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, 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 build(self):
self.W_cf = self.init([self.n_embedding, self.n_hidden])
self.W_df = self.init([self.n_distance, self.n_hidden])
self.W_fc = self.init([self.n_hidden, self.n_embedding])
self.b_cf = model_ops.zeros(shape=[
self.n_hidden,
])
self.b_df = model_ops.zeros(shape=[
self.n_hidden,
])
self.trainable_weights = [
self.W_cf, self.W_df, self.W_fc, self.b_cf, self.b_df
]
def build(self):
self.W_cf = self.init([self.n_embedding, self.n_hidden])
self.W_df = self.init([self.n_distance, self.n_hidden])
self.W_fc = self.init([self.n_hidden, self.n_embedding])
self.b_cf = model_ops.zeros(shape=[
self.n_hidden,
])
self.b_df = model_ops.zeros(shape=[
self.n_hidden,
])
self.trainable_weights = [
self.W_cf, self.W_df, self.W_fc, self.b_cf, self.b_df
]
def _build(self):
self.W = self.init(shape=(self.num_nodes, self.num_nodes))
self.b = model_ops.zeros(shape=[
self.num_nodes,
])
self.trainable_weights = [self.W, self.b]
def _build(self):
self.W = self.init(shape=(self.num_nodes, self.num_nodes))
self.b = model_ops.zeros(shape=[
self.num_nodes,
])
self.trainable_weights = [self.W, self.b]
def build(self):
self.W_list = []
self.b_list = []
prev_layer_size = self.n_embedding
for i, layer_size in enumerate(self.layer_sizes):
self.W_list.append(self.init([prev_layer_size, layer_size]))
self.b_list.append(model_ops.zeros(shape=[
layer_size,
]))
prev_layer_size = layer_size
self.W_list.append(self.init([prev_layer_size, self.n_outputs]))
self.b_list.append(model_ops.zeros(shape=[
self.n_outputs,
]))
prev_layer_size = self.n_outputs
self.trainable_weights = self.W_list + self.b_list
def build(self):
self.W_list = []
self.b_list = []
prev_layer_size = self.n_embedding
for i, layer_size in enumerate(self.layer_sizes):
self.W_list.append(self.init([prev_layer_size, layer_size]))
self.b_list.append(model_ops.zeros(shape=[
layer_size,
]))
prev_layer_size = layer_size
self.W_list.append(self.init([prev_layer_size, self.n_outputs]))
self.b_list.append(model_ops.zeros(shape=[
self.n_outputs,
]))
prev_layer_size = self.n_outputs
self.trainable_weights = self.W_list + self.b_list
def build(self):
if self.gaussian_expand:
self.W = self.init([self.n_input * 11, self.n_input])
self.b = model_ops.zeros(shape=[
self.n_input,
])
self.trainable_weights = self.W + self.b
else:
self.trainable_weights = None
def build(self):
if self.gaussian_expand:
self.W = self.init([self.n_input * 11, self.n_input])
self.b = model_ops.zeros(shape=[
self.n_input,
])
self.trainable_weights = self.W + self.b
else:
self.trainable_weights = None
def build(self):
""""Construct internal trainable weights.
"""
self.W_list = []
self.b_list = []
prev_layer_size = self.n_graph_feat
for layer_size in self.layer_sizes:
self.W_list.append(self.init([prev_layer_size, layer_size]))
self.b_list.append(model_ops.zeros(shape=[
layer_size,
]))
prev_layer_size = layer_size
self.W_list.append(self.init([prev_layer_size, self.n_outputs]))
self.b_list.append(model_ops.zeros(shape=[
self.n_outputs,
]))
self.trainable_weights = self.W_list + self.b_list
def build(self):
""""Construct internal trainable weights.
"""
self.W_list = []
self.b_list = []
prev_layer_size = self.n_graph_feat
for layer_size in self.layer_sizes:
self.W_list.append(self.init([prev_layer_size, layer_size]))
self.b_list.append(model_ops.zeros(shape=[
layer_size,
]))
prev_layer_size = layer_size
self.W_list.append(self.init([prev_layer_size, self.n_outputs]))
self.b_list.append(model_ops.zeros(shape=[
self.n_outputs,
]))
self.trainable_weights = self.W_list + self.b_list
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 = tf.nn.xw_plus_b(pair_features, W, b)
self.A = tf.reshape(self.A, (-1, n_hidden, n_hidden))
self.trainable_weights = [W, b]
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 = tf.nn.xw_plus_b(pair_features, W, b)
self.A = tf.reshape(self.A, (-1, n_hidden, n_hidden))
self.trainable_weights = [W, b]
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 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 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])
