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- import tensorflow as tf
- from sklearn.datasets import load_iris
- from sklearn.model_selection import train_test_split
- from sklearn.preprocessing import OneHotEncoder
- def get_iris_data(test_size=0.2):
- iris_data = load_iris()
- x = iris_data.data
- y = iris_data.target.reshape(-1, 1)
- encoder = OneHotEncoder(sparse=False)
- y = encoder.fit_transform(y)
- train_x, test_x, train_y, test_y = train_test_split(x, y)
- return train_x, train_y, test_x, test_y
- def set_keras_threads(threads):
- # We set threads here to avoid contention, as Keras
- # is heavily parallelized across multiple cores.
- tf.config.threading.set_inter_op_parallelism_threads(threads)
- tf.config.threading.set_intra_op_parallelism_threads(threads)
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