def test_text_cnn():
model = torch.nn.Sequential(
torchlayers.Conv(64), # specify ONLY out_channels
torch.nn.ReLU(), # use torch.nn wherever you wish
torchlayers.BatchNorm(), # BatchNormNd inferred from input
torchlayers.Conv(128), # Default kernel_size equal to 3
torchlayers.ReLU(),
torchlayers.Conv(256, kernel_size=11), # "same" padding as default
torchlayers.GlobalMaxPool(), # Known from Keras
torchlayers.Linear(10), # Output for 10 classes
)
torchlayers.build(model, torch.randn(2, 300, 1))
def model():
return torchlayers.Sequential(
torchlayers.Conv(64),
torchlayers.BatchNorm(),
torchlayers.ReLU(),
torchlayers.Conv(128),
torchlayers.BatchNorm(),
torchlayers.ReLU(),
torchlayers.Conv(256),
torchlayers.GlobalMaxPool(),
torchlayers.Linear(64),
torchlayers.BatchNorm(),
torchlayers.Linear(10),
)
def model():
return tl.Sequential(
tl.Conv(64),
tl.BatchNorm(),
tl.ReLU(),
tl.Conv(128),
tl.BatchNorm(),
tl.ReLU(),
tl.Conv(256),
tl.GlobalMaxPool(),
tl.Linear(64),
tl.BatchNorm(),
tl.Linear(10),
)
def create_bottleneck(self, labels, tasks, linear_cls):
return torch.nn.Sequential(torchlayers.GlobalMaxPool(),
linear_cls(labels * tasks))