6bi=SrSSKJr SSKJr SSKJr SSKJr SSKJr SSKJ r SSKJ r SS K J r SS K Jr SS KJr \"S /S 9"SS\ R$55r\"S/S 9"SS\ 55rg)zGated Recurrent Unit V1 layer.) activations) constraints) initializers) regularizers) InputSpec)gru) rnn_utils)RNN) tf_logging) keras_exportzkeras.layers.GRUCell)v1cN^\rSrSrSrSU4SjjrSrU=r$)GRUCell a$Cell class for the GRU layer. 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: hard sigmoid (`hard_sigmoid`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix. bias_regularizer: Regularizer function applied to the bias vector. kernel_constraint: Constraint function applied to the `kernel` weights matrix. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. reset_after: GRU convention (whether to apply reset gate after or before matrix multiplication). False = "before" (default), True = "after" (cuDNN compatible). Call arguments: inputs: A 2D tensor. states: List of state tensors corresponding to the previous timestep. 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. c >[TU]"U40SU_SU_SU_SU_SU_SU_SU_SU _S U _S U _S U _S U _S U_SU_SURSS5_SU_UD6 g)N activationrecurrent_activationuse_biaskernel_initializerrecurrent_initializerbias_initializerkernel_regularizerrecurrent_regularizerbias_regularizerkernel_constraintrecurrent_constraintbias_constraintdropoutrecurrent_dropoutimplementation reset_after)super__init__pop)selfunitsrrrrrrrrrrrrrrr"kwargs __class__s X/home/james-whalen/.local/lib/python3.13/site-packages/tf_keras/src/layers/rnn/gru_v1.pyr$GRUCell.__init__Qs(   ! "6   2  #8  .  2 #8 . 0 "6 ,  0 "::&6:! "$% )tanh hard_sigmoidTglorot_uniform orthogonalzerosNNNNNNr3F)__name__ __module__ __qualname____firstlineno____doc__r$__static_attributes__ __classcell__r)s@r*rr sB-d++* "!#' ' r,rzkeras.layers.GRUc^\rSrSrSrSU4SjjrSU4Sjjr\S5r\S5r \S5r \S5r \S 5r \S 5r \S 5r\S 5r\S 5r\S5r\S5r\S5r\S5r\S5r\S5r\S5r\S5rU4Sjr\S5rSrU=r$)GRU{aGated Recurrent Unit - Cho et al. 2014. There are two variants. The default one is based on 1406.1078v3 and has reset gate applied to hidden state before matrix multiplication. The other one is based on original 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`. Use `'reset_after'=True` and `recurrent_activation='sigmoid'`. 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: hard sigmoid (`hard_sigmoid`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix. bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to the output of the layer (its "activation").. kernel_constraint: Constraint function applied to the `kernel` weights matrix. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. return_sequences: Boolean. Whether to return the last output in the output sequence, or the full sequence. return_state: Boolean. Whether to return the last state in addition to the output. 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. time_major: The shape format of the `inputs` and `outputs` tensors. If True, the inputs and outputs will be in shape `(timesteps, batch, ...)`, whereas in the False case, it will be `(batch, timesteps, ...)`. Using `time_major = True` is a bit more efficient because it avoids transposes at the beginning and end of the RNN calculation. However, most TensorFlow data is batch-major, so by default this function accepts input and emits output in batch-major form. reset_after: GRU convention (whether to apply reset gate after or before matrix multiplication). False = "before" (default), True = "after" (cuDNN compatible). Call arguments: inputs: A 3D tensor. mask: Binary tensor of shape `(samples, timesteps)` indicating whether a given timestep should be masked. An individual `True` entry indicates that the corresponding timestep should be utilized, while a `False` entry indicates that the corresponding timestep should be ignored. 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. initial_state: List of initial state tensors to be passed to the first call of the cell. c >URSS5nUS:Xa[R"S5 SU;aSURS50nO0n[U40SU_SU_SU_S U_S U_S U_S U_S U _SU _SU _SU _SU_SU_SU_SU_SU_SUR S5_SUR SS5_SS_UD6n[ TU]"U4UUUUUS.UD6 [R"U 5Ul[SS9/Ul g)Nr r!rzm`implementation=0` has been deprecated, and now defaults to `implementation=1`.Please update your layer call.enable_caching_devicerrrrrrrrrrrrrrr"dtype trainableTnamegru_cell)return_sequences return_state go_backwardsstatefulunroll)ndim) r%loggingwarningrgetr#r$ractivity_regularizerr input_spec)r&r'rrrrrrrrrrOrrrrrrErFrGrHrIr"r(r cell_kwargscellr)s r*r$ GRU.__init__s4 $4a8 Q  OO1  #f ,'4K)LKK  ! "6   2  #8  .  2 #8 . 0 "6 ,  0 *! "$# $**W%% &jjd3' (+ .   -%%   %1$4$45I$J!$!,-r,c >[TU]XX4S9$)N)masktraining initial_state)r#call)r&inputsrUrVrWr)s r*rXGRU.callsw|   r,c.URR$N)rRr'r&s r*r' GRU.units$syyr,c.URR$r\)rRrr]s r*rGRU.activation(syy###r,c.URR$r\)rRrr]s r*rGRU.recurrent_activation,yy---r,c.URR$r\)rRrr]s r*r GRU.use_bias0syy!!!r,c.URR$r\)rRrr]s r*rGRU.kernel_initializer4yy+++r,c.URR$r\)rRrr]s r*rGRU.recurrent_initializer8yy...r,c.URR$r\)rRrr]s r*rGRU.bias_initializer<yy)))r,c.URR$r\)rRrr]s r*rGRU.kernel_regularizer@rhr,c.URR$r\)rRrr]s r*rGRU.recurrent_regularizerDrkr,c.URR$r\)rRrr]s r*rGRU.bias_regularizerHrnr,c.URR$r\)rRrr]s r*rGRU.kernel_constraintLyy***r,c.URR$r\)rRrr]s r*rGRU.recurrent_constraintPrcr,c.URR$r\)rRrr]s r*rGRU.bias_constraintTsyy(((r,c.URR$r\)rRrr]s r*r GRU.dropoutXsyy   r,c.URR$r\)rRrr]s r*rGRU.recurrent_dropout\rwr,c.URR$r\)rRr r]s r*r GRU.implementation`syy'''r,c.URR$r\)rRr"r]s r*r"GRU.reset_afterdsyy$$$r,c>0SUR_S[R"UR5_S[R"UR5_SUR _S[ R"UR5_S[ R"UR5_S[ R"UR5_S[R"UR5_S [R"UR5_S [R"UR5_S [R"UR5_S [R"UR 5_S [R"UR"5_S[R"UR$5_SUR&_SUR(_SUR*_SUR,0EnUR/[0R2"UR455 [6TU]q5nUS [;[=UR?55[=UR?55-5$)Nr'rrrrrrrrrrOrrrrrr r"rR) r'r serializerrrrrrrrrrrrOrrrrrrr r"updater config_for_enable_caching_devicerRr# get_configdictlistitems)r&config base_configr)s r*rGRU.get_confighs9! TZZ! +//@!  #K$9$9))%!  !  !,"8"8''#!  $\%;%;**&!   6 6t7L7L M!  !,"8"8''#! " $\%;%;**&#! (  6 6t7L7L M)! * #L$:$:))%+! 0 !6!6t7M7M!N1! 2 #K$9$9))%3! 8 {44T5I5IJ9! : t||;! < !7!7=! > d11?! @ 4++A! D  i@@KLg(*  D**,-V\\^0DDEEr,c:SU;aUSS:XaSUS'U"S0UD6$)Nr rr!r-r-)clsrs r* from_configGRU.from_configs. v %&1A*Ba*G'(F# $}V}r,)rOrP)r.r/Tr0r1r2NNNNNNNr3r3FFFFFF)NNN)r4r5r6r7r8r$rXpropertyr'rrrrrrrrrrrrrrr r"r classmethodrr9r:r;s@r*r=r={sVv++* "!!/H.T $$.."",,//**,,//**++..))!!++((%%&FPr,r=N)r8 tf_keras.srcrrrrtf_keras.src.engine.input_specrtf_keras.src.layers.rnnrr tf_keras.src.layers.rnn.base_rnnr tensorflow.python.platformr rL tensorflow.python.util.tf_exportr rr=r-r,r*rs%%$%%4'-0=9()*W ckkW +W t$%&X#X'Xr,