-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathtrain_samm_windows_all.py
158 lines (150 loc) · 6.09 KB
/
train_samm_windows_all.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
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")))
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)
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=30, 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=10, 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_window = cp["DEFAULT"].get("features_subdir_vad_window")
label_samm_window = cp["DEFAULT"].get("label_samm_window")
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_std_variation_max")
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")
features_engineered_root = os.path.join(fme_dir_samm, features_engineered)
features_engineered_dir = os.path.join(features_engineered_root, features_subdir_vad_window)
df_label_path = os.path.join(features_engineered_root, label_samm_window)
df_label = pd.read_csv(df_label_path)
output_dir = save_config(output_fold)
main(output_dir, optimizer)