2Vh^pddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlm Z dd l m Z dd l m Z dd lmZdd lmZe d Gdde eZe dGddeZy)) activations)backend) constraints) initializers)ops) regularizers)tree) keras_export) InputSpec)Layer)DropoutRNNCell)RNNzkeras.layers.GRUCellcheZdZdZ dfd ZfdZddZfdZd dZxZ 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) c D|dkrtd|d|jdd}t| d i|||_||_t j||_t j||_ ||_ tj||_ tj||_ tj||_tj||_tj| |_tj| |_t'j| |_t'j| |_t'j| |_t/dt1d||_t/dt1d||_|j4dk7rd|_|jdk(rd |_||_t:j<j?| |_ ||_!|j |_"|j |_#y) NrzQReceived an invalid value for argument `units`, expected a positive integer, got .implementation?)seed)$ ValueErrorpopsuper__init__runitsrget activationrecurrent_activationuse_biasrkernel_initializerrecurrent_initializerbias_initializerrkernel_regularizerrecurrent_regularizerbias_regularizerrkernel_constraintrecurrent_constraintbias_constraintminmaxdropoutrecurrent_dropoutdropout_mask_countrrrandom SeedGeneratorseed_generator reset_after state_size output_size)selfrr!r"r#r$r%r&r'r(r)r*r+r,r/r0r5rkwargsr __class__s H/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/layers/rnn/gru.pyrzGRUCell.__init__Vs* A:4497!=  $4a8 "6", %//*5$/OO4H$I!  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TZZ0   11 11// (DIDIr<cj tj|r|dn|}|jr^|js|jd}}nCdt j |j|jjddD\}}|jdk(r+|rBd|jcxkrdkr-nn*|j|}||dz}||dz} ||dz} n|}|} |} t j||jddd|jf} t j| |jdd|j|jdzf} t j| |jdd|jdzdf} |jrF| d|jz } | ||j|jdzz } | ||jdzdz } |rBd|jcxkrdkr-nn*|j|}||dz}||dz}||dz}n|}|}|}t j||j ddd|jf}t j||j dd|j|jdzf}|jr=|jr1|d|jz }|||j|jdzz }|j#| |z}|j#| |z}|jr]t j||j dd|jdzdf}|jr||jdzdz }||z}n9t j||z|j dd|jdzdf}|j%| |z}n|r.d|jcxkrdkrnn|j|}||z}t j||j}|jr|z }t j |dd \} } } |jr2t j||j }|jr<|z }n6t j||j dddd|jzf}|ddd|jf}|dd|j|jdzf}|dd|jdzdf}|j#| |z}|j#| |z}|jr||z}n9t j||z|j ddd|jzdf}|j%| |z}||zd|z |zz}tj|r|gn|}||fS) Nrc3JK|]}tj|dyw)raxisN)rsqueeze).0es r; zGRUCell.call..s&.KK**.s!#rNrrrrrr>)r is_nestedr#r5rFrsplitr@rr/get_dropout_maskmatmulr?rr0get_recurrent_dropout_maskrEr"r!)r8inputsstatestrainingh_tm1 input_biasrecurrent_biasdp_maskinputs_zinputs_rinputs_hx_zx_rx_h rec_dp_maskh_tm1_zh_tm1_rh_tm1_h recurrent_z recurrent_rzr recurrent_hhhmatrix_x matrix_innerh new_states r;callz GRUCell.calls^/F1IV  ==##-1YYN . YYtyy$))//!2D1M.* N   ! #C$,,44//7!GAJ.!GAJ.!GAJ.!!!**Xt{{1l l?'CDC**$++adjj1n)D&DEC**Xt{{1djj1n6F3F'GHC}}z,DJJ//z$**tzzA~>>z$**q."233C$"8"8>3>"==eD +a.0+a.0+a.0**..q,DJJ,?K**..q$**tzzA~2M/MNKDMM~l ;; ~djj4::>JJ ))# *;$**q.2B#CCK+o !jjK!6!6q$**q.:J7J!K {!23BC$,,44//7')zz&$++6H}}J&IIh;MCc"zz%1F1FG == N2L #zz4004Da$**n4D1DE  'q,DJJ,7K&q$**tzzA~*E'EFK&q$**q.*:':;K))# *;>&1QCq )|r<cid|jdtj|jdtj|jd|j dt j|jdt j|jdt j|jdtj|jd tj|jd tj|jd tj|jd tj|j d tj|j"d|j$d|j&d|j(d|j*}t,|]}i||S)Nrr!r"r#r$r%r&r'r(r)r*r+r,r/r0r5r)rr serializer!r"r#rr$r%r&rr'r(r)rr*r+r,r/r0r5rr get_configr8config base_configr:s r;rwzGRUCell.get_config,s TZZ +//@  #K$9$9))%    !,"8"8''#  $\%;%;**&   6 6t7L7L M  !,"8"8''# " $\%;%;**&# (  6 6t7L7L M) * !6!6t7M7M!N+ , #K$9$9))%- 2 {44T5I5IJ3 4 t||5 6 !7!77 8 4++9 : DII; >g(* (+(((r<c^tj||jf|jgS)N)dtype)rzerosr6 compute_dtype)r8 batch_sizes r;get_initial_statezGRUCell.get_initial_stateOs) IIz4??34;M;M N  r<)tanhsigmoidTglorot_uniform orthogonalr}NNNNNNrrTNFN) __name__ __module__ __qualname____doc__rrGrtrwr __classcell__r:s@r;rrsZCP&+* "! %9&v#Jtl!)F r<rzkeras.layers.GRUceZdZdZ dfd Zdfd Zdfd ZedZedZ edZ edZ ed Z ed Z ed Zed Zed ZedZedZedZedZedZedZedZfdZedZxZS)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. `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`. c Bt|fid|d|d|d|d|d|d|d| d | d | d | d |d |d|d|d|jddd|jddddd|d|jdd}t||f|||||| d|t d|_|dvrtd|||_tjdk(rPtj|j|j|j|j|r |dvrd |_yyyy)!Nr!r"r#r$r%r&r'r(r)r*r+r,r/r0r5r| trainableTrAgru_cellrrr)return_sequences return_state go_backwardsstatefulunrollactivity_regularizerr)ndim)autoTFzlInvalid valid received for argument `use_cudnn`. Expected one of {'auto', True, False}. Received: use_cudnn= tensorflow)r5)TrF)rr rrrr input_specr use_cudnnrcudnn_okr!r"rr# supports_jit)r8rr!r"r#r$r%r&r'r(r)rr*r+r,r/r0rrrrrrr5rr9cellr:s r;rz GRU.__init__s8  ! "6   2  #8  .  2 #8 . 0 "6 ,  0 $! "**Wd+# $jjd3% &' () *"::&6:+ .   -%%!5  $+ 1 1''0k3  # OO  -  ))  ' ^+ %D , .r<ctj|r|d}tj|r|d}|jdvr`|jsS |rw|jrk|j j |dddddf}tj|d}tj|tj|}||z}n|}tj||||j j|j j|j j|j j |j j"|j$|j&|j(|j j* }tjdk(rd|_|S|jdur t1d t2|i|||| S#t.$rY7wxYw) Nr)rTrrN) r?rErFr!r"rrrr5rFTzuse_cudnn=True was specified, but cuDNN is not supported for this layer configuration with this backend. 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TZZ! +//@!  #K$9$9))%!  !  !,"8"8''#!  $\%;%;**&!   6 6t7L7L M!  !,"8"8''#! " $\%;%;**&#! (  6 6t7L7L M)! * #L$:$:))%+! 0 !6!6t7M7M!N1! 2 #K$9$9))%3! 8 {44T5I5IJ9! : t||;! < !7!7=! > 4++?! @ DIINNA! Dg(*  (+(((r<c|di|S)Nrr)clsrys r; from_configzGRU.from_configs}V}r<)rrTrrr}NNNNNNNrrNFFFFFTrr)NNF)rrrrrrrtpropertyrr!r"r#r$r%r&r'r(r)r*r+r,r/r0r5rw classmethodrrrs@r;rrUsxz&+* "!! 3P&d3 j $$.."",,//**,,//**++..))!!++%%%)Nr<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;rst!!""-1(@($%B e^B &B J  !p#p"pr<