PyTorch 01. Basic PyTorch

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Rylah's Study & Daily Life

PyTorch 01. Basic PyTorch 본문

Study/Deep Learning

PyTorch 01. Basic PyTorch

Rylah 2022. 2. 11. 10:09

TorchVision, TorchSummary Install

View > Tool Windows > Terminal

pip install torchvision==0.1.8

 pip install torchsummary

import torch

# 입력 뉴런 : 5개, 출력 뉴런 : 3개
# 총 사용 시냅스 15개

hello = torch.nn.Linear(5, 3)

# 임의의 입력 벡터 2개 만들기
data = torch.randn(2, 5)

# 인공 신명망에 입력하고 결과를 출력
print(data)
print(hello(data))

# 필요한 패키지 가져오기
from torchvision.datasets import MNIST # TorchVision에서 MNIST 함수를 가져옴 (배치 설정 등)
import torchvision.transforms as transforms # Torchvision에서 Transform 함수를 수입
from torch.utils.data import DataLoader # Dataloader, 학습용 데이터를 한줄씩 가져올 때 사용
import torch.nn as nn # 인공신경망을 만드는 층 NN 패키지
from torchsummary import summary

# 데이터 변환 방식 지정
rules = transforms.Compose([ # DNN이 PyTorch로 만들어지기 때문에 Pytorch Tensor로 전환
    transforms.ToTensor(), # Numpy -> PyTorchTensor
])

# 학습용 데이터 로더
train_loader = DataLoader( # 6만개의 이미지, 한 배치 = 500개씩
    MNIST('mnist', train=True, download=True, transform=rules),
    batch_size=500, shuffle=True
)

# 평가용 데이터 로더
test_loader = DataLoader(
    MNIST('mnist', train=False, download=True, transform=rules),
    batch_size=500, shuffle=False
)


# 임의의 이미지 한 batch 가져오기
i = iter(train_loader)
images, labels = i.next()

print(images[0])
print(labels[0])

tensor([[-0.4553, -0.4855, -1.2358, -0.1158, -3.1778],
        [-0.9592,  0.4361,  0.6890,  0.6946,  0.4702]])
tensor([[-1.0712, -0.2554,  0.2080],
        [ 0.6585, -0.0785,  0.2994]], grad_fn=<AddmmBackward>)
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
Processing...
Done!
tensor([[[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
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          0.0000, 0.0000, 0.0000, 0.0000],
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tensor(9)

Process finished with exit code 0

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