)
self.max_pooling2 = nn.MaxPool2d(kernel_size=2, stride=2)
# 2*2
# 2*2
self.fc = nn.Linear(24*2*2, 2)
def forward(self, x):
batchsize = x.size(0)
out = self.conv1(x)
out = self.max_pooling1(out)
out = self.conv2_1(out)
out = self.conv2_2(out)
out = self.max_pooling2(out)
out = out.view(batchsize, -1)
out = self.fc(out)
out = F.log_softmax(out, dim=1)
return out
class TrainingDataSet(Dataset):
def __init__(self):
super(TrainingDataSet, self).__init__()
self.data_dict_X = X_train
self.data_dict_y = y_train
def __getitem__(self, index):
t = self.data_dict_X[index, 0:36]
t = torch.tensor(t).view(6, 6)
return t, self.data_dict_y[index]
def __len__(self):
return len(self.data_dict_y)
class TestDataSet(Dataset):
def __init__(self):
super(TestDataSet, self).__init__()
self.data_dict_X = X_validate
self.data_dict_y = y_validate
def __getitem__(self, index):
t = self.data_dict_X[index, 0:36]
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