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)] for epoch in range(nb_epochs): for b in range(nb_batches): X_non_mix, X_mix, _ = mixed_data.get_batch(batch_size) t = time.time() c = model.train(X_mix, X_non_mix, learning_rate, b) t_f = time.time() time_spent = time_spent[1:] +[t_f-t] print 'Step #' ,b,' loss=', c ,' ETA = ', getETA(sum(time_spent)/float(np.count_nonzero(time_spent)) , nb_batches, b, nb_epochs, epoch) # print 'length of data =', X_non_mix.shape ,'step ', b+1, mixed_data.datasets[0].index_item_split, mixed_data.selected_split_size(),getETA(sum(time_spent)/float(np.count_nonzero(time_spent)), nb_batches, b, nb_epochs, epoch) if b%20 == 0: #cost_valid < cost_valid_min: print 'DAS model saved at iteration number ', nb_batches*epoch + b,' with cost = ', c model.save(b)
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
for epoch in range(nb_epochs):
for b in range(nb_batches):
step = nb_batches * epoch + b
X_non_mix, X_mix, Ind = mixed_data.get_batch(batch_size)
t = time.time()
c = model.train(X_mix, X_non_mix, learning_rate, step, ind_train=Ind)
t_f = time.time()
time_spent = time_spent[1:] + [t_f - t]
print 'Step #', step, ' loss=', c, ' ETA = ', getETA(
sum(time_spent) / float(np.count_nonzero(time_spent)), nb_batches,
b, nb_epochs, epoch)
# print 'length of data =', X_non_mix.shape ,'step ', b+1, mixed_data.datasets[0].index_item_split, mixed_data.selected_split_size(),getETA(sum(time_spent)/float(np.count_nonzero(time_spent)), nb_batches, b, nb_epochs, epoch)
if step % 20 == 0: #cost_valid < cost_valid_min:
print 'DAS model saved at iteration number ', step, ' with cost = ', c
model.save(nb_batches * epoch + b)
mixed_data.select_split(1)
x_non_test, x_test, _ = mixed_data.get_only_first_items(8)
model.test_prediction(x_test, x_non_test, step)
mixed_data.select_split(0)
males = H5PY_RW()
males.open_h5_dataset('test_raw.h5py', subset=males_keys(H5_dico))
males.set_chunk(5 * 4 * 512)
males.shuffle()
print 'Male voices loaded: ', males.length(), ' items'
fem = H5PY_RW()
fem.open_h5_dataset('test_raw.h5py', subset=females_keys(H5_dico))
fem.set_chunk(5 * 4 * 512)
fem.shuffle()
print 'Female voices loaded: ', fem.length(), ' items'
Mixer = Mixer([males, fem], with_mask=False, with_inputs=True)
adapt_model = Adapt()
print 'Model DAS created'
adapt_model.init()
cost_valid_min = 1e10
Mixer.select_split(0)
learning_rate = 0.005
for i in range(config.max_iterations):
X_in, X_mix, Ind = Mixer.get_batch(1)
c = adapt_model.train(X_mix, X_in, learning_rate, i)
print 'Step #', i, ' ', c
if i % 20 == 0: #cost_valid < cost_valid_min:
print 'DAS model saved at iteration number ', i, ' with cost = ', c
adapt_model.save(i)
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)
print 'Step #', i, ' loss=', c
if i % 20 == 0: #cost_valid < cost_valid_min:
print 'DAS model saved at iteration number ', i, ' with cost = ', c
model.save(i)
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)]
for epoch in range(nb_epochs):
for b in range(nb_batches):
X_non_mix, X_mix, _ = mixed_data.get_batch(batch_size)
t = time.time()
c = model.train(X_mix, X_non_mix, learning_rate,
nb_batches * epoch + b)
t_f = time.time()
time_spent = time_spent[1:] + [t_f - t]
print 'Step #', b, ' loss=', c, ' ETA = ', getETA(
sum(time_spent) / float(np.count_nonzero(time_spent)), nb_batches,
b, nb_epochs, epoch)
# print 'length of data =', X_non_mix.shape ,'step ', b+1, mixed_data.datasets[0].index_item_split, mixed_data.selected_split_size(),getETA(sum(time_spent)/float(np.count_nonzero(time_spent)), nb_batches, b, nb_epochs, epoch)
if b % 20 == 0: #cost_valid < cost_valid_min:
print 'DAS model saved at iteration number ', nb_batches * epoch + b, ' with cost = ', c
model.save(b)