from torch import nn
class OrigModel(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
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(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=1)
from torchplus import nn
class TorchPlusModel(nn.Module):
def __init__(self):
super().__init__()
conv1 = nn.Conv2d(1, 10, kernel_size=5) nn.MaxPool2d(2) nn.ReLU
conv2 = nn.Conv2d(10, 20, kernel_size=5) nn.Dropout2d() \
nn.MaxPool2d(2) nn.ReLU
fc1 = nn.Linear(320, 50) nn.ReLU nn.Dropout
fc2 = nn.Linear(50, 10) nn.LogSoftmax(1)
self.seq = conv1 conv2 nn.Flatten fc1 fc2
def forward(self, x):
return self.seq(x)