def fit(self, subpolicies, X, y):
which = np.random.randint(len(subpolicies), size=len(X))
for i, subpolicy in enumerate(subpolicies):
X[which == i] = subpolicy(X[which == i])
callback = TQDMCallback(leave_inner=False, leave_outer=False)
callback.on_train_batch_begin = callback.on_batch_begin
callback.on_train_batch_end = callback.on_batch_end
self.model.fit(X,
y,
CHILD_BATCH_SIZE,
CHILD_EPOCHS,
verbose=0,
callbacks=[callback])
return self
def train_model(model, space='K', n=1):
print(model.summary(line_length=150))
run_id = f'kikinet_sep_{space}{n}_af{AF}_{int(time.time())}'
chkpt_path = f'checkpoints/{run_id}' + '-{epoch:02d}.hdf5'
print(run_id)
chkpt_cback = ModelCheckpoint(chkpt_path, period=n_epochs // 2)
log_dir = op.join('logs', run_id)
tboard_cback = TensorBoard(
profile_batch=0,
log_dir=log_dir,
histogram_freq=0,
write_graph=True,
write_images=False,
)
lrate_cback = LearningRateScheduler(learning_rate_from_epoch)
tqdm_cb = TQDMCallback(metric_format="{name}: {value:e}")
tqdm_cb.on_train_batch_begin = tqdm_cb.on_batch_begin
tqdm_cb.on_train_batch_end = tqdm_cb.on_batch_end
if space == 'K':
train_gen = train_gen_k
val_gen = val_gen_k
elif space == 'I':
if n == 2:
train_gen = train_gen_last
val_gen = val_gen_last
elif n == 1:
train_gen = train_gen_i
val_gen = val_gen_i
model.fit_generator(
train_gen,
steps_per_epoch=n_volumes_train,
epochs=n_epochs,
validation_data=val_gen,
validation_steps=1,
verbose=0,
callbacks=[
tqdm_cb,
tboard_cback,
chkpt_cback,
lrate_cback,
],
# max_queue_size=35,
use_multiprocessing=True,
workers=35,
shuffle=True,
)
return model
run_id = f'cascadenet_af{AF}_{int(time.time())}'
chkpt_path = f'checkpoints/{run_id}' + '-{epoch:02d}.hdf5'
print(run_id)
chkpt_cback = ModelCheckpoint(chkpt_path, period=100, save_weights_only=True)
log_dir = op.join('logs', run_id)
tboard_cback = TensorBoard(
profile_batch=0,
log_dir=log_dir,
histogram_freq=0,
write_graph=True,
write_images=False,
)
tqdm_cb = TQDMCallback(metric_format="{name}: {value:e}")
tqdm_cb.on_train_batch_begin = tqdm_cb.on_batch_begin
tqdm_cb.on_train_batch_end = tqdm_cb.on_batch_end
model = cascade_net(lr=1e-3, **run_params)
print(model.summary(line_length=150))
model.fit_generator(
train_gen,
steps_per_epoch=n_volumes_train,
epochs=n_epochs,
validation_data=val_gen,
validation_steps=1,
verbose=0,
callbacks=[
tqdm_cb,
tboard_cback,
chkpt_cback,