def build_parser(DROPOUT, LSTM_NUM_LAYERS, word_to_ix, pretrained_embeds):
# Predef
TEST_EMBEDDING_DIM = 4
WORD_EMBEDDING_DIM = 64
STACK_EMBEDDING_DIM = 100
NUM_FEATURES = 3
# Build Model
feat_extractor = SimpleFeatureExtractor()
# BiLSTM word embeddings will probably work best, but feel free to experiment with the others you developed
word_embedding_lookup = BiLSTMWordEmbedding(word_to_ix,
WORD_EMBEDDING_DIM,
STACK_EMBEDDING_DIM,
num_layers=LSTM_NUM_LAYERS,
dropout=DROPOUT)
initialize_with_pretrained(pretrained_embeds, word_embedding_lookup)
action_chooser = LSTMActionChooser(STACK_EMBEDDING_DIM * NUM_FEATURES,
LSTM_NUM_LAYERS,
dropout=DROPOUT)
combiner = LSTMCombiner(STACK_EMBEDDING_DIM,
num_layers=LSTM_NUM_LAYERS,
dropout=DROPOUT)
parser = TransitionParser(feat_extractor, word_embedding_lookup,
action_chooser, combiner)
return parser
def test_bilstm_word_embeds_d4_1():
global test_sent, word_to_ix, vocab
torch.manual_seed(1)
embedder = BiLSTMWordEmbedding(word_to_ix, TEST_EMBEDDING_DIM, TEST_EMBEDDING_DIM, 1, 0.0)
embeds = embedder(test_sent)
assert len(embeds) == len(test_sent)
assert isinstance(embeds, list)
assert isinstance(embeds[0], ag.Variable)
assert embeds[0].size() == (1, TEST_EMBEDDING_DIM)
embeds_list = make_list(embeds)
true = (
[0.09079286456108093, 0.06577987223863602, 0.26242679357528687, -0.004267544485628605],
[0.16868481040000916, 0.2032647728919983, 0.23663431406021118, -0.11785736680030823],
[0.35757705569267273, 0.3805052936077118, -0.006295515224337578, 0.0010524550452828407],
[0.26692214608192444, 0.3241712749004364, 0.13473030924797058, -0.026079852133989334],
[0.23157459497451782, 0.13698695600032806, 0.04000323265790939, 0.1107199415564537],
[0.22783540189266205, -0.02211562544107437, 0.06239837780594826, 0.08553065359592438],
[0.24633683264255524, 0.09283821284770966, 0.0987505242228508, -0.07646450400352478],
[0.05530695244669914, -0.4060348570346832, -0.060150448232889175, -0.003920700401067734],
[0.2099054455757141, -0.304738312959671, -0.01663055270910263, -0.05987118184566498]
)
pairs = zip(embeds_list, true)
check_tensor_correctness(pairs)
def test_bilstm_embedding_d4_1():
global test_doc
torch.manual_seed(1)
lstm = BiLSTMWordEmbedding(word_to_ix, TEST_EMBEDDING_DIM, LSTM_HIDDEN,
LSTM_LAYERS, DROPOUT)
pred_not = lstm(test_doc)[2].data.tolist()[0][:6]
true_not = [
0.11752596497535706, 0.042018793523311615, 0.06257987767457962,
-0.057494595646858215, 0.06428981572389603, -0.16254858672618866
]
list_assert(pred_not, true_not)
def test_pretrain_embeddings_d4_5():
torch.manual_seed(1)
word_to_ix = {"interest": 0, "rate": 1, "swap": 2}
pretrained = {
"interest": [6.1, 2.2, -3.5],
"swap": [5.7, 1.6, 3.2],
UNK_TOKEN: [8.5, -0.4, 2.0]
}
embedder = BiLSTMWordEmbedding(word_to_ix, 3, 2, 1, 0)
initialize_with_pretrained(pretrained, embedder)
embeddings = embedder.word_embeddings.weight.data
pairs = []
pairs.append(
(embeddings[word_to_ix["interest"]].tolist(), pretrained["interest"]))
pairs.append(
(embeddings[word_to_ix["rate"]].tolist(), pretrained[UNK_TOKEN]))
pairs.append((embeddings[word_to_ix["swap"]].tolist(), pretrained["swap"]))
check_tensor_correctness(pairs)