layers.py 文件源码

def build(self, inputs_shape):

        # Import dimensions
        (max_atoms, max_degree, num_atom_features, num_bond_features,
         num_samples) = mol_shapes_to_dims(mol_shapes=inputs_shape)

        # Add the dense layer that contains the trainable parameters
        # Initialise dense layer with specified params (kwargs) and name
        inner_layer = self.create_inner_layer_fn()
        inner_layer_type = inner_layer.__class__.__name__.lower()
        inner_layer.name = self.name + '_inner_'+ inner_layer_type

        # Initialise TimeDistributed layer wrapper in order to parallelise
        #   dense layer across atoms
        inner_3D_layer_name = self.name + '_inner_timedistributed'
        self.inner_3D_layer = layers.TimeDistributed(inner_layer, name=inner_3D_layer_name)

        # Build the TimeDistributed layer (which will build the Dense layer)
        self.inner_3D_layer.build((None, max_atoms, num_atom_features+num_bond_features))

        # Store dense_3D_layer and it's weights
        self.trainable_weights = self.inner_3D_layer.trainable_weights