def loadmodels(args):
    models = util.loadmodels(args, "cfg/classification")
    if args.mode == "top":
        for name in models.keys():
            models[name]['trainer'].max_steps = 10000  # training on top 500 never seems to need more than 10000 steps, since it's a small training set
    for cfg in models.itervalues():
        cfg["model"].conv_seq[0].fsize = 24     # Override default max motif length
    return models
Ejemplo n.º 2
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def loadmodels(args):
    models = util.loadmodels(args, "cfg/classification")
    if args.mode == "top":
        for name in models.keys():
            models[name][
                'trainer'].max_steps = 10000  # training on top 500 never seems to need more than 10000 steps, since it's a small training set
    for cfg in models.itervalues():
        cfg["model"].conv_seq[
            0].fsize = 24  # Override default max motif length
    return models
Ejemplo n.º 3
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def loadmodels(args):
    models = util.loadmodels(args, "cfg/regression/maxpool")
    for cfg in models.itervalues():
        cfg["model"].conv_seq[0].fsize = 24  # Override default max motif length
    return models
Ejemplo n.º 4
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def loadmodels(args):
    models = util.loadmodels(args, "cfg/classification")
    return models
Ejemplo n.º 5
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def loadmodels(args):
    models = util.loadmodels(args, "cfg/regression/maxpool")
    for cfg in models.itervalues():
        cfg["model"].conv_seq[
            0].fsize = 24  # Override default max motif length
    return models
Ejemplo n.º 6
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def loadmodels(args):
    models = util.loadmodels(args, "cfg/classification")
    return models
Ejemplo n.º 7
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def loadmodels(args):
    models = util.loadmodels(args, modeldir="cfg/regression/allpool")
    return models