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fix2
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s434695 2021-05-16 22:00:03 +02:00
parent 26ddaa3a0c
commit f40f237a02
1 changed files with 10 additions and 7 deletions
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train.py
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@ -26,9 +26,10 @@ def my_config():
batch_param = int(sys.argv[1]) batch_param = int(sys.argv[1])
epoch_param = int(sys.argv[2]) epoch_param = int(sys.argv[2])
@ex.capture
def prepare_model(epoch_param, batch_param, _run): def prepare_model(epoch_param, batch_param, _run):
_run.info["prepare_model_ts"] = str(datetime.now()) _run.info["prepare_model_ts"] = str(datetime.now())
# odczytanie danych z plików
vgsales_train = pd.read_csv('train.csv') vgsales_train = pd.read_csv('train.csv')
vgsales_test = pd.read_csv('test.csv') vgsales_test = pd.read_csv('test.csv')
vgsales_dev = pd.read_csv('dev.csv') vgsales_dev = pd.read_csv('dev.csv')
@ -37,33 +38,35 @@ def prepare_model(epoch_param, batch_param, _run):
vgsales_test['Nintendo'] = vgsales_test['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0) vgsales_test['Nintendo'] = vgsales_test['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
vgsales_dev['Nintendo'] = vgsales_dev['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0) vgsales_dev['Nintendo'] = vgsales_dev['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
# podzial na X i y
X_train = vgsales_train.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1) X_train = vgsales_train.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
y_train = vgsales_train[['Nintendo']] y_train = vgsales_train[['Nintendo']]
X_test = vgsales_test.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1) X_test = vgsales_test.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
y_test = vgsales_test[['Nintendo']] y_test = vgsales_test[['Nintendo']]
print(X_train.shape[1]) print(X_train.shape[1])
# keras model
model = Sequential() model = Sequential()
model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu')) model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu'))
model.add(Dense(1,kernel_initializer='normal', activation='sigmoid')) model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))
early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10) early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
# kompilacja
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# model fit
epochs = int(sys.argv[1]) epochs = int(sys.argv[1])
batch_size = int(sys.argv[2]) batch_size = int(sys.argv[2])
# trenowanie modelu
model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test)) model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))
# zapisanie modelu prediction = model.predict(X_test)
rmse = mean_squared_error(y_test, prediction)
_run.log_scalar("rmse", rmse)
model.save('vgsales_model.h5') model.save('vgsales_model.h5')
return rmse
@ex.main @ex.main
def my_main(epoch_param, batch_param): def my_main(epoch_param, batch_param):
print(prepare_model()) print(prepare_model())