我正在尝试使Mobilenetv2模型（将最后一层重新训练到我的数据中）在Google Edge TPU珊瑚上运行。
我已经按照这个教程https://www.tensorflow.org/lite/performance/post_training_quantization?hl=zh-CN进行了训练后量化。相关代码为：
...
train = tf.convert_to_tensor(np.array(train, dtype='float32'))
my_ds = tf.data.Dataset.from_tensor_slices(train).batch(1)
# POST TRAINING QUANTIZATION
def representative_dataset_gen():
for input_value in my_ds.take(30):
yield [input_value]
converter = tf.lite.TFLiteConverter.from_keras_model_file(saved_model_dir)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset_gen
converter.target_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
tflite_quant_model = converter.convert()
我已经成功生成了tflite量化模型，但是当我运行edgetpu_compiler时（此页面为https://coral.withgoogle.com/docs/edgetpu/compiler/#usage），我得到以下输出：
edgetpu_compiler Notebooks/MobileNetv2_3class_visit_split_best-val-
acc.h5.quant.tflite
Edge TPU Compiler version 2.0.258810407
INFO: Initialized TensorFlow Lite runtime.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: quantized_dimension must …