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