8000 Merge pull request #5214 from glennq/mlp_refactoring · scikit-learn/scikit-learn@965a715 · GitHub
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Merge pull request #5214 from glennq/mlp_refactoring
[MRG + 2] Mlp with adam, nesterov's momentum, early stopping
2 parents efb0179 + 917bacb commit 965a715

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‎benchmarks/bench_mnist.py

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covertype dataset, the feature space is homogenous.
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Example of output :
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[..]
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Classification performance:
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===========================
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Classifier train-time test-time error-rat
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Classifier train-time test-time error-rate
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------------------------------------------------------------
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Nystroem-SVM 105.07s 0.91s 0.0227
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ExtraTrees 48.20s 1.22s 0.0288
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RandomForest 47.17s 1.21s 0.0304
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SampledRBF-SVM 140.45s 0.84s 0.0486
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CART 22.84s 0.16s 0.1214
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dummy 0.01s 0.02s 0.8973
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MLP_adam 53.46s 0.11s 0.0224
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Nystroem-SVM 112.97s 0.92s 0.0228
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MultilayerPerceptron 24.33s 0.14s 0.0287
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ExtraTrees 42.99s 0.57s 0.0294
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RandomForest 42.70s 0.49s 0.0318
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SampledRBF-SVM 135.81s 0.56s 0.0486
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LinearRegression-SAG 16.67s 0.06s 0.0824
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CART 20.69s 0.02s 0.1219
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dummy 0.00s 0.01s 0.8973
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"""
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from __future__ import division, print_function
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.utils import check_array
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from sklearn.linear_model import LogisticRegression
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from sklearn.neural_network import MLPClassifier
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# Memoize the data extraction and memory map the resulting
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# train / test splits in readonly mode
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'CART': DecisionTreeClassifier(),
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'ExtraTrees': ExtraTreesClassifier(n_estimators=100),
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'RandomForest': RandomForestClassifier(n_estimators=100),
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'Nystroem-SVM':
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make_pipeline(Nystroem(gamma=0.015, n_components=1000), LinearSVC(C=100)),
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'SampledRBF-SVM':
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make_pipeline(RBFSampler(gamma=0.015, n_components=1000), LinearSVC(C=100)),
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'LinearRegression-SAG': LogisticRegression(solver='sag', tol=1e-1, C=1e4)
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'Nystroem-SVM': make_pipeline(
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Nystroem(gamma=0.015, n_components=1000), LinearSVC(C=100)),
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'SampledRBF-SVM': make_pipeline(
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RBFSampler(gamma=0.015, n_components=1000), LinearSVC(C=100)),
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'LinearRegression-SAG': LogisticRegression(solver='sag', tol=1e-1, C=1e4),
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'MultilayerPerceptron': MLPClassifier(
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hidden_layer_sizes=(100, 100), max_iter=400, alpha=1e-4,
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algorithm='sgd', learning_rate_init=0.2, momentum=0.9, verbose=1,
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tol=1e-4, random_state=1),
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'MLP-adam': MLPClassifier(
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hidden_layer_sizes=(100, 100), max_iter=400, alpha=1e-4,
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algorithm='adam', learning_rate_init=0.001, verbose=1,
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tol=1e-4, random_state=1)
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}
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‎doc/images/multilayerperceptron_network.png

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‎doc/modules/classes.rst

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:template: class.rst
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neural_network.BernoulliRBM
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neural_network.MLPClassifier
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neural_network.MLPRegressor
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.. _calibration_ref:

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