# 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):
step = nb_batches * epoch + b
X_non_mix, X_mix, Ind = mixed_data.get_batch(batch_size)
t = time.time()
c = model.train_no_sum(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 b % 20 == 0: #cost_valid < cost_valid_min:
print 'DAS model saved at iteration number ', step, ' with cost = ', c
model.save(b)
model.savedModel()
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)
