ёi.h*SSKJr SSKrSSKJr SSKJrJr SSKJ r J r SSK r SSK Jr SSKrSSKJrJrJrJr SSKJrJr SS KJr SS KJrJrJr SS KJr SS KJ r J!r! SS K"J#r$J%r& SSK'J(r( SSK)J*r* SSK+J,r, \ (a)SSKJ-r- SSKJ.r. SSK/J0r0J1r1J2r2J3r3J4r4 \-Sr5\-Sr6\-Sr7/r8S6S7Sjjr9"SS5r:S8Sjr;S9Sjr<S6Sjr=S6Sjr>S:S;S jjr?S<S=S!jjr@S<S>S"jjrA"S#S\,5rB"S$S%\B5rC"S&S'\B5rD"S(S)\B5rE"S*S+\,5rF"S,S-\,5rG"S.S/\*5rH"S0S1\H5rI"S2S3\H5rJ"S4S5\H5rKg)?) annotationsN)Sequence)partialreduce) TYPE_CHECKINGAny)Self)_C_ops _legacy_C_ops frameworkin_dynamic_mode) check_typecheck_variable_and_dtype)NON_PERSISTABLE_VAR_NAME_SUFFIX)default_startup_programin_dynamic_or_pir_mode program_guard)Variable)core in_pir_mode) functional initializertensor_array_to_tensor) LayerList)Layer)Literal)Tensor) DTypeLikeNestedStructure ParamAttrLike ShapeLikeTensorOrTensors)forwardbidirect bidirectional)LSTMGRURNN_RELURNN_TANHtanhrelu RNNCellBasec d[5(a[UUUUUU40UD6$[UUUUUU40UD6$)a rnn creates a recurrent neural network specified by RNNCell `cell`, which performs :code:`cell.call()` (for dygraph mode :code:`cell.forward`) repeatedly until reaches to the maximum length of `inputs`. Parameters: cell(RNNCellBase): An instance of `RNNCellBase`. inputs(Tensor): the input sequences. If time_major is True, the shape is `[time_steps, batch_size, input_size]` else the shape is `[batch_size, time_steps, input_size]`. initial_states(Tensor|tuple|list, optional): the initial state of the rnn cell. Tensor or a possibly nested structure of tensors. If not provided, `cell.get_initial_states` would be called to produce the initial state. Defaults to None. sequence_length (Tensor|None, optional): shape `[batch_size]`, dtype: int64 or int32. The valid lengths of input sequences. Defaults to None. If `sequence_length` is not None, the inputs are treated as padded sequences. In each input sequence, elements whose time step index are not less than the valid length are treated as paddings. time_major (bool, optional): Whether the first dimension of the input means the time steps. Defaults to False. is_reverse (bool, optional): Indicate whether to calculate in the reverse order of input sequences. Defaults to False. **kwargs: Additional keyword arguments to pass to `forward` of the cell. Returns: outputs (Tensor|list|tuple): the output sequence. Tensor or nested structure of Tensors. If `time_major` is True, the shape of each tensor in outputs is `[time_steps, batch_size, hidden_size]`, else `[batch_size, time_steps, hidden_size]`. final_states (Tensor|list|tuple): final states. A (possibly nested structure of) tensor[s], representing the final state for RNN. It has the same structure of initial state. Each tensor in final states has the same shape and dtype as the corresponding tensor in initial states. Examples: .. code-block:: pycon >>> import paddle >>> inputs = paddle.rand((4, 23, 16)) >>> prev_h = paddle.randn((4, 32)) >>> cell = paddle.nn.SimpleRNNCell(16, 32) >>> rnn = paddle.nn.RNN(cell) >>> outputs, final_states = rnn(inputs, prev_h) >>> print(outputs.shape) paddle.Size([4, 23, 32]) >>> print(final_states.shape) paddle.Size([4, 32]) )r _rnn_dynamic_graph_rnn_static_graph)cellinputsinitial_statessequence_length time_major is_reversekwargss S/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/nn/layer/rnn.pyrnnr;@sbB!          !          c2\rSrSrSSjrSSjrS SjrSrg) ArrayWrappercU/UlgNarrayselfxs r:__init__ArrayWrapper.__init__s S r<c<URRU5 U$rA)rCappendrDs r:rJArrayWrapper.appends ! r<c8URRU5$rA)rC __getitem__)rEitems r:rMArrayWrapper.__getitem__szz%%d++r<rBN)rFrreturnNone)rFrrPr )rNintrPr)__name__ __module__ __qualname____firstlineno__rGrJrM__static_attributes__r<r:r>r>s,r<r>c[RRRXSS9[RRRUSU- SS9-nU$)z3update rnn state or just pass the old state throughraxisr)paddletensormath_multiply_with_axis)state new_state step_masks r: _maybe_copyrcsW ""6617 ..uq9}A.NOI r<c SS/[[S[UR555Qn[R "X5$)Nrr)listrangelenshaper\ transpose)rFperms r:_transpose_batch_timerls6 q 04aQWW./ 0D   A $$r<c l^^U(aSOSm[RRU5nUSRTnUcUR X(aSOSS9nU(d$[RR [ U5nUbU[RRRRX8URS9n [R"U SS/5n U(a<[RR SU5nUb[R"W S/S9OSn Un /n [U5Hm[RR U4SjU5n U"X40UD6upUb/[RR [[ W TS9X5nUn TS:Xa![RR S U 5O [RR S X5n M [RR U4S jU 5nU(a$[RR U4S jU5nWnUU4$) Nrr batch_ref batch_dim_idxmaxlendtypec.[R"US/S9$NrrZr\reverserFs r:$_rnn_dynamic_graph..fnnQaS1r<rZc>UT$rArX)rFis r:ryrzs 1Q4r<)rbc[U5$rA)r>rxs r:ryrzsar<c$URU5$rA)rJ)rFx_arrays r:ryrzs 7>>!#4r<cB>[R"URTS9$NrZ)r\stackrCrFtime_step_indexs r:ryrzs&,,qww_=r<c.>[R"UTS9$rrvrs r:ryrzsfnnQ_=r<)r\utilsflattenriget_initial_states map_structurerlstaticnn sequence_lod sequence_maskrsrjrwrgrrc)r3r4r5r6r7r8r9 flat_inputs time_stepsmaskstatesoutputs step_inputs step_outputs new_states final_outputs final_statesr}rs @@r:r1r1s&a1O,,&&v.KQ%%o6J00A1  ++,A6J"}},,:: fll; q!f-++ 16  * NN4qc * FG : ll00H #' #Fv#F  &33 tAw7JAv LL & &'@, O++4l  LL..=wM 22 =} L , &&r<c ( ^^[US[[[[R R 4S5 [U[[45(a3[U5H$upx[US[U5-S-SS/S5 M& [US[[[[S5[R R 4S5 [US[[S5[R R 4S5 SS jn UcURX(aS OS S 9n[RRX5nU(d$[RR[U5n[R "[RR#U5S 5S n [R$"U [R&5n Ubv[R(R*R,R/UU [RR#U5S R0S 9n [R2"U S S /5n U(a<[RRSU5nUb[R4"W S /S9OSn [R6R8R;S5 [R<"/SS9mU n [R$"U S5n TU :n SSS5 [R(R*R>RAW 5n[RBRE[RR#U5S R0S9n[RRSU5n[RRU4SjUU5 URG5 UTn[RRU4SjU5nU"UU40UD6unn[U[R6R8R[R R 45(de[RRIUU5 Ub?[RJ"W TS 5m[RRU4SjUU5n[RBRMUTU5 [R6R8R;S5 [RBROTS S9mSSS5 [RRU4SjUU5 [R6R8R;S5 [RBRQTW 5n[RR"UU 5 SSS5 SSS5 [UUS SS9unn[RRSU5nUn[RRSU5nU(a![RRSU5nU(d$[RR[U5nUU4$!,(df  GNw=f!,(df  GN`=f!,(df  N=f!,(df  N=f)Nr4r;zinputs[]float32float64r5r6cXlU$rA) stop_gradient)rFstops r: _switch_grad'_rnn_static_graph.._switch_grads r<rrrnrqc.[R"US/S9$rurvrxs r:ry#_rnn_static_graph..+r{r<rZcpuint64rscP[RRURS9$)Nr)r\r] create_arrayrsrxs r:ryr@s&--,,177,;r<cF>[RRUTU5$rAr\r] array_writerFystart_is r:ryrDsV]]..q'1=r<cD>[RRUT5$rA)r\r] array_read)rFrs r:ryrMsfmm..q':r<c >UT-UST- --$)N?rX)rFrrbs r:ryr[sa)ma3?.CCr<)rFvaluecF>[RRUTU5$rArrs r:ryres221gqAr<Tr[ use_stackc[USSS9S$)NrTrrrxs r:ryrqs(dCAFr<c US$NrXrxs r:ryrus"r<c.[R"US/S9$rurvrxs r:ryryr{r<F)+rrrftupler\pirValue isinstance enumeraterstrtyperrrrlrircastint32rrrrrsrjrwbaser device_guardzeros control_flowWhiler]rblockassert_same_structure unsqueezer increment less_thanassignr)r3r4r5r6r7r8r9r}input_xr max_seq_lenrendcondwhile_op out_array init_arraystep_in pre_staterrnew_condout_ all_staterrrrbs @@r:r2r2s 8T5&**2B2BCU&4-((#F+JA $SV+c1Iy3I5 , 4T FJJ,<,<=  4:vzz//0 00A1 \\// MN ++,A6J,,v||33F;A>?BK++k6<<8K"}},,:: ,,&&~6q9??;  q!f-++ 16  * NN4qc *    + +E 2,,r1kk#w'} 3 }},,2248H **ll""6*1-33+I++;^J LL=  /LL.. :J #7I@@ fkk++44fjj6F6FG      **:yA  &((g:I  33DJ  !!'7I> [[ " " / / 6mm--q-AG7 "" A   [[ " " / / 6}}..w>> import paddle >>> cell_fw = paddle.nn.LSTMCell(16, 32) >>> cell_bw = paddle.nn.LSTMCell(16, 32) >>> rnn = paddle.nn.BiRNN(cell_fw, cell_bw) >>> inputs = paddle.rand((2, 23, 16)) >>> outputs, final_states = rnn(inputs) >>> print(outputs.shape) paddle.Size([2, 23, 64]) >>> print(final_states[0][0].shape) paddle.Size([2, 32]) rrrnr7T)r7r8c2[R"X/S5$r)r\concat)rFrs r:rybirnn..sV]]A62.r<)rr;r\rr) cell_fwcell_bwr4r5r6r7r9 states_fw states_bw outputs_fw outputs_bwrrs r:birnnrs@..A/ ..A/  .      J     Jll((. G)L   r<c US:Xa@[R"U5nU(dU$[[USSS2USSS255$[ U5U:Xde[ UVs/sHn[R"U5PM sn5nU(d[[U65$[[U65n[[USSS2USSS255$s snf)a{ Split states of RNN network into possibly nested list or tuple of states of each RNN cells of the RNN network. Parameters: states (Tensor|tuple|list): the concatenated states for RNN network. When `state_components` is 1, states in a Tensor with shape `(L*D, N, C)` where `L` is the number of layers of the RNN network, `D` is the number of directions of the RNN network(1 for unidirectional RNNs and 2 for bidirectional RNNs), `N` is the batch size of the input to the RNN network, `C` is the hidden size of the RNN network. When `state_components` is larger than 1, `states` is a tuple of `state_components` Tensors that meet the requirements described above. For SimpleRNNs and GRUs, `state_components` is 1, and for LSTMs, `state_components` is 2. bidirectional (bool): whether the state is of a bidirectional RNN network. Defaults to False. state_components (int): the number of the components of the states. see `states` above. Defaults to 1. Returns: A nested list or tuple of RNN cell states. If `bidirectional` is True, it can be indexed twice to get an RNN cell state. The first index indicates the layer, the second index indicates the direction. If `bidirectional` is False, it can be indexed once to get an RNN cell state. The index indicates the layer. Note that if `state_components` is larger than 1, an RNN cell state can be indexed one more time to get a tensor of shape(N, C), where `N` is the batch size of the input to the RNN cell, and `C` is the hidden size of the RNN cell. rNre)r\unstackrfziprhr)rr'state_componentsrNs r: split_statesrsR1'MF3Q3K167 76{....@t,@AV % %#v,'FF3Q3K167 7 As! CcpUS:Xa3[R"[RRU55$[RRU5n/n[ U5HnUR XSU25 M [ UVs/sHn[R"U5PM sn5$s snf)ah Concatenate a possibly nested list or tuple of RNN cell states into a compact form. Parameters: states (list|tuple): a possibly nested list or tuple of RNN cell states. If `bidirectional` is True, it can be indexed twice to get an RNN cell state. The first index indicates the layer, the second index indicates the direction. If `bidirectional` is False, it can be indexed once to get an RNN cell state. The index indicates the layer. Note that if `state_components` is larger than 1, an RNN cell state can be indexed one more time to get a tensor of shape(N, C), where `N` is the batch size of the input to the RNN cell, and `C` is the hidden size of the RNN cell. bidirectional (bool): whether the state is of a bidirectional RNN network. Defaults to False. state_components (int): the number of the components of the states. see `states` above. Defaults to 1. Returns: Concatenated states for RNN network. When `state_components` is 1, states in a Tensor with shape `(L\*D, N, C)` where `L` is the number of layers of the RNN network, `D` is the number of directions of the RNN network(1 for unidirectional RNNs and 2 for bidirectional RNNs), `N` is the batch size of the input to the RNN network, `C` is the hidden size of the RNN network. rN)r\rrrrgrJr)rr'r componentsr}rNs r: concat_statesr!sH1||FLL00899%%f- '(A   f%8(8%89 :)Z@ZTfll4(Z@AA@s B3cl\rSrSrSrSS Sjjr\S Sj5r\S Sj5rSr g) r/iOz RNNCellBase is the base class for abstraction representing the calculations mapping the input and state to the output and new state. It is suitable to and mostly used in RNN. NcH^^^ [RRU5SnSn"SS5m Uc UROUn[RRR nU[RRl[RR U 4SjU5nU[RRlTc UROTn [[RRU 55S:XaF[RRU 5Sm[RR U4SjU5n Un URUS:a[U [5(a*U SHn URUU RS'M! GO5[U [5(a&U Hn URUU RS'M! OURUU RS'O[U [5(aAU SH7n [R"U5UR!5U RS'M9 O[U [5(a>U H7n [R"U5UR!5U RS'M9 O4[R"U5UR!5U RS'[RR U4SjU U 5n U $![a [R"5n GNDf=f) a Generate initialized states according to provided shape, data type and value. Parameters: batch_ref (Tensor): A tensor, which shape would be used to determine the batch size, which is used to generate initial states. For `batch_ref`'s shape d, `d[batch_dim_idx]` is treated as batch size. shape (list|tuple|None, optional): A (possibly nested structure of) shape[s], where a shape is a list/tuple of integer. `-1` (for batch size) will be automatically prepended if a shape does not starts with it. If None, property `state_shape` will be used. Defaults to None. dtype (str|list|tuple|None, optional): A (possibly nested structure of) data type[s]. The structure must be same as that of `shape`, except when all tensors' in states has the same data type, a single data type can be used. If None and property `cell.state_shape` is not available, current default floating type of paddle is used. Defaults to None. init_value (float, optional): A float value used to initialize states. Defaults to 0. batch_dim_idx (int, optional): An integer indicating which dimension of the of `batch_ref` represents batch. Defaults to 0. Returns: init_states (Tensor|tuple|list): tensor of the provided shape and dtype, or list of tensors that each satisfies the requirements, packed in the same structure as `shape` and `type` does. rc[U[[45(a[SUS5(ag[U[5(ag[U[ 5=(a [U[ 5(+$)z@For shape, list/tuple of integer is the finest-grained objectionc4[U[5=(a U$rA)rrR)flagrFs r:ryLRNNCellBase.get_initial_states.._is_shape_sequence..sJq#$6$?4$?r<TF)rrfrrdictrr)seqs r:_is_shape_sequence:RNNCellBase.get_initial_states.._is_shape_sequencesX#e}--?d!#t$$c8,IZS5I1I Ir<c\rSrSrSrSrg)-RNNCellBase.get_initial_states..Shapeic^USS:Xa[U5UlgS/[U5QUlg)Nrr)rfri)rEris r:rG6RNNCellBase.get_initial_states..Shape.__init__s0#(8r>DK 9;8Jd5k8J r<)riN)rSrTrUrVrGrWrXr<r:Shapers r<rc>T"U5$rArX)rirs r:ry0RNNCellBase.get_initial_states..s %,r<rc>T$rArX)rirss r:ryrser<cD>[R"URTUS9$)Nri fill_valuers)r\fullri)rirs init_values r:ryrskk%"r<)r\rr state_shape layers_utils is_sequencer state_dtypeNotImplementedErrorr get_default_dtyperhrirrfrrN)rErorirsrrpr states_shapesis_sequence_ori states_dtypes fill_shapess init_statesrs `` @r:rRNNCellBase.get_initial_statesVsNLL((3A6  J  -2M((u  ,,33??0B !!- 22 &  1@ !!- :05 D,,5M v||##M2 3q 8LL((7:E"LL66#]M$ ??= )A -+t,,$QA!*!?AGGAJ(K//$A!*!?AGGAJ%(1}'E !!!$+t,,$QA!'i!8!G!L!L!NAGGAJ(K//$A!'i!8!G!L!L!NAGGAJ%(.||I'>!($&!!!$ll00    M# :%779M :s K>>L! L!c[S5e)a Abstract method (property). Used to initialize states. A (possibly nested structure of) shape[s], where a shape is a list/tuple of integers (-1 for batch size would be automatically inserted into a shape if shape is not started with it). Not necessary to be implemented if states are not initialized by `get_initial_states` or the `shape` argument is provided when using `get_initial_states`. z=Please add implementation for `state_shape` in the used cell.r rEs r:rRNNCellBase.state_shape" K  r<c[S5e)a Abstract method (property). Used to initialize states. A (possibly nested structure of) data types[s]. The structure must be same as that of `shape`, except when all tensors' in states has the same data type, a single data type can be used. Not necessary to be implemented if states are not initialized by `get_initial_states` or the `dtype` argument is provided when using `get_initial_states`. z=Please add implementation for `state_dtype` in the used cell.rrs r:r RNNCellBase.state_dtyperr<rX)NNr) rorriz!NestedStructure[ShapeLike] | Nonersz!NestedStructure[DTypeLike] | NonerfloatrprRrPzNestedStructure[Tensor]rPrQ) rSrTrUrV__doc__rpropertyrr rWrXr<r:r/r/Os4837 mm1m1 m  m  m !m^        r<c^\rSrSrSrSS U4SjjjrS S Sjjr\S Sj5rS Sjr Sr U=r $) SimpleRNNCelliaU Elman RNN (SimpleRNN) cell. Given the inputs and previous states, it computes the outputs and updates states. The formula used is as follows: .. math:: h_{t} & = act(W_{ih}x_{t} + b_{ih} + W_{hh}h_{t-1} + b_{hh}) y_{t} & = h_{t} where :math:`act` is for :attr:`activation`. Please refer to `Finding Structure in Time `_ for more details. Parameters: input_size (int): The input size. hidden_size (int): The hidden size. activation (str, optional): The activation in the SimpleRNN cell. It can be `tanh` or `relu`. Defaults to `tanh`. weight_ih_attr (ParamAttr|None, optional): The parameter attribute for :math:`weight_ih`. Default: None. weight_hh_attr(ParamAttr|None, optional): The parameter attribute for :math:`weight_hh`. Default: None. bias_ih_attr (ParamAttr|None, optional): The parameter attribute for the :math:`bias_ih`. Default: None. bias_hh_attr (ParamAttr|None, optional): The parameter attribute for the :math:`bias_hh`. Default: None. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Variables: - **weight_ih** (Parameter): shape (hidden_size, input_size), input to hidden weight, corresponding to :math:`W_{ih}` in the formula. - **weight_hh** (Parameter): shape (hidden_size, hidden_size), hidden to hidden weight, corresponding to :math:`W_{hh}` in the formula. - **bias_ih** (Parameter): shape (hidden_size, ), input to hidden bias, corresponding to :math:`b_{ih}` in the formula. - **bias_hh** (Parameter): shape (hidden_size, ), hidden to hidden bias, corresponding to :math:`b_{hh}` in the formula. Inputs: - **inputs** (Tensor): shape `[batch_size, input_size]`, the input, corresponding to :math:`x_{t}` in the formula. - **states** (Tensor, optional): shape `[batch_size, hidden_size]`, the previous hidden state, corresponding to :math:`h_{t-1}` in the formula. When states is None, zero state is used. Defaults to None. Returns: - **outputs** (Tensor): shape `[batch_size, hidden_size]`, the output, corresponding to :math:`h_{t}` in the formula. - **states** (Tensor): shape `[batch_size, hidden_size]`, the new hidden state, corresponding to :math:`h_{t}` in the formula. Notes: All the weights and bias are initialized with `Uniform(-std, std)` by default. Where std = :math:`\frac{1}{\sqrt{hidden\_size}}`. For more information about parameter initialization, please refer to :ref:`api_paddle_ParamAttr`. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.randn((4, 16)) >>> prev_h = paddle.randn((4, 32)) >>> cell = paddle.nn.SimpleRNNCell(16, 32) >>> y, h = cell(x, prev_h) >>> print(y.shape) paddle.Size([4, 32]) c >[T U]5 US::a%[SURRSU35eS[ R "U5- n USLa.URX!4U[R"U *U 5S9Ul O,,V5E5EFFmmFNNE << # << CmmE>>tD << # << C    *t r<cUR4$rAr7rs r:rSimpleRNNCell.state_shapes  ""r<chSnURS:waUS- nUR"S0URD6$)N{input_size}, {hidden_size}r-z, activation={activation}rX)r8format__dict__)rErs r: extra_reprSimpleRNNCell.extra_reprs4 ) ??f $ , ,Axx($--((r<)r:r8r5r4r7r6r3r1)r-NNNNN)r6rRr7rRr8_ActivationType | strr;ParamAttrLike | Noner<rUr=rUr>rUr? str | NonerPrQrA)r4rr Tensor | NonerPz tuple[int]rPr rSrTrUrVrrGr%r rrRrW __classcell__r,s@r:r"r"s?J-3/3/3-1-1UNUNUN* UN - UN - UN+UN+UNUN UNUNn ##))r<r"c^\rSrSrSrSS U4SjjjrS S Sjjr\S Sj5rS Sjr Sr U=r $)LSTMCellia Long-Short Term Memory(LSTM) RNN cell. Given the inputs and previous states, it computes the outputs and updates states. The formula used is as follows: .. math:: i_{t} & = \sigma(W_{ii}x_{t} + b_{ii} + W_{hi}h_{t-1} + b_{hi}) f_{t} & = \sigma(W_{if}x_{t} + b_{if} + W_{hf}h_{t-1} + b_{hf}) o_{t} & = \sigma(W_{io}x_{t} + b_{io} + W_{ho}h_{t-1} + b_{ho}) \widetilde{c}_{t} & = \tanh (W_{ig}x_{t} + b_{ig} + W_{hg}h_{t-1} + b_{hg}) c_{t} & = f_{t} * c_{t-1} + i_{t} * \widetilde{c}_{t} h_{t} & = o_{t} * \tanh(c_{t}) y_{t} & = h_{t} If `proj_size` is specified, the dimension of hidden state :math:`h_{t}` will be projected to `proj_size`: .. math:: h_{t} = h_{t}W_{proj\_size} where :math:`\sigma` is the sigmoid function, and * is the elementwise multiplication operator. Please refer to `An Empirical Exploration of Recurrent Network Architectures `_ for more details. Parameters: input_size (int): The input size. hidden_size (int): The hidden size. weight_ih_attr(ParamAttr|None, optional): The parameter attribute for `weight_ih`. Default: None. weight_hh_attr(ParamAttr|None, optional): The parameter attribute for `weight_hh`. Default: None. bias_ih_attr (ParamAttr|None, optional): The parameter attribute for the `bias_ih`. Default: None. bias_hh_attr (ParamAttr|None, optional): The parameter attribute for the `bias_hh`. Default: None. proj_size (int, optional): If specified, the output hidden state will be projected to `proj_size`. `proj_size` must be smaller than `hidden_size`. Default: None. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Variables: - **weight_ih** (Parameter): shape (4 * hidden_size, input_size), input to hidden weight, which corresponds to the concatenation of :math:`W_{ii}, W_{if}, W_{ig}, W_{io}` in the formula. - **weight_hh** (Parameter): shape (4 * hidden_size, hidden_size), hidden to hidden weight, which corresponds to the concatenation of :math:`W_{hi}, W_{hf}, W_{hg}, W_{ho}` in the formula. If proj_size was specified, the shape will be (4 * hidden_size, proj_size). - **weight_ho** (Parameter, optional): shape (hidden_size, proj_size), project the hidden state. - **bias_ih** (Parameter): shape (4 * hidden_size, ), input to hidden bias, which corresponds to the concatenation of :math:`b_{ii}, b_{if}, b_{ig}, b_{io}` in the formula. - **bias_hh** (Parameter): shape (4 * hidden_size, ), hidden to hidden bias, which corresponds to the concatenation of :math:`b_{hi}, b_{hf}, b_{hg}, b_{ho}` in the formula. Inputs: - **inputs** (Tensor): shape `[batch_size, input_size]`, the input, corresponding to :math:`x_t` in the formula. - **states** (list|tuple, optional): a list/tuple of two tensors, each of shape `[batch_size, hidden_size]`, the previous hidden state, corresponding to :math:`h_{t-1}, c_{t-1}` in the formula. When states is None, zero state is used. Defaults to None. Returns: - **outputs** (Tensor). Shape `[batch_size, hidden_size]`, the output, corresponding to :math:`h_{t}` in the formula. If `proj_size` is specified, output shape will be `[batch_size, proj_size]`. - **states** (tuple). A tuple of two tensors, each of shape `[batch_size, hidden_size]`, the new hidden states, corresponding to :math:`h_{t}, c_{t}` in the formula. If `proj_size` is specified, shape of :math:`h_{t}` will be `[batch_size, proj_size]`. Notes: All the weights and bias are initialized with `Uniform(-std, std)` by default. Where std = :math:`\frac{1}{\sqrt{hidden\_size}}`. For more information about parameter initialization, please refer to :ref:`api_paddle_ParamAttr`. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.randn((4, 16)) >>> prev_h = paddle.randn((4, 32)) >>> prev_c = paddle.randn((4, 32)) >>> cell = paddle.nn.LSTMCell(16, 32) >>> y, (h, c) = cell(x, (prev_h, prev_c)) >>> print(y.shape) paddle.Size([4, 32]) >>> print(h.shape) paddle.Size([4, 32]) >>> print(c.shape) paddle.Size([4, 32]) c >[T U]5 US::a%[SURRSU35eUS:a%[SURRSU35eXr:a [S5eS[ R "U5- n USLa2URSU-U4U[R"U *U 5S 9Ul O@URSU-U4S[R"S5S 9Ul S URl USLa;URSU-U=(d U4U[R"U *U 5S 9Ul OIURSU-U=(d U4S[R"S5S 9Ul S URl USLa2URSU-4US [R"U *U 5S 9UlO@URSU-4SS [R"S 5S 9UlS URl USLa2URSU-4US [R"U *U 5S 9UlO@URSU-4SS [R"S 5S 9UlS URl XplUS:a-URX'4U[R"U *U 5S 9UlX lXl[(R*Ul[.R0Ulg) Nrr$r%z proj_size of z*proj_size must be smaller than hidden_sizerFr&Tr(r)r*rGr+r,rSr^r-r.r/r0r1r2rr3r4r5 proj_size weight_hor7r6r9sigmoid_gate_activationr\r- _activation) rEr6r7r;r<r=r>rar?r@r,s r:rGLSTMCell.__init__s   ! !$.."9"9!::efqers  q= 7 788cdocpq   #IJ JDIIk**  &!22[*-$%IIsdC$83DN "22[*-$%JJsO3DN ,0DNN (  &!22[)":{;$%IIsdC$83DN "22[)":{;$%JJsO3DN ,0DNN ( u $00["$%IIsdC$8 1DL 00["$%JJsO 1DL *.DLL & u $00["$%IIsdC$8 1DL 00["$%JJsO 1DL *.DLL &" q=!22($%IIsdC$83DN '$ ! !;;r<cUcURXR5nUup4[R"XRSS9nUR bXPR -nU[R"X0R SS9- nURbXPR-n[R"USSS9nURUS5nURUS5nURUS5n X-XpRUS 5--n XRU 5-n URS:a [R"XR5n XU 44$) NTrCr`rnum_or_sectionsr[rrre) rrr\rEr1r4r3r5splitrdrerarb) rEr4r pre_hiddenpre_cellgates chunked_gatesr}focrIs r:r%LSTMCell.forwardTs+ >,,V5E5EFF%  fnn$G << #LL(E z>>tLL << #LL(E UABG  ! !-"2 3  ! !-"2 3  ! !-"2 3 L1// a0@AA A   # # >>A  a0Aa&yr<c\UR4UR=(d UR44$)z The `state_shape` of LSTMCell is a tuple with two shapes: `((hidden_size, ), (hidden_size,))`. (-1 for batch size would be automatically inserted into shape). These two shapes correspond to :math:`h_{t-1}` and :math:`c_{t-1}` separately. )r7rars r:rLSTMCell.state_shapeks*!!#dnn&H8H8H%JKKr<c:SR"S0URD6$NrOrXrPrQrs r:rRLSTMCell.extra_repru,33DdmmDDr<) rerdr5r4r7r6rar3rbr1)NNNNrN)r6rRr7rRr;rUr<rUr=rUr>rUrarRr?rVrPrQrA)r4rrzSequence[Tensor] | None)rPztuple[tuple[int], tuple[int]]rYrZr\s@r:r^r^s[B04/3-1-1]']']'- ]' - ]' + ]'+]']']' ]']'~.LLEEr<r^c^\rSrSrSrSS U4SjjjrS S Sjjr\S Sj5rS Sjr Sr U=r $)GRUCelliya Gated Recurrent Unit (GRU) RNN cell. Given the inputs and previous states, it computes the outputs and updates states. The formula for GRU used is as follows: .. math:: r_{t} & = \sigma(W_{ir}x_{t} + b_{ir} + W_{hr}h_{t-1} + b_{hr}) z_{t} & = \sigma(W_{iz}x_{t} + b_{iz} + W_{hz}h_{t-1} + b_{hz}) \widetilde{h}_{t} & = \tanh(W_{ic}x_{t} + b_{ic} + r_{t} * (W_{hc}h_{t-1} + b_{hc})) h_{t} & = z_{t} * h_{t-1} + (1 - z_{t}) * \widetilde{h}_{t} y_{t} & = h_{t} where :math:`\sigma` is the sigmoid function, and * is the elementwise multiplication operator. Please refer to `An Empirical Exploration of Recurrent Network Architectures `_ for more details. Parameters: input_size (int): The input size. hidden_size (int): The hidden size. weight_ih_attr(ParamAttr|None, optional): The parameter attribute for `weight_ih`. Default: None. weight_hh_attr(ParamAttr|None, optional): The parameter attribute for `weight_hh`. Default: None. bias_ih_attr (ParamAttr|None, optional): The parameter attribute for the `bias_ih`. Default: None. bias_hh_attr (ParamAttr|None, optional): The parameter attribute for the `bias_hh`. Default: None. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Variables: - **weight_ih** (Parameter): shape (3 * hidden_size, input_size), input to hidden weight, which corresponds to the concatenation of :math:`W_{ir}, W_{iz}, W_{ic}` in the formula. - **weight_hh** (Parameter): shape (3 * hidden_size, hidden_size), hidden to hidden weight, which corresponds to the concatenation of :math:`W_{hr}, W_{hz}, W_{hc}` in the formula. - **bias_ih** (Parameter): shape (3 * hidden_size, ), input to hidden bias, which corresponds to the concatenation of :math:`b_{ir}, b_{iz}, b_{ic}` in the formula. - **bias_hh** (Parameter): shape (3 * hidden_size, ), hidden to hidden bias, which corresponds to the concatenation of :math:`b_{hr}, b_{hz}, b_{hc}` in the formula. Inputs: - **inputs** (Tensor): A tensor with shape `[batch_size, input_size]`, corresponding to :math:`x_t` in the formula. - **states** (Tensor): A tensor with shape `[batch_size, hidden_size]`, corresponding to :math:`h_{t-1}` in the formula. Returns: - **outputs** (Tensor): shape `[batch_size, hidden_size]`, the output, corresponding to :math:`h_{t}` in the formula. - **states** (Tensor): shape `[batch_size, hidden_size]`, the new hidden state, corresponding to :math:`h_{t}` in the formula. Notes: All the weights and bias are initialized with `Uniform(-std, std)` by default. Where std = :math:`\frac{1}{\sqrt{hidden\_size}}`. For more information about parameter initialization, please refer to s:ref:`api_paddle_ParamAttr`. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.randn((4, 16)) >>> prev_h = paddle.randn((4, 32)) >>> cell = paddle.nn.GRUCell(16, 32) >>> y, h = cell(x, prev_h) >>> print(y.shape) paddle.Size([4, 32]) >>> print(h.shape) paddle.Size([4, 32]) c >[T U]5 US::a%[SURRSU35eS[ R "U5- nUSLa2URSU-U4U[R"U*U5S9Ul O@URSU-U4S[R"S5S9Ul SURl USLa2URSU-U4U[R"U*U5S9Ul O@URSU-U4S[R"S5S9Ul SURl USLa2URSU-4US[R"U*U5S 9UlO@URSU-4SS[R"S 5S 9UlSURl USLa2URSU-4US[R"U*U5S 9UlO@URSU-4SS[R"S 5S 9UlSURl X lXl[$R&Ul[*R,Ulg) Nrr$r%rFrjr&Tr(r)r*rGr+r,rSr^r-r.r/r0r1r2rr3r4r5r7r6r9rcrdr\r-re) rEr6r7r;r<r=r>r?r@r,s r:rGGRUCell.__init__s{  ! !$.."9"9!::efqers DIIk**  &!22[*-$%IIsdC$83DN "22[*-$%JJsO3DN ,0DNN (  &!22[+.$%IIsdC$83DN "22[+.$%JJsO3DN ,0DNN ( u $00["$%IIsdC$8 1DL 00["$%JJsO 1DL *.DLL & u $00["$%IIsdC$8 1DL 00["$%JJsO 1DL *.DLL &&$ ! !;;r<c UcURXR5nUn[R"XRSS9nUR bX@R -n[R"X0R SS9nURbXPR-n[R"USSS9upgn[R"USSS9upn URXi-5n URXz-5n URXU --5nX>- U -U-nX4$)NTrCrjrrh) rrr\rEr1r4r3r5rkrdre)rEr4rrlx_gatesh_gatesx_rx_zx_ch_rh_zh_crzrrrIs r:r%GRUCell.forwards >,,V5E5EFF --DI << # ,G-- NNM << # ,G WaaH # WaaH #  ! !#) ,  ! !#) ,   Ss7] + ^q 1 $t r<cUR4$)z The `state_shape` of GRUCell is a shape `[hidden_size]` (-1 for batch size would be automatically inserted into shape). The shape corresponds to the shape of :math:`h_{t-1}`. rLrs r:rGRUCell.state_shape/s  ""r<c:SR"S0URD6$rwrxrs r:rRGRUCell.extra_repr8rzr<)rerdr5r4r7r6r3r1)NNNNN)r6rRr7rRr;rUr<rUr=rUr>rUr?rVrPrQrA)r4rrrWrPztuple[Tensor, Tensor]rXrYrZr\s@r:r|r|ysKb04/3-1-1N'N'N'- N' - N' + N'+N'N' N'N'b7;&3 0##EEr<r|cf^\rSrSrSrSSU4SjjjrSS SjjrSrU=r$) RNNi<aF Wrapper for RNN, which creates a recurrent neural network with an RNN cell. It performs :code:`cell.forward()` repeatedly until reaches to the maximum length of `inputs`. Parameters: cell(RNNCellBase): An instance of `RNNCellBase`. is_reverse (bool, optional): Indicate whether to calculate in the reverse order of input sequences. Defaults to False. time_major (bool): Whether the first dimension of the input means the time steps. Defaults to False. Inputs: - **inputs** (Tensor): A (possibly nested structure of) tensor[s]. The input sequences. If time_major is False, the shape is `[batch_size, time_steps, input_size]`. If time_major is True, the shape is `[time_steps, batch_size, input_size]` where `input_size` is the input size of the cell. - **initial_states** (Tensor|list|tuple, optional): Tensor of a possibly nested structure of tensors, representing the initial state for the rnn cell. If not provided, `cell.get_initial_states` would be called to produce the initial states. Defaults to None. - **sequence_length** (Tensor, optional): shape `[batch_size]`, dtype: int64 or int32. The valid lengths of input sequences. Defaults to None.If `sequence_length` is not None, the inputs are treated as padded sequences. In each input sequence, elements whose time step index are not less than the valid length are treated as paddings. - **kwargs**: Additional keyword arguments to pass to `forward` of the cell. Outputs: - **outputs** (Tensor|list|tuple): the output sequences. If `time_major` is True, the shape is `[time_steps, batch_size, hidden_size]`, else `[batch_size, time_steps, hidden_size]`. - **final_states** (Tensor|list|tuple): final states of the cell. Tensor or a possibly nested structure of tensors which has the same structure with initial state. Each tensor in final states has the same shape and dtype as the corresponding tensor in initial states. Notes: This class is a low-level API for wrapping rnn cell into a RNN network. Users should take care of the state of the cell. If `initial_states` is passed to the `forward` method, make sure that it satisfies the requirements of the cell. Examples: .. code-block:: pycon >>> import paddle >>> inputs = paddle.rand((4, 23, 16)) >>> prev_h = paddle.randn((4, 32)) >>> cell = paddle.nn.SimpleRNNCell(16, 32) >>> rnn = paddle.nn.RNN(cell) >>> outputs, final_states = rnn(inputs, prev_h) >>> print(outputs.shape) paddle.Size([4, 23, 32]) >>> print(final_states.shape) paddle.Size([4, 32]) c>[TU]5 Xl[URS5(d%URRURlX lX0lg)Ncall)r*rGr3hasattrr%rr8r7)rEr3r8r7r,s r:rG RNN.__init__msE  tyy&))!YY..DIIN$$r<c l[URU4UUURURS.UD6upVXV4$)N)r5r6r7r8)r;r3r7r8)rEr4r5r6r9rrs r:r% RNN.forward{sK'* II ' *+ ' ' # **r<)r3r8r7)FF)r3r/r8boolr7rrPrQNN)r4rr5TensorOrTensors | Noner6rr9r rSrTrUrVrrGr%rWr[r\s@r:rr<sy.f! % % % %  % %"26"& ++/+ +  ++r<rch^\rSrSrSrSSU4SjjjrSS SjjrSrU=r$) BiRNNia Wrapper for bidirectional RNN, which builds a bidirectional RNN given the forward rnn cell and backward rnn cell. A BiRNN applies forward RNN and backward RNN with corresponding cells separately and concats the outputs along the last axis. Parameters: cell_fw (RNNCellBase): A RNNCellBase instance used for forward RNN. cell_bw (RNNCellBase): A RNNCellBase instance used for backward RNN. time_major (bool, optional): Whether the first dimension of the input means the time steps. Defaults to False. Inputs: - **inputs** (Tensor): the input sequences of both RNN. If time_major is True, the shape of is `[time_steps, batch_size, input_size]`, else the shape is `[batch_size, time_steps, input_size]`, where input_size is the input size of both cells. - **initial_states** (list|tuple|None, optional): A tuple/list of the initial states of the forward cell and backward cell. Defaults to None. If not provided, `cell.get_initial_states` would be called to produce the initial states for each cell. Defaults to None. - **sequence_length** (Tensor|None, optional): shape `[batch_size]`, dtype: int64 or int32. The valid lengths of input sequences. Defaults to None. If `sequence_length` is not None, the inputs are treated as padded sequences. In each input sequence, elements whose time step index are not less than the valid length are treated as paddings. - **kwargs**: Additional keyword arguments. Arguments passed to `forward` for each cell. Outputs: - **outputs** (Tensor): the outputs of the bidirectional RNN. It is the concatenation of the outputs from the forward RNN and backward RNN along the last axis. If time_major is True, the shape is `[time_steps, batch_size, size]`, else the shape is `[batch_size, time_steps, size]`, where size is `cell_fw.hidden_size + cell_bw.hidden_size`. - **final_states** (tuple): A tuple of the final states of the forward cell and backward cell. Notes: This class is a low level API for wrapping rnn cells into a BiRNN network. Users should take care of the states of the cells. If `initial_states` is passed to the `forward` method, make sure that it satisfies the requirements of the cells. Examples: .. code-block:: pycon >>> import paddle >>> cell_fw = paddle.nn.LSTMCell(16, 32) >>> cell_bw = paddle.nn.LSTMCell(16, 32) >>> rnn = paddle.nn.BiRNN(cell_fw, cell_bw) >>> inputs = paddle.rand((2, 23, 16)) >>> outputs, final_states = rnn(inputs) >>> print(outputs.shape) paddle.Size([2, 23, 64]) >>> print( ... final_states[0][0].shape, ... len(final_states), ... len(final_states[0]), ... ) paddle.Size([2, 32]) 2 2 cL>[TU]5 XlX lURUR:wa&[ SURSURS35eURUR4H'n[ US5(aMURUlM) X0l g)Nzinput size of forward cell(z') does not equalsthat of backward cell()r) r*rGrrr6r+rr%rr7)rErrr7r3r,s r:rGBiRNN.__init__s     !3!3 3-g.@.@-AB))0););([TU]5 SS/n XlX lX0lXplX];aSOSUlX`lX@lUS:XaSOSUl UU U U S.nXl U S:aUS:XdeUS:Xa [nXS'OFUS :Xa[nO9US :Xa [nS US 'O'US :Xa [nSUS 'O[nURUS 'U =(d UnUS;aaSnU"X#40UD6nUR[!UUU55 [#SU5H)nU"UU40UD6nUR[!UUU55 M+ OX];axU"X#40UD6nU"X#40UD6nUR[%UUU55 [#SU5H9nU"SU-U40UD6nU"SU-U40UD6nUR[%UUU55 M; O['SU35eSUlU=R([+UR-55US-X];aSOS-:H-sl/n[#UR5Hn[#UR 5HnnUS:XaSOSnUR/SS/5 U SLaURS5 U SLaURS5 UVs/sHnUR1UU5PM nnMp M [3UUR-55Hunn[5UUU5 M UR75 gs snf)Nr'r&rerr()r;r<r=r>rrar)r*r.r8r+r-)r%FzQdirection should be forward or bidirect (or bidirectional), received direction = Tr`_reversezweight_ih_l{}{}zweight_hh_l{}{}z bias_ih_l{}{}z bias_hh_l{}{})r*rGmoder6r7dropoutnum_directionsr7 num_layersrrar^r|r"r8rJrrgrr+could_use_cudnnrh parametersextendrPrsetattrflatten_parameters)rErr6r7r directionr7rr;r<r=r>rabidirectional_listr9rnn_clsin_sizer8r3rrr param_nameslayernumsuffixrFr?paramr,s r:rGRNNBase.__init__s# )8*(E $& #,#Ba$$%)V^-,(( " # q=6> !> 6>G"+;  U]G Z #G#)F< Z #G#)F< #G#'??F< *{  #J:=f=D KKD*j9 :1j)w >v> Cj*=>* ,j@@Gj@@G KKgw ; <1j)!!g+{EfE!!g+{EfE E'7J?@* ((1{4  $ DOO$5 6*q.0Aa; !   4??+ET001'*axR""$57H#IJu,&&7u,&&7@KL 1qxxv6 L 2,{DOO,=>KD% D$ &? ! MsK.c>UR(GaSURSS9nUVs/sH#n[R"UR5PM% nnS/[ U5-Ul[U5HJupBUS-S:aSOSUR-UR-nUS-nX R XVS--US--'ML UR[R"U5/USR[R"S5S9/Ul[!5(ao["R$R&RS S/S [(3["R*R,RSS 9SS 9UlS UR.lO>UR3[&R4R6R8S [(3S9Ul[;5(a["R<"5 USRn[?U[&R@5(a'["RBRDRFUn[HRJ"UR UR URSSS SSSU5 SSS5 g[M[O5[O55 ["R<"5 [!5(a8[PRJ"UR USRS SSSSSS//5 OPURRRUSSUR 0UR URS.S SUSRS.S9 SSS5 SSS5 ggs snf!,(df  N=f!,(df  N.=f!,(df  g=f)zV Resets parameter data pointer to address in continuous memory block for cudnn usage. F)include_sublayersNr`rerr)rirsr'uint8 dropout_state)r)rsrir?r trainableT)rsr? copy_data use_alignrsrcoalesce_tensorInput)Output FusedOutput)rrrsrr4rattrs)+rrnpprodrirh _all_weightsrrrr.sumrsr/r2 _flat_weightrr\rrrrr_dropout_statercreate_variableVarDescVarTypeUINT8r no_gradrDataTyperr paddle_type_to_proto_typer rrrr _helper append_op)rEparamsrrir}offset layer_idxrss r:rRNNBase.flatten_parametersXs9     __u_=F7=>veRWWU[[)vE>!%V 4D %f-1uqydoo-0C0CC F DI!!&q="81q5"@A.%%66%=/ )//() 3&!D }}&,jjoo&F&F!#()H(IJ & 5 5 > >Q > G# 'G'#59##1&*&:&:,,..44()H(IJ';'#  ^^%"1IOOE!%77 & 5 5 O O!!"11))))))!,## #&%(+-/F/H ==**))q  LL**. '):):;&*&7&7+/+<+<! *.).%+AY__+ #!A ?L&%.! s7*M',BM,'N=BM=N, M:= N N NcUR(d"[RRU/SQ5n[ 5(a[ R "UUURUURURURS:HURURURURSUR(+5 upEnGO>UR R#UR$5n['UR(5Vs/sH(nUR R#UR$5PM* nnUR R#[*R,R.R0SS9nUURUUS.nURURS:HURURURURUR(+S.n UUUURS.n UR R3S XU S 9 UR(d"[RRU/SQ5OUnU[5U5S :a [7U54$US4$s snf) N)rrrererT)rsr)r WeightListPreStateSequenceLength) dropout_prob is_bidirecr6r7rris_test)OutStateReserve DropoutStater;rr)r7r\r]rjrr r;rrrrr6r7rrtrainingr"create_variable_for_type_inferencersrgrrrrrrrhr) rEr4r5r6rrr`r}reserverrs r: _cudnn_implRNNBase._cudnn_impls ]],,VY?F ! # #"JJ!!## ##q(   MM!MCE ,,AA&,,OCt4455A ?? M5 llEEll**00FG  "//*"1 F!% "11Q6"oo#//"oo #}},E" $ 3 3 G LL " "6% #  ?? MM # #C 3 CJNE%L@@a@@Os/I!c UR(aSOSnURnUcUR=(d UR/UR/4nURUR -S/nUR US:aUR UUS'O*[R "U5UR5US'[[UR5Vs/sHn[R"XvU-SUS9PM sn5nOH[U[RR[R R"45(aU/OUnUR$(a8[R&R)5(aUcUR+XU5$[-X R S:HUR5n /n [/U5HWupUS:a+[0R2"UUR2UR4SS9nU "XUU5upU R7U 5 U nMY [9XR S:HUR5n W U 4$s snf)Nrrrrreupscale_in_train)rr)r7rsrar7rrrir\rNrrgrrrrrrrrdeviceis_compiled_with_rocmrrrr9rrrJr)rEr4r5r6 batch_indexrsdims fill_shaper}rr rnn_layerr final_states r:r%RNNBase.forwards"  ??a    !^^7t'7'784;K;K:LMD//D,?,??DJ||K(1, & [ 9 1 & V 4[ A F F H 1 " #4#8#89 :KK(72q: N"V]]%;%;VZZ=M=M$N  $     33559P##FOL L //14d6K6K  %dOLA1uLL!]]+  $-VAY#P G    ,F,% --2D4I4I  $$Us%I cSnURS:waUS- nURSLaUS- nURS:waUS- nUR"S0URD6$) NrOrz, num_layers={num_layers}Fz, time_major={time_major}rz, dropout={dropout}rX)rr7rrPrQ)rEmain_strs r:rRRNNBase.extra_repr?sb0 ??a  3 3H ??% ' 3 3H <<1  - -H///r<) rrrrrr7r6rrrrarr7) rr%FrNNNNr)rz_RNNType | strr6rRr7rRrrRr_DirectionType | strr7rrrr;rUr<rUr=rUr>rUrarRrPrQr)r4rr5rr6z int | NonerPz-tuple[Tensor, Tensor | tuple[Tensor, Tensor]]rrY) rSrTrUrVrrGrrr%rRrWr[r\s@r:rrs:*3 /3/3-1-1a"a"a" a"  a" ( a"a"a"-a"-a"+a"+a"a" a"a"FeNBABA/BA$ BA 7 BAN26&* :%:%/:%$ :% 7 :%x00r<rcz^\rSrSrSrSSU4SjjjrSrU=r$) SimpleRNNiJa Multilayer Elman network(SimpleRNN). It takes input sequences and initial states as inputs, and returns the output sequences and the final states. Each layer inside the SimpleRNN maps the input sequences and initial states to the output sequences and final states in the following manner: at each step, it takes step inputs(:math:`x_{t}`) and previous states(:math:`h_{t-1}`) as inputs, and returns step outputs(:math:`y_{t}`) and new states(:math:`h_{t}`). .. math:: h_{t} & = act(W_{ih}x_{t} + b_{ih} + W_{hh}h_{t-1} + b_{hh}) y_{t} & = h_{t} where :math:`act` is for :attr:`activation`. Using key word arguments to construct is recommended. Parameters: input_size (int): The input size of :math:`x` for the first layer's cell. hidden_size (int): The hidden size of :math:`h` for each layer's cell. num_layers (int, optional): Number of recurrent layers. Defaults to 1. direction (str, optional): The direction of the network. It can be "forward" or "bidirect"(or "bidirectional"). When "bidirect", the way to merge outputs of forward and backward is concatenating. Defaults to "forward". time_major (bool, optional): Whether the first dimension of the input means the time steps. If time_major is True, the shape of Tensor is [time_steps,batch_size,input_size], otherwise [batch_size, time_steps,input_size]. Defaults to False. `time_steps` means the length of input sequence. dropout (float, optional): The dropout probability. Dropout is applied to the input of each layer except for the first layer. The range of dropout from 0 to 1. Defaults to 0. activation (str, optional): The activation in each SimpleRNN cell. It can be `tanh` or `relu`. Defaults to `tanh`. weight_ih_attr (ParamAttr|None, optional): The parameter attribute for `weight_ih` of each cell. Defaults to None. weight_hh_attr (ParamAttr|None, optional): The parameter attribute for `weight_hh` of each cell. Defaults to None. bias_ih_attr (ParamAttr|None, optional): The parameter attribute for the `bias_ih` of each cells. Defaults to None. bias_hh_attr (ParamAttr|None, optional): The parameter attribute for the `bias_hh` of each cells. Defaults to None. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Inputs: - **inputs** (Tensor): the input sequence. If `time_major` is True, the shape is `[time_steps, batch_size, input_size]`, else, the shape is `[batch_size, time_steps, input_size]`. `time_steps` means the length of the input sequence. - **initial_states** (Tensor, optional): the initial state. The shape is `[num_layers * num_directions, batch_size, hidden_size]`. If initial_state is not given, zero initial states are used. - **sequence_length** (Tensor, optional): shape `[batch_size]`, dtype: int64 or int32. The valid lengths of input sequences. Defaults to None. If `sequence_length` is not None, the inputs are treated as padded sequences. In each input sequence, elements whose time step index are not less than the valid length are treated as paddings. Returns: - **outputs** (Tensor): the output sequence. If `time_major` is True, the shape is `[time_steps, batch_size, num_directions * hidden_size]`, else, the shape is `[batch_size, time_steps, num_directions * hidden_size]`. Note that `num_directions` is 2 if direction is "bidirectional" else 1. `time_steps` means the length of the output sequence. - **final_states** (Tensor): final states. The shape is `[num_layers * num_directions, batch_size, hidden_size]`. Note that `num_directions` is 2 if direction is "bidirectional" (the index of forward states are 0, 2, 4, 6... and the index of backward states are 1, 3, 5, 7...), else 1. Variables: - **weight_ih_l[k]**: the learnable input-hidden weights of the k-th layer. If `k = 0`, the shape is `[hidden_size, input_size]`. Otherwise, the shape is `[hidden_size, num_directions * hidden_size]`. - **weight_hh_l[k]**: the learnable hidden-hidden weights of the k-th layer, with shape `[hidden_size, hidden_size]`. - **bias_ih_l[k]**: the learnable input-hidden bias of the k-th layer, with shape `[hidden_size]`. - **bias_hh_l[k]**: the learnable hidden-hidden bias of the k-th layer, with shape `[hidden_size]`. Examples: .. code-block:: pycon >>> import paddle >>> rnn = paddle.nn.SimpleRNN(16, 32, 2) >>> x = paddle.randn((4, 23, 16)) >>> prev_h = paddle.randn((2, 4, 32)) >>> y, h = rnn(x, prev_h) >>> print(y.shape) paddle.Size([4, 23, 32]) >>> print(h.shape) paddle.Size([2, 4, 32]) c >US:XaSn OUS:XaSn O[SUS35eXpl[TU] U UUUUUUUU U U S5 g)Nr-r+r.r*zUnknown activation ''r)r+r8r*rG)rEr6r7rrr7rr8r;r<r=r>r?rr,s r:rGSimpleRNN.__init__sj  D 6 !D3JrUr?rVrPrQrSrTrUrVrrGrWr[r\s@r:rrJsRp*3 ,2/3/3-1-1# # #  # ( #  # # *# -# -# +# +# #  # # r<rcz^\rSrSrSrSSU4SjjjrSrU=r$)r(iad Multilayer LSTM. It takes a sequence and an initial state as inputs, and returns the output sequences and the final states. Each layer inside the LSTM maps the input sequences and initial states to the output sequences and final states in the following manner: at each step, it takes step inputs(:math:`x_{t}`) and previous states(:math:`h_{t-1}, c_{t-1}`) as inputs, and returns step outputs(:math:`y_{t}`) and new states(:math:`h_{t}, c_{t}`). .. math:: i_{t} & = \sigma(W_{ii}x_{t} + b_{ii} + W_{hi}h_{t-1} + b_{hi}) f_{t} & = \sigma(W_{if}x_{t} + b_{if} + W_{hf}h_{t-1} + b_{hf}) o_{t} & = \sigma(W_{io}x_{t} + b_{io} + W_{ho}h_{t-1} + b_{ho}) \widetilde{c}_{t} & = \tanh (W_{ig}x_{t} + b_{ig} + W_{hg}h_{t-1} + b_{hg}) c_{t} & = f_{t} * c_{t-1} + i_{t} * \widetilde{c}_{t} h_{t} & = o_{t} * \tanh(c_{t}) y_{t} & = h_{t} If `proj_size` is specified, the dimension of hidden state :math:`h_{t}` will be projected to `proj_size`: .. math:: h_{t} = h_{t}W_{proj\_size} where :math:`\sigma` is the sigmoid function, and * is the elementwise multiplication operator. Using key word arguments to construct is recommended. Parameters: input_size (int): The input size of :math:`x` for the first layer's cell. hidden_size (int): The hidden size of :math:`h` for each layer's cell. num_layers (int, optional): Number of recurrent layers. Defaults to 1. direction (str, optional): The direction of the network. It can be "forward" or "bidirect"(or "bidirectional"). When "bidirect", the way to merge outputs of forward and backward is concatenating. Defaults to "forward". time_major (bool, optional): Whether the first dimension of the input means the time steps. If time_major is True, the shape of Tensor is [time_steps,batch_size,input_size], otherwise [batch_size, time_steps,input_size]. Defaults to False. `time_steps` means the length of input sequence. dropout (float, optional): The dropout probability. Dropout is applied to the input of each layer except for the first layer. The range of dropout from 0 to 1. Defaults to 0. weight_ih_attr (ParamAttr|None, optional): The parameter attribute for `weight_ih` of each cell. Default: None. weight_hh_attr (ParamAttr|None, optional): The parameter attribute for `weight_hh` of each cell. Default: None. bias_ih_attr (ParamAttr|None, optional): The parameter attribute for the `bias_ih` of each cells. Default: None. bias_hh_attr (ParamAttr|None, optional): The parameter attribute for the `bias_hh` of each cells. Default: None. proj_size (int, optional): If specified, the output hidden state of each layer will be projected to `proj_size`. `proj_size` must be smaller than `hidden_size`. Default: 0. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Inputs: - **inputs** (Tensor): the input sequence. If `time_major` is True, the shape is `[time_steps, batch_size, input_size]`, else, the shape is `[batch_size, time_steps, input_size]`. `time_steps` means the length of the input sequence. - **initial_states** (list|tuple, optional): the initial state, a list/tuple of (h, c), the shape of each is `[num_layers * num_directions, batch_size, hidden_size]`. If initial_state is not given, zero initial states are used. - **sequence_length** (Tensor, optional): shape `[batch_size]`, dtype: int64 or int32. The valid lengths of input sequences. Defaults to None. If `sequence_length` is not None, the inputs are treated as padded sequences. In each input sequence, elements whose time step index are not less than the valid length are treated as paddings. Returns: - **outputs** (Tensor). The output sequence. If `time_major` is True, the shape is `[time_steps, batch_size, num_directions * hidden_size]`. If `proj_size` is specified, shape will be `[time_major, batch_size, num_directions * proj_size]`. If `time_major` is False, the shape is `[batch_size, time_steps, num_directions * hidden_size]`. Note that `num_directions` is 2 if direction is "bidirectional" else 1. `time_steps` means the length of the output sequence. - **final_states** (tuple). The final state, a tuple of two tensors, h and c. The shape of each is `[num_layers * num_directions, batch_size, hidden_size]`. If `proj_size` is specified, the last dimension of h will be proj_size. Note that `num_directions` is 2 if direction is "bidirectional" (the index of forward states are 0, 2, 4, 6... and the index of backward states are 1, 3, 5, 7...), else 1. Variables: - **weight_ih_l[k]**: the learnable input-hidden weights of the k-th layer. If `k = 0`, the shape is `[hidden_size, input_size]`. Otherwise, the shape is `[hidden_size, num_directions * hidden_size]`. - **weight_hh_l[k]**: the learnable hidden-hidden weights of the k-th layer, with shape `[hidden_size, hidden_size]`. - **bias_ih_l[k]**: the learnable input-hidden bias of the k-th layer, with shape `[hidden_size]`. - **bias_hh_l[k]**: the learnable hidden-hidden bias of the k-th layer, with shape `[hidden_size]`. Examples: .. code-block:: pycon >>> import paddle >>> rnn = paddle.nn.LSTM(16, 32, 2) >>> x = paddle.randn((4, 23, 16)) >>> prev_h = paddle.randn((2, 4, 32)) >>> prev_c = paddle.randn((2, 4, 32)) >>> y, (h, c) = rnn(x, (prev_h, prev_c)) >>> print(y.shape) paddle.Size([4, 23, 32]) >>> print(h.shape) paddle.Size([2, 4, 32]) >>> print(c.shape) paddle.Size([2, 4, 32]) c :>[T U]SUUUUUUUUU U U 5 g)Nr(r*rG)rEr6r7rrr7rr;r<r=r>rar?r,s r:rG LSTM.__init__/s6              r<rX) rr%FrNNNNrN)r6rRr7rRrrRrrr7rrrr;rUr<rUr=rUr>rUrarRr?rVrPrQrr\s@r:r(r(sgZ*3 /3/3-1-1     (     - - + +      r<r(ct^\rSrSrSrSSU4SjjjrSrU=r$)r)iNa% Multilayer GRU. It takes input sequence and initial states as inputs, and returns the output sequences and the final states. Each layer inside the GRU maps the input sequences and initial states to the output sequences and final states in the following manner: at each step, it takes step inputs(:math:`x_{t}`) and previous states(:math:`h_{t-1}`) as inputs, and returns step outputs(:math:`y_{t}`) and new states(:math:`h_{t}`). .. math:: r_{t} & = \sigma(W_{ir}x_{t} + b_{ir} + W_{hr}h_{t-1} + b_{hr}) z_{t} & = \sigma(W_{iz}x_{t} + b_{iz} + W_{hz}h_{t-1} + b_{hz}) \widetilde{h}_{t} & = \tanh(W_{ic}x_{t} + b_{ic} + r_{t} * (W_{hc}h_{t-1} + b_{hc})) h_{t} & = z_{t} * h_{t-1} + (1 - z_{t}) * \widetilde{h}_{t} y_{t} & = h_{t} where :math:`\sigma` is the sigmoid function, and * is the elementwise multiplication operator. Using key word arguments to construct is recommended. Parameters: input_size (int): The input size of :math:`x` for the first layer's cell. hidden_size (int): The hidden size of :math:`h` for each layer's cell. num_layers (int, optional): Number of recurrent layers. Defaults to 1. direction (str, optional): The direction of the network. It can be "forward" or "bidirect"(or "bidirectional"). When "bidirect", the way to merge outputs of forward and backward is concatenating. Defaults to "forward". time_major (bool, optional): Whether the first dimension of the input means the time steps. If time_major is True, the shape of Tensor is [time_steps,batch_size,input_size], otherwise [batch_size, time_steps,input_size]. Defaults to False. `time_steps` means the length of input sequence. dropout (float, optional): The dropout probability. Dropout is applied to the input of each layer except for the first layer. The range of dropout from 0 to 1. Defaults to 0. weight_ih_attr (ParamAttr|None, optional): The parameter attribute for `weight_ih` of each cell. Default: None. weight_hh_attr (ParamAttr|None, optional): The parameter attribute for `weight_hh` of each cell. Default: None. bias_ih_attr (ParamAttr|None, optional): The parameter attribute for the `bias_ih` of each cells. Default: None. bias_hh_attr (ParamAttr|None, optional): The parameter attribute for the `bias_hh` of each cells. Default: None. name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Inputs: - **inputs** (Tensor): the input sequence. If `time_major` is True, the shape is `[time_steps, batch_size, input_size]`, else, the shape is `[batch_size, time_steps, input_size]`. `time_steps` means the length of the input sequence. - **initial_states** (Tensor, optional): the initial state. The shape is `[num_layers * num_directions, batch_size, hidden_size]`. If initial_state is not given, zero initial states are used. Defaults to None. - **sequence_length** (Tensor, optional): shape `[batch_size]`, dtype: int64 or int32. The valid lengths of input sequences. Defaults to None. If `sequence_length` is not None, the inputs are treated as padded sequences. In each input sequence, elements whose time step index are not less than the valid length are treated as paddings. Returns: - **outputs** (Tensor): the output sequence. If `time_major` is True, the shape is `[time_steps, batch_size, num_directions * hidden_size]`, else, the shape is `[batch_size, time_steps, num_directions * hidden_size]`. Note that `num_directions` is 2 if direction is "bidirectional" else 1. `time_steps` means the length of the output sequence. - **final_states** (Tensor): final states. The shape is `[num_layers * num_directions, batch_size, hidden_size]`. Note that `num_directions` is 2 if direction is "bidirectional" (the index of forward states are 0, 2, 4, 6... and the index of backward states are 1, 3, 5, 7...), else 1. Variables: - **weight_ih_l[k]**: the learnable input-hidden weights of the k-th layer. If `k = 0`, the shape is `[hidden_size, input_size]`. Otherwise, the shape is `[hidden_size, num_directions * hidden_size]`. - **weight_hh_l[k]**: the learnable hidden-hidden weights of the k-th layer, with shape `[hidden_size, hidden_size]`. - **bias_ih_l[k]**: the learnable input-hidden bias of the k-th layer, with shape `[hidden_size]`. - **bias_hh_l[k]**: the learnable hidden-hidden bias of the k-th layer, with shape `[hidden_size]`. Examples: .. code-block:: pycon >>> import paddle >>> rnn = paddle.nn.GRU(16, 32, 2) >>> x = paddle.randn((4, 23, 16)) >>> prev_h = paddle.randn((2, 4, 32)) >>> y, h = rnn(x, prev_h) >>> print(y.shape) paddle.Size([4, 23, 32]) >>> print(h.shape) paddle.Size([2, 4, 32]) c :>[T U]SUUUUUUUUU U S5 g)Nr)rr) rEr6r7rrr7rr;r<r=r>r?r,s r:rG GRU.__init__s6              r<rX) rr%FrNNNNN)r6rRr7rRrrRrrr7rrrr;rUr<rUr=rUr>rUr?rVrPrQrr\s@r:r)r)NsWz*3 /3/3-1-1     (     - - + +     r<r))NNFF)r3r/r4rr5rr6rWr7rr8rr9rrPz?tuple[Tensor | tuple[Tensor, ...], Tensor | tuple[Tensor, ...]])r`rrarrbrrPr)rFrrPr)NNF)rr/rr/r4rr5rr6rWr7rr9rrPr)Fr)rr$r'rrrRrPz!list[Tensor] | list[list[Tensor]])rzSequence[Tensor]r'rrrRrPzTensor | tuple[Tensor, Tensor])L __future__rr^collections.abcr functoolsrrtypingrrnumpyrtyping_extensionsr r\r r r r paddle.base.data_feederrrpaddle.base.dygraph.baserpaddle.base.frameworkrrrpaddle.common_ops_importrpaddle.frameworkrr paddle.nnrr9rr/paddle.tensor.manipulationr containerrlayersrrrpaddle._typingr r!r"r#r$_DirectionType_RNNType_ActivationType__all__r;r>rcrlr1r2rrrr/r"r^r|rrrrr(r)rXr<r:rs# $%%" DDHD ..> CDN<=Hn-O  .2%) T T T +T # T  T  T T ET n , ,% A'N M)hCG%) a! a! a! a!@ a! # a!  a!a!*a!L 68 686868' 68v +B +B+B+B$ +B\T %T nn)Kn)b_E{_ED@Ek@EFO+%O+d^%E^%BX0iX0v x x vF 7F Ru 'u r<