更改 CLI 包装器以进行tflite_transfer_converter的问题

问：

我正在尝试更改我的模型的训练和推理签名函数，该模型将导出为 .tflite 模型，以便在智能手机上用于设备上的训练。就像现在的模型一样，它的主体是 MobileNetV2，而头部是设备上可训练的部分，只是一个与 softMax 完全连接的层。我正在尝试更改训练和推理功能，以便在我激活全连接层的 softMax 之前，mobileNetV2 的最后 2 层作为 2 个隐藏层再次在头部使用。

这是我的初始化，我试图保存倒数第二层和最后一层：

  def __init__(self, learning_rate=0.001):
    """Initializes a transfer learning model instance.

    Args:
      learning_rate: A learning rate for the optimzer.
    """
    self.num_features = NUM_FEATURES
    self.num_classes = NUM_CLASSES

    # trainable weights and bias for softmax
    self.ws = tf.Variable(
        tf.zeros((self.num_features, self.num_classes)),
        name='ws',
        trainable=True)
    self.bs = tf.Variable(
        tf.zeros((1, self.num_classes)), name='bs', trainable=True)

    # base model
    self.base = tf.keras.applications.MobileNetV2(
        input_shape=(IMG_SIZE, IMG_SIZE, 3),
        alpha=1.0,
        include_top=False,
        weights='imagenet')
    # loss function and optimizer
    self.loss_fn = tf.keras.losses.CategoricalCrossentropy()
    self.optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

    # save the last layer of the base model
    self.pre_last_layer = self.base.layers[-2].output
    self.last_layer = self.base.layers[-1].output

这是我更改的训练函数，我添加了这 2 层。

  @tf.function(input_signature=[
      tf.TensorSpec([None, NUM_FEATURES], tf.float32),
      tf.TensorSpec([None, NUM_CLASSES], tf.float32),
  ])
  def train(self, bottleneck, label):
    """Runs one training step with the given bottleneck features and labels.

    Args:
      bottleneck: A tensor of bottleneck features generated from the base model.
      label: A tensor of class labels for the given batch.

    Returns:
      Map of the training loss.
    """
    with tf.GradientTape() as tape:

      x = tf.keras.layers.Reshape((7, 7, 1280))(bottleneck)
      x = self.pre_last_layer(x, training = True)
      x = self.last_layer(x, training=True)
      x = tf.keras.layers.GlobalAveragePooling2D()(x)

      logits = tf.matmul(x, self.ws) + self.bs
      prediction = tf.nn.softmax(logits)
      loss = self.loss_fn(prediction, label)
    gradients = tape.gradient(loss, [self.ws, self.bs])
    self.optimizer.apply_gradients(zip(gradients, [self.ws, self.bs]))
    result = {'loss': loss}
    for grad in gradients:
      result[grad.name] = grad
    return result

就像现在一样，我一直收到 TypeError：'KerasTensor' 对象在我使用的地方不可调用，我找不到修复它的方法。x = self.pre_last_layer(x, training= True)

任何帮助都是值得赞赏的。请注意，我是 tensorflow 和 tensorflow-lite 的新手。干杯！

在网上搜索 TypeError，我发现这是由于您尝试将输入传递给输入张量而引起的，至少据我所知，我没有发现这里发生的情况。