def apply(self, inputs, mask=None): inputs = masked.MaskedModule( inputs, features=self.NUM_FEATURES[0], kernel_size=(5, 5), wrapped_module=flax.nn.Conv, mask=mask['MaskedModule_0'] if mask is not None else None) return masked.MaskedModule( inputs, features=self.NUM_FEATURES[1], wrapped_module=flax.nn.Dense, mask=mask['MaskedModule_1'] if mask is not None else None)
def apply(self, inputs, mask=None): inputs = inputs.reshape(inputs.shape[0], -1) inputs = masked.MaskedModule( inputs, features=self.NUM_FEATURES[0], wrapped_module=flax.nn.Dense, mask=mask['MaskedModule_0'] if mask else None) return masked.MaskedModule( inputs, features=self.NUM_FEATURES[1], wrapped_module=flax.nn.Dense, mask=mask['MaskedModule_1'] if mask else None)
def apply(self, inputs, mask=None): return masked.MaskedModule( inputs, features=self.NUM_FEATURES, kernel_size=(3, 3), wrapped_module=flax.nn.Conv, mask=mask['MaskedModule_0'] if mask is not None else None)
def apply(self, inputs, mask = None): inputs = inputs.reshape(inputs.shape[0], -1) return masked.MaskedModule( inputs, features=self.NUM_FEATURES, wrapped_module=flax.deprecated.nn.Dense, mask=mask['MaskedModule_0'] if mask is not None else None)
def apply(self, inputs, mask=None): layer_mask = mask['MaskedModule_0'] if mask else None return masked.MaskedModule( inputs, features=self.NUM_FEATURES, wrapped_module=flax.nn.Conv, kernel_size=(3, 3), mask=layer_mask, kernel_init=flax.nn.initializers.kaiming_normal())
def apply(self, inputs, mask=None): inputs = inputs.reshape(inputs.shape[0], -1) layer_mask = mask['MaskedModule_0'] if mask else None return masked.MaskedModule( inputs, features=self.NUM_FEATURES, wrapped_module=flax.nn.Dense, mask=layer_mask, kernel_init=flax.nn.initializers.kaiming_normal())
def apply(self, inputs, mask=None): inputs = masked.MaskedModule( inputs, features=self.NUM_FEATURES[0], kernel_size=(5, 5), wrapped_module=flax.deprecated.nn.Conv, mask=mask['MaskedModule_0'] if mask is not None else None) inputs = masked.MaskedModule( inputs, features=self.NUM_FEATURES[1], kernel_size=(3, 3), wrapped_module=flax.deprecated.nn.Conv, mask=mask['MaskedModule_1'] if mask is not None else None) return masked.MaskedModule( inputs, features=self.NUM_FEATURES[2], kernel_size=inputs.shape[1:-1], wrapped_module=flax.deprecated.nn.Conv, mask=mask['MaskedModule_2'] if mask is not None else None)
def apply(self, inputs, *args, mask=None, **kwargs): layer_mask = mask['MaskedModule_0'] if mask else None return masked.MaskedModule( inputs, features=self.NUM_FEATURES, wrapped_module=flax.nn.Conv, kernel_size=(3, 3), mask=layer_mask, kernel_init=init.kaiming_sparse_normal( layer_mask['kernel'] if layer_mask is not None else None), **kwargs)
def apply(self, inputs, *args, mask=None, **kwargs): inputs = inputs.reshape(inputs.shape[0], -1) layer_mask = mask['MaskedModule_0'] if mask else None return masked.MaskedModule( inputs, features=self.NUM_FEATURES, wrapped_module=flax.nn.Dense, mask=layer_mask, kernel_init=init.kaiming_sparse_normal( layer_mask['kernel'] if layer_mask is not None else None), **kwargs)