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build/scripts-2.7/train.py
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#!/users/parcollet/.pyenv/versions/2.7.13/bin/python
# -*- coding: utf-8 -*-
#
# Authors: Dmitriy Serdyuk
from __future__ import print_function
import numpy
import numpy as np
from os import path
import argparse
import mimir
import keras
import musicnet.models.complex
from musicnet.callbacks import (
SaveLastModel, Performance, Validation, LearningRateScheduler)
from musicnet.dataset import MusicNet
from musicnet import models
# input dimensions
d = 16384 / 4
window_size = d
# number of notes
m = 84
step = 512
step = step / 4
def schedule(epoch):
if epoch >= 0 and epoch < 10:
lrate = 1e-3
if epoch == 0:
print('
current learning rate value is ' + str(lrate))
elif epoch >= 10 and epoch < 100:
lrate = 1e-4
if epoch == 10:
print('
current learning rate value is ' + str(lrate))
elif epoch >= 100 and epoch < 120:
lrate = 5e-5
if epoch == 100:
print('
current learning rate value is ' + str(lrate))
elif epoch >= 120 and epoch < 150:
lrate = 1e-5
if epoch == 120:
print('
current learning rate value is ' + str(lrate))
elif epoch >= 150:
lrate = 1e-6
if epoch == 150:
print('
current learning rate value is ' + str(lrate))
return lrate
def get_model(model, feature_dim):
if model.startswith('complex'):
complex_ = True
model = model.split('_')[1]
else:
complex_ = False
if complex_:
model_module = models.complex
print('.. complex network')
else:
model_module = models
if model == 'mlp':
print('.. using MLP')
return model_module.get_mlp(window_size=numpy.prod(feature_dim))
elif model == 'shallow_convnet':
print('.. using shallow convnet')
return model_module.get_shallow_convnet(window_size=feature_dim[0],
channels=feature_dim[1])
elif model == 'deep_convnet':
print('.. using deep convnet')
return model_module.get_deep_convnet(window_size=feature_dim[0],
channels=feature_dim[1])
else:
raise ValueError
def main(model_name, in_memory, complex_, model, local_data, epochs, fourier,
stft, fast_load):
rng = numpy.random.RandomState(123)
# Warning: the full dataset is over 40GB. Make sure you have enough RAM!
# This can take a few minutes to load
if in_memory:
print('.. loading train data')
dataset = MusicNet(local_data, complex_=complex_, fourier=fourier,
stft=stft, rng=rng, fast_load=fast_load)
dataset.load()
print('.. train data loaded')
Xvalid, Yvalid = dataset.eval_set('valid')
Xtest, Ytest = dataset.eval_set('test')
else:
raise ValueError
print(".. building model")
model = get_model(model, dataset.feature_dim)
model.summary()
print(".. parameters: {:03.2f}M".format(model.count_params() / 1000000.))
if in_memory:
pass
# do nothing
else:
raise ValueError
logger = mimir.Logger(
filename='models/log_{}.jsonl.gz'.format(model_name))
it = dataset.train_iterator()
callbacks = [Validation(Xvalid, Yvalid, 'valid', logger),
Validation(Xtest, Ytest, 'test', logger),
SaveLastModel("./models/", 1, name=model),
Performance(logger),
LearningRateScheduler(schedule)]
print('.. start training')
model.fit_generator(
it, steps_per_epoch=1000, epochs=epochs,
callbacks=callbacks, workers=1)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('model_name')
parser.add_argument('--in-memory', action='store_true', default=False)
parser.add_argument('--complex', dest='complex_', action='store_true',
default=False)
parser.add_argument('--model', default='shallow_convnet')
parser.add_argument('--epochs', default=200, type=int)
parser.add_argument('--fourier', action='store_true', default=False)
parser.add_argument('--stft', action='store_true', default=False)
parser.add_argument('--fast-load', action='store_true', default=False)
parser.add_argument(
'--local-data',
default="/Tmp/serdyuk/data/musicnet_11khz.npz")
main(**parser.parse_args().__dict__)
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