57 lines
1.4 KiB
Python
57 lines
1.4 KiB
Python
import numpy as np
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def sigmoid(x):
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"""
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Calculate sigmoid
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"""
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return 1 / (1 + np.exp(-x))
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def sigmoid_prime(x):
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"""
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# Derivative of the sigmoid function
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"""
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return sigmoid(x) * (1 - sigmoid(x))
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learnrate = 0.5
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x = np.array([1, 2, 3, 4])
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y = np.array(0.5)
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# Initial weights
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w = np.array([0.5, -0.5, 0.3, 0.1])
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# Calculate one gradient descent step for each weight
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# Note: Some steps have been consolidated, so there are
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# fewer variable names than in the above sample code
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# TODO: Calculate the node's linear combination of inputs and weights
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h = np.dot(x, w)
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# TODO: Calculate output of neural network (y hat)
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nn_output = sigmoid(h)
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# TODO: Calculate error of neural network (y - y hat)
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error = y - nn_output
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# TODO: Calculate the error term
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# Remember, this requires the output gradient, which we haven't
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# specifically added a variable for.
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error_term = error * sigmoid_prime(h)
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# Note: The sigmoid_prime function calculates sigmoid(h) twice,
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# but you've already calculated it once. You can make this
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# code more efficient by calculating the derivative directly
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# rather than calling sigmoid_prime, like this:
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# error_term = error * nn_output * (1 - nn_output)
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# TODO: Calculate change in weights
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del_w = learnrate * error_term * x
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print('Neural Network output:')
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print(nn_output)
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print('Amount of Error:')
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print(error)
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print('Change in Weights:')
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print(del_w)
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