def __init__(self,
opts,
arch,
use_bias=True,
initializers=None,
regularizers=None,
custom_getter=None,
name="graphnn"):
"""
Input:
- opts (options) - object with all relevant options stored
- arch (ArchParams) - object with all relevant Architecture options
- use_bias (boolean, optional) - have biases in the network (default True)
- intializers (dict, optional) - specify custom initializers
- regularizers (dict, optional) - specify custom regularizers
- custom_getter (dict, optional) - specify custom getters
- name (string, optional) - name for module for scoping (default graphnn)
"""
super(GraphLongSkipLayerNetwork,
self).__init__(custom_getter=custom_getter, name=name)
self._nlayers = len(arch.layer_lens)
final_regularizers = None
if regularizers is not None:
lin_regularizers = {
k: v
for k, v in regularizers.items() if k in ["w", "b"]
}
else:
lin_regularizers = None
self._layers = [
layers.GraphSkipLayer(output_size=layer_len,
activation=arch.activ,
initializers=initializers,
regularizers=regularizers,
name="{}/graph_skip".format(name))
for layer_len in arch.layer_lens
] + [
layers.EmbeddingLinearLayer(output_size=opts.final_embedding_dim,
initializers=initializers,
regularizers=lin_regularizers,
name="{}/embed_lin".format(name))
]
self._skip_layer_idx = arch.skip_layers
self._skip_layers = [
layers.EmbeddingLinearLayer(output_size=arch.layer_lens[skip_idx],
initializers=initializers,
regularizers=lin_regularizers,
name="{}/skip".format(name))
for skip_idx in self._skip_layer_idx
]
self.normalize_emb = arch.normalize_emb
def __init__(self,
opts,
arch,
use_bias=True,
initializers=None,
regularizers=None,
custom_getter=None,
name="graphnn"):
"""
Input:
- opts (options) - object with all relevant options stored
- arch (ArchParams) - object with all relevant Architecture options
- use_bias (boolean, optional) - have biases in the network (default True)
- intializers (dict, optional) - specify custom initializers
- regularizers (dict, optional) - specify custom regularizers
- custom_getter (dict, optional) - specify custom getters
- name (string, optional) - name for module for scoping (default graphnn)
"""
super(GraphConvLayerNetwork,
self).__init__(custom_getter=custom_getter, name=name)
self._nlayers = len(arch.layer_lens)
self._layers = [
layers.GraphConvLayer(output_size=layer_len,
activation=arch.activ,
initializers=initializers,
regularizers=regularizers,
name="{}/graph_conv".format(name))
for layer_len in arch.layer_lens
] + [
layers.EmbeddingLinearLayer(output_size=opts.final_embedding_dim,
initializers=initializers,
regularizers=regularizers,
name="{}/embed_lin".format(name))
]
self.normalize_emb = arch.normalize_emb
def __init__(self,
opts,
arch,
use_bias=True,
initializers=None,
regularizers=None,
custom_getter=None,
name="graphnn"):
super(GraphSkipHopNormedNetwork, self).__init__(opts, arch,
use_bias=use_bias,
initializers=initializers,
regularizers=regularizers,
custom_getter=custom_getter,
name=name)
lin_regularizers = None
if regularizers is not None:
lin_regularizers = { k:v
for k, v in regularizers.items()
if k in ["w", "b"] }
self._hop_layers = [
layers.EmbeddingLinearLayer(
output_size=arch.layer_lens[skip_idx],
initializers=initializers,
regularizers=lin_regularizers,
name="{}/hop".format(name))
for skip_idx in self._skip_layer_idx[1:]
]
def __init__(self,
opts,
arch,
use_bias=True,
initializers=None,
regularizers=None,
custom_getter=None,
name="graphnn"):
super(GraphSkipLayerNetwork, self).__init__(custom_getter=custom_getter, name=name)
self._nlayers = len(arch.layer_lens)
final_regularizers = None
if regularizers is not None:
final_regularizers = { k:v
for k, v in regularizers.items()
if k in ["w", "b"] }
self._layers = [
layers.GraphSkipLayer(
output_size=layer_len,
activation=arch.activ,
initializers=initializers,
regularizers=regularizers,
name="{}/graph_skip".format(name))
for layer_len in arch.layer_lens
] + [
layers.EmbeddingLinearLayer(
output_size=opts.final_embedding_dim,
initializers=initializers,
regularizers=final_regularizers,
name="{}/embed_lin".format(name))
]
self.normalize_emb = arch.normalize_emb
def __init__(self,
opts,
arch,
use_bias=True,
initializers=None,
regularizers=None,
custom_getter=None,
name="graphnn"):
"""
Input:
- opts (options) - object with all relevant options stored
- arch (ArchParams) - object with all relevant Architecture options
- use_bias (boolean, optional) - have biases in the network (default True)
- intializers (dict, optional) - specify custom initializers
- regularizers (dict, optional) - specify custom regularizers
- custom_getter (dict, optional) - specify custom getters
- name (string, optional) - name for module for scoping (default graphnn)
"""
super(GraphSkipHopNormedNetwork,
self).__init__(opts,
arch,
use_bias=use_bias,
initializers=initializers,
regularizers=regularizers,
custom_getter=custom_getter,
name=name)
lin_regularizers = None
if regularizers is not None:
lin_regularizers = {
k: v
for k, v in regularizers.items() if k in ["w", "b"]
}
self._hop_layers = [
layers.EmbeddingLinearLayer(output_size=arch.layer_lens[skip_idx],
initializers=initializers,
regularizers=lin_regularizers,
name="{}/hop".format(name))
for skip_idx in self._skip_layer_idx[1:]
]