def __init__(self,
n_embedding=30,
n_outputs=100,
layer_sizes=[100],
output_activation=True,
init='glorot_uniform',
activation='tanh',
**kwargs):
"""
Parameters
----------
n_embedding: int, optional
Number of features for each atom
n_outputs: int, optional
Number of features for each molecule(output)
layer_sizes: list of int, optional(default=[1000])
Structure of hidden layer(s)
init: str, optional
Weight initialization for filters.
activation: str, optional
Activation function applied
"""
self.n_embedding = n_embedding
self.n_outputs = n_outputs
self.layer_sizes = layer_sizes
self.output_activation = output_activation
self.init = initializations.get(init) # Set weight initialization
self.activation = activations.get(activation) # Get activations
super(DTNNGather, self).__init__(**kwargs)
def __init__(self,
n_embedding=30,
n_distance=100,
n_hidden=60,
init='glorot_uniform',
activation='tanh',
**kwargs):
"""
Parameters
----------
n_embedding: int, optional
Number of features for each atom
n_distance: int, optional
granularity of distance matrix
n_hidden: int, optional
Number of nodes in hidden layer
init: str, optional
Weight initialization for filters.
activation: str, optional
Activation function applied
"""
self.n_embedding = n_embedding
self.n_distance = n_distance
self.n_hidden = n_hidden
self.init = initializations.get(init) # Set weight initialization
self.activation = activations.get(activation) # Get activations
super(DTNNStep, self).__init__(**kwargs)
def __init__(self,
n_embedding=30,
n_outputs=100,
layer_sizes=[100],
output_activation=True,
init='glorot_uniform',
activation='tanh',
**kwargs):
"""
Parameters
----------
n_embedding: int, optional
Number of features for each atom
n_outputs: int, optional
Number of features for each molecule(output)
layer_sizes: list of int, optional(default=[1000])
Structure of hidden layer(s)
init: str, optional
Weight initialization for filters.
activation: str, optional
Activation function applied
"""
self.n_embedding = n_embedding
self.n_outputs = n_outputs
self.layer_sizes = layer_sizes
self.output_activation = output_activation
self.init = initializations.get(init) # Set weight initialization
self.activation = activations.get(activation) # Get activations
super(DTNNGather, self).__init__(**kwargs)
def __init__(self,
n_embedding=30,
n_distance=100,
n_hidden=60,
init='glorot_uniform',
activation='tanh',
**kwargs):
"""
Parameters
----------
n_embedding: int, optional
Number of features for each atom
n_distance: int, optional
granularity of distance matrix
n_hidden: int, optional
Number of nodes in hidden layer
init: str, optional
Weight initialization for filters.
activation: str, optional
Activation function applied
"""
self.n_embedding = n_embedding
self.n_distance = n_distance
self.n_hidden = n_hidden
self.init = initializations.get(init) # Set weight initialization
self.activation = activations.get(activation) # Get activations
super(DTNNStep, self).__init__(**kwargs)
def __init__(self,
batch_size,
n_input=128,
gaussian_expand=False,
init='glorot_uniform',
activation='tanh',
epsilon=1e-3,
momentum=0.99,
**kwargs):
"""
Parameters
----------
batch_size: int
number of molecules in a batch
n_input: int, optional
number of features for each input molecule
gaussian_expand: boolean. optional
Whether to expand each dimension of atomic features by gaussian histogram
init: str, optional
Weight initialization for filters.
activation: str, optional
Activation function applied
"""
self.n_input = n_input
self.batch_size = batch_size
self.gaussian_expand = gaussian_expand
self.init = initializations.get(init) # Set weight initialization
self.activation = activations.get(activation) # Get activations
self.epsilon = epsilon
self.momentum = momentum
self.W, self.b = None, None
super(WeaveGather, self).__init__(**kwargs)
def __init__(self,
batch_size,
n_input=128,
gaussian_expand=False,
init='glorot_uniform',
activation='tanh',
epsilon=1e-3,
momentum=0.99,
**kwargs):
"""
Parameters
----------
batch_size: int
number of molecules in a batch
n_input: int, optional
number of features for each input molecule
gaussian_expand: boolean. optional
Whether to expand each dimension of atomic features by gaussian histogram
init: str, optional
Weight initialization for filters.
activation: str, optional
Activation function applied
"""
self.n_input = n_input
self.batch_size = batch_size
self.gaussian_expand = gaussian_expand
self.init = initializations.get(init) # Set weight initialization
self.activation = activations.get(activation) # Get activations
self.epsilon = epsilon
self.momentum = momentum
self.W, self.b = None, None
super(WeaveGather, self).__init__(**kwargs)
def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
"""Creates weave tensors.
parent layers: [atom_features, pair_features], pair_split, atom_to_pair
"""
activation = activations.get(self.activation) # Get activations
if in_layers is None:
in_layers = self.in_layers
in_layers = convert_to_layers(in_layers)
self.build()
atom_features = in_layers[0].out_tensor
pair_features = in_layers[1].out_tensor
pair_split = in_layers[2].out_tensor
atom_to_pair = in_layers[3].out_tensor
AA = tf.matmul(atom_features, self.W_AA) + self.b_AA
AA = activation(AA)
PA = tf.matmul(pair_features, self.W_PA) + self.b_PA
PA = activation(PA)
PA = tf.segment_sum(PA, pair_split)
A = tf.matmul(tf.concat([AA, PA], 1), self.W_A) + self.b_A
A = 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 = 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 = activation(AP_ji)
PP = tf.matmul(pair_features, self.W_PP) + self.b_PP
PP = activation(PP)
P = tf.matmul(tf.concat([AP_ij + AP_ji, PP], 1),
self.W_P) + self.b_P
P = activation(P)
else:
P = pair_features
self.out_tensors = [A, P]
if set_tensors:
self.variables = self.trainable_weights
self.out_tensor = A
return self.out_tensors
def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
"""Creates weave tensors.
parent layers: [atom_features, pair_features], pair_split, atom_to_pair
"""
activation = activations.get(self.activation) # Get activations
if in_layers is None:
in_layers = self.in_layers
in_layers = convert_to_layers(in_layers)
self.build()
atom_features = in_layers[0].out_tensor
pair_features = in_layers[1].out_tensor
pair_split = in_layers[2].out_tensor
atom_to_pair = in_layers[3].out_tensor
AA = tf.matmul(atom_features, self.W_AA) + self.b_AA
AA = activation(AA)
PA = tf.matmul(pair_features, self.W_PA) + self.b_PA
PA = activation(PA)
PA = tf.segment_sum(PA, pair_split)
A = tf.matmul(tf.concat([AA, PA], 1), self.W_A) + self.b_A
A = 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 = 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 = activation(AP_ji)
PP = tf.matmul(pair_features, self.W_PP) + self.b_PP
PP = activation(PP)
P = tf.matmul(tf.concat([AP_ij + AP_ji, PP], 1), self.W_P) + self.b_P
P = activation(P)
else:
P = pair_features
self.out_tensors = [A, P]
if set_tensors:
self.variables = self.trainable_weights
self.out_tensor = A
return self.out_tensors
def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
""" Generate Radial Symmetry Function """
init_fn = initializations.get(self.init) # Set weight initialization
if self.activation == 'ani':
activation_fn = self.ani_activate
else:
activation_fn = activations.get(self.activation)
if in_layers is None:
in_layers = self.in_layers
in_layers = convert_to_layers(in_layers)
inputs = in_layers[0].out_tensor
atom_numbers = in_layers[1].out_tensor
in_channels = inputs.get_shape().as_list()[-1]
self.W = init_fn(
[len(self.atom_number_cases), in_channels, self.out_channels])
self.b = model_ops.zeros(
(len(self.atom_number_cases), self.out_channels))
outputs = []
for i, atom_case in enumerate(self.atom_number_cases):
# optimization to allow for tensorcontraction/broadcasted mmul
# using a reshape trick. Note that the np and tf matmul behavior
# differs when dealing with broadcasts
a = inputs # (i,j,k)
b = self.W[i, :, :] # (k, l)
ai = tf.shape(a)[0]
aj = tf.shape(a)[1]
ak = tf.shape(a)[2]
bl = tf.shape(b)[1]
output = activation_fn(
tf.reshape(tf.matmul(tf.reshape(a, [ai * aj, ak]), b),
[ai, aj, bl]) + self.b[i, :])
mask = 1 - tf.cast(tf.cast(atom_numbers - atom_case, tf.bool),
tf.float32)
output = tf.reshape(output * tf.expand_dims(mask, 2),
(-1, self.max_atoms, self.out_channels))
outputs.append(output)
out_tensor = tf.add_n(outputs)
if set_tensors:
self.out_tensor = out_tensor
return out_tensor
def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
""" Generate Radial Symmetry Function """
init_fn = initializations.get(self.init) # Set weight initialization
if self.activation == 'ani':
activation_fn = self.ani_activate
else:
activation_fn = activations.get(self.activation)
if in_layers is None:
in_layers = self.in_layers
in_layers = convert_to_layers(in_layers)
inputs = in_layers[0].out_tensor
atom_numbers = in_layers[1].out_tensor
in_channels = inputs.get_shape().as_list()[-1]
self.W = init_fn(
[len(self.atom_number_cases), in_channels, self.out_channels])
self.b = model_ops.zeros((len(self.atom_number_cases), self.out_channels))
outputs = []
for i, atom_case in enumerate(self.atom_number_cases):
# optimization to allow for tensorcontraction/broadcasted mmul
# using a reshape trick. Note that the np and tf matmul behavior
# differs when dealing with broadcasts
a = inputs # (i,j,k)
b = self.W[i, :, :] # (k, l)
ai = tf.shape(a)[0]
aj = tf.shape(a)[1]
ak = tf.shape(a)[2]
bl = tf.shape(b)[1]
output = activation_fn(
tf.reshape(tf.matmul(tf.reshape(a, [ai * aj, ak]), b), [ai, aj, bl]) +
self.b[i, :])
mask = 1 - tf.cast(tf.cast(atom_numbers - atom_case, tf.bool), tf.float32)
output = tf.reshape(output * tf.expand_dims(mask, 2),
(-1, self.max_atoms, self.out_channels))
outputs.append(output)
out_tensor = tf.add_n(outputs)
if set_tensors:
self.out_tensor = out_tensor
return out_tensor
def __init__(self,
n_graph_feat=30,
n_atom_feat=75,
max_atoms=50,
layer_sizes=[100],
init='glorot_uniform',
activation='relu',
dropout=None,
batch_size=64,
**kwargs):
"""
Parameters
----------
n_graph_feat: int, optional
Number of features for each node(and the whole grah).
n_atom_feat: int, optional
Number of features listed per atom.
max_atoms: int, optional
Maximum number of atoms in molecules.
layer_sizes: list of int, optional(default=[100])
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.
dropout: float, optional
Dropout probability in hidden layer(s).
batch_size: int, optional
number of molecules in a batch.
"""
super(DAGLayer, self).__init__(**kwargs)
self.init = initializations.get(init) # Set weight initialization
self.activation = activations.get(activation) # Get activations
self.layer_sizes = layer_sizes
self.dropout = dropout
self.max_atoms = max_atoms
self.batch_size = batch_size
self.n_inputs = n_atom_feat + (self.max_atoms - 1) * n_graph_feat
# number of inputs each step
self.n_graph_feat = n_graph_feat
self.n_outputs = n_graph_feat
self.n_atom_feat = n_atom_feat
def __init__(self,
n_graph_feat=30,
n_atom_feat=75,
max_atoms=50,
layer_sizes=[100],
init='glorot_uniform',
activation='relu',
dropout=None,
batch_size=64,
**kwargs):
"""
Parameters
----------
n_graph_feat: int, optional
Number of features for each node(and the whole grah).
n_atom_feat: int, optional
Number of features listed per atom.
max_atoms: int, optional
Maximum number of atoms in molecules.
layer_sizes: list of int, optional(default=[100])
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.
dropout: float, optional
Dropout probability in hidden layer(s).
batch_size: int, optional
number of molecules in a batch.
"""
super(DAGLayer, self).__init__(**kwargs)
self.init = initializations.get(init) # Set weight initialization
self.activation = activations.get(activation) # Get activations
self.layer_sizes = layer_sizes
self.dropout = dropout
self.max_atoms = max_atoms
self.batch_size = batch_size
self.n_inputs = n_atom_feat + (self.max_atoms - 1) * n_graph_feat
# number of inputs each step
self.n_graph_feat = n_graph_feat
self.n_outputs = n_graph_feat
self.n_atom_feat = n_atom_feat
def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
act_fn = activations.get('sigmoid')
if in_layers is None:
in_layers = self.in_layers
in_layers = convert_to_layers(in_layers)
self._build()
A_tilda_k = in_layers[0].out_tensor
X = in_layers[1].out_tensor
if self.combine_method == "linear":
concatenated = tf.concat([A_tilda_k, X], axis=2)
adp_fn_val = act_fn(
tf.tensordot(concatenated, self.trainable_weights[0], axes=1))
else:
adp_fn_val = act_fn(tf.matmul(A_tilda_k, tf.tensordot(X, self.Q, axes=1)))
out_tensor = adp_fn_val
if set_tensors:
self.variables = self.trainable_weights
self.out_tensor = out_tensor
return out_tensor
def __init__(self,
n_graph_feat=30,
n_outputs=30,
max_atoms=50,
layer_sizes=[100],
init='glorot_uniform',
activation='relu',
dropout=None,
**kwargs):
"""
Parameters
----------
n_graph_feat: int, optional
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
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.
dropout: float, optional
Dropout probability in the hidden layer(s).
"""
super(DAGGather, self).__init__(**kwargs)
self.init = initializations.get(init) # Set weight initialization
self.activation = activations.get(activation) # Get activations
self.layer_sizes = layer_sizes
self.dropout = dropout
self.max_atoms = max_atoms
self.n_graph_feat = n_graph_feat
self.n_outputs = n_outputs
def __init__(self,
n_graph_feat=30,
n_outputs=30,
max_atoms=50,
layer_sizes=[100],
init='glorot_uniform',
activation='relu',
dropout=None,
**kwargs):
"""
Parameters
----------
n_graph_feat: int, optional
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
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.
dropout: float, optional
Dropout probability in the hidden layer(s).
"""
super(DAGGather, self).__init__(**kwargs)
self.init = initializations.get(init) # Set weight initialization
self.activation = activations.get(activation) # Get activations
self.layer_sizes = layer_sizes
self.dropout = dropout
self.max_atoms = max_atoms
self.n_graph_feat = n_graph_feat
self.n_outputs = n_outputs
def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
act_fn = activations.get('sigmoid')
if in_layers is None:
in_layers = self.in_layers
in_layers = convert_to_layers(in_layers)
self._build()
A_tilda_k = in_layers[0].out_tensor
X = in_layers[1].out_tensor
if self.combine_method == "linear":
concatenated = tf.concat([A_tilda_k, X], axis=2)
adp_fn_val = act_fn(
tf.tensordot(concatenated, self.trainable_weights[0], axes=1))
else:
adp_fn_val = act_fn(
tf.matmul(A_tilda_k, tf.tensordot(X, self.Q, axes=1)))
out_tensor = adp_fn_val
if set_tensors:
self.variables = self.trainable_weights
self.out_tensor = out_tensor
return out_tensor
def __init__(self,
n_graph_feat=30,
n_outputs=30,
max_atoms=50,
layer_sizes=[100],
init='glorot_uniform',
activation='relu',
dropout=None,
**kwargs):
"""
Parameters
----------
n_graph_feat: int, optional
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)
init: str, optional
Weight initialization for filters.
activation: str, optional
Activation function applied
dropout: float, optional
Dropout probability, not supported
"""
super(DAGGather, self).__init__(**kwargs)
self.init = initializations.get(init) # Set weight initialization
self.activation = activations.get(activation) # Get activations
self.layer_sizes = layer_sizes
self.dropout = dropout
self.max_atoms = max_atoms
self.n_graph_feat = n_graph_feat
self.n_outputs = n_outputs
