637 lines
24 KiB
Python
637 lines
24 KiB
Python
from tensorflow import nest
|
|
|
|
from keras_core import activations
|
|
from keras_core import backend
|
|
from keras_core import constraints
|
|
from keras_core import initializers
|
|
from keras_core import operations as ops
|
|
from keras_core import regularizers
|
|
from keras_core.api_export import keras_core_export
|
|
from keras_core.layers.input_spec import InputSpec
|
|
from keras_core.layers.layer import Layer
|
|
from keras_core.layers.rnn.dropout_rnn_cell import DropoutRNNCell
|
|
from keras_core.layers.rnn.rnn import RNN
|
|
|
|
|
|
@keras_core_export("keras_core.layers.LSTMCell")
|
|
class LSTMCell(Layer, DropoutRNNCell):
|
|
"""Cell class for the LSTM layer.
|
|
|
|
This class processes one step within the whole time sequence input, whereas
|
|
`keras_core.layer.LSTM` processes the whole sequence.
|
|
|
|
Args:
|
|
units: Positive integer, dimensionality of the output space.
|
|
activation: Activation function to use. Default: hyperbolic tangent
|
|
(`tanh`). If you pass None, no activation is applied
|
|
(ie. "linear" activation: `a(x) = x`).
|
|
recurrent_activation: Activation function to use for the recurrent step.
|
|
Default: sigmoid (`sigmoid`). If you pass `None`, no activation is
|
|
applied (ie. "linear" activation: `a(x) = x`).
|
|
use_bias: Boolean, (default `True`), whether the layer
|
|
should use a bias vector.
|
|
kernel_initializer: Initializer for the `kernel` weights matrix,
|
|
used for the linear transformation of the inputs. Default:
|
|
`"glorot_uniform"`.
|
|
recurrent_initializer: Initializer for the `recurrent_kernel`
|
|
weights matrix, used for the linear transformation
|
|
of the recurrent state. Default: `"orthogonal"`.
|
|
bias_initializer: Initializer for the bias vector. Default: `"zeros"`.
|
|
unit_forget_bias: Boolean (default `True`). If `True`,
|
|
add 1 to the bias of the forget gate at initialization.
|
|
Setting it to `True` will also force `bias_initializer="zeros"`.
|
|
This is recommended in [Jozefowicz et al.](
|
|
https://github.com/mlresearch/v37/blob/gh-pages/jozefowicz15.pdf)
|
|
kernel_regularizer: Regularizer function applied to the `kernel` weights
|
|
matrix. Default: `None`.
|
|
recurrent_regularizer: Regularizer function applied to the
|
|
`recurrent_kernel` weights matrix. Default: `None`.
|
|
bias_regularizer: Regularizer function applied to the bias vector.
|
|
Default: `None`.
|
|
kernel_constraint: Constraint function applied to the `kernel` weights
|
|
matrix. Default: `None`.
|
|
recurrent_constraint: Constraint function applied to the
|
|
`recurrent_kernel` weights matrix. Default: `None`.
|
|
bias_constraint: Constraint function applied to the bias vector.
|
|
Default: `None`.
|
|
dropout: Float between 0 and 1. Fraction of the units to drop for the
|
|
linear transformation of the inputs. Default: 0.
|
|
recurrent_dropout: Float between 0 and 1. Fraction of the units to drop
|
|
for the linear transformation of the recurrent state. Default: 0.
|
|
seed: Random seed for dropout.
|
|
|
|
Call arguments:
|
|
inputs: A 2D tensor, with shape `(batch, features)`.
|
|
states: A 2D tensor with shape `(batch, units)`, which is the state
|
|
from the previous time step.
|
|
training: Python boolean indicating whether the layer should behave in
|
|
training mode or in inference mode. Only relevant when `dropout` or
|
|
`recurrent_dropout` is used.
|
|
|
|
Example:
|
|
|
|
>>> inputs = np.random.random((32, 10, 8))
|
|
>>> rnn = keras_core.layers.RNN(keras_core.layers.LSTMCell(4))
|
|
>>> output = rnn(inputs)
|
|
>>> output.shape
|
|
(32, 4)
|
|
>>> rnn = keras_core.layers.RNN(
|
|
... keras_core.layers.LSTMCell(4),
|
|
... return_sequences=True,
|
|
... return_state=True)
|
|
>>> whole_sequence_output, final_state = rnn(inputs)
|
|
>>> whole_sequence_output.shape
|
|
(32, 10, 4)
|
|
>>> final_state.shape
|
|
(32, 4)
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
units,
|
|
activation="tanh",
|
|
recurrent_activation="sigmoid",
|
|
use_bias=True,
|
|
kernel_initializer="glorot_uniform",
|
|
recurrent_initializer="orthogonal",
|
|
bias_initializer="zeros",
|
|
unit_forget_bias=True,
|
|
kernel_regularizer=None,
|
|
recurrent_regularizer=None,
|
|
bias_regularizer=None,
|
|
kernel_constraint=None,
|
|
recurrent_constraint=None,
|
|
bias_constraint=None,
|
|
dropout=0.0,
|
|
recurrent_dropout=0.0,
|
|
seed=None,
|
|
**kwargs,
|
|
):
|
|
if units <= 0:
|
|
raise ValueError(
|
|
"Received an invalid value for argument `units`, "
|
|
f"expected a positive integer, got {units}."
|
|
)
|
|
implementation = kwargs.pop("implementation", 2)
|
|
super().__init__(**kwargs)
|
|
self.units = units
|
|
self.activation = activations.get(activation)
|
|
self.recurrent_activation = activations.get(recurrent_activation)
|
|
self.use_bias = use_bias
|
|
|
|
self.kernel_initializer = initializers.get(kernel_initializer)
|
|
self.recurrent_initializer = initializers.get(recurrent_initializer)
|
|
self.bias_initializer = initializers.get(bias_initializer)
|
|
|
|
self.kernel_regularizer = regularizers.get(kernel_regularizer)
|
|
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
|
|
self.bias_regularizer = regularizers.get(bias_regularizer)
|
|
|
|
self.kernel_constraint = constraints.get(kernel_constraint)
|
|
self.recurrent_constraint = constraints.get(recurrent_constraint)
|
|
self.bias_constraint = constraints.get(bias_constraint)
|
|
|
|
self.dropout = min(1.0, max(0.0, dropout))
|
|
self.recurrent_dropout = min(1.0, max(0.0, recurrent_dropout))
|
|
self.seed = seed
|
|
self.seed_generator = backend.random.SeedGenerator(seed=seed)
|
|
|
|
self.unit_forget_bias = unit_forget_bias
|
|
self.state_size = [self.units, self.units]
|
|
self.output_size = self.units
|
|
self.implementation = implementation
|
|
|
|
def build(self, input_shape):
|
|
super().build(input_shape)
|
|
input_dim = input_shape[-1]
|
|
self.kernel = self.add_weight(
|
|
shape=(input_dim, self.units * 4),
|
|
name="kernel",
|
|
initializer=self.kernel_initializer,
|
|
regularizer=self.kernel_regularizer,
|
|
constraint=self.kernel_constraint,
|
|
)
|
|
self.recurrent_kernel = self.add_weight(
|
|
shape=(self.units, self.units * 4),
|
|
name="recurrent_kernel",
|
|
initializer=self.recurrent_initializer,
|
|
regularizer=self.recurrent_regularizer,
|
|
constraint=self.recurrent_constraint,
|
|
)
|
|
|
|
if self.use_bias:
|
|
if self.unit_forget_bias:
|
|
|
|
def bias_initializer(_, *args, **kwargs):
|
|
return ops.concatenate(
|
|
[
|
|
self.bias_initializer(
|
|
(self.units,), *args, **kwargs
|
|
),
|
|
initializers.get("ones")(
|
|
(self.units,), *args, **kwargs
|
|
),
|
|
self.bias_initializer(
|
|
(self.units * 2,), *args, **kwargs
|
|
),
|
|
]
|
|
)
|
|
|
|
else:
|
|
bias_initializer = self.bias_initializer
|
|
self.bias = self.add_weight(
|
|
shape=(self.units * 4,),
|
|
name="bias",
|
|
initializer=bias_initializer,
|
|
regularizer=self.bias_regularizer,
|
|
constraint=self.bias_constraint,
|
|
)
|
|
else:
|
|
self.bias = None
|
|
self.built = True
|
|
|
|
def _compute_carry_and_output(self, x, h_tm1, c_tm1):
|
|
"""Computes carry and output using split kernels."""
|
|
x_i, x_f, x_c, x_o = x
|
|
h_tm1_i, h_tm1_f, h_tm1_c, h_tm1_o = h_tm1
|
|
i = self.recurrent_activation(
|
|
x_i + ops.matmul(h_tm1_i, self.recurrent_kernel[:, : self.units])
|
|
)
|
|
f = self.recurrent_activation(
|
|
x_f
|
|
+ ops.matmul(
|
|
h_tm1_f, self.recurrent_kernel[:, self.units : self.units * 2]
|
|
)
|
|
)
|
|
c = f * c_tm1 + i * self.activation(
|
|
x_c
|
|
+ ops.matmul(
|
|
h_tm1_c,
|
|
self.recurrent_kernel[:, self.units * 2 : self.units * 3],
|
|
)
|
|
)
|
|
o = self.recurrent_activation(
|
|
x_o
|
|
+ ops.matmul(h_tm1_o, self.recurrent_kernel[:, self.units * 3 :])
|
|
)
|
|
return c, o
|
|
|
|
def _compute_carry_and_output_fused(self, z, c_tm1):
|
|
"""Computes carry and output using fused kernels."""
|
|
z0, z1, z2, z3 = z
|
|
i = self.recurrent_activation(z0)
|
|
f = self.recurrent_activation(z1)
|
|
c = f * c_tm1 + i * self.activation(z2)
|
|
o = self.recurrent_activation(z3)
|
|
return c, o
|
|
|
|
def call(self, inputs, states, training=False):
|
|
h_tm1 = states[0] # previous memory state
|
|
c_tm1 = states[1] # previous carry state
|
|
|
|
dp_mask = self.get_dropout_mask(inputs)
|
|
rec_dp_mask = self.get_recurrent_dropout_mask(h_tm1)
|
|
|
|
if training and 0.0 < self.dropout < 1.0:
|
|
inputs *= dp_mask
|
|
if training and 0.0 < self.recurrent_dropout < 1.0:
|
|
h_tm1 *= rec_dp_mask
|
|
|
|
if self.implementation == 1:
|
|
inputs_i = inputs
|
|
inputs_f = inputs
|
|
inputs_c = inputs
|
|
inputs_o = inputs
|
|
k_i, k_f, k_c, k_o = ops.split(self.kernel, 4, axis=1)
|
|
x_i = ops.matmul(inputs_i, k_i)
|
|
x_f = ops.matmul(inputs_f, k_f)
|
|
x_c = ops.matmul(inputs_c, k_c)
|
|
x_o = ops.matmul(inputs_o, k_o)
|
|
if self.use_bias:
|
|
b_i, b_f, b_c, b_o = ops.split(self.bias, 4, axis=0)
|
|
x_i += b_i
|
|
x_f += b_f
|
|
x_c += b_c
|
|
x_o += b_o
|
|
|
|
h_tm1_i = h_tm1
|
|
h_tm1_f = h_tm1
|
|
h_tm1_c = h_tm1
|
|
h_tm1_o = h_tm1
|
|
x = (x_i, x_f, x_c, x_o)
|
|
h_tm1 = (h_tm1_i, h_tm1_f, h_tm1_c, h_tm1_o)
|
|
c, o = self._compute_carry_and_output(x, h_tm1, c_tm1)
|
|
else:
|
|
if 0.0 < self.dropout < 1.0:
|
|
inputs = inputs * dp_mask[0]
|
|
z = ops.matmul(inputs, self.kernel)
|
|
|
|
z += ops.matmul(h_tm1, self.recurrent_kernel)
|
|
if self.use_bias:
|
|
z += self.bias
|
|
|
|
z = ops.split(z, 4, axis=1)
|
|
c, o = self._compute_carry_and_output_fused(z, c_tm1)
|
|
|
|
h = o * self.activation(c)
|
|
return h, [h, c]
|
|
|
|
def get_config(self):
|
|
config = {
|
|
"units": self.units,
|
|
"activation": activations.serialize(self.activation),
|
|
"recurrent_activation": activations.serialize(
|
|
self.recurrent_activation
|
|
),
|
|
"use_bias": self.use_bias,
|
|
"unit_forget_bias": self.unit_forget_bias,
|
|
"kernel_initializer": initializers.serialize(
|
|
self.kernel_initializer
|
|
),
|
|
"recurrent_initializer": initializers.serialize(
|
|
self.recurrent_initializer
|
|
),
|
|
"bias_initializer": initializers.serialize(self.bias_initializer),
|
|
"kernel_regularizer": regularizers.serialize(
|
|
self.kernel_regularizer
|
|
),
|
|
"recurrent_regularizer": regularizers.serialize(
|
|
self.recurrent_regularizer
|
|
),
|
|
"bias_regularizer": regularizers.serialize(self.bias_regularizer),
|
|
"kernel_constraint": constraints.serialize(self.kernel_constraint),
|
|
"recurrent_constraint": constraints.serialize(
|
|
self.recurrent_constraint
|
|
),
|
|
"bias_constraint": constraints.serialize(self.bias_constraint),
|
|
"dropout": self.dropout,
|
|
"recurrent_dropout": self.recurrent_dropout,
|
|
"seed": self.seed,
|
|
}
|
|
base_config = super().get_config()
|
|
return {**base_config, **config}
|
|
|
|
def get_initial_state(self, batch_size=None):
|
|
return [
|
|
ops.zeros((batch_size, d), dtype=self.compute_dtype)
|
|
for d in self.state_size
|
|
]
|
|
|
|
|
|
class LSTM(RNN):
|
|
"""Long Short-Term Memory layer - Hochreiter 1997.
|
|
|
|
Based on available runtime hardware and constraints, this layer
|
|
will choose different implementations (cuDNN-based or backend-native)
|
|
to maximize the performance. If a GPU is available and all
|
|
the arguments to the layer meet the requirement of the cuDNN kernel
|
|
(see below for details), the layer will use a fast cuDNN implementation
|
|
when using the TensorFlow backend.
|
|
The requirements to use the cuDNN implementation are:
|
|
|
|
1. `activation` == `tanh`
|
|
2. `recurrent_activation` == `sigmoid`
|
|
3. `dropout` == 0 and `recurrent_dropout` == 0
|
|
4. `unroll` is `False`
|
|
5. `use_bias` is `True`
|
|
6. Inputs, if use masking, are strictly right-padded.
|
|
7. Eager execution is enabled in the outermost context.
|
|
|
|
For example:
|
|
|
|
>>> inputs = np.random.random((32, 10, 8))
|
|
>>> lstm = keras_core.layers.LSTM(4)
|
|
>>> output = lstm(inputs)
|
|
>>> output.shape
|
|
(32, 4)
|
|
>>> lstm = keras_core.layers.LSTM(
|
|
... 4, return_sequences=True, return_state=True)
|
|
>>> whole_seq_output, final_memory_state, final_carry_state = lstm(inputs)
|
|
>>> whole_seq_output.shape
|
|
(32, 10, 4)
|
|
>>> final_memory_state.shape
|
|
(32, 4)
|
|
>>> final_carry_state.shape
|
|
(32, 4)
|
|
|
|
Args:
|
|
units: Positive integer, dimensionality of the output space.
|
|
activation: Activation function to use.
|
|
Default: hyperbolic tangent (`tanh`).
|
|
If you pass `None`, no activation is applied
|
|
(ie. "linear" activation: `a(x) = x`).
|
|
recurrent_activation: Activation function to use
|
|
for the recurrent step.
|
|
Default: sigmoid (`sigmoid`).
|
|
If you pass `None`, no activation is applied
|
|
(ie. "linear" activation: `a(x) = x`).
|
|
use_bias: Boolean, (default `True`), whether the layer
|
|
should use a bias vector.
|
|
kernel_initializer: Initializer for the `kernel` weights matrix,
|
|
used for the linear transformation of the inputs. Default:
|
|
`"glorot_uniform"`.
|
|
recurrent_initializer: Initializer for the `recurrent_kernel`
|
|
weights matrix, used for the linear transformation of the recurrent
|
|
state. Default: `"orthogonal"`.
|
|
bias_initializer: Initializer for the bias vector. Default: `"zeros"`.
|
|
unit_forget_bias: Boolean (default `True`). If `True`,
|
|
add 1 to the bias of the forget gate at initialization.
|
|
Setting it to `True` will also force `bias_initializer="zeros"`.
|
|
This is recommended in [Jozefowicz et al.](
|
|
https://github.com/mlresearch/v37/blob/gh-pages/jozefowicz15.pdf)
|
|
kernel_regularizer: Regularizer function applied to the `kernel` weights
|
|
matrix. Default: `None`.
|
|
recurrent_regularizer: Regularizer function applied to the
|
|
`recurrent_kernel` weights matrix. Default: `None`.
|
|
bias_regularizer: Regularizer function applied to the bias vector.
|
|
Default: `None`.
|
|
activity_regularizer: Regularizer function applied to the output of the
|
|
layer (its "activation"). Default: `None`.
|
|
kernel_constraint: Constraint function applied to the `kernel` weights
|
|
matrix. Default: `None`.
|
|
recurrent_constraint: Constraint function applied to the
|
|
`recurrent_kernel` weights matrix. Default: `None`.
|
|
bias_constraint: Constraint function applied to the bias vector.
|
|
Default: `None`.
|
|
dropout: Float between 0 and 1. Fraction of the units to drop for the
|
|
linear transformation of the inputs. Default: 0.
|
|
recurrent_dropout: Float between 0 and 1. Fraction of the units to drop
|
|
for the linear transformation of the recurrent state. Default: 0.
|
|
seed: Random seed for dropout.
|
|
return_sequences: Boolean. Whether to return the last output
|
|
in the output sequence, or the full sequence. Default: `False`.
|
|
return_state: Boolean. Whether to return the last state in addition
|
|
to the output. Default: `False`.
|
|
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.
|
|
|
|
Call arguments:
|
|
inputs: A 3D tensor, with shape `(batch, timesteps, feature)`.
|
|
mask: Binary tensor of shape `(samples, timesteps)` indicating whether
|
|
a given timestep should be masked (optional).
|
|
An individual `True` entry indicates that the corresponding timestep
|
|
should be utilized, while a `False` entry indicates that the
|
|
corresponding timestep should be ignored. Defaults to `None`.
|
|
training: Python boolean indicating whether the layer should behave in
|
|
training mode or in inference mode. This argument is passed to the
|
|
cell when calling it. This is only relevant if `dropout` or
|
|
`recurrent_dropout` is used (optional). Defaults to `None`.
|
|
initial_state: List of initial state tensors to be passed to the first
|
|
call of the cell (optional, `None` causes creation
|
|
of zero-filled initial state tensors). Defaults to `None`.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
units,
|
|
activation="tanh",
|
|
recurrent_activation="sigmoid",
|
|
use_bias=True,
|
|
kernel_initializer="glorot_uniform",
|
|
recurrent_initializer="orthogonal",
|
|
bias_initializer="zeros",
|
|
unit_forget_bias=True,
|
|
kernel_regularizer=None,
|
|
recurrent_regularizer=None,
|
|
bias_regularizer=None,
|
|
activity_regularizer=None,
|
|
kernel_constraint=None,
|
|
recurrent_constraint=None,
|
|
bias_constraint=None,
|
|
dropout=0.0,
|
|
recurrent_dropout=0.0,
|
|
seed=None,
|
|
return_sequences=False,
|
|
return_state=False,
|
|
go_backwards=False,
|
|
stateful=False,
|
|
unroll=False,
|
|
**kwargs,
|
|
):
|
|
cell = LSTMCell(
|
|
units,
|
|
activation=activation,
|
|
recurrent_activation=recurrent_activation,
|
|
use_bias=use_bias,
|
|
kernel_initializer=kernel_initializer,
|
|
unit_forget_bias=unit_forget_bias,
|
|
recurrent_initializer=recurrent_initializer,
|
|
bias_initializer=bias_initializer,
|
|
kernel_regularizer=kernel_regularizer,
|
|
recurrent_regularizer=recurrent_regularizer,
|
|
bias_regularizer=bias_regularizer,
|
|
kernel_constraint=kernel_constraint,
|
|
recurrent_constraint=recurrent_constraint,
|
|
bias_constraint=bias_constraint,
|
|
dropout=dropout,
|
|
recurrent_dropout=recurrent_dropout,
|
|
dtype=kwargs.get("dtype", None),
|
|
trainable=kwargs.get("trainable", True),
|
|
name="lstm_cell",
|
|
seed=seed,
|
|
implementation=kwargs.pop("implementation", 2),
|
|
)
|
|
super().__init__(
|
|
cell,
|
|
return_sequences=return_sequences,
|
|
return_state=return_state,
|
|
go_backwards=go_backwards,
|
|
stateful=stateful,
|
|
unroll=unroll,
|
|
activity_regularizer=activity_regularizer,
|
|
**kwargs,
|
|
)
|
|
self.input_spec = InputSpec(ndim=3)
|
|
|
|
def inner_loop(self, sequences, initial_state, mask, training=False):
|
|
if nest.is_nested(mask):
|
|
mask = mask[0]
|
|
|
|
if not self.dropout and not self.recurrent_dropout:
|
|
try:
|
|
# Backends are allowed to specify (optionally) optimized
|
|
# implementation of the inner LSTM loop. In the case of
|
|
# TF for instance, it will leverage cuDNN when feasible, and
|
|
# it will raise NotImplementedError otherwise.
|
|
return backend.lstm(
|
|
sequences,
|
|
initial_state[0],
|
|
initial_state[1],
|
|
mask,
|
|
kernel=self.cell.kernel,
|
|
recurrent_kernel=self.cell.recurrent_kernel,
|
|
bias=self.cell.bias,
|
|
activation=self.cell.activation,
|
|
recurrent_activation=self.cell.recurrent_activation,
|
|
return_sequences=self.return_sequences,
|
|
go_backwards=self.go_backwards,
|
|
unroll=self.unroll,
|
|
)
|
|
except NotImplementedError:
|
|
pass
|
|
return super().inner_loop(
|
|
sequences, initial_state, mask=mask, training=training
|
|
)
|
|
|
|
def call(self, sequences, initial_state=None, mask=None, training=False):
|
|
return super().call(
|
|
sequences, mask=mask, training=training, initial_state=initial_state
|
|
)
|
|
|
|
@property
|
|
def units(self):
|
|
return self.cell.units
|
|
|
|
@property
|
|
def activation(self):
|
|
return self.cell.activation
|
|
|
|
@property
|
|
def recurrent_activation(self):
|
|
return self.cell.recurrent_activation
|
|
|
|
@property
|
|
def use_bias(self):
|
|
return self.cell.use_bias
|
|
|
|
@property
|
|
def unit_forget_bias(self):
|
|
return self.cell.unit_forget_bias
|
|
|
|
@property
|
|
def kernel_initializer(self):
|
|
return self.cell.kernel_initializer
|
|
|
|
@property
|
|
def recurrent_initializer(self):
|
|
return self.cell.recurrent_initializer
|
|
|
|
@property
|
|
def bias_initializer(self):
|
|
return self.cell.bias_initializer
|
|
|
|
@property
|
|
def kernel_regularizer(self):
|
|
return self.cell.kernel_regularizer
|
|
|
|
@property
|
|
def recurrent_regularizer(self):
|
|
return self.cell.recurrent_regularizer
|
|
|
|
@property
|
|
def bias_regularizer(self):
|
|
return self.cell.bias_regularizer
|
|
|
|
@property
|
|
def kernel_constraint(self):
|
|
return self.cell.kernel_constraint
|
|
|
|
@property
|
|
def recurrent_constraint(self):
|
|
return self.cell.recurrent_constraint
|
|
|
|
@property
|
|
def bias_constraint(self):
|
|
return self.cell.bias_constraint
|
|
|
|
@property
|
|
def dropout(self):
|
|
return self.cell.dropout
|
|
|
|
@property
|
|
def recurrent_dropout(self):
|
|
return self.cell.recurrent_dropout
|
|
|
|
def get_config(self):
|
|
config = {
|
|
"units": self.units,
|
|
"activation": activations.serialize(self.activation),
|
|
"recurrent_activation": activations.serialize(
|
|
self.recurrent_activation
|
|
),
|
|
"use_bias": self.use_bias,
|
|
"kernel_initializer": initializers.serialize(
|
|
self.kernel_initializer
|
|
),
|
|
"recurrent_initializer": initializers.serialize(
|
|
self.recurrent_initializer
|
|
),
|
|
"bias_initializer": initializers.serialize(self.bias_initializer),
|
|
"unit_forget_bias": self.unit_forget_bias,
|
|
"kernel_regularizer": regularizers.serialize(
|
|
self.kernel_regularizer
|
|
),
|
|
"recurrent_regularizer": regularizers.serialize(
|
|
self.recurrent_regularizer
|
|
),
|
|
"bias_regularizer": regularizers.serialize(self.bias_regularizer),
|
|
"activity_regularizer": regularizers.serialize(
|
|
self.activity_regularizer
|
|
),
|
|
"kernel_constraint": constraints.serialize(self.kernel_constraint),
|
|
"recurrent_constraint": constraints.serialize(
|
|
self.recurrent_constraint
|
|
),
|
|
"bias_constraint": constraints.serialize(self.bias_constraint),
|
|
"dropout": self.dropout,
|
|
"recurrent_dropout": self.recurrent_dropout,
|
|
"seed": self.cell.seed,
|
|
}
|
|
base_config = super().get_config()
|
|
del base_config["cell"]
|
|
return {**base_config, **config}
|
|
|
|
@classmethod
|
|
def from_config(cls, config):
|
|
return cls(**config)
|