print("\nBootstrapping to Balance - Training set size: %d (%d X %d)" %
(train_labels.shape[0], MAX, np.unique(train_labels).shape[0]))
print("=" * 30, "\n")
#n_epochs = 70
batch_size_for_generators = 32
train_datagen = DataGenerator(rotation_range=178,
horizontal_flip=True,
vertical_flip=True,
shear_range=0.6,
stain_transformation=True)
train_gen = train_datagen.flow(train_images,
Y_train,
batch_size=batch_size_for_generators)
### VALIDATION ###
valid_datagen = DataGenerator()
valid_gen = valid_datagen.flow(valid_images,
Y_valid,
batch_size=batch_size_for_generators)
start = time.time()
class Mycbk(ModelCheckpoint):
def __init__(self,
model,
print("=" * 40, "\n")
batch_size_for_generators = 64
train_datagen = DataGenerator(rotation_range=180,
horizontal_flip=True,
vertical_flip=True,
shear_range=0.6,
stain_transformation=True)
# train_gen = train_datagen.flow(train_images, Y_train, batch_size=batch_size_for_generators)
# VALIDATION
valid_datagen = DataGenerator()
valid_gen = valid_datagen.flow(valid_images,
Y_valid,
batch_size=batch_size_for_generators)
start = time.time()
class Mycbk(ModelCheckpoint):
def __init__(self,
model,
filepath,
monitor='val_loss',
mode='min',
save_best_only=True):
self.single_model = model
super(Mycbk, self).__init__(filepath, monitor, save_best_only, mode)
def set_model(self, model):
