Numpy Vs Pytorch

In [1]:

import numpy as np
import torch

1 Creating a matrix with numpy vs pytorch¶

In [2]:

arr = [[1,2], [2,3]]

In [3]:

np.array(arr)

Out[3]:

array([[1, 2],
       [2, 3]])

In [4]:

torch.Tensor(arr)

Out[4]:

 1  2
 2  3
[torch.FloatTensor of size 2x2]

In [7]:

np.ones((2,2))

Out[7]:

array([[ 1.,  1.],
       [ 1.,  1.]])

In [8]:

torch.ones((2,2))

Out[8]:

 1  1
 1  1
[torch.FloatTensor of size 2x2]

In [9]:

np.zeros((2,3))

Out[9]:

array([[ 0.,  0.,  0.],
       [ 0.,  0.,  0.]])

In [10]:

torch.zeros((2,3))

Out[10]:

 0  0  0
 0  0  0
[torch.FloatTensor of size 2x3]

2 Random numbers¶

In [11]:

np.random.rand(2,3)

Out[11]:

array([[ 0.68749833,  0.62618793,  0.34557432],
       [ 0.95888593,  0.35415139,  0.48186146]])

In [12]:

torch.rand(2,3)

Out[12]:

 0.3996  0.4675  0.3076
 0.5733  0.7604  0.5373
[torch.FloatTensor of size 2x3]

3 Seed for reproducibility¶

In [15]:

np.random.seed(8)
np.random.randn(2,2)

Out[15]:

array([[ 0.09120472,  1.09128273],
       [-1.94697031, -1.38634953]])

In [16]:

np.random.seed(8)
np.random.randn(2,2)

Out[16]:

array([[ 0.09120472,  1.09128273],
       [-1.94697031, -1.38634953]])

In [17]:

np.random.randn(2,2)

Out[17]:

array([[-2.29649157,  2.4098343 ],
       [ 1.72783617,  2.20455628]])

In [18]:

torch.manual_seed(8)
torch.randn(2,2)

Out[18]:

 0.1047 -0.1068
 0.7255 -0.1453
[torch.FloatTensor of size 2x2]

In [19]:

torch.manual_seed(8)
torch.randn(2,2)

Out[19]:

 0.1047 -0.1068
 0.7255 -0.1453
[torch.FloatTensor of size 2x2]

In [20]:

torch.randn(2,2)

Out[20]:

 0.6629 -0.7132
-1.8069 -0.3548
[torch.FloatTensor of size 2x2]

4 from_numpy()¶

In [22]:

numpy_arr = np.array(np.random.rand(2,2))
numpy_arr

Out[22]:

array([[ 0.28907503,  0.97385524],
       [ 0.33377405,  0.21880106]])

In [23]:

type(numpy_arr)

Out[23]:

numpy.ndarray

In [25]:

pytorch_arr = torch.from_numpy(numpy_arr)
pytorch_arr

Out[25]:

 0.2891  0.9739
 0.3338  0.2188
[torch.DoubleTensor of size 2x2]

In [26]:

type(pytorch_arr)

Out[26]:

torch.DoubleTensor

How to change the datatype to IntTensor?¶

In [32]:

numpy_arr = np.array(np.random.rand(2,2), dtype=np.int32)
numpy_arr

Out[32]:

array([[0, 0],
       [0, 0]], dtype=int32)

In [33]:

type(numpy_arr)

Out[33]:

numpy.ndarray

In [34]:

pytorch_arr = torch.from_numpy(numpy_arr)
pytorch_arr

Out[34]:

 0  0
 0  0
[torch.IntTensor of size 2x2]

In [35]:

type(pytorch_arr)

Out[35]:

torch.IntTensor

We can convert any numpy array to pytorch tensors using $\verb|torch.from_numpy()|$

How to convert torch tensors to numpy array?¶

In [37]:

torch_tensors = torch.randn(2,2)
torch_tensors

Out[37]:

-1.5444  0.7212
-0.8290  0.5838
[torch.FloatTensor of size 2x2]

In [38]:

numpy_arr = torch_tensors.numpy()
numpy_arr

Out[38]:

array([[-1.54443169,  0.72120529],
       [-0.82901162,  0.5837965 ]], dtype=float32)

5. Tensor Operations¶

How to resize torch tensors?¶

Numpy¶

In [43]:

numpy_arr = np.zeros((2,2))
numpy_arr

Out[43]:

array([[ 0.,  0.],
       [ 0.,  0.]])

In [45]:

numpy_arr.reshape(1,4)

Out[45]:

array([[ 0.,  0.,  0.,  0.]])

PyTorch¶

In [46]:

torch_ten = torch.zeros((2,2))
torch_ten

Out[46]:

 0  0
 0  0
[torch.FloatTensor of size 2x2]

In [47]:

torch_ten.view(1,4)

Out[47]:

 0  0  0  0
[torch.FloatTensor of size 1x4]

In [48]:

numpy_arr.shape

Out[48]:

(2, 2)

In [51]:

torch_ten.size()

Out[51]:

torch.Size([2, 2])

How to do element-wise addition?¶

Numpy¶

In [70]:

a = np.array([[1,2], [2,3]])
a

Out[70]:

array([[1, 2],
       [2, 3]])

In [71]:

b = np.array([[1,2], [2,3]])
b

Out[71]:

array([[1, 2],
       [2, 3]])

In [72]:

a+b

Out[72]:

array([[2, 4],
       [4, 6]])

In [73]:

np.add(a,b)

Out[73]:

array([[2, 4],
       [4, 6]])

PyTorch¶

In [74]:

a = torch.Tensor([[1,2], [2,3]])
a

Out[74]:

 1  2
 2  3
[torch.FloatTensor of size 2x2]

In [75]:

b = torch.Tensor([[1,2], [2,3]])
b

Out[75]:

 1  2
 2  3
[torch.FloatTensor of size 2x2]

In [76]:

a+b

Out[76]:

 2  4
 4  6
[torch.FloatTensor of size 2x2]

In [77]:

torch.add(a,b)

Out[77]:

 2  4
 4  6
[torch.FloatTensor of size 2x2]

How to perform inplace additions?¶

PyTorch¶

In [104]:

a = torch.Tensor([[1,2], [2,3]])
a

Out[104]:

 1  2
 2  3
[torch.FloatTensor of size 2x2]

In [105]:

b = torch.Tensor([[1,2], [2,3]])
b

Out[105]:

 1  2
 2  3
[torch.FloatTensor of size 2x2]

In [106]:

a.add(b) # not in place

Out[106]:

 2  4
 4  6
[torch.FloatTensor of size 2x2]

In [107]:

a

Out[107]:

 1  2
 2  3
[torch.FloatTensor of size 2x2]

In [108]:

a.add_(b) # in plcace

Out[108]:

 2  4
 4  6
[torch.FloatTensor of size 2x2]

In [109]:

a

Out[109]:

 2  4
 4  6
[torch.FloatTensor of size 2x2]

In [110]:

np.multiply(a,b)

Out[110]:

  2   8
  8  18
[torch.FloatTensor of size 2x2]

In [111]:

torch.mul(a,b)

Out[111]:

  2   8
  8  18
[torch.FloatTensor of size 2x2]

In [112]:

a.mul(b)

Out[112]:

  2   8
  8  18
[torch.FloatTensor of size 2x2]

How to perform mean operation?¶

In [118]:

numpy_arr = np.array([[1,2,3,4,5], [1,2,3,4,9]])
numpy_arr

Out[118]:

array([[1, 2, 3, 4, 5],
       [1, 2, 3, 4, 9]])

In [119]:

numpy_arr.mean()

Out[119]:

3.3999999999999999

In [120]:

numpy_arr.mean(axis=1)

Out[120]:

array([ 3. ,  3.8])

In [122]:

torch_ten = torch.Tensor([[1,2,3,4,5],[1,2,3,4,9]])
torch_ten

Out[122]:

 1  2  3  4  5
 1  2  3  4  9
[torch.FloatTensor of size 2x5]

In [123]:

torch_ten.mean()

Out[123]:

3.4

In [126]:

torch_ten.mean(dim=1)

Out[126]:

 3.0000
 3.8000
[torch.FloatTensor of size 2]

In [127]:

numpy_arr.shape

Out[127]:

(2, 5)

In [128]:

torch_ten.size()

Out[128]:

torch.Size([2, 5])