def test_batch_dot_shape(self):
x_batch = KTF.ones(shape=(32, 20))
y_batch = KTF.ones(shape=(32, 20))
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=1)
assert_allclose(KTF.eval(xy_batch_dot), np.ones((32, 1)) * 20, atol=1e-05)
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=0)
assert_allclose(KTF.eval(xy_batch_dot), np.ones((20, 1)) * 32, atol=1e-05)
# making sure swapping axes when ndim == 2 works
x_batch = KTF.ones(shape=(32, 20))
y_batch = KTF.ones(shape=(20, 32))
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=(0, 1))
assert_allclose(KTF.eval(xy_batch_dot), np.ones((20, 1)) * 32, atol=1e-05)
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=(1, 0))
assert_allclose(KTF.eval(xy_batch_dot), np.ones((32, 1)) * 20, atol=1e-05)
def test_batch_dot_shape(self):
x_batch = KTF.ones(shape=(32, 20))
y_batch = KTF.ones(shape=(32, 20))
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=1)
assert_allclose(KTF.eval(xy_batch_dot), np.ones((32, 1)) * 20, atol=1e-05)
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=0)
assert_allclose(KTF.eval(xy_batch_dot), np.ones((20, 1)) * 32, atol=1e-05)
# making sure swapping axes when ndim == 2 works
x_batch = KTF.ones(shape=(32, 20))
y_batch = KTF.ones(shape=(20, 32))
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=(0, 1))
assert_allclose(KTF.eval(xy_batch_dot), np.ones((20, 1)) * 32, atol=1e-05)
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=(1, 0))
assert_allclose(KTF.eval(xy_batch_dot), np.ones((32, 1)) * 20, atol=1e-05)
def _call_multiplicative_emission(self, inputs):
# e_{t, t'} = x_t^T W_a x_{t'} + b_a
e = K.batch_dot(K.dot(inputs, self.Wa),
K.permute_dimensions(inputs, (0, 2, 1)))
if self.use_attention_bias:
e += self.ba[0]
return e
def _attention_regularizer(self, attention):
batch_size = K.cast(K.shape(attention)[0], K.floatx())
input_len = K.shape(attention)[-1]
indices = K.expand_dims(K.arange(0, input_len), axis=0)
diagonal = K.expand_dims(K.arange(0, input_len), axis=-1)
eye = K.cast(K.equal(indices, diagonal), K.floatx())
return self.attention_regularizer_weight * K.sum(
K.square(
K.batch_dot(attention,
K.permute_dimensions(attention, (0, 2, 1))) -
eye)) / batch_size
def call(self, inputs, mask=None, **kwargs):
input_len = K.shape(inputs)[1]
if self.attention_type == Attention.ATTENTION_TYPE_ADD:
e = self._call_additive_emission(inputs)
elif self.attention_type == Attention.ATTENTION_TYPE_MUL:
e = self._call_multiplicative_emission(inputs)
if self.attention_activation is not None:
e = self.attention_activation(e)
if self.attention_width is not None:
if self.history_only:
lower = K.arange(0, input_len) - (self.attention_width - 1)
else:
lower = K.arange(0, input_len) - self.attention_width // 2
lower = K.expand_dims(lower, axis=-1)
upper = lower + self.attention_width
indices = K.expand_dims(K.arange(0, input_len), axis=0)
e -= 10000.0 * (1.0 - K.cast(lower <= indices, K.floatx()) *
K.cast(indices < upper, K.floatx()))
if mask is not None:
mask = K.expand_dims(K.cast(mask, K.floatx()), axis=-1)
e -= 10000.0 * ((1.0 - mask) *
(1.0 - K.permute_dimensions(mask, (0, 2, 1))))
# a_{t} = \text{softmax}(e_t)
e = K.exp(e - K.max(e, axis=-1, keepdims=True))
a = e / K.sum(e, axis=-1, keepdims=True)
# l_t = \sum_{t'} a_{t, t'} x_{t'}
v = K.batch_dot(a, inputs)
if self.attention_regularizer_weight > 0.0:
self.add_loss(self._attention_regularizer(a))
if self.return_attention:
return [v, a]
return v
def test_batch_dot_shape(self):
with pytest.raises(ValueError):
x_batch = KTF.ones(shape=(32, 20))
y_batch = KTF.ones(shape=(32, 20))
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=1)
def get_R(X):
U_w, vecT = X[0], X[1]
print U_w.shape, vecT.shape
ans = K.batch_dot(U_w, vecT)
return ans
def test_batch_dot_shape(self):
with pytest.raises(ValueError):
x_batch = KTF.ones(shape=(32, 20))
y_batch = KTF.ones(shape=(32, 20))
xy_batch_dot = KTF.batch_dot(x_batch, y_batch, axes=1)