def __init__(self, device, decimator_dimension, prediction_dimension,
edge_dimension, meta_data_dimension, mem_hidden_dimension, agg_hidden_dimension, mem_agg_hidden_dimension,
variable_classifier=None, function_classifier=None):
super(NeuralPredictor, self).__init__()
self._device = device
self._module_list = nn.ModuleList()
self._variable_classifier = variable_classifier
self._function_classifier = function_classifier
self._hidden_dimension = decimator_dimension
if variable_classifier is not None:
self._variable_aggregator = util.MessageAggregator(device, decimator_dimension + edge_dimension + meta_data_dimension,
decimator_dimension, mem_hidden_dimension,
mem_agg_hidden_dimension, agg_hidden_dimension, 0, include_self_message=True)
self._module_list.append(self._variable_aggregator)
self._module_list.append(self._variable_classifier)
if function_classifier is not None:
self._function_aggregator = util.MessageAggregator(device, decimator_dimension + edge_dimension + meta_data_dimension,
decimator_dimension, mem_hidden_dimension,
mem_agg_hidden_dimension, agg_hidden_dimension, 0, include_self_message=True)
self._module_list.append(self._function_aggregator)
self._module_list.append(self._function_classifier)
def __init__(self, device, edge_dimension, decimator_dimension,
meta_data_dimension, hidden_dimension, mem_hidden_dimension,
mem_agg_hidden_dimension, agg_hidden_dimension, dropout):
super(NeuralMessagePasser, self).__init__()
self._device = device
self._module_list = nn.ModuleList()
self._drop_out = dropout
self._variable_aggregator = util.MessageAggregator(
device,
decimator_dimension + edge_dimension + meta_data_dimension,
hidden_dimension,
mem_hidden_dimension,
mem_agg_hidden_dimension,
agg_hidden_dimension,
edge_dimension,
include_self_message=False)
self._function_aggregator = util.MessageAggregator(
device,
decimator_dimension + edge_dimension + meta_data_dimension,
hidden_dimension,
mem_hidden_dimension,
mem_agg_hidden_dimension,
agg_hidden_dimension,
edge_dimension,
include_self_message=False)
self._module_list.append(self._variable_aggregator)
self._module_list.append(self._function_aggregator)
self._hidden_dimension = hidden_dimension
self._mem_hidden_dimension = mem_hidden_dimension
self._agg_hidden_dimension = agg_hidden_dimension
self._mem_agg_hidden_dimension = mem_agg_hidden_dimension
def __init__(self, device, edge_dimension, decimator_dimension,
meta_data_dimension, hidden_dimension, mem_hidden_dimension,
mem_agg_hidden_dimension, agg_hidden_dimension, dropout):
super(NeuralMessagePasser, self).__init__()
self._device = device
self._module_list = nn.ModuleList()
self._drop_out = dropout
self._variable_aggregator = util.MessageAggregator(
device,
decimator_dimension + edge_dimension + meta_data_dimension,
hidden_dimension,
mem_hidden_dimension,
mem_agg_hidden_dimension,
agg_hidden_dimension,
edge_dimension,
include_self_message=False)
self._function_aggregator = util.MessageAggregator(
device,
decimator_dimension + edge_dimension + meta_data_dimension,
hidden_dimension,
mem_hidden_dimension,
mem_agg_hidden_dimension,
agg_hidden_dimension,
edge_dimension,
include_self_message=False)
self._module_list.append(self._variable_aggregator)
self._module_list.append(self._function_aggregator)
self._hidden_dimension = hidden_dimension
self._mem_hidden_dimension = mem_hidden_dimension
self._agg_hidden_dimension = agg_hidden_dimension
self._mem_agg_hidden_dimension = mem_agg_hidden_dimension
self._flag = False
self._n_head = 8
self._n_layers = 4
self._d_k = self._hidden_dimension / self._n_head
self.pre_embedding = np.load('datasets/arr_0.npy')