def pca_lda_graph(n_in=20, n_components=256, n_out=3):
input_data = optimus.Input(name='cqt', shape=(None, 1, n_in, 192))
reshape = optimus.Flatten(name='flat', ndim=2)
logscale = optimus.Log("logscale", 1.0)
pca = optimus.CenteredAffine(name='pca',
input_shape=(None, n_in * 192),
output_shape=(None, n_components),
act_type='linear')
lda = optimus.CenteredAffine(name='lda',
input_shape=(None, n_components),
output_shape=(None, n_out),
act_type='linear')
embedding = optimus.Output(name='embedding')
base_edges = [(input_data, reshape.input),
(reshape.output, logscale.input),
(logscale.output, pca.input), (pca.output, lda.input),
(lda.output, embedding)]
predictor = optimus.Graph(
name='pca-lda',
inputs=[input_data],
nodes=[logscale, reshape, pca, lda],
connections=optimus.ConnectionManager(base_edges).connections,
outputs=[embedding],
verbose=False)
return predictor
def test_convolve(self):
input_data = optimus.Input(name='x_in', shape=(None, 1, 1, 1))
flatten = optimus.Flatten(name='flatten', ndim=1)
output_data = optimus.Output(name='x_out')
edges = optimus.ConnectionManager([(input_data, flatten.input),
(flatten.output, output_data)])
transform = optimus.Graph(name='test',
inputs=[input_data],
nodes=[flatten],
connections=edges.connections,
outputs=[output_data])
x = np.arange(10).reshape(1, 10, 1)
y = np.array(['a', 'b'])
entity = biggie.Entity(x_in=x, y=y)
z = C.convolve(entity, transform, 'x_in')
np.testing.assert_equal(z.x_out, np.arange(10))
np.testing.assert_equal(z.y, y)
def i8c3_pwmse(size='large'):
k0, k1, k2 = dict(
small=(8, 16, 20),
med=(12, 24, 32),
large=(16, 32, 48))[size]
input_data = optimus.Input(
name='cqt',
shape=(None, 1, 8, 252))
target = optimus.Input(
name='target',
shape=(None, 1))
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=(k0, None, 3, 13),
pool_shape=(1, 3),
act_type='relu')
layer1 = optimus.Conv3D(
name='layer1',
input_shape=layer0.output.shape,
weight_shape=(k1, None, 3, 37),
act_type='relu')
layer2 = optimus.Conv3D(
name='layer2',
input_shape=layer1.output.shape,
weight_shape=(k2, None, 3, 33),
act_type='relu')
chord_classifier = optimus.Conv3D(
name='chord_classifier',
input_shape=layer2.output.shape,
weight_shape=(13, None, 2, 1),
act_type='sigmoid')
flatten = optimus.Flatten('flatten', 2)
null_classifier = optimus.Affine(
name='null_classifier',
input_shape=layer2.output.shape,
output_shape=(None, 1),
act_type='sigmoid')
cat = optimus.Concatenate('concatenate', num_inputs=2, axis=1)
param_nodes = [layer0, layer1, layer2, chord_classifier, null_classifier]
misc_nodes = [flatten, cat]
# 1.1 Create Loss
likelihoods = optimus.SelectIndex(name='likelihoods')
dimshuffle = optimus.Dimshuffle('dimshuffle', (0, 'x'))
squared_error = optimus.SquaredEuclidean(name='squared_error')
loss = optimus.Mean(name='mean_squared_error')
loss_nodes = [likelihoods, dimshuffle, squared_error, loss]
# 2. Define Edges
base_edges = [
(input_data, layer0.input),
(layer0.output, layer1.input),
(layer1.output, layer2.input),
(layer2.output, chord_classifier.input),
(layer2.output, null_classifier.input),
(chord_classifier.output, flatten.input),
(flatten.output, cat.input_0),
(null_classifier.output, cat.input_1)]
trainer_edges = optimus.ConnectionManager(
base_edges + [
(cat.output, likelihoods.input),
(chord_idx, likelihoods.index),
(likelihoods.output, dimshuffle.input),
(dimshuffle.output, squared_error.input_a),
(target, squared_error.input_b),
(squared_error.output, loss.input)])
update_manager = optimus.ConnectionManager(
map(lambda n: (learning_rate, n.weights), param_nodes) +
map(lambda n: (learning_rate, n.bias), param_nodes))
trainer = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data, target, chord_idx, learning_rate],
nodes=param_nodes + misc_nodes + loss_nodes,
connections=trainer_edges.connections,
outputs=[loss.output],
loss=loss.output,
updates=update_manager.connections,
verbose=True)
classifier_init(param_nodes)
posterior = optimus.Output(name='posterior')
predictor_edges = optimus.ConnectionManager(
base_edges + [(cat.output, posterior)])
predictor = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data],
nodes=param_nodes + misc_nodes,
connections=predictor_edges.connections,
outputs=[posterior])
return trainer, predictor
def i8x1a3T_nll2(size, use_dropout=False):
k0, k1, k2 = dict(
large=(2048, 2048, 40),)[size]
input_data = optimus.Input(
name='data',
shape=(None, 8, 1, 252))
chord_idx = optimus.Input(
name='class_idx',
shape=(None,),
dtype='int32')
learning_rate = optimus.Input(
name='learning_rate',
shape=None)
inputs = [input_data, chord_idx, learning_rate]
dropout = optimus.Input(
name='dropout',
shape=None)
# 1.2 Create Nodes
layer0 = optimus.Affine(
name='layer0',
input_shape=input_data.shape,
output_shape=(None, k0),
act_type='relu')
layer1 = optimus.Affine(
name='layer1',
input_shape=layer0.output.shape,
output_shape=(None, k1),
act_type='relu')
layer2 = optimus.Affine(
name='layer2',
input_shape=layer1.output.shape,
output_shape=(None, k2, 1, 12),
act_type='relu')
dropout_edges = []
if use_dropout:
layer0.enable_dropout()
layer1.enable_dropout()
layer2.enable_dropout()
inputs += [dropout]
dropout_edges += [(dropout, layer0.dropout),
(dropout, layer1.dropout),
(dropout, layer2.dropout)]
chord_classifier = optimus.Conv3D(
name='chord_classifier',
input_shape=layer2.output.shape,
weight_shape=(13, None, 1, 1),
act_type='linear')
flatten = optimus.Flatten('flatten', 2)
null_classifier = optimus.Affine(
name='null_classifier',
input_shape=layer0.output.shape,
output_shape=(None, 1),
act_type='linear')
cat = optimus.Concatenate('concatenate', num_inputs=2, axis=1)
softmax = optimus.Softmax('softmax')
prior = optimus.Multiply("prior", weight_shape=(1, 157), broadcast=[0])
prior.weight.value = np.ones([1, 157])
param_nodes = [layer0, layer1, layer2, null_classifier, chord_classifier]
misc_nodes = [flatten, cat, softmax, prior]
# 1.1 Create Loss
likelihoods = optimus.SelectIndex(name='likelihoods')
log = optimus.Log(name='log')
neg = optimus.Multiply(name='gain', weight_shape=None)
neg.weight.value = -1.0
loss = optimus.Mean(name='negative_log_likelihood')
loss_nodes = [likelihoods, log, neg, loss]
total_loss = optimus.Output(name='total_loss')
posterior = optimus.Output(name='posterior')
# 2. Define Edges
base_edges = [
(input_data, layer0.input),
(layer0.output, layer1.input),
(layer1.output, layer2.input),
(layer2.output, chord_classifier.input),
(layer0.output, null_classifier.input),
(chord_classifier.output, flatten.input),
(flatten.output, cat.input_0),
(null_classifier.output, cat.input_1),
(cat.output, softmax.input),
(softmax.output, prior.input),
(prior.output, posterior)]
trainer_edges = optimus.ConnectionManager(
base_edges + dropout_edges + [
(softmax.output, likelihoods.input),
(chord_idx, likelihoods.index),
(likelihoods.output, log.input),
(log.output, neg.input),
(neg.output, loss.input),
(loss.output, total_loss)])
update_manager = optimus.ConnectionManager(
map(lambda n: (learning_rate, n.weights), param_nodes) +
map(lambda n: (learning_rate, n.bias), param_nodes))
classifier_init(param_nodes)
trainer = optimus.Graph(
name=GRAPH_NAME,
inputs=inputs,
nodes=param_nodes + misc_nodes + loss_nodes,
connections=trainer_edges.connections,
outputs=[total_loss, posterior],
loss=total_loss,
updates=update_manager.connections,
verbose=True)
if use_dropout:
layer0.disable_dropout()
layer1.disable_dropout()
layer2.disable_dropout()
predictor = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data],
nodes=param_nodes + misc_nodes,
connections=optimus.ConnectionManager(base_edges).connections,
outputs=[posterior],
verbose=True)
return trainer, predictor
def i8c4b10_nll_dropout(size='large'):
k0, k1, k2 = dict(
large=(24, 48, 64))[size]
input_data = optimus.Input(
name='cqt',
shape=(None, 1, 8, 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=(k0, None, 3, 13),
pool_shape=(1, 3),
act_type='relu')
layer1 = optimus.Conv3D(
name='layer1',
input_shape=layer0.output.shape,
weight_shape=(k1, None, 3, 37),
act_type='relu')
layer2 = optimus.Conv3D(
name='layer2',
input_shape=layer1.output.shape,
weight_shape=(k2, None, 3, 33),
act_type='relu')
layer3 = optimus.Conv3D(
name='layer3',
input_shape=layer2.output.shape,
weight_shape=(10, None, 2, 1),
act_type='relu')
chord_classifier = optimus.Conv3D(
name='chord_classifier',
input_shape=layer3.output.shape,
weight_shape=(13, None, 1, 1),
act_type='linear')
flatten = optimus.Flatten('flatten', 2)
null_classifier = optimus.Affine(
name='null_classifier',
input_shape=layer3.output.shape,
output_shape=(None, 1),
act_type='linear')
cat = optimus.Concatenate('concatenate', num_inputs=2, axis=1)
softmax = optimus.Softmax('softmax')
param_nodes = [layer0, layer1, layer2, layer3,
null_classifier, chord_classifier]
misc_nodes = [flatten, cat, softmax]
# 1.1 Create Loss
likelihoods = optimus.SelectIndex(name='likelihoods')
log = optimus.Log(name='log')
neg = optimus.Gain(name='gain')
neg.weight.value = -1.0
loss = optimus.Mean(name='negative_log_likelihood')
loss_nodes = [likelihoods, log, neg, loss]
total_loss = optimus.Output(name='total_loss')
layer0.enable_dropout()
layer1.enable_dropout()
layer2.enable_dropout()
# 2. Define Edges
base_edges = [
(input_data, layer0.input),
(layer0.output, layer1.input),
(layer1.output, layer2.input),
(layer2.output, layer3.input),
(layer3.output, chord_classifier.input),
(layer3.output, null_classifier.input),
(chord_classifier.output, flatten.input),
(flatten.output, cat.input_0),
(null_classifier.output, cat.input_1),
(cat.output, softmax.input)]
trainer_edges = optimus.ConnectionManager(
base_edges + [
(dropout, layer0.dropout),
(dropout, layer1.dropout),
(dropout, layer2.dropout),
(softmax.output, likelihoods.input),
(chord_idx, likelihoods.index),
(likelihoods.output, log.input),
(log.output, neg.input),
(neg.output, loss.input),
(loss.output, total_loss)])
update_manager = optimus.ConnectionManager(
map(lambda n: (learning_rate, n.weights), param_nodes[:-1]) +
map(lambda n: (learning_rate, n.bias), param_nodes[:-1]))
trainer = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data, chord_idx, learning_rate, dropout],
nodes=param_nodes + misc_nodes + loss_nodes,
connections=trainer_edges.connections,
outputs=[total_loss],
loss=total_loss,
updates=update_manager.connections,
verbose=True)
classifier_init(param_nodes[:-1])
semitones = L.semitone_matrix(157)[:13, 2:]
chord_classifier.weights.value = semitones.reshape(13, 10, 1, 1)
posterior = optimus.Output(name='posterior')
predictor_edges = optimus.ConnectionManager(
base_edges + [(softmax.output, posterior)])
layer0.disable_dropout()
layer1.disable_dropout()
layer2.disable_dropout()
predictor = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data],
nodes=param_nodes + misc_nodes,
connections=predictor_edges.connections,
outputs=[posterior])
return trainer, predictor
def i20c3_mse12(size='large'):
k0, k1, k2 = dict(
small=(10, 20, 40),
med=(12, 24, 48),
large=(16, 32, 64))[size]
input_data = optimus.Input(
name='cqt',
shape=(None, 1, 20, 252))
target = optimus.Input(
name='target',
shape=(None, 12))
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=(k0, None, 5, 13),
pool_shape=(2, 3),
act_type='relu')
layer1 = optimus.Conv3D(
name='layer1',
input_shape=layer0.output.shape,
weight_shape=(k1, None, 5, 37),
pool_shape=(2, 1),
act_type='relu')
layer2 = optimus.Conv3D(
name='layer2',
input_shape=layer1.output.shape,
weight_shape=(k2, None, 1, 33),
pool_shape=(2, 1),
act_type='relu')
chroma_estimator = optimus.Conv3D(
name='chord_classifier',
input_shape=layer2.output.shape,
weight_shape=(1, None, 1, 1),
act_type='sigmoid')
flatten = optimus.Flatten('flatten', 2)
param_nodes = [layer0, layer1, layer2, chroma_estimator]
misc_nodes = [flatten]
# 1.1 Create Loss
error = optimus.SquaredEuclidean(name='squared_error')
loss = optimus.Mean(name='mean_squared_error')
loss_nodes = [error, loss]
chroma = optimus.Output(name='chroma')
total_loss = optimus.Output(name='total_loss')
# 2. Define Edges
base_edges = [
(input_data, layer0.input),
(layer0.output, layer1.input),
(layer1.output, layer2.input),
(layer2.output, chroma_estimator.input),
(chroma_estimator.output, flatten.input),
(flatten.output, chroma)]
trainer_edges = optimus.ConnectionManager(
base_edges + [
(flatten.output, error.input_a),
(target, error.input_b),
(error.output, loss.input),
(loss.output, total_loss)])
update_manager = optimus.ConnectionManager(
map(lambda n: (learning_rate, n.weights), param_nodes) +
map(lambda n: (learning_rate, n.bias), param_nodes))
classifier_init(param_nodes)
trainer = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data, target, learning_rate],
nodes=param_nodes + misc_nodes + loss_nodes,
connections=trainer_edges.connections,
outputs=[total_loss, chroma],
loss=total_loss,
updates=update_manager.connections,
verbose=True)
predictor = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data],
nodes=param_nodes + misc_nodes,
connections=optimus.ConnectionManager(base_edges).connections,
outputs=[chroma])
return trainer, predictor