&Vf,FdZddlmZddlmZdZdZdZdZdZ d Z d S) aJUtilities for handling Keras model in graph plugin. Two canonical types of Keras model are Functional and Sequential. A model can be serialized as JSON and deserialized to reconstruct a model. This utility helps with dealing with the serialized Keras model. They have distinct structures to the configurations in shapes below: Functional: config name: Name of the model. If not specified, it is 'model' with an optional suffix if there are more than one instance. input_layers: Keras.layers.Inputs in the model. output_layers: Layer names that are outputs of the model. layers: list of layer configurations. layer: [*] inbound_nodes: inputs to this layer. Sequential: config name: Name of the model. If not specified, it is 'sequential' with an optional suffix if there are more than one instance. layers: list of layer configurations. layer: [*] [*]: Note that a model can be a layer. Please refer to https://github.com/tensorflow/tfjs-layers/blob/master/src/keras_format/model_serialization.ts for more complete definition. )GraphDef)dtypesc#\Kd|fV|ddry|dd}|ddD]*}t|D]\}}|r|d|n|}||fV)dSdS)aYWalks the nested keras layer configuration in preorder. Args: keras_layer: Keras configuration from model.to_json. Yields: A tuple of (name_scope, layer_config). name_scope: a string representing a scope name, similar to that of tf.name_scope. layer_config: a dict representing a Keras layer configuration. configlayersname/N)get _walk_layers) keras_layer name_scopelayersub_name_scopesublayers a/var/www/html/software/conda/lib/python3.11/site-packages/tensorboard/plugins/graph/keras_util.pyr r 0s { x  $$X.. 1 __X..226::  __X..228<< 1 1E.:5.A.A 1 1*&$zzz>>::# &x00000  1 1 1 1 1c|r|d|S|S)a/Returns scoped name for a node as a string in the form '/'. Args: name_scope: a string representing a scope name, similar to that of tf.name_scope. node_name: a string representing the current node name. Returns A string representing a scoped name. r )r node_names r _scoped_namerHs#1$**ii00 rcV|ddduS)zReturns True if layer is a model. Args: layer: a dict representing a Keras model configuration. Returns: bool: True if layer is a model. rrN)r )rs r _is_modelrXs) 99X   " "8 , ,D 88rcDt|dtfr|n|gS)aNormalizes to a list of layers. Args: maybe_layers: A list of data[1] or a list of list of data. Returns: List of list of data. [1]: A Functional model has fields 'inbound_nodes' and 'output_layers' which can look like below: - ['in_layer_name', 0, 0] - [['in_layer_is_model', 1, 0], ['in_layer_is_model', 1, 1]] The data inside the list seems to describe [name, size, index]. r) isinstancelist) maybe_layerss r_norm_to_list_of_layersrds' #<?TG<<P <.rc|d}|dstdt||d|d}|d}|d}t|o|} t|} | rD| rBt ||D]0\} } t| } t|| d}||| <1nw| r%| s#t||ddd}nP| sN|rL| rJt |d ksJd t | z|d} t| } ||| <| r%|r#t |}fd |D}||<nY|dd }|dd}t|}|g|<|||fS) aUpdates input_to_in_layer, model_name_to_output, and prev_node_name based on the model_layer. Args: name_scope: a string representing a scope name, similar to that of tf.name_scope. model_layer: a dict representing a Keras model configuration. input_to_in_layer: a dict mapping Keras.layers.Input to inbound layer. model_name_to_output: a dict mapping Keras Model name to output layer of the model. prev_node_name: a string representing a previous, in sequential model layout, node name. Returns: A tuple of (input_to_in_layer, model_name_to_output, prev_node_name). input_to_in_layer: a dict mapping Keras.layers.Input to inbound layer. model_name_to_output: a dict mapping Keras Model name to output layer of the model. prev_node_name: a string representing a previous, in sequential model layout, node name. rrzlayer is not a model.r input_layers output_layers inbound_nodesrzhCannot have multi-input Functional model when parent model is not Functional. Number of input layers: %dc<g|]}t|dS)r)r).0rrs r z!_update_dicts..s'MMMU|IuQx88MMMr)r ValueErrorrboolziplenr)r model_layerinput_to_in_layermodel_name_to_outputprev_node_name layer_configr r!r"is_functional_modelis_parent_functional_model input_layer inbound_nodeinput_layer_nameinbound_node_namer layer_names last_layerlast_layer_name output_noders @r _update_dictsr;xsm2??8,,L   H % %20111Z)9)9&)A)ABBI##N33L $$_55MOOO44M|= >>"&m!4!4!=&9=+.|]+K+K D D '[,+I{CC  ,Za I I 2C . / / D $=,?=&j-2B12Ea2HII & =  =  = <  A%%% <>A+>N>N O&%%#1o ' ;??.<*+8}8(77MMMMfMMM *5Y''!%%h//3 $..2266v>>"9o>> +6-Y' 3^ DDrci}i}t}d}t|D]=\}}t|rt|||||\}}}-|d}t ||d}|j} || _|d:|d d} | | j d_ |d} | St| ts>tj|d} | j| j d_|d|dD]} t%| }|D]k\}}}}t ||}|||g}|t'|kr||n|d }| j|ln|| j|||vr-| j||| j}?|S) aReturns a GraphDef representation of the Keras model in a dict form. Note that it only supports models that implemented to_json(). Args: keras_layer: A dict from Keras model.to_json(). Returns: A GraphDef representation of the layers in the model. Nrr class_nameascii keras_classdtyper"r')rr rr;r rnodeaddr encodeattrsrdictras_dtypeas_datatype_enumtyperr+inputappend)r input_to_layerr.gr/rrr0rnode_defkeras_cls_namedtype_or_policytf_dtypemaybe_inbound_noder"r sizeindex_ inbound_nameinbound_node_names input_names rkeras_model_to_graph_defrYsN AN+K88D'D'U U    $   $ yy**  \-=-=f-E-EFF 6::<<! 99\ " " ."YY|44;;GDDN-;HM- ( *&**733  &z T0 0 &|'7'7'@'@AAH*2*CHM' " ' 99_ % % 1&+ii&@&@ 6 6" 78J K K .;66*T4#/ D#A#AL *>)A)A$|n**&!3'9#:#:::+511/3 N))*555516 66 ' N ! !. 1 1 1  & & N ! !."4"4Y"?"? @ @ @! HrN) __doc__"tensorboard.compat.proto.graph_pb2r"tensorboard.compat.tensorflow_stubrr rrrr;rYrrrr]s 88777775555551110    9 9 9(FEFEFERY Y Y Y Y r