#from sklearn.preprocessing import MinMaxScaler Tx_user = pd.read_csv(‘rfm_data.csv’, sep =’,’, engine=’python’) Pls can u help me check my code is right or wrong Mae = mean_absolute_error(y_teste, previsoes) Y_teste = scaler_teste.fit_transform(y_teste) #Testando com o y_teste e previsóes ainda com scalonamento Mae = mean_absolute_error(y_teste, previsoes1) Y_teste = scaler_y.inverse_transform(y_teste)įrom trics import mean_absolute_error Previsoes1 = scaler_y.inverse_transform(regressor.predict(X_teste_poly)) Previsoes = scaler.inverse_transform(previsoes) = is not working. Previsoes = regressor.predict(X_teste_poly) Score = regressor.score(X_treinamento_poly, y_treinamento) Regressor.fit(X_treinamento_poly, y_treinamento) X_treinamento_poly = poly.fit_transform(X_treinamento) X_treinamento = scaler.fit_transform(X_treinamento)įrom sklearn.linear_model import LinearRegressionįrom sklearn.preprocessing import PolynomialFeatures X_treinamento, X_teste, y_treinamento, y_teste = train_test_split(X, y,įrom sklearn.preprocessing import StandardScaler I do the scaling in my predictors, and when I do the prediction, the result comes out in scientific notation, researching I saw that there is a way to do an inverse_transform, reverse the scaling process, I tried, but I failed to successfully reverse, can you help me?īase = pd.read_csv(‘c://udemy//ia//bd//house-prices.csv’)įrom sklearn.model_selection import train_test_split Hello Jason, thanks for all information, and, lets see if you can help me. Update: See this post for a more up to date set of examples. In this post you discovered where data rescaling fits into the process of applied machine learning and two methods: Normalization and Standardization that you can use to rescale your data in Python using the scikit-learn library. Summaryĭata rescaling is an important part of data preparation before applying machine learning algorithms. This can quickly highlight the benefits (or lack there of) of rescaling your data with given models, and which rescaling method may be worthy of further investigation. If often can, but not always.Ī good tip is to create rescaled copies of your dataset and race them against each other using your test harness and a handful of algorithms you want to spot check. It is hard to know whether rescaling your data will improve the performance of your algorithms before you apply them. scale ( X )įor more information see the scale function in the API documentation. There you have your features extraction function, simply call it using the snippet below to obtain features from resnet18.avgpool layer model = models.Standardized_X = preprocessing. Transforms.Normalize(mean=, std=),Ī_hook = _forward_hook(my_hook) My_output: torch.tensor, output of avgpool layer The accepted answer is very helpful! I'm posting a complete example here (using a registered hook as described by for the lazy ones looking for a working solution: import torch Model.some_specific_layer.register_forward_hook(some_specific_layer_hook)įor example, to obtain the res5c output in ResNet, you may want to use a nonlocal variable (or global in Python 2): res5c_output = None Something like: def some_specific_layer_hook(module, input_, output): You can register a forward hook on the specific layer you want. Model2 = create_feature_extractor(model, return_nodes=return_nodes) Edit: there's a new feature in torchvision v0.11.0 that allows extracting features.įor example, if you wanna extract features from the layer layer4.2.relu_2, you can do like: import torchįrom _extraction import create_feature_extractor
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