microsoftml.rx_neural_network: Neuralt nätverk

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

Neurala nätverk för regressionsmodellering och för binär och klassificering av flera klasser.

Detaljer

Ett neuralt nätverk är en klass av förutsägelsemodeller inspirerade av den mänskliga hjärnan. Ett neuralt nätverk kan representeras som en viktad riktad graf. Varje nod i diagrammet kallas neuron. Neuronerna i grafen är ordnade i lager, där neuroner i ett lager är anslutna av en viktad kant (vikter kan vara 0 eller positiva tal) till neuroner i nästa lager. Det första lagret kallas indataskiktet, och varje neuron i indataskiktet motsvarar en av funktionerna. Det sista lagret i funktionen kallas utdataskiktet. Så när det gäller binära neurala nätverk innehåller den två utdataneuroner, en för varje klass, vars värden är sannolikheten för att tillhöra varje klass. De återstående lagren kallas dolda lager. Värdena för neuronerna i de dolda lagren och i utdataskiktet anges genom att beräkna den viktade summan av neuronernas värden i föregående lager och tillämpa en aktiveringsfunktion på den viktade summan. En neural nätverksmodell definieras av strukturen i grafen (nämligen antalet dolda lager och antalet nervceller i varje dolt lager), valet av aktiveringsfunktion och vikterna på grafkanterna. Algoritmen för neurala nätverk försöker lära sig de optimala vikterna på kanterna baserat på träningsdata.

Även om neurala nätverk är allmänt kända för att användas i djupinlärning och modellering av komplexa problem som bildigenkänning, är de också lätt anpassade till regressionsproblem. Alla klasser av statistiska modeller kan betraktas som ett neuralt nätverk om de använder anpassningsbara vikter och kan ungefärliga icke-linjära funktioner för sina indata. Neural nätverksregression passar särskilt bra för problem där en mer traditionell regressionsmodell inte passar en lösning.

Arguments

formel

Formeln som beskrivs i revoscalepy.rx_formula. Interaktionsvillkor och F() stöds för närvarande inte i microsoftml.

data

Ett datakällobjekt eller en teckensträng som anger en .xdf-fil eller ett dataramobjekt.

method

En teckensträng som anger typ av snabbt träd:

• "binary" för det neurala standardnätverket för binär klassificering.

• "multiClass" för neurala nätverk med flera klasser.

• "regression" för ett neuralt regressionsnätverk.

num_hidden_nodes

Standardantalet dolda noder i det neurala nätet. Standardvärdet är 100.

num_iterations

Antalet iterationer på den fullständiga träningsuppsättningen. Standardvärdet är 100.

optimizer

En lista som anger antingen optimeringsalgoritmen sgd eller adaptive . Den här listan kan skapas med hjälp av sgd_optimizer eller adadelta_optimizer. Standardvärdet är sgd.

net_definition

Net#-definitionen av strukturen i det neurala nätverket. Mer information om net#-språket finns i Referensguide

init_wts_diameter

Anger den inledande viktdiametern som anger det intervall från vilket värden ritas för de inledande inlärningsvikterna. Vikterna initieras slumpmässigt från det här intervallet. Standardvärdet är 0.1.

max_norm

Anger en övre gräns för att begränsa normen för den inkommande viktvektorn vid varje dold enhet. Detta kan vara mycket viktigt i maximalt antal neurala nätverk samt i fall där träning ger obundna vikter.

Acceleration

Anger vilken typ av maskinvaruacceleration som ska användas. Möjliga värden är "sse_math" och "gpu_math". För GPU-acceleration rekommenderar vi att du använder en miniBatchSize som är större än en. Om du vill använda GPU-accelerationen krävs ytterligare manuella konfigurationssteg:

• Ladda ned och installera NVidia CUDA Toolkit 6.5 (CUDA Toolkit).

• Ladda ned och installera NVidia cuDNN v2 Library (cudnn Library).

• Hitta katalogen libs för microsoftml-paketet genom att anropa import microsoftml, os, os.path.join(microsoftml.__path__[0], "mxLibs").

• Kopiera cublas64_65.dll, cudart64_65.dll och cusparse64_65.dll från CUDA Toolkit 6.5 till katalogen libs i microsoftml-paketet.

• Kopiera cudnn64_65.dll från cuDNN v2-biblioteket till katalogen libs i microsoftml-paketet.

mini_batch_size

Anger mini-batchstorleken. Rekommenderade värden är mellan 1 och 256. Den här parametern används endast när accelerationen är GPU. Om du ställer in den här parametern på ett högre värde förbättras träningshastigheten, men det kan påverka noggrannheten negativt. Standardvärdet är 1.

Normalisera

Anger vilken typ av automatisk normalisering som används:

• "Warn": Om normalisering behövs utförs den automatiskt. Det här är standardalternativet.

• "No": ingen normalisering utförs.

• "Yes": normalisering utförs.

• "Auto": Om normalisering behövs visas ett varningsmeddelande, men normaliseringen utförs inte.

Normaliseringen skalar om olika dataintervall till en standardskala. Funktionsskalning försäkrar att avstånden mellan datapunkter är proportionella och gör det möjligt för olika optimeringsmetoder som gradient descent att konvergera mycket snabbare. Om normalisering utförs används en MaxMin normaliserare. Det normaliserar värden i ett intervall [a, b] var -1 <= a <= 0 och 0 <= b <= 1 och b - a = 1. Den här normaliseraren bevarar gleshet genom att mappa noll till noll.

ml_transforms

Anger en lista över MicrosoftML-transformeringar som ska utföras på data före träning eller Ingen om inga transformeringar ska utföras. Se featurize_text, categoricaloch categorical_hash, för transformeringar som stöds. Dessa transformeringar utförs efter alla angivna Python-transformeringar. Standardvärdet är Ingen.

ml_transform_vars

Anger en teckenvektor med variabelnamn som ska användas i ml_transforms eller Ingen om ingen ska användas. Standardvärdet är Ingen.

row_selection

STÖDS INTE. Anger raderna (observationer) från datauppsättningen som ska användas av modellen med namnet på en logisk variabel från datauppsättningen (inom citattecken) eller med ett logiskt uttryck med variabler i datauppsättningen. Till exempel:

• row_selection = "old" använder endast observationer där värdet för variabeln old är True.

• row_selection = (age > 20) & (age < 65) & (log(income) > 10) använder endast observationer där variabelns age värde är mellan 20 och 65 och värdet för variabeln logincome är större än 10.

Radmarkeringen utförs efter bearbetning av datatransformeringar (se argumenten transforms eller transform_function). Som med alla uttryck kan definieras utanför funktionsanropet row_selection med hjälp av expression funktionen.

Förvandlar

STÖDS INTE. Ett uttryck för formuläret som representerar den första omgången av variabeltransformeringar. Precis som med alla uttryck kan (eller row_selection) definieras utanför funktionsanropet transforms med hjälp expression av funktionen.

transform_objects

STÖDS INTE. En namngiven lista som innehåller objekt som kan refereras till av transforms, transform_functionoch row_selection.

transform_function

Funktionen för variabeltransformeringen.

transform_variables

En teckenvektor för indatauppsättningsvariabler som behövs för transformeringsfunktionen.

transform_packages

STÖDS INTE. En teckenvektor som anger ytterligare Python-paket (utanför de som anges i RxOptions.get_option("transform_packages")) som ska göras tillgängliga och förinlästa för användning i variabeltransformeringsfunktioner. Till exempel de som uttryckligen definieras i revoscalepy-funktioner via deras transforms argument och transform_function argument eller de som definieras implicit via deras formula eller row_selection argument. Argumentet transform_packages kan också vara Ingen, vilket indikerar att inga paket utanför RxOptions.get_option("transform_packages") är förinstallerade.

transform_environment

STÖDS INTE. En användardefinierad miljö som fungerar som överordnad till alla miljöer som utvecklats internt och används för variabel datatransformering. Om transform_environment = Noneanvänds en ny "hash"-miljö med överordnad revoscalepy.baseenvis i stället.

blocks_per_read

Anger antalet block som ska läsas för varje segment av data som läss från datakällan.

report_progress

Ett heltalsvärde som anger rapporteringsnivån för radbearbetningsförloppet:

• 0: Inga förlopp rapporteras.

• 1: Antalet bearbetade rader skrivs ut och uppdateras.

• 2: Rader som bearbetas och tidsinställningar rapporteras.

• 3: bearbetade rader och alla tidsinställningar rapporteras.

verbose

Ett heltalsvärde som anger önskad mängd utdata. Om 0skrivs inga utförliga utdata ut under beräkningar. Heltalsvärden från 1 för att 4 ge ökande mängder information.

compute_context

Anger kontexten där beräkningar körs, som anges med en giltig revoscalepy. RxComputeContext. För närvarande lokal och revoscalepy. RxInSqlServer-beräkningskontexter stöds.

Ensemble

Kontrollparametrar för montering.

Retur

Ett NeuralNetwork objekt med den tränade modellen.

Anmärkning

Den här algoritmen är enkeltrådad och försöker inte läsa in hela datamängden i minnet.

Se även

adadelta_optimizer, sgd_optimizer, avx_math, clr_math, gpu_math, mkl_math, , sse_math. rx_predict

Referenser

Wikipedia: Artificiellt neuralt nätverk

Exempel på binär klassificering

'''
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))

Utdata:

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

Exempel på MultiClass-klassificering

'''
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))

Utdata:

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

Regressionsexempel

'''
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))

Utdata:

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

Optimerare

• microsoftml.adadelta_optimizer: Metod för anpassningsbar inlärningsfrekvens

• microsoftml.sgd_optimizer: Stochastic toning nedstigning

matematik

• microsoftml.avx_math: Acceleration med AVX-instruktioner

• microsoftml.clr_math: Acceleration med .NET-matematik

• microsoftml.gpu_math: Acceleration med NVidia CUDA

• microsoftml.mkl_math: Acceleration med Intel MKL

• microsoftml.sse_math: Acceleration med SSE-instruktioner