In [27]:

    

import 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()


    

In [28]:

    

sess = tf.Session()
lin_reg = datasets.load_iris()
x_vals = np.array([x[3] for x in lin_reg.data])
y_vals = np.array([y[0] for y in lin_reg.data])


    

In [29]:

    

batch_size = 25


    

In [30]:

    

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]))


    

In [31]:

    

# 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))


    

In [32]:

    

# Declare optimizer
my_opt = tf.train.GradientDescentOptimizer(0.05)
train_step = my_opt.minimize(loss)

# Initialize variables
init = tf.initialize_all_variables()
sess.run(init)


    

    




WARNING:tensorflow:From /Anaconda/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py:170: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.

In [33]:

    

# 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))


    

    




Step #25 A = [[ 2.11528826]] b = [[ 2.93739915]]
Loss = 1.48239
Step #50 A = [[ 1.63767004]] b = [[ 3.67306447]]
Loss = 0.688768
Step #75 A = [[ 1.28998399]] b = [[ 4.13345098]]
Loss = 0.341362
Step #100 A = [[ 1.11390674]] b = [[ 4.3878746]]
Loss = 0.392893

In [34]:

    

# 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)


    

In [35]:

    

# 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()


    

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