def setUpDatasets(self):
# Build training dataset
inputs, targets = self.generate_random_data(self.NUM_SAMPLES, (3, 32, 32),
num_classes=self.NUM_CLASSES,
dtype='float32')
# Split to train and split
train_inputs, train_targets = inputs[:self.NUM_TRAINING_SAMPLES], \
targets[:self.NUM_TRAINING_SAMPLES]
validate_inputs, validate_targets = inputs[self.NUM_TRAINING_SAMPLES:], \
targets[self.NUM_TRAINING_SAMPLES:]
# Convert to tensor and build dataset
train_dataset = TensorDataset(torch.from_numpy(train_inputs),
torch.from_numpy(train_targets))
validate_dataset = TensorDataset(torch.from_numpy(validate_inputs),
torch.from_numpy(validate_targets))
# Build dataloaders from dataset
self.train_loader = DataLoader(train_dataset, batch_size=16,
shuffle=True, num_workers=2, pin_memory=False)
self.validate_loader = DataLoader(validate_dataset, batch_size=16,
shuffle=True, num_workers=2, pin_memory=False)
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