#### NEW MODEL #### config_model["type"] = "L41_train_front" learning_rate = 0.01 batch_size = 8 config_model["chunk_size"] = 512 * 40 config_model["batch_size"] = batch_size config_model["alpha"] = learning_rate model = Adapt(config_model=config_model, pretraining=False) model.create_saver() model.restore_model(path, full_id) model.connect_only_front_to_separator(L41Model) 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 = 40 time_spent = [0 for _ in range(5)] print 'NB BATCHES =', nb_batches print 'NB ITERATIONS =', nb_batches * nb_epochs print 'NB SAVE = ', (nb_batches * nb_epochs) / 20
from models.adapt import Adapt
import config
full_id = 'soft-base-9900' + idd
folder = 'DAS_train_front'
model = Adapt(config_model=config_model, pretraining=False)
model.create_saver()
path = os.path.join(config.workdir, 'floydhub_model', "pretraining")
# path = os.path.join(config.log_dir, "pretraining")
model.restore_model(path, full_id)
from models.das import DAS
model.connect_only_front_to_separator(DAS)
init = model.non_initialized_variables()
# Model creation
# Pretraining the model
nb_iterations = 1000
#initialize the model
model.sess.run(init)
for i in range(nb_iterations):
X_in, X_mix, Ind = mixed_data.get_batch(batch_size)
c = model.train(X_mix, X_in, learning_rate, i, ind_train=Ind)
