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()
]
model.fit(train_set,
epochs=TOTAL_EPOCH,
batch_size=BATCH_SIZE,
steps_per_epoch=config.steps_per_epoch,
validation_data=test_set,
validation_steps=12,
callbacks=callbacks)
model.save(NAME.replace('.h5', '_SWA.h5'))
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,
patience=config.lr_patience),
SWA(start_epoch=config.epochs // 2, swa_freq=2),
ModelCheckpoint(NAME,
monitor='val_d_total',
mode='min',
save_best_only=True),
TerminateOnNaN()
]
model.fit(train_set,
epochs=config.epochs,
batch_size=config.batch_size,
steps_per_epoch=config.steps_per_epoch,
validation_data=test_set,
validation_steps=16,
callbacks=callbacks)
# TODO : BN
model.save(NAME.replace('.h5', '_SWA.h5'))
# mode='max'),
SWA(start_epoch=TOTAL_EPOCH // 2, swa_freq=2),
ModelCheckpoint(NAME,
monitor='val_auc',
mode='max',
save_best_only=True),
TensorBoard(log_dir=f'./logs/{NAME.replace(".h5", "")}',
histogram_freq=0,
profile_batch=2),
TerminateOnNaN()
]
model.fit(train_dataset,
epochs=TOTAL_EPOCH,
batch_size=BATCH_SIZE,
validation_data=(test_x, test_y),
steps_per_epoch=config.steps_per_epoch,
class_weight=class_weight,
callbacks=callbacks)
result = model.evaluate(test_x, test_y, verbose=1)
with open(NAME.replace('.h5', '.log'), 'a') as f:
f.write(f'\n{result}\n')
# SWA
model.compile(tf.keras.optimizers.SGD(0.),
'sparse_categorical_crossentropy')
model.fit(test_x, test_y, batch_size=BATCH_SIZE)
result = model.evaluate(test_x, test_y, verbose=1)
with open(NAME.replace('.h5', '.log'), 'a') as f:
mode='max'),
SWA(start_epoch=TOTAL_EPOCH//2, swa_freq=2),
ModelCheckpoint(NAME,
monitor='val_auc',
mode='max',
save_best_only=True),
TensorBoard(log_dir=f'./logs/{NAME.replace(".h5", "")}',
histogram_freq=0,
profile_batch=2),
TerminateOnNaN()
]
model.fit(train_dataset,
epochs=TOTAL_EPOCH,
batch_size=BATCH_SIZE,
validation_data=(val_x, val_y),
steps_per_epoch=x.shape[0]//BATCH_SIZE,
class_weight=class_weight,
callbacks=callbacks)
result = model.evaluate(test_x, test_y, verbose=1)
with open(NAME.replace('.h5', '.log'), 'a') as f:
f.write(f'\n{result}\n')
# For SWA
repeat = x.shape[0] // BATCH_SIZE
for x, y in train_dataset:
model(x, training=True)
repeat -= 1
if repeat <= 0:
break
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')
