def train(filenames=None, settings=None, **kwargs):
dataset = MNIST(filenames=filenames)
print("=" * 30)
print("DATASET SETTINGS:")
print(dataset.pretty_settings())
print("=" * 30)
net = AAE1D(filenames=filenames)
net.define_net()
print("=" * 30)
print("NETWORK SETTINGS:")
print(net.pretty_settings())
print("=" * 30)
nb_batches = settings['nb_batches']
net.load()
ptp = ProgressTimer(nb_batches)
# for i in range(nb_batches // 3 * 2):
# loss_ae = train_ae(net, dataset)
# msg = 'T:AuE, loss=%05f' % (loss_ae)
# ptp.event(net.step, msg)
# net.lr_decay()
# for i in range(nb_batches // 3):
# loss_ae = train_ae(net, dataset)
# msg = 'T:AuE, loss=%05f' % (loss_ae)
# ptp.event(net.step, msg)
for i in range(nb_batches // 3):
loss_cri = train_cri(net, dataset)
msg = '|T:Cri, loss=%05f|' % (loss_cri)
loss_gen = train_gen(net, dataset)
msg += '|T:Gen, loss=%05f|' % (loss_gen)
loss_ae = train_ae(net, dataset)
msg = '|T:AuE, loss=%05f|' % (loss_ae)
ptp.event(net.step, msg)
ptp = ProgressTimer(nb_batches)
# for i in range(nb_batches // 2):
# loss_ae = train_ae(net, dataset)
# msg = 'step #%5d, AuE, loss=%05f' % (net.step, loss_ae)
# ptp.event(net.step, msg)
# net.lr_decay()
# for i in range(nb_batches // 2):
# loss_ae = train_ae(net, dataset)
# msg = 'step #%5d, AuE, loss=%05f' % (net.step, loss_ae)
# ptp.event(net.step, msg)
# for i in range(nb_batches):
# loss_cri = train_cri(net, dataset)
# msg = 'Cri, loss=%05f ' % (loss_cri)
# loss_gen = train_gen(net, dataset)
# msg += 'Gen, loss=%05f ' % (loss_gen)
# ptp.event(net.step, msg)
net.save('net', is_print=True)
def train(nb_batches, cfs):
dataset = MNIST(filenames=cfs)
print("=" * 30)
print("DATASET SETTINGS:")
print(dataset.pretty_settings())
print("=" * 30)
net = LSGAN(filenames=cfs)
net.define_net()
print("=" * 30)
print("NETWORK SETTINGS:")
print(net.pretty_settings())
print("=" * 30)
ptp = ProgressTimer(net.pre_train)
for i in range(net.pre_train):
s = next(dataset)
z = net.gen_latent()
loss_c = net.train_on_batch('Cri', [s[0], z], [])
msg = 'loss_c= %f' % loss_c
ptp.event(i, msg)
pt = ProgressTimer(nb_batches)
loss_c = np.nan
loss_g = np.nan
for i in range(nb_batches):
s = next(dataset)
z = net.gen_latent()
if i % net.gen_freq > 0:
loss_c = net.train_on_batch('Cri', [s[0], z], [])
msg = 'c_step, loss_c= %f; loss_g= %f' % (loss_c, loss_g)
pt.event(i, msg)
else:
loss_g = net.train_on_batch('WGan', [s[0], z], [])
msg = 'g_step, loss_c= %f; loss_g= %f' % (loss_c, loss_g)
pt.event(i, msg)
if i % 1000 == 0:
net.save('AutoEncoder')
net.save('AutoEncoder', is_print=True)