a Sic9!@shdZddlZddlmmZddlmZdddZ ddd Z Gd d d Z d d Z ddZ ddZdS)zTraining-related utilities.N) generic_utilsTcsd}t|tsd}|g}tdd|Drd|sPfdd|D}dd|D}qpfdd|D}n t|}|r||d }|S) aSlices batches out of provided arrays (workaround for eager tensors). Unfortunately eager tensors don't have the same slicing behavior as Numpy arrays (they follow the same slicing behavior as symbolic TF tensors), hence we cannot use `generic_utils.slice_arrays` directly and we have to implement this workaround based on `concat`. This has a performance cost. Args: arrays: Single array or list of arrays. indices: List of indices in the array that should be included in the output batch. contiguous: Boolean flag indicating whether the indices are contiguous. Returns: Slice of data (either single array or list of arrays). FTcss|]}t|VqdSN)tf is_tensor.0xr W/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/engine/training_utils.py -zslice_arrays..csg|]fddDqS)csg|]}||dqS)r )rirr r /r z+slice_arrays...r )rindicesrr r/r z slice_arrays..cSsg|]}tj|ddqS)r)axis)rconcatrr r r r0r cs$g|]}|dddqS)rr r rrr r r2r r) isinstancelistanyr slice_arrays)arraysr contiguousconverted_to_listentriesslicesr rr rs  rFcCsf|duotdd|D}|o.tdd|D}|s>d||fS|sL|||fS|rtjt|ttj|tjt|ttj||durtj|tj|g}t|D]\}}|durLt||tj } ||} | r| j nt | } |duo||dk} | r | d| dfn| df} | | r>t | nt | q| |qt|||fS)aAdds 1.0 as sample weights for the outputs for which there is no weight. Args: outputs: List of model outputs. sample_weights: List of sample weight inputs. sample_weight_modes: List of sample weight modes or None. check_all_flat: Ensure that inputs are not nested structures. This is not a free check, so we may not want to run it eagerly every iteration. Returns: Tuple of sample weights, one sample weight for every output, and booleans describing the raw sample weights. Ncss|]}|duVqdSrr rwr r r r Ksz0handle_partial_sample_weights..css|]}|duVqdSrr rr r r r Nstemporalrr ) rrnestassert_same_structure list_to_tupleflatten enumeraternpndarrayshapeappendones)outputssample_weightssample_weight_modescheck_all_flatany_sample_weightpartial_sample_weightnew_sample_weightsrswas_numpyoutput output_shape is_temporalsw_shaper r r handle_partial_sample_weights;sV        r9c@s(eZdZdZddZddZddZdS) RespectCompiledTrainableStateaSet and restore trainable state if it has changed since compile. The keras API guarantees that the value of each Layer's `trainable` property at `Model.compile` time will be used when training that model. In order to respect this requirement, it may be necessary to set the trainable value of layers to their compile time values before beginning a training endpoint and restore the values before returning from said endpoint. This scope checks if any layer's trainable state has changed since Model compile, and performs this set and un-set bookkeeping. However, the trainable state of a layer changes quite infrequently, if ever, for many kinds of workflows. Moreover, updating every layer in a model is an expensive operation. As a result, we will only explicitly set and unset the trainable state of a model if a trainable value has changed since compile. cCs||_d|_d|_d|_dSNF)_model_current_trainable_state_compiled_trainable_state_should_set_trainable)selfmodelr r r __init__sz&RespectCompiledTrainableState.__init__cCsd|j|_|jj|_|jD]*\}}||jvr ||j|kr d|_qLq |jr`|j|jdS)NT)r<_get_trainable_stater=r>itemsr?_set_trainable_state)r@layer trainabler r r __enter__s   z'RespectCompiledTrainableState.__enter__cCs|jr|j|jdSr;)r?r<rEr=)r@type_arg value_arg traceback_argr r r __exit__sz&RespectCompiledTrainableState.__exit__N)__name__ __module__ __qualname____doc__rBrHrLr r r r r:sr:cCsFdd}||r*|jstd|jd}qt|ddrB|j|jfSdS)aRRetrieves input shape and input dtype of layer if applicable. Args: layer: Layer (or model) instance. Returns: Tuple (input_shape, input_dtype). Both could be None if the layer does not have a defined input shape. Raises: ValueError: in case an empty Sequential or Functional model is passed. cSst|dr|jp|jjdkS)N_is_graph_network Sequential)hasattrrQ __class__rM)rFr r r _is_graph_models z2get_input_shape_and_dtype.._is_graph_modelz)An empty Model cannot be used as a Layer.r_batch_input_shapeN)NN)layers ValueErrorgetattrrVdtype)rFrUr r r get_input_shape_and_dtypes   r[cCs,t|\}}|dur(tjj|djSdS)zGets the static batch size of a Layer. Args: layer: a `Layer` instance. Returns: The static batch size of a Layer. Nr)r[rcompatv1 Dimensionvalue)rFbatch_input_shape_r r r get_static_batch_sizes rbcCst|trt|S|S)zADatasets will stack the list of tensor, so switch them to tuples.)rrtuple) maybe_listr r r r$s r$)T)F)rPnumpyr'tensorflow.compat.v2r\v2r keras.utilsrrr9r:r[rbr$r r r r s  % H4