1from sklearn.neural_network import MLPClassifier
 2from skimage import io
 3
 4img1 = io.imread("imgs/25.png")
 5img2 = io.imread("imgs/24.png")
 6img3 = io.imread("imgs/104.png")
 7
 8img4 = io.imread("otherimgs/image_0008.jpg")
 9
10
11data_train = [img1, img2, img3, img4]
12data_labels = [1, 1, 1, 0]
13data_test = [img4, img3]
14clf = MLPClassifier(solver='lbfgs', alpha=1e-5,
15                    hidden_layer_sizes=(5,2), random_state=1)
16clf.fit(data_train, data_labels)
17
18clf.predict(data_test)
19
20print "MPLClassifier values:"
21[coef.shape for coef in clf.coefs_]
22
23
24
25'''
26images_and_predictions = list(zip(digits.images[n_samples // 2:], predicted))
27for index, (image, prediction) in enumerate(images_and_predictions[:4]):
28    plt.subplot(2, 4, index + 5)
29    plt.axis('off')
30    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
31    plt.title('Prediction: %i' % prediction)
32'''