Activation functions

Introduction to Deep Learning in Python

Dan Becker

Data Scientist and contributor to Keras and TensorFlow libraries

Linear vs. non-linear Functions

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Activation functions

Applied to node inputs to produce node output

Improving our neural network

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Activation functions

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ReLU (Rectified Linear Activation)

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Activation functions

import numpy as np
input_data = np.array([-1, 2])
weights = {'node_0': np.array([3, 3]),
               'node_1': np.array([1, 5]),
               'output': np.array([2, -1])}
node_0_input = (input_data * weights['node_0']).sum()
node_0_output = np.tanh(node_0_input)
node_1_input = (input_data * weights['node_1']).sum()
node_1_output = np.tanh(node_1_input)
hidden_layer_outputs = np.array([node_0_output, node_1_output])
output = (hidden_layer_output * weights['output']).sum()

print(output)

1.2382242525694254

Let's practice!

Introduction to Deep Learning in Python