2VhlddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlm 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.LSTMCellcteZdZdZ d fd ZfdZdZdZd dZfdZ d dZ xZ S) LSTMCellax Cell class for the LSTM layer. This class processes one step within the whole time sequence input, whereas `keras.layer.LSTM` 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"`. unit_forget_bias: Boolean (default `True`). If `True`, add 1 to the bias of the forget gate at initialization. Setting it to `True` will also force `bias_initializer="zeros"`. This is recommended in [Jozefowicz et al.]( https://github.com/mlresearch/v37/blob/gh-pages/jozefowicz15.pdf) 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. 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.LSTMCell(4)) >>> output = rnn(inputs) >>> output.shape (32, 4) >>> rnn = keras.layers.RNN( ... keras.layers.LSTMCell(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 \|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 g|_"|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_generatorunit_forget_bias state_size output_size)selfrr!r"r#r$r%r&r5r'r(r)r*r+r,r/r0rkwargsr __class__s I/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/layers/rnn/lstm.pyrzLSTMCell.__init__Xs* A:4497!=  $4a8 "6", %//*5$/OO4H$I!  "."2"23E"F%1%5%56K%L" , 0 01A B"."2"23E"F%1%5%56K%L" , 0 01A B!,1B!C$/OO4H$I!*?3C 12 !$S#c3D*E!F  ! !S ("#D    ! #&'D # %nn:::E 0::tzz2::cBt||d}j|jdzfdjj j _jjjdzfdjjj_ jr\jrfd}n j}jjdzfd|jj _yd_y)Nrkernel)shapename initializer regularizer constraintrecurrent_kernelc tjjjfg|i|t j djfg|i|jjdzfg|i|gS)Nonesr)r concatenater&rrr )_argsr9r8s r;r&z(LSTMCell.build..bias_initializers??1D11!% 048>5L,,V4!% 048>2D11!%a 148;K8K"LM N !t r<c|\}}}}|j|}|j|}||z||j|zz} |j|} | | fS)z.Computes carry and output using fused kernels.)r"r!) r8zrUz0z1z2z3r^r_r`ras r;_compute_carry_and_output_fusedz(LSTMCell._compute_carry_and_output_fusedsdBB  % %b )  % %b ) IDOOB// /  % %b )!t r<c|d}|d}|jdk(r|rJd|jcxkrdkr5nn2|j|}||dz}||dz}||dz} ||dz} n|}|}|} |} tj|j dd\} } } }tj || }tj || }tj | | }tj | |}|jr;tj|jdd\}}}}||z }||z }||z }||z }|rJd|jcxkrdkr5nn2|j|}||dz}||dz}||dz}||dz}n|}|}|}|}||||f}||||f}|j|||\}}n|r.d|jcxkrdkrnn|j|}||z}tj ||j }|tj ||jz }|jr||jz }tj|dd}|j||\}}||j|z} | | |gfS) NrrrrrrQraxis)rr/get_dropout_maskrsplitr?rRr#rKr0get_recurrent_dropout_maskrbrErir!)!r8inputsstatestrainingrTrUdp_maskinputs_iinputs_finputs_cinputs_ok_ik_fk_ck_orVrWrXrYb_ib_fb_cb_o rec_dp_maskrZr[r\r]rSr`rardhs! r;callz LSTMCell.callsq q    ! #C$,,44//7!GAJ.!GAJ.!GAJ.!GAJ.!!!!!$4;;!B Cc3**Xs+C**Xs+C**Xs+C**Xs+C}}%(YYtyy!!%D"S#ss s s s C$"8"8>3>"==eD +a.0+a.0+a.0+a.0c3$Agw8E11!UEBDAqC$,,44//7') 64;;/A E4#8#89 9A}}TYY !QQ'A775ADAq " "1a&yr<cid|jdtj|jdtj|jd|j d|j dtj|jdtj|jdtj|jd tj|jd tj|jd tj|jd tj|j d tj|j"dtj|j$d|j&d|j(d|j*}t,|]}i||S)Nrr!r"r#r5r$r%r&r'r(r)r*r+r,r/r0r)rr serializer!r"r#r5rr$r%r&rr'r(r)rr*r+r,r/r0rr get_configr8config base_configr:s r;rzLSTMCell.get_config!s TZZ +//@  #K$9$9))%     5 5  !,"8"8''#  $\%;%;**&   6 6t7L7L M  !,"8"8''# $ $\%;%;**&% *  6 6t7L7L M+ , !6!6t7M7M!N- . #K$9$9))%/ 4 {44T5I5IJ5 6 t||7 8 !7!79 : DII; >g(* (+(((r<c|jDcgc]%}tj||f|j'c}Scc}w)N)dtype)r6rzeros compute_dtype)r8 batch_sizeds r;get_initial_statezLSTMCell.get_initial_stateDs=__  IIz1oT-?-? @   s*<)tanhsigmoidTglorot_uniform orthogonalrTNNNNNNrrNFN) __name__ __module__ __qualname____doc__rrLrbrirrr __classcell__r:s@r;rrscET&+* "! %9&v.`49v!)F r<rzkeras.layers.LSTMceZdZdZ dfd Zdfd Zdfd ZedZedZ edZ edZ ed Z ed Z ed Zed Zed ZedZedZedZedZedZedZedZfdZedZxZS)LSTMa@Long Short-Term Memory layer - Hochreiter 1997. 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. Inputs, if use masking, are strictly right-padded. 7. Eager execution is enabled in the outermost context. For example: >>> inputs = np.random.random((32, 10, 8)) >>> lstm = keras.layers.LSTM(4) >>> output = lstm(inputs) >>> output.shape (32, 4) >>> lstm = keras.layers.LSTM( ... 4, return_sequences=True, return_state=True) >>> whole_seq_output, final_memory_state, final_carry_state = lstm(inputs) >>> whole_seq_output.shape (32, 10, 4) >>> final_memory_state.shape (32, 4) >>> final_carry_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"`. unit_forget_bias: Boolean (default `True`). If `True`, add 1 to the bias of the forget gate at initialization. Setting it to `True` will also force `bias_initializer="zeros"`. This is recommended in [Jozefowicz et al.]( https://github.com/mlresearch/v37/blob/gh-pages/jozefowicz15.pdf) 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. 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 >t|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(rNtj|j|j|j|jr |dvrd|_yyyy) Nr!r"r#r$r5r%r&r'r(r)r*r+r,r/r0r trainableTrA lstm_cellrrr)return_sequences return_state go_backwardsstatefulunrollactivity_regularizerrQ)ndim)autoTFzlInvalid valid received for argument `use_cudnn`. Expected one of {'auto', True, False}. Received: use_cudnn= tensorflow)TrF)rr rrrr input_specr use_cudnnrcudnn_okr!r"rr# supports_jit)r8rr!r"r#r$r%r&r5r'r(r)rr*r+r,r/r0rrrrrrrr9cellr:s r;rz LSTM.__init__s8  ! "6   2  .  #8 .  2 #8 . 0 "6 ,  0! "**Wd+# $jjd3% &' () *"::&6:+ .   -%%!5  $+ 1 1''0k3  # OO  -  ))   ^+ %D ,  .r<cptj|r|d}|jdvrR|jsE |rw|jrk|j j |dddddf}tj|d}tj|tj|}||z}n|}tj||d|d||j j|j j|j j|j j |j j"|j$|j&|j( }tjdk(rd|_|S|jdur t/d t0|e|||| S#t,$rY7wxYw) Nr)rTrrk)r?rErKr!r"rrrrFTzuse_cudnn=True was specified, but cuDNN is not supported for this layer configuration with this backend. Pass use_cudnn='auto' to fallback to a non-cuDNN implementation.)maskrr)r is_nestedrr0r/rrmr expand_dims broadcast_tor@rlstmr?rErKr!r"rrrrNotImplementedErrorrr inner_loop) r8 sequences initial_staterrrrs dp_sequencesoutr:s r;rzLSTM.inner_loops >>$ 7D >>^ +))#DLL"&))"<")>%)%6%6#{{ C (L8,1)J >>T !1  w! }4("  +sEF)) F54F5c*t|||||S)N)rrrr)rr)r8rrrrrr:s r;rz LSTM.callFs"w| D8=  r<c.|jjSr)rrr8s r;rz LSTM.unitsKsyyr<c.|jjSr)rr!rs r;r!zLSTM.activationOsyy###r<c.|jjSr)rr"rs r;r"zLSTM.recurrent_activationSyy---r<c.|jjSr)rr#rs r;r#z LSTM.use_biasWsyy!!!r<c.|jjSr)rr5rs r;r5zLSTM.unit_forget_bias[yy)))r<c.|jjSr)rr$rs r;r$zLSTM.kernel_initializer_yy+++r<c.|jjSr)rr%rs r;r%zLSTM.recurrent_initializercyy...r<c.|jjSr)rr&rs r;r&zLSTM.bias_initializergrr<c.|jjSr)rr'rs r;r'zLSTM.kernel_regularizerkrr<c.|jjSr)rr(rs r;r(zLSTM.recurrent_regularizerorr<c.|jjSr)rr)rs r;r)zLSTM.bias_regularizersrr<c.|jjSr)rr*rs r;r*zLSTM.kernel_constraintwyy***r<c.|jjSr)rr+rs r;r+zLSTM.recurrent_constraint{rr<c.|jjSr)rr,rs r;r,zLSTM.bias_constraintsyy(((r<c.|jjSr)rr/rs r;r/z LSTM.dropoutsyy   r<c.|jjSr)rr0rs r;r0zLSTM.recurrent_dropoutrr<cid|jdtj|jdtj|jd|j dt j|jdt j|jdt j|jd|jd tj|jd tj|jd tj|jd tj|jd t!j|j"dt!j|j$dt!j|j&d|j(d|j*d|j,j.i}t0|e}|d=i||S)Nrr!r"r#r$r%r&r5r'r(r)rr*r+r,r/r0rr)rrrr!r"r#rr$r%r&r5rr'r(r)rrr*r+r,r/r0rrrrrs r;rzLSTM.get_configs! TZZ! +//@!  #K$9$9))%!  !  !,"8"8''#!  $\%;%;**&!   6 6t7L7L M!   5 5!  !,"8"8''#! $ $\%;%;**&%! *  6 6t7L7L M+! , #L$:$:))%-! 2 !6!6t7M7M!N3! 4 #K$9$9))%5! : {44T5I5IJ;! < t||=! > !7!7?! @ DIINNA! Dg(*  (+(((r<c|di|S)Nrr)clsrs r; from_configzLSTM.from_configs}V}r<)rrTrrrTNNNNNNNrrNFFFFFrr)NNF)rrrrrrrpropertyrr!r"r#r5r$r%r&r'r(r)r*r+r,r/r0r classmethodrrrs@r;rrKsql&+* "!! 3O&b3 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(@(%&x unx 'x v !"h3h#hr<