| 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) |
