimal coefficients
[slope] = sess.run(A)
[y_intercept] = sess.run(b)# Get best fit line
best_fit = []
for i in x_vals:best_fit.append(slope*i+y_intercept)# Plot the result
plt.plot(x_vals, y_vals, 'o', label='Data Points')
plt.plot(x_vals, best_fit, 'r-', label='Best fit line', linewidth=3)
plt.legend(loc='upper left')
plt.title('Sepal Length vs Pedal Width')
plt.xlabel('Pedal Width')
plt.ylabel('Sepal Length')
plt.show()# Plot loss over time
plt.plot(loss_vec, 'k-')
plt.title('L2 Loss per Generation')
plt.xlabel('Generation')
plt.ylabel('L2 Loss')
plt.show()