2Vh]iddlZddlZddlZddlZddlZddlmZddlmZddlmZddl m Z ddl m Z ddl mZddlmZdd lmZe d Gd d e Zdd Zy)N)backend)ops)tree) keras_export)Callback) Embedding) Optimizer) file_utilszkeras.callbacks.TensorBoardceZdZdZ d!fd ZdZedZedZedZ dZ dZ d Z d Z d Zd Zd Zd"dZd"dZd"dZd"dZd"dZd"dZd"dZd"dZd"dZdZd"dZdZdZdZdZdZ dZ!dZ"d#d Z#xZ$S)$ TensorBoardaEnable visualizations for TensorBoard. TensorBoard is a visualization tool provided with TensorFlow. A TensorFlow installation is required to use this callback. This callback logs events for TensorBoard, including: * Metrics summary plots * Training graph visualization * Weight histograms * Sampled profiling When used in `model.evaluate()` or regular validation in addition to epoch summaries, there will be a summary that records evaluation metrics vs `model.optimizer.iterations` written. The metric names will be prepended with `evaluation`, with `model.optimizer.iterations` being the step in the visualized TensorBoard. If you have installed TensorFlow with pip, you should be able to launch TensorBoard from the command line: ``` tensorboard --logdir=path_to_your_logs ``` You can find more information about TensorBoard [here](https://www.tensorflow.org/get_started/summaries_and_tensorboard). Args: log_dir: the path of the directory where to save the log files to be parsed by TensorBoard. e.g., `log_dir = os.path.join(working_dir, 'logs')`. This directory should not be reused by any other callbacks. histogram_freq: frequency (in epochs) at which to compute weight histograms for the layers of the model. If set to 0, histograms won't be computed. Validation data (or split) must be specified for histogram visualizations. write_graph: (Not supported at this time) Whether to visualize the graph in TensorBoard. Note that the log file can become quite large when `write_graph` is set to `True`. write_images: whether to write model weights to visualize as image in TensorBoard. write_steps_per_second: whether to log the training steps per second into TensorBoard. This supports both epoch and batch frequency logging. update_freq: `"batch"` or `"epoch"` or integer. When using `"epoch"`, writes the losses and metrics to TensorBoard after every epoch. If using an integer, let's say `1000`, all metrics and losses (including custom ones added by `Model.compile`) will be logged to TensorBoard every 1000 batches. `"batch"` is a synonym for 1, meaning that they will be written every batch. Note however that writing too frequently to TensorBoard can slow down your training, especially when used with distribution strategies as it will incur additional synchronization overhead. Batch-level summary writing is also available via `train_step` override. Please see [TensorBoard Scalars tutorial]( https://www.tensorflow.org/tensorboard/scalars_and_keras#batch-level_logging) for more details. profile_batch: Profile the batch(es) to sample compute characteristics. profile_batch must be a non-negative integer or a tuple of integers. A pair of positive integers signify a range of batches to profile. By default, profiling is disabled. embeddings_freq: frequency (in epochs) at which embedding layers will be visualized. If set to 0, embeddings won't be visualized. embeddings_metadata: Dictionary which maps embedding layer names to the filename of a file in which to save metadata for the embedding layer. In case the same metadata file is to be used for all embedding layers, a single filename can be passed. Examples: ```python tensorboard_callback = keras.callbacks.TensorBoard(log_dir="./logs") model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback]) # Then run the tensorboard command to view the visualizations. ``` Custom batch-level summaries in a subclassed Model: ```python class MyModel(keras.Model): def build(self, _): self.dense = keras.layers.Dense(10) def call(self, x): outputs = self.dense(x) tf.summary.histogram('outputs', outputs) return outputs model = MyModel() model.compile('sgd', 'mse') # Make sure to set `update_freq=N` to log a batch-level summary every N # batches. In addition to any `tf.summary` contained in `model.call()`, # metrics added in `Model.compile` will be logged every N batches. tb_callback = keras.callbacks.TensorBoard('./logs', update_freq=1) model.fit(x_train, y_train, callbacks=[tb_callback]) ``` Custom batch-level summaries in a Functional API Model: ```python def my_summary(x): tf.summary.histogram('x', x) return x inputs = keras.Input(10) x = keras.layers.Dense(10)(inputs) outputs = keras.layers.Lambda(my_summary)(x) model = keras.Model(inputs, outputs) model.compile('sgd', 'mse') # Make sure to set `update_freq=N` to log a batch-level summary every N # batches. In addition to any `tf.summary` contained in `Model.call`, # metrics added in `Model.compile` will be logged every N batches. tb_callback = keras.callbacks.TensorBoard('./logs', update_freq=1) model.fit(x_train, y_train, callbacks=[tb_callback]) ``` Profiling: ```python # Profile a single batch, e.g. the 5th batch. tensorboard_callback = keras.callbacks.TensorBoard( log_dir='./logs', profile_batch=5) model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback]) # Profile a range of batches, e.g. from 10 to 20. tensorboard_callback = keras.callbacks.TensorBoard( log_dir='./logs', profile_batch=(10,20)) model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback]) ``` c pt |t||_||_||_||_||_|dk(rdn||_||_ | |_ |rtjdvr$tdtjd|dtjdk(rCtjddkr-tj d tjd d }|j#|d |_d |_d |_d |_d |_d|_i|_g|_y) Nbatch)jax tensorflowz(Profiling is not yet available with the zV backend. Please open a PR if you'd like to add this feature. Received: profile_batch=z (must be 0)r zProfiling with the z! backend requires python >= 3.12.r)super__init__strlog_dirhistogram_freq write_graph write_imageswrite_steps_per_second update_freqembeddings_freqembeddings_metadatar ValueErrorsys version_infowarningswarn_init_profile_batch_global_train_batch_global_test_batch_previous_epoch_iterations_train_accumulated_time_batch_start_time_summary_module_writers_prev_summary_state) selfrrrrrr profile_batchrr __class__s O/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/callbacks/tensorboard.pyrzTensorBoard.__init__s? 7| ,&(&<# +w 61K.#6  (== >()*%%2O<A "e+##A&+MM-"??,--NP%&M   /#$ "#*+''($!"# $& c||_|j|_tjj |jd|_tjj |jd|_i|_d|_ |jr|jd|_ |jr|jyy)z/Sets Keras model and writes graph if specified.train validationFTN)_modelr_log_write_dirospathjoin _train_dir_val_dirr*_should_write_train_graphr_write_keras_model_summaryr_configure_embeddings)r,models r/ set_modelzTensorBoard.set_models "ll'',,t':':GD T%8%8,G  ).&     + + --1D *     & & ( r0cL|j ddlm}||_|jSNr)r)tensorflow.summarysummary)r,rCs r/rCzTensorBoard.summarys%    ' 0#*D ###r0cd|jvr2|jj|j|jd<|jdS)Nr2)r*rCcreate_file_writerr9r,s r/ _train_writerzTensorBoard._train_writersC $-- '%)\\%D%D&DMM' "}}W%%r0cd|jvr2|jj|j|jd<|jdS)Nval)r*rCrEr:rFs r/ _val_writerzTensorBoard._val_writersC  %#'<<#B#B $DMM% }}U##r0c||jj5|jj}t |drkt |dr0|j j |jj n/|j j |jj dddy#1swYyxYw)z7Writes Keras model train_function graph to TensorBoard. function_spec_concrete_stateful_fnN) rG as_defaultr>train_functionhasattrrCgraphrM_concrete_variable_creation_fn)r,train_fns r/_write_keras_model_train_graphz*TensorBoard._write_keras_model_train_graphs    * * , zz00Hx18%<=LL&&x'E'E'K'KLLL&& ??EE   s BB22B;c|jj5|jjjdk(s#|jjjdk(rt d|jddddy#1swYyxYw)z2Writes Keras graph network summary to TensorBoard. Functional SequentialkerasrstepN)rGrNr>r.__name__keras_model_summaryrFs r/r<z&TensorBoard._write_keras_model_summary sm    * * , A $$--=::''00L@#GTZZa@  A A As ABB ctddlm}ddlm}|j }|j j D]}t|ts|jj}d}||_ |jDt|jtr|j|_p|j|jj!vs|jj#|j|_|jr@t|jts&t%d|jj!|j'|}t(j*j-|j.d}t1j2|d5} | j5|dddy#1swYyxYw) z'Configure the Projector for embeddings.r) text_format) projectorz:layer_with_weights-0/embeddings/.ATTRIBUTES/VARIABLE_VALUENzmUnrecognized `Embedding` layer names passed to `keras.callbacks.TensorBoard` `embeddings_metadata` argument: zprojector_config.pbtxtw)google.protobufr^tensorboard.pluginsr_ProjectorConfigr>layers isinstancer embeddingsadd tensor_namerr metadata_pathnamekeyspoprMessageToStringr6r7r8r5r Filewrite) r,r^r_configlayer embeddingrj config_pbtxtr7fs r/r=z!TensorBoard._configure_embeddingssp/1**,ZZ&& E%+"--113 Q)- %++7!$":":C@262J2J / ::)A)A)F)F)HH $ 8 8 < .should_record>s$***a/ /r0rr)rrNrC record_ifr+append __enter__)r,writerrZrxsummary_contexts` ` r/ _push_writerzTensorBoard._push_writer9sz   w &  0   d # LL " "= 1    ''8$$&$$&r0c|jdk(ry|jj}|djt j |djt j y)zPops the current writer.rvNrr)rr+rl__exit__rexc_info)r,previous_contexts r/ _pop_writerzTensorBoard._pop_writerIsb   w &  33779$$$clln5$$$clln5r0cb|jjD]}|jyN)r*valuesclose)r,r|s r/_close_writerszTensorBoard._close_writersTs'mm**, F LLN r0cd|}t|tr4t|jd}tjt |}t|t r||_||_n?t|ttfrt|dk(r|\|_|_n t||j dks|j|j kr t|d|_ d|_ |j dkDr$|jd|jd d|_|j dk(xr|jdk( |_y) aValidate profile_batch value and set the range of batches to profile. Sets values of _start_batch and _stop_batch attributes, specifying the start and stop batch to profile. Setting `profile_batch=0` disables profiling. Args: profile_batch: The range of batches to profile. Should be a non-negative integer or a comma separated string of pair of positive integers. A pair of positive integers signify a range of batches to profile. Raises: ValueError: If profile_batch is not an integer or a comma separated pair of positive integers. zprofile_batch must be a non-negative integer or 2-tuple of positive integers. A pair of positive integers signifies a range of batches to profile. Found: ,rFNlogdirsave)rersplitr map_structureint _start_batch _stop_batchtuplelistlenr_profiler_started_batch_trace_context_start_profiler_stop_profiler _is_tracing _should_trace)r,r-profile_batch_error_messages r/r#zTensorBoard._init_profile_batchXs<& "#0  2 $ mS ) .44S9M ..sMBM mS ) -D ,D  }udm 4]9Kq9P2? /D t/89 9   q D$4$4t7H7H$H89 9 "'$(!   q   +   U  +     " optimizerrPrJrNitemsrCscalarrr)r,rrjvalues r/ on_test_endzTensorBoard.on_test_ends ::  GDJJ,@,@,$O!!,,. #'::<KD%LL''%,/??!ZZ11<<(    s ACCcF|xjdz c_|jrtj|_|jsy|j|j k(r|j |jr%tjj||_ yyNr) r$rtimer(rr _start_tracerr tensorboardstart_batch_tracerr,rrs r/on_train_batch_beginz TensorBoard.on_train_batch_begins   A%  & &%)YY[D "!!   # #t'8'8 8      ! !(/(;(;(M(M)D % "r0c|jr|jd|_|jrLtj|jz }|j j dd|z |jt|trC|jD]0\}}|j j d|z||j2|jsy|jrs|jr<|j0tj j#|jd|_|j|j$k\r|j'yyy)NFbatch_steps_per_secondg?rYbatch_)r;rTrrr(rCrr$redictrrrrrrrstop_batch_tracerr)r,rrbatch_run_timerjrs r/on_train_batch_endzTensorBoard.on_train_batch_ends-  ) )  / / 1-2D *  & &!YY[4+A+AAN LL  (n$--   dD !#zz|  e ##tOU1I1I$  !!    %%$*C*C*O##44T5N5NO,0)''4+;+;;  "< r0c.|xjdz c_yr)r%rs r/on_test_batch_beginzTensorBoard.on_test_batch_begins 1$r0c|jrStj|jjj d|_tj|_yyNfloat32) rrconvert_to_tensorr>rrr&r_epoch_start_timer,rvrs r/on_epoch_beginzTensorBoard.on_epoch_beginsH  & &.1.C.C $$///D +&*YY[D " 'r0c|j|||jr#||jzdk(r|j||jr%||jzdk(r|j |yyy)z2Runs metrics and histogram summaries at epoch end.rN)_log_epoch_metricsr _log_weightsr_log_embeddingsrs r/ on_epoch_endzTensorBoard.on_epoch_endsm t,   54+>+>#>!#C   e $   ED,@,@$@A$E   '%F r0c|jjdd|j|jd|_y)NTF)rQprofilerr)rCtrace_onrr9rrFs r/rzTensorBoard._start_traces6 D59 DOO4r0c| |j}|jj5|jj d|z|ddd|j d|_y#1swY!xYw)z$Logs the trace graph to TensorBoard.Nzbatch_%d)rjrZF)rrGrNrC trace_exportrr)r,rs r/rzTensorBoard._stop_traceso =$$E    * * , K LL % %:+=E % J K    K Ks !A**A3ct|jjtr?t t j |jjj|d<|S)N learning_rate)rer>rr floatrconvert_to_numpyrrs r/_collect_learning_ratez"TensorBoard._collect_learning_ratesJ djj**I 6$)$$TZZ%9%9%G%GH%D ! r0c|jjj}tj|jz }t j |d}t j |d}||jz |z }t|Sr) r>rrrrrrr&r)r,current_iterationtime_since_epoch_beginsteps_per_seconds r/_compute_steps_per_secondz%TensorBoard._compute_steps_per_seconds JJ00;;!%t/E/E!E112CYO!$!6!6 "I"   ? ? ? "#%&&r0c|sy|jDcic]\}}|jdr||}}}|jDcic]\}}|jds||}}}|j|}|jr|j |d<|r]|j j 5|jD]&\}}|jjd|z||( ddd|rc|jj 5|jD]+\}}|dd}|jjd|z||- dddyycc}}wcc}}w#1swY{xYw#1swYyxYw)zWrites epoch metrics out as scalar summaries. Args: epoch: Int. The global step to use for TensorBoard. logs: Dict. Keys are scalar summary names, values are scalars. Nval_repoch_rY) r startswithrrrrGrNrCrrJ) r,rvrkv train_logsval_logsrjrs r/rzTensorBoard._log_epoch_metricss '+zz|Ptq!1<<;OadP P%)ZZ\JTQQ\\&5IAqDJJ00<  & &-1-K-K-MJ) * ##..0 L#-#3#3#5LKD%LL''4U'KL L !!,,. L#+>>#3LKD%8DLL''4U'KL L L QJ  L L L Ls.E#E# E)&E)::E/?E;/E8;Fc|jj5|jjD]w}|jD]f}|j j dd}|dz}|jj||||jsO|dz}|j|||hy|jjdddy#1swYyxYw)z-Logs the weights of the Model to TensorBoard.:_z /histogramrYz/imageN) rGrNr>rdweightsrjreplacerC histogramr_log_weight_as_imageflush)r,rvrqweight weight_namehistogram_weight_nameimage_weight_names r/rzTensorBoard._log_weights.s    * * , '** #mm F"(++"5"5c3"?K,7,,F)LL**-vE+((-8(,B)11"$5u      $ $ &! ' ' 'sA4C6CCctj|}|j}t|dk(rtj|d|dddg}nt|dk(rM|d|dkDr!tj |}|j}tj|d|d|ddg}nlt|dk(r^t jdk(r$tj |gd}|j}tj||d|d|ddg}t j|}|j}t|dk(r'|dd vr|jj||| y y y ) z%Logs a weight as a TensorBoard image.rrr channels_last)rrrr)rrrrYN) rsqueezeshaperreshape transposerimage_data_formatrrCimage)r,rrrvw_imgrs r/rz TensorBoard._log_weight_as_imageBs? F#  u:?KK58Q':;E Z1_Qx%(" e, KK58U1Xq'ABE Z1_((*o= eY7 KKa%(E!Ha'HIE((/  u:?uRyI5 LL  {E  > 6?r0ctjj|jdd|d}|jj |y)Nr2zkeras_embedding.ckpt-z .weights.h5)r6r7r8r5r> save_weights)r,rvembeddings_ckpts r/rzTensorBoard._log_embeddings[s@'',,    #E7+ 6  0r0c|jry tjj|d|_y#t$r }t j d|Yd}~yd}~wwxYw)zStarts the profiler if currently inactive. Args: logdir: Directory where profiler results will be saved. NTzFailed to start profiler: %s)rrr start_trace Exceptionloggingerror)r,res r/rzTensorBoard._start_profilercsS  ! !  =    + +F 3%)D " = MM8! < < =s&6 AAAc|jsy tjj|d|_y#t$r }t j d|Yd}~,d}~wwxYw#d|_wxYw)zStops the profiler if currently active. Args: save: Whether to save the profiler results to TensorBoard. NrzFailed to stop profiler: %sF)rrr stop_tracerrr)r,rrs r/rzTensorBoard._stop_profilerrse %%  +    * * * 5 &+D "  < MM7 ; ; <&+D "s' 7 A AA#A  A## A,) rrTFFrvrrNr)T)%r[ __module__ __qualname____doc__rr?propertyrCrGrJrTr<r=r~rrr#rrrrrrrrrrrrrrrrrrr __classcell__)r.s@r/r r sGV$ 4&l) $$&&$$ A%"N' 6< |H E  #:%1( ! 'L4'(?21 =+r0r cddlm}ddlm}|}d|j_d|j_ |j}|jj|d||g5\}} |j||||cdddS#t$r"}tjd|Yd}~yd}~wwxYw#1swYyxYw) aWrites a Keras model as JSON to as a Summary. Writing the Keras model configuration allows the TensorBoard graph plugin to render a conceptual graph, as opposed to graph of ops. In case the model fails to serialize as JSON, it ignores and returns False. Args: name: A name for this summary. The summary tag used for TensorBoard will be this name prefixed by any active name scopes. data: A Keras Model to write. step: Explicit `int64`-castable monotonic step value for this summary. If omitted, this defaults to `tf.summary.experimental.get_step()`, which must not be `None`. Returns: True on success, or False if no summary was written because no default summary writer was available. Raises: ValueError: if a default writer exists, but no step was provided and `tf.summary.experimental.get_step()` is `None`. rN)SummaryMetadatagraph_keras_model1z/Model failed to serialize as JSON. Ignoring... F)tagtensorrZmetadata)rBrCtensorflow.compat.v1r plugin_data plugin_namecontentto_jsonrr!r" experimental summary_scopero) rjdatarZrCrsummary_metadata json_stringexcrrs r/r\r\s.)4&(0C  ,+/  (lln    + + !D$<  #q}}Kd=M    GuMN   s#B&B6 B3B..B36B?r)rr6rrr! keras.srcrrrkeras.src.api_exportrkeras.src.callbacks.callbackrkeras.src.layersrkeras.src.optimizersr keras.src.utilsr r r\r0r/r'sV -1&*&+,n +(n +-n +b- r0