ValueError：尺寸必须相等，但对于'{{node Equal}} = Equal [T = DT_FLOAT，incompatible_shape_error = true]（Cast_2，Cast_3）'

Question

我正在尝试制作姿势估计模型。我编写了自己的损失函数，该函数获取网络（640、640、16）-（16640 x 640个关键点的热图）和标签（16、2）-（带有x和y坐标的16个关键点）的输出。然后，我根据标签生成热图并计算均方误差。但是，它会产生形状错误，我真的不知道如何解决。 这是带有生殖示例的Colab笔记本：https://colab.research.google.com/drive/1-R8Y7Ke7Ip9n7gA_BfOSgJmkucuKjNsS?usp=sharing

这是错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-8-6228f8c4eebf> in <module>()
----> 1 autoencoder.fit(dataset.batch(1), epochs=1, batch_size=1)

10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    971           except Exception as e:  # pylint:disable=broad-except
    972             if hasattr(e, "ag_error_metadata"):
--> 973               raise e.ag_error_metadata.to_exception(e)
    974             else:
    975               raise

ValueError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:759 train_step
        self.compiled_metrics.update_state(y, y_pred, sample_weight)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:409 update_state
        metric_obj.update_state(y_t, y_p, sample_weight=mask)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/metrics_utils.py:90 decorated
        update_op = update_state_fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/metrics.py:176 update_state_fn
        return ag_update_state(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/metrics.py:612 update_state  **
        matches = ag_fn(y_true, y_pred, **self._fn_kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/metrics.py:3309 sparse_categorical_accuracy
        return math_ops.cast(math_ops.equal(y_true, y_pred), K.floatx())
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:1614 equal
        return gen_math_ops.equal(x, y, name=name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_math_ops.py:3224 equal
        name=name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:744 _apply_op_helper
        attrs=attr_protos, op_def=op_def)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py:593 _create_op_internal
        compute_device)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:3485 _create_op_internal
        op_def=op_def)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:1975 __init__
        control_input_ops, op_def)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:1815 _create_c_op
        raise ValueError(str(e))

    ValueError: Dimensions must be equal, but are 16 and 640 for '{{node Equal}} = Equal[T=DT_FLOAT, incompatible_shape_error=true](Cast_3, Cast_4)' with input shapes: [?,16,2], [?,640,640].