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17,300 | using pipeline in grid search works the same way as using any other estimator we define parameter grid to search overand construct gridsearchcv from the pipeline and the parameter grid when specifying the parameter gridthere is slight changethough we need to specify for each parameter which step of the pipeline it belo... |
17,301 | pipeline the impact of leaking information in the cross-validation varies depending on the nature of the preprocessing step estimating the scale of the data using the test fold usually doesn' have terrible impactwhile using the test fold in feature extraction and feature selection can lead to substantial differences in... |
17,302 | from sklearn feature_selection import selectpercentilef_regression select selectpercentile(score_func=f_regressionpercentile= fit(xyx_selected select transform(xprint("x_selected shape{}format(x_selected shape)out[ ]x_selected shape( in[ ]from sklearn model_selection import cross_val_score from sklearn linear_model imp... |
17,303 | the pipeline class is not restricted to preprocessing and classificationbut can in fact join any number of estimators together for exampleyou could build pipeline containing feature extractionfeature selectionscalingand classificationfor total of four steps similarlythe last step could be regression or clustering inste... |
17,304 | classifier (called classifierfigure - overview of the pipeline training and prediction process the pipeline is actually even more general than this there is no requirement for the last step in pipeline to have predict functionand we could create pipeline just containingfor examplea scaler and pca thenbecause the last s... |
17,305 | pipe_short has steps that were automatically named we can see the names of the steps by looking at the steps attributein[ ]print("pipeline steps:\ {}format(pipe_short steps)out[ ]pipeline steps[('minmaxscaler'minmaxscaler(copy=truefeature_range=( )))('svc'svc( = cache_size= class_weight=nonecoef = decision_function_sha... |
17,306 | fit the pipeline defined before to the cancer dataset pipe fit(cancer dataextract the first two principal components from the "pcastep components pipe named_steps["pca"components_ print("components shape{}format(components shape)out[ ]components shape( accessing attributes in grid-searched pipeline as we discussed earl... |
17,307 | print("best estimator:\ {}format(grid best_estimator_)out[ ]best estimatorpipeline(steps=('standardscaler'standardscaler(copy=truewith_mean=truewith_std=true))('logisticregression'logisticregression( = class_weight=nonedual=falsefit_intercept=trueintercept_scaling= max_iter= multi_class='ovr'n_jobs= penalty=' 'random_s... |
17,308 | parameters using pipelineswe can encapsulate all the processing steps in our machine learning workflow in single scikit-learn estimator another benefit of doing this is that we can now adjust the parameters of the preprocessing using the outcome of supervised task like regression or classification in previous we used p... |
17,309 | plt yticks(range(len(param_grid['polynomialfeatures__degree']))param_grid['polynomialfeatures__degree']plt colorbar(figure - heat map of mean cross-validation score as function of the degree of the polynomial features and alpha parameter of ridge looking at the results produced by the cross-validationwe can see that us... |
17,310 | score without poly features as we would expect looking at the grid search results visualized in figure - using no polynomial features leads to decidedly worse results searching over preprocessing parameters together with model parameters is very powerful strategy howeverkeep in mind that gridsearchcv tries all possible... |
17,311 | in[ ]x_trainx_testy_trainy_test train_test_splitcancer datacancer targetrandom_state= grid gridsearchcv(pipeparam_gridcv= grid fit(x_trainy_trainprint("best params:\ {}\nformat(grid best_params_)print("best cross-validation score{ }format(grid best_score_)print("test-set score{ }format(grid score(x_testy_test))out[ ]be... |
17,312 | evaluate whether every component you are including in your model is necessary with this we have completed our survey of general-purpose tools and algorithms provided by scikit-learn you now possess all the required skills and know the necessary mechanisms to apply machine learning in practice in the next we will dive i... |
17,313 | working with text data in we talked about two kinds of features that can represent properties of the datacontinuous features that describe quantityand categorical features that are items from fixed list there is third kind of feature that can be found in many applicationswhich is text for exampleif we want to classify ... |
17,314 | categorical data free strings that can be semantically mapped to categories structured string data text data categorical data is data that comes from fixed list say you collect data via survey where you ask people their favorite colorwith drop-down menu that allows them to select from "red,"green,"blue,"yellow,"black,"... |
17,315 | their treatment is highly dependent on context and domain systematic treatment of these cases is beyond the scope of this book the final category of string data is freeform text data that consists of phrases or sentences examples include tweetschat logsand hotel reviewsas well as the collected works of shakespearethe c... |
17,316 | !tree - data/aclimdb out[ ]data/aclimdb +-test +-neg +-pos +-train +-neg +-pos directories files the pos folder contains all the positive reviewseach as separate text fileand similarly for the neg folder there is helper function in scikit-learn to load files stored in such folder structurewhere each subfolder correspon... |
17,317 | remove this formatting before we proceedin[ ]text_train [doc replace( "" "for doc in text_trainthe type of the entries of text_train will depend on your python version in python they will be of type bytes which represents binary encoding of the string data in python text_train contains strings we won' go into the detai... |
17,318 | mental image of representing text as "bag computing the bag-of-words representation for corpus of documents consists of the following three steps tokenization split each document into the words that appear in it (called tokens)for example by splitting them on whitespace and punctuation vocabulary building collect vocab... |
17,319 | the bag-of-words representation is implemented in countvectorizerwhich is transformer let' first apply it to toy datasetconsisting of two samplesto see it workingin[ ]bards_words =["the fool doth think he is wise,""but the wise man knows himself to be fool"we import and instantiate the countvectorizer and fit it to our... |
17,320 | words in the english language (which is what the vocabulary modelsstoring all those zeros would be prohibitiveand waste of memory to look at the actual content of the sparse matrixwe can convert it to "densenumpy array (that also stores all the entriesusing the toarray method: in[ ]print("dense representation of bag_of... |
17,321 | , , indicating that the vocabulary contains , entries againthe data is stored as scipy sparse matrix let' look at the vocabulary in bit more detail another way to access the vocabulary is using the get_feature_name method of the vectorizerwhich returns convenient list where each entry corresponds to one featurein[ ]fea... |
17,322 | performance by actually building classifier we have the training labels stored in y_train and the bag-of-words representation of the training data in x_trainso we can train classifier on this data for high-dimensionalsparse data like thislinear models like logisticregression often work best let' start by evaluating log... |
17,323 | extracts tokens using regular expression by defaultthe regular expression that is used is "\ \ \ +\bif you are not familiar with regular expressionsthis means it finds all sequences of characters that consist of at least two letters or numbers (\wand that are separated by word boundaries (\bit does not find single-lett... |
17,324 | [' ''affections''appropriately''barbra''blurbs''butchered''cheese''commitment''courts''deconstructed''disgraceful''dvds''eschews''fell''freezer''goriest''hauser''hungary''insinuate''juggle''leering''maelstrom''messiah''music''occasional''parking''pleasantville''pronunciation''recipient''reviews''sas''shea''sneers''stei... |
17,325 | from sklearn feature_extraction text import english_stop_words print("number of stop words{}format(len(english_stop_words))print("every th stopword:\ {}format(list(english_stop_words)[:: ])out[ ]number of stop words every th stopword['above''elsewhere''into''well''rather''fifteen''had''enough''herein''should''third''al... |
17,326 | influences the number of features and the performance rescaling the data with tf-idf instead of dropping features that are deemed unimportantanother approach is to rescale features by how informative we expect them to be one of the most common ways to do this is using the term frequency-inverse document frequency (tf-i... |
17,327 | from sklearn feature_extraction text import tfidfvectorizer from sklearn pipeline import make_pipeline pipe make_pipeline(tfidfvectorizer(min_df= norm=none)logisticregression()param_grid {'logisticregression__c'[ ]grid gridsearchcv(pipeparam_gridcv= grid fit(text_trainy_trainprint("best cross-validation score{ }format(... |
17,328 | ments or are only used sparinglyand only in very long documents interestinglymany of the high-tf-idf features actually identify certain shows or movies these terms only appear in reviews for this particular show or franchisebut tend to appear very often in these particular reviews this is very clearfor examplefor "poke... |
17,329 | mglearn tools visualize_coefficientsgrid best_estimator_ named_steps["logisticregression"coef_feature_namesn_top_features= figure - largest and smallest coefficients of logistic regression trained on tf-idf features the negative coefficients on the left belong to words that according to the model are indicative of nega... |
17,330 | that are considered here is an example on the toy data we used earlierin[ ]print("bards_words:\ {}format(bards_words)out[ ]bards_words['the fool doth think he is wise,''but the wise man knows himself to be fool'the default is to create one feature per sequence of tokens that is at least one token long and at most one t... |
17,331 | print("transformed data (dense):\ {}format(cv transform(bards_wordstoarray())out[ ]transformed data (dense)[[ [ ]for most applicationsthe minimum number of tokens should be oneas single words often capture lot of meaning adding bigrams helps in most cases adding longer sequences--up to -grams--might help toobut this wi... |
17,332 | best cross-validation score best parameters{'tfidfvectorizer__ngram_range'( )'logisticregression__c' as you can see from the resultswe improved performance by bit more than percent by adding bigram and trigram features we can visualize the cross-validation accuracy as function of the ngram_range and parameter as heat m... |
17,333 | figure - )in[ ]extract feature names and coefficients vect grid best_estimator_ named_steps['tfidfvectorizer'feature_names np array(vect get_feature_names()coef grid best_estimator_ named_steps['logisticregression'coef_ mglearn tools visualize_coefficients(coeffeature_namesn_top_features= figure - most important featur... |
17,334 | advanced tokenizationstemmingand lemmatization as mentioned previouslythe feature extraction in the countvectorizer and tfidf vectorizer is relatively simpleand much more elaborate methods are possible one particular step that is often improved in more sophisticated text-processing applications is the first step in the... |
17,335 | --the porter stemmera widely used collection of heuristics (here imported from the nltk package)--to lemmatization as implemented in the spacy package: in[ ]import spacy import nltk load spacy' english-language models en_nlp spacy load('en'instantiate nltk' porter stemmer stemmer nltk stem porterstemmer(define function... |
17,336 | to "meetin generallemmatization is much more involved process than stemmingbut it usually produces better results than stemming when used for normalizing tokens for machine learning while scikit-learn implements neither form of normalizationcountvectorizer allows specifying your own tokenizer to convert each document i... |
17,337 | x_train_lemma shape( x_train shape( as you can see from the outputlemmatization reduced the number of features from , (with the standard countvectorizer processingto , lemmatization can be seen as kind of regularizationas it conflates certain features thereforewe expect lemmatization to improve performance most when th... |
17,338 | learn then corresponds to one topicand the coefficients of the components in the representation of document tell us how strongly related that document is to particular topic oftenwhen people talk about topic modelingthey refer to one particular decomposition method called latent dirichlet allocation (often lda for shor... |
17,339 | of them similarly to the components in nmftopics don' have an inherent orderingand changing the number of topics will change all of the topics we'll use the "batchlearning methodwhich is somewhat slower than the default ("online"but usually provides better resultsand increase "max_iter"which can also lead to better mod... |
17,340 | topic between young family real performance beautiful work each both director topic war world us our american documentary history new own point topic funny worst comedy thing guy re stupid actually nothing want topic show series episode tv episodes shows season new television years topic didn saw am thought years book ... |
17,341 | topics np array([ ]sorting np argsort(lda components_axis= )[:::- feature_names np array(vect get_feature_names()mglearn tools print_topics(topics=topicsfeature_names=feature_namessorting=sortingtopics_per_chunk= n_words= out[ ]topic thriller suspense horror atmosphere mystery house director quite bit de performances d... |
17,342 | scott gary streisand star hart lundgren dolph career sabrina role temple phantom judy melissa zorro gets barbra cast short serial topic money budget actors low worst waste give want nothing terrible crap must reviews imdb director thing believe am actually topic funny comedy laugh jokes humor hilarious laughs fun re fu... |
17,343 | believe \'ve only just got round to watching "purple rainthe brand new -disc anniversary special edition led me to buy it \nb"this film is worth seeing alone for jared harrisoutstanding portrayal of john lennon it doesn' matter that harris doesn' exactly resemble lennonhis mannerismsexpressionspostureaccent and attitud... |
17,344 | the most important topics are which seems to consist mostly of stopwordspossibly with slight negative directiontopic which is clearly about bad reviewsfollowed by some genre-specific topics and and both of which seem to contain laudatory words it seems like lda mostly discovered two kind of topicsgenre-specific and rat... |
17,345 | draw from an unsupervised model should be taken with grain of saltand we recommend verifying your intuition by looking at the documents in specific topic the topics produced by the lda transform method can also sometimes be used as compact representation for supervised learning this is particularly helpful when few tra... |
17,346 | follow-ups another direction in nlp that has picked up momentum in recent years is the use of recurrent neural networks (rnnsfor text processing rnns are particularly powerful type of neural network that can produce output that is again textin contrast to classification models that can only assign class labels the abil... |
17,347 | wrapping up you now know how to apply the important machine learning algorithms for supervised and unsupervised learningwhich allow you to solve wide variety of machine learning problems before we leave you to explore all the possibilities that machine learning offerswe want to give you some final words of advicepoint ... |
17,348 | how do measure if my fraud prediction is actually workingdo have the right data to evaluate an algorithmif am successfulwhat will be the business impact of my solutionas we discussed in it is best if you can measure the performance of your algorithm directly using business metriclike increased profit or decreased losse... |
17,349 | the tools we've discussed in this book are great for many machine learning applicationsand allow very quick analysis and prototyping python and scikit-learn are also used in production systems in many organizations--even very large ones like international banks and global social media companies howevermany companies ha... |
17,350 | website or service using algorithm awhile the rest of the users will be provided with algorithm for both groupsrelevant success metrics will be recorded for set period of time thenthe metrics of algorithm and algorithm will be comparedand selection between the two approaches will be made according to these metrics usin... |
17,351 | from sklearn base import baseestimatortransformermixin class mytransformer(baseestimatortransformermixin)def __init__(selffirst_parameter= second_parameter= )all parameters must be specified in the __init__ function self first_parameter self second_parameter def fit(selfxy=none)fit should only take and as parameters ev... |
17,352 | tist there have been many good books written about the theory of machine learningand if we were able to excite you about the possibilities that machine learning opens upwe suggest you pick up at least one of them and dig deeper we already mentioned hastietibshiraniand friedman' book the elements of statistical learning... |
17,353 | because this is an introductory bookwe focused on the most common machine learning tasksclassification and regression in supervised learningand clustering and signal decomposition in unsupervised learning there are many more kinds of machine learning out therewith many important applications there are two particularly ... |
17,354 | you user is going northand the gps is telling you the user is going southyou probably can' trust the gps if your position estimate tells you the user just walked through wallyou should also be highly skeptical it' possible to express this situation using probabilistic modeland then use machine learning or probabilistic... |
17,355 | out-of-core learning describes learning from data that cannot be stored in main memorybut where the learning takes place on single computer (or even single processor within computerthe data is read from source like the hard disk or the network either one sample at time or in chunks of multiple samplesso that each chunk... |
17,356 | ing with these datasets can provide great opportunity to practice your machine learning skills disadvantage of competitions is that they already provide particular metric to optimizeand usually fixedpreprocessed dataset keep in mind that defining the problem and collecting the data are also important aspects of real-wo... |
17,357 | / testing accuracy acknowledgmentsxi adjusted rand index (ari) agglomerative clustering evaluating and comparing example of hierarchical clustering linkage choices principle of algorithm chains and pipelines - building pipelines building pipelines with make_pipeline - grid search preprocessing steps grid-searching for ... |
17,358 | area under the curve (auc) - attributionsx average precision bag-of-words representation applying to movie reviews - applying to toy dataset more than one word ( -grams) - steps in computing bernoullinb bigrams binary classification - binning - bootstrap samples boston housing dataset boundary points bunch objects busi... |
17,359 | data pointsdefined data representation - (see also feature extraction/feature engineeringtext dataautomatic feature selection - binning and - categorical features - effect on model performance integer features model complexity vs dataset size overview of table analogy in training vs test sets understanding your data un... |
17,360 | forge dataset frameworks free string data freeform text data high-dimensional datasets histograms hit rate hold-out sets human involvement/oversight gamma parameter gaussian kernels of svc gaussiannb generalization building models for defined examples of get_dummies function get_support method of feature selection grad... |
17,361 | parameters predictions with regression strengths and weaknesses kaggle kernelized support vector machines (svmskernel trick linear models and nonlinear features vs linear support vector machines mathematics of parameters predictions with preprocessing data for strengths and weaknesses tuning svm parameters understandin... |
17,362 | effect of data representation choices on evaluation and improvement - evaluation metrics and scoring - iris classification application - overfitting vs underfitting pipeline preprocessing and selecting selecting with grid search theory behind tuning parameters with grid search - movie reviews multiclass classification ... |
17,363 | problem solving building your own estimators business metrics and initial approach to resources - simple vs complicated cases steps of testing your system tool choice production systems testing tool choice pruning for decision trees pseudorandom number generators pure leafs pymc language python benefits of prepackaged ... |
17,364 | download our free python ebookhow to code in python which is available via do co/python-book for other programming languages and devops engineering articlesour knowledge base of over , tutorials is available as creativecommons-licensed resource via do co/tutorials |
17,365 | written by lisa tagliaferri python is flexible and versatile programming language suitable for many use caseswith strengths in scriptingautomationdata analysismachine learningand back-end development first published in the python development team was inspired by the british comedy group monty python to make programming... |
17,366 | the version number while this number may varythe output will be similar to thisoutput python if you received alternate outputyou can navigate in web browser to python org in order to download python and install it to your machine by following the instructions once you are able to type the python - command above and rec... |
17,367 | as django for web development or numpy for scientific computing so if you would like to install numpyyou can do so with the command pip install numpy there are few more packages and development tools to install to ensure that we have robust set-up for our programming environmentsudo apt install build-essential libssl-d... |
17,368 | install it with the followingsudo apt install - python -venv with venv installedwe can now create environments let' either choose which directory we would like to put our python programming environments inor create new directory with mkdiras inmkdir environments cd environments once you are in the directory where you w... |
17,369 | once you run the appropriate commandyou can verify that the environment is set up be continuing essentiallypyvenv sets up new directory that contains few items which we can view with the ls commandls my_env output bin include lib lib pyvenv cfg share togetherthese files work to make sure that your projects are isolated... |
17,370 | the first thing you see on your line(my_envsammy@sammy:~/environmentsthis prefix lets us know that the environment my_env is currently activemeaning that when we create programs here they will use only this particular environment' settings and packages notewithin the virtual environmentyou can use the command python in... |
17,371 | the file press once you exit out of nano and return to your shelllet' run the program(my_envsammy@sammy:~/environmentspython hello py hello py program that you just created should cause your terminal to produce the following outputoutput helloworldto leave the environmentsimply type the command deactivate and you will ... |
17,372 | written by lisa tagliaferri machine learning is subfield of artificial intelligence (aithe goal of machine learning generally is to understand the structure of data and fit that data into models that can be understood and utilized by people although machine learning is field within computer scienceit differs from tradi... |
17,373 | supervised and unsupervised learningand common algorithmic approaches in machine learningincluding the -nearest neighbor algorithmdecision tree learningand deep learning we'll explore which programming languages are most used in machine learningproviding with some of the positive and negative attributes of each additio... |
17,374 | with images of sharks labeled as fish and images of oceans labeled as water by being trained on this datathe supervised learning algorithm should be able to later identify unlabeled shark images as fish and unlabeled ocean images as water common use case of supervised learning is to use historical data to predict stati... |
17,375 | audience in order to increase their number of purchases without being told "correctanswerunsupervised learning methods can look at complex data that is more expansive and seemingly unrelated in order to organize it in potentially meaningful ways unsupervised learning is often used for anomaly detection including for fr... |
17,376 | the -nearest neighbor algorithm is pattern recognition model that can be used for classification as well as regression often abbreviated as knnthe in -nearest neighbor is positive integerwhich is typically small in either classification or regressionthe input will consist of the closest training examples within space w... |
17,377 | when new object is added to the space -in this case green heart -we will want the machine learning algorithm to classify the heart to certain class |
17,378 | when we choose the algorithm will find the three nearest neighbors of the green heart in order to classify it to either the diamond class or the star class in our diagramthe three nearest neighbors of the green heart are one diamond and two stars thereforethe algorithm will classify the heart with the star class |
17,379 | among the most basic of machine learning algorithmsk-nearest neighbor is considered to be type of "lazy learningas generalization beyond the training data does not occur until query is made to the system decision tree learning for general usedecision trees are employed to visually represent decisions and show or inform... |
17,380 | in the predictive modelthe data' attributes that are determined through observation are represented by the brancheswhile the conclusions about the data' target value are represented in the leaves when "learninga treethe source data is divided into subsets based on an attribute value testwhich is repeated on each of the... |
17,381 | whether or not it is suitable for going fishing true classification tree data set would have lot more features than what is outlined abovebut relationships should be straightforward to determine when working with decision tree learningseveral determinations need to be madeincluding what features to choosewhat condition... |
17,382 | although data and computational analysis may make us think that we are receiving objective informationthis is not the casebeing based on data does not mean that machine learning outputs are neutral human bias plays role in how data is collectedorganizedand ultimately in the algorithms that determine how machine learnin... |
17,383 | third parties to monitor and audit algorithmsbuilding alternative systems that can detect biasesand ethics reviews as part of data science project planning raising awareness about biasesbeing mindful of our own unconscious biasesand structuring equity in our machine learning projects and pipelines can work to combat bi... |
17,384 | python with scikit-learn written by michelle morales edited by brian hogan in this tutorialyou'll implement simple machine learning algorithm in python using scikit-learna machine learning tool for python using database of breast cancer tumor informationyou'll use naive bayes (nbclassifier that predicts whether or not ... |
17,385 | let' begin by installing the python module scikit-learnone of the best and most documented machine learning libraries for python to begin our coding projectlet' activate our python programming environment make sure you're in the directory where your environment is locatedand run the following commandmy_env/bin/activate... |
17,386 | in jupytercreate new python notebook called ml tutorial in the first cell of the notebookimport the sklearn moduleml tutorial import sklearn your notebook should look like the following figurejupyter notebook with one python cellwhich imports sklearn now that we have sklearn imported in our notebookwe can begin working... |
17,387 | scikit-learn comes installed with various datasets which we can load into pythonand the dataset we want is included import and load the datasetml tutorial from sklearn datasets import load_breast_cancer load dataset data load_breast_cancer( data variable represents python object that works like dictionary the important... |
17,388 | labels data['target'feature_names data['feature_names'features data['data'we now have lists for each set of information to get better understanding of our datasetlet' take look at our data by printing our class labelsthe first data instance' labelour feature namesand the feature values for the first data instanceml tut... |
17,389 | are then mapped to binary values of and where represents malignant tumors and represents benign tumors thereforeour first data instance is malignant tumor whose mean radius is + now that we have our data loadedwe can work with our data to build our machine learning classifier step -organizing data into sets to evaluate... |
17,390 | random_state= the function randomly splits the data using the test_size parameter in this examplewe now have test set (testthat represents of the original dataset the remaining data (trainthen makes up the training data we also have the respective labels for both the train/test variablesi train_labels and test_labels w... |
17,391 | after we train the modelwe can then use the trained model to make predictions on our test setwhich we do using the predict(function the predict(function returns an array of predictions for each data instance in the test set we can then print our predictions to get sense of what the model determined use the predict(func... |
17,392 | now that we have our predictionslet' evaluate how well our classifier is performing step -evaluating the model' accuracy using the array of true class labelswe can evaluate the accuracy of our model' predicted values by comparing the two arrays (test_labels vs predswe will use the sklearn function accuracy_score(to det... |
17,393 | indicators of tumor class you have successfully built your first machine learning classifier let' reorganize the code by placing all import statements at the top of the notebook or script the final version of the code should look like thisml tutorial from sklearn datasets import load_breast_cancer from sklearn model_se... |
17,394 | traintesttrain_labelstest_labels train_test_split(featureslabelstest_size= random_state= initialize our classifier gnb gaussiannb(train our classifier model gnb fit(traintrain_labelsmake predictions preds gnb predict(testprint(predsevaluate accuracy print(accuracy_score(test_labelspreds)now you can continue to work wit... |
17,395 | python now you can load dataorganize datatrainpredictand evaluate machine learning classifiers in python using scikit-learn the steps in this tutorial should help you facilitate the process of working with your own data in python |
17,396 | handwritten digits with tensorflow written by ellie birbeck edited by brian hogan neural networks are used as method of deep learningone of the many subfields of artificial intelligence they were first proposed around years ago as an attempt at simulating the way the human brain worksthough in much more simplified form... |
17,397 | network to recognize and predict the correct label for the digit displayed while you won' need prior experience in practical deep learning or tensorflow to follow along with this tutorialwe'll assume some familiarity with machine learning terms and concepts such as training and testingfeatures and labelsoptimizationand... |
17,398 | versions of these libraries by creating requirements txt file in the project directory which specifies the requirement and the version we need create the requirements txt file(tensorflow-demotouch requirements txt open the file in your text editor and add the following lines to specify the imagenumpyand tensorflow libr... |
17,399 | let' create python program to work with this dataset we will use one file for all of our work in this tutorial create new file called main py(tensorflow-demotouch main py now open this file in your text editor of choice and add this line of code to the file to import the tensorflow librarymain py import tensorflow as t... |
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