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51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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import tensorflow as tf
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gpus = tf.config.experimental.list_physical_devices("GPU")
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if gpus:
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try:
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# tf.config.experimental.set_visible_devices(gpus[0], "GPU")
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tf.config.experimental.set_memory_growth(gpus[0], True)
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except RuntimeError as e:
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print(e)
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from tensorflow import keras
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from tensorflow.keras import layers
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from tensorflow.keras.models import Sequential
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from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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def make_model():
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model = Sequential()
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model.add(layers.Dense(10, activation='relu', input_shape=(4,)))
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model.add(layers.Dense(10, activation='relu'))
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model.add(layers.Dense(3, activation='softmax'))
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return model
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def load_data():
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iris = load_iris()
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x = iris.data
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y = iris.target
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y = keras.utils.to_categorical(y, 3)
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x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, shuffle=True, stratify=y)
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return x_train, x_test, y_train, y_test
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if __name__ == "__main__":
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model = make_model()
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x_train, x_test, y_train, y_test = load_data()
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print(x_train.shape, y_train.shape)
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loss = ['categorical_crossentropy', 'accuracy','mse']
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metrics = ['accuracy']
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model.compile(optimizer='sgd', loss=loss[0], metrics=metrics[0])
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model.fit(x_train, y_train, epochs=200, batch_size=32, validation_split=0.2)
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model.evaluate(x_test, y_test, batch_size=32) |