Hello,
I’d like to know why my validation and training accuracy are so low; in fact, they’re the same. I am working on torchvisions MNIST dataset.
Accuracy train: 0.10; Accuracy val: 0.10
Here is the model:
class Net(nn.Module):
"""A representation of a convolutional neural network comprised of VGG blocks."""
def __init__(self, n_channels):
super(Net, self).__init__()
# VGG block 1
self.conv1 = nn.Conv2d(n_channels, 64, (3,3))
self.act1 = nn.ReLU()
self.pool1 = nn.MaxPool2d((2,2), stride=(2,2))
self.dropout = nn.Dropout(0.2)
# VGG block 2
self.conv2 = nn.Conv2d(64, 64, (3,3))
self.act2 = nn.ReLU()
self.pool2 = nn.MaxPool2d((2,2), stride=(2,2))
self.dropout2 = nn.Dropout(0.2)
# VGG block 3
self.conv3 = nn.Conv2d(64, 128, (3,3))
self.act3 = nn.ReLU()
self.pool3 = nn.MaxPool2d((2,2), stride=(2,2))
self.dropout3 = nn.Dropout(0.2)
# Fully connected layer
self.f1 = nn.Linear(128 * 1 * 1, 1000)
self.dropout4 = nn.Dropout(0.5)
self.act4 = nn.ReLU()
# Output layer
self.f2 = nn.Linear(1000, 10)
self.act5 = nn.Softmax(dim=1)
def forward(self, X):
"""This function forward propagates the input."""
# VGG block 1
X = self.conv1(X)
X = self.act1(X)
X = self.pool1(X)
X = self.dropout(X)
# VGG block 2
X = self.conv2(X)
X = self.act2(X)
X = self.pool2(X)
X = self.dropout2(X)
# VGG block 3
X = self.conv3(X)
X = self.act3(X)
X = self.pool3(X)
X = self.dropout3(X)
# Flatten
X = X.view(-1, 128)
# Fully connected layer
X = self.f1(X)
X = self.act4(X)
X = self.dropout4(X)
# Output layer
X = self.f2(X)
X = self.act5(X)
return X
and here is how I am computing accuracy:
def validate(model, train_loader, val_loader):
for name, loader in [("train", train_loader), ("val", val_loader)]:
correct = 0
total = 0
with torch.no_grad():
for imgs, labels in loader:
outputs = model(imgs)
_, predicted = torch.max(outputs, dim=1)
total += labels.shape[0]
correct += int((predicted == labels).sum())
print("Accuracy {}: {:.2f}".format(name, correct / total))
