scikit-learn
diff --git a/‎benchmarks/bench_mnist.py
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+25-14Lines changed: 25 additions & 14 deletions b/‎benchmarks/bench_mnist.py
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+25-14Lines changed: 25 additions & 14 deletions
diff --git a/‎doc/images/multilayerperceptron_network.png
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87.3 KB b/‎doc/images/multilayerperceptron_network.png
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87.3 KB
diff --git a/‎doc/modules/classes.rst
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@@ -9,19 +9,21 @@
 covertype dataset, the feature space is homogenous.
 
 Example of output :
-
     [..]
+
     Classification performance:
     ===========================
-    Classifier               train-time   test-time   error-rat
+    Classifier               train-time   test-time   error-rate
     ------------------------------------------------------------
-    Nystroem-SVM                105.07s       0.91s       0.0227
-    ExtraTrees                   48.20s       1.22s       0.0288
-    RandomForest                 47.17s       1.21s       0.0304
-    SampledRBF-SVM              140.45s       0.84s       0.0486
-    CART                         22.84s       0.16s       0.1214
-    dummy                         0.01s       0.02s       0.8973
-
+    MLP_adam                     53.46s       0.11s       0.0224
+    Nystroem-SVM                112.97s       0.92s       0.0228
+    MultilayerPerceptron         24.33s       0.14s       0.0287
+    ExtraTrees                   42.99s       0.57s       0.0294
+    RandomForest                 42.70s       0.49s       0.0318
+    SampledRBF-SVM              135.81s       0.56s       0.0486
+    LinearRegression-SAG         16.67s       0.06s       0.0824
+    CART                         20.69s       0.02s       0.1219
+    dummy                         0.00s       0.01s       0.8973
 """
 from __future__ import division, print_function
 
@@ -48,6 +50,7 @@
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.utils import check_array
 from sklearn.linear_model import LogisticRegression
+from sklearn.neural_network import MLPClassifier
 
 # Memoize the data extraction and memory map the resulting
 # train / test splits in readonly mode
@@ -84,11 +87,19 @@ def load_data(dtype=np.float32, order='F'):
     'CART': DecisionTreeClassifier(),
     'ExtraTrees': ExtraTreesClassifier(n_estimators=100),
     'RandomForest': RandomForestClassifier(n_estimators=100),
-    'Nystroem-SVM':
-    make_pipeline(Nystroem(gamma=0.015, n_components=1000), LinearSVC(C=100)),
-    'SampledRBF-SVM':
-    make_pipeline(RBFSampler(gamma=0.015, n_components=1000), LinearSVC(C=100)),
-    'LinearRegression-SAG': LogisticRegression(solver='sag', tol=1e-1, C=1e4)
+    'Nystroem-SVM': make_pipeline(
+        Nystroem(gamma=0.015, n_components=1000), LinearSVC(C=100)),
+    'SampledRBF-SVM': make_pipeline(
+        RBFSampler(gamma=0.015, n_components=1000), LinearSVC(C=100)),
+    'LinearRegression-SAG': LogisticRegression(solver='sag', tol=1e-1, C=1e4),
+    'MultilayerPerceptron': MLPClassifier(
+        hidden_layer_sizes=(100, 100), max_iter=400, alpha=1e-4,
+        algorithm='sgd', learning_rate_init=0.2, momentum=0.9, verbose=1,
+        tol=1e-4, random_state=1),
+    'MLP-adam': MLPClassifier(
+        hidden_layer_sizes=(100, 100), max_iter=400, alpha=1e-4,
+        algorithm='adam', learning_rate_init=0.001, verbose=1,
+        tol=1e-4, random_state=1)
 }
 
 
 
@@ -1048,6 +1048,8 @@ See the :ref:`metrics` section of the user guide for further details.
    :template: class.rst
 
    neural_network.BernoulliRBM
+   neural_network.MLPClassifier
+   neural_network.MLPRegressor
 
 
 .. _calibration_ref: