- self.nb_batches = nb_samples // batch_size
- self.nb_samples = self.nb_batches * self.batch_size
+
+ self.batch_size = batch_size
+ self.nb_samples = nb_samples
+ self.nb_batches = self.nb_samples // self.batch_size
- self.nb_batches = nb_samples // batch_size
- self.nb_samples = self.nb_batches * self.batch_size
+
+ self.batch_size = batch_size
+ self.nb_samples = nb_samples
+ self.nb_batches = self.nb_samples // self.batch_size
+
for b in range(0, self.nb_batches):
target = torch.LongTensor(self.batch_size).bernoulli_(0.5)
input = svrt.generate_vignettes(problem_number, target)
for b in range(0, self.nb_batches):
target = torch.LongTensor(self.batch_size).bernoulli_(0.5)
input = svrt.generate_vignettes(problem_number, target)
self.targets.append(target)
self.input_storages.append(svrt.compress(input.storage()))
if logger is not None: logger(self.nb_batches * self.batch_size, b * self.batch_size)
self.targets.append(target)
self.input_storages.append(svrt.compress(input.storage()))
if logger is not None: logger(self.nb_batches * self.batch_size, b * self.batch_size)