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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 Activation, Dropout, Embedding, TimeDistributed
from keras.layers import Bidirectional, GRU from keras.layers import Bidirectional, GRU
import keras.backend as K import keras.backend as K
import theano.tensor as T
from keras.models import Sequential, Model, model_from_json from keras.models import Sequential, Model, model_from_json
from keras.regularizers import l2 from keras.regularizers import l2
from keras.optimizers import Adam from keras.optimizers import Adam
@ -106,8 +105,8 @@ class _Attention(object):
def __call__(self, sent1, sent2): def __call__(self, sent1, sent2):
def _outer(AB): def _outer(AB):
att_ji = T.batched_dot(AB[1], AB[0].dimshuffle((0, 2, 1))) att_ji = K.batch_dot(AB[1], K.permute_dimensions(AB[0], (0, 2, 1)))
return att_ji.dimshuffle((0, 2, 1)) return K.permute_dimensions(att_ji,(0, 2, 1))
return merge( return merge(
@ -126,12 +125,12 @@ class _SoftAlignment(object):
att = attmat[0] att = attmat[0]
mat = attmat[1] mat = attmat[1]
if transpose: if transpose:
att = att.dimshuffle((0, 2, 1)) att = K.permute_dimensions(att,(0, 2, 1))
# 3d softmax # 3d softmax
e = K.exp(att - K.max(att, axis=-1, keepdims=True)) e = K.exp(att - K.max(att, axis=-1, keepdims=True))
s = K.sum(e, axis=-1, keepdims=True) s = K.sum(e, axis=-1, keepdims=True)
sm_att = e / s 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, return merge([attention, sentence], mode=_normalize_attention,
output_shape=(self.max_length, self.nr_hidden)) # Shape: (i, n) output_shape=(self.max_length, self.nr_hidden)) # Shape: (i, n)
@ -191,7 +190,7 @@ class _GlobalSumPooling1D(Layer):
def call(self, x, mask=None): def call(self, x, mask=None):
if mask is not 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: else:
return K.sum(x, axis=1) return K.sum(x, axis=1)
@ -199,11 +198,12 @@ class _GlobalSumPooling1D(Layer):
def test_build_model(): def test_build_model():
vectors = numpy.ndarray((100, 8), dtype='float32') vectors = numpy.ndarray((100, 8), dtype='float32')
shape = (10, 16, 3) 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) model = build_model(vectors, shape, settings)
def test_fit_model(): def test_fit_model():
def _generate_X(nr_example, length, nr_vector): def _generate_X(nr_example, length, nr_vector):
X1 = numpy.ndarray((nr_example, length), dtype='int32') X1 = numpy.ndarray((nr_example, length), dtype='int32')
X1 *= X1 < nr_vector X1 *= X1 < nr_vector
@ -212,6 +212,7 @@ def test_fit_model():
X2 *= X2 < nr_vector X2 *= X2 < nr_vector
X2 *= 0 <= X2 X2 *= 0 <= X2
return [X1, X2] return [X1, X2]
def _generate_Y(nr_example, nr_class): def _generate_Y(nr_example, nr_class):
ys = numpy.zeros((nr_example, nr_class), dtype='int32') ys = numpy.zeros((nr_example, nr_class), dtype='int32')
for i in range(nr_example): for i in range(nr_example):
@ -220,7 +221,7 @@ def test_fit_model():
vectors = numpy.ndarray((100, 8), dtype='float32') vectors = numpy.ndarray((100, 8), dtype='float32')
shape = (10, 16, 3) 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) model = build_model(vectors, shape, settings)
train_X = _generate_X(20, shape[0], vectors.shape[1]) train_X = _generate_X(20, shape[0], vectors.shape[1])