早上好
我正在尝试实现本文所述的1D数据的改进WGAN: https://arxiv.org/pdf/1704.00028.pdf
它已在keras contrib github中作为示例实施: https://github.com/keras-team/keras-contrib/blob/master/examples/improved_wgan.py 尽管如此,梯度惩罚损失的这种实现不再适用于tf2。K.gradients()返回[None]
ValueError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:505 train_function *
outputs = self.distribute_strategy.run(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 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:2290 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:2649 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:467 train_step **
y, y_pred, sample_weight, regularization_losses=self.losses)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:204 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:143 __call__
losses = self.call(y_true, y_pred)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:246 call
return self.fn(y_true, y_pred, **self._fn_kwargs)
<ipython-input-7-4f0896d0107b>:104 gradient_penalty_loss
gradients_sqr = K.square(gradients)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py:2189 square
return math_ops.square(x)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_math_ops.py:9964 square
"Square", x=x, name=name)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:488 _apply_op_helper
(input_name, err))
ValueError: Tried to convert 'x' to a tensor and failed. Error: None values not supported.
以下是该问题的完整示例: https://colab.research.google.com/drive/11dcMKoiCigTnEn7QvmjqLNrJdmFztByT
有人知道发生了什么变化吗?你知道怎么解决这个问题吗
更新:这会在构造反计算图时忽略错误。然后它就好像跑了
def gradient_penalty_loss(y_true, y_pred, averaged_samples):
gradients = K.gradients(y_pred, averaged_samples)[0]
try:
gradients_sqr = K.square(gradients)
except ValueError:
print("Gradients returned None")
return 0
gradients_sqr_sum = K.sum(gradients_sqr, axis=np.arange(1, len(gradients_sqr.shape)))
gradient_l2_norm = K.sqrt(gradients_sqr_sum)
gradient_penalty = K.square(1 - gradient_l2_norm)
return K.mean(gradient_penalty)
如果您按照更新中的建议进行操作,tf将忽略损失函数
对于Tensorflow 2,似乎不可能以旧的方式实现这一点。我最终修改了代码,使其适应这种创建模型的方式。我的建议是什么
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