If verifications in your system are expected to produce as many false positives as true positives, a three-state selector would be appropriate (like the Holly's Coffee example in another answer): Unknown (Default state) | Correct | Incorrect If false positives are an exception and are usually rare, a better pattern would be a flag. Since verifying items is optional, perhaps there is simply a "Mark as invalid" option somewhere in the row actions. Invalid/false positive rows can then be treated differently - red text, moved to another table, different status, etc.

Image autogation function:def augment(image, label): new_height = new_width = 32 # размер изображения image = tf.image.resize(image, (new_height, new_width)) if tf.random.uniform((), minval=0, maxval=1) < 0.1: image = tf.tile(tf.image.rgb_to_grayscale(image), [1, 1, 3]) # с вероятностью 0.1 делаем изображение серым image = tf.image.random_brightness(image, max_delta=0.1) image = tf.image.random_contrast(image, lower=0.1, upper=0.2) # a left upside down flipped is still a dog ;) image = tf.image.random_flip_left_right(image) # 50% # image = tf.image.random_flip_up_down(image) #%50% return image, label If the date is tf.tensor, this function may be applied to it:ds_augmented = ds_train.map(augment) The function does not supplement the dateset, but changes it. Therefore, a changed date should be added to the reference date.(Source: https://youtu.be/8wwfVV7ixyY )

I'm not gonna give you the exact answer, because the identification of people (self-conscious rather than ready nets) is done by a whole team of scientists, looking for these very patterns, etc. One thing I can say is, I'm often very negative about trying to use neurosis where I don't get caught on the basis of "modity." But there are tasks in which neuronets are indeed the most reasonable method of solution. Recognition of persons (thinks in general) is one of these tasks. Because I can naphantize now, for example, the voyage of the faces, the distance between the lower (or upper) point of the lower lip and the lower point of the chin, or the left edge of the right eye relative to the right edge of the nose... But it's the good thing that tries to find such meaningful patterns.So if it's not just a training project, it's probably sooner or later that you're gonna come to build a real neurosis, and then you're gonna re-open those networks that are already available.But if the training project is a fantasy: the width of the nose, the attitude of the width of the nose to the thickness of the lip, the eye cut (the ratio of height to width) etc.

Well, it's such an initial outfit, not vectored, on a pure python, no library. Just think of something like that. quadramatic deviation And we take the root from him to keep the size. dataset = { "Класс 1": [[16, 18, 11, 18], [14, 11, 14, 16], [13, 10, 11, 17]], "Класс 2": [[38, 38, 34, 37], [30, 38, 39, 34], [30, 32, 38, 38]], "Класс 3": [[57, 50, 51, 51], [58, 50, 50, 56], [50, 58, 56, 58]] } input_data = [[21, 11, 25, 16]] for i in input_data: print(i) for k in dataset: print(k, sum(sum((a-b)**2 for a,b in zip(row,i))/len(dataset[k]) for row in dataset[k])**0.5) Conclusion:[21, 11, 25, 16] Класс 1 15.340577998671801 Класс 2 36.71965867669978 Класс 3 72.1295131459146 The lowest deviation is here. Класс 1 It worked.Updated the formulathe result now does not depend on how many samples each class represents. In the previous version, the result could be distorted if the number of samples differed by grade.

Create a test machine generator.test_generator = datagen.flow_from_directory( '/path/to/test/images', target_size=target_size, batch_size=batch_size, class_mode='categorical') model.evaluate_generator(test_generator, test_generator.samples // batch_size) NOTE: The test pieces shall be displayed in sub-divisions with the same names as those from the training kit.PS https://ru.stackoverflow.com/a/939732/211923 UPD: to classify stranger images: img_generator = datagen.flow_from_directory( '/path/to/images/for/classification', target_size=target_size, batch_size=batch_size, class_mode='categorical') probabilities = model.predict_generator(generator, img_generator.samples // batch_size)

It's good that your neurosis should not be just two things out, like, 1 - it's a cat. 2 - it's a dog. The normal option is when you're out. probability to a class. Like, "80 per cent of the probability that it's a cat, 40 per cent of the probability that it's a dog" and what's more likely, that's the end result and that's out.Accordingly, you can look at these probability, and if neurosis makes it possible for a cat and for a dog less than a threshold, then it's some other object that neurosis couldn't I.D., you can see it as a separate category.You can still look at the roadblockers, they can also find "anomalia" in data, that is, images that don't look very much like the ones that were trained in.But in general, of course, we need to look at specific data. Maybe it's just that the pictures of the tables will somehow fit into some kind of pathetic neurosis found for cats or dogs, and it's not gonna be that simple, and then we're gonna have to separate pictures of the tables and teach them to have your neurosis, too, so she can know them.Neuronet training is still some art and research. There are certain prescriptions for a case, but their ability to work needs to be checked for specific data.

How could you not find an example? https://medium.com/@nathaliejeans/how-i-classified-images-with-recurrent-neural-networks-28eb4b57fc79 https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/recurrent_network.ipynb https://github.com/aymericdamien/TensorFlow-Examples https://stats.stackexchange.com/questions/129411/how-can-recurrent-neural-networks-be-used-for-sequence-classification Yeah. https://paperswithcode.com/task/sequential-image-classification or https://stats.stackexchange.com/questions/129411/how-can-recurrent-neural-networks-be-used-for-sequence-classification

But you do realize that in the case of a text, you'll be able to do what you say 100,500+? And then you'll have to count the distances "everyone." It's a real long time. There could be two choices:It's hard to limit the number of dates, and the quality that you're not very good is gonna fall.Use only methods that understand the dissolved matrices. That's what we need to clean up. StandardScaler (it is usually not used for texts - there's no point, plus it makes it normal from the blended matrices) and try to find a replacement. distance.cdistthe same fast (or faster) but the understandable matrices. Something like that. NMSLIB or https://github.com/erikbern/ann-benchmarks ♪ You can try instead. CountVectorizer Use HashingVectorizerhe doesn't need to keep his fortune, so he's supposed to work faster.There's no easy way out here, because it's like the distance "everyone" for the lines, in each of which hundreds of thousands of fitches can't be fast. Here, in the case of a disbanded matrix of significant figs, each sample has a little bit and the calculation of the distances goes much faster.

It's totally classic, you know, the basic task of the classification theory. It's not even a book, it's a blog of machine training. Get in one click: https://ru.wikipedia.org/wiki/%D0%97%D0%B0%D0%B4%D0%B0%D1%87%D0%B0_%D0%BA%D0%BB%D0%B0%D1%81%D1%81%D0%B8%D1%84%D0%B8%D0%BA%D0%B0%D1%86%D0%B8%D0%B8 https://tproger.ru/translations/scikit-learn-in-python/ https://evergreens.com.ua/ru/articles/classical-machine-learning.html https://wiki.loginom.ru/articles/classification-problem.html http://www.aiportal.ru/articles/autoclassification/methods-class.html and. This is what method of use - is determined on the basis of both the respective theoretical limitations and the comparison of their application.

weights = res def my_score_func(y_true, y_pred): global weights return weights[(y_pred==1) & (y_true==1) & (weights>=0)].sum() my_scorer = make_scorer(my_score_func, greater_is_better=True) P. S. https://scikit-learn.org/stable/auto_examples/model_selection/plot_multi_metric_evaluation.html#sphx-glr-auto-examples-model-selection-plot-multi-metric-evaluation-py or so (more preferred):def my_score_func(y_true, y_pred, weights): return weights[(y_pred==1) & (y_true==1) & (weights>=0)].sum() my_scorer = make_scorer(my_score_func, greater_is_better=True, weights=res)

The accuracy of the model should be assessed on two parameters: (1) Dispersion (2) Removal :: Removal is a mistake resulting from an erroneous assumption in the learning algorithm. As a result of large shifts, the algorithm may miss the link between the signs and the conclusion (non-learning). • Disaspersia is a misrepresentation of small deviations in the training set. At high dispersion, algorithm may somehow interpret accidental noise[en] in a training set, rather than the desired result (training).If your model shows a good precision on the training data, you'll run it through the new ones she's not familiar with. You your algorithm has a very good precision in the training set, now check that there's a retraining (dissemination) on new data.The different parameters (accuracy, loss or caste options) can also be assessed, but the loss is the most universal.

If you want the replicated model results, always use the parameter. random_stateOtherwise you can get different results on exactly the same dates.Example - double the same code (one and the same date)In [29]: %paste dt_wine = tree.DecisionTreeClassifier() dt_wine = dt_wine.fit(X_train,y_train) y_pred = dt_wine.predict(X_test) test_accuracy= metrics.accuracy_score(y_test, y_pred) print("Accuracy: ", '%.2f'% (test_accuracy*100),"%") -- End pasted text -- Accuracy: 96.55 % In [30]: %paste dt_wine = tree.DecisionTreeClassifier() dt_wine = dt_wine.fit(X_train,y_train) y_pred = dt_wine.predict(X_test) test_accuracy= metrics.accuracy_score(y_test, y_pred) print("Accuracy: ", '%.2f'% (test_accuracy*100),"%") -- End pasted text -- Accuracy: 94.83 % The accuracy of the predictions decreased in the second case. A parameter should be used to avoid this. random_state:With regard to the selection of the criterion, if one criterion was Always. Better than the other, it might not make sense to leave the criterion that always produces the worst result. ;The criterion is one of their hypermeters of the model that can and should be built ( https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html ♪ https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV ♪ https://hyperopt.github.io/hyperopt/ , etc.)If you're using algorithms based on tree decisions, I'd advise you to use it. https://towardsdatascience.com/decision-tree-ensembles-bagging-and-boosting-266a8ba60fd9#:~:text=Ensemble%20methods%2C%20which%20combines%20several,to%20form%20a%20strong%20learner. - they are much more resilient and almost always better than single trees.Example for your data:In [52]: from sklearn.ensemble import RandomForestClassifier In [53]: rf = RandomForestClassifier(random_state=123) In [54]: rf.fit(X_train, y_train) Out[54]: RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None, oob_score=False, random_state=123, verbose=0, warm_start=False) In [55]: y_pred2 = rf.predict(X_test) In [56]: metrics.accuracy_score(y_test, y_pred2) Out[56]: 1.0

Try using the parameter. stratified=True♪ import xgboost as xgb xgb.cv(params=params, dtrain=dtrain, num_boost_round=1000, nfold=8, stratified=True) When using this parameter, in the setting up of a test sub-sample (single)fold() The algorithm will try to maintain the same percentage of classes as the whole dateset.If your dateset is highly balanced (e.g. number of positive class 1% and number of negative class tags is 99%), then you should think about the pre-balancing of the dateset. A suitable module is available to deal with disaggregated data https://imbalanced-learn.readthedocs.io/en/stable/api.html to balance data with virtually all known and popular methods: RUS♪ ROS♪ SMOTE♪ SMOTEENC Many others.

Somehow:import os from collections import defaultdict from zipfile import ZipFile from glob import glob def unpack(filename, registry, to_dir="."): with ZipFile(filename) as zf: for file in zf.filelist: fn = os.path.join(to_dir, file.filename) kind = fn.partition("_")[0] zf.extract(file, to_dir) _, ext = os.path.splitext(fn) os.rename(fn, f"{kind}.{registry[kind]}{ext}") registry[kind] += 1 file_registry = defaultdict(int) for file in glob(r"C:\temp\test*.zip"): unpack(file, file_registry, to_dir=r"C:\temp\test\result")

If I wanted to be specific, I would have created vocabularies unique for each topic, and then I would have weighed the line on the basis of the words associated with the topic. That kind of sentimental analysis.Look at the book. https://www.tidytextmining.com/ there is a detailed discussion of both the LDA and other NLP tasks.

This warning usually suggests that in predicted values Y_pred No classes available Y_test♪ For example, Y_test There are classes: {0, 1, 2, 3}♪ Y_pred: {0, 1, 2}♪In this case, some metrics cannot be properly calculated.

In the next code block:rf = RandomForestClassifier(n_estimators=350) rf.fit(X_train, Y_train) Y_pred = random_forest.predict(X_test) Subject training rfand foresee the object random_forestwhich is not declared anywhere.Try and replace random_forest ♪ rf♪

Class marks (labels) are class names. For example with cats and dogs, it is a data column or a list of these two values (кот or собака)The neural network is numerical. Different uses are common. encoders♪ One column with figures corresponding to unique classes may eventually be produced or https://www.google.com/search?q=one+hot+encoding&pws=0&gl=us&gws_rd=cr ♪

I think it's easier to add a third category. unknown - Put pictures of no cats or dogs in there and teach the INS in a data set of three categories.Besides, if you use it, loss='categorical_crossentropy', the last layer should be activated as an activation function activation='softmax'Oh, well. sigmoid used for tasks Binary classifications.

The question is formulated so that it is difficult to provide a clear answer.On the one hand, the SciKit-Learn Library offers http://scikit-learn.org/stable/modules/classes.html#classification-metrics and, on the other hand, it may always be necessary to use its metric, the most useful for this particular task.In this case, use may be made. http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer :>>> from sklearn.metrics import fbeta_score, make_scorer >>> ftwo_scorer = make_scorer(fbeta_score, beta=2) >>> ftwo_scorer make_scorer(fbeta_score, beta=2) >>> from sklearn.model_selection import GridSearchCV >>> from sklearn.svm import LinearSVC >>> grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, ... scoring=ftwo_scorer) https://stackoverflow.com/questions/32401493/how-to-create-customize-your-own-scorer-function-in-scikit-learn