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train_samm_all.py
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train_samm_all.py
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import pandas as pd
import matplotlib.pyplot as plt
import os
from configparser import ConfigParser
from generator_vad import FeatruesSequence
from keras.optimizers import Adam, SGD
from sklearn.model_selection import train_test_split, KFold, cross_val_score, cross_validate, StratifiedKFold
import shutil
from datetime import datetime
from model import Conv1D_model, LSTM_model, GRU_model, ConcatCNN_model, ConcatCNN_SAMM_model
from clr_callback import *
from callback import SaveMinLoss
import pickle
from sklearn.preprocessing import StandardScaler
from math import ceil
from pathlib import Path
from keras.utils import multi_gpu_model, plot_model
from model_resnet1d import Resnet1D
import tensorflow as tf
import gc
def set_sess_cfg():
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
for device in gpu_devices:
tf.config.experimental.set_memory_growth(device, True)
def save_config(output):
if not os.path.exists(output):
os.makedirs(output)
dir = os.path.dirname(__file__)
output = os.path.join(output, str(datetime.now().strftime("%Y%m%d-%H%M%S")+'_samm_'+model_name+str(n_gru_units)))
output_dir_src = os.path.join(output, 'src')
os.makedirs(output_dir_src, exist_ok=True)
print(f"backup config file to {output_dir_src}")
shutil.copy("config.ini", os.path.join(output_dir_src, 'config.ini'))
shutil.copy("model_resnet1d.py", os.path.join(output_dir_src, 'model_resnet1d.py'))
train_file = os.path.basename(__file__)
shutil.copy(os.path.join(dir, train_file), os.path.join(output_dir_src, train_file))
return output
def main(output_dir, optimizer):
train_sequence = FeatruesSequence(
dataset_csv_file=str(df_label_path),
batch_size=batch_size,
random_state=seed,
)
validation_sequence = FeatruesSequence(
dataset_csv_file=str(df_label_path),
batch_size=batch_size,
shuffle_on_epoch_end=False,
test=True,
)
model_method = eval(f'{model_name}_model')
model = model_method(n_timesteps=n_timesteps, n_features=n_features, n_gru_units=n_gru_units)
print(model.summary())
plot_model(model, to_file=os.path.join(output_dir, 'model.png'))
if optimizer == 'adam':
optimizer = Adam(lr=initial_learning_rate)
base_lr = base_lr_adam
max_lr = max_lr_adam
elif optimizer == 'sgd':
optimizer = SGD(momentum=momentum, decay=decay)
base_lr = base_lr_sgd
max_lr = max_lr_sgd
model.compile(optimizer=optimizer, loss=loss_func, metrics=['mse', 'mae'])
earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=32, verbose=0, mode='min')
# if cyclicLR_mode == 'exp_range':
# gamma = 0.99994
# else:
# gamma = 1.
# clr = CyclicLR(mode=cyclicLR_mode, step_size=train_steps*2, base_lr=base_lr, max_lr=max_lr, gamma=gamma)
# change_lr = LearningRateScheduler(scheduler, verbose=1)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=9, verbose=1)
model_save_path = os.path.join(output_dir, output_model_name)
checkpoint = ModelCheckpoint(
model_save_path,
save_weights_only=False,
save_best_only=True,
verbose=1,
)
csv_logger = CSVLogger(os.path.join(output_dir, 'training.csv'))
save_min_loss = SaveMinLoss(filepath=output_dir)
tensor_board = TensorBoard(log_dir=os.path.join(output_dir, "logs"), batch_size=batch_size)
callbacks = [
checkpoint,
tensor_board,
csv_logger,
# clr,
reduce_lr,
save_min_loss,
earlystop,
]
print("** start training **")
# Training.
history = model.fit_generator(
generator=train_sequence,
# steps_per_epoch=train_steps,
epochs=epochs,
validation_data=validation_sequence,
# validation_steps=validation_steps,
callbacks=callbacks,
# class_weight='auto',
workers=generator_workers,
shuffle=False,
)
# dump history
print("** dump history **")
with open(os.path.join(output_dir, "history.pkl"), "wb") as f:
pickle.dump({
"history": history.history,
}, f)
print("** done! **")
mse = history.history["mse"]
val_mse = history.history["val_mse"]
results_file = os.path.join(output_dir, "results.log")
with open(results_file, 'a') as f:
f.write(f'train_mses:{mse}')
f.write(f'val_mses:{val_mse}')
if __name__ == "__main__":
set_sess_cfg()
# parser config
config_file = "./config.ini"
cp = ConfigParser()
cp.read(config_file)
fme_dir_samm = cp["DEFAULT"].get("fme_dir_samm")
features_engineered = cp["DEFAULT"].get("features_engineered")
features_subdir_vad = cp["DEFAULT"].get("features_subdir_vad")
label_samm = cp["DEFAULT"].get("label_samm")
batch_size = cp["TRAIN"].getint("batch_size")
seed = cp["TRAIN"].getint("seed")
output_fold = cp["DEFAULT"].get("output_fold")
n_timesteps = cp["DEFAULT"].getint("n_timesteps_samm")
n_features = cp["DEFAULT"].getint("n_features_vad")
initial_learning_rate = cp["TRAIN"].getfloat("initial_learning_rate")
output_model_name = cp["TRAIN"].get("output_model_name")
base_lr_adam = cp["TRAIN"].getfloat("base_lr_adam")
max_lr_adam = cp["TRAIN"].getfloat("max_lr_adam")
momentum = cp["TRAIN"].getfloat("momentum")
decay = cp["TRAIN"].getfloat("decay")
base_lr_sgd = cp["TRAIN"].getfloat("base_lr_sgd")
max_lr_sgd = cp["TRAIN"].getfloat("max_lr_sgd")
loss_func = cp["TRAIN"].get("loss_func")
cyclicLR_mode = cp["TRAIN"].get("cyclicLR_mode")
epochs = cp["TRAIN"].getint("epochs")
generator_workers = cp["TRAIN"].getint("generator_workers")
optimizer = cp["TRAIN"].get("optimizer")
model_name = cp["TRAIN"].get("model_name")
n_gru_units = cp["TRAIN"].getint("n_gru_units")
features_engineered_root = os.path.join(fme_dir_samm, features_engineered)
features_engineered_dir = os.path.join(features_engineered_root, features_subdir_vad)
df_label_path = os.path.join(features_engineered_root, label_samm)
df_label = pd.read_csv(df_label_path)
output_dir = save_config(output_fold)
main(output_dir, optimizer)