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32 lines
869 B
32 lines
869 B
from sklearn.neural_network import MLPClassifier
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from skimage import io
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img1 = io.imread("imgs/25.png")
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img2 = io.imread("imgs/24.png")
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img3 = io.imread("imgs/104.png")
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img4 = io.imread("otherimgs/image_0008.jpg")
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data_train = [img1, img2, img3, img4]
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data_labels = [1, 1, 1, 0]
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data_test = [img4, img3]
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clf = MLPClassifier(solver='lbfgs', alpha=1e-5,
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hidden_layer_sizes=(5,2), random_state=1)
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clf.fit(data_train, data_labels)
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clf.predict(data_test)
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print "MPLClassifier values:"
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[coef.shape for coef in clf.coefs_]
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'''
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images_and_predictions = list(zip(digits.images[n_samples // 2:], predicted))
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for index, (image, prediction) in enumerate(images_and_predictions[:4]):
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plt.subplot(2, 4, index + 5)
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plt.axis('off')
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plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
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plt.title('Prediction: %i' % prediction)
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'''
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