def svm(): # ********************* load the dataset and divide to X&y *********************** from sklearn.datasets import make_blobs X, Y = make_blobs(cluster_std=0.9, random_state=20, n_samples=1000, centers=10, n_features=10) from Algorithms.ML_.helper.data_helper import split_train_val_test X, Xv, y, Yv, Xt, Yt = split_train_val_test(X, Y) print(X.shape, y.shape, Xv.shape, Yv.shape, Xt.shape, Yt.shape) # ********************* build model *********************** from model import SVM from activation import Activation, Softmax, Hinge from regularization import Regularization, L1, L2, L12 from optimizer import Vanilla model = SVM() learning_rate, reg_rate = 1e-3, 5e-1 model.compile(alpha=learning_rate, lambda_=reg_rate, activation=Softmax(), reg=L2(), opt=Vanilla()) model.describe() # ********************* train *********************** loss_train, loss_val = model.train(X, y, val=(Xv, Yv), iter_=1000, return_loss=True, verbose=True, eps=1e-3) import matplotlib.pyplot as plt plt.plot(range(len(loss_train)), loss_train) plt.plot(range(len(loss_val)), loss_val) plt.legend(['train', 'val']) plt.xlabel('Iteration') plt.ylabel('Training loss') plt.title('Training Loss history') plt.show() # ********************* predict *********************** pred_train = model.predict(X) pred_val = model.predict(Xv) pred_test = model.predict(Xt) import metrics print('train accuracy=', metrics.accuracy(y, pred_train)) print('val accuracy=', metrics.accuracy(Yv, pred_val)) print('test accuracy=', metrics.accuracy(Yt, pred_test)) print('null accuracy=', metrics.null_accuracy(y)) import metrics metrics.print_metrics(Yt, pred_test)
X, Xv, Xte, Xd = X - mu, Xv - mu, Xte - mu, Xd - mu # ********************* train *********************** # model = SVM() # model.compile(lambda_=2.5e4, alpha=1e-7) # 1e-7, reg=2.5e4, # loss_history = model.train(X, y, eps=0.001, batch=200, iter_=1500) # # plt.plot(range(len(loss_history)), loss_history) # plt.xlabel('Iteration number') # plt.ylabel('Loss value') # plt.show() # print(loss_history[::100]) # lr, rg = SVM.ff(X, y, Xv, Yv, [1e-7, 1e-6],[2e4, 2.5e4, 3e4, 3.5e4, 4e4, 4.5e4, 5e4, 6e4]) # print(lr, rg) model = SVM() model.compile(alpha=1e-7, lambda_=2, activation=Softmax, reg=L2) # model.compile(alpha=0, lambda_=0, activation=Hinge, Reg=L2, dReg=dL2) history = model.train(Xd, Yd, iter_=0, eps=0.0001) print(model.loss(model.X, model.y, add_ones=False), np.sum(model.grad(model.X, model.y, False))) L, dW = model.grad(model.X, model.y, True) print(L, np.sum(dW)) # print(np.sum(model.W)) # print(np.sum(model.grad(model.X, model.y, loss_=False))) # print(np.sum(model.grad1(model.X, model.y))) # L, dW = model.activation.loss_grad_loop(model.X, model.W, model.y) # print(L, np.sum(dW)) loss_history = model.train(X, y, eps=0.0001, batch=200, iter_=1500) plt.plot(range(len(loss_history)), loss_history)