根据 TF 2 中的文档：

       steps_per_epoch: Integer or `None`.
            Total number of steps (batches of samples)
            before declaring one epoch finished and starting the
            next epoch. When training with input tensors such as
            TensorFlow data tensors, the default `None` is equal to
            the number of samples in your dataset divided by
            the batch size, or 1 if that cannot be determined. If x is a
            `tf.data` dataset, and 'steps_per_epoch'
            is None, the epoch will run until the input dataset is exhausted.
            This argument is not supported with array inputs.
        validation_steps: Only relevant if `validation_data` is provided and
            is a `tf.data` dataset. Total number of steps (batches of
            samples) to draw before stopping when performing validation
            at the end of every epoch. If validation_data is a `tf.data` dataset
            and 'validation_steps' is None, validation
            will run until the `validation_data` dataset is exhausted.

我不明白这部分：

Total number of steps (batches of samples)
                before declaring one epoch finished and starting the
                next epoch.

如果我有 1000 个样本，批量大小 = 100，那么一个 epoch 将需要 10 个步骤才能达到。为什么需要另一个steps_per_epoch？如果两者都使用，则它们是冲突的。不是吗？如果批量大小为 100，则需要 10 个步骤。如果steps_per_epoch = 20，则意味着一个epoch需要20个批次，这与通过批次大小参数100计算的“10”步相冲突。

我哪里错了？