Beispiel #1
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def train_mlp(NET, sgd_params, datasets):
    """Run mlp training test."""
    # Train the net
    NT.train_mlp(NET=NET, \
        sgd_params=sgd_params, \
        datasets=datasets)
    return
def train_mlp(NET, sgd_params, datasets):
    """Run mlp training test."""
    # Train the net
    NT.train_mlp(NET=NET, \
        sgd_params=sgd_params, \
        datasets=datasets)
    return
Beispiel #3
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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
Beispiel #5
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def train_dae(NET, dae_layer, sgd_params, datasets):
    """Run DAE training test."""
    # Run denoising autoencoder training on the given layer of NET
    NT.train_dae(NET=NET, \
        dae_layer=dae_layer, \
        sgd_params=sgd_params, \
        datasets=datasets)
    return
def train_dae(NET, dae_layer, sgd_params, datasets):
    """Run DAE training test."""
    # Run denoising autoencoder training on the given layer of NET
    NT.train_dae(NET=NET, \
        dae_layer=dae_layer, \
        sgd_params=sgd_params, \
        datasets=datasets)
    return
Beispiel #7
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def train_dae(NET, dae_layer, mlp_params, sgd_params):
    """Run DAE training test."""

    # Load some data to train/validate/test with
    dataset = 'data/mnist.pkl.gz'
    datasets = load_udm(dataset)

    # Run denoising autoencoder training on the given layer of NET
    NT.train_dae(NET=NET, \
        dae_layer=dae_layer, \
        mlp_params=mlp_params, \
        sgd_params=sgd_params, \
        datasets=datasets)
    return 1
Beispiel #8
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def train_dae(NET, dae_layer, mlp_params, sgd_params):
    """Run DAE training test."""

    # Load some data to train/validate/test with
    dataset = 'data/mnist.pkl.gz'
    datasets = load_udm(dataset)

    # Run denoising autoencoder training on the given layer of NET
    NT.train_dae(NET=NET, \
        dae_layer=dae_layer, \
        mlp_params=mlp_params, \
        sgd_params=sgd_params, \
        datasets=datasets)
    return
Beispiel #9
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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
Beispiel #10
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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
Beispiel #11
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def train_mlp(NET, mlp_params, sgd_params):
    """Run mlp training test."""

    # Load some data to train/validate/test with
    #dataset = 'data/mnist.pkl.gz'
    #datasets = load_udm(dataset)
    dataset = 'data/mnist_batches.npz'
    datasets = load_mnist(dataset)

    # Tell the net that it's not semisupervised, which will force it to use
    # _all_ examples for computing the DEV regularizer.
    NET.is_semisupervised = 0

    # Train the net
    NT.train_mlp(NET=NET, \
        mlp_params=mlp_params, \
        sgd_params=sgd_params, \
        datasets=datasets)
    return 1
Beispiel #12
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def train_mlp(NET, mlp_params, sgd_params):
    """Run mlp training test."""

    # Load some data to train/validate/test with
    #dataset = 'data/mnist.pkl.gz'
    #datasets = load_udm(dataset)
    dataset = 'data/mnist_batches.npz'
    datasets = load_mnist(dataset)

    # Tell the net that it's not semisupervised, which will force it to use
    # _all_ examples for computing the DEV regularizer.
    NET.is_semisupervised = 0

    # Train the net
    NT.train_mlp(NET=NET, \
        mlp_params=mlp_params, \
        sgd_params=sgd_params, \
        datasets=datasets)
    return