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)
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="ann", help="Model to train.")
args = parser.parse_args()
lag = 6
config = {"batch": 256, "epochs": 200}
file1 = 'data/compareData/train_data_compare(after6am).csv'
file2 = 'data/compareData/test_data_cluster0_compare(after6am).csv'
X_train, y_train, X_test, y_test, scaler = 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([6, 100, 100, 1])
train_model(m, X_train, y_train, args.model, config)
if args.model == 'ann':
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1]))
m = model.get_ann([6, 20, 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([6, 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([6, 100, 100, 100, 1])
train_seas(m, X_train, y_train, args.model, config)
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="lstm", help="Model to train.")
parser.add_argument("--lag", default="12", help="specify number of lags")
parser.add_argument("--file_name",
default="970_1_data.csv",
help="Csv file name")
args = parser.parse_args()
lag = int(args.lag)
config = {"batch": 256, "epochs": 600} # training config
file = args.file_name
model_name = os.path.splitext(file)[0]
X_train, y_train, _, _, _ = process_data(file, lag)
if args.model == 'lstm':
m = model.get_lstm(
[[3, lag], 64, 64,
1]) # changing the input to 3, lags for rain data model
train_model(m, X_train, y_train, "{}_lstm".format(model_name), config)
if args.model == 'gru':
m = model.get_gru([[3, lag], 64, 64, 1])
train_model(
m, X_train, y_train, "{}_gru".format(model_name),
config) # changing the input to 3, lags for rain data model
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument(
"--model",
default="saes",
help="Model to train.")
args = parser.parse_args()
lag = 12
config = {"batch": 256, "epochs": 50}
file1 = '/home/VICOMTECH/icejudo/PROYECTOS/MiRepoGithub/MLflow-dvc-data/data/train.csv'
file2 = '/home/VICOMTECH/icejudo/PROYECTOS/MiRepoGithub/MLflow-dvc-data/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)
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="lstm",
help="Model to train.") #model name
parser.add_argument("--lag", default="12", help="lags") # number of lags
parser.add_argument("--file_name",
default="970_1_data.csv",
help="Csv file name") # file name of the csv
args = parser.parse_args()
lag = int(args.lag)
config = {"batch": 256, "epochs": 600}
file = args.file_name
X_train, y_train, _, _, _ = process_data(file, lag)
if args.model == 'lstm': # training lstm model
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
m = model.get_lstm([lag, 64, 64, 1])
train_model(m, X_train, y_train, args.model, config)
if args.model == 'gru': # training gru model
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
m = model.get_gru([lag, 64, 64, 1])
train_model(m, X_train, y_train, args.model, config)
if args.model == 'saes': # training saes model
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1]))
m = model.get_saes([lag, 400, 400, 400, 1])
train_seas(m, X_train, y_train, args.model, config)
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument(
"--model",
default="lstm",
help="Model to train.")
args = parser.parse_args()
config = {"batch": 64, "epochs": 150}
X_train, y_train, _, _, _ = data_process(12)
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 args.model == 'bilstm':
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
m = model.get_bilstm([12, 64, 1])
train_model(m, X_train, y_train, args.model, config)
def start_train():
config = {"batch": 256, "epochs": 10}
file_path1=fileStr1.get()
file_path2='data/100211data/100211_all_test.csv'
if file_path1=="" or file_path2=="":
tkinter.messagebox.askokcancel(title='请选择文件~', message='请选择两个文件')
return
print("start_train")
callBack = keras.callbacks.LambdaCallback(
on_epoch_end=lambda epoch, logs: print("epoch",epoch)
)
needLstm =modelName.get()=="lstm" or modelName.get()=="all"
needAllDense = modelName.get()=="allDense" or modelName.get()=="all"
# _thread.start_new_thread(show_progress,())
if needLstm:
for i in range(lagIntStart.get(),lagIntEnd.get(),2):
lag = i
X_train, y_train, _, _, _ = process_data(file_path1, file_path2, lag)
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
m = model.get_lstm([lag, 64, 64, 1])
train_model(m, X_train, y_train, "lstm", config,lag,callBack)
if needAllDense:
for i in range(lagIntStart.get(), lagIntEnd.get(), 2):
lag=i
X_train, y_train, _, _, _ = process_data(file_path1, file_path2, lag)
m = model.get_AllDense([lag, 64, 64, 1])
train_allDense_model(m, X_train, y_train, "AllDense", config, lag,callBack)
tkinter.messagebox.askokcancel(title='ok~', message='训练完成，结果保存在model文件夹下')
return
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument(
"--model",
default="bidirectional",
help="Model to train.")
args = parser.parse_args()
lag = 4 # how far to look back
config = {"batch": 256, "epochs": 15 }
file1 = './data/train1.csv'
file2 = './data/test1.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([4, 64, 64, 1])
train_model(m, X_train, y_train, args.model, config)
if args.model == 'bidirectional':
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
m = model.get_bidirectional([4, 64, 64, 1])
train_model(m, X_train, y_train, args.model, config)
if args.model == 'simplernn':
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
m = model.get_simplernn([4, 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([4, 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([4, 400, 400, 400, 1])
train_seas(m, X_train, y_train, args.model, config)
def run_thread(X_train, y_train, batchs, epochs, number_input):
config = {"batch": batchs, "epochs": epochs}
m1 = model.get_lstm(
[X_train.shape[1], number_input, number_input, 1, X_train.shape[2]])
train_model(
m1, X_train, y_train,
'lstm' + str(number_input) + '_' + str(batchs) + '_' + str(epochs),
config)
def generate_new_model(model_to_train, input_shape):
if model_to_train == 'seas':
m = model.get_saes(input_shape, [400, 400, 400, 1])
elif model_to_train == 'lstm':
m = model.get_lstm(input_shape, [64, 64, 1])
if model_to_train == 'gru':
m = model.get_gru(input_shape, [64, 64, 1])
if model_to_train == "feedfwd":
m = model.get_feed_fwd(input_shape, [64, 1])
if model_to_train == "deepfeedfwd":
m = model.get_deep_feed_fwd(input_shape, [64, 128, 64, 1])
return m
def main(target_model):
config = {"batch": 10, "epochs": 25000}
train = 'data/train/'
test = 'data/test/'
X_train, y_train, _, _ = process_data(train, test)
if target_model == 'lstm':
m = model.get_lstm([58, 64, 64, 2])
train_model(m, X_train, y_train, target_model, config)
if target_model == 'gru':
m = model.get_gru([58, 64, 64, 2])
train_model(m, X_train, y_train, target_model, config)
if target_model == 'saes':
X_train = np.reshape(X_train, (X_train.shape[0], -1))
m = model.get_saes([4800, 400, 400, 400, 2])
train_seas(m, X_train, y_train, target_model, 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":500}
path=r'D:\data\2018_data\process_data\M30f.csv'
if args.model in ['lstm','gru','saes']:
X_train,y_train,_,_,_=process_data(path,lag)
if args.model == 'merge_lstm':
lag = [12, 96, 672]
X_train, y_train, _, _, _ =min_day_week_data(path,lag)
# X_train = np.reshape(X_train,[X_train.shape[0], X_train.shape[1], 1])
m = model.merge_lstm([12,24,7,1])
train_merge_lstm(m, X_train, y_train, args.model, config)
if args.model=='lstm':
X_train=np.reshape(X_train,[X_train.shape[0],X_train.shape[1],1])
m=model.get_lstm([12,64,128,1])
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([12,64,128,1])
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 args.model=='cnn':
lag=96
X_train,y_train,_,_,_=cnn_data(path,lag)
X_train=np.reshape(X_train,[X_train.shape[0],X_train.shape[1],X_train.shape[2],1])
# y_train=np.reshape(y_train,[y_train.shape[0],y_train.shape[1],1])
m=model.get_cnn([68,96,16,32,64])
train_model(m,X_train,y_train,args.model,config)
def training(self):
gru_model = get_gru(self.units)
train_model(gru_model, self.X_train, self.y_train, 'GRU', self.config)
lstm_model = get_lstm(self.units)
train_model(lstm_model, self.X_train, self.y_train, 'LSTM',
self.config)
