X:                  Y0 0                 00 1                 11 0                 11 1                 0Code:from NeuralNetwork import NeuralNetworkimport numpy as npnn = NeuralNetwork([2,2,1], ‘tanh‘)     X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])     y = np.array([0, 1, 1, 0])     nn.fit(X, y)     for i in [[0, 0], [0, 1], [1, 0], [1,1]]:        print(i, nn.predict(i))2. 手写数字识别：每个图片8x8 识别数字：0,1,2,3,4,5,6,7,8,9Code:import numpy as np from sklearn.datasets import load_digits from sklearn.metrics import confusion_matrix, classification_report from sklearn.preprocessing import LabelBinarizer from NeuralNetwork import NeuralNetworkfrom sklearn.cross_validation import train_test_splitdigits = load_digits()  X = digits.data  y = digits.target  X -= X.min() # normalize the values to bring them into the range 0-1  X /= X.max()nn = NeuralNetwork([64,100,10],‘logistic‘)  X_train, X_test, y_train, y_test = train_test_split(X, y)  labels_train = LabelBinarizer().fit_transform(y_train)  labels_test = LabelBinarizer().fit_transform(y_test)print "start fitting"nn.fit(X_train,labels_train,epochs=3000)  predictions = []  for i in range(X_test.shape[0]):      o = nn.predict(X_test[i] )      predictions.append(np.argmax(o))  print confusion_matrix(y_test,predictions)  print classification_report(y_test,predictions)