问题描述
我有一个神经网络，输出为NxM，其中N是批处理大小，M是网络需要进行预测的输出数量.我想为网络的每个M输出计算一个指标，即跨批次的所有实例，但分别为每个M输出计算一个指标，以便该指标有M个值.我试图按如下方法创建自定义指标.
I have a neural network with an output NxM, where N is the batch size and M are the number of outputs where the network needs to make a prediction. I would like to compute a metric for each of the M outputs of the network, i.e. across all instances of the batch but separately for each of the M outputs, so that there would be M values of this metric. I tried to create a custom metric as follows.
def my_metric(y_true, y_pred):
return [3.1, 5.2] # a list of dummy values
，然后将此度量传递到模型的compile方法的度量列表，然后Keras输出一个数字，该数字是3.1和5.2的平均值(在本例中为(3.1 + 5.2)/2 = 4.15)，而不是而不是打印实际列表.那么，是否有一种方法可以返回并打印列表(或numpy数组)作为度量?当然，在我的特定情况下，我不会在上面的示例中返回虚拟列表，但是我的自定义指标更加复杂.
and then pass this metric to the list of metrics of the compile method of the model, then Keras outputs a number that is the average of 3.1 and 5.2 (in this case, (3.1 + 5.2)/2 = 4.15) rather than printing the actual list. So, is there a way of returning and printing a list (or numpy array) as the metric? Of course, in my specific case, I will not return the dummy list in the example above, but my custom metric is more complex.
推荐答案
每M设置一个指标.
一个输出的工作代码:
from keras.layers import Dense, Input
from keras.models import Model
import keras.backend as K
import numpy as np
inputs = Input((5,))
outputs = Dense(3)(inputs)
model = Model(inputs, outputs)
def metricWrapper(m):
def meanMetric(true, pred):
return pred[:, m]
meanMetric.__name__ = 'meanMetric_' + str(m)
return meanMetric
metrics = [metricWrapper(m) for m in range(3)]
model.compile(loss='mse', metrics=metrics, optimizer='adam')
model.fit(np.random.rand(10,5), np.zeros((10,3)))
这篇关于是否有可能返回一个数组(或张量)而不是数字的度量?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！