microsoftml.rx_neural_network: Neuraal netwerk

Usage

microsoftml.rx_neural_network(formula: str,
    data: [revoscalepy.datasource.RxDataSource.RxDataSource,
    pandas.core.frame.DataFrame], method: ['binary', 'multiClass',
    'regression'] = 'binary', num_hidden_nodes: int = 100,
    num_iterations: int = 100,
    optimizer: [<function adadelta_optimizer at 0x0000007156EAC048>,
    <function sgd_optimizer at 0x0000007156E9FB70>] = {'Name': 'SgdOptimizer',
    'Settings': {}}, net_definition: str = None,
    init_wts_diameter: float = 0.1, max_norm: float = 0,
    acceleration: [<function avx_math at 0x0000007156E9FEA0>,
    <function clr_math at 0x0000007156EAC158>,
    <function gpu_math at 0x0000007156EAC1E0>,
    <function mkl_math at 0x0000007156EAC268>,
    <function sse_math at 0x0000007156EAC2F0>] = {'Name': 'AvxMath',
    'Settings': {}}, mini_batch_size: int = 1, normalize: ['No',
    'Warn', 'Auto', 'Yes'] = 'Auto', ml_transforms: list = None,
    ml_transform_vars: list = None, row_selection: str = None,
    transforms: dict = None, transform_objects: dict = None,
    transform_function: str = None,
    transform_variables: list = None,
    transform_packages: list = None,
    transform_environment: dict = None, blocks_per_read: int = None,
    report_progress: int = None, verbose: int = 1,
    ensemble: microsoftml.modules.ensemble.EnsembleControl = None,
    compute_context: revoscalepy.computecontext.RxComputeContext.RxComputeContext = None)

Description

Neurale netwerken voor regressiemodellering en voor binaire en classificatie met meerdere klassen.

Bijzonderheden

Een neuraal netwerk is een klasse voorspellingsmodellen die geïnspireerd zijn op het menselijk brein. Een neuraal netwerk kan worden weergegeven als een gewogen grafiek. Elk knooppunt in de grafiek wordt een neuron genoemd. De neuronen in de grafiek zijn gerangschikt in lagen, waarbij neuronen in één laag worden verbonden door een gewogen rand (gewichten kunnen 0 of positieve getallen zijn) aan neuronen in de volgende laag. De eerste laag wordt de invoerlaag genoemd en elke neuron in de invoerlaag komt overeen met een van de functies. De laatste laag van de functie wordt de uitvoerlaag genoemd. In het geval van binaire neurale netwerken bevat het dus twee uitvoerneuronen, één voor elke klasse, waarvan de waarden de waarschijnlijkheid zijn van het behoren tot elke klasse. De resterende lagen worden verborgen lagen genoemd. De waarden van de neuronen in de verborgen lagen en in de uitvoerlaag worden ingesteld door de gewogen som van de waarden van de neuronen in de vorige laag te berekenen en een activeringsfunctie toe te passen op die gewogen som. Een neuraal netwerkmodel wordt gedefinieerd door de structuur van de grafiek (namelijk het aantal verborgen lagen en het aantal neuronen in elke verborgen laag), de keuze van de activeringsfunctie en de gewichten op de graafranden. Het neurale netwerkalgoritmen proberen de optimale gewichten aan de randen te leren op basis van de trainingsgegevens.

Hoewel neurale netwerken algemeen bekend zijn voor gebruik in deep learning en het modelleren van complexe problemen zoals beeldherkenning, zijn ze ook eenvoudig aangepast aan regressieproblemen. Elke klasse van statistische modellen kan worden beschouwd als een neuraal netwerk als ze adaptieve gewichten gebruiken en niet-lineaire functies van hun invoer kunnen benaderen. Regressie van neuraal netwerk is vooral geschikt voor problemen waarbij een meer traditioneel regressiemodel niet past bij een oplossing.

Arguments

formule

De formule zoals beschreven in revoscalepy.rx_formula. Interactietermen en F() worden momenteel niet ondersteund in microsoftml.

gegevens

Een gegevensbronobject of een tekenreeks die een .xdf-bestand of een gegevensframeobject opgeeft.

method

Een tekenreeks die het type Fast Tree aangeeft:

• "binary" voor het standaard neurale netwerk voor binaire classificatie.

• "multiClass" voor neuraal netwerk voor classificatie met meerdere klassen.

• "regression" voor een regressieneuraal netwerk.

num_hidden_nodes

Het standaardaantal verborgen knooppunten in het neurale net. De standaardwaarde is 100.

num_iterations

Het aantal iteraties voor de volledige trainingsset. De standaardwaarde is 100.

optimizer

Een lijst die het sgd algoritme of adaptive optimalisatie-algoritme opgeeft. Deze lijst kan worden gemaakt met sgd_optimizer of adadelta_optimizer. De standaardwaarde is sgd.

net_definition

De Net#-definitie van de structuur van het neurale netwerk. Zie Referentiehandleiding voor meer informatie over de Net#-taal

init_wts_diameter

Hiermee stelt u de initiële gewichtsdiameter in waarmee het bereik wordt opgegeven van waaruit waarden worden getrokken voor de aanvankelijke leergewichten. De gewichten worden willekeurig geïnitialiseerd vanuit dit bereik. De standaardwaarde is 0,1.

max_norm

Hiermee geeft u een bovengrens om de norm van de binnenkomende gewichtsvector te beperken op elke verborgen eenheid. Dit kan erg belangrijk zijn in maximaal uitgaande neurale netwerken en in gevallen waarin training niet-gebonden gewichten produceert.

Versnelling

Hiermee geeft u het type hardwareversnelling dat moet worden gebruikt. Mogelijke waarden zijn 'sse_math' en 'gpu_math'. Voor GPU-versnelling is het raadzaam om een miniBatchSize groter dan één te gebruiken. Als u de GPU-versnelling wilt gebruiken, zijn er aanvullende handmatige installatiestappen vereist:

• Download en installeer NVidia CUDA Toolkit 6.5 (CUDA Toolkit).

• Download en installeer NVidia cuDNN v2 Library (cudnn Library).

• Zoek de map bibliotheken van het microsoftml-pakket door aan te roepen import microsoftml, os, os.path.join(microsoftml.__path__[0], "mxLibs").

• Kopieer cublas64_65.dll, cudart64_65.dll en cusparse64_65.dll van de CUDA Toolkit 6.5 naar de map bibliotheken van het microsoftml-pakket.

• Kopieer cudnn64_65.dll uit de cuDNN v2 Library naar de map bibliotheken van het microsoftml-pakket.

mini_batch_size

Hiermee stelt u de grootte van de minibatch in. Aanbevolen waarden liggen tussen 1 en 256. Deze parameter wordt alleen gebruikt wanneer de versnelling GPU is. Als u deze parameter instelt op een hogere waarde, wordt de snelheid van de training verbeterd, maar dit kan een negatieve invloed hebben op de nauwkeurigheid. De standaardwaarde is 1.

Normalize

Hiermee geeft u het type automatische normalisatie gebruikt:

• "Warn": als normalisatie nodig is, wordt deze automatisch uitgevoerd. Dit is de standaardoptie.

• "No": er wordt geen normalisatie uitgevoerd.

• "Yes": normalisatie wordt uitgevoerd.

• "Auto": als normalisatie nodig is, wordt er een waarschuwingsbericht weergegeven, maar wordt er geen normalisatie uitgevoerd.

Normalisatie schaalt verschillende gegevensbereiken opnieuw op een standaardschaal. Door het schalen van functies worden de afstanden tussen gegevenspunten proportioneel en kunnen verschillende optimalisatiemethoden, zoals gradiëntafname, veel sneller worden geconvergeerd. Als normalisatie wordt uitgevoerd, wordt een MaxMin normalizer gebruikt. Het normaliseert waarden in een interval [a, b] waar -1 <= a <= 0 en 0 <= b <= 1 .b - a = 1 Deze normalizer behoudt spaarzaamheid door nul aan nul toe te brengen.

ml_transforms

Hiermee geeft u een lijst op met MicrosoftML-transformaties die moeten worden uitgevoerd op de gegevens vóór de training of Geen als er geen transformaties moeten worden uitgevoerd. Zie featurize_text, categoricalen categorical_hash, voor transformaties die worden ondersteund. Deze transformaties worden uitgevoerd na opgegeven Python-transformaties. De standaardwaarde is None.

ml_transform_vars

Hiermee geeft u een tekenvector op van namen van variabelen die moeten worden gebruikt in ml_transforms of Geen als er geen moet worden gebruikt. De standaardwaarde is None.

row_selection

NIET ONDERSTEUND. Hiermee geeft u de rijen (waarnemingen) op uit de gegevensset die door het model moeten worden gebruikt met de naam van een logische variabele uit de gegevensset (tussen aanhalingstekens) of met een logische expressie met behulp van variabelen in de gegevensset. Voorbeeld:

• row_selection = "old" gebruikt alleen waarnemingen waarin de waarde van de variabele old is True.

• row_selection = (age > 20) & (age < 65) & (log(income) > 10) gebruikt alleen waarnemingen waarbij de waarde van de age variabele tussen 20 en 65 ligt en de waarde van de logincome variabele groter is dan 10.

De rijselectie wordt uitgevoerd na het verwerken van gegevenstransformaties (zie de argumenten transforms of transform_function). Net als bij alle expressies row_selection kunt u buiten de functieoproep definiëren met behulp van de expression functie.

Transformeert

NIET ONDERSTEUND. Een expressie van het formulier dat de eerste ronde van variabeletransformaties vertegenwoordigt. Net als bij alle expressies transforms kan (of row_selection) buiten de functie-aanroep worden gedefinieerd met behulp van de expression functie.

transform_objects

NIET ONDERSTEUND. Een benoemde lijst met objecten waarnaar kan worden verwezen door transforms, transform_functionen row_selection.

transform_function

De functie voor variabeletransformatie.

transform_variables

Een tekenvector van variabelen voor invoergegevenssets die nodig zijn voor de transformatiefunctie.

transform_packages

NIET ONDERSTEUND. Een tekenvector die extra Python-pakketten opgeeft (buiten de pakketten die zijn opgegeven in RxOptions.get_option("transform_packages")) die beschikbaar moeten worden gemaakt en vooraf moeten worden geladen voor gebruik in functies voor variabele transformatie. Bijvoorbeeld degenen die expliciet zijn gedefinieerd in revoscalepy-functies via hun transforms en transform_function argumenten of die impliciet zijn gedefinieerd via hun formula of row_selection argumenten. Het transform_packages argument kan ook Geen zijn, wat aangeeft dat er geen pakketten buiten RxOptions.get_option("transform_packages") zijn geladen.

transform_environment

NIET ONDERSTEUND. Een door de gebruiker gedefinieerde omgeving die als bovenliggend element fungeert voor alle omgevingen die intern zijn ontwikkeld en worden gebruikt voor variabele gegevenstransformatie. Als transform_environment = Nonein plaats daarvan een nieuwe hash-omgeving met bovenliggende revoscalepy.baseenvis wordt gebruikt.

blocks_per_read

Hiermee geeft u het aantal blokken op dat moet worden gelezen voor elk segment gegevens dat uit de gegevensbron wordt gelezen.

report_progress

Een geheel getal dat het rapportageniveau voor de voortgang van de rijverwerking aangeeft:

• 0: er wordt geen voortgang gerapporteerd.

• 1: het aantal verwerkte rijen wordt afgedrukt en bijgewerkt.

• 2: rijen verwerkt en tijdsinstellingen worden gerapporteerd.

• 3: rijen verwerkt en alle tijdsinstellingen worden gerapporteerd.

verbose

Een geheel getal dat de gewenste hoeveelheid uitvoer aangeeft. Als 0er tijdens berekeningen geen uitgebreide uitvoer wordt afgedrukt. Gehele getallen van waaruit 14 grotere hoeveelheden informatie moeten worden opgegeven.

compute_context

Hiermee stelt u de context in waarin berekeningen worden uitgevoerd, opgegeven met een geldige revoscalepy. RxComputeContext. Momenteel lokaal en opnieuw schalen. RxInSqlServer-rekencontexten worden ondersteund.

Ensemble

Besturingsparameters voor het opgeven van overeenkomsten.

Retouren

Een NeuralNetwork object met het getrainde model.

Opmerking

Dit algoritme is één threaded en probeert de volledige gegevensset niet in het geheugen te laden.

Zie ook

adadelta_optimizer avx_math, , , clr_mathsgd_optimizer, , gpu_math, , mkl_math, . sse_mathrx_predict

References

Wikipedia: Kunstmatig neuraal netwerk

Voorbeeld van binaire classificatie

'''
Binary Classification.
'''
import numpy
import pandas
from microsoftml import rx_neural_network, rx_predict
from revoscalepy.etl.RxDataStep import rx_data_step
from microsoftml.datasets.datasets import get_dataset

infert = get_dataset("infert")

import sklearn
if sklearn.__version__ < "0.18":
    from sklearn.cross_validation import train_test_split
else:
    from sklearn.model_selection import train_test_split

infertdf = infert.as_df()
infertdf["isCase"] = infertdf.case == 1
data_train, data_test, y_train, y_test = train_test_split(infertdf, infertdf.isCase)

forest_model = rx_neural_network(
    formula=" isCase ~ age + parity + education + spontaneous + induced ",
    data=data_train)
    
# RuntimeError: The type (RxTextData) for file is not supported.
score_ds = rx_predict(forest_model, data=data_test,
                     extra_vars_to_write=["isCase", "Score"])
                     
# Print the first five rows
print(rx_data_step(score_ds, number_rows_read=5))

Uitvoer:

Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Beginning processing data.
Rows Read: 186, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 186, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 186, Read Time: 0, Transform Time: 0
Beginning processing data.
Using: AVX Math

***** Net definition *****
  input Data [5];
  hidden H [100] sigmoid { // Depth 1
    from Data all;
  }
  output Result [1] sigmoid { // Depth 0
    from H all;
  }
***** End net definition *****
Input count: 5
Output count: 1
Output Function: Sigmoid
Loss Function: LogLoss
PreTrainer: NoPreTrainer
___________________________________________________________________
Starting training...
Learning rate: 0.001000
Momentum: 0.000000
InitWtsDiameter: 0.100000
___________________________________________________________________
Initializing 1 Hidden Layers, 701 Weights...
Estimated Pre-training MeanError = 0.742343
Iter:1/100, MeanErr=0.680245(-8.37%), 119.87M WeightUpdates/sec
Iter:2/100, MeanErr=0.637843(-6.23%), 122.52M WeightUpdates/sec
Iter:3/100, MeanErr=0.635404(-0.38%), 122.24M WeightUpdates/sec
Iter:4/100, MeanErr=0.634980(-0.07%), 73.36M WeightUpdates/sec
Iter:5/100, MeanErr=0.635287(0.05%), 128.26M WeightUpdates/sec
Iter:6/100, MeanErr=0.634572(-0.11%), 131.05M WeightUpdates/sec
Iter:7/100, MeanErr=0.634827(0.04%), 124.27M WeightUpdates/sec
Iter:8/100, MeanErr=0.635359(0.08%), 123.69M WeightUpdates/sec
Iter:9/100, MeanErr=0.635244(-0.02%), 119.35M WeightUpdates/sec
Iter:10/100, MeanErr=0.634712(-0.08%), 127.80M WeightUpdates/sec
Iter:11/100, MeanErr=0.635105(0.06%), 122.69M WeightUpdates/sec
Iter:12/100, MeanErr=0.635226(0.02%), 98.61M WeightUpdates/sec
Iter:13/100, MeanErr=0.634977(-0.04%), 127.88M WeightUpdates/sec
Iter:14/100, MeanErr=0.634347(-0.10%), 123.25M WeightUpdates/sec
Iter:15/100, MeanErr=0.634891(0.09%), 124.27M WeightUpdates/sec
Iter:16/100, MeanErr=0.635116(0.04%), 123.06M WeightUpdates/sec
Iter:17/100, MeanErr=0.633770(-0.21%), 122.05M WeightUpdates/sec
Iter:18/100, MeanErr=0.634992(0.19%), 128.79M WeightUpdates/sec
Iter:19/100, MeanErr=0.634385(-0.10%), 122.95M WeightUpdates/sec
Iter:20/100, MeanErr=0.634752(0.06%), 127.14M WeightUpdates/sec
Iter:21/100, MeanErr=0.635043(0.05%), 123.44M WeightUpdates/sec
Iter:22/100, MeanErr=0.634845(-0.03%), 121.81M WeightUpdates/sec
Iter:23/100, MeanErr=0.634850(0.00%), 125.11M WeightUpdates/sec
Iter:24/100, MeanErr=0.634617(-0.04%), 122.18M WeightUpdates/sec
Iter:25/100, MeanErr=0.634675(0.01%), 125.69M WeightUpdates/sec
Iter:26/100, MeanErr=0.634911(0.04%), 122.44M WeightUpdates/sec
Iter:27/100, MeanErr=0.634311(-0.09%), 121.90M WeightUpdates/sec
Iter:28/100, MeanErr=0.634798(0.08%), 123.54M WeightUpdates/sec
Iter:29/100, MeanErr=0.634674(-0.02%), 127.53M WeightUpdates/sec
Iter:30/100, MeanErr=0.634546(-0.02%), 100.96M WeightUpdates/sec
Iter:31/100, MeanErr=0.634859(0.05%), 124.40M WeightUpdates/sec
Iter:32/100, MeanErr=0.634747(-0.02%), 128.21M WeightUpdates/sec
Iter:33/100, MeanErr=0.634842(0.02%), 125.82M WeightUpdates/sec
Iter:34/100, MeanErr=0.634703(-0.02%), 77.48M WeightUpdates/sec
Iter:35/100, MeanErr=0.634804(0.02%), 122.21M WeightUpdates/sec
Iter:36/100, MeanErr=0.634690(-0.02%), 112.48M WeightUpdates/sec
Iter:37/100, MeanErr=0.634654(-0.01%), 119.18M WeightUpdates/sec
Iter:38/100, MeanErr=0.634885(0.04%), 137.19M WeightUpdates/sec
Iter:39/100, MeanErr=0.634723(-0.03%), 113.80M WeightUpdates/sec
Iter:40/100, MeanErr=0.634714(0.00%), 127.50M WeightUpdates/sec
Iter:41/100, MeanErr=0.634794(0.01%), 129.54M WeightUpdates/sec
Iter:42/100, MeanErr=0.633835(-0.15%), 133.05M WeightUpdates/sec
Iter:43/100, MeanErr=0.634401(0.09%), 128.95M WeightUpdates/sec
Iter:44/100, MeanErr=0.634575(0.03%), 123.42M WeightUpdates/sec
Iter:45/100, MeanErr=0.634673(0.02%), 123.78M WeightUpdates/sec
Iter:46/100, MeanErr=0.634692(0.00%), 119.04M WeightUpdates/sec
Iter:47/100, MeanErr=0.634476(-0.03%), 122.95M WeightUpdates/sec
Iter:48/100, MeanErr=0.634583(0.02%), 97.87M WeightUpdates/sec
Iter:49/100, MeanErr=0.634706(0.02%), 121.41M WeightUpdates/sec
Iter:50/100, MeanErr=0.634564(-0.02%), 120.58M WeightUpdates/sec
Iter:51/100, MeanErr=0.634118(-0.07%), 120.17M WeightUpdates/sec
Iter:52/100, MeanErr=0.634699(0.09%), 127.27M WeightUpdates/sec
Iter:53/100, MeanErr=0.634123(-0.09%), 110.51M WeightUpdates/sec
Iter:54/100, MeanErr=0.634390(0.04%), 123.74M WeightUpdates/sec
Iter:55/100, MeanErr=0.634461(0.01%), 113.66M WeightUpdates/sec
Iter:56/100, MeanErr=0.634415(-0.01%), 118.61M WeightUpdates/sec
Iter:57/100, MeanErr=0.634453(0.01%), 114.99M WeightUpdates/sec
Iter:58/100, MeanErr=0.634478(0.00%), 104.53M WeightUpdates/sec
Iter:59/100, MeanErr=0.634010(-0.07%), 124.62M WeightUpdates/sec
Iter:60/100, MeanErr=0.633901(-0.02%), 118.93M WeightUpdates/sec
Iter:61/100, MeanErr=0.634088(0.03%), 40.46M WeightUpdates/sec
Iter:62/100, MeanErr=0.634046(-0.01%), 94.65M WeightUpdates/sec
Iter:63/100, MeanErr=0.634233(0.03%), 27.18M WeightUpdates/sec
Iter:64/100, MeanErr=0.634596(0.06%), 123.94M WeightUpdates/sec
Iter:65/100, MeanErr=0.634185(-0.06%), 125.01M WeightUpdates/sec
Iter:66/100, MeanErr=0.634469(0.04%), 119.41M WeightUpdates/sec
Iter:67/100, MeanErr=0.634333(-0.02%), 124.11M WeightUpdates/sec
Iter:68/100, MeanErr=0.634203(-0.02%), 112.68M WeightUpdates/sec
Iter:69/100, MeanErr=0.633854(-0.05%), 118.62M WeightUpdates/sec
Iter:70/100, MeanErr=0.634319(0.07%), 123.59M WeightUpdates/sec
Iter:71/100, MeanErr=0.634423(0.02%), 122.51M WeightUpdates/sec
Iter:72/100, MeanErr=0.634388(-0.01%), 126.15M WeightUpdates/sec
Iter:73/100, MeanErr=0.634230(-0.02%), 126.51M WeightUpdates/sec
Iter:74/100, MeanErr=0.634011(-0.03%), 128.32M WeightUpdates/sec
Iter:75/100, MeanErr=0.634294(0.04%), 127.48M WeightUpdates/sec
Iter:76/100, MeanErr=0.634372(0.01%), 123.51M WeightUpdates/sec
Iter:77/100, MeanErr=0.632020(-0.37%), 122.12M WeightUpdates/sec
Iter:78/100, MeanErr=0.633770(0.28%), 119.55M WeightUpdates/sec
Iter:79/100, MeanErr=0.633504(-0.04%), 124.21M WeightUpdates/sec
Iter:80/100, MeanErr=0.634154(0.10%), 125.94M WeightUpdates/sec
Iter:81/100, MeanErr=0.633491(-0.10%), 120.83M WeightUpdates/sec
Iter:82/100, MeanErr=0.634212(0.11%), 128.60M WeightUpdates/sec
Iter:83/100, MeanErr=0.634138(-0.01%), 73.58M WeightUpdates/sec
Iter:84/100, MeanErr=0.634244(0.02%), 124.08M WeightUpdates/sec
Iter:85/100, MeanErr=0.634065(-0.03%), 96.43M WeightUpdates/sec
Iter:86/100, MeanErr=0.634174(0.02%), 124.28M WeightUpdates/sec
Iter:87/100, MeanErr=0.633966(-0.03%), 125.24M WeightUpdates/sec
Iter:88/100, MeanErr=0.633989(0.00%), 130.31M WeightUpdates/sec
Iter:89/100, MeanErr=0.633767(-0.04%), 115.73M WeightUpdates/sec
Iter:90/100, MeanErr=0.633831(0.01%), 122.81M WeightUpdates/sec
Iter:91/100, MeanErr=0.633219(-0.10%), 114.91M WeightUpdates/sec
Iter:92/100, MeanErr=0.633589(0.06%), 93.29M WeightUpdates/sec
Iter:93/100, MeanErr=0.634086(0.08%), 123.31M WeightUpdates/sec
Iter:94/100, MeanErr=0.634075(0.00%), 120.99M WeightUpdates/sec
Iter:95/100, MeanErr=0.634071(0.00%), 122.49M WeightUpdates/sec
Iter:96/100, MeanErr=0.633523(-0.09%), 116.48M WeightUpdates/sec
Iter:97/100, MeanErr=0.634103(0.09%), 128.85M WeightUpdates/sec
Iter:98/100, MeanErr=0.633836(-0.04%), 123.87M WeightUpdates/sec
Iter:99/100, MeanErr=0.633772(-0.01%), 128.17M WeightUpdates/sec
Iter:100/100, MeanErr=0.633684(-0.01%), 123.65M WeightUpdates/sec
Done!
Estimated Post-training MeanError = 0.631268
___________________________________________________________________
Not training a calibrator because it is not needed.
Elapsed time: 00:00:00.2454094
Elapsed time: 00:00:00.0082325
Beginning processing data.
Rows Read: 62, Read Time: 0.001, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:00.0297006
Finished writing 62 rows.
Writing completed.
Rows Read: 5, Total Rows Processed: 5, Total Chunk Time: 0.001 seconds 
  isCase PredictedLabel     Score  Probability
0   True          False -0.689636     0.334114
1   True          False -0.710219     0.329551
2   True          False -0.712912     0.328956
3  False          False -0.700765     0.331643
4   True          False -0.689783     0.334081

Voorbeeld van classificatie van MultiClass

'''
MultiClass Classification.
'''
import numpy
import pandas
from microsoftml import rx_neural_network, rx_predict
from revoscalepy.etl.RxDataStep import rx_data_step
from microsoftml.datasets.datasets import get_dataset

iris = get_dataset("iris")

import sklearn
if sklearn.__version__ < "0.18":
    from sklearn.cross_validation import train_test_split
else:
    from sklearn.model_selection import train_test_split

irisdf = iris.as_df()
irisdf["Species"] = irisdf["Species"].astype("category")
data_train, data_test, y_train, y_test = train_test_split(irisdf, irisdf.Species)

model = rx_neural_network(
    formula="  Species ~ Sepal_Length + Sepal_Width + Petal_Length + Petal_Width ",
    method="multiClass",
    data=data_train)
    
# RuntimeError: The type (RxTextData) for file is not supported.
score_ds = rx_predict(model, data=data_test,
                     extra_vars_to_write=["Species", "Score"])
                     
# Print the first five rows
print(rx_data_step(score_ds, number_rows_read=5))

Uitvoer:

Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Beginning processing data.
Rows Read: 112, Read Time: 0.001, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 112, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 112, Read Time: 0, Transform Time: 0
Beginning processing data.
Using: AVX Math

***** Net definition *****
  input Data [4];
  hidden H [100] sigmoid { // Depth 1
    from Data all;
  }
  output Result [3] softmax { // Depth 0
    from H all;
  }
***** End net definition *****
Input count: 4
Output count: 3
Output Function: SoftMax
Loss Function: LogLoss
PreTrainer: NoPreTrainer
___________________________________________________________________
Starting training...
Learning rate: 0.001000
Momentum: 0.000000
InitWtsDiameter: 0.100000
___________________________________________________________________
Initializing 1 Hidden Layers, 803 Weights...
Estimated Pre-training MeanError = 1.949606
Iter:1/100, MeanErr=1.937924(-0.60%), 98.43M WeightUpdates/sec
Iter:2/100, MeanErr=1.921153(-0.87%), 96.21M WeightUpdates/sec
Iter:3/100, MeanErr=1.920000(-0.06%), 95.55M WeightUpdates/sec
Iter:4/100, MeanErr=1.917267(-0.14%), 81.25M WeightUpdates/sec
Iter:5/100, MeanErr=1.917611(0.02%), 102.44M WeightUpdates/sec
Iter:6/100, MeanErr=1.918476(0.05%), 106.16M WeightUpdates/sec
Iter:7/100, MeanErr=1.916096(-0.12%), 97.85M WeightUpdates/sec
Iter:8/100, MeanErr=1.919486(0.18%), 77.99M WeightUpdates/sec
Iter:9/100, MeanErr=1.916452(-0.16%), 95.67M WeightUpdates/sec
Iter:10/100, MeanErr=1.916024(-0.02%), 102.06M WeightUpdates/sec
Iter:11/100, MeanErr=1.917155(0.06%), 99.21M WeightUpdates/sec
Iter:12/100, MeanErr=1.918543(0.07%), 99.25M WeightUpdates/sec
Iter:13/100, MeanErr=1.919120(0.03%), 85.38M WeightUpdates/sec
Iter:14/100, MeanErr=1.917713(-0.07%), 103.00M WeightUpdates/sec
Iter:15/100, MeanErr=1.917675(0.00%), 98.70M WeightUpdates/sec
Iter:16/100, MeanErr=1.917982(0.02%), 99.10M WeightUpdates/sec
Iter:17/100, MeanErr=1.916254(-0.09%), 103.41M WeightUpdates/sec
Iter:18/100, MeanErr=1.915691(-0.03%), 102.00M WeightUpdates/sec
Iter:19/100, MeanErr=1.914844(-0.04%), 86.64M WeightUpdates/sec
Iter:20/100, MeanErr=1.919268(0.23%), 94.68M WeightUpdates/sec
Iter:21/100, MeanErr=1.918748(-0.03%), 108.11M WeightUpdates/sec
Iter:22/100, MeanErr=1.917997(-0.04%), 96.33M WeightUpdates/sec
Iter:23/100, MeanErr=1.914987(-0.16%), 82.84M WeightUpdates/sec
Iter:24/100, MeanErr=1.916550(0.08%), 99.70M WeightUpdates/sec
Iter:25/100, MeanErr=1.915401(-0.06%), 96.69M WeightUpdates/sec
Iter:26/100, MeanErr=1.916092(0.04%), 101.62M WeightUpdates/sec
Iter:27/100, MeanErr=1.916381(0.02%), 98.81M WeightUpdates/sec
Iter:28/100, MeanErr=1.917414(0.05%), 102.29M WeightUpdates/sec
Iter:29/100, MeanErr=1.917316(-0.01%), 100.17M WeightUpdates/sec
Iter:30/100, MeanErr=1.916507(-0.04%), 82.09M WeightUpdates/sec
Iter:31/100, MeanErr=1.915786(-0.04%), 98.33M WeightUpdates/sec
Iter:32/100, MeanErr=1.917581(0.09%), 101.70M WeightUpdates/sec
Iter:33/100, MeanErr=1.913680(-0.20%), 79.94M WeightUpdates/sec
Iter:34/100, MeanErr=1.917264(0.19%), 102.54M WeightUpdates/sec
Iter:35/100, MeanErr=1.917377(0.01%), 100.67M WeightUpdates/sec
Iter:36/100, MeanErr=1.912060(-0.28%), 70.37M WeightUpdates/sec
Iter:37/100, MeanErr=1.917009(0.26%), 80.80M WeightUpdates/sec
Iter:38/100, MeanErr=1.916216(-0.04%), 94.56M WeightUpdates/sec
Iter:39/100, MeanErr=1.916362(0.01%), 28.22M WeightUpdates/sec
Iter:40/100, MeanErr=1.910658(-0.30%), 100.87M WeightUpdates/sec
Iter:41/100, MeanErr=1.916375(0.30%), 85.99M WeightUpdates/sec
Iter:42/100, MeanErr=1.916257(-0.01%), 102.06M WeightUpdates/sec
Iter:43/100, MeanErr=1.914505(-0.09%), 99.86M WeightUpdates/sec
Iter:44/100, MeanErr=1.914638(0.01%), 103.11M WeightUpdates/sec
Iter:45/100, MeanErr=1.915141(0.03%), 107.62M WeightUpdates/sec
Iter:46/100, MeanErr=1.915119(0.00%), 99.65M WeightUpdates/sec
Iter:47/100, MeanErr=1.915379(0.01%), 107.03M WeightUpdates/sec
Iter:48/100, MeanErr=1.912565(-0.15%), 104.78M WeightUpdates/sec
Iter:49/100, MeanErr=1.915466(0.15%), 110.43M WeightUpdates/sec
Iter:50/100, MeanErr=1.914038(-0.07%), 98.44M WeightUpdates/sec
Iter:51/100, MeanErr=1.915015(0.05%), 96.28M WeightUpdates/sec
Iter:52/100, MeanErr=1.913771(-0.06%), 89.27M WeightUpdates/sec
Iter:53/100, MeanErr=1.911621(-0.11%), 72.67M WeightUpdates/sec
Iter:54/100, MeanErr=1.914969(0.18%), 111.17M WeightUpdates/sec
Iter:55/100, MeanErr=1.913894(-0.06%), 98.68M WeightUpdates/sec
Iter:56/100, MeanErr=1.914871(0.05%), 95.41M WeightUpdates/sec
Iter:57/100, MeanErr=1.912898(-0.10%), 80.72M WeightUpdates/sec
Iter:58/100, MeanErr=1.913334(0.02%), 103.71M WeightUpdates/sec
Iter:59/100, MeanErr=1.913362(0.00%), 99.57M WeightUpdates/sec
Iter:60/100, MeanErr=1.913915(0.03%), 106.21M WeightUpdates/sec
Iter:61/100, MeanErr=1.913310(-0.03%), 112.27M WeightUpdates/sec
Iter:62/100, MeanErr=1.913395(0.00%), 50.86M WeightUpdates/sec
Iter:63/100, MeanErr=1.912814(-0.03%), 58.91M WeightUpdates/sec
Iter:64/100, MeanErr=1.911468(-0.07%), 72.06M WeightUpdates/sec
Iter:65/100, MeanErr=1.912313(0.04%), 86.34M WeightUpdates/sec
Iter:66/100, MeanErr=1.913320(0.05%), 114.39M WeightUpdates/sec
Iter:67/100, MeanErr=1.912914(-0.02%), 105.97M WeightUpdates/sec
Iter:68/100, MeanErr=1.909881(-0.16%), 105.73M WeightUpdates/sec
Iter:69/100, MeanErr=1.911649(0.09%), 105.23M WeightUpdates/sec
Iter:70/100, MeanErr=1.911192(-0.02%), 110.24M WeightUpdates/sec
Iter:71/100, MeanErr=1.912480(0.07%), 106.86M WeightUpdates/sec
Iter:72/100, MeanErr=1.909881(-0.14%), 97.28M WeightUpdates/sec
Iter:73/100, MeanErr=1.911678(0.09%), 109.57M WeightUpdates/sec
Iter:74/100, MeanErr=1.911137(-0.03%), 91.01M WeightUpdates/sec
Iter:75/100, MeanErr=1.910706(-0.02%), 99.41M WeightUpdates/sec
Iter:76/100, MeanErr=1.910869(0.01%), 84.18M WeightUpdates/sec
Iter:77/100, MeanErr=1.911643(0.04%), 105.07M WeightUpdates/sec
Iter:78/100, MeanErr=1.911438(-0.01%), 110.12M WeightUpdates/sec
Iter:79/100, MeanErr=1.909590(-0.10%), 84.16M WeightUpdates/sec
Iter:80/100, MeanErr=1.911181(0.08%), 92.30M WeightUpdates/sec
Iter:81/100, MeanErr=1.910534(-0.03%), 110.60M WeightUpdates/sec
Iter:82/100, MeanErr=1.909340(-0.06%), 54.07M WeightUpdates/sec
Iter:83/100, MeanErr=1.908275(-0.06%), 104.08M WeightUpdates/sec
Iter:84/100, MeanErr=1.910364(0.11%), 107.19M WeightUpdates/sec
Iter:85/100, MeanErr=1.910286(0.00%), 102.55M WeightUpdates/sec
Iter:86/100, MeanErr=1.909155(-0.06%), 79.72M WeightUpdates/sec
Iter:87/100, MeanErr=1.909384(0.01%), 102.37M WeightUpdates/sec
Iter:88/100, MeanErr=1.907751(-0.09%), 105.48M WeightUpdates/sec
Iter:89/100, MeanErr=1.910164(0.13%), 102.53M WeightUpdates/sec
Iter:90/100, MeanErr=1.907935(-0.12%), 105.03M WeightUpdates/sec
Iter:91/100, MeanErr=1.909510(0.08%), 99.97M WeightUpdates/sec
Iter:92/100, MeanErr=1.907405(-0.11%), 100.03M WeightUpdates/sec
Iter:93/100, MeanErr=1.905757(-0.09%), 113.21M WeightUpdates/sec
Iter:94/100, MeanErr=1.909167(0.18%), 107.86M WeightUpdates/sec
Iter:95/100, MeanErr=1.907593(-0.08%), 106.09M WeightUpdates/sec
Iter:96/100, MeanErr=1.908358(0.04%), 111.25M WeightUpdates/sec
Iter:97/100, MeanErr=1.906484(-0.10%), 95.81M WeightUpdates/sec
Iter:98/100, MeanErr=1.908239(0.09%), 105.89M WeightUpdates/sec
Iter:99/100, MeanErr=1.908508(0.01%), 103.05M WeightUpdates/sec
Iter:100/100, MeanErr=1.904747(-0.20%), 106.81M WeightUpdates/sec
Done!
Estimated Post-training MeanError = 1.896338
___________________________________________________________________
Not training a calibrator because it is not needed.
Elapsed time: 00:00:00.1620840
Elapsed time: 00:00:00.0096627
Beginning processing data.
Rows Read: 38, Read Time: 0, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:00.0312987
Finished writing 38 rows.
Writing completed.
Rows Read: 5, Total Rows Processed: 5, Total Chunk Time: Less than .001 seconds 
      Species   Score.0   Score.1   Score.2
0  versicolor  0.350161  0.339557  0.310282
1      setosa  0.358506  0.336593  0.304901
2   virginica  0.346957  0.340573  0.312470
3   virginica  0.346685  0.340748  0.312567
4   virginica  0.348469  0.340113  0.311417

Voorbeeld van regressie

'''
Regression.
'''
import numpy
import pandas
from microsoftml import rx_neural_network, rx_predict
from revoscalepy.etl.RxDataStep import rx_data_step
from microsoftml.datasets.datasets import get_dataset

attitude = get_dataset("attitude")

import sklearn
if sklearn.__version__ < "0.18":
    from sklearn.cross_validation import train_test_split
else:
    from sklearn.model_selection import train_test_split

attitudedf = attitude.as_df()
data_train, data_test = train_test_split(attitudedf)

model = rx_neural_network(
    formula="rating ~ complaints + privileges + learning + raises + critical + advance",
    method="regression",
    data=data_train)
    
# RuntimeError: The type (RxTextData) for file is not supported.
score_ds = rx_predict(model, data=data_test,
                     extra_vars_to_write=["rating"])
                     
# Print the first five rows
print(rx_data_step(score_ds, number_rows_read=5))

Uitvoer:

Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Beginning processing data.
Rows Read: 22, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 22, Read Time: 0.001, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 22, Read Time: 0, Transform Time: 0
Beginning processing data.
Using: AVX Math

***** Net definition *****
  input Data [6];
  hidden H [100] sigmoid { // Depth 1
    from Data all;
  }
  output Result [1] linear { // Depth 0
    from H all;
  }
***** End net definition *****
Input count: 6
Output count: 1
Output Function: Linear
Loss Function: SquaredLoss
PreTrainer: NoPreTrainer
___________________________________________________________________
Starting training...
Learning rate: 0.001000
Momentum: 0.000000
InitWtsDiameter: 0.100000
___________________________________________________________________
Initializing 1 Hidden Layers, 801 Weights...
Estimated Pre-training MeanError = 4458.793673
Iter:1/100, MeanErr=1624.747024(-63.56%), 27.30M WeightUpdates/sec
Iter:2/100, MeanErr=139.267390(-91.43%), 30.50M WeightUpdates/sec
Iter:3/100, MeanErr=116.382316(-16.43%), 29.16M WeightUpdates/sec
Iter:4/100, MeanErr=114.947244(-1.23%), 32.06M WeightUpdates/sec
Iter:5/100, MeanErr=112.886818(-1.79%), 32.96M WeightUpdates/sec
Iter:6/100, MeanErr=112.406547(-0.43%), 30.29M WeightUpdates/sec
Iter:7/100, MeanErr=110.502757(-1.69%), 30.92M WeightUpdates/sec
Iter:8/100, MeanErr=111.499645(0.90%), 31.20M WeightUpdates/sec
Iter:9/100, MeanErr=111.895816(0.36%), 32.46M WeightUpdates/sec
Iter:10/100, MeanErr=110.171443(-1.54%), 34.61M WeightUpdates/sec
Iter:11/100, MeanErr=106.975524(-2.90%), 22.14M WeightUpdates/sec
Iter:12/100, MeanErr=107.708220(0.68%), 7.73M WeightUpdates/sec
Iter:13/100, MeanErr=105.345097(-2.19%), 28.99M WeightUpdates/sec
Iter:14/100, MeanErr=109.937833(4.36%), 31.04M WeightUpdates/sec
Iter:15/100, MeanErr=106.672340(-2.97%), 30.04M WeightUpdates/sec
Iter:16/100, MeanErr=108.474555(1.69%), 32.41M WeightUpdates/sec
Iter:17/100, MeanErr=109.449054(0.90%), 31.60M WeightUpdates/sec
Iter:18/100, MeanErr=105.911830(-3.23%), 34.05M WeightUpdates/sec
Iter:19/100, MeanErr=106.045172(0.13%), 33.80M WeightUpdates/sec
Iter:20/100, MeanErr=108.360427(2.18%), 33.60M WeightUpdates/sec
Iter:21/100, MeanErr=106.506436(-1.71%), 33.77M WeightUpdates/sec
Iter:22/100, MeanErr=99.167335(-6.89%), 32.26M WeightUpdates/sec
Iter:23/100, MeanErr=108.115797(9.02%), 25.86M WeightUpdates/sec
Iter:24/100, MeanErr=106.292283(-1.69%), 31.03M WeightUpdates/sec
Iter:25/100, MeanErr=99.397875(-6.49%), 31.33M WeightUpdates/sec
Iter:26/100, MeanErr=104.805299(5.44%), 31.57M WeightUpdates/sec
Iter:27/100, MeanErr=101.385085(-3.26%), 22.92M WeightUpdates/sec
Iter:28/100, MeanErr=100.064656(-1.30%), 35.01M WeightUpdates/sec
Iter:29/100, MeanErr=100.519013(0.45%), 32.74M WeightUpdates/sec
Iter:30/100, MeanErr=99.273143(-1.24%), 35.12M WeightUpdates/sec
Iter:31/100, MeanErr=100.465649(1.20%), 33.68M WeightUpdates/sec
Iter:32/100, MeanErr=102.402320(1.93%), 33.79M WeightUpdates/sec
Iter:33/100, MeanErr=97.517196(-4.77%), 32.32M WeightUpdates/sec
Iter:34/100, MeanErr=102.597511(5.21%), 32.46M WeightUpdates/sec
Iter:35/100, MeanErr=96.187788(-6.25%), 32.32M WeightUpdates/sec
Iter:36/100, MeanErr=101.533507(5.56%), 21.44M WeightUpdates/sec
Iter:37/100, MeanErr=99.339624(-2.16%), 21.53M WeightUpdates/sec
Iter:38/100, MeanErr=98.049306(-1.30%), 15.27M WeightUpdates/sec
Iter:39/100, MeanErr=97.508282(-0.55%), 23.21M WeightUpdates/sec
Iter:40/100, MeanErr=99.894288(2.45%), 27.94M WeightUpdates/sec
Iter:41/100, MeanErr=95.190566(-4.71%), 32.47M WeightUpdates/sec
Iter:42/100, MeanErr=91.234977(-4.16%), 31.29M WeightUpdates/sec
Iter:43/100, MeanErr=98.824414(8.32%), 32.35M WeightUpdates/sec
Iter:44/100, MeanErr=96.759533(-2.09%), 22.37M WeightUpdates/sec
Iter:45/100, MeanErr=95.275106(-1.53%), 32.09M WeightUpdates/sec
Iter:46/100, MeanErr=95.749031(0.50%), 26.49M WeightUpdates/sec
Iter:47/100, MeanErr=96.267879(0.54%), 31.81M WeightUpdates/sec
Iter:48/100, MeanErr=97.383752(1.16%), 31.01M WeightUpdates/sec
Iter:49/100, MeanErr=96.605199(-0.80%), 32.05M WeightUpdates/sec
Iter:50/100, MeanErr=96.927400(0.33%), 32.42M WeightUpdates/sec
Iter:51/100, MeanErr=96.288491(-0.66%), 28.89M WeightUpdates/sec
Iter:52/100, MeanErr=92.751171(-3.67%), 33.68M WeightUpdates/sec
Iter:53/100, MeanErr=88.655001(-4.42%), 34.53M WeightUpdates/sec
Iter:54/100, MeanErr=90.923513(2.56%), 32.00M WeightUpdates/sec
Iter:55/100, MeanErr=91.627261(0.77%), 25.74M WeightUpdates/sec
Iter:56/100, MeanErr=91.132907(-0.54%), 30.00M WeightUpdates/sec
Iter:57/100, MeanErr=95.294092(4.57%), 33.13M WeightUpdates/sec
Iter:58/100, MeanErr=90.219024(-5.33%), 31.70M WeightUpdates/sec
Iter:59/100, MeanErr=92.727605(2.78%), 30.71M WeightUpdates/sec
Iter:60/100, MeanErr=86.910488(-6.27%), 33.07M WeightUpdates/sec
Iter:61/100, MeanErr=92.350984(6.26%), 32.46M WeightUpdates/sec
Iter:62/100, MeanErr=93.208298(0.93%), 31.08M WeightUpdates/sec
Iter:63/100, MeanErr=90.784723(-2.60%), 21.19M WeightUpdates/sec
Iter:64/100, MeanErr=88.685225(-2.31%), 33.17M WeightUpdates/sec
Iter:65/100, MeanErr=91.668555(3.36%), 30.65M WeightUpdates/sec
Iter:66/100, MeanErr=82.607568(-9.88%), 29.72M WeightUpdates/sec
Iter:67/100, MeanErr=88.787842(7.48%), 32.98M WeightUpdates/sec
Iter:68/100, MeanErr=88.793186(0.01%), 34.67M WeightUpdates/sec
Iter:69/100, MeanErr=88.918795(0.14%), 14.09M WeightUpdates/sec
Iter:70/100, MeanErr=87.121434(-2.02%), 33.02M WeightUpdates/sec
Iter:71/100, MeanErr=86.865602(-0.29%), 34.87M WeightUpdates/sec
Iter:72/100, MeanErr=87.261979(0.46%), 32.34M WeightUpdates/sec
Iter:73/100, MeanErr=87.812460(0.63%), 31.35M WeightUpdates/sec
Iter:74/100, MeanErr=87.818462(0.01%), 32.54M WeightUpdates/sec
Iter:75/100, MeanErr=87.085672(-0.83%), 34.80M WeightUpdates/sec
Iter:76/100, MeanErr=85.773668(-1.51%), 35.39M WeightUpdates/sec
Iter:77/100, MeanErr=85.338703(-0.51%), 34.59M WeightUpdates/sec
Iter:78/100, MeanErr=79.370105(-6.99%), 30.14M WeightUpdates/sec
Iter:79/100, MeanErr=83.026209(4.61%), 32.32M WeightUpdates/sec
Iter:80/100, MeanErr=89.776417(8.13%), 33.14M WeightUpdates/sec
Iter:81/100, MeanErr=85.447100(-4.82%), 32.32M WeightUpdates/sec
Iter:82/100, MeanErr=83.991969(-1.70%), 22.12M WeightUpdates/sec
Iter:83/100, MeanErr=85.065064(1.28%), 30.41M WeightUpdates/sec
Iter:84/100, MeanErr=83.762008(-1.53%), 31.29M WeightUpdates/sec
Iter:85/100, MeanErr=84.217726(0.54%), 34.92M WeightUpdates/sec
Iter:86/100, MeanErr=82.395181(-2.16%), 34.26M WeightUpdates/sec
Iter:87/100, MeanErr=82.979145(0.71%), 22.87M WeightUpdates/sec
Iter:88/100, MeanErr=83.656685(0.82%), 28.51M WeightUpdates/sec
Iter:89/100, MeanErr=81.132468(-3.02%), 32.43M WeightUpdates/sec
Iter:90/100, MeanErr=81.311106(0.22%), 30.91M WeightUpdates/sec
Iter:91/100, MeanErr=81.953897(0.79%), 31.98M WeightUpdates/sec
Iter:92/100, MeanErr=79.018074(-3.58%), 33.13M WeightUpdates/sec
Iter:93/100, MeanErr=78.220412(-1.01%), 31.47M WeightUpdates/sec
Iter:94/100, MeanErr=80.833884(3.34%), 25.16M WeightUpdates/sec
Iter:95/100, MeanErr=81.550135(0.89%), 32.64M WeightUpdates/sec
Iter:96/100, MeanErr=77.785628(-4.62%), 32.54M WeightUpdates/sec
Iter:97/100, MeanErr=76.438158(-1.73%), 34.34M WeightUpdates/sec
Iter:98/100, MeanErr=79.471621(3.97%), 33.12M WeightUpdates/sec
Iter:99/100, MeanErr=76.038475(-4.32%), 33.01M WeightUpdates/sec
Iter:100/100, MeanErr=75.349164(-0.91%), 32.68M WeightUpdates/sec
Done!
Estimated Post-training MeanError = 75.768932
___________________________________________________________________
Not training a calibrator because it is not needed.
Elapsed time: 00:00:00.1178557
Elapsed time: 00:00:00.0088299
Beginning processing data.
Rows Read: 8, Read Time: 0, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:00.0293893
Finished writing 8 rows.
Writing completed.
Rows Read: 5, Total Rows Processed: 5, Total Chunk Time: 0.001 seconds 
   rating      Score
0    82.0  70.120613
1    64.0  66.344688
2    68.0  68.862373
3    58.0  68.241341
4    63.0  67.196869

Optimizers

• microsoftml.adadelta_optimizer: Methode voor adaptieve leersnelheid

• microsoftml.sgd_optimizer: Stochastische gradiëntafname

wiskunde

• microsoftml.avx_math: Acceleratie met AVX-instructies

• microsoftml.clr_math: Acceleratie met .NET-

• microsoftml.gpu_math: Versnelling met NVidia CUDA

• microsoftml.mkl_math: Versnelling met Intel MKL-

• microsoftml.sse_math: Acceleratie met SSE-instructies