## How to load a Computer Vision dataset in PyTorch? Updated on 25-Jan-2022 08:33:23
There are many datasets available in Pytorch related to computer vision tasks. The torch.utils.data.Dataset provides different types of datasets. The torchvision.datasets is a subclass of torch.utils.data.Dataset and has many datasets related to images and videos. PyTorch also provides us a torch.utils.data.DataLoader which is used to load multiple samples from a dataset.StepsWe could use the following steps to load computer vision datasets −Import the required libraries. In all the following examples, the required Python libraries are torch, Matplotlib, and torchvision. Make sure you have already installed them.import torch import torchvision from torchvision import datasets from torchvision.transforms import ToTensor import matplotlib.pyplot as ... Read More

## How to measure the Binary Cross Entropy between the target and the input probabilities in PyTorch? Updated on 25-Jan-2022 08:13:43
We apply the BCELoss() method to compute the binary cross entropy loss between the input and target (predicted and actual) probabilities. BCELoss() is accessed from the torch.nn module. It creates a criterion that measures the binary cross entropy loss. It is a type of loss function provided by the torch.nn module.The loss functions are used to optimize a deep neural network by minimizing the loss. Both the input and target should be torch tensors having the class probabilities. Make sure that the target is between 0 and 1. Both the input and target tensors may have any number of dimensions. ... Read More

## torch.nn.Dropout() Method in Python PyTorch Updated on 25-Jan-2022 08:08:00
Making some of the random elements of an input tensor zero has been proven to be an effective technique for regularization during the training of a neural network. To achieve this task, we can apply torch.nn.Dropout(). It zeroes some of the elements of the input tensor.An element will be zeroed with the given probability p. It uses a Bernoulli distribution to take samples of the element being zeroed. It does not support complex-valued inputs.Syntaxtorch.nn.Dropout(p=0.5)The default probability of an element to zeroed is set to 0.5StepsWe could use the following steps to randomly zero some of the elements of an input ... Read More

## How to rescale a tensor in the range [0, 1] and sum to 1 in PyTorch? Updated on 25-Jan-2022 08:03:26
We can rescale an n-dimensional input Tensor such that the elements lie within the range [0, 1] and sum to 1. To do this, we can apply the Softmax() function. We can rescale the n-dimensional input tensor along a particular dimension. The size of the output tensor is the same as the input tensor.Syntaxtorch.nn.Softmax(dim)Parametersdim – The dimension along which the Softmax is computed.StepsWe could use the following steps to crop an image at random location with given size −Import the required library. In all the following examples, the required Python library is torch. Make sure you have already installed it.import ... Read More

## How to apply rectified linear unit function element-wise in PyTorch? Updated on 25-Jan-2022 07:59:27
To apply a rectified linear unit (ReLU) function element-wise on an input tensor, we use torch.nn.ReLU(). It replaces all the negative elements in the input tensor with 0 (zero), and all the non-negative elements are left unchanged. It supports only real-valued input tensors. ReLU is used as an activation function in neural networks.Syntaxrelu = torch.nn.ReLU() output = relu(input)StepsYou could use the following steps to apply rectified linear unit (ReLU) function element-wise −Import the required library. In all the following examples, the required Python library is torch. Make sure you have already installed it.import torch import torch.nn as nnDefine input tensor ... Read More

## How to apply a 2D Average Pooling in PyTorch? Updated on 25-Jan-2022 07:48:40
We can apply a 2D Average Pooling over an input image composed of several input planes using the torch.nn.AvgPool2d() module. The input to a 2D Average Pooling layer must be of size [N, C, H, W] where N is the batch size, C is the number of channels, H and W are the height and width of the input image.The main feature of an Average Pooling operation is the filter or kernel size and stride. This module supports TensorFloat32.Syntaxtorch.nn.AvgPool2d(kernel_size)Parameterskernel_size – The size of the window to take an average over.Along with this parameter, there are some optional parameters also such ... Read More

## How to pad the input tensor boundaries with a constant value in PyTorch? Updated on 25-Jan-2022 07:40:43
The torch.nn.ConstantPad2D() pads the input tensor boundaries with constant value. The size of the input tensor must be in 3D or 4D in (C, H, W) or (N, C, H, W) format respectively. Where N, C, H, W represents the mini batch size, number of channels, height and width respectively. The padding is done along the height and width of the input tensor.It takes the size of padding (padding) and constant values (value) as the parameters. The size of padding may be an integer or a tuple. The padding may be the same for all boundaries or different for each ... Read More

## How to pad the input tensor boundaries with zero in PyTorch? Updated on 25-Jan-2022 07:31:55  