preds = []
gt = []
for id in ids:
test_ids = {id}
train_ids = set([x.split("/")[-1][:5] for x in files]) - test_ids
train_val, test = [x for x in files if x.split("/")[-1][:5] in train_ids],\
[x for x in files if x.split("/")[-1][:5] in test_ids]
train, val = train_test_split(train_val, test_size=0.1, random_state=1337)
train_dict = {k: np.load(k) for k in train}
test_dict = {k: np.load(k) for k in test}
val_dict = {k: np.load(k) for k in val}
model = get_model_cnn_crf(lr=0.0001)
file_path = "cnn_crf_model_20_folds.h5"
# model.load_weights(file_path)
checkpoint = ModelCheckpoint(file_path,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
early = EarlyStopping(monitor="val_acc",
mode="max",
patience=20,
verbose=1)
redonplat = ReduceLROnPlateau(monitor="val_acc",
mode="max",
files = sorted(glob(os.path.join(base_path, "*.npz")))
ids = sorted(list(set([x.split("/")[-1][:5] for x in files])))
#split by test subject
train_ids, test_ids = train_test_split(ids, test_size=0.15, random_state=1338)
train_val, test = [x for x in files if x.split("/")[-1][:5] in train_ids],\
[x for x in files if x.split("/")[-1][:5] in test_ids]
train, val = train_test_split(train_val, test_size=0.1, random_state=1337)
train_dict = {k: np.load(k) for k in train}
test_dict = {k: np.load(k) for k in test}
val_dict = {k: np.load(k) for k in val}
model = get_model_cnn_crf()
file_path = "cnn_crf_model.h5"
# model.load_weights(file_path)
checkpoint = ModelCheckpoint(file_path,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
early = EarlyStopping(monitor="val_acc", mode="max", patience=20, verbose=1)
redonplat = ReduceLROnPlateau(monitor="val_acc",
mode="max",
patience=5,
verbose=2)
callbacks_list = [checkpoint, early, redonplat] # early