from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import warnings
from ..engine import Layer, InputSpec
from .. import backend as K
from ..utils import conv_utils
from ..utils.generic_utils import to_list
from .. import regularizers
from .. import constraints
from .. import activations
from .. import initializers
class MaxoutDense(Layer):
"""A dense maxout layer.
A `MaxoutDense` layer takes the element-wise maximum of
`nb_feature` `Dense(input_dim, output_dim)` linear layers.
This allows the layer to learn a convex,
piecewise linear activation function over the inputs.
Note that this is a *linear* layer;
if you wish to apply activation function
(you shouldn't need to --they are universal function approximators),
an `Activation` layer must be added after.
# Arguments
output_dim: int > 0.
nb_feature: number of Dense layers to use internally.
init: name of initialization function for the weights of the layer
(see [initializations](../initializations.md)),
or alternatively, Theano function to use for weights
initialization. This parameter is only relevant
if you don't pass a `weights` argument.
weights: list of Numpy arrays to set as initial weights.
The list should have 2 elements, of shape `(input_dim, output_dim)`
and (output_dim,) for weights and biases respectively.
W_regularizer: instance of [WeightRegularizer](../regularizers.md)
(eg. L1 or L2 regularization), applied to the main weights matrix.
b_regularizer: instance of [WeightRegularizer](../regularizers.md),
applied to the bias.
activity_regularizer: instance of [ActivityRegularizer](../regularizers.md),
applied to the network output.
W_constraint: instance of the [constraints](../constraints.md) module
(eg. maxnorm, nonneg), applied to the main weights matrix.
b_constraint: instance of the [constraints](../constraints.md) module,
applied to the bias.
bias: whether to include a bias
(i.e. make the layer affine rather than linear).
input_dim: dimensionality of the input (integer). This argument
(or alternatively, the keyword argument `input_shape`)
is required when using this layer as the first layer in a model.
# Input shape
2D tensor with shape: `(nb_samples, input_dim)`.
# Output shape
2D tensor with shape: `(nb_samples, output_dim)`.
# References
- [Maxout Networks](http://arxiv.org/abs/1302.4389)
"""
def __init__(self, output_dim,
nb_feature=4,
init='glorot_uniform',
weights=None,
W_regularizer=None,
b_regularizer=None,
activity_regularizer=None,
W_constraint=None,
b_constraint=None,
bias=True,
input_dim=None,
**kwargs):
warnings.warn('The `MaxoutDense` layer is deprecated '
'and will be removed after 06/2017.')
self.output_dim = output_dim
self.nb_feature = nb_feature
self.init = initializers.get(init)
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
self.initial_weights = weights
self.input_spec = InputSpec(ndim=2)
self.input_dim = input_dim
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(MaxoutDense, self).__init__(**kwargs)
def build(self, input_shape):
input_dim = input_shape[1]
self.input_spec = InputSpec(dtype=K.floatx(),
shape=(None, input_dim))
self.W = self.add_weight((self.nb_feature, input_dim, self.output_dim),
initializer=self.init,
name='W',
regularizer=self.W_regularizer,
constraint=self.W_constraint)
if self.bias:
self.b = self.add_weight((self.nb_feature, self.output_dim,),
initializer='zero',
name='b',
regularizer=self.b_regularizer,
constraint=self.b_constraint)
else:
self.b = None
if self.initial_weights is not None:
self.set_weights(self.initial_weights)
del self.initial_weights
self.built = True
def compute_output_shape(self, input_shape):
assert input_shape and len(input_shape) == 2
return (input_shape[0], self.output_dim)
def call(self, x):
# no activation, this layer is only linear.
output = K.dot(x, self.W)
if self.bias:
output += self.b
output = K.max(output, axis=1)
return output
def get_config(self):
config = {'output_dim': self.output_dim,
'init': initializers.serialize(self.init),
'nb_feature': self.nb_feature,
'W_regularizer': regularizers.serialize(self.W_regularizer),
'b_regularizer': regularizers.serialize(self.b_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'W_constraint': constraints.serialize(self.W_constraint),
'b_constraint': constraints.serialize(self.b_constraint),
'bias': self.bias,
'input_dim': self.input_dim}
base_config = super(MaxoutDense, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class Highway(Layer):
"""Densely connected highway network.
Highway layers are a natural extension of LSTMs to feedforward networks.
# Arguments
init: name of initialization function for the weights of the layer
(see [initializations](../initializations.md)),
or alternatively, Theano function to use for weights
initialization. This parameter is only relevant
if you don't pass a `weights` argument.
activation: name of activation function to use
(see [activations](../activations.md)),
or alternatively, elementwise Theano function.
If you don't specify anything, no activation is applied
(ie. "linear" activation: a(x) = x).
weights: list of Numpy arrays to set as initial weights.
The list should have 2 elements, of shape `(input_dim, output_dim)`
and (output_dim,) for weights and biases respectively.
W_regularizer: instance of [WeightRegularizer](../regularizers.md)
(eg. L1 or L2 regularization), applied to the main weights matrix.
b_regularizer: instance of [WeightRegularizer](../regularizers.md),
applied to the bias.
activity_regularizer: instance of [ActivityRegularizer](../regularizers.md),
applied to the network output.
W_constraint: instance of the [constraints](../constraints.md) module
(eg. maxnorm, nonneg), applied to the main weights matrix.
b_constraint: instance of the [constraints](../constraints.md) module,
applied to the bias.
bias: whether to include a bias
(i.e. make the layer affine rather than linear).
input_dim: dimensionality of the input (integer). This argument
(or alternatively, the keyword argument `input_shape`)
is required when using this layer as the first layer in a model.
# Input shape
2D tensor with shape: `(nb_samples, input_dim)`.
# Output shape
2D tensor with shape: `(nb_samples, input_dim)`.
# References
- [Highway Networks](http://arxiv.org/abs/1505.00387v2)
"""
def __init__(self,
init='glorot_uniform',
activation=None,
weights=None,
W_regularizer=None,
b_regularizer=None,
activity_regularizer=None,
W_constraint=None,
b_constraint=None,
bias=True,
input_dim=None,
**kwargs):
warnings.warn('The `Highway` layer is deprecated '
'and will be removed after 06/2017.')
if 'transform_bias' in kwargs:
kwargs.pop('transform_bias')
warnings.warn('`transform_bias` argument is deprecated and '
'has been removed.')
self.init = initializers.get(init)
self.activation = activations.get(activation)
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
self.initial_weights = weights
self.input_spec = InputSpec(ndim=2)
self.input_dim = input_dim
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(Highway, self).__init__(**kwargs)
def build(self, input_shape):
input_dim = input_shape[1]
self.input_spec = InputSpec(dtype=K.floatx(),
shape=(None, input_dim))
self.W = self.add_weight((input_dim, input_dim),
initializer=self.init,
name='W',
regularizer=self.W_regularizer,
constraint=self.W_constraint)
self.W_carry = self.add_weight((input_dim, input_dim),
initializer=self.init,
name='W_carry')
if self.bias:
self.b = self.add_weight((input_dim,),
initializer='zero',
name='b',
regularizer=self.b_regularizer,
constraint=self.b_constraint)
self.b_carry = self.add_weight((input_dim,),
initializer='one',
name='b_carry')
else:
self.b_carry = None
if self.initial_weights is not None:
self.set_weights(self.initial_weights)
del self.initial_weights
self.built = True
def call(self, x):
y = K.dot(x, self.W_carry)
if self.bias:
y += self.b_carry
transform_weight = activations.sigmoid(y)
y = K.dot(x, self.W)
if self.bias:
y += self.b
act = self.activation(y)
act *= transform_weight
output = act + (1 - transform_weight) * x
return output
def get_config(self):
config = {'init': initializers.serialize(self.init),
'activation': activations.serialize(self.activation),
'W_regularizer': regularizers.serialize(self.W_regularizer),
'b_regularizer': regularizers.serialize(self.b_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'W_constraint': constraints.serialize(self.W_constraint),
'b_constraint': constraints.serialize(self.b_constraint),
'bias': self.bias,
'input_dim': self.input_dim}
base_config = super(Highway, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def AtrousConvolution1D(*args, **kwargs):
from ..layers import Conv1D
if 'atrous_rate' in kwargs:
rate = kwargs.pop('atrous_rate')
else:
rate = 1
kwargs['dilation_rate'] = rate
warnings.warn('The `AtrousConvolution1D` layer '
' has been deprecated. Use instead '
'the `Conv1D` layer with the `dilation_rate` '
'argument.')
return Conv1D(*args, **kwargs)
def AtrousConvolution2D(*args, **kwargs):
from ..layers import Conv2D
if 'atrous_rate' in kwargs:
rate = kwargs.pop('atrous_rate')
else:
rate = 1
kwargs['dilation_rate'] = rate
warnings.warn('The `AtrousConvolution2D` layer '
' has been deprecated. Use instead '
'the `Conv2D` layer with the `dilation_rate` '
'argument.')
return Conv2D(*args, **kwargs)
class Recurrent(Layer):
"""Abstract base class for recurrent layers.
Do not use in a model -- it's not a valid layer!
Use its children classes `LSTM`, `GRU` and `SimpleRNN` instead.
All recurrent layers (`LSTM`, `GRU`, `SimpleRNN`) also
follow the specifications of this class and accept
the keyword arguments listed below.
# Example
```python
# as the first layer in a Sequential model
model = Sequential()
model.add(LSTM(32, input_shape=(10, 64)))
# now model.output_shape == (None, 32)
# note: `None` is the batch dimension.
# for subsequent layers, no need to specify the input size:
model.add(LSTM(16))
# to stack recurrent layers, you must use return_sequences=True
# on any recurrent layer that feeds into another recurrent layer.
# note that you only need to specify the input size on the first layer.
model = Sequential()
model.add(LSTM(64, input_dim=64, input_length=10, return_sequences=True))
model.add(LSTM(32, return_sequences=True))
model.add(LSTM(10))
```
# Arguments
weights: list of Numpy arrays to set as initial weights.
The list should have 3 elements, of shapes:
`[(input_dim, output_dim), (output_dim, output_dim), (output_dim,)]`.
return_sequences: Boolean. Whether to return the last output
in the output sequence, or the full sequence.
return_state: Boolean. Whether to return the last state
in addition to the output.
go_backwards: Boolean (default False).
If True, process the input sequence backwards and return the
reversed sequence.
stateful: Boolean (default False). If True, the last state
for each sample at index i in a batch will be used as initial
state for the sample of index i in the following batch.
unroll: Boolean (default False).
If True, the network will be unrolled,
else a symbolic loop will be used.
Unrolling can speed-up a RNN,
although it tends to be more memory-intensive.
Unrolling is only suitable for short sequences.
implementation: one of {0, 1, or 2}.
If set to 0, the RNN will use
an implementation that uses fewer, larger matrix products,
thus running faster on CPU but consuming more memory.
If set to 1, the RNN will use more matrix products,
but smaller ones, thus running slower
(may actually be faster on GPU) while consuming less memory.
If set to 2 (LSTM/GRU only),
the RNN will combine the input gate,
the forget gate and the output gate into a single matrix,
enabling more time-efficient parallelization on the GPU.
Note: RNN dropout must be shared for all gates,
resulting in a slightly reduced regularization.
input_dim: dimensionality of the input (integer).
This argument (or alternatively, the keyword argument `input_shape`)
is required when using this layer as the first layer in a model.
input_length: Length of input sequences, to be specified
when it is constant.
This argument is required if you are going to connect
`Flatten` then `Dense` layers upstream
(without it, the shape of the dense outputs cannot be computed).
Note that if the recurrent layer is not the first layer
in your model, you would need to specify the input length
at the level of the first layer
(e.g. via the `input_shape` argument)
# Input shapes
3D tensor with shape `(batch_size, timesteps, input_dim)`,
(Optional) 2D tensors with shape `(batch_size, output_dim)`.
# Output shape
- if `return_state`: a list of tensors. The first tensor is
the output. The remaining tensors are the last states,
each with shape `(batch_size, units)`.
- if `return_sequences`: 3D tensor with shape
`(batch_size, timesteps, units)`.
- else, 2D tensor with shape `(batch_size, units)`.
# Masking
This layer supports masking for input data with a variable number
of timesteps. To introduce masks to your data,
use an [Embedding](embeddings.md) layer with the `mask_zero` parameter
set to `True`.
# Note on using statefulness in RNNs
You can set RNN layers to be 'stateful', which means that the states
computed for the samples in one batch will be reused as initial states
for the samples in the next batch. This assumes a one-to-one mapping
between samples in different successive batches.
To enable statefulness:
- specify `stateful=True` in the layer constructor.
- specify a fixed batch size for your model, by passing
if sequential model:
`batch_input_shape=(...)` to the first layer in your model.
else for functional model with 1 or more Input layers:
`batch_shape=(...)` to all the first layers in your model.
This is the expected shape of your inputs
*including the batch size*.
It should be a tuple of integers, e.g. `(32, 10, 100)`.
- specify `shuffle=False` when calling fit().
To reset the states of your model, call `.reset_states()` on either
a specific layer, or on your entire model.
# Note on specifying the initial state of RNNs
You can specify the initial state of RNN layers symbolically by
calling them with the keyword argument `initial_state`. The value of
`initial_state` should be a tensor or list of tensors representing
the initial state of the RNN layer.
You can specify the initial state of RNN layers numerically by
calling `reset_states` with the keyword argument `states`. The value of
`states` should be a numpy array or list of numpy arrays representing
the initial state of the RNN layer.
"""
def __init__(self, return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
unroll=False,
implementation=0,
**kwargs):
super(Recurrent, self).__init__(**kwargs)
self.return_sequences = return_sequences
self.return_state = return_state
self.go_backwards = go_backwards
self.stateful = stateful
self.unroll = unroll
self.implementation = implementation
self.supports_masking = True
self.input_spec = [InputSpec(ndim=3)]
self.state_spec = None
self.dropout = 0
self.recurrent_dropout = 0
def compute_output_shape(self, input_shape):
if isinstance(input_shape, list):
input_shape = input_shape[0]
if self.return_sequences:
output_shape = (input_shape[0], input_shape[1], self.units)
else:
output_shape = (input_shape[0], self.units)
if self.return_state:
state_shape = [(input_shape[0], self.units) for _ in self.states]
return [output_shape] + state_shape
else:
return output_shape
def compute_mask(self, inputs, mask):
if isinstance(mask, list):
mask = mask[0]
output_mask = mask if self.return_sequences else None
if self.return_state:
state_mask = [None for _ in self.states]
return [output_mask] + state_mask
else:
return output_mask
def step(self, inputs, states):
raise NotImplementedError
def get_constants(self, inputs, training=None):
return []
def get_initial_state(self, inputs):
# build an all-zero tensor of shape (samples, output_dim)
initial_state = K.zeros_like(inputs) # (samples, timesteps, input_dim)
initial_state = K.sum(initial_state, axis=(1, 2)) # (samples,)
initial_state = K.expand_dims(initial_state) # (samples, 1)
initial_state = K.tile(initial_state, [1, self.units]) # (samples, output_dim)
initial_state = [initial_state for _ in range(len(self.states))]
return initial_state
def preprocess_input(self, inputs, training=None):
return inputs
def __call__(self, inputs, initial_state=None, **kwargs):
# If there are multiple inputs, then
# they should be the main input and `initial_state`
# e.g. when loading model from file
if isinstance(inputs, (list, tuple)) and len(inputs) > 1 and initial_state is None:
initial_state = inputs[1:]
inputs = inputs[0]
# If `initial_state` is specified,
# and if it a Keras tensor,
# then add it to the inputs and temporarily
# modify the input spec to include the state.
if initial_state is None:
return super(Recurrent, self).__call__(inputs, **kwargs)
if not isinstance(initial_state, (list, tuple)):
initial_state = [initial_state]
is_keras_tensor = hasattr(initial_state[0], '_keras_history')
for tensor in initial_state:
if hasattr(tensor, '_keras_history') != is_keras_tensor:
raise ValueError('The initial state of an RNN layer cannot be'
' specified with a mix of Keras tensors and'
' non-Keras tensors')
if is_keras_tensor:
# Compute the full input spec, including state
input_spec = self.input_spec
state_spec = self.state_spec
input_spec = to_list(input_spec)
state_spec = to_list(state_spec)
self.input_spec = input_spec + state_spec
# Compute the full inputs, including state
inputs = [inputs] + list(initial_state)
# Perform the call
output = super(Recurrent, self).__call__(inputs, **kwargs)
# Restore original input spec
self.input_spec = input_spec
return output
else:
kwargs['initial_state'] = initial_state
return super(Recurrent, self).__call__(inputs, **kwargs)
def call(self, inputs, mask=None, training=None, initial_state=None):
# input shape: `(samples, time (padded with zeros), input_dim)`
# note that the .build() method of subclasses MUST define
# self.input_spec and self.state_spec with complete input shapes.
if isinstance(inputs, list):
initial_state = inputs[1:]
inputs = inputs[0]
elif initial_state is not None:
pass
elif self.stateful:
initial_state = self.states
else:
initial_state = self.get_initial_state(inputs)
if isinstance(mask, list):
mask = mask[0]
if len(initial_state) != len(self.states):
raise ValueError('Layer has ' + str(len(self.states)) +
' states but was passed ' +
str(len(initial_state)) +
' initial states.')
input_shape = K.int_shape(inputs)
timesteps = input_shape[1]
if self.unroll and timesteps in [None, 1]:
raise ValueError('Cannot unroll a RNN if the '
'time dimension is undefined or equal to 1. \n'
'- If using a Sequential model, '
'specify the time dimension by passing '
'an `input_shape` or `batch_input_shape` '
'argument to your first layer. If your '
'first layer is an Embedding, you can '
'also use the `input_length` argument.\n'
'- If using the functional API, specify '
'the time dimension by passing a `shape` '
'or `batch_shape` argument to your Input layer.')
constants = self.get_constants(inputs, training=None)
preprocessed_input = self.preprocess_input(inputs, training=None)
last_output, outputs, states = K.rnn(self.step,
preprocessed_input,
initial_state,
go_backwards=self.go_backwards,
mask=mask,
constants=constants,
unroll=self.unroll,
input_length=timesteps)
if self.stateful:
updates = []
for i in range(len(states)):
updates.append((self.states[i], states[i]))
self.add_update(updates, inputs)
# Properly set learning phase
if 0 < self.dropout + self.recurrent_dropout:
last_output._uses_learning_phase = True
outputs._uses_learning_phase = True
if self.return_sequences:
output = outputs
else:
output = last_output
if self.return_state:
if not isinstance(states, (list, tuple)):
states = [states]
else:
states = list(states)
return [output] + states
else:
return output
def reset_states(self, states=None):
if not self.stateful:
raise AttributeError('Layer must be stateful.')
batch_size = self.input_spec[0].shape[0]
if not batch_size:
raise ValueError('If a RNN is stateful, it needs to know '
'its batch size. Specify the batch size '
'of your input tensors: \n'
'- If using a Sequential model, '
'specify the batch size by passing '
'a `batch_input_shape` '
'argument to your first layer.\n'
'- If using the functional API, specify '
'the time dimension by passing a '
'`batch_shape` argument to your Input layer.')
# initialize state if None
if self.states[0] is None:
self.states = [K.zeros((batch_size, self.units))
for _ in self.states]
elif states is None:
for state in self.states:
K.set_value(state, np.zeros((batch_size, self.units)))
else:
if not isinstance(states, (list, tuple)):
states = [states]
if len(states) != len(self.states):
raise ValueError('Layer ' + self.name + ' expects ' +
str(len(self.states)) + ' states, '
'but it received ' + str(len(states)) +
' state values. Input received: ' +
str(states))
for index, (value, state) in enumerate(zip(states, self.states)):
if value.shape != (batch_size, self.units):
raise ValueError('State ' + str(index) +
' is incompatible with layer ' +
self.name + ': expected shape=' +
str((batch_size, self.units)) +
', found shape=' + str(value.shape))
K.set_value(state, value)
def get_config(self):
config = {'return_sequences': self.return_sequences,
'return_state': self.return_state,
'go_backwards': self.go_backwards,
'stateful': self.stateful,
'unroll': self.unroll,
'implementation': self.implementation}
base_config = super(Recurrent, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class ConvRecurrent2D(Recurrent):
"""Abstract base class for convolutional recurrent layers.
Do not use in a model -- it's not a functional layer!
# Arguments
filters: Integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution).
kernel_size: An integer or tuple/list of n integers, specifying the
dimensions of the convolution window.
strides: An integer or tuple/list of n integers,
specifying the strides of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"` or `"same"` (case-insensitive).
data_format: A string,
one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch, time, ..., channels)`
while `channels_first` corresponds to
inputs with shape `(batch, time, channels, ...)`.
It defaults to the `image_data_format` value found in your
Keras config file at `~/.keras/keras.json`.
If you never set it, then it will be "channels_last".
dilation_rate: An integer or tuple/list of n integers, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any `strides` value != 1.
return_sequences: Boolean. Whether to return the last output
in the output sequence, or the full sequence.
go_backwards: Boolean (default False).
If True, process the input sequence backwards.
stateful: Boolean (default False). If True, the last state
for each sample at index i in a batch will be used as initial
state for the sample of index i in the following batch.
# Input shape
5D tensor with shape `(num_samples, timesteps, channels, rows, cols)`.
# Output shape
- if `return_sequences`: 5D tensor with shape
`(num_samples, timesteps, channels, rows, cols)`.
- else, 4D tensor with shape `(num_samples, channels, rows, cols)`.
# Masking
This layer supports masking for input data with a variable number
of timesteps. To introduce masks to your data,
use an [Embedding](embeddings.md) layer with the `mask_zero` parameter
set to `True`.
**Note:** for the time being, masking is only supported with Theano.
# Note on using statefulness in RNNs
You can set RNN layers to be 'stateful', which means that the states
computed for the samples in one batch will be reused as initial states
for the samples in the next batch.
This assumes a one-to-one mapping between
samples in different successive batches.
To enable statefulness:
- specify `stateful=True` in the layer constructor.
- specify a fixed batch size for your model, by passing
a `batch_input_size=(...)` to the first layer in your model.
This is the expected shape of your inputs *including the batch
size*.
It should be a tuple of integers, e.g. `(32, 10, 100)`.
To reset the states of your model, call `.reset_states()` on either
a specific layer, or on your entire model.
"""
def __init__(self, filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
return_sequences=False,
go_backwards=False,
stateful=False,
**kwargs):
super(ConvRecurrent2D, self).__init__(**kwargs)
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = K.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2, 'dilation_rate')
self.return_sequences = return_sequences
self.go_backwards = go_backwards
self.stateful = stateful
self.input_spec = [InputSpec(ndim=5)]
self.state_spec = None
def compute_output_shape(self, input_shape):
if isinstance(input_shape, list):
input_shape = input_shape[0]
if self.data_format == 'channels_first':
rows = input_shape[3]
cols = input_shape[4]
elif self.data_format == 'channels_last':
rows = input_shape[2]
cols = input_shape[3]
rows = conv_utils.conv_output_length(rows,
self.kernel_size[0],
padding=self.padding,
stride=self.strides[0],
dilation=self.dilation_rate[0])
cols = conv_utils.conv_output_length(cols,
self.kernel_size[1],
padding=self.padding,
stride=self.strides[1],
dilation=self.dilation_rate[1])
if self.return_sequences:
if self.data_format == 'channels_first':
output_shape = (input_shape[0], input_shape[1],
self.filters, rows, cols)
elif self.data_format == 'channels_last':
output_shape = (input_shape[0], input_shape[1],
rows, cols, self.filters)
else:
if self.data_format == 'channels_first':
output_shape = (input_shape[0], self.filters, rows, cols)
elif self.data_format == 'channels_last':
output_shape = (input_shape[0], rows, cols, self.filters)
if self.return_state:
if self.data_format == 'channels_first':
output_shape = [output_shape] + [(input_shape[0], self.filters, rows, cols) for _ in range(2)]
elif self.data_format == 'channels_last':
output_shape = [output_shape] + [(input_shape[0], rows, cols, self.filters) for _ in range(2)]
return output_shape
def get_config(self):
config = {'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'return_sequences': self.return_sequences,
'go_backwards': self.go_backwards,
'stateful': self.stateful}
base_config = super(ConvRecurrent2D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
