29 lines
785 B
Python
29 lines
785 B
Python
import numpy as np
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import tensorflow as tf
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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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from tensorflow.keras import layers
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from tensorflow.keras.utils import to_categorical
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# Getting data
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data_set = load_iris()
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x = data_set['data']
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y = to_categorical(data_set['target'])
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train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.2)
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# Building the model
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model = tf.keras.Sequential()
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model.add(layers.Dense(20, activation='relu', input_dim=4))
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model.add(layers.Dense(3, activation='sigmoid'))
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model.compile(optimizer='adam', loss='categorical_crossentropy',
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metrics=['accuracy'])
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# Training the model
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model.fit(train_x, train_y, validation_data=(test_x, test_y), epochs=1000)
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model.save('iris_model.h5')
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