72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
import sys
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from keras.backend import mean
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import pandas as pd
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import numpy as np
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from sklearn import preprocessing
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_squared_error
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras.layers import Input, Dense, Activation,Dropout
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from tensorflow.keras.models import Model
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from tensorflow.keras.callbacks import EarlyStopping
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from keras.models import Sequential
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from sacred import Experiment
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from sacred.observers import MongoObserver
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ex = Experiment("434742-mongo", interactive=False, save_git_info=False)
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ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))
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@ex.config
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def my_config():
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epochs = 10
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batch_size = 16
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@ex.capture
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def prepare_model(epochs, batch_size, _run):
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# odczytanie danych z plików
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avocado_train = pd.read_csv('avocado_train.csv')
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avocado_test = pd.read_csv('avocado_test.csv')
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avocado_validate = pd.read_csv('avocado_validate.csv')
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# podzial na X i y
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X_train = avocado_train[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
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y_train = avocado_train[['type']]
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X_test = avocado_test[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
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y_test = avocado_test[['type']]
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print(X_train.shape[1])
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# keras model
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model = Sequential()
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model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu'))
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model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))
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early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
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# kompilacja
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model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
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# trenowanie modelu
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model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))
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# predykcja
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prediction = model.predict(X_test)
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# ewaluacja
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rmse = mean_squared_error(y_test, prediction)
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_run.log_scalar("rmse", rmse)
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# zapisanie modelu
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model.save('avocado_model.h5')
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return rmse
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@ex.automain
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def my_main(epochs, batch_size):
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print(prepare_model())
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ex.run()
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ex.add_artifact('avocado_model.h5') |