&Vf5lddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlm Z dd l m Z dd l m Z dd lmZdd lmZe d Gdde eZe dGddeZdS)) activations)backend) constraints) initializers)ops) regularizers)tree) keras_export) InputSpec)Layer)DropoutRNNCell)RNNzkeras.layers.GRUCellcheZdZdZ dfd Zfd Zdd ZfdZddZxZ S)GRUCella Cell class for the GRU layer. This class processes one step within the whole time sequence input, whereas `keras.layer.GRU` processes the whole sequence. Args: units: Positive integer, dimensionality of the output space. activation: Activation function to use. Default: hyperbolic tangent (`tanh`). If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step. Default: sigmoid (`sigmoid`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, (default `True`), whether the layer should use a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. Default: `"glorot_uniform"`. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. Default: `"orthogonal"`. bias_initializer: Initializer for the bias vector. Default: `"zeros"`. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. Default: `None`. recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix. Default: `None`. bias_regularizer: Regularizer function applied to the bias vector. Default: `None`. kernel_constraint: Constraint function applied to the `kernel` weights matrix. Default: `None`. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. Default: `None`. bias_constraint: Constraint function applied to the bias vector. Default: `None`. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0. reset_after: GRU convention (whether to apply reset gate after or before matrix multiplication). False = "before", True = "after" (default and cuDNN compatible). seed: Random seed for dropout. Call arguments: inputs: A 2D tensor, with shape `(batch, features)`. states: A 2D tensor with shape `(batch, units)`, which is the state from the previous time step. training: Python boolean indicating whether the layer should behave in training mode or in inference mode. Only relevant when `dropout` or `recurrent_dropout` is used. Example: >>> inputs = np.random.random((32, 10, 8)) >>> rnn = keras.layers.RNN(keras.layers.GRUCell(4)) >>> output = rnn(inputs) >>> output.shape (32, 4) >>> rnn = keras.layers.RNN( ... keras.layers.GRUCell(4), ... return_sequences=True, ... return_state=True) >>> whole_sequence_output, final_state = rnn(inputs) >>> whole_sequence_output.shape (32, 10, 4) >>> final_state.shape (32, 4) tanhsigmoidTglorot_uniform orthogonalzerosNc |dkrtd|d|dd}tjd i|||_||_t j||_t j||_ ||_ tj||_ tj||_ tj||_tj||_tj| |_tj| |_t'j| |_t'j| |_t'j| |_t/dt1d||_t/dt1d||_||_t8j||_||_ |j|_!|j|_"dS) NrzQReceived an invalid value for argument `units`, expected a positive integer, got .implementation?r)seed)# ValueErrorpopsuper__init__runitsrget activationrecurrent_activationuse_biasrkernel_initializerrecurrent_initializerbias_initializerrkernel_regularizerrecurrent_regularizerbias_regularizerrkernel_constraintrecurrent_constraintbias_constraintminmaxdropoutrecurrent_dropoutrrrandom SeedGeneratorseed_generator reset_after state_size output_size)selfr"r$r%r&r'r(r)r*r+r,r-r.r/r2r3r7rkwargsr __class__s U/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/layers/rnn/gru.pyr!zGRUCell.__init__Vs* A::=49===  $4a88""6""", %/*55$/O4H$I$I!  "."23E"F"F%1%56K%L%L" , 01A B B"."23E"F"F%1%56K%L%L" , 01A B B!,1B!C!C$/O4H$I$I!*??3C 1 122 !$S#c3D*E*E!F!F %n:::EE&*:c t||d}|||jdzfd|j|j|j|_||j|jdzfd|j|j |j |_ |j rN|j s d|jzf}n dd|jzf}||d|j|j|j|_nd|_d|_dS) Nkernel)shapename initializer regularizer constraintrecurrent_kernelrbiasT)r build add_weightr"r'r*r-rBr(r+r.rHr&r7r)r,r/rIbuilt)r: input_shape input_dim bias_shaper<s r=rJz GRUCell.builds"  k"""O oodj1n-//- &   !%:tzA~.#220 !0! !  = # 1$*n.  TZ0   1 1/ (DIIDI r>Fct tj|r|dn|}||}||}|jrJ|js |jd}}n9dtj|j|jj ddD\}}|rd|j cxkrdkrnn||z}|rd|j cxkrdkrnn||z}|j dkrJ|} |} |} tj | |jddd|jf} tj | |jdd|j|jdzf} tj | |jdd|jdzdf}|jrA| |d|jz } | ||j|jdzz } |||jdzdz }|}|}|}tj ||jddd|jf}tj ||jdd|j|jdzf}|jr3|jr,||d|jz }|||j|jdzz }|| |z}|| |z}|jrPtj ||jdd|jdzdf}|jr|||jdzdz }||z}n1tj ||z|jdd|jdzdf}|||z}ngtj ||j}|jr||z }tj|dd \} } }|jr'tj ||j}|jr||z }n.tj ||jdddd|jzf}|ddd|jf}|dd|j|jdzf}|dd|jdzdf}|| |z}|| |z}|jr||z}n1tj ||z|jddd|jzdf}|||z}||zd|z |zz}tj|r|gn|}||fS) Nrc3BK|]}tj|dVdS)raxisN)rsqueeze).0es r= zGRUCell.call..sE..K***......r>rRrrrrAr@)r is_nestedget_dropout_maskget_recurrent_dropout_maskr&r7rIrsplitrCr2r3rmatmulrBr"rHr%r$)r:inputsstatestrainingh_tm1dp_mask rec_dp_mask input_biasrecurrent_biasinputs_zinputs_rinputs_hx_zx_rx_hh_tm1_zh_tm1_rh_tm1_h recurrent_z recurrent_rzr recurrent_hhhmatrix_x matrix_innerh new_states r=callz GRUCell.calls// ;F1IIV ''//55e<< = # -1YN .. Yty$)/!2D1MMM...* N  &dl0000S00000g%F  (d4::::s:::::K'E  ! # #HHH*Xt{111l l?'CDDC*$+aaadj1n)D&DEC*Xt{111dj1n6F6F3F'GHHC} 4z,DJ,//z$*tzA~"=>>z$*q."2"233GGG*.qqq,DJ,?K*.qqq$*tzA~2M/MNK KDM K~l l;; ~dj4:>.IJJ ))# *;<$*q.2B2B#CCK+o !jK!6qqq$*q.:J:J7J!K {!233BBz&$+66H} 'J&Ih;;;MCc "z%1FGG =3 N2L #z404Da$*n4D1DE   'qqq,DJ,7K&qqq$*tzA~*E'EFK&qqq$*q.*:*:':;K))# *;<&118QCCq )|r>cid|jdtj|jdtj|jd|jdt j|jdt j|jdt j|j dtj|j d tj|j d tj|j d tj|jd tj|jd tj|jd|jd|jd|jd|j}t-}i||S)Nr"r$r%r&r'r(r)r*r+r,r-r.r/r2r3r7r)r"r serializer$r%r&rr'r(r)rr*r+r,rr-r.r/r2r3r7rr get_configr:config base_configr<s r=r|zGRUCell.get_config!s TZ +/@@  #K$9)%%    !,"8'##  $\%;*&&   6t7L M M  !,"8'## " $\%;*&&# (  6t7L M M) * !6t7M!N!N+ , #K$9)%%- 2 {4T5IJJ3 4 t|5 6 !77 8 4+9 : DI; >gg((** (+(((r>cHtj||jf|jgS)N)dtype)rrr8 compute_dtype)r: batch_sizes r=get_initial_statezGRUCell.get_initial_stateDs( Iz4?34;M N N N  r>)rrTrrrNNNNNNrrTNFN) __name__ __module__ __qualname____doc__r!rJryr|r __classcell__r<s@r=rrsCCP&+* "! %5&5&5&5&5&5&n$$$$$Lllll\!)!)!)!)!)F        r>rzkeras.layers.GRUceZdZdZ d!fd Zd"fd Zd#fd ZedZedZ edZ edZ edZ edZ edZedZedZedZedZedZedZedZedZedZfdZed ZxZS)$GRUaGated Recurrent Unit - Cho et al. 2014. Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or backend-native) to maximize the performance. If a GPU is available and all the arguments to the layer meet the requirement of the cuDNN kernel (see below for details), the layer will use a fast cuDNN implementation when using the TensorFlow backend. The requirements to use the cuDNN implementation are: 1. `activation` == `tanh` 2. `recurrent_activation` == `sigmoid` 3. `dropout` == 0 and `recurrent_dropout` == 0 4. `unroll` is `False` 5. `use_bias` is `True` 6. `reset_after` is `True` 7. Inputs, if use masking, are strictly right-padded. 8. Eager execution is enabled in the outermost context. There are two variants of the GRU implementation. The default one is based on [v3](https://arxiv.org/abs/1406.1078v3) and has reset gate applied to hidden state before matrix multiplication. The other one is based on [original](https://arxiv.org/abs/1406.1078v1) and has the order reversed. The second variant is compatible with CuDNNGRU (GPU-only) and allows inference on CPU. Thus it has separate biases for `kernel` and `recurrent_kernel`. To use this variant, set `reset_after=True` and `recurrent_activation='sigmoid'`. For example: >>> inputs = np.random.random((32, 10, 8)) >>> gru = keras.layers.GRU(4) >>> output = gru(inputs) >>> output.shape (32, 4) >>> gru = keras.layers.GRU(4, return_sequences=True, return_state=True) >>> whole_sequence_output, final_state = gru(inputs) >>> whole_sequence_output.shape (32, 10, 4) >>> final_state.shape (32, 4) Args: units: Positive integer, dimensionality of the output space. activation: Activation function to use. Default: hyperbolic tangent (`tanh`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step. Default: sigmoid (`sigmoid`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, (default `True`), whether the layer should use a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. Default: `"glorot_uniform"`. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. Default: `"orthogonal"`. bias_initializer: Initializer for the bias vector. Default: `"zeros"`. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. Default: `None`. recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix. Default: `None`. bias_regularizer: Regularizer function applied to the bias vector. Default: `None`. activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). Default: `None`. kernel_constraint: Constraint function applied to the `kernel` weights matrix. Default: `None`. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. Default: `None`. bias_constraint: Constraint function applied to the bias vector. Default: `None`. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0. seed: Random seed for dropout. return_sequences: Boolean. Whether to return the last output in the output sequence, or the full sequence. Default: `False`. return_state: Boolean. Whether to return the last state in addition to the output. Default: `False`. go_backwards: Boolean (default `False`). If `True`, process the input sequence backwards and return the reversed sequence. stateful: Boolean (default: `False`). If `True`, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch. unroll: Boolean (default: `False`). If `True`, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. reset_after: GRU convention (whether to apply reset gate after or before matrix multiplication). `False` is `"before"`, `True` is `"after"` (default and cuDNN compatible). use_cudnn: Whether to use a cuDNN-backed implementation. `"auto"` will attempt to use cuDNN when feasible, and will fallback to the default implementation if not. Call arguments: inputs: A 3D tensor, with shape `(batch, timesteps, feature)`. mask: Binary tensor of shape `(samples, timesteps)` indicating whether a given timestep should be masked (optional). An individual `True` entry indicates that the corresponding timestep should be utilized, while a `False` entry indicates that the corresponding timestep should be ignored. Defaults to `None`. training: Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is only relevant if `dropout` or `recurrent_dropout` is used (optional). Defaults to `None`. initial_state: List of initial state tensors to be passed to the first call of the cell (optional, `None` causes creation of zero-filled initial state tensors). Defaults to `None`. rrTrrrNrFautoc 0t|fid|d|d|d|d|d|d|d| d | d | d | d |d |d|d|d|ddd|ddddd|}tj|f|||||| d|t d|_|dvrt d|||_tjdkr:tj |j |j |j |j |r|dvr d|_dSdSdSdS)Nr$r%r&r'r(r)r*r+r,r-r.r/r2r3r7r trainableTrDgru_cellr)return_sequences return_state go_backwardsstatefulunrollactivity_regularizerrA)ndim)rTFzlInvalid valid received for argument `use_cudnn`. Expected one of {'auto', True, False}. Received: use_cudnn= tensorflow)r7)TrF)rr#r r!r input_specr use_cudnnrcudnn_okr$r%rr& supports_jit)r:r"r$r%r&r'r(r)r*r+r,rr-r.r/r2r3rrrrrrr7rr;cellr<s r=r!z GRU.__init__s/8    !z "6!5 X  21  #8"7  .-  21 #8"7 .- 0/ "6!5 ,O G 0/ $ ! "**Wd+++# $jjd333% &' () ,   -%%!5  $+++ 1 1 13'033  # O   - - )  '  .^++ %D    . - - -,+r>cTtj|r|d}tj|r|d}|jdvr|js|js t j||||jj|jj |jj |jj |jj |j |j|j|jj }t jdkrd|_|S#t$$rYnwxYw|jdurt'dt)||||S) Nr)rT) rBrHrIr$r%rrrr7rFTzuse_cudnn=True was specified, but cuDNN is not supported for this layer configuration with this backend. Pass use_cudnn='auto' to fallback to a non-cuDNN implementation.)maskr`)r rYrr2r3rgrurrBrHrIr$r%rrrr7rNotImplementedErrorrr inner_loop)r: sequences initial_staterr`outr<s r=rzGRU.inner_loopsS >- ( ( -)!,M >$   7D >^ + +< (>  "+!%#y/)-)C!Y^#'9#7-1Y-K)-)>%)%6#{$(I$9   C ((L88,1)J*D >T ! !1  ww!! }4("   sB C C*)C*cNt||||S)N)rr`r)r ry)r:rrrr`r<s r=ryzGRU.callCs+ww|| D8=   r>c|jjSr)rr"r:s r=r"z GRU.unitsHs yr>c|jjSr)rr$rs r=r$zGRU.activationLs y##r>c|jjSr)rr%rs r=r%zGRU.recurrent_activationP y--r>c|jjSr)rr&rs r=r&z GRU.use_biasTs y!!r>c|jjSr)rr'rs r=r'zGRU.kernel_initializerX y++r>c|jjSr)rr(rs r=r(zGRU.recurrent_initializer\ y..r>c|jjSr)rr)rs r=r)zGRU.bias_initializer` y))r>c|jjSr)rr*rs r=r*zGRU.kernel_regularizerdrr>c|jjSr)rr+rs r=r+zGRU.recurrent_regularizerhrr>c|jjSr)rr,rs r=r,zGRU.bias_regularizerlrr>c|jjSr)rr-rs r=r-zGRU.kernel_constraintp y**r>c|jjSr)rr.rs r=r.zGRU.recurrent_constrainttrr>c|jjSr)rr/rs r=r/zGRU.bias_constraintxs y((r>c|jjSr)rr2rs r=r2z GRU.dropout|s y  r>c|jjSr)rr3rs r=r3zGRU.recurrent_dropoutrr>c|jjSr)rr7rs r=r7zGRU.reset_afters y$$r>c6id|jdtj|jdtj|jd|jdt j|jdt j|jdt j|j dtj|j d tj|j d tj|j d tj|jd tj|jd tj|jdtj|jd|jd|jd|jd|jji}t1}|d=i||S)Nr"r$r%r&r'r(r)r*r+r,rr-r.r/r2r3r7rr)r"rr{r$r%r&rr'r(r)rr*r+r,rrr-r.r/r2r3r7rrr r|r}s r=r|zGRU.get_configs! TZ! +/@@!  #K$9)%%!  !  !,"8'##!  $\%;*&&!   6t7L M M!  !,"8'##! " $\%;*&&#! (  6t7L M M)! * #L$:)%%+! 0 !6t7M!N!N1! 2 #K$9)%%3! 8 {4T5IJJ9! : t|;! < !7=! > 4+?! @ DINA! ! Dgg((**  (+(((r>c|di|S)Nrr)clsr~s r= from_configzGRU.from_configss}}V}}r>)rrTrrrNNNNNNNrrNFFFFFTrr)NNF)rrrrr!rrypropertyr"r$r%r&r'r(r)r*r+r,r-r.r/r2r3r7r| classmethodrrrs@r=rrJsxxz&+* "!! 3O&O&O&O&O&O&b* * * * * * X      X$$X$..X.""X",,X,//X/**X*,,X,//X/**X*++X+..X.))X)!!X!++X+%%X%%)%)%)%)%)N[r>rN) keras.srcrrrrrrr keras.src.api_exportr keras.src.layers.input_specr keras.src.layers.layerr %keras.src.layers.rnn.dropout_rnn_cellr keras.src.layers.rnn.rnnrrrrr>r=rs!!!!!!!!!!!!""""""""""""------111111((((((@@@@@@(((((($%%w w w w w e^w w &%w t  !!fffff#ff"!fffr>