Loading deepchem/models/tf_new_models/graph_models.py +32 −2 Original line number Diff line number Diff line Loading @@ -11,7 +11,7 @@ __license__ = "MIT" import tensorflow as tf from deepchem.nn.layers import GraphGather from deepchem.models.tf_new_models.graph_topology import GraphTopology, DTNNGraphTopology, DAGGraphTopology, WeaveGraphTopology from deepchem.models.tf_new_models.graph_topology import GraphTopology, DTNNGraphTopology, DAGGraphTopology, WeaveGraphTopology, WeaveGraphTopology_v2 class SequentialGraph(object): Loading Loading @@ -195,6 +195,36 @@ class SequentialWeaveGraph(SequentialGraph): self.output = layer(self.output) self.layers.append(layer) class SequentialWeaveGraph_v2(SequentialGraph): """SequentialGraph for Weave models """ def __init__(self, batch_size, max_atoms=50, n_atom_feat=75, n_pair_feat=14): self.graph = tf.Graph() self.batch_size = batch_size self.max_atoms = max_atoms self.n_atom_feat = n_atom_feat self.n_pair_feat = n_pair_feat with self.graph.as_default(): self.graph_topology = WeaveGraphTopology_v2(self.batch_size, self.max_atoms, self.n_atom_feat,self.n_pair_feat) self.output = self.graph_topology.get_atom_features_placeholder() self.output_P = self.graph_topology.get_pair_features_placeholder() self.layers = [] def add(self, layer): """Adds a new layer to model.""" with self.graph.as_default(): if type(layer).__name__ in ['WeaveLayer_v2']: self.output, self.output_P = layer([ self.output, self.output_P ] + self.graph_topology.get_topology_placeholders()) elif type(layer).__name__ in ['WeaveGather_v2']: self.output = layer( [self.output, self.graph_topology.atom_split_placeholder]) else: self.output = layer(self.output) self.layers.append(layer) class SequentialSupportGraph(object): Loading deepchem/models/tf_new_models/graph_topology.py +111 −0 Original line number Diff line number Diff line Loading @@ -492,3 +492,114 @@ class WeaveGraphTopology(GraphTopology): self.membership_placeholder: membership } return dict_DTNN class WeaveGraphTopology_v2(GraphTopology): """Manages placeholders associated with batch of graphs and their topology""" def __init__(self, batch_size, max_atoms, n_atom_feat, n_pair_feat, name='Weave_topology'): """ Parameters ---------- max_atoms: int maximum number of atoms in a molecule n_atom_feat: int number of basic features of each atom n_pair_feat: int number of basic features of each pair """ #self.n_atoms = n_atoms self.name = name self.batch_size = batch_size self.max_atoms = max_atoms * batch_size self.n_atom_feat = n_atom_feat self.n_pair_feat = n_pair_feat self.atom_features_placeholder = tf.placeholder( dtype='float32', shape=(None, self.n_atom_feat), name=self.name + '_atom_features') self.pair_features_placeholder = tf.placeholder( dtype='float32', shape=(None, self.n_pair_feat), name=self.name + '_pair_features') self.pair_split_placeholder = tf.placeholder( dtype='int32', shape=(self.max_atoms,), name=self.name + '_pair_split') self.pair_membership_placeholder = tf.placeholder( dtype='bool', shape=(self.max_atoms,), name=self.name + '_pair_membership') self.atom_split_placeholder = tf.placeholder( dtype='int32', shape=(self.batch_size,), name=self.name + '_atom_split') self.atom_to_pair_placeholder = tf.placeholder( dtype='int32', shape=(None,2), name=self.name + '_atom_to_pair') # Define the list of tensors to be used as topology self.topology = [self.pair_split_placeholder, self.pair_membership_placeholder, self.atom_split_placeholder, self.atom_to_pair_placeholder] self.inputs = [self.atom_features_placeholder] self.inputs += self.topology def get_pair_features_placeholder(self): return self.pair_features_placeholder def batch_to_feed_dict(self, batch): """Converts the current batch of WeaveMol into tensorflow feed_dict. Assigns the atom features and pair features to the placeholders tensors params ------ batch : np.ndarray Array of WeaveMol returns ------- feed_dict : dict Can be merged with other feed_dicts for input into tensorflow """ # Extract atom numbers atom_feat = [] pair_feat = [] atom_split = [] atom_to_pair = [] pair_split = [] max_atoms = self.max_atoms start = 0 for im, mol in enumerate(batch): n_atoms = mol.get_num_atoms() # number of atoms in each molecule atom_split.append(n_atoms) # index of pair features C0, C1 = np.meshgrid(np.arange(n_atoms), np.arange(n_atoms)) atom_to_pair.append(np.transpose(np.array([C1.flatten()+start, C0.flatten()+start]))) start = start + n_atoms # number of pairs for each atom pair_split.extend([n_atoms]*n_atoms) # atom features atom_feat.append(mol.get_atom_features()) # pair features pair_feat.append(np.reshape(mol.get_pair_features(), (n_atoms*n_atoms, self.n_pair_feat))) atom_feat = np.concatenate(atom_feat, axis=0) pair_feat = np.concatenate(pair_feat, axis=0) atom_to_pair = np.concatenate(atom_to_pair, axis=0) atom_split = np.array(atom_split) n_pair = len(pair_split) pair_split = np.pad(pair_split, ((0, max_atoms-n_pair)), 'constant') pair_membership = np.array([True]*n_pair + [False]*(max_atoms-n_pair)) # Generate dicts dict_DTNN = { self.atom_features_placeholder: atom_feat, self.pair_features_placeholder: pair_feat, self.pair_split_placeholder: pair_split, self.pair_membership_placeholder: pair_membership, self.atom_split_placeholder: atom_split, self.atom_to_pair_placeholder: atom_to_pair } return dict_DTNN No newline at end of file deepchem/molnet/preset_hyper_parameters.py +1 −1 Original line number Diff line number Diff line Loading @@ -68,7 +68,7 @@ hps['dag'] = { hps['weave'] = { 'batch_size': 64, 'nb_epoch': 40, 'learning_rate': 0.001, 'learning_rate': 0.0001, 'n_graph_feat': 128, 'n_pair_feat': 14, 'seed': 123 Loading deepchem/nn/__init__.py +4 −0 Original line number Diff line number Diff line Loading @@ -21,8 +21,10 @@ from deepchem.nn.layers import DAGLayer from deepchem.nn.layers import DAGGather from deepchem.nn.weave_layers import WeaveLayer from deepchem.nn.weave_layers import WeaveLayer_v2 from deepchem.nn.weave_layers import WeaveConcat from deepchem.nn.weave_layers import WeaveGather from deepchem.nn.weave_layers import WeaveGather_v2 from deepchem.nn.model_ops import weight_decay from deepchem.nn.model_ops import optimizer Loading @@ -36,8 +38,10 @@ from deepchem.models.tf_new_models.graph_topology import GraphTopology from deepchem.models.tf_new_models.graph_topology import DTNNGraphTopology from deepchem.models.tf_new_models.graph_topology import DAGGraphTopology from deepchem.models.tf_new_models.graph_topology import WeaveGraphTopology from deepchem.models.tf_new_models.graph_topology import WeaveGraphTopology_v2 from deepchem.models.tf_new_models.graph_models import SequentialGraph from deepchem.models.tf_new_models.graph_models import SequentialDTNNGraph from deepchem.models.tf_new_models.graph_models import SequentialDAGGraph from deepchem.models.tf_new_models.graph_models import SequentialWeaveGraph from deepchem.models.tf_new_models.graph_models import SequentialWeaveGraph_v2 from deepchem.models.tf_new_models.graph_models import SequentialSupportGraph deepchem/nn/weave_layers.py +97 −1 Original line number Diff line number Diff line Loading @@ -175,6 +175,66 @@ class WeaveLayer(Layer): return A, P class WeaveLayer_v2(WeaveLayer): def call(self, x, mask=None): """Execute this layer on input tensors. x = [atom_features, pair_features, pair_split, pair_membership, atom_split] Parameters ---------- x: list list of Tensors of form described above. mask: bool, optional Ignored. Present only to shadow superclass call() method. Returns ------- A: Tensor Tensor of atom_features P: Tensor Tensor of pair_features """ # Add trainable weights self.build() atom_features = x[0] pair_features = x[1] pair_split = x[2] pair_membership = x[3] atom_split = x[4] atom_to_pair = x[5] AA = tf.matmul(atom_features, self.W_AA) + self.b_AA AA = self.activation(AA) PA = tf.matmul(pair_features, self.W_PA) + self.b_PA PA = self.activation(PA) PAs = tf.split(PA, pair_split, axis=0) PA = [tf.reduce_sum(molecule, 0) for molecule in PAs] PA = tf.boolean_mask(PA, pair_membership) A = tf.matmul(tf.concat([AA, PA], 1), self.W_A) + self.b_A A = self.activation(A) if self.update_pair: AP_ij = tf.matmul(tf.reshape(tf.gather(atom_features, atom_to_pair), [-1, 2*self.n_atom_input_feat]), self.W_AP) + self.b_AP AP_ij = self.activation(AP_ij) AP_ji = tf.matmul(tf.reshape(tf.gather(atom_features, tf.reverse(atom_to_pair, [1])), [-1, 2*self.n_atom_input_feat]), self.W_AP) + self.b_AP AP_ji = self.activation(AP_ji) PP = tf.matmul(pair_features, self.W_PP) + self.b_PP PP = self.activation(PP) P = tf.matmul(tf.concat([AP_ij + AP_ji, PP], 1), self.W_P) + self.b_P P = self.activation(P) else: P = pair_features return A, P class WeaveConcat(Layer): """" Concat a batch of molecules into a batch of atoms """ Loading Loading @@ -342,3 +402,39 @@ class WeaveGather(Layer): outputs = outputs / tf.reduce_sum(outputs, axis=2, keep_dims=True) outputs = tf.reshape(outputs, [-1, self.n_input * 11]) return outputs class WeaveGather_v2(WeaveGather): def call(self, x, mask=None): """Execute this layer on input tensors. x = [atom_features, atom_split] Parameters ---------- x: list Tensors as listed above mask: bool, optional Ignored. Present only to shadow superclass call() method. Returns ------- outputs: Tensor Tensor of molecular features """ # Add trainable weights self.build() outputs = x[0] atom_split = x[1] if self.gaussian_expand: outputs = self.gaussian_histogram(outputs) outputs = tf.split(outputs, atom_split, axis=0) output_molecules = [tf.reduce_sum(molecule, 0) for molecule in outputs] output_molecules = tf.stack(output_molecules) if self.gaussian_expand: output_molecules = tf.matmul(output_molecules, self.W) + self.b output_molecules = self.activation(output_molecules) return output_molecules No newline at end of file Loading
deepchem/models/tf_new_models/graph_models.py +32 −2 Original line number Diff line number Diff line Loading @@ -11,7 +11,7 @@ __license__ = "MIT" import tensorflow as tf from deepchem.nn.layers import GraphGather from deepchem.models.tf_new_models.graph_topology import GraphTopology, DTNNGraphTopology, DAGGraphTopology, WeaveGraphTopology from deepchem.models.tf_new_models.graph_topology import GraphTopology, DTNNGraphTopology, DAGGraphTopology, WeaveGraphTopology, WeaveGraphTopology_v2 class SequentialGraph(object): Loading Loading @@ -195,6 +195,36 @@ class SequentialWeaveGraph(SequentialGraph): self.output = layer(self.output) self.layers.append(layer) class SequentialWeaveGraph_v2(SequentialGraph): """SequentialGraph for Weave models """ def __init__(self, batch_size, max_atoms=50, n_atom_feat=75, n_pair_feat=14): self.graph = tf.Graph() self.batch_size = batch_size self.max_atoms = max_atoms self.n_atom_feat = n_atom_feat self.n_pair_feat = n_pair_feat with self.graph.as_default(): self.graph_topology = WeaveGraphTopology_v2(self.batch_size, self.max_atoms, self.n_atom_feat,self.n_pair_feat) self.output = self.graph_topology.get_atom_features_placeholder() self.output_P = self.graph_topology.get_pair_features_placeholder() self.layers = [] def add(self, layer): """Adds a new layer to model.""" with self.graph.as_default(): if type(layer).__name__ in ['WeaveLayer_v2']: self.output, self.output_P = layer([ self.output, self.output_P ] + self.graph_topology.get_topology_placeholders()) elif type(layer).__name__ in ['WeaveGather_v2']: self.output = layer( [self.output, self.graph_topology.atom_split_placeholder]) else: self.output = layer(self.output) self.layers.append(layer) class SequentialSupportGraph(object): Loading
deepchem/models/tf_new_models/graph_topology.py +111 −0 Original line number Diff line number Diff line Loading @@ -492,3 +492,114 @@ class WeaveGraphTopology(GraphTopology): self.membership_placeholder: membership } return dict_DTNN class WeaveGraphTopology_v2(GraphTopology): """Manages placeholders associated with batch of graphs and their topology""" def __init__(self, batch_size, max_atoms, n_atom_feat, n_pair_feat, name='Weave_topology'): """ Parameters ---------- max_atoms: int maximum number of atoms in a molecule n_atom_feat: int number of basic features of each atom n_pair_feat: int number of basic features of each pair """ #self.n_atoms = n_atoms self.name = name self.batch_size = batch_size self.max_atoms = max_atoms * batch_size self.n_atom_feat = n_atom_feat self.n_pair_feat = n_pair_feat self.atom_features_placeholder = tf.placeholder( dtype='float32', shape=(None, self.n_atom_feat), name=self.name + '_atom_features') self.pair_features_placeholder = tf.placeholder( dtype='float32', shape=(None, self.n_pair_feat), name=self.name + '_pair_features') self.pair_split_placeholder = tf.placeholder( dtype='int32', shape=(self.max_atoms,), name=self.name + '_pair_split') self.pair_membership_placeholder = tf.placeholder( dtype='bool', shape=(self.max_atoms,), name=self.name + '_pair_membership') self.atom_split_placeholder = tf.placeholder( dtype='int32', shape=(self.batch_size,), name=self.name + '_atom_split') self.atom_to_pair_placeholder = tf.placeholder( dtype='int32', shape=(None,2), name=self.name + '_atom_to_pair') # Define the list of tensors to be used as topology self.topology = [self.pair_split_placeholder, self.pair_membership_placeholder, self.atom_split_placeholder, self.atom_to_pair_placeholder] self.inputs = [self.atom_features_placeholder] self.inputs += self.topology def get_pair_features_placeholder(self): return self.pair_features_placeholder def batch_to_feed_dict(self, batch): """Converts the current batch of WeaveMol into tensorflow feed_dict. Assigns the atom features and pair features to the placeholders tensors params ------ batch : np.ndarray Array of WeaveMol returns ------- feed_dict : dict Can be merged with other feed_dicts for input into tensorflow """ # Extract atom numbers atom_feat = [] pair_feat = [] atom_split = [] atom_to_pair = [] pair_split = [] max_atoms = self.max_atoms start = 0 for im, mol in enumerate(batch): n_atoms = mol.get_num_atoms() # number of atoms in each molecule atom_split.append(n_atoms) # index of pair features C0, C1 = np.meshgrid(np.arange(n_atoms), np.arange(n_atoms)) atom_to_pair.append(np.transpose(np.array([C1.flatten()+start, C0.flatten()+start]))) start = start + n_atoms # number of pairs for each atom pair_split.extend([n_atoms]*n_atoms) # atom features atom_feat.append(mol.get_atom_features()) # pair features pair_feat.append(np.reshape(mol.get_pair_features(), (n_atoms*n_atoms, self.n_pair_feat))) atom_feat = np.concatenate(atom_feat, axis=0) pair_feat = np.concatenate(pair_feat, axis=0) atom_to_pair = np.concatenate(atom_to_pair, axis=0) atom_split = np.array(atom_split) n_pair = len(pair_split) pair_split = np.pad(pair_split, ((0, max_atoms-n_pair)), 'constant') pair_membership = np.array([True]*n_pair + [False]*(max_atoms-n_pair)) # Generate dicts dict_DTNN = { self.atom_features_placeholder: atom_feat, self.pair_features_placeholder: pair_feat, self.pair_split_placeholder: pair_split, self.pair_membership_placeholder: pair_membership, self.atom_split_placeholder: atom_split, self.atom_to_pair_placeholder: atom_to_pair } return dict_DTNN No newline at end of file
deepchem/molnet/preset_hyper_parameters.py +1 −1 Original line number Diff line number Diff line Loading @@ -68,7 +68,7 @@ hps['dag'] = { hps['weave'] = { 'batch_size': 64, 'nb_epoch': 40, 'learning_rate': 0.001, 'learning_rate': 0.0001, 'n_graph_feat': 128, 'n_pair_feat': 14, 'seed': 123 Loading
deepchem/nn/__init__.py +4 −0 Original line number Diff line number Diff line Loading @@ -21,8 +21,10 @@ from deepchem.nn.layers import DAGLayer from deepchem.nn.layers import DAGGather from deepchem.nn.weave_layers import WeaveLayer from deepchem.nn.weave_layers import WeaveLayer_v2 from deepchem.nn.weave_layers import WeaveConcat from deepchem.nn.weave_layers import WeaveGather from deepchem.nn.weave_layers import WeaveGather_v2 from deepchem.nn.model_ops import weight_decay from deepchem.nn.model_ops import optimizer Loading @@ -36,8 +38,10 @@ from deepchem.models.tf_new_models.graph_topology import GraphTopology from deepchem.models.tf_new_models.graph_topology import DTNNGraphTopology from deepchem.models.tf_new_models.graph_topology import DAGGraphTopology from deepchem.models.tf_new_models.graph_topology import WeaveGraphTopology from deepchem.models.tf_new_models.graph_topology import WeaveGraphTopology_v2 from deepchem.models.tf_new_models.graph_models import SequentialGraph from deepchem.models.tf_new_models.graph_models import SequentialDTNNGraph from deepchem.models.tf_new_models.graph_models import SequentialDAGGraph from deepchem.models.tf_new_models.graph_models import SequentialWeaveGraph from deepchem.models.tf_new_models.graph_models import SequentialWeaveGraph_v2 from deepchem.models.tf_new_models.graph_models import SequentialSupportGraph
deepchem/nn/weave_layers.py +97 −1 Original line number Diff line number Diff line Loading @@ -175,6 +175,66 @@ class WeaveLayer(Layer): return A, P class WeaveLayer_v2(WeaveLayer): def call(self, x, mask=None): """Execute this layer on input tensors. x = [atom_features, pair_features, pair_split, pair_membership, atom_split] Parameters ---------- x: list list of Tensors of form described above. mask: bool, optional Ignored. Present only to shadow superclass call() method. Returns ------- A: Tensor Tensor of atom_features P: Tensor Tensor of pair_features """ # Add trainable weights self.build() atom_features = x[0] pair_features = x[1] pair_split = x[2] pair_membership = x[3] atom_split = x[4] atom_to_pair = x[5] AA = tf.matmul(atom_features, self.W_AA) + self.b_AA AA = self.activation(AA) PA = tf.matmul(pair_features, self.W_PA) + self.b_PA PA = self.activation(PA) PAs = tf.split(PA, pair_split, axis=0) PA = [tf.reduce_sum(molecule, 0) for molecule in PAs] PA = tf.boolean_mask(PA, pair_membership) A = tf.matmul(tf.concat([AA, PA], 1), self.W_A) + self.b_A A = self.activation(A) if self.update_pair: AP_ij = tf.matmul(tf.reshape(tf.gather(atom_features, atom_to_pair), [-1, 2*self.n_atom_input_feat]), self.W_AP) + self.b_AP AP_ij = self.activation(AP_ij) AP_ji = tf.matmul(tf.reshape(tf.gather(atom_features, tf.reverse(atom_to_pair, [1])), [-1, 2*self.n_atom_input_feat]), self.W_AP) + self.b_AP AP_ji = self.activation(AP_ji) PP = tf.matmul(pair_features, self.W_PP) + self.b_PP PP = self.activation(PP) P = tf.matmul(tf.concat([AP_ij + AP_ji, PP], 1), self.W_P) + self.b_P P = self.activation(P) else: P = pair_features return A, P class WeaveConcat(Layer): """" Concat a batch of molecules into a batch of atoms """ Loading Loading @@ -342,3 +402,39 @@ class WeaveGather(Layer): outputs = outputs / tf.reduce_sum(outputs, axis=2, keep_dims=True) outputs = tf.reshape(outputs, [-1, self.n_input * 11]) return outputs class WeaveGather_v2(WeaveGather): def call(self, x, mask=None): """Execute this layer on input tensors. x = [atom_features, atom_split] Parameters ---------- x: list Tensors as listed above mask: bool, optional Ignored. Present only to shadow superclass call() method. Returns ------- outputs: Tensor Tensor of molecular features """ # Add trainable weights self.build() outputs = x[0] atom_split = x[1] if self.gaussian_expand: outputs = self.gaussian_histogram(outputs) outputs = tf.split(outputs, atom_split, axis=0) output_molecules = [tf.reduce_sum(molecule, 0) for molecule in outputs] output_molecules = tf.stack(output_molecules) if self.gaussian_expand: output_molecules = tf.matmul(output_molecules, self.W) + self.b output_molecules = self.activation(output_molecules) return output_molecules No newline at end of file
