PyTorch

Overview The behavior of the torch.matmul() function The torch.matmul() function performs a matrix product of two tensors. The behavior depends on the dimensionality of the tensors as follows: If both tensors are......

In the world of PyTorch and deep learning, transposing a tensor means rearranging its dimensions. For example, a tensor with a shape (3, 4) becomes (4, 3) after transposing. Transposition is useful when the original tensor’s shape......

The shape of a PyTorch tensor The shape of a PyTorch tensor is the number of elements in each dimension. You can use the shape attribute or the size() method to get the shape of a tensor as a torch.Size object, which is a subclass......

In order to calculate the memory used by a PyTorch tensor in bytes, you can use the following formula: memory_in_bytes = tensor.element_size() * tensor.nelement() his will give you the size of the tensor data in memory, whether......

When working with PyTorch, there might be cases where you need to change the data type of a tensor for some reason, such as to match the data type of another tensor or a scalar operand in an arithmetic operation or to reduce the memory......

A tensor range in PyTorch is a sequence of numbers that are stored in a one-dimensional tensor. In order to create a tensor range, you can use the torch.arange() function. Syntax Below is the syntax of the torch.arange()......

PyTorch is a popular framework for creating and manipulating tensors for deep learning and other applications. Tensors are multidimensional arrays that can store numerical data and perform various operations on them. This practical,......

When developing neural networks with PyTorch, there might be cases where you find random tensors are useful, such as when you need synthetic data for testing or experimentation purposes or when you want to sample from a probability......

This concise, practical article shows you how to convert NumPy arrays into PyTorch tensors and vice versa. Without any further ado, let’s get straight to the main points. Turning NumPy arrays into PyTorch tensors There are......

Exact equality comparison Exactly equality comparison means checking if two PyTorch tensors have the same shape, dtype, and values. It returns True if they are exactly the same and False otherwise. To deal with this kind of......

Why use torch.manual_seed() torch.manual_seed() is a function that helps you control the randomness in PyTorch. A lot of times, you want to use random numbers in your code, such as when you create a tensor with torch.rand() or when......

When working with PyTorch, there might be cases where you want to create a tensor from a Python list. For example, you want to create a custom tensor with some specific values that are not easily generated by the built-in tensor creation......