Question

我正在尝试将MAE作为我的DNN回归模型的性能度量。我正在使用DNN来预测facebook帖子的评论数量。据我了解，如果是分类问题，那么我们使用准确性。如果是回归问题，那么我们使用RMSE或MAE。我的代码如下：

with tf.name_scope("eval"):
    correct = tf.metrics.mean_absolute_error(labels = y, predictions = logits)
    mae = tf.reduce_mean(tf.cast(correct, tf.int64))
    mae_summary = tf.summary.scalar('mae', accuracy)

出于某种原因，我收到以下错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-396-313ddf858626> in <module>()
      1 with tf.name_scope("eval"):
----> 2     correct = tf.metrics.mean_absolute_error(labels = y, predictions = logits)
      3     mae = tf.reduce_mean(tf.cast(correct, tf.int64))
      4     mae_summary = tf.summary.scalar('mae', accuracy)

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/metrics_impl.py in mean_absolute_error(labels, predictions, weights, metrics_collections, updates_collections, name)
    736   predictions, labels, weights = _remove_squeezable_dimensions(
    737       predictions=predictions, labels=labels, weights=weights)
--> 738   absolute_errors = math_ops.abs(predictions - labels)
    739   return mean(absolute_errors, weights, metrics_collections,
    740               updates_collections, name or 'mean_absolute_error')

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py in binary_op_wrapper(x, y)
    883       if not isinstance(y, sparse_tensor.SparseTensor):
    884         try:
--> 885           y = ops.convert_to_tensor(y, dtype=x.dtype.base_dtype, name="y")
    886         except TypeError:
    887           # If the RHS is not a tensor, it might be a tensor aware object

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, preferred_dtype)
    834       name=name,
    835       preferred_dtype=preferred_dtype,
--> 836       as_ref=False)
    837 
    838 

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx)
    924 
    925     if ret is None:
--> 926       ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
    927 
    928     if ret is NotImplemented:

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in _TensorTensorConversionFunction(t, dtype, name, as_ref)
    772     raise ValueError(
    773         "Tensor conversion requested dtype %s for Tensor with dtype %s: %r" %
--> 774         (dtype.name, t.dtype.name, str(t)))
    775   return t
    776 

ValueError: Tensor conversion requested dtype float32 for Tensor with dtype int64: 'Tensor("eval_9/remove_squeezable_dimensions/cond_1/Merge:0", dtype=int64)'

Answer 1

代码中的这一行：

correct = tf.metrics.mean_absolute_error(labels = y, predictions = logits)

以TensorFlow首先从标签中减去预测的方式执行，如后面所示：

absolute_errors = math_ops.abs(predictions - labels)

为了进行减法，两个张量需要是相同的数据类型。据推测，您的预测（logits）是float32，并且从错误消息中您的标签是int64。您必须使用tf.to_float进行显式转换，或者在评论中建议使用隐式转换：将占位符定义为float32以开始，并在处理订阅源字典时信任TensorFlow进行转换。

DNN线性回归。 MAE测量误差

1 个答案: