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To perform element-wise subtraction on tensors, we can use the torch.sub() method of PyTorch. The corresponding elements of the tensors are subtracted. We can subtract a scalar or tensor from another tensor. We can subtract a tensor from a tensor with same or different dimension. The dimension of the final tensor will be same as the dimension of the higher-dimensional tensor.StepsImport the required library. In all the following Python examples, the required Python library is torch. Make sure you have already installed it.Define two or more PyTorch tensors and print them. If you want to subtract a scalar quantity, define ... Read More
We can use torch.add() to perform element-wise addition on tensors in PyTorch. It adds the corresponding elements of the tensors. We can add a scalar or tensor to another tensor. We can add tensors with same or different dimensions. The dimension of the final tensor will be same as the dimension of the higher dimension tensor.StepsImport the required library. In all the following Python examples, the required Python library is torch. Make sure you have already installed it.Define two or more PyTorch tensors and print them. If you want to add a scalar quantity, define it.Add two or more tensors ... Read More
To resize a PyTorch tensor, we use the .view() method. We can increase or decrease the dimension of the tensor, but we have to make sure that the total number of elements in a tensor must match before and after the resize.StepsImport the required library. In all the following Python examples, the required Python library is torch. Make sure you have already installed it.Create a PyTorch tensor and print it.Resize the above-created tensor using .view() and assign the value to a variable. .view() does not resize the original tensor; it only gives a view with the new size, as its ... Read More
We access the size (or shape) of a tensor and the number of elements in the tensor as the metadata of the tensor. To access the size of a tensor, we use the .size() method and the shape of a tensor is accessed using .shape.Both .size() and .shape produce the same result. We use the torch.numel() function to find the total number of elements in the tensor.StepsImport the required library. Here, the required library is torch. Make sure that you have installed torch.Define a PyTorch tensor.Find the metadata of the tensor. Use .size() and .shape to access the size and ... Read More
We use Indexing and Slicing to access the values of a tensor.Indexing is used to access the value of a single element of the tensor, whereasSlicing is used to access the values of a sequence of elements.We use the assignment operator to modify the values of a tensor. Assigning new value/s using the assignment operator will modify the tensor with new value/s.StepsImport the required libraries. Here, the required library is torch.Define a PyTorch tensor.Access the value of a single element at particular index using indexing or access the values of sequence of elements using slicing.Modify the accessed values with new ... Read More
A PyTorch tensor is an n-dimensional array (matrix) containing elements of a single data type. A tensor is like a numpy array. The difference between numpy arrays and PyTorch tensors is that the tensors utilize the GPUs to accelerate the numeric computations. For the accelerated computations, the images are converted to the tensors.To convert an image to a PyTorch tensor, we can take the following steps −StepsImport the required libraries. The required libraries are torch, torchvision, Pillow.Read the image. The image must be either a PIL image or a numpy.ndarray (HxWxC) in the range [0, 255]. Here H, W, and ... Read More
To change the name of single column using setNames, we would need to specify the column name that needs to be changed.For example, if we have a data frame called df that contains three columns say Var1, var2, and Var3 and we want to change var2 to Var2 then we can use the command as follows −setNames(df,replace(names(df),names(df)=="var2","Var2"))Example 1Following snippet creates a sample data frame −x
To add single quotes to strings in an R data frame column, we can use paste0 function. This will cover the strings with single quotes from both the sides but we can add them at the initial or only at the last position.To add them on both the sides, we can use the following syntax −Data_frame$Column
When we write a long number in R, by default the printed output of that number is in scientific notation. To stop this printing behavior we can use sprint function.For example, if we have a data frame called df that contains a column say X having long numbers then we can stop printing of these numbers in scientific notation by using the below given command −df$X
Sometimes a variable has a character associated with the numerical values such as variable name etc. If we want to associate a character to numbers then paste0 function can be used.For example, if we have a data frame called df that contains a column say X then we can associate X to each value in the column by using the command given below −df$X