39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
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import simplenn
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import MNIST
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import numpy as np
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from time import sleep
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from copy import deepcopy
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sigmoid = simplenn.Sigmoid()
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cross_entropy = simplenn.CrossEntropy()
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x, y = MNIST.load_data('./data/mnist_train.csv', rescale=True)
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m, n = x.shape
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units = np.array([25, 15, 1])
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model = simplenn.Model(units, n, sigmoid, cross_entropy)
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# model.evaluate(x, y)
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# sleep(5)
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print('\n============Train===========')
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model.train_2(x, y, 20, 0.001, 1)
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print('\n=====Evaluate(training)=====')
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model.evaluate(x, y)
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print('\n=======Evaluate(test)=======')
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x, y = MNIST.load_data('./data/mnist_test.csv', rescale=True)
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model.evaluate(x, y)
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# x, y = MNIST.load_data('./data/mnist_train.csv', max_size=100)
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# m, n = x.shape
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# units = np.array([25, 15, 1])
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# model = simplenn.Model(units, n, sigmoid, cross_entropy)
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# model1 = deepcopy(model)
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# model1.train_ng(x, y, 20, 0.6)
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# model1.evaluate(x, y)
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# x = np.array([[0, 0],
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# [1, 1]])
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# y = np.array([0, 1])
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#
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# units = np.array([3, 1])
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# model = simplenn.Model(units, 2, sigmoid, cross_entropy)
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# model.train_ng(x, y, 500, 0.6)
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# model.evaluate(x, y)
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