31 lines
1.0 KiB
Python
31 lines
1.0 KiB
Python
import pandas as pd
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import numpy as np
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# Make numpy values easier to read.
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np.set_printoptions(precision=3, suppress=True)
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import tensorflow as tf
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from tensorflow.keras import layers
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ecvalve = pd.read_csv(
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"/home/fferreira/Documents/teste_ecvalve.csv",
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names=[ "frequency", "state", "pressure", "capacity"])
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print("Data shape:", ecvalve.shape)
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print("Columns:", ecvalve.columns)
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ecvalve.head()
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ecvalve_features = ecvalve.copy()
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ecvalve_labels = ecvalve_features.pop('state')
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ecvalve_features = np.array(ecvalve_features)
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ecvalve_model = tf.keras.Sequential([
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layers.Dense(64, activation='relu', input_shape=(ecvalve_features.shape[1],)),
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layers.Dense(32, activation='relu'), # Add another layer
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layers.Dense(1, activation='sigmoid') # Sigmoid for binary classification
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])
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ecvalve_model.compile(loss=tf.keras.losses.BinaryCrossentropy(), optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])
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ecvalve_model.fit(ecvalve_features, ecvalve_labels, epochs=20)
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ecvalve_model.save('EcValve_1.h5') |