&VfgddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlm 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.LSTMCellcteZdZdZ dfd Zfd Zd Zd ZddZfdZ ddZ 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) 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-dt/d||_t-dt/d||_||_t6j||_||_|j|jg|_ |j|_!||_"dS) NrzQReceived an invalid value for argument `units`, expected a positive integer, got .implementation?r)seed)# ValueErrorpopsuper__init__unitsrget activationrecurrent_activationuse_biasrkernel_initializerrecurrent_initializerbias_initializerrkernel_regularizerrecurrent_regularizerbias_regularizerrkernel_constraintrecurrent_constraintbias_constraintminmaxdropoutrecurrent_dropoutrrrandom SeedGeneratorseed_generatorunit_forget_bias state_size output_sizer)selfr"r$r%r&r'r(r)r7r*r+r,r-r.r/r2r3rkwargsr __class__s V/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/layers/rnn/lstm.pyr!zLSTMCell.__init__Xs* 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 0:tz2:,ct||d}|jdzfdjjj_jjdzfdjj j _ j rGj rfd}nj}jdzfd|jj_nd_d_dS) Nkernel)shapename initializer regularizer constraintrecurrent_kernelc tjjjfg|Ri|t jdjfg|Ri|jjdzfg|Ri|gS)Nonesr)r concatenater)r"rr#)_argsr;r:s r=r)z(LSTMCell.build..bias_initializers?1D1!% 048>5L,V44!% 048>2D1!%a 148biasT)r build add_weightr"r'r*r-rBr(r+r.rHr&r7r)r,r/rNbuilt)r: input_shape input_dimr)r<s` r=rOzLSTMCell.builds0  k"""O oodj1n-//- &   !%:tzA~.#220 !0! !  = $ 9       $(#8 zA~', 1/ (DIIDI r>c l|\}}}}|\}} } } ||tj||jddd|jfz} ||tj| |jdd|j|jdzfz} | |z| ||tj| |jdd|jdz|jdzfzzz}||tj| |jdd|jdzdfz}||fS)z.Computes carry and output using split kernels.Nr)r%rmatmulrHr"r$)r:xh_tm1c_tm1x_ix_fx_cx_oh_tm1_ih_tm1_fh_tm1_ch_tm1_oifcos r=_compute_carry_and_outputz"LSTMCell._compute_carry_and_outputskS#s-2*'7  % % #*Wd&;AAA||O&LMM M    % % j.qqq$*tzA~2M/MN    IDOO j%aaaa$*q.)H&HI       % % j$"74:>;K;K8K"LMM N  !t r>c|\}}}}||}||}||z|||zz} ||} | | fS)z.Computes carry and output using fused kernels.)r%r$) r:zrYz0z1z2z3rbrcrdres r=_compute_carry_and_output_fusedz(LSTMCell._compute_carry_and_output_fusedspBB  % %b ) )  % %b ) ) IDOOB/// /  % %b ) )!t r>Fc|d}|d}||}||}|rd|jcxkrdkrnn||z}|rd|jcxkrdkrnn||z}|jdkr|}|} |} |} t j|jdd\} } }}t j|| }t j| | }t j| |}t j| |}|j r5t j|j dd\}}}}||z }||z }||z }||z }|}|}|}|}||||f}||||f}| |||\}}nxt j||j}|t j||j z }|j r ||j z }t j|dd}| ||\}}|||z} | | |gfS)NrrrrA)axis)get_dropout_maskget_recurrent_dropout_maskr2r3rrsplitrBrVr&rNrfrHrmr$)!r:inputsstatestrainingrXrYdp_mask rec_dp_maskinputs_iinputs_finputs_cinputs_ok_ik_fk_ck_orZr[r\r]b_ib_fb_cb_or^r_r`rarWrdrerhhs! r=callz LSTMCell.calls\q q ''//55e<<  &dl0000S00000g%F  (d4::::s:::::K'E  ! # #HHHH!$4;!B!B!B Cc3*Xs++C*Xs++C*Xs++C*Xs++C} %(Yty!!%D%D%D"S#ss s s s GGGGc3$Agw8E11!UEBBDAqq 64;//A E4#899 9A} TY !QQ'''A775AADAq "" "1a&yr>cid|jdtj|jdtj|jd|jd|jdtj|jdtj|j dtj|j d tj|j d tj|j d tj|jd tj|jd tj|jdtj|jd|jd|jd|j}t-}i||S)Nr"r$r%r&r7r'r(r)r*r+r,r-r.r/r2r3r)r"r serializer$r%r&r7rr'r(r)rr*r+r,rr-r.r/r2r3rr get_configr:config base_configr<s r=rzLSTMCell.get_configs TZ +/@@  #K$9)%%     5  !,"8'##  $\%;*&&   6t7L M M  !,"8'## $ $\%;*&&% *  6t7L M M+ , !6t7M!N!N- . #K$9)%%/ 4 {4T5IJJ5 6 t|7 8 !79 : DI; >gg((** (+(((r>c.fdjDS)NcJg|]}tj|fj S))dtype)rr compute_dtype).0d batch_sizer:s r= z.LSTMCell.get_initial_state..8s?    Iz1oT-? @ @ @   r>)r8)r:rs``r=get_initial_statezLSTMCell.get_initial_state7s6     _    r>)rrTrrrTNNNNNNrrNFN) __name__ __module__ __qualname____doc__r!rOrfrmrrr __classcell__r<s@r=rrsEET&+* "! %5-5-5-5-5-5-n/////b4////b!)!)!)!)!)F        r>rzkeras.layers.LSTMceZdZdZ d!fd Zd"fd Zd#fd ZedZedZ edZ edZ edZ edZ edZedZedZedZedZedZedZedZedZedZfdZed ZxZS)$LSTMaSLong 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. `dropout` == 0 and `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`. rrTrrrNrFautoc Zt|fid|d|d|d|d|d|d|d| d | d | d | d |d |d|d|d|ddd|ddddd|d|dd}tj|f|||||| d|t d|_|dvrtd|||_tj dkr8tj |j |j |j |jr|dvr d|_dSdSdSdS) Nr$r%r&r'r7r(r)r*r+r,r-r.r/r2r3r trainableTrD lstm_cellrrr)return_sequences return_state go_backwardsstatefulunrollactivity_regularizerrU)ndim)rTFzlInvalid valid received for argument `use_cudnn`. Expected one of {'auto', True, False}. Received: use_cudnn= tensorflow)TrF)rr#rr r!r input_specr use_cudnnrcudnn_okr$r%rr& supports_jit)r:r"r$r%r&r'r(r)r7r*r+r,rr-r.r/r2r3rrrrrrrr;cellr<s r=r!z LSTM.__init__sD8    !z "6!5 X  21  .-  #8"7 .-  21 #8"7 .- 0/ "6!5 ,O G 0/! "**Wd+++# $jjd333% &' () *"::&6:::+ .   -%%!5  $+++ 1 1 13'033  # O   - - )   .^++ %D    . - - -,+r>c tj|r|d}|jdvr|js|js t j||d|d||jj|jj |jj |jj |jj |j |j|j }t jdkrd|_|S#t"$rYnwxYw|jdurt%dt'|||| S) Nr)rTro)rBrHrNr$r%rrrrFTzuse_cudnn=True was specified, but cuDNN is not supported for this layer configuration with this backend. Pass use_cudnn='auto' to fallback to a non-cuDNN implementation.)maskrv)r is_nestedrr2r3rlstmrrBrHrNr$r%rrrrNotImplementedErrorrr inner_loop)r: sequences initial_staterrvoutr<s r=rzLSTM.inner_loops< >$   7D >^ + +< (>  ",!%a(%a(#y/)-)C!Y^#'9#7-1Y-K)-)>%)%6#{   C ((L88,1)J*D >T ! !1  ww!! }4("   sB C CCcNt||||S)N)rrvr)r r)r:rrrrvr<s r=rz LSTM.call/s+ww|| D8=   r>c|jjSr)rr"r:s r=r"z LSTM.units4s yr>c|jjSr)rr$rs r=r$zLSTM.activation8s y##r>c|jjSr)rr%rs r=r%zLSTM.recurrent_activation< y--r>c|jjSr)rr&rs r=r&z LSTM.use_bias@s y!!r>c|jjSr)rr7rs r=r7zLSTM.unit_forget_biasD y))r>c|jjSr)rr'rs r=r'zLSTM.kernel_initializerH y++r>c|jjSr)rr(rs r=r(zLSTM.recurrent_initializerL y..r>c|jjSr)rr)rs r=r)zLSTM.bias_initializerPrr>c|jjSr)rr*rs r=r*zLSTM.kernel_regularizerTrr>c|jjSr)rr+rs r=r+zLSTM.recurrent_regularizerXrr>c|jjSr)rr,rs r=r,zLSTM.bias_regularizer\rr>c|jjSr)rr-rs r=r-zLSTM.kernel_constraint` y**r>c|jjSr)rr.rs r=r.zLSTM.recurrent_constraintdrr>c|jjSr)rr/rs r=r/zLSTM.bias_constrainths y((r>c|jjSr)rr2rs r=r2z LSTM.dropoutls y  r>c|jjSr)rr3rs r=r3zLSTM.recurrent_dropoutprr>c6id|jdtj|jdtj|jd|jdt j|jdt j|jdt j|j d|j d tj|j d tj|j d tj|jd tj|jd t!j|jdt!j|jdt!j|jd|jd|jd|jji}t1}|d=i||S)Nr"r$r%r&r'r(r)r7r*r+r,rr-r.r/r2r3rr)r"rrr$r%r&rr'r(r)r7rr*r+r,rrr-r.r/r2r3rrr rrs r=rzLSTM.get_configts! TZ! +/@@!  #K$9)%%!  !  !,"8'##!  $\%;*&&!   6t7L M M!   5!  !,"8'##! $ $\%;*&&%! *  6t7L M M+! , #L$:)%%-! 2 !6t7M!N!N3! 4 #K$9)%%5! : {4T5IJJ;! < t|=! > !7?! @ DINA! ! Dgg((**  (+(((r>c|di|S)Nrr)clsrs r= from_configzLSTM.from_configss}}V}}r>)rrTrrrTNNNNNNNrrNFFFFFrr)NNF)rrrrr!rrpropertyr"r$r%r&r7r'r(r)r*r+r,r-r.r/r2r3r classmethodrrrs@r=rr>sqql&+* "!! 3O&O&O&O&O&O&b) ) ) ) ) ) V      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((((((@@@@@@((((((%&&k k k k k unk k '&k \ !""^^^^^3^^#"^^^r>