def main(args): # 1.1 Create Inputs input_data = optimus.Input(name='cqt', shape=(None, 1, TIME_DIM, 252)) target_chroma = optimus.Input( name='target_chroma', 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=(12, 1, 3, 19), pool_shape=(1, 3), act_type='relu') layer1 = optimus.Conv3D(name='layer1', input_shape=layer0.output.shape, weight_shape=(16, None, 3, 15), act_type='relu') layer2 = optimus.Conv3D(name='layer2', input_shape=layer1.output.shape, weight_shape=(20, None, 1, 15), act_type='relu') layer3 = optimus.Affine(name='layer3', input_shape=layer2.output.shape, output_shape=( None, 12, ), act_type='sigmoid') all_nodes = [layer0, layer1, layer2, layer3] # 1.1 Create Losses chroma_xentropy = optimus.CrossEntropy(name="chroma_xentropy") # 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, chroma_xentropy.prediction), (target_chroma, chroma_xentropy.target) ]) 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) ]) trainer = optimus.Graph(name=GRAPH_NAME, inputs=[input_data, target_chroma, learning_rate], nodes=all_nodes, connections=trainer_edges.connections, outputs=[optimus.Graph.TOTAL_LOSS], losses=[chroma_xentropy], updates=update_manager.connections) optimus.random_init(layer0.weights) optimus.random_init(layer1.weights) optimus.random_init(layer2.weights) optimus.random_init(layer3.weights) validator = optimus.Graph(name=GRAPH_NAME, inputs=[input_data, target_chroma], nodes=all_nodes, connections=trainer_edges.connections, outputs=[optimus.Graph.TOTAL_LOSS], losses=[chroma_xentropy]) chroma_out = optimus.Output(name='chroma') predictor_edges = optimus.ConnectionManager([(input_data, layer0.input), (layer0.output, layer1.input), (layer1.output, layer2.input), (layer2.output, layer3.input), (layer3.output, chroma_out)]) predictor = optimus.Graph(name=GRAPH_NAME, inputs=[input_data], nodes=all_nodes, connections=predictor_edges.connections, outputs=[chroma_out]) # 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_chroma ], **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)) fret_bitmap = optimus.Input(name='fret_bitmap', shape=(None, 6, FRET_DIM)) 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, 3, 19), pool_shape=(1, 3), act_type='relu') layer1 = optimus.Conv3D(name='layer1', input_shape=layer0.output.shape, weight_shape=(16, None, 3, 15), act_type='relu') layer2 = optimus.Conv3D(name='layer2', input_shape=layer1.output.shape, weight_shape=(20, None, 1, 15), act_type='relu') layer3 = optimus.Affine(name='layer3', input_shape=layer2.output.shape, output_shape=( None, 512, ), act_type='relu') fretboard = optimus.MultiSoftmax(name='fretboard', input_shape=layer3.output.shape, output_shape=(None, 6, FRET_DIM), act_type='linear') all_nodes = [layer0, layer1, layer2, layer3, fretboard] # 1.1 Create Losses mse = optimus.MeanSquaredError(name="mean_squared_error") # 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, fretboard.input), (fretboard.output, mse.prediction), (fret_bitmap, mse.target) ]) 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, fretboard.weights), (learning_rate, fretboard.bias) ]) trainer = optimus.Graph(name=GRAPH_NAME, inputs=[input_data, fret_bitmap, learning_rate], nodes=all_nodes, connections=trainer_edges.connections, outputs=[optimus.Graph.TOTAL_LOSS], losses=[mse], updates=update_manager.connections) optimus.random_init(fretboard.weights) validator = optimus.Graph(name=GRAPH_NAME, inputs=[input_data, fret_bitmap], nodes=all_nodes, connections=trainer_edges.connections, outputs=[optimus.Graph.TOTAL_LOSS], losses=[mse]) 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, fretboard.input), (fretboard.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.cqt_sample(input_data.shape[2]), T.pitch_shift(MAX_FRETS, bins_per_pitch=3), T.fret_indexes_to_bitmap(FRET_DIM) ], **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) # 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)