explain these python codes with comments , explain briefly >>> import numpy as np >>> example_input = [1, .2, .1, .05, .2] >>> example_weights = [.2, .12, .4, .6, .90] >>> input_vector = np.array(example_input) >>> weights = np.array(example_weights) >>> bias_weight = .2 >>> activation_level = np.dot(input_vector, weights) +\ ... (bias_weight * 1) >>> activation_level 0.674 With that, if you use a simple threshold activation function and choose a threshold of .5, your next step is the following: >>> threshold = 0.5 >>> if activation_level >= threshold: ... perceptron_output = 1 ... else: ... perceptron_output = 0 >>> perceptron_
explain these python codes with comments , explain briefly >>> import numpy as np >>> example_input = [1, .2, .1, .05, .2] >>> example_weights = [.2, .12, .4, .6, .90] >>> input_vector = np.array(example_input) >>> weights = np.array(example_weights) >>> bias_weight = .2 >>> activation_level = np.dot(input_vector, weights) +\ ... (bias_weight * 1) >>> activation_level 0.674 With that, if you use a simple threshold activation function and choose a threshold of .5, your next step is the following: >>> threshold = 0.5 >>> if activation_level >= threshold: ... perceptron_output = 1 ... else: ... perceptron_output = 0 >>> perceptron_
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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explain these python codes with comments , explain briefly
>>> import numpy as np
>>> example_input = [1, .2, .1, .05, .2]
>>> example_weights = [.2, .12, .4, .6, .90]
>>> input_vector = np.array(example_input)
>>> weights = np.array(example_weights)
>>> bias_weight = .2
>>> activation_level = np.dot(input_vector, weights) +\
... (bias_weight * 1)
>>> activation_level
0.674
With that, if you use a simple threshold activation function and choose a threshold of
.5, your next step is the following:
>>> threshold = 0.5
>>> if activation_level >= threshold:
... perceptron_output = 1
... else:
... perceptron_output = 0
>>> perceptron_
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