train_dl, test_dl = data.prepare_data('heart.csv') print('Training ', len(train_dl.dataset)) print('Test ', len(test_dl.dataset)) # print(train_dl) xTest, yTest = [], [] for i, (inputs, targets) in enumerate(test_dl): xTest.append(inputs.numpy().flatten()) yTest.append(targets.numpy().flatten()) xTrain, yTrain = [], [] for i, (inputs, targets) in enumerate(train_dl): xTrain.append(inputs.numpy().flatten()) yTrain.append(targets.item()) xTrain = np.array(xTrain) yTrain = np.array(yTrain) # define the NN model = nn.MLP(13) # Train NN and save the trained model for future use. # trainAndSaveNN(train_dl, test_dl, model) # Load trained model. model.load_state_dict(torch.load('trainedNN.pt')) # test the NN acc = nn.evaluate_model(test_dl, model) print('NN Accuracy: %.3f' % (acc * 100.0)) # Calling first pipeline firstPipeline(train_dl, model, xTest, yTest) # Calling second pipeline secondPipeline(train_dl, model, xTest, yTest) gbtWithHardLabels(xTrain, yTrain, xTest, yTest)
for image in train_data_set.X: dst = pre.eqHist(image) dst = pre.reshape(dst) reshaped_train_images.append(dst) train_data_set.X = reshaped_train_images reshaped_test_images = [] for image in test_data_set.X: dst = pre.eqHist(image) dst = pre.reshape(dst) reshaped_test_images.append(dst) test_data_set.X = reshaped_test_images reshaped_train_images = [] for image in train_data_set.X: dst = image.reshape(-1, 1) reshaped_train_images.append(dst) train_data_set.X = np.asarray(reshaped_train_images) reshaped_test_images = [] for image in test_data_set.X: dst = image.reshape(-1, 1) reshaped_test_images.append(dst) test_data_set.X = np.asarray(reshaped_test_images) mlp = nn.MLP("NN.dat", train_data_set, print_step=1, verbose=1) #mlp.train(n_epochs=10, learning_rate=2, decay=1.) #mlp.make_plot() # #mlp.setdataset(test_data_set) #mlp.print_accuracy()