rep = 3
elif val_groups[i] in ['c', 'g', 'k', 'l']:
rep = 2
val_idx.extend([val_idx0[i]] * rep)
val_idx = np.asarray(val_idx)
validation_steps = len(val_idx)
steps_per_epoch = 5 * int(len(train_idx) / batch_size)
print('Training fold', it)
print('steps_per_epoch', steps_per_epoch, 'validation_steps',
validation_steps)
data_gen = BaseMaskDatasetIterator(train_idx,
random_transformers=[
aug_mega_hardcore((-0.25, 0.6)),
aug_mega_hardcore((-0.6, 0.25))
],
batch_size=batch_size,
shuffle=True,
seed=1)
np.random.seed(it + 111)
random.seed(it + 111)
tf.set_random_seed(it + 111)
# tbCallback = TensorBoard(log_dir="tb_logs/densenet_softmax_{0}".format(it), histogram_freq=0, write_graph=True, write_images=False)
lrSchedule = LearningRateScheduler(lambda epoch: schedule_steps(
epoch, [(1e-5, 2), (3e-4, 4), (1e-4, 6)]))
rep = 1
if val_groups[i] in ['b', 'd', 'e', 'n']:
rep = 4
elif val_groups[i] in ['c']:
rep = 3
val_idx.extend([val_idx0[i]] * rep)
val_idx = np.asarray(val_idx)
validation_steps = len(val_idx)
steps_per_epoch = 5 * int(len(train_idx) / batch_size)
print('Training fold', it)
print('steps_per_epoch', steps_per_epoch, 'validation_steps', validation_steps)
data_gen = BaseMaskDatasetIterator(train_idx,
random_transformers=[aug_mega_hardcore((-0.25, 0.6)), aug_mega_hardcore((-0.6, 0.25))],
batch_size=batch_size,
shuffle=True,
seed=1
)
np.random.seed(it+111)
random.seed(it+111)
tf.set_random_seed(it+111)
# tbCallback = TensorBoard(log_dir="tb_logs/inception_softmax_{0}".format(it), histogram_freq=0, write_graph=True, write_images=False)
model = get_inception_resnet_v2_unet_softmax((None, None), weights=None)
model.load_weights(path.join(models_folder, 'inception_resnet_v2_weights_{0}.h5'.format(it)))
lrSchedule = LearningRateScheduler(lambda epoch: schedule_steps(epoch, [(1e-6, 2), (1e-5, 10), (5e-6, 20), (3e-6, 25)]))