34 lines
845 B
Python
34 lines
845 B
Python
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#!/usr/bin/env python3
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from sklearn.datasets import load_digits
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from sklearn.model_selection import train_test_split
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from sklearn.neural_network import MLPClassifier
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from sklearn.metrics import accuracy_score
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import matplotlib.pyplot as plt
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import pickle
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digits = load_digits()
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# input
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x = digits.images.reshape((len(digits.images), -1))
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# what the output should be
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y = digits.target
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mlp = MLPClassifier(hidden_layer_sizes=(18,),
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activation='logistic',
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alpha=1e-4, solver='sgd',
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tol=1e-4, random_state=1,
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learning_rate_init=.1,
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verbose=True)
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mlp.fit(x, y)
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fig, axes = plt.subplots(1, 1)
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axes.plot(mlp.loss_curve_, 'o-')
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axes.set_xlabel("iterations")
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axes.set_ylabel("loss")
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plt.show()
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pickle.dump(mlp.coefs_, open( 'weights.pkl', 'wb'))
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