使用tflite_convert可以将训练好的TensorFlow模型转换为轻量级的.tflite文件,以便在移动设备上运行和推理。 2. tflite_convert的主要参数 tflite_convert工具具有多个参数,用于控制转换过程的各个方面。以下是tflite_convert的一些主要参数: - --output_file:指定输出的.tflite文件的名称和路径。 - --saved_model_...
tflite_convert是一个命令行工具,用于将TensorFlow模型转换为TensorFlow Lite模型。TensorFlow Lite是一种用于在移动、嵌入式和物联网设备上部署机器学习模型的轻量级解决方案。 使用tflite_convert命令行工具,可以将训练好的TensorFlow模型转换为适用于移动设备的TensorFlow Lite模型。以下是一个tflite_convert命令行工作示例:...
tflite_convert -- --graph_def_file=$STRIPPED_PB --output_file=/absolute/path/to/tensorflow/tensorflow/contrib/lite/examples/android/assets/tflite_convert_example.tflite --input_arrays=Preprocessor/sub --output_arrays=concat,concat_1 --output_format=TFLITE --input_shapes=1,300,300,3 --...
tflite_convert--graph_def_file=tflite/tflite_graph.pb --output_file=tflite/detect.tflite --output_format=TFLITE --input_shapes=1,300,300,3--input_arrays=normalized_input_image_tensor --output_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostPro...
tflite_convert \ --output_file=tf.tflite \ --graph_def_file=tf.pb \ --input_arrays=convolution2d_1_input \ --output_arrays=dense_3/BiasAdd \ --input_shape=1,3,448,448 ``` 参数说明,input_arrays和output_arrays是model的起始输入变量名和结束变量名,input_shape是和input_arrays对应 ...
tflite on Android: Didn't find op for builtin opcode 'CONV_2D' version '5'#41943 Closed dtch1997mentioned this issueAug 5, 2020 tensorflow.lite.python.convert.ConverterError: <unknown>:0: error: loc("Func/StatefulPartitionedCall/input/_0"): requires all operands and results to have comp...
You can use utility tflite_convert which is the part of tensorflow 1.10 (or higher) package. The simple use for float inference is something like: tflite_convert \ --output_file=/tmp/retrained_graph.tflite \ --graph_def_file=/tmp/retrained_graph.pb \ --input_arrays=input \ --output...
将生成的facenet.pb转化为tflite格式: tflite_convert --output_file model_mobile_eval/facenet.tflite --graph_def_file facenet.pb --input_arrays "input" --input_shapes "1,160,160,3" --output_arrays output --output_format TFLITE 祝贺你,你会在文件夹model_mobile_eval中找到facenet.tflite文件...
解决方法 解决问题 The scripts freeze_graph.exe, saved_model_cli.exe, tensorboard.exe, tflite_convert.exe, toco.exe and toco_from_protos.exe are installed in 'C:\Users\niu\AppData\Roaming\Python\Python36\Scripts' which is not on PATH. ...
converter = tf.lite.TFLiteConverter.from_keras_model(loaded_model) tflite_model = converter.convert() with tf.io.gfile.GFile('final.tflite', 'wb') as f: f.write(tflite_model) Training: Model: "retinanet" ___ Layer (type) Output Shape Param # Connected to ===...