45 lines
961 B
Python
45 lines
961 B
Python
import numpy as np
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import matplotlib.pyplot as plt
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start = np.array([0, 0])
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bias = np.array([-3.37, 0.125])
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weights = np.array([
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[-4,1.5],
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[-1.5,0]
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])
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def activate(input):
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# calculate activation as matrix
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# o1 = w11 * o1 + w12 * o2 + b1
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# o2 = w21 * o1 + w22 * o2 + b2
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return np.matmul(weights, input) + bias
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def predict(n):
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current_output = start.copy()
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points = np.zeros((2, n))
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for i in range(n):
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# calculate output with tanh(x)
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current_output = np.tanh(activate(current_output))
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#save datapoint
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points[:, i] = current_output.copy()
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return points
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timespan = 50
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timespan_range = range(timespan)
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predictions = predict(timespan)
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# fetch o1, o2 from datapoint prediction
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o1 = predictions[0, :]
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o2 = predictions[1, :]
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plt.title("Recurrent Neural Network")
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plt.plot(timespan_range, o1)
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plt.plot(timespan_range, o2)
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plt.legend(["o1", "o2"], loc="upper left")
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plt.show()
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