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- %matplotlib nbagg
-
- from sklearn.metrics import accuracy_score
-
- import matplotlib.pyplot as plt
- import matplotlib.animation as animation
-
- fig = plt.figure()
- imgs = []
-
- y_true = np.array(nn.y).astype(float)
-
- # back-propagation
- def backpropagation(n, X, y):
- for i in range(n.n_epoch):
- # forward to calculate each node's output
- forward(n, X)
-
- # print loss, accuracy
- L = np.sum((n.z2 - y)**2)
-
- y_pred = np.zeros(nn.z2.shape[0])
- y_pred[np.where(nn.z2[:,0]<nn.z2[:,1])] = 1
- acc = accuracy_score(y_true, y_pred)
-
- print("epoch [%4d] L = %f, acc = %f" % (i, L, acc))
-
- # calc weights update
- d2 = n.z2*(1-n.z2)*(y - n.z2)
- d1 = n.z1*(1-n.z1)*(np.dot(d2, n.W2.T))
-
- # update weights
- n.W2 += n.epsilon * np.dot(n.z1.T, d2)
- n.b2 += n.epsilon * np.sum(d2, axis=0)
- n.W1 += n.epsilon * np.dot(X.T, d1)
- n.b1 += n.epsilon * np.sum(d1, axis=0)
-
- # plot animation
- #img = plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap=plt.cm.Spectral)
- #imgs.append(img)
-
- nn.n_epoch = 2000
- backpropagation(nn, X, t)
-
- #ani = animation.ArtistAnimation(fig, imgs)
- #plt.show()
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