chore: import upstream snapshot with attribution

examples/nnet_mlp.py

@@ -0,0 +1,95 @@
+import logging
+
+try:
+    from sklearn.model_selection import train_test_split
+except ImportError:
+    from sklearn.cross_validation import train_test_split
+from sklearn.datasets import make_classification
+from sklearn.datasets import make_regression
+from sklearn.metrics import roc_auc_score
+
+from mla.metrics.metrics import mean_squared_error
+from mla.neuralnet import NeuralNet
+from mla.neuralnet.constraints import MaxNorm
+from mla.neuralnet.layers import Activation, Dense, Dropout
+from mla.neuralnet.optimizers import Adadelta, Adam
+from mla.neuralnet.parameters import Parameters
+from mla.neuralnet.regularizers import L2
+from mla.utils import one_hot
+
+logging.basicConfig(level=logging.DEBUG)
+
+
+def classification():
+    # Generate a random binary classification problem.
+    X, y = make_classification(
+        n_samples=1000,
+        n_features=100,
+        n_informative=75,
+        random_state=1111,
+        n_classes=2,
+        class_sep=2.5,
+    )
+    y = one_hot(y)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.15, random_state=1111
+    )
+
+    model = NeuralNet(
+        layers=[
+            Dense(256, Parameters(init="uniform", regularizers={"W": L2(0.05)})),
+            Activation("relu"),
+            Dropout(0.5),
+            Dense(128, Parameters(init="normal", constraints={"W": MaxNorm()})),
+            Activation("relu"),
+            Dense(2),
+            Activation("softmax"),
+        ],
+        loss="categorical_crossentropy",
+        optimizer=Adadelta(),
+        metric="accuracy",
+        batch_size=64,
+        max_epochs=25,
+    )
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    print("classification accuracy", roc_auc_score(y_test[:, 0], predictions[:, 0]))
+
+
+def regression():
+    # Generate a random regression problem
+    X, y = make_regression(
+        n_samples=5000,
+        n_features=25,
+        n_informative=25,
+        n_targets=1,
+        random_state=100,
+        noise=0.05,
+    )
+    y *= 0.01
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.1, random_state=1111
+    )
+
+    model = NeuralNet(
+        layers=[
+            Dense(64, Parameters(init="normal")),
+            Activation("linear"),
+            Dense(32, Parameters(init="normal")),
+            Activation("linear"),
+            Dense(1),
+        ],
+        loss="mse",
+        optimizer=Adam(),
+        metric="mse",
+        batch_size=256,
+        max_epochs=15,
+    )
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    print("regression mse", mean_squared_error(y_test, predictions.flatten()))
+
+
+if __name__ == "__main__":
+    classification()
+    regression()