Permalink
Find file Copy path
655 lines (561 sloc) 25.2 KB
# -*- coding: utf-8 -*-
"""Layers that augment the functionality of a base layer.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
from ..engine.base_layer import Layer
from ..engine.base_layer import InputSpec
from ..utils.generic_utils import has_arg
from ..utils.generic_utils import object_list_uid
from .. import backend as K
from . import recurrent
class Wrapper(Layer):
"""Abstract wrapper base class.
Wrappers take another layer and augment it in various ways.
Do not use this class as a layer, it is only an abstract base class.
Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers.
# Arguments
layer: The layer to be wrapped.
"""
def __init__(self, layer, **kwargs):
self.layer = layer
# Tracks mapping of Wrapper inputs to inner layer inputs. Useful when
# the inner layer has update ops that depend on its inputs (as opposed
# to the inputs to the Wrapper layer).
self._input_map = {}
super(Wrapper, self).__init__(**kwargs)
def build(self, input_shape=None):
self.built = True
@property
def activity_regularizer(self):
if hasattr(self.layer, 'activity_regularizer'):
return self.layer.activity_regularizer
else:
return None
@property
def trainable(self):
return self.layer.trainable
@trainable.setter
def trainable(self, value):
self.layer.trainable = value
@property
def trainable_weights(self):
return self.layer.trainable_weights
@property
def non_trainable_weights(self):
return self.layer.non_trainable_weights
@property
def updates(self):
if hasattr(self.layer, 'updates'):
return self.layer.updates
return []
def get_updates_for(self, inputs=None):
# If the wrapper modifies the inputs, use the modified inputs to
# get the updates from the inner layer.
inner_inputs = inputs
if inputs is not None:
uid = object_list_uid(inputs)
if uid in self._input_map:
inner_inputs = self._input_map[uid]
updates = self.layer.get_updates_for(inner_inputs)
updates += super(Wrapper, self).get_updates_for(inputs)
return updates
@property
def losses(self):
if hasattr(self.layer, 'losses'):
return self.layer.losses
return []
def get_losses_for(self, inputs=None):
if inputs is None:
losses = self.layer.get_losses_for(None)
return losses + super(Wrapper, self).get_losses_for(None)
return super(Wrapper, self).get_losses_for(inputs)
def get_weights(self):
return self.layer.get_weights()
def set_weights(self, weights):
self.layer.set_weights(weights)
def get_config(self):
config = {'layer': {'class_name': self.layer.__class__.__name__,
'config': self.layer.get_config()}}
base_config = super(Wrapper, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
@classmethod
def from_config(cls, config, custom_objects=None):
from . import deserialize as deserialize_layer
layer = deserialize_layer(config.pop('layer'),
custom_objects=custom_objects)
return cls(layer, **config)
class TimeDistributed(Wrapper):
"""This wrapper applies a layer to every temporal slice of an input.
The input should be at least 3D, and the dimension of index one
will be considered to be the temporal dimension.
Consider a batch of 32 samples,
where each sample is a sequence of 10 vectors of 16 dimensions.
The batch input shape of the layer is then `(32, 10, 16)`,
and the `input_shape`, not including the samples dimension, is `(10, 16)`.
You can then use `TimeDistributed` to apply a `Dense` layer
to each of the 10 timesteps, independently:
```python
# as the first layer in a model
model = Sequential()
model.add(TimeDistributed(Dense(8), input_shape=(10, 16)))
# now model.output_shape == (None, 10, 8)
```
The output will then have shape `(32, 10, 8)`.
In subsequent layers, there is no need for the `input_shape`:
```python
model.add(TimeDistributed(Dense(32)))
# now model.output_shape == (None, 10, 32)
```
The output will then have shape `(32, 10, 32)`.
`TimeDistributed` can be used with arbitrary layers, not just `Dense`,
for instance with a `Conv2D` layer:
```python
model = Sequential()
model.add(TimeDistributed(Conv2D(64, (3, 3)),
input_shape=(10, 299, 299, 3)))
```
# Arguments
layer: a layer instance.
"""
def __init__(self, layer, **kwargs):
super(TimeDistributed, self).__init__(layer, **kwargs)
self.supports_masking = True
def _get_shape_tuple(self, init_tuple, tensor, start_idx, int_shape=None):
"""Finds non-specific dimensions in the static shapes
and replaces them by the corresponding dynamic shapes of the tensor.
# Arguments
init_tuple: a tuple, the first part of the output shape
tensor: the tensor from which to get the (static and dynamic) shapes
as the last part of the output shape
start_idx: int, which indicate the first dimension to take from
the static shape of the tensor
int_shape: an alternative static shape to take as the last part
of the output shape
# Returns
The new int_shape with the first part from init_tuple
and the last part from either `int_shape` (if provided)
or K.int_shape(tensor), where every `None` is replaced by
the corresponding dimension from K.shape(tensor)
"""
# replace all None in int_shape by K.shape
if int_shape is None:
int_shape = K.int_shape(tensor)[start_idx:]
if not any(not s for s in int_shape):
return init_tuple + int_shape
tensor_shape = K.shape(tensor)
int_shape = list(int_shape)
for i, s in enumerate(int_shape):
if not s:
int_shape[i] = tensor_shape[start_idx + i]
return init_tuple + tuple(int_shape)
def build(self, input_shape):
assert len(input_shape) >= 3
self.input_spec = InputSpec(shape=input_shape)
child_input_shape = (input_shape[0],) + input_shape[2:]
if not self.layer.built:
self.layer.build(child_input_shape)
self.layer.built = True
super(TimeDistributed, self).build()
def compute_output_shape(self, input_shape):
child_input_shape = (input_shape[0],) + input_shape[2:]
child_output_shape = self.layer.compute_output_shape(child_input_shape)
timesteps = input_shape[1]
return (child_output_shape[0], timesteps) + child_output_shape[1:]
def call(self, inputs, training=None, mask=None):
kwargs = {}
if has_arg(self.layer.call, 'training'):
kwargs['training'] = training
uses_learning_phase = False
input_shape = K.int_shape(inputs)
if input_shape[0]:
# batch size matters, use rnn-based implementation
def step(x, _):
global uses_learning_phase
output = self.layer.call(x, **kwargs)
if hasattr(output, '_uses_learning_phase'):
uses_learning_phase = (output._uses_learning_phase or
uses_learning_phase)
return output, []
_, outputs, _ = K.rnn(step, inputs,
initial_states=[],
input_length=input_shape[1],
unroll=False)
y = outputs
else:
# No batch size specified, therefore the layer will be able
# to process batches of any size.
# We can go with reshape-based implementation for performance.
input_length = input_shape[1]
if not input_length:
input_length = K.shape(inputs)[1]
inner_input_shape = self._get_shape_tuple((-1,), inputs, 2)
# Shape: (num_samples * timesteps, ...). And track the
# transformation in self._input_map.
input_uid = object_list_uid(inputs)
inputs = K.reshape(inputs, inner_input_shape)
self._input_map[input_uid] = inputs
# (num_samples * timesteps, ...)
if has_arg(self.layer.call, 'mask') and mask is not None:
inner_mask_shape = self._get_shape_tuple((-1,), mask, 2)
kwargs['mask'] = K.reshape(mask, inner_mask_shape)
y = self.layer.call(inputs, **kwargs)
if hasattr(y, '_uses_learning_phase'):
uses_learning_phase = y._uses_learning_phase
# Shape: (num_samples, timesteps, ...)
output_shape = self.compute_output_shape(input_shape)
output_shape = self._get_shape_tuple(
(-1, input_length), y, 1, output_shape[2:])
y = K.reshape(y, output_shape)
# Apply activity regularizer if any:
if (hasattr(self.layer, 'activity_regularizer') and
self.layer.activity_regularizer is not None):
regularization_loss = self.layer.activity_regularizer(y)
self.add_loss(regularization_loss, inputs)
if uses_learning_phase:
y._uses_learning_phase = True
return y
def compute_mask(self, inputs, mask=None):
"""Computes an output mask tensor for Embedding layer
based on the inputs, mask, and the inner layer.
If batch size is specified:
Simply return the input `mask`. (An rnn-based implementation with
more than one rnn inputs is required but not supported in Keras yet.)
Otherwise we call `compute_mask` of the inner layer at each time step.
If the output mask at each time step is not `None`:
(E.g., inner layer is Masking or RNN)
Concatenate all of them and return the concatenation.
If the output mask at each time step is `None` and
the input mask is not `None`:
(E.g., inner layer is Dense)
Reduce the input_mask to 2 dimensions and return it.
Otherwise (both the output mask and the input mask are `None`):
(E.g., `mask` is not used at all)
Return `None`.
# Arguments
inputs: Tensor
mask: Tensor
# Returns
None or a tensor
"""
# cases need to call the layer.compute_mask when input_mask is None:
# Masking layer and Embedding layer with mask_zero
input_shape = K.int_shape(inputs)
if input_shape[0]:
# batch size matters, we currently do not handle mask explicitly
return mask
inner_mask = mask
if inner_mask is not None:
inner_mask_shape = self._get_shape_tuple((-1,), mask, 2)
inner_mask = K.reshape(inner_mask, inner_mask_shape)
input_uid = object_list_uid(inputs)
inner_inputs = self._input_map[input_uid]
output_mask = self.layer.compute_mask(inner_inputs, inner_mask)
if output_mask is None:
if mask is None:
return None
# input_mask is not None, and output_mask is None:
# we should return a not-None mask
output_mask = mask
for _ in range(2, len(K.int_shape(mask))):
output_mask = K.any(output_mask, axis=-1)
else:
# output_mask is not None. We need to reshape it
input_length = input_shape[1]
if not input_length:
input_length = K.shape(inputs)[1]
output_mask_int_shape = K.int_shape(output_mask)
if output_mask_int_shape is None:
# if the output_mask does not have a static shape,
# its shape must be the same as mask's
if mask is not None:
output_mask_int_shape = K.int_shape(mask)
else:
output_mask_int_shape = K.compute_output_shape(input_shape)[:-1]
output_mask_shape = self._get_shape_tuple(
(-1, input_length), output_mask, 1, output_mask_int_shape[1:])
output_mask = K.reshape(output_mask, output_mask_shape)
return output_mask
class Bidirectional(Wrapper):
"""Bidirectional wrapper for RNNs.
# Arguments
layer: `Recurrent` instance.
merge_mode: Mode by which outputs of the
forward and backward RNNs will be combined.
One of {'sum', 'mul', 'concat', 'ave', None}.
If None, the outputs will not be combined,
they will be returned as a list.
# Raises
ValueError: In case of invalid `merge_mode` argument.
# Examples
```python
model = Sequential()
model.add(Bidirectional(LSTM(10, return_sequences=True),
input_shape=(5, 10)))
model.add(Bidirectional(LSTM(10)))
model.add(Dense(5))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
```
"""
def __init__(self, layer, merge_mode='concat', weights=None, **kwargs):
if merge_mode not in ['sum', 'mul', 'ave', 'concat', None]:
raise ValueError('Invalid merge mode. '
'Merge mode should be one of '
'{"sum", "mul", "ave", "concat", None}')
self.forward_layer = copy.copy(layer)
config = layer.get_config()
config['go_backwards'] = not config['go_backwards']
self.backward_layer = layer.__class__.from_config(config)
self.forward_layer.name = 'forward_' + self.forward_layer.name
self.backward_layer.name = 'backward_' + self.backward_layer.name
self.merge_mode = merge_mode
if weights:
nw = len(weights)
self.forward_layer.initial_weights = weights[:nw // 2]
self.backward_layer.initial_weights = weights[nw // 2:]
self.stateful = layer.stateful
self.return_sequences = layer.return_sequences
self.return_state = layer.return_state
self.supports_masking = True
self._trainable = True
super(Bidirectional, self).__init__(layer, **kwargs)
self.input_spec = layer.input_spec
self._num_constants = None
@property
def trainable(self):
return self._trainable
@trainable.setter
def trainable(self, value):
self._trainable = value
self.forward_layer.trainable = value
self.backward_layer.trainable = value
def get_weights(self):
return self.forward_layer.get_weights() + self.backward_layer.get_weights()
def set_weights(self, weights):
nw = len(weights)
self.forward_layer.set_weights(weights[:nw // 2])
self.backward_layer.set_weights(weights[nw // 2:])
def compute_output_shape(self, input_shape):
output_shape = self.forward_layer.compute_output_shape(input_shape)
if self.return_state:
state_shape = output_shape[1:]
output_shape = output_shape[0]
if self.merge_mode == 'concat':
output_shape = list(output_shape)
output_shape[-1] *= 2
output_shape = tuple(output_shape)
elif self.merge_mode is None:
output_shape = [output_shape, copy.copy(output_shape)]
if self.return_state:
if self.merge_mode is None:
return output_shape + state_shape + copy.copy(state_shape)
return [output_shape] + state_shape + copy.copy(state_shape)
return output_shape
def __call__(self, inputs, initial_state=None, constants=None, **kwargs):
inputs, initial_state, constants = recurrent._standardize_args(
inputs, initial_state, constants, self._num_constants)
if initial_state is None and constants is None:
return super(Bidirectional, self).__call__(inputs, **kwargs)
# Applies the same workaround as in `RNN.__call__`
additional_inputs = []
additional_specs = []
if initial_state is not None:
# Check if `initial_state` can be splitted into half
num_states = len(initial_state)
if num_states % 2 > 0:
raise ValueError(
'When passing `initial_state` to a Bidirectional RNN, '
'the state should be a list containing the states of '
'the underlying RNNs. '
'Found: ' + str(initial_state))
kwargs['initial_state'] = initial_state
additional_inputs += initial_state
state_specs = [InputSpec(shape=K.int_shape(state))
for state in initial_state]
self.forward_layer.state_spec = state_specs[:num_states // 2]
self.backward_layer.state_spec = state_specs[num_states // 2:]
additional_specs += state_specs
if constants is not None:
kwargs['constants'] = constants
additional_inputs += constants
constants_spec = [InputSpec(shape=K.int_shape(constant))
for constant in constants]
self.forward_layer.constants_spec = constants_spec
self.backward_layer.constants_spec = constants_spec
additional_specs += constants_spec
self._num_constants = len(constants)
self.forward_layer._num_constants = self._num_constants
self.backward_layer._num_constants = self._num_constants
is_keras_tensor = K.is_keras_tensor(additional_inputs[0])
for tensor in additional_inputs:
if K.is_keras_tensor(tensor) != is_keras_tensor:
raise ValueError('The initial state of a Bidirectional'
' layer cannot be specified with a mix of'
' Keras tensors and non-Keras tensors'
' (a "Keras tensor" is a tensor that was'
' returned by a Keras layer, or by `Input`)')
if is_keras_tensor:
# Compute the full input spec, including state
full_input = [inputs] + additional_inputs
full_input_spec = self.input_spec + additional_specs
# Perform the call with temporarily replaced input_spec
original_input_spec = self.input_spec
self.input_spec = full_input_spec
output = super(Bidirectional, self).__call__(full_input, **kwargs)
self.input_spec = original_input_spec
return output
else:
return super(Bidirectional, self).__call__(inputs, **kwargs)
def call(self,
inputs,
mask=None,
training=None,
initial_state=None,
constants=None):
kwargs = {}
if has_arg(self.layer.call, 'training'):
kwargs['training'] = training
if has_arg(self.layer.call, 'mask'):
kwargs['mask'] = mask
if has_arg(self.layer.call, 'constants'):
kwargs['constants'] = constants
if initial_state is not None and has_arg(self.layer.call, 'initial_state'):
forward_inputs = [inputs[0]]
backward_inputs = [inputs[0]]
pivot = len(initial_state) // 2 + 1
# add forward initial state
forward_state = inputs[1:pivot]
forward_inputs += forward_state
if self._num_constants is None:
# add backward initial state
backward_state = inputs[pivot:]
backward_inputs += backward_state
else:
# add backward initial state
backward_state = inputs[pivot:-self._num_constants]
backward_inputs += backward_state
# add constants for forward and backward layers
forward_inputs += inputs[-self._num_constants:]
backward_inputs += inputs[-self._num_constants:]
y = self.forward_layer.call(forward_inputs,
initial_state=forward_state, **kwargs)
y_rev = self.backward_layer.call(backward_inputs,
initial_state=backward_state, **kwargs)
else:
y = self.forward_layer.call(inputs, **kwargs)
y_rev = self.backward_layer.call(inputs, **kwargs)
if self.return_state:
states = y[1:] + y_rev[1:]
y = y[0]
y_rev = y_rev[0]
if self.return_sequences:
y_rev = K.reverse(y_rev, 1)
if self.merge_mode == 'concat':
output = K.concatenate([y, y_rev])
elif self.merge_mode == 'sum':
output = y + y_rev
elif self.merge_mode == 'ave':
output = (y + y_rev) / 2
elif self.merge_mode == 'mul':
output = y * y_rev
elif self.merge_mode is None:
output = [y, y_rev]
else:
raise ValueError('Unrecognized value for argument '
'merge_mode: %s' % (self.merge_mode))
# Properly set learning phase
if (getattr(y, '_uses_learning_phase', False) or
getattr(y_rev, '_uses_learning_phase', False)):
if self.merge_mode is None:
for out in output:
out._uses_learning_phase = True
else:
output._uses_learning_phase = True
if self.return_state:
if self.merge_mode is None:
return output + states
return [output] + states
return output
def reset_states(self):
self.forward_layer.reset_states()
self.backward_layer.reset_states()
def build(self, input_shape):
with K.name_scope(self.forward_layer.name):
self.forward_layer.build(input_shape)
with K.name_scope(self.backward_layer.name):
self.backward_layer.build(input_shape)
self.built = True
def compute_mask(self, inputs, mask):
if isinstance(mask, list):
mask = mask[0]
if self.return_sequences:
if not self.merge_mode:
output_mask = [mask, mask]
else:
output_mask = mask
else:
output_mask = [None, None] if not self.merge_mode else None
if self.return_state:
states = self.forward_layer.states
state_mask = [None for _ in states]
if isinstance(output_mask, list):
return output_mask + state_mask * 2
return [output_mask] + state_mask * 2
return output_mask
@property
def trainable_weights(self):
if hasattr(self.forward_layer, 'trainable_weights'):
return (self.forward_layer.trainable_weights +
self.backward_layer.trainable_weights)
return []
@property
def non_trainable_weights(self):
if hasattr(self.forward_layer, 'non_trainable_weights'):
return (self.forward_layer.non_trainable_weights +
self.backward_layer.non_trainable_weights)
return []
@property
def updates(self):
if hasattr(self.forward_layer, 'updates'):
return self.forward_layer.updates + self.backward_layer.updates
return []
def get_updates_for(self, inputs=None):
forward_updates = self.forward_layer.get_updates_for(inputs)
backward_updates = self.backward_layer.get_updates_for(inputs)
return (super(Wrapper, self).get_updates_for(inputs) +
forward_updates + backward_updates)
@property
def losses(self):
if hasattr(self.forward_layer, 'losses'):
return self.forward_layer.losses + self.backward_layer.losses
return []
def get_losses_for(self, inputs=None):
forward_losses = self.forward_layer.get_losses_for(inputs)
backward_losses = self.backward_layer.get_losses_for(inputs)
return (super(Wrapper, self).get_losses_for(inputs) +
forward_losses + backward_losses)
@property
def constraints(self):
constraints = {}
if hasattr(self.forward_layer, 'constraints'):
constraints.update(self.forward_layer.constraints)
constraints.update(self.backward_layer.constraints)
return constraints
def get_config(self):
config = {'merge_mode': self.merge_mode}
if self._num_constants is not None:
config['num_constants'] = self._num_constants
base_config = super(Bidirectional, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
@classmethod
def from_config(cls, config, custom_objects=None):
from . import deserialize as deserialize_layer
rnn_layer = deserialize_layer(config.pop('layer'),
custom_objects=custom_objects)
num_constants = config.pop('num_constants', None)
layer = cls(rnn_layer, **config)
layer._num_constants = num_constants
return layer