a Sic;@sdZddlZddlmmZddlmZddl m Z ddl m Z ddl m Z ddlmZddlmZdd lmZd d Zdd d ZdddZdddZdddZdddZdS)z;Keras training and evaluation routines for eager execution.N)backend)training_utils)training_utils_v1)loss_scale_optimizer) losses_utils) GradientTape) tf_loggingcCs<t|d|||}Wdn1s.0Y|S)N_loss)r name_scope)outputstargetsloss_fn output_namelossrZ/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/engine/training_eager_v1.py_eager_loss_fns(rcstj|}tjg}rdtjtkrJfddjD}|j|||dd}|fddjD|S)aCalculates the metrics for each output of the given model. Args: model: The model on which metrics are being calculated. outputs: The outputs of the given model. targets: The predictions or targets of the given model. sample_weights: Optional list of sample weights for each output. masks: Optional list of masks for each output. Returns: Returns the metric results for each output of the model. cs"g|]}|durdndqS)Nr)pop.0t)r rr :sz%_eager_metrics_fn..T)r sample_weightsmasks&return_weighted_and_unweighted_metricsskip_target_maskscsg|]}|jvr|qSr)_compile_metric_functionsresult)rm)modelrrrJs ) tfnestflattenlen_targets_handle_metrics_prepare_skip_target_masksextendmetrics)rr r rrmetric_results new_targetsr)rr r_eager_metrics_fn%s.     r+Fc Csd}i}|jr||d<t|dkr4t|ts4|d}tddtj|Dr^tjtj |}||fi|}tj|}|rt ||}|rdd|D}dd|D} tj|}g} t d d d|jD} |j} | s| s|rtd ntd t| D]~\} }|r|| nd }| | }t |j| d|d urt||| j}|d urj|}n.t||| j}tj||d\}}}||9}t|dr||| || }tj||tjjd}|j}|tjjkrtjj}tj||d}n||| || |d}tjj}Wd n1s,0Yt|j dkrX| !|| ||tjjkrpt"|}||j#| |7}q| r|t"t$| 7}Wd n1s0Y||| | fS)aCalculates the loss for a given model. Args: model: The model on which metrics are being calculated. inputs: Either a dictionary of inputs to the model or a list of input arrays. targets: List of target arrays. output_loss_metrics: List of metrics that are used to aggregated output loss values. sample_weights: Optional list of sample weight arrays. training: Whether the model should be run in inference or training mode. Returns: Returns the model output, total loss, loss value calculated using the specified loss function and masks for each output. The total loss includes regularization losses and applies masking and sample weighting to the loss value. rtrainingcss |]}t|tjttfVqdSN) isinstancenpndarrayfloatint)rinput_trrr zsz_model_loss..cSs(g|] }|dur tt|ndqSr.)rcast_if_floating_dtyper convert_to_tensor)rvalrrrrsz_model_loss..cSsg|]}t|ddqS) _keras_maskN)getattrrrrrrrcSsg|]}|dur|qSr.r)rr rrrrsz?The model cannot be trained because it has no loss to optimize.z@The model cannot be evaluated because it has no loss to compute.Nr ) sample_weight reduction)r<r=)r=)%_expects_training_argr#r/dictanyr r!r" map_structurer7r#cast_if_floating_dtype_and_mismatchrr loss_functionslosses ValueError enumerate output_namescastdtypersqueeze_or_expand_dimensionshasattrcallcompute_weighted_loss ReductionV2NONEr=AUTOSUM_OVER_BATCH_SIZEreduce_weighted_lossr appendscale_loss_for_distribution_loss_weights_listadd_n)rinputsr output_loss_metricsrr, total_losskwargsoutsr output_lossesloss_fns custom_lossesir weightsmask_per_sample_lossesweighted_lossesloss_reduction output_lossrrr _model_lossSs       "&rgc CsNt|r dnd$t|tL}t||||||d\}}} } t|jtj rd|j |} n|} Wdn1s|0Y|r|j } | rt |dr| || q|| | } t|jtj r|j| } |jt| | n td||| | fWdWdS1s 0YWdn1s@0YdS)a$Calculate the loss and gradient for one input batch. The model weights are updated if training is set to True. Args: model: Model whose loss has to be calculated. inputs: List of input arrays. targets: List of target arrays. output_loss_metrics: List of metrics that are used to aggregated output loss values. sample_weights: Optional list of sample weight arrays. training: The boolean represents if the weights of the model are updated. 'fit' methods will set this to True while 'evaluate' methods will set this to False. Returns: output of the model, total loss, the loss and the mask associated with each output. Raises: ValueError: If the model has no loss to optimize. r-r)rXrr,N _backwardszThe list of trainable weights is empty. Make sure that you are not setting model.trainable to False before compiling the model.)reager_learning_phase_scoperRespectCompiledTrainableStaterrgr/ optimizerrLossScaleOptimizerget_scaled_losstrainable_weightsrKrhgradientget_unscaled_gradientsapply_gradientsziploggingwarning)rrWr rXrr,taper[rYr\rscaled_total_lossrngradsrrr_process_single_batchsH "   rxc Csbt||}t||||d|d\}}}}t|ts8|g}t|||||d} tj|}||| dS)aCalculates the loss and gradient updates for one input batch. Args: model: Model whose loss has to be calculated. inputs: Input batch data. targets: Target batch data. sample_weights: Sample weight batch data. output_loss_metrics: List of metrics that are used to aggregated output loss values. Returns: Dict with three items: 'total_loss': list with a single tensor for overall loss, 'output_losses': list of tensors for loss corresponding to each of the model output. Could be a empty list when model has only one output. 'metrics': list of tensors for metric specified. Trr,rXrrrYr\r() rcast_to_model_input_dtypesrxr/listr+r r!r" rrWr rrXr[rYr\rmetrics_resultsrrrtrain_on_batchAs&    rc Cst||}td,t||||d|d\}}}}Wdn1sH0Yt|tsb|g}t|||||d} tj |}||| dS)aCalculates the loss for one input batch. Args: model: Model whose loss has to be calculated. inputs: Input batch data. targets: Target batch data. sample_weights: Sample weight batch data. output_loss_metrics: List of metrics that are used to aggregated output loss values. Returns: Dict with three items: 'total_loss': single tensor for overall loss, 'output_losses': list of tensors for loss corresponding to each of the model output. Could be a empty list when model has only one output. 'metrics': list of tensors for metric specified. rFryNrzr{) rr|rrirgr/r}r+r r!r"r~rrr test_on_batchks(  ,   r)NN)NNF)NNF)NN)NN)__doc__numpyr0tensorflow.compat.v2compatv2r kerasr keras.enginerrkeras.mixed_precisionr keras.utilsr tensorflow.python.eager.backproprtensorflow.python.platformrrsrr+rgrxrrrrrrs0        2 * J +