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Add BiRNN for entailment

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Hastily add bidirectional RNN to entailment example
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Matthew Honnibal 2016-11-12 01:15:01 +11:00 committed by GitHub
parent f123f92e0c
commit ca996fc01a
1 changed files with 14 additions and 1 deletions
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15
examples/keras_parikh_entailment/keras_decomposable_attention.py
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@ -12,7 +12,7 @@ from keras.optimizers import Adam
from keras.layers.normalization import BatchNormalization from keras.layers.normalization import BatchNormalization
def build_model(vectors, shape, settings): def build_model(vectors, shape, settings, use_rnn_encoding=False):
'''Compile the model.''' '''Compile the model.'''
max_length, nr_hidden, nr_class = shape max_length, nr_hidden, nr_class = shape
# Declare inputs. # Declare inputs.
@ -21,6 +21,8 @@ def build_model(vectors, shape, settings):
# Construct operations, which we'll chain together. # Construct operations, which we'll chain together.
embed = _StaticEmbedding(vectors, max_length, nr_hidden) embed = _StaticEmbedding(vectors, max_length, nr_hidden)
if use_rnn_encoding:
encode = _BiLSTMEncode(max_length, nr_hidden)
attend = _Attention(max_length, nr_hidden) attend = _Attention(max_length, nr_hidden)
align = _SoftAlignment(max_length, nr_hidden) align = _SoftAlignment(max_length, nr_hidden)
compare = _Comparison(max_length, nr_hidden) compare = _Comparison(max_length, nr_hidden)
@ -30,6 +32,10 @@ def build_model(vectors, shape, settings):
sent1 = embed(ids1) # Shape: (i, n) sent1 = embed(ids1) # Shape: (i, n)
sent2 = embed(ids2) # Shape: (j, n) sent2 = embed(ids2) # Shape: (j, n)
if use_rnn_encoding:
sent1 = encode(sent1)
sent2 = encode(sent2)
attention = attend(sent1, sent2) # Shape: (i, j) attention = attend(sent1, sent2) # Shape: (i, j)
align1 = align(sent2, attention) align1 = align(sent2, attention)
@ -73,6 +79,13 @@ class _StaticEmbedding(object):
def __call__(self, sentence): def __call__(self, sentence):
return self.project(self.embed(sentence)) return self.project(self.embed(sentence))
class _BiRNNEncoding(object):
def __init__(self, max_length, nr_out):
self.model = Sequential()
self.model.add(Bidirectional(LSTM(nr_out, input_length=max_length)))
def __call__(self, sentence):
return self.model(sentence)
class _Attention(object): class _Attention(object):
def __init__(self, max_length, nr_hidden, dropout=0.0, L2=1e-4, activation='relu'): def __init__(self, max_length, nr_hidden, dropout=0.0, L2=1e-4, activation='relu'):