Programming Articles - Page 640 of 2501

In this problem, we are given an array aar[] of n integer values which are not sorted and an integer val. Our task is to find the start and ending index of an element in an unsorted array.For the occurrence of the element in the array, we will return, "Starting index and ending index " if it is found in the array twice or more."Single index " if it is found in the array once."Element not present " if it is not present in the array.Let's take an example to understand the problem, Example 1Input : arr[] = {2, 1, ... Read More

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

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

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

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

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

In this problem, we are given two values n and a prime number p. Our task is to find Square Root under Modulo p.Let's take an example to understand the problem, Input : n = 4, p = 11 Output : 9Solution ApproachHere, we will be using Tonelli-Shanks Algorithm.Tonelli-Shanks Algorithm is used in modular arithmetic to solve for a value x in congruence of the form x2 = n (mod p).The algorithm to find square root modulo using shank's Tonelli Algorithm −Step 1 − Find the value of $(n^{((p-1)/2)})(mod\:p)$, if its value is p -1, then modular square root is ... Read More

In this problem, we are given two values n and a prime number p. Our task is to find Square Root under Modulo p (When p is in form of 4*i + 3). Here, p is of the form (4*i + 3) i.e. p % 4 = 3 for i > 1 and p being a prime number.Here are some numbers, 7, 11, 19, 23, 31...Let's take an example to understand the problem, Input : n = 3, p = 7 Output :Solution ApproachA simple solution to the problem is using a loop. We will loop from 2 to (p ... Read More

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

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 More