# patdbro/pytorch-tutorial-ipynb-c5057

2 years ago
In [2]:
``import torch``
In [4]:
``````t1 = torch.tensor(4.)
t1
``````
Out[4]:
``tensor(4.)``
In [5]:
``t1.dtype``
Out[5]:
``torch.float32``
In [7]:
``````t2 = torch.tensor([1.,2,3,4])
t2
``````
Out[7]:
``tensor([1., 2., 3., 4.])``
In [9]:
``````t3 = torch.tensor([[5,6], [7,8], [9,10]])
t3
``````
Out[9]:
``````tensor([[ 5,  6],
[ 7,  8],
[ 9, 10]])``````
In [10]:
``````t4 = torch.tensor([
[[11,12,13],
[13,14,15]],
[[15,16, 17],
[17,18,19.]]
])
t4
``````
Out[10]:
``````tensor([[[11., 12., 13.],
[13., 14., 15.]],

[[15., 16., 17.],
[17., 18., 19.]]])``````
In [11]:
``t4.shape``
Out[11]:
``torch.Size([2, 2, 3])``
In [13]:
``t4.dtype``
Out[13]:
``torch.float32``
In [14]:
``````x = torch.tensor(3.)
w = torch.tensor(4.,requires_grad = True)
b = torch.tensor(5.,requires_grad = True)``````
In [15]:
``````y = x * w +b
y
``````
Out[15]:
``tensor(17., grad_fn=<AddBackward0>)``
In [17]:
``y.backward()``
In [18]:
``````print('dy/dx: ', x.grad)
print('dy/dw: ', w.grad)
print('dy/db: ', b.grad)``````
```dy/dx: None dy/dw: tensor(3.) dy/db: tensor(1.) ```
In [19]:
``import numpy as np``
In [21]:
``````x = np.array([[1,2],[3,4]])
x
``````
Out[21]:
``````array([[1, 2],
[3, 4]])``````
In [22]:
``````y = torch.from_numpy(x)
y
``````
Out[22]:
``````tensor([[1, 2],
[3, 4]])``````
In [23]:
``x.dtype, y.dtype``
Out[23]:
``(dtype('int64'), torch.int64)``
In [24]:
``````z = y.numpy()
z
``````
Out[24]:
``````array([[1, 2],
[3, 4]])``````
In [ ]:
``````import jovian
jovian.commit()
``````
```[jovian] Saving notebook.. ```
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`` ``