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Articles by Shahid Akhtar Khan
Page 11 of 17
torch.nn.Dropout() Method in Python PyTorch
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 MoreHow to rescale a tensor in the range [0, 1] and sum to 1 in PyTorch?
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 MoreHow to apply rectified linear unit function element-wise in PyTorch?
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 MoreHow to apply a 2D Average Pooling in PyTorch?
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 MoreHow to pad the input tensor boundaries with a constant value in PyTorch?
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 MoreHow to apply a 2D Max Pooling in PyTorch?
We can apply a 2D Max Pooling over an input image composed of several input planes using the torch.nn.MaxPool2d() module. The input to a 2D Max Pool 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, respectively.The main feature of a Max Pool operation is the filter or kernel size and stride. This module supports TensorFloat32.Syntaxtorch.nn.MaxPool2d(kernel_size)Parameterskernel_size – The size of the window to take a max over.Along with this parameter, there are some optional parameters also ...
Read MoreHow to apply a 2D transposed convolution operation in PyTorch?
We can apply a 2D transposed convolution operation over an input image composed of several input planes using the torch.nn.ConvTranspose2d() module. This module can be seen as the gradient of Conv2d with respect to its input.The input to a 2D transpose convolution 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, respectively.Generally a 2D transposed convolution operation is applied on the image tensors. For a RGB image, the number of channels is 3. The main feature ...
Read MoreHow to apply a 2D convolution operation in PyTorch?
We can apply a 2D convolution operation over an input image composed of several input planes using the torch.nn.Conv2d() module. It is implemented as a layer in a convolutional neural network (CNN). The input to a 2D convolution 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 tensor.Generally a 2D convolution operation is applied on the image tensors. For an RGB image, the number of channels is 3. The main feature of a convolution operation is the ...
Read MoreHow to upsample a given multi-channel temporal, spatial or volumetric data in PyTorch?
A temporal data can be represented as a 1D tensor, and spatial data as 2D tensor while a volumetric data can be represented as a 3D tensor. The Upsample class provided by torch.nn module supports these types of data to be upsampled. But these data must be in the form N ☓ C ☓ D (optional) ☓ H (optional) ☓ W (optional), Where N is the minibatch size, C is the numberchannels, D, H and W are depth, height and width of the data, respectively. Hence, to upsample a temporal data (1D), we need it to be in 3D in ...
Read MoreHow to adjust saturation of an image in PyTorch?
The saturation of an image refers to the intensity of a color. The higher the saturation of a color, the more vivid it is. The lower the saturation of a color, the closer it is to gray.To adjust the saturation of an image, we apply adjust_saturation(). It's one of the functional transforms provided by the torchvision.transforms module. adjust_saturation() transformation accepts both PIL and tensor images. A tensor image is a PyTorch tensor with shape [C, H, W], where C is the number of channels, H is the image height, and W is the image width.This transform also accepts a batch ...
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