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Page 1835 of 2109
How to use global variables in Ruby?
Global variables have a global scope and they can be accessed from anywhere in a program. Assignments to global variables can be made from anywhere in the program. Global variables are always prefixed with a dollar sign.It is necessary to define a global variable to have a variable that is available across classes. When a global variable is uninitialized, it has no value by default and its use is nil.Now let's make use of the global variable in an example to understand it better. Consider the code shown below.Example 1# Global Variable example # global variable $global_var = 15 ...
Read MoreYield keyword in Ruby Programming
There are often cases where we would want to execute a normal expression multiple times inside a method but without having to repeat the same expression again and again. With the yield keyword, we can do the same.We can also pass arguments to the yield keyword and get values in return as well. Now let's explore some examples to see how the yield keyword works in Ruby.Example 1Consider the code shown below where we are declaring a normal yield keyword twice inside a method and then calling it.def tuts puts "In the tuts method" # using yield keyword ...
Read MoreTrue, False and Nil in Ruby Programming
We know that everything in Ruby is treated as an object, and so the true, false and nil as well. They are built-in types that Ruby provides to do different conditional checks and more. In this article, we will explore different examples of the true, false and nil data types and how to use them.True, False in RubyLet's start with a very simple example where we will check if two variables are equal or not.Example 1Consider the code shown belowfirst = 10 second = 10 if first == second # If Condition is true puts "True! First ...
Read MoreStatic Members in Ruby Programming
Static Members in Ruby are declared with the help of the class. Since Ruby doesn't provide a reserved keyword such as static, when we make use of the class variable, then we create a static variable and then we can declare a method of that class in which the static variable is defined as a static method as well.In Ruby, there are two implementations for the static keyword −Static variableStatic methodIn this article, we will explore both these implementations where first, we will explore a code example of how to declare a static variable and then we will see how ...
Read MoreHow to adjust the sharpness of an image in PyTorch?
To adjust the sharpness of an image, we apply adjust_sharpness(). It's one of the functional transforms provided by the torchvision.transforms module. adjust_sharpness() transformation accepts both PIL and tensor images.A tensor image is a PyTorch tensor with shape [C, H, W], where C is number of channels, H is image height, and W is image width. This transform also accepts a batch of tensor images. If the image is neither a PIL image nor tensor image, then we first convert it to a tensor image and then apply the adjust_sharpness(). The sharpness should be any non-negative number.Syntaxtorchvision.transforms.functional.adjust_sharpness(img, sharpness_factor)Parametersimg – Image of ...
Read MoreHow to construct a complex tensor with the given real and imaginary parts in PyTorch?
With given real and imaginary parts, we can construct a complex number in PyTorch using torch.complex() method. The real and imaginary parts must be float or double. Both the real and imaginary parts must be of the same type. If the real part is float, then the imaginary must also be float.If the inputs are torch.float32, then the constructed complex tensor must be torch.complex64.If the inputs are torch.float64, then the complex tensor must be torch.complex128.Syntaxtorch.complex(real, imag)Parametersreal and imag − Real and imaginary parts of the complex tensor. Both must be of the same dtype, float or double only.StepsWe could use the ...
Read MoreFind start and ending index of an element in an unsorted array in C++
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 MoreHow to define a simple Convolutional Neural Network in PyTorch?
To define a simple convolutional neural network (CNN), we could use the following steps −StepsFirst we import the important libraries and packages. We try to implement a simple CNN in PyTorch. In all the following examples, the required Python library is torch. Make sure you have already installed it.import torch import torch.nn as nn import torch.nn.functional as FOur next step is to build a simple CNN model. Here, we use the nn package to implement our model. For this, we define a class MyNet and pass nn.Module as the parameter.class MyNet(nn.Module):We need to create two functions inside the class to ...
Read MoreFind Square Root under Modulo p (Shanks Tonelli algorithm) in C++
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 MoreHow to measure the Binary Cross Entropy between the target and the input probabilities in PyTorch?
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. ...
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