Lab 10 Convolutional Neural Network - Mnist CNN Model
18990 ワード
学習段階に深く入り込む.
CNN構造確認
CNNのMNISTに対する応用コード
# Lab 11 MNIST and Convolutional Neural Network
import torch
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import torch.nn.init
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# for reproducibility
torch.manual_seed(777)
if device == 'cuda':
torch.cuda.manual_seed_all(777)
# parameters
learning_rate = 0.001
training_epochs = 15
batch_size = 100
# MNIST dataset
mnist_train = dsets.MNIST(root='MNIST_data/',
train=True,
transform=transforms.ToTensor(),
download=True)
mnist_test = dsets.MNIST(root='MNIST_data/',
train=False,
transform=transforms.ToTensor(),
download=True)
# dataset loader
data_loader = torch.utils.data.DataLoader(dataset=mnist_train,
batch_size=batch_size,
shuffle=True,
drop_last=True)
# CNN Model (2 conv layers)
class CNN(torch.nn.Module):
def __init__(self):
super(CNN, self).__init__()
# L1 ImgIn shape=(?, 28, 28, 1)
# Conv -> (?, 28, 28, 32)
# Pool -> (?, 14, 14, 32)
self.layer1 = torch.nn.Sequential(
torch.nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(),
torch.nn.MaxPool2d(kernel_size=2, stride=2))
# L2 ImgIn shape=(?, 14, 14, 32)
# Conv ->(?, 14, 14, 64)
# Pool ->(?, 7, 7, 64)
self.layer2 = torch.nn.Sequential(
torch.nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(),
torch.nn.MaxPool2d(kernel_size=2, stride=2))
# Final FC 7x7x64 inputs -> 10 outputs
self.fc = torch.nn.Linear(7 * 7 * 64, 10, bias=True)
torch.nn.init.xavier_uniform_(self.fc.weight)
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.view(out.size(0), -1) # Flatten them for FC
out = self.fc(out)
return out
# instantiate CNN model
model = CNN().to(device)
# define cost/loss & optimizer
criterion = torch.nn.CrossEntropyLoss().to(device) # Softmax is internally computed.
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# train my model
total_batch = len(data_loader)
print('Learning started. It takes sometime.')
for epoch in range(training_epochs):
avg_cost = 0
for X, Y in data_loader:
# image is already size of (28x28), no reshape
# label is not one-hot encoded
X = X.to(device)
Y = Y.to(device)
optimizer.zero_grad()
hypothesis = model(X)
cost = criterion(hypothesis, Y)
cost.backward()
optimizer.step()
avg_cost += cost / total_batch
print('[Epoch: {:>4}] cost = {:>.9}'.format(epoch + 1, avg_cost))
print('Learning Finished!')
# Test model and check accuracy
with torch.no_grad():
X_test = mnist_test.test_data.view(len(mnist_test), 1, 28, 28).float().to(device)
Y_test = mnist_test.test_labels.to(device)
prediction = model(X_test)
correct_prediction = torch.argmax(prediction, 1) == Y_test
accuracy = correct_prediction.float().mean()
print('Accuracy:', accuracy.item())
Reference
この問題について(Lab 10 Convolutional Neural Network - Mnist CNN Model), 我々は、より多くの情報をここで見つけました https://velog.io/@uonmf97/Lab-10-Convolutional-Neural-Network-Mnist-CNN-Modelテキストは自由に共有またはコピーできます。ただし、このドキュメントのURLは参考URLとして残しておいてください。
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