| import torch | |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| print(torch.__version__) | |
| scalar = torch.tensor(7) | |
| scalar | |
| scalar.ndim | |
| scalar.item() | |
| vector = torch.tensor([7, 7]) | |
| vector.ndim | |
| vector.shape | |
| MATRIX = torch.tensor[[7, 8],[9, 10]] | |
| MATRIX | |
| MATRIX.ndim | |
| MATRIX[1] | |
| MATRIX.shape | |
| TENSOR = torch.tensor([[[1, 2, 3], | |
| [3, 6, 9], | |
| [2, 4, 5]]]) | |
| TENSOR.ndim | |
| TENSOR.shape | |
| TENSOR[0] | |
| random_tensor = torch.rand(3, 4) | |
| random_tensor | |
| random_tensor.ndim | |
| random_image_size_tensor = torch.rand(size=(224, 224, 3)) | |
| random_image_size_tensor.shape, random_image_size_tensor.ndim | |
| zeros = torch.zeros(size=(3, 4)) | |
| zeros | |
| ones = torch.ones(size=(3, 4)) | |
| ones | |
| ones.dtype | |
| random_tensor.dtype | |
| one_to_ten = torch.arange(start=1, end=11, step=1) | |
| ten_zeros = torch.zeros_like(input=one_to_ten) | |
| ten_zeros | |
| float_32_tensor - torch.tensor([3.0, 6.0, 9.0], | |
| dtype=None, | |
| device=None, | |
| requires_grad=False) |