o i ea@sdZddlmmZddlmZddlmZddlmZddlm Z ddlm Z ddl m Z dd l mZdd lmZdd lmZGd d d ee jZGdddeZdS)z-Base class for N-D convolutional LSTM layers.N) activations)backend) constraints) initializers) regularizers) base_layer)ConvRNN)DropoutRNNCellMixin) conv_utilscseZdZdZ                dfd d ZfddZdddZeddZdddZ ddZ fddZ Z S) ConvLSTMCellag Cell class for the ConvLSTM layer. Args: rank: Integer, rank of the convolution, e.g. "2" for 2D convolutions. filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of n integers, specifying the dimensions of the convolution window. strides: An integer or tuple/list of n integers, specifying the strides of the convolution. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format: A string, one of `channels_last` (default) or `channels_first`. When unspecified, uses `image_data_format` value found in your Keras config file at `~/.keras/keras.json` (if exists) else 'channels_last'. Defaults to 'channels_last'. dilation_rate: An integer or tuple/list of n integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any `strides` value != 1. activation: Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step. 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. unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate at initialization. Use in combination with `bias_initializer="zeros"`. This is recommended in [Jozefowicz et al., 2015]( http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) 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. Call arguments: inputs: A (2+ `rank`)D 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. validNtanh hard_sigmoidTglorot_uniform orthogonalzerosc sTtjd i|||_|jdkrtd|d|||_t||jd|_tj||jddd|_t ||_ t ||_ t||jd|_ t||_t| |_| |_t| |_t| |_t| |_||_t||_t||_t||_t||_t||_t||_t d t!d ||_"t d t!d ||_#|j|jf|_$dS) NzRank z< convolutions are not currently implemented. Received: rank= kernel_sizestridesT allow_zero dilation_rate?r)%super__init__rank ValueErrorfiltersr normalize_tuplerrnormalize_paddingpaddingnormalize_data_format data_formatrrget activationrecurrent_activationuse_biasrkernel_initializerrecurrent_initializerbias_initializerunit_forget_biasrkernel_regularizerrecurrent_regularizerbias_regularizerrkernel_constraintrecurrent_constraintbias_constraintminmaxdropoutrecurrent_dropout state_size)selfrr rrr#r%rr'r(r)r*r+r,r-r.r/r0r1r2r3r6r7kwargs __class__rR/var/www/myenv/lib/python3.10/site-packages/keras/src/layers/rnn/base_conv_lstm.pyr\sH               zConvLSTMCell.__init__cst|jdkrd}nd}||durtd|||}j|jdf_jjjdf}jjjdj j d_ j|j dj jd_jrvjrafd d }nj}jjdfd |jjd _nd_d _dS)Nchannels_firstr zvThe channel dimension of the inputs (last axis) should be defined. Found None. Full input shape received: input_shape=kernel)shape initializername regularizer constraintrecurrent_kernelcs`tjjfg|Ri|tdjfg|Ri|jjdfg|Ri|gS)Nones)r concatenater,r rr&)_argsr:r9rr=r,s0 z,ConvLSTMCell.build..bias_initializerbias)rBrDrCrErFT)rbuildr%rrr kernel_shape add_weightr*r.r1rAr+r/r2rGr)r-r,r0r3rNbuilt)r9 input_shape channel_axis input_dimrecurrent_kernel_shaper,r;rMr=rOsV      zConvLSTMCell.buildc)Cs0|d}|d}|j||dd}|j||dd}d|jkr#dkr>nn||d}||d} ||d} ||d} n|}|} |} |} d|jkrQdkrlnn||d} ||d} ||d}||d}n|} |} |}|}tj|jd|jdd\}}}}tj|jd|jdd\}}}}|j rt|j d\}}}}nd \}}}}|j ||||j d }|j | |||j d }|j | |||j d }|j | |||j d }| | |} | | |}!| ||}"| ||}#||| }$|||!}%|%||$|||"}&|||#}'|'||&}(|(|(|&gfS) Nrr r@)countrrIr)axis)NNNN)r#)get_dropout_mask_for_cell#get_recurrent_dropout_mask_for_cellr6r7tfsplitrArrGr)rN input_convr#recurrent_convr(r'))r9inputsstatestrainingh_tm1c_tm1dp_mask rec_dp_maskinputs_iinputs_finputs_cinputs_oh_tm1_ih_tm1_fh_tm1_ch_tm1_okernel_ikernel_fkernel_ckernel_orecurrent_kernel_irecurrent_kernel_frecurrent_kernel_crecurrent_kernel_obias_ibias_fbias_cbias_ox_ix_fx_cx_oh_ih_fh_ch_oifcohrrr=callsd            zConvLSTMCell.callcCs4|jdkrtjS|jdkrtjS|jdkrtjSdS)Nr rIr)rrconv1dconv2dconv3drMrrr= _conv_funcs   zConvLSTMCell._conv_funccCs:|j|||j||j|jd}|durtj|||jd}|S)N)rr#r%r)r%)rrr%rrbias_add)r9xwbr#conv_outrrr=r](szConvLSTMCell.input_convcCs.tjd|jddd}|j|||d|jd}|S)Nr rTrsame)rr#r%)r r!rrr%)r9rrrrrrr=r^7s  zConvLSTMCell.recurrent_convcs id|jd|jd|jd|jd|jd|jdt|jdt|j d |j d t |j d t |j d t |jd |jdt|jdt|jdt|jdt|jt|jt|j|j|jd}t}tt|t|S)Nr rrr#r%rr'r(r)r*r+r,r-r.r/r0r1)r2r3r6r7)r rrr#r%rr serializer'r(r)rr*r+r,r-rr.r/r0rr1r2r3r6r7r get_configdictlistitemsr9config base_configr;rr=r@sl        #zConvLSTMCell.get_config)r r Nr rrTrrrTNNNNNNrrN)Nr ) __name__ __module__ __qualname____doc__rrOrpropertyrr]r^r __classcell__rrr;r=r s:A B A@   r csteZdZdZ                 d?fd d Zd@fdd ZeddZeddZeddZ eddZ eddZ eddZ eddZ edd Zed!d"Zed#d$Zed%d&Zed'd(Zed)d*Zed+d,Zed-d.Zed/d0Zed1d2Zed3d4Zed5d6Zed7d8Zed9d:Zfd;d<Zed=d>ZZS)AConvLSTMaGAbstract N-D Convolutional LSTM layer (used as implementation base). Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional. Args: rank: Integer, rank of the convolution, e.g. "2" for 2D convolutions. filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of n integers, specifying the dimensions of the convolution window. strides: An integer or tuple/list of n integers, specifying the strides of the convolution. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. data_format: A string, one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs. `channels_last` corresponds to inputs with shape `(batch, time, ..., channels)` while `channels_first` corresponds to inputs with shape `(batch, time, channels, ...)`. When unspecified, uses `image_data_format` value found in your Keras config file at `~/.keras/keras.json` (if exists) else 'channels_last'. Defaults to 'channels_last'. dilation_rate: An integer or tuple/list of n integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any `strides` value != 1. activation: Activation function to use. By default hyperbolic tangent activation function is applied (`tanh(x)`). recurrent_activation: Activation function to use for the recurrent step. 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. unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate at initialization. Use in combination with `bias_initializer="zeros"`. This is recommended in [Jozefowicz et al., 2015]( http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) 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. 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. 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. 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. 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. r r NrrTrrrFrc stdid|d|d|d|d|d|d|d|d | d | d | d | d | d|d|d|d|d|d|d|d|d|ddd|d}tj||f||||d|t||_dS)Nrr rrr#r%rr'r(r)r*r+r,r-r.r/r0r1r2r3r6r7rDconv_lstm_celldtype)return_sequences return_state go_backwardsstatefulr)r r&rrractivity_regularizer)r9rr rrr#r%rr'r(r)r*r+r,r-r.r/r0rr1r2r3rrrrr6r7r:cellr;rr=rsz       zConvLSTM.__init__cstj||||dS)N)maskra initial_state)rr)r9r_rrarr;rr=rsz ConvLSTM.callcC|jjSr)rr rMrrr=r zConvLSTM.filterscCrr)rrrMrrr=rrzConvLSTM.kernel_sizecCrr)rrrMrrr=r rzConvLSTM.stridescCrr)rr#rMrrr=r# rzConvLSTM.paddingcCrr)rr%rMrrr=r%rzConvLSTM.data_formatcCrr)rrrMrrr=rrzConvLSTM.dilation_ratecCrr)rr'rMrrr=r'rzConvLSTM.activationcCrr)rr(rMrrr=r(rzConvLSTM.recurrent_activationcCrr)rr)rMrrr=r)!rzConvLSTM.use_biascCrr)rr*rMrrr=r*%rzConvLSTM.kernel_initializercCrr)rr+rMrrr=r+)rzConvLSTM.recurrent_initializercCrr)rr,rMrrr=r,-rzConvLSTM.bias_initializercCrr)rr-rMrrr=r-1rzConvLSTM.unit_forget_biascCrr)rr.rMrrr=r.5rzConvLSTM.kernel_regularizercCrr)rr/rMrrr=r/9rzConvLSTM.recurrent_regularizercCrr)rr0rMrrr=r0=rzConvLSTM.bias_regularizercCrr)rr1rMrrr=r1ArzConvLSTM.kernel_constraintcCrr)rr2rMrrr=r2ErzConvLSTM.recurrent_constraintcCrr)rr3rMrrr=r3IrzConvLSTM.bias_constraintcCrr)rr6rMrrr=r6MrzConvLSTM.dropoutcCrr)rr7rMrrr=r7QrzConvLSTM.recurrent_dropoutcsid|jd|jd|jd|jd|jd|jdt|jdt|j d |j d t |j d t |j d t |jd |jdt|jdt|jdt|jdt|jt|jt|jt|j|j|jd}t}|d=tt|t|S)Nr rrr#r%rr'r(r)r*r+r,r-r.r/r0r)r1r2r3r6r7r) r rrr#r%rrrr'r(r)rr*r+r,r-rr.r/r0rrr1r2r3r6r7rrrrrrr;rr=rUst        &zConvLSTM.get_configcCs|di|S)Nrr)clsrrrr= from_configszConvLSTM.from_config)r r Nr rrTrrrTNNNNNNNFFFFrr)NNN) rrrrrrrr rrr#r%rr'r(r)r*r+r,r-r.r/r0r1r2r3r6r7r classmethodrrrrr;r=rhsTD                      +r)rtensorflow.compat.v2compatv2r[ keras.srcrrrrrkeras.src.enginer"keras.src.layers.rnn.base_conv_rnnr+keras.src.layers.rnn.dropout_rnn_cell_mixinr keras.src.utilsr BaseRandomLayerr rrrrr=s         K