def dnn_class(self):
     """Create DenseNet with classes fixture."""
     from vulcanai.models.dnn import DenseNet
     return DenseNet(name='Test_DenseNet_class',
                     in_dim=(200),
                     config={
                         'dense_units': [100, 50],
                         'dropouts': 0.3,
                     },
                     num_classes=3)
Exemple #2
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 def dnn_class(self, cnn_noclass):
     """Create dnn module prediction leaf node."""
     return DenseNet(
         name='Test_DenseNet_class',
         input_networks=cnn_noclass,
         config={
             'dense_units': [100, 50],
             'initializer': None,
             'bias_init': None,
             'norm': None,
             'dropout': 0.5,  # Single value or List
         },
         num_classes=3
     )
Exemple #3
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 def dnn_class(self):
     """Create DenseNet with no prediction layer."""
     return DenseNet(name='Test_DenseNet_class',
                     in_dim=(200),
                     activation=torch.nn.SELU(),
                     num_classes=10,
                     config={
                         'dense_units': [100],
                         'dropout': [0.3],
                     },
                     optim_spec={
                         'name': 'Adam',
                         'lr': 0.001
                     })
 def dnn_class_two(self):
     """Create DenseNet with no prediction layer."""
     from vulcanai.models.dnn import DenseNet
     return DenseNet(name='Test_DenseNet_class',
                     in_dim=(200),
                     activation=torch.nn.SELU(),
                     num_classes=2,
                     input_networks=None,
                     config={
                         'dense_units': [100],
                         'dropout': [0.3],
                         'initializer': None,
                         'bias_init': None,
                         'norm': None
                     },
                     optim_spec={
                         'name': 'Adam',
                         'lr': 0.001
                     })