def main(args):
trainer, predictor = build_model()
if args.init_param_file:
print "Loading parameters: %s" % args.init_param_file
trainer.load_param_values(args.init_param_file)
optimus.random_init(trainer.params['layer3'].weights)
optimus.random_init(trainer.params['layer3'].bias)
# 3. Create Data
print "Loading %s" % args.training_file
stash = biggie.Stash(args.training_file)
stream = D.create_stash_stream(
stash, TIME_DIM, pitch_shift=0, vocab_dim=VOCAB, pool_size=25)
if args.secondary_source:
stash2 = biggie.Stash(args.secondary_source)
stream2 = D.create_uniform_chord_stream(
stash2, TIME_DIM, pitch_shift=0, vocab_dim=VOCAB, working_size=5)
stream = S.mux([stream, stream2], [0.5, 0.5])
stream = S.minibatch(stream, batch_size=BATCH_SIZE)
print "Starting '%s'" % args.trial_name
driver = optimus.Driver(
graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
hyperparams = dict(learning_rate=LEARNING_RATE)
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
def main(args):
trainer, predictor = build_model()
if args.init_param_file:
print "Loading parameters: %s" % args.init_param_file
trainer.load_param_values(args.init_param_file)
optimus.random_init(trainer.params['layer3'].weights)
optimus.random_init(trainer.params['layer3'].bias)
# 3. Create Data
print "Loading %s" % args.training_file
stash = biggie.Stash(args.training_file)
stream = D.create_stash_stream(stash,
TIME_DIM,
pitch_shift=0,
vocab_dim=VOCAB,
pool_size=25)
if args.secondary_source:
stash2 = biggie.Stash(args.secondary_source)
stream2 = D.create_uniform_chord_stream(stash2,
TIME_DIM,
pitch_shift=0,
vocab_dim=VOCAB,
working_size=5)
stream = S.mux([stream, stream2], [0.5, 0.5])
stream = S.minibatch(stream, batch_size=BATCH_SIZE)
print "Starting '%s'" % args.trial_name
driver = optimus.Driver(graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
hyperparams = dict(learning_rate=LEARNING_RATE)
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
def main(args):
# 1.1 Create Inputs
input_data = optimus.Input(
name='cqt',
shape=(None, OCTAVE_DIM, TIME_DIM, PITCH_DIM))
chord_idx = optimus.Input(
name='chord_idx',
shape=(None,),
dtype='int32')
learning_rate = optimus.Input(
name='learning_rate',
shape=None)
# 1.2 Create Nodes
layer0 = optimus.Conv3D(
name='layer0',
input_shape=input_data.shape,
weight_shape=(32, None, 5, 5),
pool_shape=(2, 3),
act_type='relu')
layer1 = optimus.Conv3D(
name='layer1',
input_shape=layer0.output.shape,
weight_shape=(64, None, 5, 7),
act_type='relu')
layer2 = optimus.Conv3D(
name='layer2',
input_shape=layer1.output.shape,
weight_shape=(128, None, 3, 6),
act_type='relu')
layer3 = optimus.Affine(
name='layer3',
input_shape=layer2.output.shape,
output_shape=(None, 1024,),
act_type='relu')
chord_classifier = optimus.Softmax(
name='chord_classifier',
input_shape=layer3.output.shape,
n_out=VOCAB,
act_type='linear')
all_nodes = [layer0, layer1, layer2, layer3, chord_classifier]
# 1.1 Create Losses
chord_nll = optimus.NegativeLogLikelihood(
name="chord_nll")
# 2. Define Edges
trainer_edges = optimus.ConnectionManager([
(input_data, layer0.input),
(layer0.output, layer1.input),
(layer1.output, layer2.input),
(layer2.output, layer3.input),
(layer3.output, chord_classifier.input),
(chord_classifier.output, chord_nll.likelihood),
(chord_idx, chord_nll.target_idx)])
update_manager = optimus.ConnectionManager([
(learning_rate, layer0.weights),
(learning_rate, layer0.bias),
(learning_rate, layer1.weights),
(learning_rate, layer1.bias),
(learning_rate, layer2.weights),
(learning_rate, layer2.bias),
(learning_rate, layer3.weights),
(learning_rate, layer3.bias),
(learning_rate, chord_classifier.weights),
(learning_rate, chord_classifier.bias)])
trainer = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data, chord_idx, learning_rate],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_nll],
updates=update_manager.connections)
for n in all_nodes:
optimus.random_init(n.weights)
optimus.random_init(n.bias)
if args.init_param_file:
print "Loading parameters: %s" % args.init_param_file
trainer.load_param_values(args.init_param_file)
posterior = optimus.Output(
name='posterior')
predictor_edges = optimus.ConnectionManager([
(input_data, layer0.input),
(layer0.output, layer1.input),
(layer1.output, layer2.input),
(layer2.output, layer3.input),
(layer3.output, chord_classifier.input),
(chord_classifier.output, posterior)])
predictor = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data],
nodes=all_nodes,
connections=predictor_edges.connections,
outputs=[posterior])
# 3. Create Data
print "Loading %s" % args.training_file
stash = biggie.Stash(args.training_file)
stream = D.create_uniform_chord_stream(
stash, TIME_DIM, pitch_shift=0, vocab_dim=VOCAB, working_size=10,
valid_idx=range(60) + [156])
if args.secondary_source:
stash2 = biggie.Stash(args.secondary_source)
stream2 = D.create_uniform_chord_stream(
stash2, TIME_DIM, pitch_shift=0, vocab_dim=VOCAB, working_size=5)
stream = S.mux([stream, stream2], [0.5, 0.5])
stream = S.minibatch(stream, batch_size=BATCH_SIZE)
print "Starting '%s'" % args.trial_name
driver = optimus.Driver(
graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
hyperparams = {learning_rate.name: LEARNING_RATE}
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
def main(args):
# 1.1 Create Inputs
input_data = optimus.Input(name='cqt',
shape=(None, OCTAVE_DIM, TIME_DIM, PITCH_DIM))
chord_idx = optimus.Input(name='chord_idx', shape=(None, ), dtype='int32')
learning_rate = optimus.Input(name='learning_rate', shape=None)
# 1.2 Create Nodes
layer0 = optimus.Conv3D(name='layer0',
input_shape=input_data.shape,
weight_shape=(32, None, 5, 5),
pool_shape=(2, 3),
act_type='relu')
layer1 = optimus.Conv3D(name='layer1',
input_shape=layer0.output.shape,
weight_shape=(64, None, 5, 7),
act_type='relu')
layer2 = optimus.Conv3D(name='layer2',
input_shape=layer1.output.shape,
weight_shape=(128, None, 3, 6),
act_type='relu')
layer3 = optimus.Affine(name='layer3',
input_shape=layer2.output.shape,
output_shape=(
None,
1024,
),
act_type='relu')
chord_classifier = optimus.Softmax(name='chord_classifier',
input_shape=layer3.output.shape,
n_out=VOCAB,
act_type='linear')
all_nodes = [layer0, layer1, layer2, layer3, chord_classifier]
# 1.1 Create Losses
chord_nll = optimus.NegativeLogLikelihood(name="chord_nll")
# 2. Define Edges
trainer_edges = optimus.ConnectionManager([
(input_data, layer0.input), (layer0.output, layer1.input),
(layer1.output, layer2.input), (layer2.output, layer3.input),
(layer3.output, chord_classifier.input),
(chord_classifier.output, chord_nll.likelihood),
(chord_idx, chord_nll.target_idx)
])
update_manager = optimus.ConnectionManager([
(learning_rate, layer0.weights), (learning_rate, layer0.bias),
(learning_rate, layer1.weights), (learning_rate, layer1.bias),
(learning_rate, layer2.weights), (learning_rate, layer2.bias),
(learning_rate, layer3.weights), (learning_rate, layer3.bias),
(learning_rate, chord_classifier.weights),
(learning_rate, chord_classifier.bias)
])
trainer = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data, chord_idx, learning_rate],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_nll],
updates=update_manager.connections)
for n in all_nodes:
optimus.random_init(n.weights)
optimus.random_init(n.bias)
if args.init_param_file:
print "Loading parameters: %s" % args.init_param_file
trainer.load_param_values(args.init_param_file)
posterior = optimus.Output(name='posterior')
predictor_edges = optimus.ConnectionManager([
(input_data, layer0.input), (layer0.output, layer1.input),
(layer1.output, layer2.input), (layer2.output, layer3.input),
(layer3.output, chord_classifier.input),
(chord_classifier.output, posterior)
])
predictor = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data],
nodes=all_nodes,
connections=predictor_edges.connections,
outputs=[posterior])
# 3. Create Data
print "Loading %s" % args.training_file
stash = biggie.Stash(args.training_file)
stream = D.create_uniform_chord_stream(stash,
TIME_DIM,
pitch_shift=0,
vocab_dim=VOCAB,
working_size=4)
if args.secondary_source:
stash2 = biggie.Stash(args.secondary_source)
stream2 = D.create_uniform_chord_stream(stash2,
TIME_DIM,
pitch_shift=0,
vocab_dim=VOCAB,
working_size=5)
stream = S.mux([stream, stream2], [0.5, 0.5])
stream = S.minibatch(stream, batch_size=BATCH_SIZE)
print "Starting '%s'" % args.trial_name
driver = optimus.Driver(graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
hyperparams = {learning_rate.name: LEARNING_RATE}
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
def main(args):
# 1.1 Create Inputs
input_data = optimus.Input(
name='cqt',
shape=(None, OCTAVE_DIM, TIME_DIM, PITCH_DIM))
chord_idx = optimus.Input(
name='chord_idx',
shape=(None,),
dtype='int32')
learning_rate = optimus.Input(
name='learning_rate',
shape=None)
margin = optimus.Input(
name='margin',
shape=None)
# 1.2 Create Nodes
layer0 = optimus.Conv3D(
name='layer0',
input_shape=input_data.shape,
weight_shape=(32, None, 5, 5),
pool_shape=(2, 3),
act_type='relu')
layer1 = optimus.Conv3D(
name='layer1',
input_shape=layer0.output.shape,
weight_shape=(64, None, 5, 7),
act_type='relu')
layer2 = optimus.Conv3D(
name='layer2',
input_shape=layer1.output.shape,
weight_shape=(128, None, 3, 6),
act_type='relu')
layer3 = optimus.Affine(
name='layer3',
input_shape=layer2.output.shape,
output_shape=(None, 1024,),
act_type='relu')
chord_estimator = optimus.Softmax(
name='chord_estimator',
input_shape=layer3.output.shape,
output_shape=(None, VOCAB,),
act_type='sigmoid')
all_nodes = [layer0, layer1, layer2, layer3, chord_classifier]
log = optimus.Log(name='log')
neg = optimus.Gain(name='gain')
neg.weight = np.array(-1)
energy = optimus.SelectIndex(name='selector')
loss = optimus.Mean(name='total_loss')
# 1.1 Create Losses
chord_margin = optimus.Margin(
name="chord_margin",
mode='max')
# 2. Define Edges
trainer_edges = optimus.ConnectionManager([
(input_data, layer0.input),
(layer0.output, layer1.input),
(layer1.output, layer2.input),
(layer2.output, layer3.input),
(layer3.output, chord_estimator.input),
(chord_estimator.output, log.input),
(log.output, neg.input),
(neg.output, energy.input),
(chord_idx, energy.index),
(energy.output, loss.input)])
update_manager = optimus.ConnectionManager([
(learning_rate, layer0.weights),
(learning_rate, layer0.bias),
(learning_rate, layer1.weights),
(learning_rate, layer1.bias),
(learning_rate, layer2.weights),
(learning_rate, layer2.bias),
(learning_rate, layer3.weights),
(learning_rate, layer3.bias),
(learning_rate, chord_estimator.weights),
(learning_rate, chord_estimator.bias)])
trainer = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data, chord_idx, learning_rate, margin],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
loss=[loss.output],
updates=update_manager.connections)
for n in all_nodes:
optimus.random_init(n.weights)
optimus.random_init(n.bias)
if args.init_param_file:
print "Loading parameters: %s" % args.init_param_file
trainer.load_param_values(args.init_param_file)
for n in all_nodes[-2:]:
optimus.random_init(n.weights)
optimus.random_init(n.bias)
posterior = optimus.Output(
name='posterior')
predictor_edges = optimus.ConnectionManager([
(input_data, layer0.input),
(layer0.output, layer1.input),
(layer1.output, layer2.input),
(layer2.output, layer3.input),
(layer3.output, chord_classifier.input),
(chord_classifier.output, posterior)])
predictor = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data],
nodes=all_nodes,
connections=predictor_edges.connections,
outputs=[posterior])
# 3. Create Data
print "Loading %s" % args.training_file
stash = biggie.Stash(args.training_file)
stream = D.create_stash_stream(
stash, TIME_DIM, pitch_shift=0, vocab_dim=VOCAB, pool_size=25)
if args.secondary_source:
stash2 = biggie.Stash(args.secondary_source)
stream2 = D.create_uniform_chord_stream(
stash2, TIME_DIM, pitch_shift=0, vocab_dim=VOCAB, working_size=5)
stream = S.mux([stream, stream2], [0.5, 0.5])
stream = S.minibatch(stream, batch_size=BATCH_SIZE)
print "Starting '%s'" % args.trial_name
driver = optimus.Driver(
graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
hyperparams = {learning_rate.name: LEARNING_RATE,
margin.name: MARGIN}
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)