metrics

问题 I have an auto scaling group of instances in Amazon cloud and I want to monitor some metrics across all instances in auto-scaling group. For example it would be nice to have a metric which reports the maximum memory usage across all instances that belong to the group. Thus I would have an opportunity to detect memleaks. I know that I can monitor a group via load balancer's metrics, but I don't have one and don't want to. Group metrics described on this page http://docs.aws.amazon.com/cli

问题 I am computing my confusion matrix as shown below for image semantic segmentation which is a pretty verbose approach: def confusion_matrix(preds, labels, conf_m, sample_size): preds = normalize(preds,0.9) # returns [0,1] tensor preds = preds.flatten() labels = labels.flatten() for i in range(len(preds)): if preds[i]==1 and labels[i]==1: conf_m[0,0] += 1/(len(preds)*sample_size) # TP elif preds[i]==1 and labels[i]==0: conf_m[0,1] += 1/(len(preds)*sample_size) # FP elif preds[i]==0 and labels[i

问题 I'm using Branch lib an Android to generate links that I send afterwards via sms. If user has no app installed on the phone, your link correctly transfers to Play Store ( the link in the dashboard ). After installing and running the application it receives all data from the link as expected. However, if I have the app already installed on the phone, pressing the link does not open the app but redirects me again to Play Store. If I press the "Open" button there, the app receives the

问题 I'm using Branch lib an Android to generate links that I send afterwards via sms. If user has no app installed on the phone, your link correctly transfers to Play Store ( the link in the dashboard ). After installing and running the application it receives all data from the link as expected. However, if I have the app already installed on the phone, pressing the link does not open the app but redirects me again to Play Store. If I press the "Open" button there, the app receives the

问题 I am using DescriptiveStatistics to track the moving average of some metrics. I have a thread that submits the metric value every minute, and I track the 10 minute moving average of the metric by using the setWindowSize(10) method on DescriptiveStatistics. This works fine for tracking a single moving average but I actually need to track multiple moving averages, i.e. the 1 minute average, the 5 minute average, and the 10 minute average. Currently I have the following options: Have 3 different

问题 I am using DescriptiveStatistics to track the moving average of some metrics. I have a thread that submits the metric value every minute, and I track the 10 minute moving average of the metric by using the setWindowSize(10) method on DescriptiveStatistics. This works fine for tracking a single moving average but I actually need to track multiple moving averages, i.e. the 1 minute average, the 5 minute average, and the 10 minute average. Currently I have the following options: Have 3 different

问题 I have implemented a custom metric based on SIM and when i try the code it works. I have implemented it using tensors and np arrays and both give the same results. However when I start fitting the model the values given back are a lot higher then the values I get when i load the weights generated by the training and applying the same function. My function is: def SIM(y_true,y_pred): n_y_true=y_true/(K.sum(y_true)+K.epsilon()) n_y_pred=y_pred/(K.sum(y_pred)+K.epsilon()) return K.mean(K.sum( K

问题 I tried to pass to GridSearchCV other scoring metrics like balanced_accuracy for Binary Classification (instead of the default accuracy ) scoring = ['balanced_accuracy','recall','roc_auc','f1','precision'] validator = GridSearchCV(estimator=clf, param_grid=param_grid, scoring=scoring, refit=refit_scorer, cv=cv) and got this error ValueError: 'balanced_accuracy' is not a valid scoring value. Valid options are ['accuracy','adjusted_mutual_info_score','adjusted_rand_score','average_precision',

问题 I have project for school. Now I need to make from it report of all metrics CK (Chidamber Kemerer metrics). The report has to be like table of all those metrics. Question is how to make it from Ndepend this report which it generates it is not what I am looking for. Please help and say how to do it... maybe some tips, documents or something this is very important... 回答1: Ok, so if we are talking of these Chidamber Kemerer metrics, the NDepend ability to write Code Queries and Rules over LINQ

问题 I try to follow the tensorflow API 1.4 document to achieve what I need in a learning process. I am now at this stage, can produce a predict object for example: classifier = tf.estimator.DNNClassifier(feature_columns=feature_cols,hidden_units=[10, 20, 10], n_classes=3, model_dir="/tmp/xlz_model") predict = classifier.predict(input_fn=input_pd_fn_prt (test_f),predict_keys=["class_ids"]) label =tf.constant(test_l.values, tf.int64) how can I use predict and label in tf.metrics.auc for example: