mirror of
https://github.com/jung-geun/PSO.git
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70 lines
1.8 KiB
Python
70 lines
1.8 KiB
Python
import os
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import pandas as pd
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import tensorflow as tf
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from sklearn.model_selection import train_test_split
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from tensorflow import keras
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from tensorflow.keras import layers
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from tensorflow.keras.layers import Dense
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.utils import to_categorical
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from ucimlrepo import fetch_ucirepo
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
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def make_model():
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model = Sequential()
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model.add(Dense(12, input_dim=16, activation='relu'))
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model.add(Dense(8, activation='relu'))
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model.add(Dense(7, activation='softmax'))
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return model
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def get_data():
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# fetch dataset
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dry_bean_dataset = fetch_ucirepo(id=602)
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# data (as pandas dataframes)
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X = dry_bean_dataset.data.features
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y = dry_bean_dataset.data.targets
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x = X.to_numpy()
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# object to categorical
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x = x.astype('float32')
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y_class = to_categorical(y)
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# metadata
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# print(dry_bean_dataset.metadata)
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# variable information
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# print(dry_bean_dataset.variables)
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# print(X.head())
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# print(y.head())
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# y_class = to_categorical(y)
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x_train, x_test, y_train, y_test = train_test_split(
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x, y_class, test_size=0.2, random_state=42, shuffle=True)
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return x_train, x_test, y_train, y_test
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x_train, x_test, y_train, y_test = get_data()
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model = make_model()
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early_stopping = keras.callbacks.EarlyStopping(
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patience=10, min_delta=0.001, restore_best_weights=True)
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model.compile(loss='sparse_categorical_crossentropy',
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optimizer='adam', metrics=['accuracy', "mse"])
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model.summary()
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history = model.fit(x_train, y_train, epochs=150,
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batch_size=10, callbacks=[early_stopping])
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score = model.evaluate(x_test, y_test, verbose=2)
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