def test_accUpdateGradParameters(self):
module = nn.LookupTable(5, 3)
module.weight.fill_(2)
input = torch.LongTensor([1, 3])
output = module.updateOutput(input)
module.backwardUpdate(input, output, 0.1)
self.assertEqual(module.weight[0, 0], 2)
self.assertEqual(module.weight[3, 0], 1.8)
def __init__(self, pipeline):
"""
:param pipeline: holds all necessary information for nnsimulation
"""
# global dictionary
self.dictionary = pipeline.dictionary
# vector length
self.emb_dim = len(pipeline.emb_matrix[0])
self.emb_matrix = pipeline.emb_matrix
# embedding matrix as lookup table
self.emb_matrix_look = nn.LookupTable(len(pipeline.emb_matrix),
self.emb_dim)
self.emb_matrix_look.weight = self.emb_matrix
# length of input sequence for RNN
self.in_len = 5
# rnn properties
# memory dimension
self.mem_dim = 150
# learning rate
self.learning_rate = 0.05
# word vector embedding learning rate
self.emb_learning_rate = 0.0
# minibatch size
self.batch_size = 25
# regulation strength
self.reg = 1e-4
# simulation module hidden dimension
self.sim_nhidden = 50
# optimization configuration
self.optim_state = {self.learning_rate}
# negative log likeligood optimization objective
self.criterion = nn.ClassNLLCriterion()
# models
# initialize code learning model
self.rnn = rnn.RNN_Example(self)
# initialize classification model
self.log_reg = lm.LogisticRegression()