def run_mnist_prediction(prediction_hparams):
# DATA
datamodule = MNISTDataModule(data_dir=DATA_PATH + '/mnist_')
datamodule.prepare_data() # downloads data to given path
train_dataloader = datamodule.train_dataloader()
val_dataloader = datamodule.val_dataloader()
test_dataloader = datamodule.test_dataloader()
encoder_model = SimpleConvNet()
return run_prediction(prediction_hparams, train_dataloader, val_dataloader, test_dataloader,
encoder_model, encoder_out_dim=50)
def run_mnist_dvrl(prediction_hparams, dvrl_hparams):
# DATA
datamodule = MNISTDataModule(data_dir=DATA_PATH + '/mnist_')
datamodule.prepare_data() # downloads data to given path
train_dataloader = datamodule.train_dataloader(
batch_size=dvrl_hparams.get('outer_batch_size', 32))
val_dataloader = datamodule.val_dataloader(
batch_size=dvrl_hparams.get('outer_batch_size', 32))
test_dataloader = datamodule.test_dataloader(
batch_size=dvrl_hparams.get('outer_batch_size', 32))
val_split = datamodule.val_split
encoder_model = SimpleConvNet()
return run_gumbel(dvrl_hparams,
prediction_hparams,
train_dataloader,
val_dataloader,
test_dataloader,
val_split,
encoder_model,
encoder_out_dim=50)
def mnist_data():
mnist = MNISTDataModule('../data/', batch_size=512)
mnist.prepare_data()
mnist.setup()
return mnist