normalize probabilities nn pytorch code example
Example 1: batchnorm1d pytorch
class network(nn.Module):
def __init__(self):
super(network, self).__init__()
self.linear1 = nn.Linear(in_features=40, out_features=320)
self.bn1 = nn.BatchNorm1d(num_features=320)
self.linear2 = nn.Linear(in_features=320, out_features=2)
def forward(self, input):
y = F.relu(self.bn1(self.linear1(input)))
y = F.softmax(self.linear2(y), dim=1)
return y
model = network()
x = torch.randn(10, 40)
output = model(x)
Example 2: dropout2d pytorch
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(F.dropout2d(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x)
x = F.log_softmax(self.fc2(x))
return x