def build_model():
x_in = optimus.Input(name="x", shape=(None, 2))
class_idx = optimus.Input(name="y", shape=(None, ), dtype='int32')
learning_rate = optimus.Input(name='learning_rate', shape=None)
layer0 = optimus.Affine(name='layer0',
input_shape=x_in.shape,
output_shape=(None, 100),
act_type='relu')
layer1 = optimus.Affine(name='layer1',
input_shape=layer0.output.shape,
output_shape=(None, 100),
act_type='relu')
classifier = optimus.Softmax(name='classifier',
input_shape=layer1.output.shape,
n_out=N_CLASSES,
act_type='linear')
nll = optimus.NegativeLogLikelihood(name="nll")
posterior = optimus.Output(name='posterior')
trainer_edges = optimus.ConnectionManager([
(x_in, layer0.input), (layer0.output, layer1.input),
(layer1.output, classifier.input), (classifier.output, nll.likelihood),
(class_idx, 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, classifier.weights), (learning_rate, classifier.bias)
])
trainer = optimus.Graph(name='trainer',
inputs=[x_in, class_idx, learning_rate],
nodes=[layer0, layer1, classifier],
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[nll],
updates=update_manager.connections)
optimus.random_init(layer0.weights)
optimus.random_init(layer1.weights)
optimus.random_init(classifier.weights)
predictor_edges = optimus.ConnectionManager([
(x_in, layer0.input), (layer0.output, layer1.input),
(layer1.output, classifier.input), (classifier.output, posterior)
])
predictor = optimus.Graph(name='predictor',
inputs=[x_in],
nodes=[layer0, layer1, classifier],
connections=predictor_edges.connections,
outputs=[posterior])
driver = optimus.Driver(graph=trainer, name='test')
return driver, predictor
def main(args):
# 1.1 Create Inputs
input_data = optimus.Input(name='cqt', shape=(None, 1, TIME_DIM, 252))
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)
margin_weight = optimus.Input(name='margin_weight', shape=None)
nll_weight = optimus.Input(name='nll_weight', shape=None)
# 1.2 Create Nodes
layer0 = optimus.Conv3D(name='layer0',
input_shape=input_data.shape,
weight_shape=(12, 1, 9, 19),
pool_shape=(1, 3),
act_type='relu')
layer1 = optimus.Conv3D(name='layer1',
input_shape=layer0.output.shape,
weight_shape=(16, None, 7, 15),
act_type='relu')
layer2 = optimus.Conv3D(name='layer2',
input_shape=layer1.output.shape,
weight_shape=(20, None, 6, 15),
act_type='relu')
layer3 = optimus.Affine(name='layer3',
input_shape=layer2.output.shape,
output_shape=(None, 512),
act_type='relu')
chord_classifier = optimus.Affine(name='chord_classifier',
input_shape=layer3.output.shape,
output_shape=(None, VOCAB),
act_type='sigmoid')
all_nodes = [layer0, layer1, layer2, layer3, chord_classifier]
# 1.1 Create Losses
nll = optimus.NegativeLogLikelihood(name="nll", weighted=True)
likelihood_margin = optimus.LikelihoodMargin(name="likelihood_margin",
mode='l1',
weighted=True)
# likelihood_margin = optimus.NLLMargin(
# name="likelihood_margin",
# mode='l2',
# weighted=True)
# 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, likelihood_margin.likelihood),
(chord_idx, likelihood_margin.target_idx),
(margin, likelihood_margin.margin),
(margin_weight, likelihood_margin.weight),
(chord_classifier.output, nll.likelihood), (chord_idx, nll.target_idx),
(nll_weight, nll.weight)
])
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, margin, learning_rate,
margin_weight, nll_weight
],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[nll, likelihood_margin],
updates=update_manager.connections)
optimus.random_init(chord_classifier.weights)
optimus.random_init(chord_classifier.bias)
validator = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data, chord_idx, margin, margin_weight, nll_weight],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[nll, likelihood_margin])
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
stash = biggie.Stash(args.training_file)
stream = S.minibatch(D.create_uniform_quality_stream(stash,
TIME_DIM,
vocab_dim=VOCAB),
batch_size=50)
driver = optimus.Driver(graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
hyperparams = {
learning_rate.name: LEARNING_RATE,
margin_weight.name: MARGIN_WEIGHT,
nll_weight.name: NLL_WEIGHT,
margin.name: MARGIN
}
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
validator_file = path.join(driver.output_directory, args.validator_file)
optimus.save(validator, def_file=validator_file)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
def main(args):
# 1.1 Create Inputs
input_data = optimus.Input(name='cqt',
shape=(None, 1, 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
input_scalar = optimus.Normalize(name='input_scalar',
mode='l2',
scale_factor=50.0)
layer0 = optimus.Conv3D(name='layer0',
input_shape=input_data.shape,
weight_shape=(32, 1, 5, 19),
pool_shape=(2, 3),
act_type='relu')
layer1 = optimus.Conv3D(name='layer1',
input_shape=layer0.output.shape,
weight_shape=(64, None, 5, 15),
act_type='relu')
layer2 = optimus.Conv3D(name='layer2',
input_shape=layer1.output.shape,
weight_shape=(128, None, 3, 15),
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 = [
input_scalar, 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, input_scalar.input), (input_scalar.output, 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[1:]:
optimus.random_init(n.weights)
optimus.random_init(n.bias)
if args.init_param_file:
param_values = dict(np.load(args.init_param_file))
keys = param_values.keys()
for key in keys:
if chord_classifier.name in key or layer3.name in key:
print "skipping %s" % key
del param_values[key]
trainer.param_values = param_values
posterior = optimus.Output(name='posterior')
predictor_edges = optimus.ConnectionManager([
(input_data, input_scalar.input), (input_scalar.output, 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 = S.minibatch(D.create_uniform_chord_stream(stash,
TIME_DIM,
pitch_shift=0,
vocab_dim=VOCAB,
working_size=10),
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, 1, TIME_DIM, 252))
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=(12, 1, 9, 19),
pool_shape=(1, 3),
act_type='relu')
layer1 = optimus.Conv3D(
name='layer1',
input_shape=layer0.output.shape,
weight_shape=(16, None, 7, 15),
act_type='relu')
layer2 = optimus.Conv3D(
name='layer2',
input_shape=layer1.output.shape,
weight_shape=(20, None, 6, 15),
act_type='relu')
layer3 = optimus.Affine(
name='layer3',
input_shape=layer2.output.shape,
output_shape=(None, 512,),
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)])
print "Building trainer"
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)
optimus.random_init(chord_classifier.weights)
print "Building validator"
validator = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data, chord_idx],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_nll])
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)])
print "Building predictor"
predictor = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data],
nodes=all_nodes,
connections=predictor_edges.connections,
outputs=[posterior])
# 3. Create Data
print "Opening Data"
stash = biggie.Stash(args.training_file)
stream = S.minibatch(
D.create_uniform_quality_stream(stash, TIME_DIM),
batch_size=50,
functions=[FX.pitch_shift(), FX.map_to_chord_index(VOCAB)])
driver = optimus.Driver(
graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
hyperparams = {learning_rate.name: LEARNING_RATE}
print "...aaand we're off!"
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
validator_file = path.join(driver.output_directory, args.validator_file)
optimus.save(validator, def_file=validator_file)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
def main(args):
# 1.1 Create Inputs
input_data = optimus.Input(name='cqt', shape=(None, 1, TIME_DIM, 252))
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=(30, 1, 9, 19),
pool_shape=(1, 3),
act_type='relu')
layer1 = optimus.Conv3D(name='layer1',
input_shape=layer0.output.shape,
weight_shape=(50, None, 7, 15),
act_type='relu')
layer2 = optimus.Affine(name='layer2',
input_shape=layer1.output.shape,
output_shape=(
None,
1024,
),
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)
validator = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data, chord_idx],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_nll])
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
stash = biggie.Stash(args.training_file)
hyperparams = {learning_rate.name: LEARNING_RATE}
valid_idx = [0]
for q in range(1, 13):
valid_idx.append(q)
stream = S.minibatch(D.create_uniform_quality_stream(
stash, TIME_DIM, vocab_dim=VOCAB, valid_idx=valid_idx),
batch_size=BATCH_SIZE)
driver = optimus.Driver(graph=trainer,
name=args.trial_name + "_c%02d" % q,
output_directory=args.model_directory)
driver.fit(stream,
hyperparams=hyperparams,
max_iter=20000,
**DRIVER_ARGS)
validator_file = path.join(driver.output_directory, args.validator_file)
optimus.save(validator, def_file=validator_file)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
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, 1, TIME_DIM, 252))
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=(12, 1, 5, 19),
pool_shape=(1, 3),
act_type='relu')
layer1 = optimus.Conv3D(name='layer1',
input_shape=layer0.output.shape,
weight_shape=(16, None, 5, 15),
act_type='relu')
layer2 = optimus.Conv3D(name='layer2',
input_shape=layer1.output.shape,
weight_shape=(20, None, 2, 15),
act_type='relu')
layer3 = optimus.Affine(name='layer3',
input_shape=layer2.output.shape,
output_shape=(
None,
512,
),
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)
optimus.random_init(chord_classifier.weights)
validator = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data, chord_idx],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_nll])
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
source = optimus.Queue(optimus.File(args.training_file),
transformers=[
T.chord_sample(input_data.shape[2]),
T.pitch_shift(8),
T.map_to_index(VOCAB)
],
**SOURCE_ARGS)
driver = optimus.Driver(graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
hyperparams = {learning_rate.name: LEARNING_RATE}
driver.fit(source, hyperparams=hyperparams, **DRIVER_ARGS)
validator_file = path.join(driver.output_directory, args.validator_file)
optimus.save(validator, def_file=validator_file)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
def main(args):
# 1.1 Create Inputs
input_data = optimus.Input(name='cqt', shape=(None, 1, TIME_DIM, 252))
chord_idx = optimus.Input(name='chord_idx', shape=(None, ), dtype='int32')
learning_rate = optimus.Input(name='learning_rate', shape=None)
dropout = optimus.Input(name='dropout', shape=None)
# 1.2 Create Nodes
layer0 = optimus.Conv3D(name='layer0',
input_shape=input_data.shape,
weight_shape=(30, 1, 9, 19),
pool_shape=(1, 3),
act_type='relu')
layer1 = optimus.Conv3D(name='layer1',
input_shape=layer0.output.shape,
weight_shape=(50, None, 7, 15),
act_type='relu')
layer2 = optimus.Affine(name='layer2',
input_shape=layer1.output.shape,
output_shape=(
None,
1024,
),
act_type='relu')
layer3 = optimus.Affine(name='layer3',
input_shape=layer2.output.shape,
output_shape=(
None,
1024,
),
act_type='relu')
for n in [layer2, layer3]:
n.enable_dropout()
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), (dropout, layer2.dropout),
(dropout, layer3.dropout)
])
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, dropout],
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)
validator_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)
])
for n in [layer2, layer3]:
n.disable_dropout()
validator = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data, chord_idx],
nodes=all_nodes,
connections=validator_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_nll])
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)
s = D.create_uniform_chord_stream(stash,
TIME_DIM,
pitch_shift=6,
vocab_dim=VOCAB,
working_size=10)
stream = S.minibatch(FX.drop_frames(FX.awgn(s, 0.05), 0.1),
batch_size=BATCH_SIZE)
driver = optimus.Driver(graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
hyperparams = {learning_rate.name: LEARNING_RATE, dropout.name: DROPOUT}
validator_file = path.join(driver.output_directory, args.validator_file)
optimus.save(validator, def_file=validator_file)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
print "Starting '%s'" % args.trial_name
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
def main(args):
# 1.1 Create Inputs
input_data = optimus.Input(name='cqt', shape=(None, 1, TIME_DIM, 252))
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=(30, 1, 9, 19),
pool_shape=(1, 3),
act_type='relu')
layer1 = optimus.Conv3D(name='layer1',
input_shape=layer0.output.shape,
weight_shape=(50, None, 7, 15),
act_type='relu')
layer2 = optimus.Affine(name='layer2',
input_shape=layer1.output.shape,
output_shape=(
None,
1024,
),
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,
momentum=None)
for n in all_nodes:
optimus.random_init(n.weights)
optimus.random_init(n.bias)
trainer.load_param_values(
"/media/attic/dl4mir/chord_estimation/models/nll_chord_uniform_2big/synth_data_01/0/classifier-V157-synth_data_01-041750-2014-08-25_21h59m56s.npz"
)
validator = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data, chord_idx],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_nll])
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=False,
vocab_dim=VOCAB,
working_size=3)
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}
validator_file = path.join(driver.output_directory, args.validator_file)
optimus.save(validator, def_file=validator_file)
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, 1, TIME_DIM, 252))
chord_idx = optimus.Input(
name='chord_idx',
shape=(None,),
dtype='int32')
learning_rate = optimus.Input(
name='learning_rate',
shape=None)
limiter_weight = optimus.Input(
name='limiter_weight',
shape=None)
likelihood_threshold = optimus.Input(
name='likelihood_threshold',
shape=None)
# 1.2 Create Nodes
layer0 = optimus.Conv3D(
name='layer0',
input_shape=input_data.shape,
weight_shape=(12, 1, 9, 19),
pool_shape=(1, 3),
act_type='relu')
layer1 = optimus.Conv3D(
name='layer1',
input_shape=layer0.output.shape,
weight_shape=(16, None, 7, 15),
act_type='relu')
layer2 = optimus.Conv3D(
name='layer2',
input_shape=layer1.output.shape,
weight_shape=(20, None, 6, 15),
act_type='relu')
layer3 = optimus.Affine(
name='layer3',
input_shape=layer2.output.shape,
output_shape=(None, 512,),
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")
max_likelihood = optimus.Max(
name="max_likelihood")
# 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),
(chord_classifier.output, max_likelihood.input),
(limiter_weight, max_likelihood.weight),
(likelihood_threshold, max_likelihood.threshold)])
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)])
print "Trainer"
trainer = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data, chord_idx, learning_rate,
limiter_weight, likelihood_threshold],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_nll, max_likelihood],
updates=update_manager.connections)
optimus.random_init(chord_classifier.weights)
print "Validator"
validator = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data, chord_idx, limiter_weight, likelihood_threshold],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_nll, max_likelihood])
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)])
print "Predictor"
predictor = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data],
nodes=all_nodes,
connections=predictor_edges.connections,
outputs=[posterior])
# 3. Create Data
stash = biggie.Stash(args.training_file)
stream = S.minibatch(
D.create_uniform_quality_stream(stash, TIME_DIM, vocab_dim=VOCAB),
batch_size=50)
driver = optimus.Driver(
graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
hyperparams = {learning_rate.name: LEARNING_RATE,
likelihood_threshold.name: LIKELIHOOD_THRESHOLD,
limiter_weight.name: LIMITER_WEIGHT}
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
validator_file = path.join(driver.output_directory, args.validator_file)
optimus.save(validator, def_file=validator_file)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
