ium_434742/sacred-training.py
patrycjalazna 89ee49bca3
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params in function added
2021-05-14 21:01:05 +02:00

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2.1 KiB
Python

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