import torch
a = torch.FloatTensor([float('inf')])
a
tensor([inf])
t1 = torch.tensor(4.)
t1
tensor(4.)
t0 = torch.tensor(float('inf'))
t0
tensor(inf)
t2 = torch.tensor([1,2,3,4])
t2
tensor([1, 2, 3, 4])
t1.shape
torch.Size([])
t2.shape
torch.Size([4])
t2.dtype
torch.int64
x = torch.tensor(3.)
w = torch.tensor(4.,requires_grad=True)
b = torch.tensor(5.,requires_grad=True)
y = w*x+b
y
tensor(17., grad_fn=<AddBackward0>)
y.backward()
x.grad
w.grad
tensor(3.)
b.grad
tensor(1.)
import numpy as np
a = np.ones((2,2))
b = torch.tensor(a)
b[0,1] = 3
b
tensor([[1., 3.],
[1., 1.]], dtype=torch.float64)
a
array([[1., 1.],
[1., 1.]])
c = torch.from_numpy(a)
c[0,1] = 4
a
array([[1., 4.],
[1., 1.]])
pip install jovian --upgrade
Requirement already up-to-date: jovian in c:\users\qwx753507\desktop\mypy\lib\site-packages (0.1.62)
Requirement already satisfied, skipping upgrade: requests in c:\users\qwx753507\desktop\mypy\lib\site-packages (from jovian) (2.22.0)
Requirement already satisfied, skipping upgrade: uuid in c:\users\qwx753507\desktop\mypy\lib\site-packages (from jovian) (1.30)
Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\users\qwx753507\desktop\mypy\lib\site-packages (from requests->jovian) (1.25.3)
Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in c:\users\qwx753507\desktop\mypy\lib\site-packages (from requests->jovian) (2019.6.16)
Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in c:\users\qwx753507\desktop\mypy\lib\site-packages (from requests->jovian) (3.0.4)
Requirement already satisfied, skipping upgrade: idna<2.9,>=2.5 in c:\users\qwx753507\desktop\mypy\lib\site-packages (from requests->jovian) (2.8)
Note: you may need to restart the kernel to use updated packages.
import jovian
jovian.commit()
[jovian] Saving notebook..