i * batch_number + j)
# PARAMS
for name, param in net.named_parameters():
writer.add_histogram(name,
param.clone().cpu().data.numpy(),
i * batch_number + j)
if j % logging_text_step == 0:
net.train(False)
# STEP
s = "non sopporto i giocatori di biliardo, i soprannomi, gli indecisi, i no"[
0:75]
s_final = s
s = numpy.asarray([
CHARS.index(c) if c in CHARS else CHARS.index(FILL_CHAR)
for c in s
])
s = to_categorical(s, num_classes=features_size)
for k in xrange(500):
c = net(
Variable(torch.FloatTensor(
s[numpy.newaxis, ...])).cuda()).cpu().data.numpy()
c = numpy.where(c < numpy.max(c), 0, 1)
s = numpy.append(s, c, 0)
s = s[1:]
c = numpy.argmax(c, 1)[0]
s_final += CHARS[c]
writer.add_text("text_sample", s_final, i * batch_number + j)
progress.finish()
default=1,
help='number of GPUs to use')
parser.add_argument('--netG',
default='',
help="path to netG (to continue training)")
parser.add_argument('--netD',
default='',
help="path to netD (to continue training)")
parser.add_argument('--outf',
default='.',
help='folder to output images and model checkpoints')
parser.add_argument('--manualSeed', type=int, help='manual seed')
opt = parser.parse_args()
print(opt)
writer.add_text('opt', str(opt), 0)
try:
os.makedirs(opt.outf)
except OSError:
pass
if opt.manualSeed is None:
opt.manualSeed = random.randint(1, 10000)
print("Random Seed: ", opt.manualSeed)
writer.add_text('Random Seed: ', str(opt.manualSeed), 0)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
if opt.cuda:
torch.cuda.manual_seed_all(opt.manualSeed)
cudnn.benchmark = True
import torch
import torchvision.utils as vutils
import numpy as np
import torchvision.models as models
from datetime import datetime
from tensorboard import SummaryWriter
resnet18 = models.resnet18(True)
writer = SummaryWriter('runs/' + datetime.now().strftime('%B%d %H:%M:%S'))
sample_rate = 44100
freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]
for n_iter in range(100):
M_global = torch.rand(1) # value to keep
writer.add_scalar('M_global', M_global[0], n_iter)
x = torch.rand(32, 3, 64, 64) # output from network
if n_iter % 10 == 0:
x = vutils.make_grid(x, normalize=True, scale_each=True)
writer.add_image('Image', x, n_iter)
x = torch.zeros(sample_rate * 2)
for i in range(x.size(0)):
x[i] = np.cos(
freqs[n_iter // 10] * np.pi * float(i) /
float(sample_rate)) # sound amplitude should in [-1, 1]
writer.add_audio('Audio', x, n_iter)
for name, param in resnet18.named_parameters():
writer.add_histogram(name,
param.clone().cpu().data.numpy(), n_iter)
writer.add_text('Text', 'text logged at step:' + str(n_iter), n_iter)
writer.add_text('another Text',
'another text logged at step:' + str(n_iter), n_iter)
writer.close()
# Fix the number of epochs to train for
n_epochs = 10
threshold = 0.01 * 10 ** (-21)
# -------------------------------------------------------------------------
# SET UP A LOGGER FOR TENSORBOARD VISUALIZATION
# -------------------------------------------------------------------------
run_start = datetime.datetime.now()
log_name = [run_start, distances, sample_size, initial_lr, threshold]
log_name_formatted = '[{:%Y-%m-%d_%H:%M}]-[{}]-[{}]-[lr_{:.1e}]-'\
'[thresh_{:.2e}]'.format(*log_name)
writer = SummaryWriter(log_dir='logs/{}'.format(log_name_formatted))
writer.add_text(tag='Description',
text_string='(Description missing.)')
# -------------------------------------------------------------------------
# TRAIN THE NET FOR THE GIVEN NUMBER OF EPOCHS
# -------------------------------------------------------------------------
print('\nStart training: Training on {} examples, validating on {} '
'examples\n'.format(len(data_train), len(data_validation)))
# -------------------------------------------------------------------------
for epoch in range(n_epochs):
print('Epoch {}/{}'.format(epoch+1, n_epochs))
running_loss = 0
metrics = {'loss': [], 'hamming': [], 'val_loss': [], 'val_hamming': []}
# -------------------------------------------------------------------------
# SET UP A LOGGER FOR TENSORBOARD VISUALIZATION
# -------------------------------------------------------------------------
# Define a log directory and set up a writer
run_start = datetime.datetime.now()
log_name = [
run_start, noise_source, distances, sample_size, initial_lr, threshold,
regularization_parameter
]
log_name_formatted = '[{:%Y-%m-%d_%H:%M}]-[{}]-[{}]-[{}]-[LR_{:.1e}]-'\
'[THR_{:.1f}]_[REG_{:.2e}]'.format(*log_name)
writer = SummaryWriter(log_dir='logs/{}'.format(log_name_formatted))
writer.add_text(tag='Description', text_string=description)
# Define a shortcut to write metrics to the log
def log_metric(name, value, epoch):
writer.add_scalar(name, value, epoch)
# -------------------------------------------------------------------------
# TRAIN THE NET FOR THE GIVEN NUMBER OF EPOCHS
# -------------------------------------------------------------------------
print('\nStart training: Training on {} examples, validating on {} '
'examples\n'.format(len(data_train), len(data_validation)))
# -------------------------------------------------------------------------
#
