"""
Train the NN model.
"""
import sys
import warnings
import argparse
import numpy as np
import pandas as pd
from data.data import process_data
from model import model
from keras.models import Model
from keras.callbacks import EarlyStopping
warnings.filterwarnings("ignore")
def train_model(model, X_train, y_train, name, config):
"""train
train a single model.
# Arguments
model: Model, NN model to train.
X_train: ndarray(number, lags), Input data for train.
y_train: ndarray(number, ), result data for train.
name: String, name of model.
config: Dict, parameter for train.
"""
model.compile(loss="mse", optimizer="rmsprop", metrics=['mape'])
# early = EarlyStopping(monitor='val_loss', patience=30, verbose=0, mode='auto')
hist = model.fit(
X_train, y_train,
batch_size=config["batch"],
epochs=config["epochs"],
validation_split=0.05)
model.save('model/' + name + '.h5')
df = pd.DataFrame.from_dict(hist.history)
df.to_csv('model/' + name + ' loss.csv', encoding='utf-8', index=False)
def train_seas(models, X_train, y_train, name, config):
"""train
train the SAEs model.
# Arguments
models: List, list of SAE model.
X_train: ndarray(number, lags), Input data for train.
y_train: ndarray(number, ), result data for train.
name: String, name of model.
config: Dict, parameter for train.
"""
temp = X_train
# early = EarlyStopping(monitor='val_loss', patience=30, verbose=0, mode='auto')
for i in range(len(models) - 1):
if i > 0:
p = models[i - 1]
hidden_layer_model = Model(input=p.input,
output=p.get_layer('hidden').output)
temp = hidden_layer_model.predict(temp)
m = models[i]
m.compile(loss="mse", optimizer="rmsprop", metrics=['mape'])
m.fit(temp, y_train, batch_size=config["batch"],
epochs=config["epochs"],
validation_split=0.05)
models[i] = m
saes = models[-1]
for i in range(len(models) - 1):
weights = models[i].get_layer('hidden').get_weights()
saes.get_layer('hidden%d' % (i + 1)).set_weights(weights)
train_model(saes, X_train, y_train, name, config)
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument(
"--model",
default="lstm",
help="Model to train.")
args = parser.parse_args()
lag = 12
config = {"batch": 256, "epochs": 600}
file1 = 'data/train.csv'
file2 = 'data/test.csv'
X_train, y_train, _, _, _ = process_data(file1, file2, lag)
if args.model == 'lstm':
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
m = model.get_lstm([12, 64, 64, 1])
train_model(m, X_train, y_train, args.model, config)
if args.model == 'gru':
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
m = model.get_gru([12, 64, 64, 1])
train_model(m, X_train, y_train, args.model, config)
if args.model == 'saes':
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1]))
m = model.get_saes([12, 400, 400, 400, 1])
train_seas(m, X_train, y_train, args.model, config)
if __name__ == '__main__':
main(sys.argv)
