55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
from sacred import Experiment
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from sacred.observers import FileStorageObserver, MongoObserver
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import pandas as pd
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import numpy as np
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from tensorflow.keras import Sequential
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from tensorflow.keras.layers import Dense
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from sklearn.preprocessing import MinMaxScaler
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ex = Experiment("464962")
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ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
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ex.observers.append(FileStorageObserver('my_runs'))
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@ex.config
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def my_config():
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data_path = './data/car_prices_train.csv'
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epochs = 20
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batch_size = 32
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model_path = './car_prices_predict_model.h5'
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@ex.main
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def train_model(data_path, epochs, batch_size, model_path, _run):
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train_data = pd.read_csv(data_path)
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train_data.dropna(inplace=True)
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y_train = train_data['sellingprice'].astype(np.float32)
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X_train = train_data[['year', 'condition', 'transmission']]
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scaler_x = MinMaxScaler()
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X_train['condition'] = scaler_x.fit_transform(X_train[['condition']])
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scaler_y = MinMaxScaler()
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y_train = scaler_y.fit_transform(y_train.values.reshape(-1, 1))
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X_train = pd.get_dummies(X_train, columns=['transmission'])
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model = Sequential([
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Dense(64, activation='relu'),
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Dense(32, activation='relu'),
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Dense(1)
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])
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model.compile(optimizer='adam', loss='mean_squared_error')
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model.summary(print_fn=lambda x: _run.info.setdefault('model_summary', []).append(x))
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history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size)
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for epoch, loss in enumerate(history.history['loss']):
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_run.log_scalar("loss", loss, epoch)
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model.save(model_path)
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if __name__ == "__main__":
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ex.run()
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