o ©i etã@sFdZddlmZddlmZddlmZedƒGdd„dejƒƒZdS) zBase class for RNN cells.é)Ú base_layer)Ú rnn_utils)Ú keras_exportzkeras.layers.AbstractRNNCellc@s:eZdZdZdd„Zedd„ƒZedd„ƒZd d d „ZdS) ÚAbstractRNNCellaRAbstract object representing an RNN cell. See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn) for details about the usage of RNN API. This is the base class for implementing RNN cells with custom behavior. Every `RNNCell` must have the properties below and implement `call` with the signature `(output, next_state) = call(input, state)`. Examples: ```python class MinimalRNNCell(AbstractRNNCell): def __init__(self, units, **kwargs): self.units = units super(MinimalRNNCell, self).__init__(**kwargs) @property def state_size(self): return self.units def build(self, input_shape): self.kernel = self.add_weight(shape=(input_shape[-1], self.units), initializer='uniform', name='kernel') self.recurrent_kernel = self.add_weight( shape=(self.units, self.units), initializer='uniform', name='recurrent_kernel') self.built = True def call(self, inputs, states): prev_output = states[0] h = backend.dot(inputs, self.kernel) output = h + backend.dot(prev_output, self.recurrent_kernel) return output, output ``` This definition of cell differs from the definition used in the literature. In the literature, 'cell' refers to an object with a single scalar output. This definition refers to a horizontal array of such units. An RNN cell, in the most abstract setting, is anything that has a state and performs some operation that takes a matrix of inputs. This operation results in an output matrix with `self.output_size` columns. If `self.state_size` is an integer, this operation also results in a new state matrix with `self.state_size` columns. If `self.state_size` is a (possibly nested tuple of) TensorShape object(s), then it should return a matching structure of Tensors having shape `[batch_size].concatenate(s)` for each `s` in `self.batch_size`. cCót‚)a«The function that contains the logic for one RNN step calculation. Args: inputs: the input tensor, which is a slide from the overall RNN input by the time dimension (usually the second dimension). states: the state tensor from previous step, which has the same shape as `(batch, state_size)`. In the case of timestep 0, it will be the initial state user specified, or zero filled tensor otherwise. Returns: A tuple of two tensors: 1. output tensor for the current timestep, with size `output_size`. 2. state tensor for next step, which has the shape of `state_size`. ©ÚNotImplementedError)ÚselfÚinputsÚstates©r úU/var/www/myenv/lib/python3.10/site-packages/keras/src/layers/rnn/abstract_rnn_cell.pyÚcallQszAbstractRNNCell.callcCr)z›size(s) of state(s) used by this cell. It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes. r©r r r r Ú state_sizebszAbstractRNNCell.state_sizecCr)z>Integer or TensorShape: size of outputs produced by this cell.rrr r r Ú output_sizekszAbstractRNNCell.output_sizeNcCst ||||¡S)N)rÚ#generate_zero_filled_state_for_cell)r r Ú batch_sizeÚdtyper r r Úget_initial_statepsÿz!AbstractRNNCell.get_initial_state)NNN) Ú__name__Ú __module__Ú __qualname__Ú__doc__rÚpropertyrrrr r r r rs6  rN) rÚkeras.src.enginerÚkeras.src.layers.rnnrÚ tensorflow.python.util.tf_exportrÚLayerrr r r r Ús