def train_ss_mlp(NET, sgd_params, datasets):
"""Run semi-supervised EA-regularized test."""
# Run training on the given NET
NT.train_ss_mlp(NET=NET, \
sgd_params=sgd_params, \
datasets=datasets)
return
def train_ss_mlp(NET, sgd_params, datasets):
"""Run semi-supervised EA-regularized test."""
# Run training on the given NET
NT.train_ss_mlp(NET=NET, \
sgd_params=sgd_params, \
datasets=datasets)
return
def train_ss_mlp(NET, mlp_params, sgd_params, rng, su_count=1000):
"""Run semisupervised DEV-regularized test."""
# Load some data to train/validate/test with
dataset = 'data/mnist.pkl.gz'
datasets = load_udm_ss(dataset, su_count, rng)
# Tell the net that it's semisupervised, which will force it to use only
# unlabeled examples for computing the DEV regularizer.
NET.is_semisupervised = 1
# Run training on the given NET
NT.train_ss_mlp(NET=NET, \
mlp_params=mlp_params, \
sgd_params=sgd_params, \
datasets=datasets)
return 1
def train_ss_mlp(NET, mlp_params, sgd_params, rng, su_count=1000):
"""Run semisupervised DEV-regularized test."""
# Load some data to train/validate/test with
dataset = 'data/mnist.pkl.gz'
datasets = load_udm_ss(dataset, su_count, rng)
# Tell the net that it's semisupervised, which will force it to use only
# unlabeled examples for computing the DEV regularizer.
NET.is_semisupervised = 1
# Run training on the given NET
NT.train_ss_mlp(NET=NET, \
mlp_params=mlp_params, \
sgd_params=sgd_params, \
datasets=datasets)
return
