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最近两天刚开始用mxnet，训练时发现log只有accuracy，没有loss，训练半天到跑验证的时候才发现loss为NAN了。
这样不能随时看到loss的变化而及时做出调整，比较浪费时间精力。
在python mxnet安装路径下有相关接口和文件。
我用的Anaconda2，路径为Anaconda2\Lib\site-packages\mxnet\metric.py
这是一个在线评价模块Online evaluation metric module。里面给出了一些精度、loss等的评价示例类，并且给出了详细直观的示例。这些评价类的基类都是class EvalMetric(object)。
如精度评价：
class Accuracy(EvalMetric):
"""Computes accuracy classification score.
The accuracy score is defined as
.. math::
\\text{accuracy}(y, \\hat{y}) = \\frac{1}{n} \\sum_{i=0}^{n-1}
\\text{1}(\\hat{y_i} == y_i)
Parameters
----------
axis : int, default=1
The axis that represents classes
name : str
Name of this metric instance for display.
output_names : list of str, or None
Name of predictions that should be used when updating with update_dict.
By default include all predictions.
label_names : list of str, or None
Name of labels that should be used when updating with update_dict.
By default include all labels.
Examples
--------
>>> predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]
>>> labels = [mx.nd.array([0, 1, 1])]
>>> acc = mx.metric.Accuracy()
>>> acc.update(preds = predicts, labels = labels)
>>> print acc.get()
('accuracy', 0.6666666666666666)
"""
def __init__(self, axis=1, name='accuracy',
output_names=None, label_names=None):
super(Accuracy, self).__init__(
name, axis=axis,
output_names=output_names, label_names=label_names)
self.axis = axis
def update(self, labels, preds):
"""Updates the internal evaluation result.
Parameters
----------
labels : list of `NDArray`
The labels of the data.
preds : list of `NDArray`
Predicted values.
"""
check_label_shapes(labels, preds)
for label, pred_label in zip(labels, preds):
if pred_label.shape != label.shape:
pred_label = ndarray.argmax(pred_label, axis=self.axis)
pred_label = pred_label.asnumpy().astype('int32')
label = label.asnumpy().astype('int32')
check_label_shapes(label, pred_label)
self.sum_metric += (pred_label.flat == label.flat).sum()
self.num_inst += len(pred_label.flat)如cross entropy loss：
@register
@alias('ce')
class CrossEntropy(EvalMetric):
"""Computes Cross Entropy loss.
The cross entropy over a batch of sample size :math:`N` is given by
.. math::
-\\sum_{n=1}^{N}\\sum_{k=1}^{K}t_{nk}\\log (y_{nk}),
where :math:`t_{nk}=1` if and only if sample :math:`n` belongs to class :math:`k`.
:math:`y_{nk}` denotes the probability of sample :math:`n` belonging to
class :math:`k`.
Parameters
----------
eps : float
Cross Entropy loss is undefined for predicted value is 0 or 1,
so predicted values are added with the small constant.
name : str
Name of this metric instance for display.
output_names : list of str, or None
Name of predictions that should be used when updating with update_dict.
By default include all predictions.
label_names : list of str, or None
Name of labels that should be used when updating with update_dict.
By default include all labels.
Examples
--------
>>> predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]
>>> labels = [mx.nd.array([0, 1, 1])]
>>> ce = mx.metric.CrossEntropy()
>>> ce.update(labels, predicts)
>>> print ce.get()
('cross-entropy', 0.57159948348999023)
"""
def __init__(self, eps=1e-12, name='cross-entropy',
output_names=None, label_names=None):
super(CrossEntropy, self).__init__(
name, eps=eps,
output_names=output_names, label_names=label_names)
self.eps = eps
def update(self, labels, preds):
"""Updates the internal evaluation result.
Parameters
----------
labels : list of `NDArray`
The labels of the data.
preds : list of `NDArray`
Predicted values.
"""
check_label_shapes(labels, preds)
for label, pred in zip(labels, preds):
label = label.asnumpy()
pred = pred.asnumpy()
label = label.ravel()
assert label.shape[0] == pred.shape[0]
prob = pred[numpy.arange(label.shape[0]), numpy.int64(label)]
self.sum_metric += (-numpy.log(prob + self.eps)).sum()
self.num_inst += label.shape[0]你还可以根据自己定义的评价标准按照上面的示例去写自己的评价接口。
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使用的时候
import mxnet as mx。。。。。。略。。。
eval_metrics = mx.metric.CompositeEvalMetric()
metric1 = mx.metric.Accuracy()
metric2 = mx.metric.CrossEntropy()
metric3 = mx.metric.MSE()
for child_metric in [metric1, metric2, metric3]:
eval_metrics.add(child_metric)然后再mod.fit中使用eval_metrics，如下：
mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
optimizer='sgd', optimizer_params=optimizer_params,
arg_params=args, aux_params=auxs, begin_epoch=begin_epoch, num_epoch=end_epoch)更多详细代码解释可以参考
https://blog.csdn.net/u014380165/article/details/78311231
修改后可以定制输出loss，如下
