def get_input_decay(name):
"""
Generates the appropriate decay function for a given variable. The decays
that can be generated are static (no decay over time), linear and
exponential.
:param name: name of the variable that will be generated
:return: decay function for the variable with appropriate parameters
"""
decay = input('What kind of decay for {}? [s/l/e]'.format(name)) or 'e'
if decay == 's':
value = float(input('Static value: '))
return StaticDecay(value)
if decay == 'l':
value = float(input('Start value: '))
rate = float(input('Rate:'))
return LinearDecay(value, rate)
if decay == 'e':
value = float(input('Start value: '))
rate = float(input('Rate:'))
return ExponentialDecay(value, rate)
exit('Not a valid option!')
import matplotlib.pyplot as plt
import os
from decay import StaticDecay, LinearDecay, ExponentialDecay
from neighborhood import gaussian, bubble
DEFAULTS_EXP = (
'rgbg_beergardens', #dataset
2, #number of neurons per city
5000, #iterations
1000, #k
1000, #plot_k
bubble, #neighborhood
ExponentialDecay(0.7, 0.9999), #learning rate
ExponentialDecay(30, 0.999)) #radius
DEFAULTS_LIN = ('rgbg_beergardens', 8, 40000, 5000, 5000, gaussian,
LinearDecay(0.9, 0.0000089), LinearDecay(20, 0.0005))
DEFAULT_STA = ('rgbg_beergardens', 2, 50000, 5000, 5000, gaussian,
StaticDecay(0.5), StaticDecay(1))
plt.figure()
def plot_map(cities, neurons, iteration):
"""
Generates the required map of cities and neurons at a given moment and
stores the result in a png image. The map contains all the cities
represented as red dots and all the neurons as green, crossed by a line
dots. The method plots the iteration in which the snapshot was taken.
:param cities: the cities to be plotted, passed as a list of (x, y)
coordinates
('uruguay', 8, 10000, 1000, 500, bubble, ExponentialDecay(0.7, 0.9999),
ExponentialDecay(734 * 8 / 10, 0.999)),
'western_sahara':
('western_sahara', 8, 500, 50, 50, gaussian, ExponentialDecay(0.7, 0.999),
ExponentialDecay(29 * 8 / 10, 0.995)),
'qatar': ('qatar', 8, 5000, 500, 500, gaussian,
ExponentialDecay(0.9,
0.9999), ExponentialDecay(194 * 8 / 10, 0.997)),
'djibouti': ('djibouti', 8, 1000, 50, 50, gaussian,
ExponentialDecay(0.7,
0.999), ExponentialDecay(89 * 8 / 10, 0.995))
}
DEFAULTS_LIN = {
'uruguay': ('uruguay', 8, 10000, 1000, 500, gaussian,
LinearDecay(0.9, 0.000089), LinearDecay(734 * 8 / 10, 0.055)),
'western_sahara': ('western_sahara', 8, 500, 50, 50, gaussian,
LinearDecay(0.7, 0.001), LinearDecay(29 * 8 / 10,
0.04)),
'qatar': ('qatar', 8, 10000, 500, 500, gaussian,
LinearDecay(0.7, 0.000065), LinearDecay(194 * 8 / 10, 0.015)),
'djibouti':
('djibouti', 8, 5000, 50, 50, gaussian, LinearDecay(0.7, 0.0001),
LinearDecay(89 * 8 / 10, 0.014))
}
DEFAULT_STA = {
'uruguay': ('uruguay', 8, 10000, 1000, 200, gaussian, StaticDecay(0.4),
StaticDecay(500)),
'western_sahara': ('western_sahara', 6, 5000, 1000, 200, gaussian,
StaticDecay(0.3), StaticDecay(5)),
def train(self, generator, discriminator, tool):
print("using device: %s" % (self.device))
'''
print ("discriminator parameters!")
utils.print_parameter_list(discriminator)
print ("______________________")
input(">")
'''
# build training and validation data
tool.close_corruption()
tool.set_batch_size(self.hps.batch_size)
tool.build_train_data(self.hps.unpaired_train_data,
self.hps.paired_train_data, None)
tool.build_valid_data(self.hps.unpaired_valid_data,
self.hps.paired_valid_data, None)
print("train batch num: %d" % (tool.train_batch_num))
print("valid batch num: %d" % (tool.valid_batch_num))
# training logger
logger = StyInsLogger('train')
logger.set_batch_num(tool.train_batch_num)
logger.set_log_steps(self.hps.log_steps)
logger.set_log_path(self.hps.train_log_path)
logger.set_rate('learning_rate', 0.0)
logger.set_rate('teach_ratio', 1.0)
# build optimizer
optGen = torch.optim.Adam(generator.parameters(),
lr=0.0,
betas=(0.9, 0.999))
optDis = torch.optim.Adam(discriminator.parameters(),
lr=0.0,
betas=(0.5, 0.999))
optimizerGen = ISRScheduler(optimizer=optGen,
warmup_steps=self.hps.warmup_steps,
max_lr=self.hps.max_lr,
min_lr=self.hps.min_lr,
init_lr=self.hps.init_lr,
beta=0.75)
optimizerDis = ISRScheduler(optimizer=optDis,
warmup_steps=self.hps.warmup_steps,
max_lr=self.hps.max_lr,
min_lr=self.hps.min_lr,
init_lr=self.hps.init_lr,
beta=0.5)
# ----------------------------------------------
criterion = Criterion(self.hps.pad_idx)
# when paired data is available, at the beginning,
# we give larger weight for supervised loss and then decrease it.
self.decay_tool = LinearDecay(
burn_down_steps=self.hps.superv_burn_down_steps,
decay_steps=self.hps.superv_decay_steps,
max_v=self.hps.superv_max,
min_v=self.hps.superv_min)
generator.train()
discriminator.train()
print("In training mode: %d, %d" %
(generator.training, discriminator.training))
#input("Please check the parameters and press enter to continue>")
# --------------------------------------------------------------------------------------
for epoch in range(1, self.hps.max_epoches + 1):
self.run_train(generator, discriminator, tool, optimizerGen,
optimizerDis, criterion, logger)
if (self.hps.save_epoches >= 1) and (epoch %
self.hps.save_epoches) == 0:
# save checkpoint
print("saving model...")
utils.save_checkpoint_multiple(self.hps.ckpt_path, generator,
discriminator, optimizerGen,
optimizerDis, epoch)
if epoch % self.hps.valid_epoches == 0:
print("run validation...")
generator.eval()
discriminator.eval()
print("in training mode: %d, %d" %
(generator.training, discriminator.training))
self.run_validation(epoch, generator, discriminator, criterion,
tool, optimizerGen.rate())
generator.train()
discriminator.train()
print("validation Done, mode: %d, %d" %
(generator.training, discriminator.training))
logger.add_epoch()
# each epoch, we rebuild the data, including shuffling the data, re-assigning
# transfer direction and resampling instances for each batch
print("shuffle data...")
tool.shuffle_training_data()
ExponentialDecay(734*8/10, 0.999)),
'western_sahara': ('western_sahara', 8, 500, 50, 50, gaussian,
ExponentialDecay(0.7, 0.999),
ExponentialDecay(29*8/10, 0.995)),
'qatar': ('qatar', 8, 5000, 500, 500, gaussian,
ExponentialDecay(0.9, 0.9999),
ExponentialDecay(194*8/10, 0.997)),
'djibouti': ('djibouti', 8, 1000, 50, 50, gaussian,
ExponentialDecay(0.7, 0.999),
ExponentialDecay(89*8/10, 0.995)),
'rgbg_beergardens': ('rgbg_beergardens', 8, 500, 50, 50, gaussian,
ExponentialDecay(0.7, 0.999),
ExponentialDecay(17*8/10, 0.995))}
DEFAULTS_LIN = {'uruguay': ('uruguay', 8, 10000, 1000, 500, gaussian,
LinearDecay(0.9, 0.000089),
LinearDecay(734*8/10, 0.055)),
'western_sahara': ('western_sahara', 8, 500, 50, 50, gaussian,
LinearDecay(0.7, 0.001),
LinearDecay(29*8/10, 0.04)),
'qatar': ('qatar', 8, 10000, 500, 500, gaussian,
LinearDecay(0.7, 0.000065),
LinearDecay(194*8/10, 0.015)),
'djibouti': ('djibouti', 8, 5000, 50, 50, gaussian,
LinearDecay(0.7, 0.0001),
LinearDecay(89*8/10, 0.014)),
'rgbg_beergardens': ('rgbg_beergardens', 8, 500, 50, 50, gaussian,
LinearDecay(0.7, 0.001))}
DEFAULT_STA = {'uruguay': ('uruguay', 8, 10000, 1000, 200, gaussian,
StaticDecay(0.4),