tf.keras

[xuchengggg]

def compile(self, learning_rate, momentum):
    """Gets the model ready for training. Adds losses, regularization, and
    metrics. Then calls the Keras compile() function.
    """
    # Optimizer object
    optimizer = keras.optimizers.SGD(
        lr=learning_rate, momentum=momentum,
        clipnorm=self.config.GRADIENT_CLIP_NORM)
    # Add Losses
    # First, clear previously set losses to avoid duplication
    self.keras_model._losses = []
    self.keras_model._per_input_losses = {}
    loss_names = [
        "rpn_class_loss",  "rpn_bbox_loss",
        "mrcnn_class_loss", "mrcnn_bbox_loss", "mrcnn_mask_loss"]
    for name in loss_names:
        layer = self.keras_model.get_layer(name)
        if layer.output in self.keras_model.losses:
            continue
        loss = (
            tf.reduce_mean(layer.output, keepdims=True)
            * self.config.LOSS_WEIGHTS.get(name, 1.))
        self.keras_model.add_loss(loss)

    # Add L2 Regularization
    # Skip gamma and beta weights of batch normalization layers.
    reg_losses = [
        keras.regularizers.l2(self.config.WEIGHT_DECAY)(w) / tf.cast(tf.size(w), tf.float32)
        for w in self.keras_model.trainable_weights
        if 'gamma' not in w.name and 'beta' not in w.name]
    self.keras_model.add_loss(tf.add_n(reg_losses))

    # Compile
    self.keras_model.compile(
        optimizer=optimizer,
        loss=[None] * len(self.keras_model.outputs))

    # Add metrics for losses
    for name in loss_names:
        if name in self.keras_model.metrics_names:
            continue
        layer = self.keras_model.get_layer(name)
        self.keras_model.metrics_names.append(name)
        loss = (
            tf.reduce_mean(layer.output, keepdims=True)
            * self.config.LOSS_WEIGHTS.get(name, 1.))
        self.keras_model.metrics_tensors.append(loss)

这个是mask rcnn的代码，这部分要怎么修改才能在tensorflow 2.0, tf.keras下运行呢