Y_pred = (batch_size, *) and y_train = (batch_size, *). The input and output have to be the same size and have the dtype float. y_pred = torch.tensor(, ], requires_grad=True) print("Y Pred: \n", y_pred) print("\nY Pred shape: ", y_pred.shape, "\n") print("=" * 50) y_train = torch.tensor(, ]) print("\nY Train: \n", y_train) print("\nY Train shape: ", y_train.shape) # OUTPUT # Y Pred: tensor(, ], requires_grad=True) Y Pred shape: torch.Size() = Y Train: tensor(, ]) Y Train shape: torch.Size() Mean Absolute Error - torch.nn.L1Loss() Let’s begin by defining the actual and predicted output tensors in order to calculate the loss. Import Libraries import torch import torch.nn as nn Regression Learn more about the loss functions from the official PyTorch docs. Negative Log Likelihood - torch.nn.NLLLoss().Binary Cross Entropy with Logits Loss - torch.nn.BCEWithLogitsLoss().Binary Cross Entropy Loss - torch.nn.BCELoss().Mean Squared Error - torch.nn.MSELoss().Mean Absolute Error - torch.nn.L1Loss().for i,j in data_loader: print(i,j) # OUTPUT # tensor() tensor() tensor() tensor() tensor() tensor() tensor() tensor() tensor() tensor()įollowing are the commonly used loss functions for different deep learning tasks. Let’s use the dataloader with a for loop. data_loader = DataLoader(dataset=data, batch_size=2, shuffle=True) data_loader_iter = iter(data_loader) print(next(data_loader_iter)) # OUTPUT # ), tensor()] Here we specify the batch size and shuffle. Printing out the 4th element (3rd index) from out data. Let’s use the methods _len_() and _getitem_(). data = CustomDataset(torch.FloatTensor(X), torch.FloatTensor(y)) The inputs have to be of the type Tensor. class CustomDataset(Dataset): def _init_(self, X_data, y_data): self.X_data = X_data self.y_data = y_data def _getitem_(self, index): return self.X_data, self.y_data def _len_ (self): return len(self.X_data) We will return a tuple of (input, output). Assume you had input and output data as. Let’s implement a basic PyTorch dataset and dataloader. train_loader = DataLoader(custom_dataset_object, batch_size=32, shuffle=True) _getitem_: returns data (input and output) in batches.Ī dataloader is then used on this dataset class to read the data in batches. _len_: returns the size of the input data. _init_ : used to perform initializing operations such as reading data and preprocessing. A custom dataset class is created using 3 main components.Ĭlass CustomDataset(Dataset): def _init_(self): pass def _getitem_(self, index): pass def _len_(self): pass
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