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torch.tensor()

时间: 2024-02-25 14:29:20 浏览: 57

torch.tensor() is a function in PyTorch that creates a new tensor with the specified data. It can take in various data types like lists, tuples, arrays, and other tensors, and returns a new tensor with the same data type as the input. Example usage: ``` import torch # create a new tensor from a list my_list = [1, 2, 3] my_tensor = torch.tensor(my_list) # create a new tensor from a numpy array import numpy as np my_array = np.array([[1, 2], [3, 4]]) my_tensor2 = torch.tensor(my_array) # create a new tensor from another tensor my_tensor3 = torch.tensor([5, 6, 7]) my_tensor4 = torch.tensor(my_tensor3) ```

torch.tensor

torch.tensor is a class in the PyTorch library that creates a multi-dimensional matrix of numbers. It is used for numerical computations and machine learning applications. It can be initialized with a list, tuple, NumPy array, or another tensor. It supports a variety of operations such as addition, subtraction, multiplication, and division. It can also be used for gradient calculations and automatic differentiation.

torch.Tensor

torch.Tensor is a multi-dimensional array in PyTorch, similar to a NumPy array. It is the fundamental data structure of PyTorch and is used to store and manipulate data in deep learning models. A tensor can be created with the torch.Tensor() constructor or by converting a NumPy array to a PyTorch tensor using the torch.from_numpy() function. Tensors can be manipulated using a wide range of mathematical operations and can be moved to different devices, such as a GPU, using the to() method.

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