07. Quiz: TensorFlow ReLUs

TensorFlow ReLUs

A Rectified linear unit (ReLU) is type of activation function that is defined as f(x) = max(0, x). The function returns 0 if x is negative, otherwise it returns x. TensorFlow provides the ReLU function as tf.nn.relu(), as shown below.

# Hidden Layer with ReLU activation function
hidden_layer = tf.add(tf.matmul(features, hidden_weights), hidden_biases)
hidden_layer = tf.nn.relu(hidden_layer)

output = tf.add(tf.matmul(hidden_layer, output_weights), output_biases)

The above code applies the tf.nn.relu() function to the hidden_layer, effectively turning off any negative weights and acting like an on/off switch. Adding additional layers, like the output layer, after an activation function turns the model into a nonlinear function. This nonlinearity allows the network to solve more complex problems.

Quiz

In this quiz, you'll use TensorFlow's ReLU function to turn the linear model below into a nonlinear model.

Start Quiz:

# Solution is available in the other "solution.py" tab
import tensorflow as tf

output = None
hidden_layer_weights = [
    [0.1, 0.2, 0.4],
    [0.4, 0.6, 0.6],
    [0.5, 0.9, 0.1],
    [0.8, 0.2, 0.8]]
out_weights = [
    [0.1, 0.6],
    [0.2, 0.1],
    [0.7, 0.9]]

# Weights and biases
weights = [
    tf.Variable(hidden_layer_weights),
    tf.Variable(out_weights)]
biases = [
    tf.Variable(tf.zeros(3)),
    tf.Variable(tf.zeros(2))]

# Input
features = tf.Variable([[1.0, 2.0, 3.0, 4.0], [-1.0, -2.0, -3.0, -4.0], [11.0, 12.0, 13.0, 14.0]])

# TODO: Create Model

# TODO: Print session results

# Quiz Solution
# Note: You can't run code in this tab
import tensorflow as tf

output = None
hidden_layer_weights = [
    [0.1, 0.2, 0.4],
    [0.4, 0.6, 0.6],
    [0.5, 0.9, 0.1],
    [0.8, 0.2, 0.8]]
out_weights = [
    [0.1, 0.6],
    [0.2, 0.1],
    [0.7, 0.9]]

# Weights and biases
weights = [
    tf.Variable(hidden_layer_weights),
    tf.Variable(out_weights)]
biases = [
    tf.Variable(tf.zeros(3)),
    tf.Variable(tf.zeros(2))]

# Input
features = tf.Variable([[1.0, 2.0, 3.0, 4.0], [-1.0, -2.0, -3.0, -4.0], [11.0, 12.0, 13.0, 14.0]])

# TODO: Create Model
hidden_layer = tf.add(tf.matmul(features, weights[0]), biases[0])
hidden_layer = tf.nn.relu(hidden_layer)
logits = tf.add(tf.matmul(hidden_layer, weights[1]), biases[1])

# TODO: Print session results
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(logits))

User's Answer:

(Note: The answer done by the user is not guaranteed to be correct)

# Solution is available in the other "solution.py" tab
import tensorflow as tf

output = None
hidden_layer_weights = [
    [0.1, 0.2, 0.4],
    [0.4, 0.6, 0.6],
    [0.5, 0.9, 0.1],
    [0.8, 0.2, 0.8]]
out_weights = [
    [0.1, 0.6],
    [0.2, 0.1],
    [0.7, 0.9]]

# Weights and biases
weights = [
    tf.Variable(hidden_layer_weights),
    tf.Variable(out_weights)]
biases = [
    tf.Variable(tf.zeros(3)),
    tf.Variable(tf.zeros(2))]

# Input
features = tf.Variable([[1.0, 2.0, 3.0, 4.0], [-1.0, -2.0, -3.0, -4.0], [11.0, 12.0, 13.0, 14.0]])

# TODO: Create Model

hidden_layer = tf.add(tf.matmul(features, hidden_layer_weights), biases[0])
hidden_layer = tf.nn.relu(hidden_layer)

output = tf.add(tf.matmul(hidden_layer, out_weights), biases[1])

# TODO: Print session results
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    print(sess.run(output))
# Quiz Solution
# Note: You can't run code in this tab
import tensorflow as tf

output = None
hidden_layer_weights = [
    [0.1, 0.2, 0.4],
    [0.4, 0.6, 0.6],
    [0.5, 0.9, 0.1],
    [0.8, 0.2, 0.8]]
out_weights = [
    [0.1, 0.6],
    [0.2, 0.1],
    [0.7, 0.9]]

# Weights and biases
weights = [
    tf.Variable(hidden_layer_weights),
    tf.Variable(out_weights)]
biases = [
    tf.Variable(tf.zeros(3)),
    tf.Variable(tf.zeros(2))]

# Input
features = tf.Variable([[1.0, 2.0, 3.0, 4.0], [-1.0, -2.0, -3.0, -4.0], [11.0, 12.0, 13.0, 14.0]])

# TODO: Create Model
hidden_layer = tf.add(tf.matmul(features, weights[0]), biases[0])
hidden_layer = tf.nn.relu(hidden_layer)
logits = tf.add(tf.matmul(hidden_layer, weights[1]), biases[1])

# TODO: Print session results
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(logits))