Loading deepchem/models/tensorgraph/graph_layers.py +11 −5 Original line number Diff line number Diff line Loading @@ -581,7 +581,7 @@ class DAGLayer(Layer): Number of features listed per atom. max_atoms: int, optional Maximum number of atoms in molecules. layer_sizes: list of int, optional(default=[1000]) layer_sizes: list of int, optional(default=[100]) Structure of hidden layer(s) init: str, optional Weight initialization for filters. Loading Loading @@ -683,7 +683,8 @@ class DAGLayer(Layer): # DAGgraph_step maps from batch_inputs to a batch of graph_features # of shape: (batch_size*max_atoms) * n_graph_features # representing the graph features of target atoms in each graph batch_outputs = self.DAGgraph_step(batch_inputs, self.W_list, self.b_list) batch_outputs = self.DAGgraph_step(batch_inputs, self.W_list, self.b_list, **kwargs) # index for targe atoms target_index = tf.stack([tf.range(n_atoms), parents[:, count, 0]], axis=1) Loading @@ -698,11 +699,13 @@ class DAGLayer(Layer): self.out_tensor = out_tensor return out_tensor def DAGgraph_step(self, batch_inputs, W_list, b_list): def DAGgraph_step(self, batch_inputs, W_list, b_list, **kwargs): outputs = batch_inputs for idw, W in enumerate(W_list): outputs = tf.nn.xw_plus_b(outputs, W, b_list[idw]) outputs = self.activation(outputs) if 'training' in kwargs and kwargs['training'] == 1.0 and not self.dropout is None: outputs = tf.nn.dropout(outputs, 1.0 - self.dropout) return outputs def none_tensors(self): Loading Loading @@ -794,18 +797,21 @@ class DAGGather(Layer): # Extract atom_features graph_features = tf.segment_sum(atom_features, membership) # sum all graph outputs outputs = self.DAGgraph_step(graph_features, self.W_list, self.b_list) outputs = self.DAGgraph_step(graph_features, self.W_list, self.b_list, **kwargs) out_tensor = outputs if set_tensors: self.variables = self.trainable_weights self.out_tensor = out_tensor return out_tensor def DAGgraph_step(self, batch_inputs, W_list, b_list): def DAGgraph_step(self, batch_inputs, W_list, b_list, **kwargs): outputs = batch_inputs for idw, W in enumerate(W_list): outputs = tf.nn.xw_plus_b(outputs, W, b_list[idw]) outputs = self.activation(outputs) if 'training' in kwargs and kwargs['training'] == 1.0 and not self.dropout is None: outputs = tf.nn.dropout(outputs, 1.0 - self.dropout) return outputs def none_tensors(self): Loading deepchem/models/tensorgraph/models/graph_models.py +16 −0 Original line number Diff line number Diff line Loading @@ -366,6 +366,9 @@ class DAGModel(TensorGraph): n_atom_feat=75, n_graph_feat=30, n_outputs=30, layer_sizes=[100], layer_sizes_gather=[100], dropout=None, mode="classification", **kwargs): """ Loading @@ -381,6 +384,12 @@ class DAGModel(TensorGraph): Number of features for atom in the graph n_outputs: int, optional Number of features for each molecule layer_sizes: list of int, optional Structure of hidden layer(s) in DAG propagation step layer_sizes_gather: list of int, optional Structure of hidden layer(s) in DAG gather step dropout: None or float Dropout probability, applied after each propagation step and gather step mode: str Either "classification" or "regression" for type of model. """ Loading @@ -389,6 +398,9 @@ class DAGModel(TensorGraph): self.n_atom_feat = n_atom_feat self.n_graph_feat = n_graph_feat self.n_outputs = n_outputs self.layer_sizes = layer_sizes self.layer_sizes_gather = layer_sizes_gather self.dropout = dropout self.mode = mode super(DAGModel, self).__init__(**kwargs) self.build_graph() Loading @@ -410,6 +422,8 @@ class DAGModel(TensorGraph): n_graph_feat=self.n_graph_feat, n_atom_feat=self.n_atom_feat, max_atoms=self.max_atoms, layer_sizes=self.layer_sizes, dropout=self.dropout, batch_size=self.batch_size, in_layers=[ self.atom_features, self.parents, self.calculation_orders, Loading @@ -419,6 +433,8 @@ class DAGModel(TensorGraph): n_graph_feat=self.n_graph_feat, n_outputs=self.n_outputs, max_atoms=self.max_atoms, layer_sizes=self.layer_sizes_gather, dropout=self.dropout, in_layers=[dag_layer1, self.membership]) costs = [] Loading Loading
deepchem/models/tensorgraph/graph_layers.py +11 −5 Original line number Diff line number Diff line Loading @@ -581,7 +581,7 @@ class DAGLayer(Layer): Number of features listed per atom. max_atoms: int, optional Maximum number of atoms in molecules. layer_sizes: list of int, optional(default=[1000]) layer_sizes: list of int, optional(default=[100]) Structure of hidden layer(s) init: str, optional Weight initialization for filters. Loading Loading @@ -683,7 +683,8 @@ class DAGLayer(Layer): # DAGgraph_step maps from batch_inputs to a batch of graph_features # of shape: (batch_size*max_atoms) * n_graph_features # representing the graph features of target atoms in each graph batch_outputs = self.DAGgraph_step(batch_inputs, self.W_list, self.b_list) batch_outputs = self.DAGgraph_step(batch_inputs, self.W_list, self.b_list, **kwargs) # index for targe atoms target_index = tf.stack([tf.range(n_atoms), parents[:, count, 0]], axis=1) Loading @@ -698,11 +699,13 @@ class DAGLayer(Layer): self.out_tensor = out_tensor return out_tensor def DAGgraph_step(self, batch_inputs, W_list, b_list): def DAGgraph_step(self, batch_inputs, W_list, b_list, **kwargs): outputs = batch_inputs for idw, W in enumerate(W_list): outputs = tf.nn.xw_plus_b(outputs, W, b_list[idw]) outputs = self.activation(outputs) if 'training' in kwargs and kwargs['training'] == 1.0 and not self.dropout is None: outputs = tf.nn.dropout(outputs, 1.0 - self.dropout) return outputs def none_tensors(self): Loading Loading @@ -794,18 +797,21 @@ class DAGGather(Layer): # Extract atom_features graph_features = tf.segment_sum(atom_features, membership) # sum all graph outputs outputs = self.DAGgraph_step(graph_features, self.W_list, self.b_list) outputs = self.DAGgraph_step(graph_features, self.W_list, self.b_list, **kwargs) out_tensor = outputs if set_tensors: self.variables = self.trainable_weights self.out_tensor = out_tensor return out_tensor def DAGgraph_step(self, batch_inputs, W_list, b_list): def DAGgraph_step(self, batch_inputs, W_list, b_list, **kwargs): outputs = batch_inputs for idw, W in enumerate(W_list): outputs = tf.nn.xw_plus_b(outputs, W, b_list[idw]) outputs = self.activation(outputs) if 'training' in kwargs and kwargs['training'] == 1.0 and not self.dropout is None: outputs = tf.nn.dropout(outputs, 1.0 - self.dropout) return outputs def none_tensors(self): Loading
deepchem/models/tensorgraph/models/graph_models.py +16 −0 Original line number Diff line number Diff line Loading @@ -366,6 +366,9 @@ class DAGModel(TensorGraph): n_atom_feat=75, n_graph_feat=30, n_outputs=30, layer_sizes=[100], layer_sizes_gather=[100], dropout=None, mode="classification", **kwargs): """ Loading @@ -381,6 +384,12 @@ class DAGModel(TensorGraph): Number of features for atom in the graph n_outputs: int, optional Number of features for each molecule layer_sizes: list of int, optional Structure of hidden layer(s) in DAG propagation step layer_sizes_gather: list of int, optional Structure of hidden layer(s) in DAG gather step dropout: None or float Dropout probability, applied after each propagation step and gather step mode: str Either "classification" or "regression" for type of model. """ Loading @@ -389,6 +398,9 @@ class DAGModel(TensorGraph): self.n_atom_feat = n_atom_feat self.n_graph_feat = n_graph_feat self.n_outputs = n_outputs self.layer_sizes = layer_sizes self.layer_sizes_gather = layer_sizes_gather self.dropout = dropout self.mode = mode super(DAGModel, self).__init__(**kwargs) self.build_graph() Loading @@ -410,6 +422,8 @@ class DAGModel(TensorGraph): n_graph_feat=self.n_graph_feat, n_atom_feat=self.n_atom_feat, max_atoms=self.max_atoms, layer_sizes=self.layer_sizes, dropout=self.dropout, batch_size=self.batch_size, in_layers=[ self.atom_features, self.parents, self.calculation_orders, Loading @@ -419,6 +433,8 @@ class DAGModel(TensorGraph): n_graph_feat=self.n_graph_feat, n_outputs=self.n_outputs, max_atoms=self.max_atoms, layer_sizes=self.layer_sizes_gather, dropout=self.dropout, in_layers=[dag_layer1, self.membership]) costs = [] Loading
