40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import keras
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from autoencoder import Autoencoder
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from utils import relu
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def mnist_test(
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bottleneck: int,
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max_epoch: int,
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patience: int,
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):
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(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
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x_train = np.divide(x_train, 255)
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x_test = np.divide(x_train, 255)
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in_len = x_train[0].flatten().shape[0]
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autoencoder = Autoencoder(in_len, bottleneck, 0.001, relu)
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autoencoder.train_dataset(x_train, max_epoch, patience, display_loss=True)
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example: np.ndarray = x_test[np.random.randint(0, len(x_test))]
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code = autoencoder.encode(example.flatten())
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output = autoencoder.decode(code)
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plt.subplot(1, 2, 1)
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plt.matshow(example, fignum=False)
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plt.subplot(1, 2, 2)
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plt.matshow(output.reshape(example.shape), fignum=False)
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plt.show()
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if __name__ == "__main__":
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import argparse
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import sys
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options = "b:e:p:"
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parser = argparse.ArgumentParser()
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parser.add_argument('-b', type=int, nargs='?', default=50)
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parser.add_argument('-e', type=int, nargs='?', default=1000)
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parser.add_argument('-p', type=int, nargs='?', default=5)
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args = parser.parse_args(sys.argv[1:])
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mnist_test(args.b, args.e, args.p)
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