BVh4dZddlZddlZddlZddlmZddlmZddlm Z ddlm Z ddl m Z ddlmZdd lmZdd lmZdd lmZdd lmZ ddlZd ZededZddZddZ ddZ dZ!dZ"dZ#dZ$dZ% ddZ&dZ'dZ(dZ)y#e$rdZYDwxYw) z@Functions for saving and loading a Keras Model from HDF5 format.N)backend) optimizer_v1) model_config) saving_utils) json_utils) LazyLoader)ask_to_proceed_with_overwrite) variables)gfile) tf_loggingisequential_libz)tensorflow.python.keras.engine.sequentialct tdt|jt|jk7rt j dt|tjs|s-tjj|rt|}|sytjj|}tjj|stj |tj|d}d}n|}d} t#j$||}|j'D]q\} } t| t(t*t,frBt/j0| t2j4j7d |j8| <c| |j8| <s|j;d } |j<} t?| | |rF|j@r:t|j@tBjDstG||j@|jI|r|jKyy#|r|jKwwxYw) aSaves a model to a HDF5 file. The saved model contains: - the model's configuration (topology) - the model's weights - the model's optimizer's state (if any) Thus the saved model can be reinstantiated in the exact same state, without any of the code used for model definition or training. Args: model: Keras model instance to be saved. filepath: One of the following: - String, path where to save the model - `h5py.File` object where to save the model overwrite: Whether we should overwrite any existing model at the target location, or instead ask the user with a manual prompt. include_optimizer: If True, save optimizer's state together. Raises: ImportError: if h5py is not available. Nz`save_model` requires h5py.aFound duplicated `Variable`s in Model's `weights`. This is usually caused by `Variable`s being shared by Layers in the Model. These `Variable`s will be treated as separate `Variable`s when the Model is restored. To avoid this, please save with `save_format="tf"`.wmodeTF)defaultutf8 model_weights)&h5py ImportErrorlenweights_undeduplicated_weightsloggingwarning isinstanceFileospathisfiler dirnameexistsr MakeDirsrmodel_metadataitemsdictlisttuplejsondumpsr get_json_typeencodeattrs create_grouplayerssave_weights_to_hdf5_group optimizerr TFOptimizer$save_optimizer_weights_to_hdf5_groupflushclose) modelfilepath overwriteinclude_optimizerproceeddirpathfopened_new_filer$kvmodel_weights_group model_layerss Z/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/keras/saving/hdf5_format.pysave_model_to_hdf5rC4s4 \ 3 44  3u<<== OOGH Hdii ( 1-h7g ggooh'G 77>>' " nnW (%AOAO!008IJN$$&1 AdE* +ZZ z//117   ..9<GGIggiggis DH<<Ict td|si}t|tj }|rtj|d}n|}d} |jj d}| t dt|dr|jd}tj|}tj|| }t|d |j|r |jj d }t|dr|jd}|*tjd ||r|j!SStj|}|j"dit%j&||d dit%j(|d|vrM |j*j-|j.t5|} |j*j7||r|j!|S#t0t2f$rtjdYawxYw#t $rtjdY^wxYw#|r|j!wwxYw)aNLoads a model saved via `save_model_to_hdf5`. Args: filepath: One of the following: - String, path to the saved model - `h5py.File` object from which to load the model custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization. compile: Boolean, whether to compile the model after loading. Returns: A Keras model instance. If an optimizer was found as part of the saved model, the model is already compiled. Otherwise, the model is uncompiled and a warning will be displayed. When `compile` is set to False, the compilation is omitted without any warning. Raises: ImportError: if h5py is not available. ValueError: In case of an invalid savefile. Nz`load_model` requires h5py.rrrzNo model found in config file.decodezutf-8)custom_objectsrtraining_configzgNo training configuration found in the save file, so the model was *not* compiled. Compile it manually.from_serializedToptimizer_weightszError when creating the weights of optimizer {}, making it impossible to restore the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.zuError in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.)rrrrr-get ValueErrorhasattrrFrmodel_config_libmodel_from_configload_weights_from_hdf5_groupr/rrr5compiler!compile_args_from_training_configtry_build_compiled_argumentsr1_create_all_weightstrainable_variablesNotImplementedErrorAttributeError&load_optimizer_weights_from_hdf5_group set_weights) r7rGrRr=r<r6rrHoptimizer_weight_valuess rBload_model_from_hdf5r\s=2 \ 3 44 N"8TYY77/ (%AA %277;;~.L 7 88|X&!((1l$$\2L  . .|>L NE!?!3U\\B $56o ( +)009  M N6ggi5#))/:oemmBlDD >+B<@B//6  ! N // - -e.G.G H#I"K ( // % %&= >ggi ,#$^4 N //M N N ( //' ( ( ggisVCIAI%G:? I H$:$H!I H!!I$IIIIIc( fd}fd}fd}jjdk(r||}jjdk(r ||}n jjdvr||}dk(rjjdk(rtj|}jjdk(r|d j}|d d j d d fk7s|d j k7rG|d j k(r|d d j d d fk(sJtj|d d|d <|d d d d d d d d f|d <jjdk(r+jdk(rtj|d d|d <jjdk(rVjdk(rtj|d d|d <jdk(rtj|d d|d <jjdk(r+jdk(rtj|d d|d <jjdk(r|t|dk(rntj|d |d |dgd}tj|d |d|dgd} tj|d |d|dgd} || | g}jjd k(rt|d!k(rztj|d |d|d |dgd}tj|d |d|d|d"gd} tj|d |d|d|d#gd} || | g}jjd$k(rt|d!k(rtj|d |d|d |dgd}tj|d |d|d|d"gd} tj|d |d|d|d#gd} jdk(r,tj|d}tj| d} || | g}gd%} jj| vrtjjd |d jk7rQtj|d d&|d <jjd$k(rtj|d d&|d <t|S)'aPreprocess layer weights between different Keras formats. Converts layers weights from Keras 1 format to Keras 2 and also weights of CuDNN layers in Keras 2. Args: layer: Layer instance. weights: List of weights values (Numpy arrays). original_keras_version: Keras version for the weights, as a string. original_backend: Keras backend the weights were trained with, as a string. Returns: A list of weights values (Numpy arrays). ct|dz}tj|d|}tj||d}||zS)aConverts layers nested in `Bidirectional` wrapper. This function uses `preprocess_weights_for_loading()` for converting layers. Args: weights: List of weights values (Numpy arrays). Returns: A list of weights values (Numpy arrays). N)rpreprocess_weights_for_loading forward_layerbackward_layer)rnum_weights_per_layerforward_weightsbackward_weightslayeroriginal_backendoriginal_keras_versions rBconvert_nested_bidirectionalzDpreprocess_weights_for_loading..convert_nested_bidirectionalsm LA-4 W%;&;< 02O6 g&;&<= 02 - --c4tj|S)aConverts layers nested in `TimeDistributed` wrapper. This function uses `preprocess_weights_for_loading()` for converting nested layers. Args: weights: List of weights values (Numpy arrays). Returns: A list of weights values (Numpy arrays). )r`rf)rrfrgrhs rBconvert_nested_time_distributedzGpreprocess_weights_for_loading..convert_nested_time_distributed s# * W46F HHrjc|dt j}|t jd}g}g} jD]}t|j}t|j}|js:t ||d||d|z }|j |d||j ||d||d}||d}||zS)aConverts layers nested in `Model` or `Sequential`. This function uses `preprocess_weights_for_loading()` for converting nested layers. Args: weights: List of weights values (Numpy arrays). Returns: A list of weights values (Numpy arrays). N)rfrrhrg)rtrainable_weightsr/non_trainable_weightsrr`extend) rrnronew_trainable_weightsnew_non_trainable_weightssublayernum_trainable_weightsnum_non_trainable_weights preprocessedrfrgrhs rBconvert_nested_modelz.convert_nested_models =U%<%#C(?(?$@$AB "LL(!("<"<="%h&D&D"E   5&'=(=>*+E,EFG#9- / $$\2H3H%IJ!((6K6L)MN-.C.DE 5 % &!((" !#< <.> >>\\'!*l; 1:aAqj)gaj 8+   . .\\'!*l;  #44   o -\\'!*l;   . .\\'!*l;  8+   . .\\'!*o>  5( W WQZ D2N>> QZWQZ 0r;~~wqz71:wqzBL+T2 6) W  QZWQZ <2G>> QZWQZ =BH~~ QZWQZ =BH+T2 </ W  QZWQZ <2G>> QZWQZ =BH~~ QZWQZ =BH    0 0<< 5&\\*:LI +T2O+ __,q)*gaj.>.>><< L9gaj  ! !\ 1\\'!*l;  eW --rjc d d |jj}|dvr}t|dk(ro|djd}|dj}d |d|z zfk(rd }n#|| zfk(rd }nt d t |zd fd }||k7r |||d k(}|dvrt|dk(r|djd}|dj}d d fd }|d|z zfk(rd}n0|d| zfk(rd}n#|| zfk(rd}nt d t |z|dk(rd}n|j rd}nd}||k7r6||f} d| vrt d| z|dk(r ||d }|S|dk(r ||d}|S)aNConverts weights for RNN layers between native and CuDNN format. Input kernels for each gate are transposed and converted between Fortran and C layout, recurrent kernels are transposed. For LSTM biases are summed/ split in half, for GRU biases are reshaped. Weights can be converted in both directions between `LSTM` and`CuDNNSLTM` and between `CuDNNGRU` and `GRU(reset_after=True)`. Default `GRU` is not compatible with `CuDNNGRU`. For missing biases in `LSTM`/`GRU` (`use_bias=False`) no conversion is made. Args: layer: Target layer instance. weights: List of source weights values (input kernels, recurrent kernels, [biases]) (Numpy arrays). Returns: A list of converted weights values (Numpy arrays). Raises: ValueError: for incompatible GRU layer/weights or incompatible biases ctjtj||Dcgc] }|| c}Scc}w)a=Transforms kernel for each gate separately using given function. Args: kernels: Stacked array of kernels for individual gates. func: Function applied to kernel of each gate. n_gates: Number of gates (4 for LSTM, 3 for GRU). Returns: Stacked array of transformed kernels. )rhstackhsplit)kernelsfuncn_gatesr>s rBtransform_kernelsz/_convert_rnn_weights..transform_kernelss0 99ryy''BC!d1gC DDCs>c|rdndfd}|S)aMakes a function that transforms input kernels from/to CuDNN format. It keeps the shape, but changes between the layout (Fortran/C). Eg.: ``` Keras CuDNN [[0, 1, 2], <---> [[0, 2, 4], [3, 4, 5]] [1, 3, 5]] ``` It can be passed to `transform_kernels()`. Args: from_cudnn: `True` if source weights are in CuDNN format, `False` if they're in plain Keras format. Returns: Function that converts input kernel to the other format. FCcR|jj|jS)N)order)Treshaper)rrs rB transformz@_convert_rnn_weights..transpose_input..transforms! XX  fll%  88rjrK) from_cudnnrrs @rBtranspose_inputz-_convert_rnn_weights..transpose_inputs(C3E9 rj)r CuDNNLSTMrrrr_rrrzInvalid bias shape: Tc|d|}|dd}|r1tjtj|dddd}ntjd|dzd}|||gS)zConverts the weights between CuDNNLSTM and LSTM. Args: weights: Original weights. from_cudnn: Indicates whether original weights are from CuDNN layer. Returns: Updated weights compatible with LSTM. rrc|jSNrr>s rBzD_convert_rnn_weights..convert_lstm_weights.. !##rjr_rg?)rsumsplittilerrrrecurrent_kernelsbiasesrrrs rBconvert_lstm_weightsz2_convert_rnn_weights..convert_lstm_weightss"'!*oj.I")+g+GAJ wO QQ7a@wqz)1-(& 11rj)r)rCuDNNGRUc|d|}|dd}tj|dj|rdnd}|||gS)zConverts the weights between CuDNNGRU and GRU. Args: weights: Original weights. from_cudnn: Indicates whether original weights are from CuDNN layer. Returns: Updated weights compatible with GRU. rrc|jSrrrs rBrzC_convert_rnn_weights..convert_gru_weights.."rrjr_)r_rr)rarrayrrs rBconvert_gru_weightsz1_convert_rnn_weights..convert_gru_weightssa"'!*oj.I")+g+GAJ wOxx #++zGrJf(& 11rjrzGRU(reset_after=True)zGRU(reset_after=False)z%s is not compatible with %sF)T)rrrrrMstr reset_after) rfr target_classunits bias_shapesourcerrtargettypesrrrs @@@rBrrs2 E6)),**s7|q/@ AJ  Q E!!JGa%i')++f ) )f -J? @@20$W;9NOg((S\Q-> AJ  Q E!!JG2"a%i')++f 57?+ +&f ) )'f -J? @@z!f   &f'fve !U *7%?@@ : %g$? . , ,%g%@ .rjct|d}|r|jd}|Dcgc]&}t|jj d(}}t |d|t j|}t||D]D\}}|j||j|j} |js|| d<@|| ddFyycc}w)zSaves optimizer weights of a optimizer to a HDF5 group. Args: hdf5_group: HDF5 group. optimizer: optimizer instance. rrJr weight_namesdtyperKN) getattrr.rnamer,save_attributes_to_hdf5_grouprbatch_get_valuezipcreate_datasetrr) hdf5_groupr1symbolic_weights weights_grouprr weight_valuesrval param_dsets rBr3r3CsY 2++,?@M8HI1CK&&v.ILI!-N++,<=M}5 c //  0,j YY 2 1  Is+CcT|d}t|d}|Dcgc]}|| c}Scc}w)zLoad optimizer weights from a HDF5 group. Args: hdf5_group: A pointer to a HDF5 group. Returns: data: List of optimizer weight names. rJr)load_attributes_from_hdf5_group)rroptimizer_weight_names weight_names rBrYrY[s801-:^%8N O- $ OO Os %c ddlm}t|d|Dcgc]}|jj dc}t j j d|j d<t|j d|j d<t|dD]}|j|j}t|}t j|}|Dcgc]}|jj d}}t|d |t||D]D\} } |j| | j| j } | js| | d <@| | d d Fy cc}wcc}w) z~Saves the weights of a list of layers to a HDF5 group. Args: f: HDF5 group. layers: List of layer instances. r) __version__ layer_namesrr keras_versionc|jSr)r)xs rBrz,save_weights_to_hdf5_group..zs AFFrj)keyrrrKN)tensorflow.python.kerasrrrr,rr-rsortedr._legacy_weightsrrrrr) r<r/rrfgrrrrrrrs rBr0r0js>CGu**62GI(//7!'') /66v>!''/f"23 e uzz"Ae$G++G4M3:;aAFFMM&);L;!!^\B}5 c##D#))399#Ej YY 2 1   Hrrrs rBrQrQsWW_5%x05< > ??3/??!@" -.QsIc d|jvr-|jd}t|dr|jd}nd}d|jvr-|jd}t|dr|jd}nd}t|d}i}|D]:}|js|j |jgj |<g}t|D]r\} } || } t| d} | D cgc]} tj| | }} |j| gD] }t|}t||||}t|t|k7r|rTtjd j!|jd j!t|t|zt#d t%| zd z|jzd zt%t|zdzdzt%t|zdzt't|D]"}t)j*||||j,k7r|r\tjd j!|jdj!||j,||j,zt#d t%| zd z|jzdzt%||zdj!t)j*||zdzt%||j,zdz|j ||||f%#ut)j.|ycc} w)aImplements name-based weight loading. (instead of topological weight loading). Layers that have no matching name are skipped. Args: f: A pointer to a HDF5 group. layers: a list of target layers. skip_mismatch: Boolean, whether to skip loading of layers where there is a mismatch in the number of weights, or a mismatch in the shape of the weights. Raises: ValueError: in case of mismatch between provided layers and weights file and skip_match=False. rrFrr}rNrrz(Skipping loading of weights for layer {}z1 due to mismatch in number of weights ({} vs {}).rrz ") expects z! weight(s), but the saved weightsz have z element(s).z% due to mismatch in shape ({} vs {}).z "), weight z has shape {}z!, but the saved weight has shape .)r-rNrFrr setdefaultrrrrrLrr`rrrformatrMrrangerrrr)r<r/ skip_mismatchrhrgrindexrfrr>rrrrrris rB$load_weights_from_hdf5_group_by_namers&WW_5%x05<II J SV+k9EJJF&'),S1A-B)CD<=?GHS/014BBC C S'(N!   -a0 1]15E5K5K K  OO''-vejj'9=< >  $ $&6q&9=;K%L M#N!!N &NN -.IQs1M!c|Dcgc]}t|tkDs|}}|r#tdtdj|fzt j |}d}t j ||}td|Dr.|dz }t j ||}td|Dr.|dkDr(t|D]\}} | |jd||fz<y||j|<ycc}w)aSaves attributes (data) of the specified name into the HDF5 group. This method deals with an inherent problem of HDF5 file which is not able to store data larger than HDF5_OBJECT_HEADER_LIMIT bytes. Args: group: A pointer to a HDF5 group. name: A name of the attributes to save. data: Attributes data to store. Raises: RuntimeError: If any single attribute is too large to be saved. z_The following attributes cannot be saved to HDF5 file because they are larger than %d bytes: %sz, rc3BK|]}|jtkDywr)nbytesHDF5_OBJECT_HEADER_LIMIT).0rs rB z0save_attributes_to_hdf5_group..As FAAHH/ / Fs%s%dN) rr RuntimeErrorjoinrr array_splitanyrr-) grouprdatarbad_attributesdata_npy num_chunks chunked_datachunk_id chunk_datas rBrr"s" $I!s1v0H'HAI.I H0$))N2KLM NNZZ (**5, F FF!OJ>>(J7L F FF!^ ), 7:*/9ekk&D(++,:EKK-Js C2C2c ||jvr;|j|Dcgc]!}t|dr|jdn|#}}|Sg}d}d||fz|jvrg|j|jd||fzDcgc]!}t|dr|jdn|#c}|dz }d||fz|jvrg|Scc}wcc}w)aQLoads attributes of the specified name from the HDF5 group. This method deals with an inherent problem of HDF5 file which is not able to store data larger than HDF5_OBJECT_HEADER_LIMIT bytes. Args: group: A pointer to a HDF5 group. name: A name of the attributes to load. Returns: data: Attributes data. rFrrr r)r-rNrFrp)rrnrrs rBrrLs U[[T"  $Ax0a7 D  + DH D(# #u{{ 2 kk;;vx(889&a2!((6  9 !mh D(# #u{{ 2 + s &C &Cc|j|jz}td|Dr/tdj |j j ||S)aDO NOT USE. For legacy reason, the layer.weights was in the order of [self.trainable_weights + self.non_trainable_weights], and this order was used for preserving the weights in h5 format. The new order of layer.weights are the same as layer.get_weights() which is more intuitive for user. To keep supporting the existing saved h5 file, this method should be used to save/load weights. In future version, we will delete this method and introduce a breaking change for h5 and stay with the new order for weights. Args: layer: a `tf.keras.Model` or `tf.keras.layers.Layer` instance. Returns: A list of variables with the order of trainable_weights, followed by non_trainable_weights. c3RK|]}t|tj !ywr)rvariables_moduleVariable)rrs rBr z"_legacy_weights..~s!G!Z+44 5 5Gs%'zSave or restore weights that is not an instance of `tf.Variable` is not supported in h5, use `save_format='tf'` instead. Got a model or layer {} with weights {})rnrorrWrrr)rfrs rBrrksY$  # #e&A&A A'GwGG  &&,fU__-E-Ew&O QQ .rj)TT)NT)NN)F)*__doc__r)rnumpyrrrrtensorflow.python.keras.savingrrOr*tensorflow.python.keras.saving.saved_modelr+tensorflow.python.keras.utils.generic_utilsr&tensorflow.python.keras.utils.io_utilsr tensorflow.python.opsr rtensorflow.python.platformr r rrrrglobalsr rCr\r`rr3rYr0rQrrrrrKrjrBr&s G +0K7ABP?,< "gi/1 OdY|;?48w.tdN0 P>@/H#S/l'T>I $s B BB