Ch `04`: Concept `01`

Linear regression for classification (just for demonstrative purposes)

Import the usual libraries:

``````

In [1]:

%matplotlib inline
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

``````

Let's say we have numbers that we want to classify. They'll just be 1-dimensional values. Numbers close to 5 will be given the label `[0]`, and numbers close to 2 will be given the label `[1]`, as designed here:

``````

In [2]:

x_label0 = np.random.normal(5, 1, 10)
x_label1 = np.random.normal(2, 1, 10)
xs = np.append(x_label0, x_label1)
labels = [0.] * len(x_label0) + [1.] * len(x_label1)

plt.scatter(xs, labels)

``````
``````

Out[2]:

<matplotlib.collections.PathCollection at 0x7f5be2699fd0>

``````

Define the hyper-parameters, placeholders, and variables:

``````

In [3]:

learning_rate = 0.001
training_epochs = 1000

X = tf.placeholder("float")
Y = tf.placeholder("float")

w = tf.Variable([0., 0.], name="parameters")

``````

Define the model:

``````

In [4]:

def model(X, w):
tf.multiply(w[0], tf.pow(X, 0)))

``````

Given a model, define the cost function:

``````

In [5]:

y_model = model(X, w)
cost = tf.reduce_sum(tf.square(Y-y_model))

``````

Set up the training op, and also introduce a couple ops to calculate some metrics, such as accuracy:

``````

In [6]:

correct_prediction = tf.equal(Y, tf.to_float(tf.greater(y_model, 0.5)))
accuracy = tf.reduce_mean(tf.to_float(correct_prediction))

``````

Prepare the session:

``````

In [7]:

sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)

``````

Run the training op multiple times on the input data:

``````

In [8]:

for epoch in range(training_epochs):
sess.run(train_op, feed_dict={X: xs, Y: labels})
current_cost = sess.run(cost, feed_dict={X: xs, Y: labels})
if epoch % 100 == 0:
print(epoch, current_cost)

``````
``````

0 8.63226
100 3.23953
200 2.14632
300 1.90881
400 1.8572
500 1.84599
600 1.84356
700 1.84303
800 1.84291
900 1.84289

``````

Show some final metrics/results:

``````

In [9]:

w_val = sess.run(w)
print('learned parameters', w_val)

print('accuracy', sess.run(accuracy, feed_dict={X: xs, Y: labels}))

sess.close()

``````
``````

learned parameters [ 1.28786051 -0.25033307]
accuracy 0.95

``````

Plot the learned function

``````

In [10]:

all_xs = np.linspace(0, 10, 100)
plt.plot(all_xs, all_xs*w_val[1] + w_val[0])
plt.scatter(xs, labels)
plt.show()

``````
``````

``````