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Edits to make the script actually compile and achieve 99% on MNIST

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Anthony Wang 2021-08-25 21:01:46 -05:00
parent 60e12b8eca
commit 57eb6ecea7
1 changed files with 11 additions and 12 deletions
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23
mnist.py
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@ -6,6 +6,7 @@ from torchvision import datasets
from torchvision.transforms import ToTensor, Lambda, Compose
import matplotlib.pyplot as plt
training_data = datasets.MNIST(
root="data",
train=True,
@ -20,7 +21,7 @@ test_data = datasets.MNIST(
transform=ToTensor(),
)
batch_size = 64
batch_size = 100
train_loader = DataLoader(training_data, batch_size=batch_size)
test_loader = DataLoader(test_data, batch_size=batch_size)
@ -47,16 +48,15 @@ class CNN(nn.Module):
self.fc2 = nn.Linear(in_features=600, out_features=120)
self.fc3 = nn.Linear(in_features=120, out_features=10)
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.view(out.size(0), -1)
out = self.fc1(out)
out = self.drop(out)
out = self.fc2(out)
out = self.fc3(out)
return out
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.view(out.size(0), -1)
out = self.fc1(out)
out = self.drop(out)
out = self.fc2(out)
out = self.fc3(out)
return out
model = CNN()
@ -93,7 +93,6 @@ for epoch in range(num_epochs):
total = 0
correct = 0
for images, labels in test_loader:
images, labels = images.to(device), labels.to(device)
labels_list.append(labels)
test = Variable(images.view(batch_size, 1, 28, 28))