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Merge pull request #628 from chenb67/master

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Remove theano dependency from parikh model + small bug fix
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Matthew Honnibal 2016-11-14 01:28:22 +11:00 committed by GitHub
commit fb8acc1dfb
1 changed files with 9 additions and 8 deletions
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17
examples/keras_parikh_entailment/keras_decomposable_attention.py
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@ -6,7 +6,6 @@ from keras.layers import InputSpec, Layer, Input, Dense, merge
from keras.layers import Activation, Dropout, Embedding, TimeDistributed
from keras.layers import Bidirectional, GRU
import keras.backend as K
import theano.tensor as T
from keras.models import Sequential, Model, model_from_json
from keras.regularizers import l2
from keras.optimizers import Adam
@ -106,8 +105,8 @@ class _Attention(object):
def __call__(self, sent1, sent2):
def _outer(AB):
att_ji = T.batched_dot(AB[1], AB[0].dimshuffle((0, 2, 1)))
return att_ji.dimshuffle((0, 2, 1))
att_ji = K.batch_dot(AB[1], K.permute_dimensions(AB[0], (0, 2, 1)))
return K.permute_dimensions(att_ji,(0, 2, 1))
return merge(
@ -126,12 +125,12 @@ class _SoftAlignment(object):
att = attmat[0]
mat = attmat[1]
if transpose:
att = att.dimshuffle((0, 2, 1))
att = K.permute_dimensions(att,(0, 2, 1))
# 3d softmax
e = K.exp(att - K.max(att, axis=-1, keepdims=True))
s = K.sum(e, axis=-1, keepdims=True)
sm_att = e / s
return T.batched_dot(sm_att, mat)
return K.batch_dot(sm_att, mat)
return merge([attention, sentence], mode=_normalize_attention,
output_shape=(self.max_length, self.nr_hidden)) # Shape: (i, n)
@ -191,7 +190,7 @@ class _GlobalSumPooling1D(Layer):
def call(self, x, mask=None):
if mask is not None:
return K.sum(x * T.clip(mask, 0, 1), axis=1)
return K.sum(x * K.clip(mask, 0, 1), axis=1)
else:
return K.sum(x, axis=1)
@ -199,11 +198,12 @@ class _GlobalSumPooling1D(Layer):
def test_build_model():
vectors = numpy.ndarray((100, 8), dtype='float32')
shape = (10, 16, 3)
settings = {'lr': 0.001, 'dropout': 0.2}
settings = {'lr': 0.001, 'dropout': 0.2, 'gru_encode':True}
model = build_model(vectors, shape, settings)
def test_fit_model():
def _generate_X(nr_example, length, nr_vector):
X1 = numpy.ndarray((nr_example, length), dtype='int32')
X1 *= X1 < nr_vector
@ -212,6 +212,7 @@ def test_fit_model():
X2 *= X2 < nr_vector
X2 *= 0 <= X2
return [X1, X2]
def _generate_Y(nr_example, nr_class):
ys = numpy.zeros((nr_example, nr_class), dtype='int32')
for i in range(nr_example):
@ -220,7 +221,7 @@ def test_fit_model():
vectors = numpy.ndarray((100, 8), dtype='float32')
shape = (10, 16, 3)
settings = {'lr': 0.001, 'dropout': 0.2}
settings = {'lr': 0.001, 'dropout': 0.2, 'gru_encode':True}
model = build_model(vectors, shape, settings)
train_X = _generate_X(20, shape[0], vectors.shape[1])