def setUp(self):
self._vocab_size = 10
self._max_time = 16
self._batch_size = 8
self._emb_dim = 20
self._attention_dim = 256
self._inputs = torch.randint(self._vocab_size,
size=(self._batch_size, self._max_time))
embedding = torch.rand(self._vocab_size,
self._emb_dim,
dtype=torch.float)
self._embedder = WordEmbedder(init_value=embedding)
self._encoder_output = torch.rand(self._batch_size, self._max_time, 64)
self._test_hparams = {} # (cell_type, is_multi) -> hparams
for cell_type in ["RNNCell", "LSTMCell", "GRUCell"]:
hparams = {
"rnn_cell": {
'type': cell_type,
'kwargs': {
'num_units': 256,
},
},
"attention": {
"kwargs": {
"num_units": self._attention_dim
},
}
}
self._test_hparams[(cell_type, False)] = HParams(
hparams, AttentionRNNDecoder.default_hparams())
hparams = {
"rnn_cell": {
'type': 'LSTMCell',
'kwargs': {
'num_units': 256,
},
'num_layers': 3,
},
"attention": {
"kwargs": {
"num_units": self._attention_dim
},
}
}
self._test_hparams[("LSTMCell", True)] = HParams(
hparams, AttentionRNNDecoder.default_hparams())
def setUp(self):
self._vocab_size = 10
self._max_time = 16
self._batch_size = 8
self._emb_dim = 20
self._attention_dim = 256
self._inputs = torch.rand(self._batch_size,
self._max_time,
self._emb_dim,
dtype=torch.float32)
self._embedding = torch.rand(self._vocab_size,
self._emb_dim,
dtype=torch.float32)
self._encoder_output = torch.rand(self._batch_size, self._max_time, 64)
hparams = {
"rnn_cell": {
'type': 'RNNCell',
'kwargs': {
'num_units': 256,
},
},
"attention": {
"kwargs": {
"num_units": self._attention_dim
},
}
}
self._hparams_rnn = HParams(hparams,
AttentionRNNDecoder.default_hparams())
hparams = {
"rnn_cell": {
'type': 'LSTMCell',
'kwargs': {
'num_units': 256,
},
},
"attention": {
"kwargs": {
"num_units": self._attention_dim
},
}
}
self._hparams_lstm = HParams(hparams,
AttentionRNNDecoder.default_hparams())
hparams = {
"rnn_cell": {
'type': 'GRUCell',
'kwargs': {
'num_units': 256,
},
},
"attention": {
"kwargs": {
"num_units": self._attention_dim
},
}
}
self._hparams_gru = HParams(hparams,
AttentionRNNDecoder.default_hparams())
hparams = {
"rnn_cell": {
'type': 'RNNCell',
'kwargs': {
'num_units': 256,
},
'num_layers': 3,
},
"attention": {
"kwargs": {
"num_units": self._attention_dim
},
}
}
self._hparams_multicell = HParams(
hparams, AttentionRNNDecoder.default_hparams())
