pytorch学習1インストールと入門

インストール

condaとpipはすべてできて、pipは速く自分でダウンロードして、端末で入力してリンクすることができます:link.vscodeはデバッグでjypter notebookかipythonを使いました

入門(python参照)

torchとnumpy変換

import torch
import numpy as np
np_data = np.arange(6).reshape((2, 3))
torch_data = torch.from_numpy(np_data)
tensor2array = torch_data.numpy()
print(
    '
numpy array:', np_data,          # [[0 1 2], [3 4 5]]
    '
torch tensor:', torch_data,      #  0  1  2 
 3  4  5    [torch.LongTensor of size 2x3]
    '
tensor to array:', tensor2array, # [[0 1 2], [3 4 5]]
)

tensor
tensorはpytorchのデータ構造で、高次元配列1.5*3配列に似ています.

x=t.tensor(5,3)

2.ランダム2 D配列

x=t.rand(5,3)
print(x.size(),x.size()[0],x.size(1))#   

3.add

t.add(x,y)
x+y
result=t.Tensor(5,3)
t.add(x,y,out=result)
print(result)
y.add(x)
print(y,"y")
##inplace add,y   ，        tensor  
y.add_(x)
print(y,"y")

4.5行1行

##    2
x[:,2]
a=t.ones(5)
print(a)

5.numpyとtensor変換

import numpy as np
np_data = np.arange(6).reshape((2, 3))
torch_data = torch.from_numpy(np_data)
tensor2array = torch_data.numpy()
print(
    '
numpy array:', np_data,          # [[0 1 2], [3 4 5]]
    '
torch tensor:', torch_data,      #  0  1  2 
 3  4  5    [torch.LongTensor of size 2x3]
    '
tensor to array:', tensor2array, # [[0 1 2], [3 4 5]]
)

6.gpu

if t.cuda.is_available():    
    x=x.cuda()

7.others

# abs      
data = [-1, -2, 1, 2]
tensor = torch.FloatTensor(data)  #    32    tensor
print(
    '
abs',
    '
numpy: ', np.abs(data),          # [1 2 1 2]
    '
torch: ', torch.abs(tensor)      # [1 2 1 2]
)

# sin        sin
print(
    '
sin',
    '
numpy: ', np.sin(data),      # [-0.84147098 -0.90929743  0.84147098  0.90929743]
    '
torch: ', torch.sin(tensor)  # [-0.8415 -0.9093  0.8415  0.9093]
)

# mean    
print(
    '
mean',
    '
numpy: ', np.mean(data),         # 0.0
    '
torch: ', torch.mean(tensor)     # 0.0
)

# matrix multiplication
data = [[1,2], [3,4]]
tensor = torch.FloatTensor(data)  # 32-bit floating point
# correct method
print(
    '
matrix multiplication (matmul)',
    '
numpy: ', np.matmul(data, data),     # [[7, 10], [15, 22]]
    '
torch: ', torch.mm(tensor, tensor)   # [[7, 10], [15, 22]]
)
# incorrect method
data = np.array(data)
print(
    '
matrix multiplication (dot)',
    '
numpy: ', data.dot(data),        # [[7, 10], [15, 22]]
    '
torch: ', tensor.dot(tensor)     # this will convert tensor to [1,2,3,4], you'll get 30.0
)