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* Fix python linter Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Format e2e tests Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Format hack, docs Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Add py-fmt target Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Update python lint workflow Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Rebase master Signed-off-by: Sivanantham Chinnaiyan <[email protected]> --------- Signed-off-by: Sivanantham Chinnaiyan <[email protected]>
34 lines
962 B
Python
34 lines
962 B
Python
import warnings
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from sklearn.datasets import fetch_openml
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.neural_network import MLPClassifier
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import joblib
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# Load data from https://www.openml.org/d/554
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X, y = fetch_openml("mnist_784", version=1, return_X_y=True)
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X = X / 255.0
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X_train, X_test = X[:60000], X[60000:]
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y_train, y_test = y[:60000], y[60000:]
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mlp = MLPClassifier(
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hidden_layer_sizes=(500, 500, 500),
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max_iter=10,
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alpha=1e-4,
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solver="sgd",
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verbose=10,
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random_state=1,
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learning_rate_init=0.1,
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)
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# this example won't converge because of CI's time constraints, so we catch the
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# warning and are ignore it here
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", category=ConvergenceWarning, module="sklearn")
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mlp.fit(X_train, y_train)
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print("Training set score: %f" % mlp.score(X_train, y_train))
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print("Test set score: %f" % mlp.score(X_test, y_test))
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joblib.dump(mlp, "model.pkl")
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