def test(net: nn.Module, loss_fn: loss, x_test: np.array, y_test: np.array) -> Tuple[float, np.array]:
"""
Run the model on x_test and calculate the loss of the predictions.
The model run on evaluation mode and without updating the computational graph (no_grad)
"""
net.eval()
with torch.no_grad():
y_test_pred = net(x_test.float())
loss = loss_fn(input=y_test_pred.reshape(-1), target=y_test.float())
test_loss = loss.item()
return test_loss, y_test_pred
def np2tensor(x: np.array) -> torch.Tensor:
x = np.transpose(x, (2, 0, 1))
x = torch.from_numpy(x)
with torch.no_grad():
while x.dim() < 4:
x.unsqueeze_(0)
x = x.float() / 255
return x
def forward(self, x: np.array):
x = self.fc(x.float())
x = self.sigmoid(x)
return x
def __init__(self, features: np.array, labels: np.array):
assert features.shape[0] == labels.shape[0]
self.features = features.float()
self.labels = torch.Tensor(labels).long()
