Loading deepchem/models/tf_new_models/graph_models.py +19 −16 Original line number Diff line number Diff line Loading @@ -26,7 +26,9 @@ class SequentialGraph(object): n_feat: int Number of features per atom. """ #self.graph_topology = GraphTopology(n_atoms, n_feat) self.graph = tf.Graph() self.session = tf.Session(graph=self.graph, config=config) with self.graph.as_default(): self.graph_topology = GraphTopology(n_feat) self.output = self.graph_topology.get_atom_features_placeholder() # Keep track of the layers Loading @@ -34,6 +36,7 @@ class SequentialGraph(object): def add(self, layer): """Adds a new layer to model.""" with self.graph.as_default(): # For graphical layers, add connectivity placeholders if type(layer).__name__ in ['GraphConv', 'GraphGather', 'GraphPool']: if (len(self.layers) > 0 and hasattr(self.layers[-1], "__name__")): Loading deepchem/models/tf_new_models/graph_topology.py +8 −12 Original line number Diff line number Diff line Loading @@ -52,22 +52,18 @@ class GraphTopology(object): self.max_deg = max_deg self.min_deg = min_deg self.atom_features_placeholder = Input( tensor=tf.placeholder( self.atom_features_placeholder = tensor=tf.placeholder( dtype='float32', shape=(None, self.n_feat), name=self.name+'_atom_features')) name=self.name+'_atom_features') self.deg_adj_lists_placeholders = [ Input(tensor=tf.placeholder( dtype='int32', shape=(None, deg), name=self.name+'_deg_adj'+str(deg))) tf.placeholder( dtype='int32', shape=(None, deg), name=self.name+'_deg_adj'+str(deg)) for deg in range(1, self.max_deg+1)] self.deg_slice_placeholder = Input( tensor=tf.placeholder( self.deg_slice_placeholder = tf.placeholder( dtype='int32', shape=(self.max_deg-self.min_deg+1,2), name="deg_slice",), name=self.name+'_deg_slice') self.membership_placeholder = Input( tensor=tf.placeholder(dtype='int32', shape=(None,), name="membership"), name=self.name+'_membership') self.membership_placeholder = tf.placeholder( dtype='int32', shape=(None,), name=self.name+'_membership') # Define the list of tensors to be used as topology self.topology = [self.deg_slice_placeholder, self.membership_placeholder] Loading Loading
deepchem/models/tf_new_models/graph_models.py +19 −16 Original line number Diff line number Diff line Loading @@ -26,7 +26,9 @@ class SequentialGraph(object): n_feat: int Number of features per atom. """ #self.graph_topology = GraphTopology(n_atoms, n_feat) self.graph = tf.Graph() self.session = tf.Session(graph=self.graph, config=config) with self.graph.as_default(): self.graph_topology = GraphTopology(n_feat) self.output = self.graph_topology.get_atom_features_placeholder() # Keep track of the layers Loading @@ -34,6 +36,7 @@ class SequentialGraph(object): def add(self, layer): """Adds a new layer to model.""" with self.graph.as_default(): # For graphical layers, add connectivity placeholders if type(layer).__name__ in ['GraphConv', 'GraphGather', 'GraphPool']: if (len(self.layers) > 0 and hasattr(self.layers[-1], "__name__")): Loading
deepchem/models/tf_new_models/graph_topology.py +8 −12 Original line number Diff line number Diff line Loading @@ -52,22 +52,18 @@ class GraphTopology(object): self.max_deg = max_deg self.min_deg = min_deg self.atom_features_placeholder = Input( tensor=tf.placeholder( self.atom_features_placeholder = tensor=tf.placeholder( dtype='float32', shape=(None, self.n_feat), name=self.name+'_atom_features')) name=self.name+'_atom_features') self.deg_adj_lists_placeholders = [ Input(tensor=tf.placeholder( dtype='int32', shape=(None, deg), name=self.name+'_deg_adj'+str(deg))) tf.placeholder( dtype='int32', shape=(None, deg), name=self.name+'_deg_adj'+str(deg)) for deg in range(1, self.max_deg+1)] self.deg_slice_placeholder = Input( tensor=tf.placeholder( self.deg_slice_placeholder = tf.placeholder( dtype='int32', shape=(self.max_deg-self.min_deg+1,2), name="deg_slice",), name=self.name+'_deg_slice') self.membership_placeholder = Input( tensor=tf.placeholder(dtype='int32', shape=(None,), name="membership"), name=self.name+'_membership') self.membership_placeholder = tf.placeholder( dtype='int32', shape=(None,), name=self.name+'_membership') # Define the list of tensors to be used as topology self.topology = [self.deg_slice_placeholder, self.membership_placeholder] Loading
