out = tf.keras.layers.Dense(N_CLASSES, activation='sigmoid')(out)
model = tf.keras.models.Model(inputs=model.input, outputs=out)
if config.optimizer == 'adam':
opt = Adam(config.lr)
elif config.optimizer == 'sgd':
opt = SGD(config.lr, momentum=0.9)
else:
opt = RMSprop(config.lr, momentum=0.9)
if config.l2 > 0:
model = apply_kernel_regularizer(
model, tf.keras.regularizers.l2(config.l2))
model.compile(optimizer=opt,
loss='binary_crossentropy',
metrics=['AUC'])
model.summary()
model.load_weights(NAME)
print('loaded pretrained model')
""" DATA """
# wavs = glob.glob('/codes/2020_track3/t3_audio/*.wav')
wavs = glob.glob(
'/media/data1/datasets/ai_challenge/2020_track3/t3_audio/*.wav')
wavs.sort()
to_mel = magphase_to_mel(config.n_mels)
for wav in wavs:
sample = load_wav(wav)[None, :] # [1, freq, time, chan2]
sample = complex_to_magphase(sample)
sample = to_mel(sample)
if config.pretrain:
model.load_weights(NAME)
print('loaded pretrained model')
if config.optimizer == 'adam':
opt = Adam(config.lr, clipvalue=0.01)
elif config.optimizer == 'sgd':
opt = SGD(config.lr, momentum=0.9)
else:
opt = RMSprop(config.lr, momentum=0.9)
if config.l2 > 0:
model = apply_kernel_regularizer(model,
tf.keras.regularizers.l2(config.l2))
model.compile(optimizer=opt, loss=custom_loss, metrics=[d_total, cos_sim])
model.summary()
""" DATA """
train_set = make_dataset(config, training=True)
test_set = make_dataset(config, training=False)
""" TRAINING """
callbacks = [
CSVLogger(NAME.replace('.h5', '.log'), append=True),
LearningRateScheduler(custom_scheduler(4096, TOTAL_EPOCH / 12)),
SWA(start_epoch=TOTAL_EPOCH // 2, swa_freq=2),
ModelCheckpoint(NAME,
monitor='val_d_total',
save_best_only=True,
verbose=1),
TerminateOnNaN()
]
out = tf.keras.layers.ReLU()(out)
out = tf.keras.layers.Dense(30, activation=None)(out)
model = tf.keras.models.Model(inputs=model.input, outputs=out)
if config.optimizer == 'adam':
opt = Adam(config.lr, clipvalue=config.clipvalue)
elif config.optimizer == 'sgd':
opt = SGD(config.lr, momentum=0.9, clipvalue=config.clipvalue)
else:
opt = RMSprop(config.lr, momentum=0.9, clipvalue=config.clipvalue)
if config.l2 > 0:
model = apply_kernel_regularizer(model,
tf.keras.regularizers.l2(config.l2))
model.compile(
optimizer=opt,
loss=mse, # 'binary_crossentropy',
metrics=[d_total, baseline])
model.summary()
""" DATA """
train_set = make_dataset(config, training=True)
test_set = make_dataset(config, training=False)
print(train_set)
for x, y in train_set.take(1):
print(tf.shape(x), tf.shape(y))
""" TRAINING """
from train_frame import custom_scheduler
callbacks = [
CSVLogger(NAME.replace('.h5', '.log'), append=True),
# LearningRateScheduler(custom_scheduler(config.n_dim*8, config.epochs/10)),
ReduceLROnPlateau(monitor='val_loss',
factor=config.lr_factor,
model = tf.keras.models.Model(inputs=x, outputs=out)
model.summary()
lr = config.lr
if config.optimizer == 'adam':
opt = Adam(lr, clipvalue=config.clipvalue)
elif config.optimizer == 'sgd':
opt = SGD(lr, momentum=0.9, clipvalue=config.clipvalue)
else:
opt = RMSprop(lr, momentum=0.9, clipvalue=config.clipvalue)
if config.l2 > 0:
model = apply_kernel_regularizer(
model, tf.keras.regularizers.l2(config.l2))
model.compile(optimizer=opt,
loss=custom_loss(alpha=0.8, l2=config.loss_l2),
metrics=[d_total(config.multiplier), cos_sim])
model.summary()
if config.pretrain:
model.load_weights(NAME)
print('loaded pretrained model')
""" DATA """
train_set = make_dataset(config, training=True)
test_set = make_dataset(config, training=False)
""" TRAINING """
callbacks = [
CSVLogger(NAME.replace('.h5', '.log'), append=True),
LearningRateScheduler(
custom_scheduler(4096, TOTAL_EPOCH / 12, config.lr_div)),
SWA(start_epoch=TOTAL_EPOCH // 2, swa_freq=2),
config.lr = cosine_decay_with_warmup(
config.steps_per_epoch * 5, config.lr,
config.steps_per_epoch * config.steps)
if config.optimizer == 'adam':
opt = Adam(config.lr)
elif config.optimizer == 'sgd':
opt = SGD(config.lr, momentum=0.9)
else:
opt = RMSprop(config.lr, momentum=0.9)
if config.l2 > 0:
model = apply_kernel_regularizer(
model, tf.keras.regularizers.l2(config.l2))
model.compile(optimizer=opt,
loss='categorical_crossentropy',
metrics=['accuracy', 'AUC'])
model.summary()
if config.pretrain:
model.load_weights(NAME)
print('loadded pretrained model')
""" DATA """
# 1. IMPORTING TRAINING DATA & PRE-PROCESSING
PATH = '/datasets/ai_challenge/icassp'
prefixes = ['gen'] # , 'noise_train']
x = [np.load(os.path.join(PATH, f'{pre}_x.npy')) for pre in prefixes]
y = [np.load(os.path.join(PATH, f'{pre}_y.npy')) for pre in prefixes]
test_x = [np.load(os.path.join(PATH, '50_x.npy'))]
test_y = [np.load(os.path.join(PATH, '50_y.npy'))]
config.lr = tf.keras.experimental.CosineDecay(config.lr,
config.epochs *
config.steps_per_epoch,
alpha=config.lr * 1e-2)
if config.optimizer == 'adam':
opt = Adam(config.lr)
elif config.optimizer == 'sgd':
opt = SGD(config.lr, momentum=0.9)
else:
opt = RMSprop(config.lr, momentum=0.9)
if config.l2 > 0:
model = apply_kernel_regularizer(
model, tf.keras.regularizers.l2(config.l2))
model.compile(optimizer=opt,
loss='categorical_crossentropy',
metrics=['accuracy', 'AUC'])
# model.summary()
if config.pretrain:
model.load_weights(NAME)
print('loadded pretrained model')
""" DATA """
# TRAINING DATA
PATH = '/codes/generate_wavs'
backgrounds = pickle.load(
open(os.path.join(PATH, 'drone_normed_complex.pickle'), 'rb'))
voices = pickle.load(
open(os.path.join(PATH, 'voice_normed_complex.pickle'), 'rb'))
labels = np.load(os.path.join(PATH, 'voice_labels.npy'))
y_test = np.loadtxt('D:/study/data/test_label.txt')
x_test = x_test[:5138,:,:,:]
print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)
print('끝')
# 2. 모델
model = Sequential()
model.add(EfficientNetB0(include_top=False))
model.add(Dense(20, activation='softmax',name='s1'))
model.summary()
# 3. 훈련
model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['acc'])
model.fit(x_train, y_train, batch_size=12, epochs=2, validation_split=0.1)
# 4. 평가, 예측
loss, acc = model.evaluate(x_train, y_train)
y_predict = model.predict(x_test)
print("loss : ", loss)
print("acc : ", acc)
print(y_predict)
print('진짜 끝')
