class Adapt_Pretrainer(Trainer): def __init__(self, **kwargs): super(Adapt_Pretrainer, self).__init__(trainer_type='pretraining', **kwargs) def build(self): self.model = Adapt(**self.args) self.model.tensorboard_init() self.model.init_all()
model = Adapt(config_model=config_model, pretraining=False)
model.create_saver()
path = os.path.join(config.model_root, 'log', 'DPCL_train_front')
model.restore_model(path, full_id)
model.connect_front_back_to_separator(DPCL)
with model.graph.as_default():
model.create_saver()
model.restore_model(path, full_id)
# model.freeze_front()
model.optimize
model.tensorboard_init()
init = model.non_initialized_variables()
model.sess.run(init)
print 'Total name :'
print model.runID
# nb_iterations = 500
mixed_data.adjust_split_size_to_batchsize(batch_size)
nb_batches = mixed_data.nb_batches(batch_size)
nb_epochs = 1
time_spent = [ 0 for _ in range(5)]
config_model["alpha"] = learning_rate
config_model["reg"] = 1e-3
config_model["beta"] = 0.1
config_model["rho"] = 0.01
config_model["same_filter"] = True
config_model["optimizer"] = 'Adam'
####
####
adapt_model = Adapt(config_model=config_model,
pretraining=True,
folder='pretraining')
adapt_model.tensorboard_init()
adapt_model.init()
print 'Total name :'
print adapt_model.runID
# nb_iterations = 500
mixed_data.adjust_split_size_to_batchsize(batch_size)
nb_batches = mixed_data.nb_batches(batch_size)
nb_epochs = 2
time_spent = [0 for _ in range(5)]
for epoch in range(nb_epochs):
for b in range(nb_batches):
X_non_mix, _, _ = mixed_data.get_batch(batch_size)