wehub-resource-sync
73 lines
1.8 KiB
Python
73 lines
1.8 KiB
Python
from sklearn.datasets import make_classification
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from sklearn.metrics import roc_auc_score
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from sklearn.model_selection import train_test_split
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from mla.neuralnet import NeuralNet
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from mla.neuralnet.layers import Dense, Activation, Dropout, Parameters
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from mla.neuralnet.optimizers import *
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from mla.utils import one_hot
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def clasifier(optimizer):
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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 -= np.mean(X, axis=0)
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X /= np.std(X, axis=0)
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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(128, Parameters(init="uniform")),
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Activation("relu"),
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Dropout(0.5),
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Dense(64, Parameters(init="normal")),
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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=optimizer,
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metric="accuracy",
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batch_size=64,
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max_epochs=10,
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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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return roc_auc_score(y_test[:, 0], predictions[:, 0])
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def test_adadelta():
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assert clasifier(Adadelta()) > 0.9
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def test_adam():
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assert clasifier(Adam()) > 0.9
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def test_adamax():
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assert clasifier(Adamax()) > 0.9
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def test_rmsprop():
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assert clasifier(RMSprop()) > 0.9
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def test_adagrad():
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assert clasifier(Adagrad()) > 0.9
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def test_sgd():
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assert clasifier(SGD(learning_rate=0.0001)) > 0.9
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assert clasifier(SGD(learning_rate=0.0001, nesterov=True, momentum=0.9)) > 0.9
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assert clasifier(SGD(learning_rate=0.0001, nesterov=False, momentum=0.0)) > 0.9
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