arduino / arduino/ArduinoTensorFlowLiteTutorials
TFLite MicroInterpreter() never returns on Arduino Nano BLE 33 Sense
- 主要语言
- Jupyter Notebook
- 星标
- 270
- 派生
- 122
- PR 合并指标
- 30 天内没有已合并 PR
描述
I am trying out the gesture recognization example using the **IMU_Classifier** example for detecting a "punch" gesture.
I see that the Arduino setup() code is kind of blocked when trying to initialize a MicroInterpreter() instance as below.
```
// Create a static memory buffer for TFLM, the size may need to
// be adjusted based on the model you are using
constexpr int tensorArenaSize = 8 * 1024;
byte tensorArena[tensorArenaSize];
tfLiteInterpreter = new tflite::MicroInterpreter(tfLiteModel,tfLiteOpsResolver, tensorArena, tensorArenaSize, &tfLiteErrorReporter);
```
Could you please share if this could be due to model size or any other dependency not being met? I gave it a try by shrinking model layers to reduce the model size as below but I still see an issue. Here, I am only trying to classify the punch gesture.
```
`# build the model and train it
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(16, activation='relu'))
model.add(tf.keras.layers.Dense(8, activation='relu'))
model.add(tf.keras.layers.Dense(NUM_GESTURES, activation='sigmoid')) # softmax is used, because we only expect one gesture to occur per input
model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
history = model.fit(inputs_train, outputs_train, epochs=600, batch_size=1, validation_data=(inputs_validate, outputs_validate))
`
```
贡献指南
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调研方向
从 Arduino Nano BLE 33 Sense 上 IMU_Classifier 示例的 setup() 以及其中展示的 tflite::MicroInterpreter 初始化开始检查。检查模型、8 KB tensor arena 和所述依赖项,以确定初始化为何始终不返回。确定原因,并使 punch-classification 示例能够在不阻塞的情况下完成 setup,即视为完成。
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评估
- 技术栈
- arduino, jupyter-notebook, keras, tensorflow
- 领域
- embedded-iot, machine-learning
- Issue 类型
- 缺陷
- 难度
- 4/5
- 预计耗时
- 3-5 天
- 活跃度
- 停滞
- 描述清晰度
- 需要澄清
- 新手友好度
- 25/100