Saravanan666/pypi-org huggingface dataset:
Package: lpu
LPULPUis a collection of utility classes/functions for language processingInstallationInstalling from PyPI release$pipinstall--userlpuInstalling from GitHub master$pipinstall--userhttps://github.com/akivajp/lpu/archive/master.zipModulesmodules inlpupackagelpu.common.configinheritable and serializable configurutation classes, useful for calling functions with many argumentslpu.common.environhandling environment variables with stacks, useful for changing/reverting global settings, such as debugging modeslpu.common.filesutility functions for file handling, including transparent file access of gzip fileslpu.common.loggingenhanced logging objects (from standard logging library) with colorizing features and operations with environment variableslpu.common.progressutility classes and functions for progress reporting (as known as progress bars), working with file-type objects and iteratorslpu.smt.align.ibm_modelsutility classes to train and estimate word alignemt based on IBM modelsCommandsLPU package also includes directly executable commandscommands inlpupackagelpu-abspath$lpu-abspath[-h]filepath[filepath...]get absolute paths of given files or directorieslpu-clean-parallel$lpu-clean-parallel[-h][--minmin_length][--maxmax_length]\[--ratioratio][--target-directorydirectory_path][--escape]\[--normalize]filepath[filepath...]output_taglpu-dialog$lpu-dialog[-h][--existfilepath][--continue][--yes][--no]Show message on condition, wait and receive user's responselpu-exec-parallel$lpu-exec-parallel[-h][--inputfilepath][--outputfilepath]\[--splitsizenum_lines][--chunksnum_files][--threadsnum_threads]\[--tmpdirdirectory_path][--verbose][--intervalseconds]commandExecute command in multiple processes by splitting the targe filelpu-guess-langcode$lpu-guess-langcode[-h]filepath[filepath...]Guess the language codes from given fileslpu-progress$lpu-progress[-h][--lines][--refreshseconds][--headerstring]\[filepathfilepath...]]Show the progress of pipe I/Olpu-random-split$lpu-random-split[-h]--inputfilepath[filepath...]\[--prefixesprefix[prefix...]][--suffixessuffix[suffix...]]\--tagstag[tag...]--split-sizessize[size...][--ignore-empty]\[--quiet][--debug][--random-seedseed][--ids[suffix]]lpu-wait-files$lpu-wait-files[-h][--quiet][--debug][--delayseconds]\[--intervalseconds][--timeoutseconds]filepath[filepath...]Wait until file will be foundlpu-word-align-train$lpu-word-align-train[-h][--save-soresfilepath][--decode-alignfilepath]\[--iteration-limitnum_iterations][--thresholdmin_probability]\[--nbestinteger][--character][--debug][--quiet]\src_pathtrg_pathsave_trans_path[save_align_path]lpu-word-align-score$lpu-word-align-score[-h][--save-scoresfilepath]\[--decode-alignfilepath][--character][--debug][--quiet]\src_pathtrg_pathtrans_path[align_path]
Package: lpynn
lpynnThis is a basic neural network package consisting of a single perceptron node. It can do binary classification.UsageExample:fromlpynn.perceptronimportinit_perceptron,print_perceptron,normalize_input_vectors,Perceptron,train_datasetfromlpythonimporti32,f64defmain0():p:Perceptron=Perceptron(0,[0.0],0.0,0,0.0,0.0,0)init_perceptron(p,2,0.05,10000,90.0)print_perceptron(p)print("=================================")input_vectors:list[list[f64]]=[[-1.0,-1.0],[-1.0,1.0],[1.0,-1.0],[1.0,1.0]]outputs:list[i32]=[1,1,1,-1]normalize_input_vectors(input_vectors)train_dataset(p,input_vectors,outputs)print_perceptron(p)main0()
Package: lpython-emulation
lpython_emulationThis is a python package to add typing information to python code.Examplefromlpythonimporti32,f64defadd(a:i32,b:i32)->i32:returna+bdefarea_of_circle(radius:f64)->f64:pi:f64=3.14returnpi*(radius*radius)defmain0():print("The sum of 5 and 3 is",end=" ")print(add(5,3))print("Area of circle with radius 5.0 is",end=" ")print(area_of_circle(5.0))main0()$pythonmain.py
Thesumof5and3is8Areaofcirclewithradius5.0is78.5
Package: lqa
Local Quadratic Approximation (LQA)In deep learning tasks, the learning rate determines the update step size in each iteration, which plays a critical role in gradient-based optimization. However, the determination of the appropriate learning rate in practice typically replies on subjective judgement. In this work, we propose a novel optimization method based on local quadratic approximation (LQA). In each update step, given the gradient direction, we locally approximate the loss function by a standard quadratic function of the learning rate. Then, we propose an approximation step to obtain a nearly optimal learning rate in a computationally efficient way. The proposed LQA method has three important features. First, the learning rate is automatically determined in each update step. Second, it is dynamically adjusted according to the current loss function value and the parameter estimates. Third, with the gradient direction fixed, the proposed method leads to nearly the greatest reduction in terms of the loss function. Extensive experiments have been conducted to prove the strengths of the proposed LQA method. The detailed description of the LQA method can be found athttps://arxiv.org/abs/2004.03260.How to use LQA1. Import the packageimport lqa2. Initialize a LQA worker and train your model.The following example shows the necessary parameters:model: your model (based on tensorflow.keras)epochs: the number of epochstrain: training dataset (e.g., [X0, Y0], or a data generator)test: testing (or validation) dataset (e.g., [X1, Y1], or a data generator)loss: loss functionbestmodel = lqa.gd(model, epochs=10, train=[X0,X1], test=[Y0,Y1]], loss='sparse_categorical_crossentropy')3. Get the tuned model from the worker.The tuned model and training records are organized as attributes of the LQA worker.# tuned model
bestmodel.model

# training records, including:
# history[train_loss] - loss on the training dataset
# history[train_acc] - accuracy on the training dataset
# history[test_loss] - loss on the testing dataset
# history[test_acc] - accuracy on the testing dataset
bestmodel.history
Package: lqg
Inverse optimal control for continuous psychophysicsThis repository contains the officialJAXimplementation of the inverse optimal control method presented in the paper:Straub, D., & Rothkopf, C. A. (2022). Putting perception into action with inverse optimal control for continuous psychophysics. eLife, 11, e76635.InstallationThe package can be installed viapippython-mpipinstalllqgSince publication of oureLife paper, I have substantially updated the package. If you want to use the package as described in the paper, please install an older version<0.20:python-mpipinstalllqg==0.1.9If you want the latest development version, I recommend cloning the repository and installing locally in a virtual environment:gitclonegit@github.com:dominikstrb/lqg.gitcdlqg
python-mvenvenvsourceenv/bin/activate
python-mpipinstall-e.Usage examplesmain.pyshows how to simulate data and infer parameters using the LQG model of the tracking task.notebooks/Tutorial.ipynbexplains the model and its parameters in more detail, including the extension to subjective internal models (based onmy tutorial at CCN 2022)CitationIf you use our method or code in your research, please cite our paper:@article{straub2022putting,
  title={Putting perception into action with inverse optimal control for continuous psychophysics},
  author={Straub, Dominik and Rothkopf, Constantin A},
  journal={eLife},
  volume={11},
  pages={e76635},
  year={2022},
  publisher={eLife Sciences Publications Limited}
}ExtensionsSignal-dependent noiseThis implementation supports the basic LQG framework. For the extension to signal-dependent noise(Todorov, 2005), please seeour NeurIPS 2021 paperandits implementation.Non-linear dynamicsWe are currently working on extending this approach to non-linear dynamics and non-quadratic costs. Please check out ourpreprint. Code will be released soon.
Package: lquery
Try bring C# linq to python 馃巿.The library is a demo, not for production (yet).Compare with OthersDifferent between lquery and others (linq for python like asq):lquery try convert func (from bytecode) to SQL and query from database process.Compare with CSharpFor C#:IQueryable<?>query=null;varitems=query.Where(z=>z.Name=='s').Select(z=>z.Value).ToList();So for python:query:Queryable=None;expr=query.where(z=>z.name=='s').select(z=>z.value).to_list();for in-memory iterablefromlqueryimportenumerablequery:Queryable=enumerable([1,2,3])# then query itfor mongodbfromlquery.extras.mongodbimportMongoDbQuerycollection=# get a collection from pymongoquery:Queryable=MongoDbQuery(collection)# then query itlinq APIsto_memory- same asAsEnumerable()from C#whereselectselect_manytakeskipread more examples from unittests.Othersprint reduce infoPrint reduce info is easy way to check what query will compile to SQL.code example:>>>fromlquery.extras.mongodbimportMongoDbQuery>>>mongo_query=MongoDbQuery(None)>>>reduce_info=mongo_query\....where(lambdax:(x['size']['h']==14)&(x['size']['uom']=='cm'))\....skip(1)\....where(lambdax:x['size']['w']>15)\....get_reduce_info()>>>reduce_info.print()reduceinfoof:Queryable().where(<function<lambda>at0x0000025DBC661EA0>).skip(1).where(<function<lambda>at0x0000025DBE957840>)=>[SQL]where(<function<lambda>at0x0000025DBC661EA0>)[SQL]skip(1)[MEM]where(<function<lambda>at0x0000025DBE957840>)you can see the 1stwhere()and 1stskip()was success compile to SQL, and 2ndwhere()only work inside python process.
Package: lr35902dis
lr35902disA python disassembler library for the LR35902 CPU (the CPU of the GameBoy, similar to a Z80 processor).
This is an adaptation ofz80dis. Any errors are probably mine, PRs welcome :).Use>>> from lr35902dis import lr35902
>>> lr35902.disasm(b'\xCB\xE7', 0)
'set 4,a'Or, if you'd like access to the instruction internals, like opcode identifier, length, and operands:>>> decoded = lr35902.decode(b'\xCB\xE7', 0)
>>> decoded.op
<OP.SET: 58>
>>> decoded.operands[0]
(<OPER_TYPE.IMM: 45>, 4)
>>> decoded.operands[1]
(<OPER_TYPE.REG_A: 1>,)
>>> decoded.len
2The decoded structure can be supplied to disasm() to make a string:>>> lr35902.disasm(decoded)
'set 4,a'
Package: lrange
lrangeis a lazy range function for Python 2.x orxrangedrop-in
replacement for long integers.lrangeis a pure Python analog of the builtinrangefunction from
Python 3.x.>>> from lrange import lrange
>>> r = lrange(2**100, 2**101, 2**100)
>>> len(r)
1
>>> for i in r:
...     print i,
1267650600228229401496703205376
>>> for i in r:
...     print i,
1267650600228229401496703205376
>>> 2**100 in r
True
>>> r[0], r[-1]
(1267650600228229401496703205376L, 1267650600228229401496703205376L)
>>> L = list(r)
>>> L2 = [1, 2, 3]
>>> L2[:] = r
>>> L == L2 == [2**100]
TrueFiles are licensed under the MIT License. See the file MIT-LICENSE.txt
for details.The latest version is athttps://github.com/zed/lrange/
Package: lrbenchmark
LRBenchmarkRepository for benchmarking Likelihood Ratio systems.PrerequisitesThis repository is developed for Python 3.8.DependenciesAll dependencies can either be installed by runningpip install -r requirements.txtorpip install ..Add new dependencies to the setup.py and always update the requirements by running:pip-compile --output-file=requirements.txt setup.py.UsageRunning the benchmark can be done as follows:Specify the parameters for the benchmark in thelrbenchmark.yamlRunpython run.pyThe parameters for the benchmark must be provided in the following structure:experiment:
  repeats: 10
  scorer:
    - 'name scorer 1'
    - 'name scorer 2'
  calibrator: 
    - 'name calibrator'
  dataset:
    - 'name dataset'
  preprocessor:
    - 'name preprocessor 1'
    - 'name preprocessor 2'At least 1 setting needs to be provided for each parameter, but more settings per parameter can be provided. The pipeline will
create the cartesian product over all parameter settings (exceptrepeats) and will execute the experiments accordingly.All possible settings can be found inparams.py. The parameters that need to be set are:repeats: Number of repeats for each experiment.scorer: Scoring models for generating scores.calibrator: Models for calibrating scores.dataset: Datasets on which the experiments can be executed.preprocessor: Data preprocessing steps. You can use the value'dummy'if no preprocessing is needed.Example: Benchmark feature rank based LR systemThis repository supports several data transformations, such as the possibility to transform X features from values to ranks.
To benchmark these models against models without any transformations on X features, the following experiments (among others) could be
defined inlrbenchmark.yaml.experiment:
  repeats: 10
  scorer:
    - 'LR'
    - 'XGB'
  calibrator:
    - 'logit'
  dataset:
    drugs_xtc:
      n_splits: 2
    glass:
      n_splits: 2
  preprocessor:
    - 'dummy'
    - 'rank_transformer'When executingpython run.pyan experiment for all possible combination of parameters will be executed. The results for each experiment (metrics + plots)
will be stored in separate folders within theoutputfolder.DatasetsThere are currently two datasets implemented for this project:drugs_xtc: will be published on our github soonglass: LA-ICPMS measurements of elemental concentration from floatglass. The data will be downloaded automatically fromhttps://github.com/NetherlandsForensicInstitute/elemental_composition_glasswhen used in the pipeline for the first time.
Package: lrboost
lrboost is asckit-learncompatible package for linear residual boosting. lrboost uses a linear estimator to first remove any linear trends from the data, and then uses a separate non-linear estimator to model the remaining non-linear trends. We find that extrapolation tasks or data with linear and non-linear components are the best use cases. Not every modeling task will benefit from lrboost, but we use this in our own work and wanted to share something that made it easy to use.For a stable version, install usingpip:pipinstalllrboostlrboost was inspired by'Regression-Enhanced Random Forests' by Haozhe Zhang, Dan Nettleton, and Zhengyuan Zhu.An excellent PyData talk by Gabby Shklovsky explaining the intuition underlying the approach may also be found here:'Random Forest Best Practices for the Business World'.LRBoostRegressor can be used like any other sklearn estimator and is built off a sklearn template.predict(X)returns an array-like of final predictionsAddingpredict(X, detail=True)returns a dictionary with primary, secondary, and final predictions.fromsklearn.datasetsimportload_diabetesfromlrboostimportLRBoostRegressorX,y=load_diabetes(return_X_y=True)lrb=LRBoostRegressor().fit(X,y)predictions=lrb.predict(X)detailed_predictions=lrb.predict(X,detail=True)print(lrb.primary_model.score(X,y))#R2print(lrb.score(X,y))#R2>>>0.512>>>0.933More detailed documentation can be found here!Andrew Patton,Nathan Walker, andKostya Medvedovsky
Package: lrc-adjuster
LRC Lyrics Timestamp Adjuster 🎶Simple Python package to adjust the timestamps in a MidiCo-variant synchronised lyrics LRC fileInstallationYou can installlrc-adjusterusingpiporPoetry.Using pip:pipinstalllrc-adjusterUsing Poetry:poetryaddlrc-adjusterUsageThe package provides a command-line interface (CLI) tool called lrc-adjuster that allows you to adjust the timestamps in an LRC file by a specified offset.lrc-adjuster<input_file><offset><input_file>:PathtotheinputLRCfile.<offset>:Thetimeoffsettoapplytoeachtimestampinseconds.Itcanbeapositiveornegativefloating-pointnumber.For example, to adjust the timestamps in the file input.lrc by an offset of -5.2 seconds:lrc-adjusterinput.lrc-5.2The adjusted LRC file will be saved with the suffix -adjusted and the offset value before the file extension.ExampleHere's an example of an LRC file:[re:MidiCo][00:10.862]1:/This[00:11.080]1:is[00:11.240]1:a[00:11.400]1:man's[00:12.080]1:world...Running the command:lrc-adjusterinput.lrc3.5will produce an adjusted LRC file:[re:MidiCo][00:14.362]1:/This[00:14.580]1:is[00:14.740]1:a[00:14.900]1:man's[00:15.580]1:world...ContributingContributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or a pull request on the GitHub repository.LicenseThis project is licensed under the MIT License. Do with it what you will!
Package: lrcalc
This package provides Python bindings for theLittlewood-Richardson Calculator library.Example>>>importlrcalc>>>lrcalc.mult([2,1],[2]){(2,2,1):1,(4,1):1,(3,1,1):1,(3,2):1}>>>lrcalc.skew([3,2,1],[2,1]){(2,1):2,(1,1,1):1,(3,):1}InstallationYou need the LR Calculator library version 2 to be already installed on your
system, follow the instructionshere. Once this is done, the Python
bindings can be installed using pip:$ pip install lrcalcSourceYou can find the latest version of the source code on bitbucket:https://bitbucket.org/asbuch/lrcalc.LicenseThis package is distributed under the terms of the GNU General Public License
(GPL) published by the Free Software Foundation; either version 3 of the
License, or (at your option) any later version. Seehttp://www.gnu.org/licenses/.
Package: lrcloud
This project extend Adobe Lightroom with cloud support making it possible to access a catalog from different machines safely without corrupting the catalog.Features:Have a catalog file in a shared location, such as a folder in Google Drive, Dropbox, or a NAS, and have lrcloud synchronize the catalog between a local and the shared location.Only synchronizing the changes not the whole catalogSupport Smart PreviewsOn-the-fly catalog compressionCurrent limitations:The paths in the shared catalog are not converted thus a catalog cannot be shared between Window and OSX.No GUINo simultaneous catalog accessUsageNotePlease note thatlrcloudis still inbetaso backup your catalog frequently :)$ python -m lrcloud -h
usage: __main__.py [-h] [--init-push-to-cloud | --init-pull-from-cloud]
                   [--cloud-catalog CLOUD_CATALOG]
                   [--local-catalog LOCAL_CATALOG]
                   [--lightroom-exec LIGHTROOM_EXEC | --lightroom-exec-debug LIGHTROOM_EXEC_DEBUG]
                   [-v] [--no-smart-previews] [--config-file CONFIG_FILE]
                   [--diff-cmd DIFF_CMD] [--patch-cmd PATCH_CMD]

Cloud extension to Lightroom

optional arguments:
  -h, --help            show this help message and exit
  --init-push-to-cloud  Initiate the local catalog and push it to the cloud
                        (default: False)
  --init-pull-from-cloud
                        Download the cloud catalog and initiate a
                        corresponding local catalog (default: False)
  --cloud-catalog CLOUD_CATALOG
                        The cloud/shared catalog file e.g. located in Google
                        Drive or Dropbox (default: None)
  --local-catalog LOCAL_CATALOG
                        The local Lightroom catalog file (default: None)
  --lightroom-exec LIGHTROOM_EXEC
                        The Lightroom executable file (default: None)
  --lightroom-exec-debug LIGHTROOM_EXEC_DEBUG
                        Instead of running Lightroom, append data to the end
                        of the catalog file (default: None)
  -v, --verbose         Increase output verbosity (default: False)
  --no-smart-previews   Don't Sync Smart Previews (default: False)
  --config-file CONFIG_FILE
                        Path to the configure (.ini) file (default:
                        /home/madsbk/.lrcloud.ini)
  --diff-cmd DIFF_CMD   The command that given two files, $in1 and $in2,
                        produces a diff file $out (default: ./jdiff -f $in1
                        $in2 $out)
  --patch-cmd PATCH_CMD
                        The command that given a file, $in1, and a path,
                        $patch, produces a file $out (default: ./jptch $in1
                        $patch $out)
Package: lrcparser
[WIP]lrcparserA simple lyric file parser.Why this parser?Pros:...Well, to be honest, there's no strong reasons.Cons:Lack of testsPotential bugs that may screw your files up......But if you have tried this library and think it useful...then just keep using it, thank you for your support :)Usage[ti:test_lyric]
[al:TEST ~AVOIDING ERRORS~]
[by:283375]
[offset:250]

[00:00.02]Line 1
[00:00.28]Line 2
[00:02.83]Line 3
[00:28.33]Line 4 with translation | 一般大家都这么打翻译
[00:28.33]可惜我更喜欢换行
[00:28.33]你说得对，但是《lrcparser》是由……
[28:33.75]Line 6fromlrcparserimport*withopen('example.lrc','r',encoding='utf-8')aslrc_file:parsed=LrcParser.parse(lrc_file.read(),parse_translations=True)offset,lrc_lines,attributes=parsed.values()>>>offset250>>>lrc_lines[LrcLine(start_time=LrcTime(0,0,20),text=LrcText(LrcTextSegment(LrcTime(0,0,20),"Line 1")),translations=None,),LrcLine(start_time=LrcTime(0,0,280),text=LrcText(LrcTextSegment(LrcTime(0,0,280),"Line 2")),translations=None,),LrcLine(start_time=LrcTime(0,2,830),text=LrcText(LrcTextSegment(LrcTime(0,2,830),"Line 3")),translations=None,),LrcLine(start_time=LrcTime(0,28,330),text=LrcText(LrcTextSegment(LrcTime(0,28,330),"Line 4 with translation")),translations=[LrcText(LrcTextSegment(LrcTime(0,28,330),"一般大家都这么打翻译")),LrcText(LrcTextSegment(LrcTime(0,28,330),"可惜我更喜欢换行")),LrcText(LrcTextSegment(LrcTime(0,28,330),"你说得对，但是《lrcparser》是由……")),],),LrcLine(start_time=LrcTime(28,33,750),text=LrcText(LrcTextSegment(LrcTime(28,33,750),"Line 6")),translations=None,),]>>>attributes{"ti":"test_lyric","al":"TEST ~AVOIDING ERRORS~","by":"283375","offset":"250",}
Package: lrctoolbox
LRC ToolboxToolkit to work withLRC Filesin PythonUsagefromlrctoolboximportSyncedLyrics# Load LRC filelyrics=SyncedLyrics.load_from_file("example.lrc")# check if lyrics are syncedassertlyrics.is_synced# get lyrics as stringprint(lyrics.lyrics)# shift lyrics by 1 secondforlineinlyrics.lines:line.timestamp+=1000Developmentpoetry is used for dependency management. Install it withpip install poetryand then runpoetry installto install all dependencies.
Package: lrcup
LRCUPPython CLI and library for interacting with theLRCLIB.net API.InstallationInstall via PyPI:pipinstalllrcupCLI Usage# If you have an unsynced/synced LRC file:lrcupuploadexample.lrc# If you have a track with embedded lyrics:lrcupuploadfile.flacModule UsageThe class method names are based off of the LRCLIB API endpoints.Please refer to them for more information.fromlrcupimportLRCLiblrclib=LRCLib()# Example of getting synced lyrics via searchresults=lrclib.search(track="Never gonna give you up",artist="Rick Astley")print(results[0]["syncedLyrics"])
Package: lrcurve
lrcurveCreates a learning-curve plot for Jupyter/Colab notebooks that is
updated in real-time.There is a framework agnostic interfacelrcurve.PlotLearningCurvethat works well with PyTorch and Tensorflow and a keras wrapperlrcurve.KerasLearningCurvethat uses the keras callback interface.lrcurveworks with python 3.6 or newer and is distributed under the
MIT license.Installpipinstall-UlrcurveAPIlrcurve.PlotLearningCurvelrcurve.KerasLearningCurveExamplesKeras exampleFramework agnostic examplePyTorch exampleKeras examplefromlrcurveimportKerasLearningCurvemodel.compile(optimizer=keras.optimizers.Adam(),loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=[keras.metrics.SparseCategoricalAccuracy()])model.fit(train.x,train.y,epochs=100,verbose=0,validation_data=(validation.x,validation.y),callbacks=[KerasLearningCurve()])Framework agnostic examplewithPlotLearningCurve()asplot:foriinrange(100):plot.append(i,{'loss':math.exp(-(i+1)/10),'val_loss':math.exp(-i/10)})plot.draw()time.sleep(0.1)PyTorch examplefromlrcurveimportPlotLearningCurveplot=PlotLearningCurve(mappings={'loss':{'line':'train','facet':'loss'},'val_loss':{'line':'validation','facet':'loss'},'acc':{'line':'train','facet':'acc'},'val_acc':{'line':'validation','facet':'acc'}},facet_config={'loss':{'name':'Cross-Entropy','limit':[0,None],'scale':'linear'},'acc':{'name':'Accuracy','limit':[0,1],'scale':'linear'}},xaxis_config={'name':'Epoch','limit':[0,500]})withplot:# optimize modelforepochinrange(500):# compute lossz_test=network(x_test)loss_test=criterion(z_test,y_test)optimizer.zero_grad()z_train=network(x_train)loss_train=criterion(z_train,y_train)loss_train.backward()optimizer.step()# compute accuacyaccuacy_test=sklearn.metrics.accuracy_score(torch.argmax(z_test,1).numpy(),y_test)accuacy_train=sklearn.metrics.accuracy_score(torch.argmax(z_train,1).numpy(),y_train)# append and updateplot.append(epoch,{'loss':loss_train,'val_loss':loss_test,'acc':accuacy_train,'val_acc':accuacy_test})plot.draw()SponsorSponsored byNearForm Research.