After post-training quantization, is it possible to change the dense-layer weights in TF Lite models?
An example of what I would like to do:
  interpreter = tf.lite.Interpreter(model_path=Flags.tfl_file_name)
  interpreter.allocate_tensors()
  tensor_details = interpreter.get_tensor_details()
  weight_idx = 0
  for tensor in tensor_details:
    if tensor['name'] == 'sequential/dense/MatMul':
      weight_shape = tensor['shape']
      weight_idx = tensor['index']
      weight = interpreter.get_tensor(weight_idx)
      weight = np.zeros(weight_shape,dtype='int8')
      print(weight)
      interpreter.set_tensor(weight_idx, weight)

This feature is needed for my hardware-accelerated Fully_Connected kernel.