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',
eps=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.eps = eps
self.momentum = momentum
self.W, self.b = None, None
super(WeaveGather, self).__init__(**kwargs)
def __init__(self, n_hidden=100, init='glorot_uniform'):
self.n_hidden = n_hidden
self.init = initializations.get(init)
Wz = self.init([n_hidden, n_hidden])
Wr = self.init([n_hidden, n_hidden])
Wh = self.init([n_hidden, n_hidden])
Uz = self.init([n_hidden, n_hidden])
Ur = self.init([n_hidden, n_hidden])
Uh = self.init([n_hidden, n_hidden])
bz = model_ops.zeros(shape=(n_hidden, ))
br = model_ops.zeros(shape=(n_hidden, ))
bh = model_ops.zeros(shape=(n_hidden, ))
self.trainable_weights = [Wz, Wr, Wh, Uz, Ur, Uh, bz, br, bh]
def __init__(self,
pair_features,
n_pair_features=8,
n_hidden=100,
init='glorot_uniform'):
self.n_pair_features = n_pair_features
self.n_hidden = n_hidden
self.init = initializations.get(init)
W = self.init([n_pair_features, n_hidden * n_hidden])
b = model_ops.zeros(shape=(n_hidden * n_hidden, ))
self.A = torch.matmul(pair_features, W) + b
self.A = torch.reshape(self.A, (-1, n_hidden, n_hidden))
self.trainable_weights = [W, b]
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 build(self):
""" Construct internal trainable weights.
TODO(rbharath): Need to make this not set instance variables to
follow style in other layers.
"""
init = initializations.get(self.init) # Set weight initialization
self.W_AA = init([self.n_atom_input_feat, self.n_hidden_AA])
self.b_AA = model_ops.zeros(shape=[
self.n_hidden_AA,
])
self.W_PA = init([self.n_pair_input_feat, self.n_hidden_PA])
self.b_PA = model_ops.zeros(shape=[
self.n_hidden_PA,
])
self.W_A = init([self.n_hidden_A, self.n_atom_output_feat])
self.b_A = model_ops.zeros(shape=[
self.n_atom_output_feat,
])
self.trainable_weights = [
self.W_AA, self.b_AA, self.W_PA, self.b_PA, self.W_A, self.b_A
]
if self.update_pair:
self.W_AP = init([self.n_atom_input_feat * 2, self.n_hidden_AP])
self.b_AP = model_ops.zeros(shape=[
self.n_hidden_AP,
])
self.W_PP = init([self.n_pair_input_feat, self.n_hidden_PP])
self.b_PP = model_ops.zeros(shape=[
self.n_hidden_PP,
])
self.W_P = init([self.n_hidden_P, self.n_pair_output_feat])
self.b_P = model_ops.zeros(shape=[
self.n_pair_output_feat,
])
self.trainable_weights.extend([
self.W_AP, self.b_AP, self.W_PP, self.b_PP, self.W_P, self.b_P
])
def __init__(self,
n_embedding=30,
periodic_table_length=30,
init='glorot_uniform',
**kwargs):
"""
Parameters
----------
n_embedding: int, optional
Number of features for each atom
periodic_table_length: int, optional
Length of embedding, 83=Bi
init: str, optional
Weight initialization for filters.
"""
self.n_embedding = n_embedding
self.periodic_table_length = periodic_table_length
self.init = initializations.get(init) # Set weight initialization
super(DTNNEmbedding, self).__init__(**kwargs)
def __init__(self,
M,
batch_size,
n_hidden=100,
init='orthogonal',
**kwargs):
"""
Parameters
----------
M: int
Number of LSTM steps
batch_size: int
Number of samples in a batch(all batches must have same size)
n_hidden: int, optional
number of hidden units in the passing phase
"""
self.M = M
self.batch_size = batch_size
self.n_hidden = n_hidden
self.init = initializations.get(init)
super(SetGather, self).__init__(**kwargs)
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