31 lines
903 B
Python
31 lines
903 B
Python
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#!/usr/bin/env python3
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# coding: utf-8
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import numpy as np
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import sklearn.datasets as ds
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import sklearn.metrics as mt
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import sklearn.preprocessing as prep
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import neuralNetwork as nn
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import sklearn.cross_validation as cross
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digits = ds.load_digits()
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x = digits.data
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y = digits.target
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# 将x中的值设置到0-1之间
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x -= x.min()
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x /= x.max()
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# n = nn.NeuralNetwork([64, 100, 10], 'tanh')
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n = nn.NeuralNetwork([64, 100, 10], 'logistic')
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x_train, x_test, y_train, y_test = cross.train_test_split(x, y)
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labels_train = prep.LabelBinarizer().fit_transform(y_train)
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labels_test = prep.LabelBinarizer().fit_transform(y_test)
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print("start fitting")
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n.fit(x_train, labels_train, epochs=10000)
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preddctions = []
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for i in range(x_test.shape[0]):
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o = n.predit(x_test[i])
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preddctions.append(np.argmax(o))
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print(mt.confusion_matrix(y_test, preddctions))
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print(mt.classification_report(y_test, preddctions))
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