microsoftml.rx_neural_network: Rede Neural

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

Redes neurais para modelagem de regressão e classificação binária e de várias classes.

Detalhes

Uma rede neural é uma classe de modelos de previsão inspirados no cérebro humano. Uma rede neural pode ser representada como um grafo direcionado ponderado. Cada nó no grafo é chamado de neurônio. Os neurônios no grafo são organizados em camadas, onde os neurônios em uma camada são conectados por uma borda ponderada (os pesos podem ser 0 ou números positivos) aos neurônios na próxima camada. A primeira camada é chamada de camada de entrada e cada neurônio na camada de entrada corresponde a um dos recursos. A última camada da função é chamada de camada de saída. Portanto, no caso de redes neurais binárias, ele contém dois neurônios de saída, um para cada classe, cujos valores são as probabilidades de pertencer a cada classe. As camadas restantes são chamadas de camadas ocultas. Os valores dos neurônios nas camadas ocultas e na camada de saída são definidos calculando a soma ponderada dos valores dos neurônios na camada anterior e aplicando uma função de ativação a essa soma ponderada. Um modelo de rede neural é definido pela estrutura de seu grafo (ou seja, o número de camadas ocultas e o número de neurônios em cada camada oculta), a escolha da função de ativação e os pesos nas bordas do grafo. O algoritmo de rede neural tenta aprender os pesos ideais nas bordas com base nos dados de treinamento.

Embora as redes neurais sejam amplamente conhecidas para uso em problemas complexos de aprendizado profundo e modelagem, como reconhecimento de imagem, elas também são facilmente adaptadas a problemas de regressão. Qualquer classe de modelos estatísticos pode ser considerada uma rede neural se usar pesos adaptáveis e pode aproximar funções não lineares de suas entradas. A regressão de rede neural é especialmente adequada para problemas em que um modelo de regressão mais tradicional não pode se ajustar a uma solução.

Arguments

fórmula

A fórmula, conforme descrito em revoscalepy.rx_formula. No momento, não há suporte para termos F() de interação no microsoftml.

dados

Um objeto de fonte de dados ou uma cadeia de caracteres especificando um arquivo .xdf ou um objeto de quadro de dados.

método

Uma cadeia de caracteres que indica o tipo árvore rápida:

• "binary" para a rede neural de classificação binária padrão.

• "multiClass" para a rede neural de classificação de várias classes.

• "regression" para uma rede neural de regressão.

num_hidden_nodes

O número padrão de nós ocultos na rede neural. O valor padrão é 100.

num_iterations

O número de iterações no conjunto de treinamento completo. O valor padrão é 100.

optimizer

Uma lista que especifica o sgd algoritmo ou adaptive a otimização. Essa lista pode ser criada usando sgd_optimizer ou adadelta_optimizer. O valor padrão é sgd.

net_definition

A definição net# da estrutura da rede neural. Para obter mais informações sobre o idioma net#, consulte o Guia de Referência

init_wts_diameter

Define o diâmetro dos pesos iniciais que especifica o intervalo do qual os valores são desenhados para os pesos de aprendizado iniciais. Os pesos são inicializados aleatoriamente dentro desse intervalo. O valor padrão é 0,1.

max_norm

Especifica um limite superior para restringir a norma do vetor de peso de entrada em cada unidade oculta. Isso pode ser muito importante na máx. das redes neurais, bem como nos casos em que o treinamento produz pesos não associados.

Aceleração

Especifica o tipo de aceleração de hardware a ser usado. Os valores possíveis são "sse_math" e "gpu_math". Para aceleração de GPU, é recomendável usar um miniBatchSize maior que um. Se você quiser usar a aceleração de GPU, serão necessárias etapas de instalação manuais adicionais:

• Baixe e instale o NVidia CUDA Toolkit 6.5 (CUDA Toolkit).

• Baixe e instale a Biblioteca NVidia cuDNN v2 (Biblioteca cudnn).

• Localize o diretório libs do pacote microsoftml chamando import microsoftml, os, os.path.join(microsoftml.__path__[0], "mxLibs").

• Copie cublas64_65.dll, cudart64_65.dll e cusparse64_65.dll do CUDA Toolkit 6.5 para o diretório libs do pacote microsoftml.

• Copie cudnn64_65.dll da Biblioteca cuDNN v2 para o diretório libs do pacote microsoftml.

mini_batch_size

Define o tamanho do mini-lote. Os valores recomendados estão entre 1 e 256. Esse parâmetro só é usado quando a aceleração é GPU. Definir esse parâmetro como um valor mais alto melhora a velocidade do treinamento, mas pode afetar negativamente a precisão. O valor padrão é 1.

Normalizar

Especifica o tipo de normalização automática usado:

• "Warn": se a normalização for necessária, ela será executada automaticamente. Essa é a opção padrão.

• "No": nenhuma normalização é executada.

• "Yes": a normalização é executada.

• "Auto": se a normalização for necessária, uma mensagem de aviso será exibida, mas a normalização não será executada.

A normalização redimensiona intervalos de dados diferentes para uma escala padrão. O dimensionamento de recursos garante que as distâncias entre os pontos de dados sejam proporcionais e permite que vários métodos de otimização, como descendente de gradiente, convergam muito mais rapidamente. Se a normalização for executada, um MaxMin normalizador será usado. Normaliza valores em um intervalo [a, b] onde -1 <= a <= 0 e 0 <= b <= 1b - a = 1. Esse normalizador preserva a moderação mapeando zero a zero.

ml_transforms

Especifica uma lista de transformações do MicrosoftML a serem executadas nos dados antes do treinamento ou Nenhuma se nenhuma transformação deve ser executada. Consulte featurize_text, categoricale categorical_hash, para transformações com suporte. Essas transformações são executadas após quaisquer transformações do Python especificadas. O valor padrão é None.

ml_transform_vars

Especifica um vetor de caracteres de nomes de variáveis a serem usados ou ml_transformsNenhum se nenhum for usado. O valor padrão é None.

row_selection

SEM SUPORTE. Especifica as linhas (observações) do conjunto de dados que devem ser usadas pelo modelo com o nome de uma variável lógica do conjunto de dados (entre aspas) ou com uma expressão lógica usando variáveis no conjunto de dados. Por exemplo:

• row_selection = "old" usará apenas observações nas quais o valor da variável old é True.

• row_selection = (age > 20) & (age < 65) & (log(income) > 10) usa apenas observações nas quais o valor da age variável está entre 20 e 65 e o valor da logincome variável é maior que 10.

A seleção de linha é executada após o processamento de transformações de dados (consulte os argumentos transforms ou transform_function). Assim como acontece com todas as expressões, row_selection pode ser definido fora da chamada de função usando a expression função.

Transforma

SEM SUPORTE. Uma expressão do formulário que representa a primeira rodada de transformações variáveis. Assim como acontece com todas as expressões, transforms (ou row_selection) pode ser definido fora da chamada de função usando a expression função.

transform_objects

SEM SUPORTE. Uma lista nomeada que contém objetos que podem ser referenciados por transforms, transform_functione row_selection.

transform_function

A função de transformação variável.

transform_variables

Um vetor de caractere das variáveis de conjunto de dados de entrada necessárias para a função de transformação.

transform_packages

SEM SUPORTE. Um vetor de caracteres que especifica pacotes python adicionais (fora daqueles especificados em RxOptions.get_option("transform_packages")) a serem disponibilizados e pré-carregados para uso em funções de transformação variável. Por exemplo, aqueles explicitamente definidos em funções revoscalepy por meio de seus transforms argumentos ou transform_function argumentos ou aqueles definidos implicitamente por meio de seus formula argumentos ou row_selection argumentos. O transform_packages argumento também pode ser Nenhum, indicando que nenhum pacote externo RxOptions.get_option("transform_packages") está pré-carregado.

transform_environment

SEM SUPORTE. Um ambiente definido pelo usuário para servir como pai para todos os ambientes desenvolvidos internamente e usados para transformação de dados variáveis. Se transform_environment = None, um novo ambiente "hash" com parent revoscalepy.baseenvis usado em vez disso.

blocks_per_read

Especifica o número de blocos a serem lidos para cada parte dos dados lidos da fonte de dados.

report_progress

Um valor inteiro que especifica o nível de relatório sobre o progresso do processamento de linhas:

• 0: nenhum progresso é relatado.

• 1: o número de linhas processadas é impresso e atualizado.

• 2: linhas processadas e intervalos são relatados.

• 3: linhas processadas e todos os intervalos são relatados.

detalhada

Um valor inteiro que especifica a quantidade de saída desejada. Se 0, nenhuma saída detalhada será impressa durante os cálculos. Valores inteiros de 1 para 4 fornecer quantidades crescentes de informações.

compute_context

Define o contexto no qual os cálculos são executados, especificado com um revoscalepy válido. RxComputeContext. Atualmente local e revoscalepy. Há suporte para contextos de computação RxInSqlServer .

Conjunto

Controlar parâmetros para ensembling.

Devoluções

Um NeuralNetwork objeto com o modelo treinado.

Observação

Esse algoritmo é de thread único e não tentará carregar todo o conjunto de dados na memória.

Consulte também

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

Referências

Wikipédia: Rede neural artificial

Exemplo de classificação binária

'''
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))

Saída:

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

Exemplo de classificação multiclasse

'''
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))

Saída:

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

Exemplo de regressão

'''
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))

Saída:

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

Otimizadores

• microsoftml.adadelta_optimizer: método de taxa de aprendizagem adaptável

• microsoftml.sgd_optimizer: descendente de gradiente estocástico

matemáticas

• microsoftml.avx_math: Aceleração com instruções do AVX

• microsoftml.clr_math: Aceleração com de matemática do .NET

• microsoftml.gpu_math: Aceleração com o NVidia CUDA

• microsoftml.mkl_math: Aceleração com o Intel MKL

• microsoftml.sse_math: Aceleração com instruções de SSE