model=tf.keras.applications.MobileNetV3Large()fb_model=tf.lite.TFLiteConverterV2.from_keras_model(model).convert()tf.lite.experimental.Analyzer.analyze(model_content=fb_model)# === TFLite ModelAnalyzer ===## Your TFLite model has ‘1’ subgraph(s). In the subgraph description below,# T# represents the Tensor numbers. For example, in Subgraph#0, the MUL op# takes tensor #0 and tensor #19 as input and produces tensor #136 as output.## Subgraph#0 main(T#0) -> [T#263]# Op#0 MUL(T#0, T#19) -> [T#136]# Op#1 ADD(T#136, T#18) -> [T#137]# Op#2 CONV_2D(T#137, T#44, T#93) -> [T#138]# Op#3 HARD_SWISH(T#138) -> [T#139]# Op#4 DEPTHWISE_CONV_2D(T#139, T#94, T#24) -> [T#140]# ...
Analyzes the given tflite_model with dumping model structure.
This tool provides a way to understand users' TFLite flatbuffer model by
dumping internal graph structure. It also provides additional features
like checking GPU delegate compatibility.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[]]