def compute_mask(self, inputs, mask=None):
if mask is None:
return None
if not isinstance(mask, list):
raise ValueError('`mask` should be a list.')
if not isinstance(inputs, list):
raise ValueError('`inputs` should be a list.')
if len(mask) != len(inputs):
raise ValueError('The lists `inputs` and `mask` '
'should have the same length.')
if all([m is None for m in mask]):
return None
# Make a list of masks while making sure
# the dimensionality of each mask
# is the same as the corresponding input.
masks = []
for input_i, mask_i in zip(inputs, mask):
if mask_i is None:
# Input is unmasked. Append all 1s to masks,
# but cast it to bool first
masks.append(K.cast(K.ones_like(input_i), 'bool'))
elif K.ndim(mask_i) < K.ndim(input_i):
# Mask is smaller than the input, expand it
masks.append(K.expand_dims(mask_i))
else:
masks.append(mask_i)
concatenated = K.concatenate(masks, axis=self.axis)
return K.all(concatenated, axis=-1, keepdims=False)
def call(self, inputs):
if self._reshape_required:
reshaped_inputs = []
input_ndims = list(map(K.ndim, inputs))
if None not in input_ndims:
# If ranks of all inputs are available,
# we simply expand each of them at axis=1
# until all of them have the same rank.
max_ndim = max(input_ndims)
for x in inputs:
x_ndim = K.ndim(x)
for _ in range(max_ndim - x_ndim):
x = K.expand_dims(x, 1)
reshaped_inputs.append(x)
return self._merge_function(reshaped_inputs)
else:
# Transpose all inputs so that batch size is the last dimension.
# (batch_size, dim1, dim2, ... ) -> (dim1, dim2, ... , batch_size)
transposed = False
for x in inputs:
x_ndim = K.ndim(x)
if x_ndim is None:
x_shape = K.shape(x)
batch_size = x_shape[0]
new_shape = K.concatenate([x_shape[1:], K.expand_dims(batch_size)])
x_transposed = K.reshape(x,
K.stack([batch_size,
K.prod(x_shape[1:])]))
x_transposed = K.permute_dimensions(x_transposed, (1, 0))
x_transposed = K.reshape(x_transposed, new_shape)
reshaped_inputs.append(x_transposed)
transposed = True
elif x_ndim > 1:
dims = list(range(1, x_ndim)) + [0]
reshaped_inputs.append(K.permute_dimensions(x, dims))
transposed = True
else:
# We don't transpose inputs if they are 1D vectors or scalars.
reshaped_inputs.append(x)
y = self._merge_function(reshaped_inputs)
y_ndim = K.ndim(y)
if transposed:
# If inputs have been transposed, we have to transpose the output too.
if y_ndim is None:
y_shape = K.shape(y)
y_ndim = K.shape(y_shape)[0]
batch_size = y_shape[y_ndim - 1]
new_shape = K.concatenate(
[K.expand_dims(batch_size), y_shape[:y_ndim - 1]])
y = K.reshape(y, (-1, batch_size))
y = K.permute_dimensions(y, (1, 0))
y = K.reshape(y, new_shape)
elif y_ndim > 1:
dims = [y_ndim - 1] + list(range(y_ndim - 1))
y = K.permute_dimensions(y, dims)
return y
else:
return self._merge_function(inputs)
def call(self, inputs):
x1 = inputs[0]
x2 = inputs[1]
if isinstance(self.axes, int):
if self.axes < 0:
axes = [self.axes % K.ndim(x1), self.axes % K.ndim(x2)]
else:
axes = [self.axes] * 2
else:
axes = []
for i in range(len(self.axes)):
if self.axes[i] < 0:
axes.append(self.axes[i] % K.ndim(inputs[i]))
else:
axes.append(self.axes[i])
if self.normalize:
x1 = K.l2_normalize(x1, axis=axes[0])
x2 = K.l2_normalize(x2, axis=axes[1])
output = K.batch_dot(x1, x2, axes)
return output
def softmax(x):
ndim = K.ndim(x)
if ndim == 2:
return K.softmax(x)
elif ndim == 3:
e = K.exp(x - K.max(x, axis=-1, keepdims=True))
s = K.sum(e, axis=-1, keepdims=True)
return e / s
else:
raise ValueError('Cannot apply softmax to a tensor '
'that is not 2D or 3D. '
'Here, ndim=' + str(ndim))
def softmax(x, axis=-1):
"""Softmax activation function.
Arguments:
x : Tensor.
axis: Integer, axis along which the softmax normalization is applied.
Returns:
Tensor, output of softmax transformation.
Raises:
ValueError: In case `dim(x) == 1`.
"""
ndim = K.ndim(x)
if ndim == 2:
return K.softmax(x)
elif ndim > 2:
e = K.exp(x - K.max(x, axis=axis, keepdims=True))
s = K.sum(e, axis=axis, keepdims=True)
return e / s
else:
raise ValueError('Cannot apply softmax to a tensor that is 1D')
def softmax(x, axis=-1):
"""Softmax activation function.
Arguments:
x : Tensor.
axis: Integer, axis along which the softmax normalization is applied.
Returns:
Tensor, output of softmax transformation.
Raises:
ValueError: In case `dim(x) == 1`.
"""
ndim = K.ndim(x)
if ndim == 2:
return K.softmax(x)
elif ndim > 2:
e = K.exp(x - K.max(x, axis=axis, keepdims=True))
s = K.sum(e, axis=axis, keepdims=True)
return e / s
else:
raise ValueError('Cannot apply softmax to a tensor that is 1D')
