451 lines
17 KiB
Python
451 lines
17 KiB
Python
from keras_core import activations
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from keras_core import backend
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from keras_core import constraints
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from keras_core import initializers
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from keras_core import operations as ops
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from keras_core import regularizers
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from keras_core.api_export import keras_core_export
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from keras_core.layers.input_spec import InputSpec
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from keras_core.layers.layer import Layer
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from keras_core.layers.rnn.dropout_rnn_cell import DropoutRNNCell
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from keras_core.layers.rnn.rnn import RNN
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@keras_core_export("keras_core.layers.SimpleRNNCell")
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class SimpleRNNCell(Layer, DropoutRNNCell):
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"""Cell class for SimpleRNN.
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This class processes one step within the whole time sequence input, whereas
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`keras_core.layer.SimpleRNN` processes the whole sequence.
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Args:
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units: Positive integer, dimensionality of the output space.
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activation: Activation function to use.
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Default: hyperbolic tangent (`tanh`).
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If you pass `None`, no activation is applied
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(ie. "linear" activation: `a(x) = x`).
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use_bias: Boolean, (default `True`), whether the layer
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should use a bias vector.
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kernel_initializer: Initializer for the `kernel` weights matrix,
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used for the linear transformation of the inputs. Default:
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`"glorot_uniform"`.
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recurrent_initializer: Initializer for the `recurrent_kernel`
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weights matrix, used for the linear transformation
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of the recurrent state. Default: `"orthogonal"`.
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bias_initializer: Initializer for the bias vector. Default: `"zeros"`.
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kernel_regularizer: Regularizer function applied to the `kernel` weights
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matrix. Default: `None`.
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recurrent_regularizer: Regularizer function applied to the
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`recurrent_kernel` weights matrix. Default: `None`.
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bias_regularizer: Regularizer function applied to the bias vector.
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Default: `None`.
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kernel_constraint: Constraint function applied to the `kernel` weights
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matrix. Default: `None`.
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recurrent_constraint: Constraint function applied to the
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`recurrent_kernel` weights matrix. Default: `None`.
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bias_constraint: Constraint function applied to the bias vector.
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Default: `None`.
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dropout: Float between 0 and 1. Fraction of the units to drop for the
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linear transformation of the inputs. Default: 0.
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recurrent_dropout: Float between 0 and 1. Fraction of the units to drop
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for the linear transformation of the recurrent state. Default: 0.
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seed: Random seed for dropout.
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Call arguments:
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sequence: A 2D tensor, with shape `(batch, features)`.
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states: A 2D tensor with shape `(batch, units)`, which is the state
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from the previous time step.
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training: Python boolean indicating whether the layer should behave in
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training mode or in inference mode. Only relevant when `dropout` or
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`recurrent_dropout` is used.
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Example:
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```python
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inputs = np.random.random([32, 10, 8]).astype(np.float32)
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rnn = keras_core.layers.RNN(keras_core.layers.SimpleRNNCell(4))
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output = rnn(inputs) # The output has shape `(32, 4)`.
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rnn = keras_core.layers.RNN(
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keras_core.layers.SimpleRNNCell(4),
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return_sequences=True,
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return_state=True
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)
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# whole_sequence_output has shape `(32, 10, 4)`.
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# final_state has shape `(32, 4)`.
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whole_sequence_output, final_state = rnn(inputs)
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```
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"""
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def __init__(
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self,
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units,
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activation="tanh",
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use_bias=True,
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kernel_initializer="glorot_uniform",
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recurrent_initializer="orthogonal",
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bias_initializer="zeros",
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kernel_regularizer=None,
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recurrent_regularizer=None,
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bias_regularizer=None,
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kernel_constraint=None,
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recurrent_constraint=None,
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bias_constraint=None,
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dropout=0.0,
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recurrent_dropout=0.0,
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seed=None,
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**kwargs,
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):
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if units <= 0:
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raise ValueError(
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"Received an invalid value for argument `units`, "
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f"expected a positive integer, got {units}."
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)
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super().__init__(**kwargs)
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self.seed = seed
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self.seed_generator = backend.random.SeedGenerator(seed)
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self.units = units
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self.activation = activations.get(activation)
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self.use_bias = use_bias
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self.kernel_initializer = initializers.get(kernel_initializer)
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self.recurrent_initializer = initializers.get(recurrent_initializer)
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self.bias_initializer = initializers.get(bias_initializer)
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self.kernel_regularizer = regularizers.get(kernel_regularizer)
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self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
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self.bias_regularizer = regularizers.get(bias_regularizer)
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self.kernel_constraint = constraints.get(kernel_constraint)
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self.recurrent_constraint = constraints.get(recurrent_constraint)
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self.bias_constraint = constraints.get(bias_constraint)
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self.dropout = min(1.0, max(0.0, dropout))
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self.recurrent_dropout = min(1.0, max(0.0, recurrent_dropout))
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self.state_size = self.units
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self.output_size = self.units
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def build(self, input_shape):
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self.kernel = self.add_weight(
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shape=(input_shape[-1], self.units),
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name="kernel",
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initializer=self.kernel_initializer,
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regularizer=self.kernel_regularizer,
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constraint=self.kernel_constraint,
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)
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self.recurrent_kernel = self.add_weight(
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shape=(self.units, self.units),
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name="recurrent_kernel",
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initializer=self.recurrent_initializer,
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regularizer=self.recurrent_regularizer,
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constraint=self.recurrent_constraint,
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)
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if self.use_bias:
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self.bias = self.add_weight(
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shape=(self.units,),
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name="bias",
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initializer=self.bias_initializer,
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regularizer=self.bias_regularizer,
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constraint=self.bias_constraint,
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)
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else:
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self.bias = None
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self.built = True
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def call(self, sequence, states, training=False):
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prev_output = states[0] if isinstance(states, (list, tuple)) else states
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dp_mask = self.get_dropout_mask(sequence)
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rec_dp_mask = self.get_recurrent_dropout_mask(prev_output)
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if training and dp_mask is not None:
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sequence *= dp_mask
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h = ops.matmul(sequence, self.kernel)
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if self.bias is not None:
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h += self.bias
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if training and rec_dp_mask is not None:
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prev_output *= rec_dp_mask
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output = h + ops.matmul(prev_output, self.recurrent_kernel)
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if self.activation is not None:
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output = self.activation(output)
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new_state = [output] if isinstance(states, (list, tuple)) else output
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return output, new_state
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def get_initial_state(self, batch_size=None):
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return [
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ops.zeros((batch_size, self.state_size), dtype=self.compute_dtype)
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]
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def get_config(self):
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config = {
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"units": self.units,
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"activation": activations.serialize(self.activation),
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"use_bias": self.use_bias,
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"kernel_initializer": initializers.serialize(
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self.kernel_initializer
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),
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"recurrent_initializer": initializers.serialize(
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self.recurrent_initializer
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),
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"bias_initializer": initializers.serialize(self.bias_initializer),
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"kernel_regularizer": regularizers.serialize(
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self.kernel_regularizer
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),
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"recurrent_regularizer": regularizers.serialize(
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self.recurrent_regularizer
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),
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"bias_regularizer": regularizers.serialize(self.bias_regularizer),
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"kernel_constraint": constraints.serialize(self.kernel_constraint),
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"recurrent_constraint": constraints.serialize(
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self.recurrent_constraint
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),
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"bias_constraint": constraints.serialize(self.bias_constraint),
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"dropout": self.dropout,
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"recurrent_dropout": self.recurrent_dropout,
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"seed": self.seed,
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}
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base_config = super().get_config()
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return {**base_config, **config}
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@keras_core_export("keras_core.layers.SimpleRNN")
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class SimpleRNN(RNN):
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"""Fully-connected RNN where the output is to be fed back as the new input.
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Args:
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units: Positive integer, dimensionality of the output space.
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activation: Activation function to use.
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Default: hyperbolic tangent (`tanh`).
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If you pass None, no activation is applied
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(ie. "linear" activation: `a(x) = x`).
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use_bias: Boolean, (default `True`), whether the layer uses
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a bias vector.
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kernel_initializer: Initializer for the `kernel` weights matrix,
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used for the linear transformation of the inputs. Default:
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`"glorot_uniform"`.
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recurrent_initializer: Initializer for the `recurrent_kernel`
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weights matrix, used for the linear transformation of the recurrent
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state. Default: `"orthogonal"`.
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bias_initializer: Initializer for the bias vector. Default: `"zeros"`.
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kernel_regularizer: Regularizer function applied to the `kernel` weights
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matrix. Default: `None`.
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recurrent_regularizer: Regularizer function applied to the
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`recurrent_kernel` weights matrix. Default: `None`.
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bias_regularizer: Regularizer function applied to the bias vector.
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Default: `None`.
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activity_regularizer: Regularizer function applied to the output of the
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layer (its "activation"). Default: `None`.
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kernel_constraint: Constraint function applied to the `kernel` weights
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matrix. Default: `None`.
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recurrent_constraint: Constraint function applied to the
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`recurrent_kernel` weights matrix. Default: `None`.
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bias_constraint: Constraint function applied to the bias vector.
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Default: `None`.
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dropout: Float between 0 and 1.
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Fraction of the units to drop for the linear transformation
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of the inputs. Default: 0.
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recurrent_dropout: Float between 0 and 1.
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Fraction of the units to drop for the linear transformation of the
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recurrent state. Default: 0.
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return_sequences: Boolean. Whether to return the last output
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in the output sequence, or the full sequence. Default: `False`.
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return_state: Boolean. Whether to return the last state
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in addition to the output. Default: `False`.
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go_backwards: Boolean (default: `False`).
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If `True`, process the input sequence backwards and return the
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reversed sequence.
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stateful: Boolean (default: `False`). If `True`, the last state
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for each sample at index i in a batch will be used as initial
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state for the sample of index i in the following batch.
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unroll: Boolean (default: `False`).
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If `True`, the network will be unrolled,
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else a symbolic loop will be used.
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Unrolling can speed-up a RNN,
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although it tends to be more memory-intensive.
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Unrolling is only suitable for short sequences.
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Call arguments:
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sequence: A 3D tensor, with shape `[batch, timesteps, feature]`.
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mask: Binary tensor of shape `[batch, timesteps]` indicating whether
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a given timestep should be masked. An individual `True` entry
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indicates that the corresponding timestep should be utilized,
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while a `False` entry indicates that the corresponding timestep
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should be ignored.
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training: Python boolean indicating whether the layer should behave in
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training mode or in inference mode.
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This argument is passed to the cell when calling it.
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This is only relevant if `dropout` or `recurrent_dropout` is used.
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initial_state: List of initial state tensors to be passed to the first
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call of the cell.
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Example:
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```python
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inputs = np.random.random((32, 10, 8))
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simple_rnn = keras_core.layers.SimpleRNN(4)
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output = simple_rnn(inputs) # The output has shape `(32, 4)`.
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simple_rnn = keras_core.layers.SimpleRNN(
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4, return_sequences=True, return_state=True
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)
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# whole_sequence_output has shape `(32, 10, 4)`.
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# final_state has shape `(32, 4)`.
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whole_sequence_output, final_state = simple_rnn(inputs)
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```
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"""
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def __init__(
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self,
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units,
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activation="tanh",
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use_bias=True,
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kernel_initializer="glorot_uniform",
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recurrent_initializer="orthogonal",
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bias_initializer="zeros",
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kernel_regularizer=None,
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recurrent_regularizer=None,
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bias_regularizer=None,
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activity_regularizer=None,
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kernel_constraint=None,
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recurrent_constraint=None,
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bias_constraint=None,
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dropout=0.0,
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recurrent_dropout=0.0,
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return_sequences=False,
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return_state=False,
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go_backwards=False,
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stateful=False,
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unroll=False,
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seed=None,
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**kwargs,
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):
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cell = SimpleRNNCell(
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units,
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activation=activation,
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use_bias=use_bias,
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kernel_initializer=kernel_initializer,
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recurrent_initializer=recurrent_initializer,
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bias_initializer=bias_initializer,
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kernel_regularizer=kernel_regularizer,
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recurrent_regularizer=recurrent_regularizer,
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bias_regularizer=bias_regularizer,
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kernel_constraint=kernel_constraint,
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recurrent_constraint=recurrent_constraint,
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bias_constraint=bias_constraint,
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dropout=dropout,
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recurrent_dropout=recurrent_dropout,
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seed=seed,
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dtype=kwargs.get("dtype", None),
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trainable=kwargs.get("trainable", True),
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name="simple_rnn_cell",
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)
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super().__init__(
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cell,
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return_sequences=return_sequences,
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return_state=return_state,
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go_backwards=go_backwards,
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stateful=stateful,
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unroll=unroll,
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**kwargs,
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)
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self.input_spec = [InputSpec(ndim=3)]
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def call(self, sequences, initial_state=None, mask=None, training=False):
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return super().call(
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sequences, mask=mask, training=training, initial_state=initial_state
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)
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@property
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def units(self):
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return self.cell.units
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@property
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def activation(self):
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return self.cell.activation
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@property
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def use_bias(self):
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return self.cell.use_bias
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@property
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def kernel_initializer(self):
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return self.cell.kernel_initializer
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@property
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def recurrent_initializer(self):
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return self.cell.recurrent_initializer
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@property
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def bias_initializer(self):
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return self.cell.bias_initializer
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@property
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def kernel_regularizer(self):
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return self.cell.kernel_regularizer
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@property
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def recurrent_regularizer(self):
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return self.cell.recurrent_regularizer
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@property
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def bias_regularizer(self):
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return self.cell.bias_regularizer
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@property
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def kernel_constraint(self):
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return self.cell.kernel_constraint
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@property
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def recurrent_constraint(self):
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return self.cell.recurrent_constraint
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@property
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def bias_constraint(self):
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return self.cell.bias_constraint
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@property
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def dropout(self):
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return self.cell.dropout
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@property
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def recurrent_dropout(self):
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return self.cell.recurrent_dropout
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def get_config(self):
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config = {
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"units": self.units,
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"activation": activations.serialize(self.activation),
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"use_bias": self.use_bias,
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"kernel_initializer": initializers.serialize(
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self.kernel_initializer
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),
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"recurrent_initializer": initializers.serialize(
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self.recurrent_initializer
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),
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"bias_initializer": initializers.serialize(self.bias_initializer),
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"kernel_regularizer": regularizers.serialize(
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self.kernel_regularizer
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),
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"recurrent_regularizer": regularizers.serialize(
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self.recurrent_regularizer
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),
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"bias_regularizer": regularizers.serialize(self.bias_regularizer),
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"activity_regularizer": regularizers.serialize(
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self.activity_regularizer
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),
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"kernel_constraint": constraints.serialize(self.kernel_constraint),
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"recurrent_constraint": constraints.serialize(
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self.recurrent_constraint
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),
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"bias_constraint": constraints.serialize(self.bias_constraint),
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"dropout": self.dropout,
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"recurrent_dropout": self.recurrent_dropout,
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}
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base_config = super().get_config()
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del base_config["cell"]
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return {**base_config, **config}
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@classmethod
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def from_config(cls, config):
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return cls(**config)
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