for f in sorted(os.listdir(env.dataset())):
if f.endswith("sparse_acoustic_data.dump"):
print "Considering {} as input".format(f)
source_data_file_list.append(env.dataset(f))
data_file_list = source_data_file_list[:]
max_t, input_size = 0, None
data_denoms = []
data_corpus = None
data_ends = []
for source_id, inp_file in enumerate(data_file_list):
print "Reading {}".format(inp_file)
d = SparseAcoustic.deserialize(inp_file)
max_t = max(d.data.shape[0], max_t)
assert input_size is None or input_size == d.data.shape[1], "Got spikes with another neurons number"
input_size = d.data.shape[1]
if data_corpus is None:
data_corpus = d.data
else:
scipy.sparse.vstack([data_corpus, d.data])
data_ends.append(data_corpus.shape[0])
data_denoms.append(d.data_denom)
batch_size = len(data_ends)*20
# data_ends = np.asarray([5000, data_ends[0]], dtype=np.int32)
def sigmoid(x):
return 1.0/(1.0 +np.exp(-x))
sp.append(sparsity)
print "Epoch {}, cost {}, sparsity {}".format(e, sum(mc)/len(mc), sum(sp)/len(sp))
if epochs > 0:
for source_id in xrange(len(data_source)):
if not sel is None and not source_id in sel:
continue
song_data, source_sr, data_denom = read_song(source_id)
_, sparsity, filter_v, rfilter_v, bias_v, hidden_final, _ = roll_around(song_data, True)
# hidden_final[np.where(hidden_final < 1e-05)] = 0.0
sa = SparseAcoustic(hidden_final, data_denom)
sa.serialize(env.dataset("{}_sparse_acoustic_data.dump".format(source_id)))
out_final = restore_hidden(sess, rfilter_v.reshape(L, filters_num), hidden_final, k)
out_final *= data_denom
data_recov = lr.resample(out_final, target_sr, source_sr, scale=True)
lr.output.write_wav(env.result("{}_recovery.wav".format(source_id)), data_recov, source_sr)
print "Saving in {}".format(saver.save(sess, model_fname))
rfilter_v = sess.run(cm.recov_filter)
np.save(open(env.run("recov_filter.pkl"), "w"), rfilter_v)
# else:
# source_sr = 22000
# src = env.run("nn_dream.dump")
