def __init__(self, max_atoms=50, n_atom_feat=75, n_pair_feat=14):
"""
Parameters
----------
max_atoms: int, optional
Maximum number of atoms in a molecule, should be defined based on dataset
n_atom_feat: int, optional
Number of features per atom.
n_pair_feat: int, optional
Number of features per pair of atoms.
"""
warnings.warn(
"SequentialWeaveGraph is deprecated. "
"Will be removed in DeepChem 1.4.", DeprecationWarning)
self.graph = tf.Graph()
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(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 = []
class SequentialWeaveGraph(SequentialGraph):
"""SequentialGraph for Weave models
"""
def __init__(self, max_atoms=50, n_atom_feat=75, n_pair_feat=14):
self.graph = tf.Graph()
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(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']:
self.output, self.output_P = layer(
[self.output, self.output_P] +
self.graph_topology.get_topology_placeholders())
elif type(layer).__name__ in ['WeaveConcat']:
self.output = layer(
[self.output, self.graph_topology.atom_mask_placeholder])
elif type(layer).__name__ in ['WeaveGather']:
self.output = layer(
[self.output, self.graph_topology.membership_placeholder])
else:
self.output = layer(self.output)
self.layers.append(layer)
class SequentialWeaveGraph(SequentialGraph):
"""SequentialGraph for Weave models
"""
def __init__(self, max_atoms=50, n_atom_feat=75, n_pair_feat=14):
self.graph = tf.Graph()
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(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']:
self.output, self.output_P = layer([
self.output, self.output_P
] + self.graph_topology.get_topology_placeholders())
elif type(layer).__name__ in ['WeaveConcat']:
self.output = layer(
[self.output, self.graph_topology.atom_mask_placeholder])
elif type(layer).__name__ in ['WeaveGather']:
self.output = layer(
[self.output, self.graph_topology.membership_placeholder])
else:
self.output = layer(self.output)
self.layers.append(layer)
def __init__(self, max_atoms=50, n_atom_feat=75, n_pair_feat=14):
self.graph = tf.Graph()
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(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 = []
class SequentialWeaveGraph(SequentialGraph):
"""SequentialGraph for Weave models
"""
def __init__(self, max_atoms=50, n_atom_feat=75, n_pair_feat=14):
"""
Parameters
----------
max_atoms: int, optional
Maximum number of atoms in a molecule, should be defined based on dataset
n_atom_feat: int, optional
Number of features per atom.
n_pair_feat: int, optional
Number of features per pair of atoms.
"""
warnings.warn(
"SequentialWeaveGraph is deprecated. "
"Will be removed in DeepChem 1.4.", DeprecationWarning)
self.graph = tf.Graph()
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(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']:
self.output, self.output_P = layer(
[self.output, self.output_P] +
self.graph_topology.get_topology_placeholders())
elif type(layer).__name__ in ['WeaveConcat']:
self.output = layer(
[self.output, self.graph_topology.atom_mask_placeholder])
elif type(layer).__name__ in ['WeaveGather']:
self.output = layer(
[self.output, self.graph_topology.membership_placeholder])
else:
self.output = layer(self.output)
self.layers.append(layer)
class SequentialWeaveGraph(SequentialGraph):
"""SequentialGraph for Weave models
"""
def __init__(self, max_atoms=50, n_atom_feat=75, n_pair_feat=14):
"""
Parameters
----------
max_atoms: int, optional
Maximum number of atoms in a molecule, should be defined based on dataset
n_atom_feat: int, optional
Number of features per atom.
n_pair_feat: int, optional
Number of features per pair of atoms.
"""
warnings.warn("SequentialWeaveGraph is deprecated. "
"Will be removed in DeepChem 1.4.", DeprecationWarning)
self.graph = tf.Graph()
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(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']:
self.output, self.output_P = layer([
self.output, self.output_P
] + self.graph_topology.get_topology_placeholders())
elif type(layer).__name__ in ['WeaveConcat']:
self.output = layer(
[self.output, self.graph_topology.atom_mask_placeholder])
elif type(layer).__name__ in ['WeaveGather']:
self.output = layer(
[self.output, self.graph_topology.membership_placeholder])
else:
self.output = layer(self.output)
self.layers.append(layer)
def __init__(self, max_atoms=50, n_atom_feat=75, n_pair_feat=14):
self.graph = tf.Graph()
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(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 __init__(self, max_atoms=50, n_atom_feat=75, n_pair_feat=14):
"""
Parameters
----------
max_atoms: int, optional
Maximum number of atoms in a molecule, should be defined based on dataset
n_atom_feat: int, optional
Number of features per atom.
n_pair_feat: int, optional
Number of features per pair of atoms.
"""
self.graph = tf.Graph()
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(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 __init__(self, max_atoms=50, n_atom_feat=75, n_pair_feat=14):
"""
Parameters
----------
max_atoms: int, optional
Maximum number of atoms in a molecule, should be defined based on dataset
n_atom_feat: int, optional
Number of features per atom.
n_pair_feat: int, optional
Number of features per pair of atoms.
"""
warnings.warn("SequentialWeaveGraph is deprecated. "
"Will be removed in DeepChem 1.4.", DeprecationWarning)
self.graph = tf.Graph()
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(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 = []