Pytorch replaces some components in numpy. / / please indicate the source of the reprint

numpy It can process two kinds of data at the same time ：list and numpy.ndarray.

and torch Can only deal with tensors (Tensor) data .

---

1. Replace np.asarray()

import torch

data=[[1,2],[3,4]]

data = torch.Tensor(data)

# data = np.asarray(data)

print(data.shape)

2. Replace nn.prod()

# Use torch.prod() Replace nn.prod(), among torch.prod() The parameters inside must be tensor.

import torch

data=[1,2]

data = torch.Tensor(data)

print(data.shape)

c = torch.prod(data)

c

---

tensor(2.)

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3. Replace np.max()

# Use torch.max() Replace np.max(), among torch.max() The parameters inside must be tensor.

import torch

data=[1,2,3,9,4]

data = torch.Tensor(data)

a = torch.max(data)

a

---

tensor(9.)

4.torch.Size Convert to np.arrary

import torch

# data = np.asarray(data)

data=[[1,2],[3,4]]

data = torch.Tensor(data)

print(data.shape)

print(np.asarray(data.shape))

c = torch.prod(torch.Tensor(np.asarray(data.shape)))

c

---

torch.Size([2, 2])

[2 2]

tensor(4.)

5.numpy And torch Of ravel, They are used in the same way , Be careful tensor.ravel() The data after is still Tensor

import torch

# data = np.asarray(data)

data=[[1,2],[3,4]]

data = torch.Tensor(data)

data = data.ravel()

print("ravel:",data)

---

ravel: tensor([1., 2., 3., 4.])

6.numpy And torch Of ndim, They are used in the same way

import torch

data=[[1,2],[3,4]]

data_numpy = np.asarray(data).ravel()

data = torch.Tensor(data)

data = data.ravel()

print("ravel:",data)

print(data.shape)

print("tensor_ndim:",data.ndim)

print("data_numpy_ndim:",data_numpy.ndim)

---

ravel: tensor([1., 2., 3., 4.])

torch.Size([4])

tensor_ndim: 1

data_numpy_ndim: 1

 7.numpy And torch.tensor Of data.T The effect is the same

import torch

data=[[1,2],[3,4]]

data_numpy = np.asarray(data)

data = torch.Tensor(data)

# data = data.ravel()

data_trans_tensor=data.T

print("data:",data)

print("data_trans:",data_trans_tensor)

data_trans_numpy=data_numpy.T

print("data_numpy:",data_numpy)

print("data_trans_numpy:",data_trans_numpy)

---

data: tensor([[1., 2.], [3., 4.]])

data_trans: tensor([[1., 3.], [2., 4.]])

data_numpy: [[1 2] [3 4]]

data_trans_numpy: [[1 3] [2 4]]

8.np.arange() And torch.arange() Use the same

timesteps=5

a = np.arange(timesteps)

b = torch.arange(timesteps)

print("a:",a)

print("b:",b)

---

a: [0 1 2 3 4]

b: tensor([0, 1, 2, 3, 4])

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9.np.ceil() And torch.ceil() Use the same , but torch.ceil() The input of must be tensor.

a=[0.1,1.1,2.1,3.1]

b=np.ceil(a)

c=torch.ceil(torch.Tensor(a))

print("b:",b)

print("c:",c)

---

b: [1. 2. 3. 4.]

c: tensor([1., 2., 3., 4.])

10.torch.sort() Replace np.sort(),torch.sort() The index corresponding to the original sequence will also be returned

import numpy as np

import torch

a=[5,1.1,2.1,3.1]

b=np.sort(a)

value,indices=torch.sort(torch.Tensor(a))

print("b:",b)

print("c:",value,indices)

---

b: [1.1 2.1 3.1 5. ]

c: tensor([1.1000, 2.1000, 3.1000, 5.0000])  tensor([1, 2, 3, 0])

np.sort() Function function _wx5ba0c87f1984b Technology blog _51CTO Blog

torch.sort()_James_Bobo The blog of -CSDN Blog _torch.sort()

11.torch.abs() Replace np.abs()

data = [-1 , -2 , 1 , 2]

tensor = torch.FloatTensor(data)

print(

"abs in numpy:\n",np.abs(data),

"\nabs in torch:\n",torch.abs(tensor)

)

---

abs in numpy: [1 2 1 2] abs in torch: tensor([1., 2., 1., 2.])