Activatiefuncties

Introductie tot Deep Learning in Python

Dan Becker

Data Scientist and contributor to Keras and TensorFlow libraries

Lineaire vs. niet-lineaire functies

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Activatiefuncties

Toegepast op node-inputs om node-output te krijgen

Ons neuraal netwerk verbeteren

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Activatiefuncties

ch1_3.009.png

ReLU (Rectified Linear Activation)

ch1_3.011.png

Activatiefuncties

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

Laten we oefenen!

Introductie tot Deep Learning in Python