Loading deepchem/models/tensorgraph/graph_layers.py +18 −12 Original line number Diff line number Diff line Loading @@ -582,15 +582,17 @@ class DAGLayer(Layer): max_atoms: int, optional Maximum number of atoms in molecules. layer_sizes: list of int, optional(default=[100]) Structure of hidden layer(s) List of hidden layer size(s): length of this list represents the number of hidden layers, and each element is the width of corresponding hidden layer. init: str, optional Weight initialization for filters. activation: str, optional Activation function applied Activation function applied. dropout: float, optional Dropout probability, not supported here Dropout probability in hidden layer(s). batch_size: int, optional number of molecules in a batch number of molecules in a batch. """ super(DAGLayer, self).__init__(**kwargs) Loading Loading @@ -704,8 +706,9 @@ class DAGLayer(Layer): 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) training = kwargs['training'] if 'training' in kwargs else 1.0 if not self.dropout is None: outputs = tf.nn.dropout(outputs, 1.0 - self.dropout * training) return outputs def none_tensors(self): Loading Loading @@ -736,19 +739,21 @@ class DAGGather(Layer): Parameters ---------- n_graph_feat: int, optional Number of features for each atom Number of features for each atom. n_outputs: int, optional Number of features for each molecule. max_atoms: int, optional Maximum number of atoms in molecules. layer_sizes: list of int, optional Structure of hidden layer(s) List of hidden layer size(s): length of this list represents the number of hidden layers, and each element is the width of corresponding hidden layer. init: str, optional Weight initialization for filters. activation: str, optional Activation function applied Activation function applied. dropout: float, optional Dropout probability, not supported Dropout probability in the hidden layer(s). """ super(DAGGather, self).__init__(**kwargs) Loading Loading @@ -810,8 +815,9 @@ class DAGGather(Layer): 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) training = kwargs['training'] if 'training' in kwargs else 1.0 if not self.dropout is None: outputs = tf.nn.dropout(outputs, 1.0 - self.dropout * training) return outputs def none_tensors(self): Loading deepchem/models/tensorgraph/models/graph_models.py +11 −9 Original line number Diff line number Diff line Loading @@ -375,22 +375,24 @@ class DAGModel(TensorGraph): Parameters ---------- n_tasks: int Number of tasks Number of tasks. max_atoms: int, optional Maximum number of atoms in a molecule, should be defined based on dataset Maximum number of atoms in a molecule, should be defined based on dataset. n_atom_feat: int, optional Number of features per atom. n_graph_feat: int, optional Number of features for atom in the graph Number of features for atom in the graph. n_outputs: int, optional Number of features for each molecule Number of features for each molecule. layer_sizes: list of int, optional Structure of hidden layer(s) in DAG propagation step List of hidden layer size(s) in the propagation step: length of this list represents the number of hidden layers, and each element is the width of corresponding hidden layer. 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 List of hidden layer size(s) in the gather step. dropout: None or float, optional Dropout probability, applied after each propagation step and gather step. mode: str, optional Either "classification" or "regression" for type of model. """ self.n_tasks = n_tasks Loading Loading
deepchem/models/tensorgraph/graph_layers.py +18 −12 Original line number Diff line number Diff line Loading @@ -582,15 +582,17 @@ class DAGLayer(Layer): max_atoms: int, optional Maximum number of atoms in molecules. layer_sizes: list of int, optional(default=[100]) Structure of hidden layer(s) List of hidden layer size(s): length of this list represents the number of hidden layers, and each element is the width of corresponding hidden layer. init: str, optional Weight initialization for filters. activation: str, optional Activation function applied Activation function applied. dropout: float, optional Dropout probability, not supported here Dropout probability in hidden layer(s). batch_size: int, optional number of molecules in a batch number of molecules in a batch. """ super(DAGLayer, self).__init__(**kwargs) Loading Loading @@ -704,8 +706,9 @@ class DAGLayer(Layer): 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) training = kwargs['training'] if 'training' in kwargs else 1.0 if not self.dropout is None: outputs = tf.nn.dropout(outputs, 1.0 - self.dropout * training) return outputs def none_tensors(self): Loading Loading @@ -736,19 +739,21 @@ class DAGGather(Layer): Parameters ---------- n_graph_feat: int, optional Number of features for each atom Number of features for each atom. n_outputs: int, optional Number of features for each molecule. max_atoms: int, optional Maximum number of atoms in molecules. layer_sizes: list of int, optional Structure of hidden layer(s) List of hidden layer size(s): length of this list represents the number of hidden layers, and each element is the width of corresponding hidden layer. init: str, optional Weight initialization for filters. activation: str, optional Activation function applied Activation function applied. dropout: float, optional Dropout probability, not supported Dropout probability in the hidden layer(s). """ super(DAGGather, self).__init__(**kwargs) Loading Loading @@ -810,8 +815,9 @@ class DAGGather(Layer): 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) training = kwargs['training'] if 'training' in kwargs else 1.0 if not self.dropout is None: outputs = tf.nn.dropout(outputs, 1.0 - self.dropout * training) return outputs def none_tensors(self): Loading
deepchem/models/tensorgraph/models/graph_models.py +11 −9 Original line number Diff line number Diff line Loading @@ -375,22 +375,24 @@ class DAGModel(TensorGraph): Parameters ---------- n_tasks: int Number of tasks Number of tasks. max_atoms: int, optional Maximum number of atoms in a molecule, should be defined based on dataset Maximum number of atoms in a molecule, should be defined based on dataset. n_atom_feat: int, optional Number of features per atom. n_graph_feat: int, optional Number of features for atom in the graph Number of features for atom in the graph. n_outputs: int, optional Number of features for each molecule Number of features for each molecule. layer_sizes: list of int, optional Structure of hidden layer(s) in DAG propagation step List of hidden layer size(s) in the propagation step: length of this list represents the number of hidden layers, and each element is the width of corresponding hidden layer. 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 List of hidden layer size(s) in the gather step. dropout: None or float, optional Dropout probability, applied after each propagation step and gather step. mode: str, optional Either "classification" or "regression" for type of model. """ self.n_tasks = n_tasks Loading
