def loadPieces(dirpath):
pieces = {}
# for fname in os.listdir(dirpath):
# if fname[-4:] not in ('.mid','.MID'):
# continue
# name = fname[:-4]
# outMatrix = midiToNoteStateMatrix(os.path.join(dirpath, fname))
# if len(outMatrix) < batch_len:
# continue
# pieces[name] = outMatrix
# print "Loaded {}".format(name)
# sys.stdout.flush()
# return pieces
tmp = readxml.createStateMatrices('../musicxml', batch_len)
for key in tmp:
pieces[key] = tmp[key][1]
return pieces
def loadPieces(dirpath):
pieces = {}
# for fname in os.listdir(dirpath):
# if fname[-4:] not in ('.mid','.MID'):
# continue
# name = fname[:-4]
# outMatrix = midiToNoteStateMatrix(os.path.join(dirpath, fname))
# if len(outMatrix) < batch_len:
# continue
# pieces[name] = outMatrix
# print "Loaded {}".format(name)
# sys.stdout.flush()
# return pieces
tmp = readxml.createStateMatrices('../musicxml', batch_len)
for key in tmp:
pieces[key] = tmp[key][1]
return pieces
def train_statematrix_net(net,
batch_size=128,
dropout=.5,
output_rate=100,
output_length=128,
total_epochs=5000,
path='net_output/'):
"""
Trains a neural network, taking time steps from state matrices as input
and output.
"""
statematrices = readxml.createStateMatrices()
batches = []
for song in statematrices.values():
matrix = song[1]
while len(matrix) > 0:
batch = []
for i in range(batch_size):
if len(matrix) == 0:
break
batch.append([[n * 20 - 19 for l in matrix.pop() for n in l]])
batches.append(zip(batch[:-1], batch[1:]))
if not os.path.exists(path):
os.makedirs(path)
print('\nTraining network:')
for i in range(0, total_epochs, output_rate):
last = [[-1] * 156]
statematrix = []
for j in range(output_length):
new = net.run(last)
statematrix.append(
estimate_statematrix_from_output(new[0], last[0]))
last = new
midi_to_statematrix.noteStateMatrixToMidi(
statematrix, name=(path + 'example{0}'.format(i)))
print('\tfile example{0}.mid created'.format(i))
net.save(path + 'weights{0}'.format(i))
print('\tweights saved in weights{0}'.format(i))
for j in range(output_rate):
print('\t\t' + str(i + j))
for batch in batches:
net.train(batch, 1, .1, dropout, .5)
net.reset()
def train_statematrix_net(net, batch_size=128, dropout=.5, output_rate = 100,
output_length=128, total_epochs=5000,
path = 'net_output/'):
"""
Trains a neural network, taking time steps from state matrices as input
and output.
"""
statematrices = readxml.createStateMatrices()
batches = []
for song in statematrices.values():
matrix = song[1]
while len(matrix) > 0:
batch = []
for i in xrange(batch_size):
if len(matrix) == 0:
break
batch.append([[n * 20 - 19 for l in matrix.pop() for n in l]])
batches.append(zip(batch[:-1], batch[1:]))
if not os.path.exists(path):
os.makedirs(path)
print('\nTraining network:')
for i in xrange(0, total_epochs, output_rate):
last = [[-1] * 156]
statematrix = []
for j in xrange(output_length):
new = net.run(last)
statematrix.append(estimate_statematrix_from_output(new[0], last[0]))
last = new
midi_to_statematrix.noteStateMatrixToMidi(statematrix,
name=(path + 'example{0}'.format(i)))
print('\tfile example{0}.mid created'.format(i))
net.save(path + 'weights{0}'.format(i))
print('\tweights saved in weights{0}'.format(i))
for j in xrange(output_rate):
print('\t\t' + str(i + j))
for batch in batches:
net.train(batch, 1, .1, dropout, .5)
net.reset()