如何解决 TensorFlow 中的“BiasGrad 要求张量大小 <= int32 max”InvalidArgumentError？

Question

我正在尝试使用 Keras/Tensorflow 训练卷积神经网络。我的模型编译正确，但是一旦训练开始，就会返回以下错误：

Using TensorFlow backend.

Epoch 1/3

Traceback (most recent call last):
  File "./main.py", line 17, in <module>
    history = CNN.fit(TrainImages, TrainMasks, epochs = 3)

  File "/home/tomhalmos/.local/lib/python3.6/site-packages/keras/engine/training.py", line 1239, in fit
    validation_freq=validation_freq)

  File "/home/tomhalmos/.local/lib/python3.6/site-packages/keras/engine/training_arrays.py", line 196, in fit_loop
    outs = fit_function(ins_batch)

  File "/home/tomhalmos/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/backend.py", line 3727, in __call__
    outputs = self._graph_fn(*converted_inputs)

  File "/home/tomhalmos/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 1551, in __call__
    return self._call_impl(args, kwargs)

  File "/home/tomhalmos/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 1591, in _call_impl
    return self._call_flat(args, self.captured_inputs, cancellation_manager)

  File "/home/tomhalmos/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 1692, in _call_flat
    ctx, args, cancellation_manager=cancellation_manager))

  File "/home/tomhalmos/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 545, in call
    ctx=ctx)

  File "/home/tomhalmos/.local/lib/python3.6/site-packages/tensorflow_core/python/eager/execute.py", line 67, in quick_execute
    six.raise_from(core._status_to_exception(e.code, message), None)

  File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.**InvalidArgumentError:  BiasGrad requires tensor size <= int32 max**
         [[node gradients/conv2d_22/BiasAdd_grad/BiasAddGrad (defined at /home/tomhalmos/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3009) ]] [Op:__inference_keras_scratch_graph_5496]

Function call stack:
keras_scratch_graph

如果上述内容还不够，很乐意提供任何进一步的详细信息。

Answer 1

边界检查针对张量中的元素数量。大小限制为 21.47 亿个值 ( int32 )。

将图像大小 (hxv) 乘以样本批量大小。将其乘以操作中的通道数（例如 Conv2D）。计数大于 2.1e9 的地方就是有罪的操作。除了减少其中一个数字之外，我看不到任何解决方案。