def TTA_fitting(tr_gen, val_gen, test_gen, dummy_test_gen):
fold_val, fold_test = [], []
for i in range(N_ENSMBLE):
callbacks = [
# val_lossが下がらなくなった際に学習を終了するコールバック
EarlyStopping(monitor='val_loss', patience=30, verbose=1),
# val_lossが下がらなくなった際に学習率を下げるコールバック
ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=10,
min_lr=1e-9),
# val_categorical_accuracyが最大のmodelを保存するコールバック
ModelCheckpoint(savePath,
monitor='val_categorical_accuracy',
save_best_only=True)
]
# load data
RCNN = Residual_CNN(N_BLOCK, N_LAYER, FILTER, KERNEL)
model = RCNN.build_model()
# model training
model.fit_generator(tr_gen,
callbacks,
steps_per_epoch=tr_x.shape[0] // BATCH_SIZE,
epochs=EPOCH,
validation_data=val_gen,
validation_steps=val_x.shape[0] // BATCH_SIZE)
# TTA dummy test data
val_pred = TTA(model, dummy_test_gen, sub_epoch=10)
tta_val_acc = accuracy_score(np.argmax(val_y, 1),
np.argmax(val_pred, 1))
print(f"TTA_vaild_accuracy : {tta_val_acc}")
# TTA test data
test_pred = TTA(model, test_gen, sub_epoch=10)
# stacking TTA prediction
fold_val.append(val_pred)
fold_test.append(test_pred)
# TTA ensmble accuracy
if i != 0:
mean_val_pred = np.mean(fold_val, axis=0)
mean_test_pred = np.mean(fold_test, axis=0)
ensmble_tta_val_acc = accuracy_score(np.argmax(val_y, 1),
np.argmax(mean_val_pred, 1))
print(f"ensmbleTTA_vaild_accuracy : {ensmble_tta_val_acc}")
# saving TTA ensmble prediction of test data
submission = pd.Series(mean_test_pred, name='label')
submission.to_csv(os.path.join(
PATH, 'submission_ensemble_' + str(i + 1) + ".csv"),
header=True,
index_label='id')
def Fold_fitting(train_x, train_y, test):
N_FOLD = 5
RCNN = Residual_CNN(N_BLOCK, N_LAYER, FILTER, KERNEL)
model = RCNN.build_model()
fold = StratifiedKFold(n_splits=N_FOLD, random_state=1103)
pred = []
acc = []
for i, (tr_idx,
val_idx) in enumerate(fold.split(train_x, np.argmax(train_y, 1))):
callbacks = [
# val_lossが下がらなくなった際に学習を終了するコールバック
EarlyStopping(monitor='val_loss', patience=30, verbose=1),
# val_lossが下がらなくなった際に学習率を下げるコールバック
ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=10,
min_lr=1e-9),
# val_categorical_accuracyが最大のmodelを保存するコールバック
ModelCheckpoint(savePath,
monitor='val_categorical_accuracy',
save_best_only=True)
]
print(f"==========={i}_fold start ================= ")
train_split = train_x[tr_idx], train_y[val_idx]
val_split = val_y[tr_idx], val_y[val_idx]
tr_gen, val_gen, test_gen, dummy_test_gen = get_argment_generator(
train_split, val_split, train, test, BATCH_SIZE)
h = model.fit_generator(tr_gen,
epochs=EPOCH,
steps_per_epoch=len(tr_x) // BATCH_SIZE,
validation_data=val_gen,
validation_steps=len(val_x) // BATCH_SIZE,
callbacks=callbacks)
val_pred = model.predict(dummy_test_gen)
test_pred = model.predict(test_gen)
val_score = accuracy_score(np.argmax(val_split[1], 1), val_pred)
print(f"fold_{i} val accuracy : {val_score}")
pred.append(test_pred)
mean_pred = np.mean(pred, axis=0)
pred_y = np.argmax(mean_pred, 1)
submission = pd.Series(pred_y, name='label')
submission.to_csv(os.path.join(PATH, f"submission_fold{N_FOLD}.csv"),
header=True,
index_label='id')