qurator-spk/sbb_pixelwise_segmentation
1
0
Fork 0
mirror of https://github.com/qurator-spk/sbb_pixelwise_segmentation.git synced 2025-06-09 11:50:04 +02:00
Code Issues Releases Wiki Activity

Merge pull request #18 from johnlockejrr/unifying-training-models

Browse source 
Deprecations in train.py and check an argument in inference.py
This commit is contained in:
Clemens Neudecker 2025-04-17 15:43:28 +02:00 • committed by GitHub
commit d57de478eb
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
2 changed files with 7 additions and 6 deletions
Download patch file Download diff file Expand all files Collapse all files

12
train.py
View file

@ -278,16 +278,16 @@ def run(_config, n_classes, n_epochs, input_height,
if (task == "segmentation" or task == "binarization"):
if not is_loss_soft_dice and not weighted_loss:
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
if is_loss_soft_dice:
model.compile(loss=soft_dice_loss,
optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
if weighted_loss:
model.compile(loss=weighted_categorical_crossentropy(weights),
optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
elif task == "enhancement":
model.compile(loss='mean_squared_error',
optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
# generating train and evaluation data
@ -300,7 +300,7 @@ def run(_config, n_classes, n_epochs, input_height,
##score_best=[]
##score_best.append(0)
for i in tqdm(range(index_start, n_epochs + index_start)):
model.fit_generator(
model.fit(
train_gen,
steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs)) / n_batch) - 1,
validation_data=val_gen,
@ -388,7 +388,7 @@ def run(_config, n_classes, n_epochs, input_height,
#f1score_tot = [0]
indexer_start = 0
opt = SGD(lr=0.01, momentum=0.9)
opt = SGD(learning_rate=0.01, momentum=0.9)
opt_adam = tf.keras.optimizers.Adam(learning_rate=0.0001)
model.compile(loss="binary_crossentropy",
optimizer = opt_adam,metrics=['accuracy'])