How to use the deephyper.search.models.base.param.discrete function in deephyper

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github deephyper / deephyper / deephyper / benchmarks_hps / reutersmlp / space.py View on Github external
def __init__(self):
        self.space = [
                activation1,
                activation2,
                activation3,
                activation4,
                activation5,
                param.discrete("batch_size", 8, 128, step.GEOMETRIC, 2),
                dropout,
                param.discrete("epochs", 2, 20, step.ARITHMETIC, 1),
                param.discrete("max_words", 800, 1300, step.ARITHMETIC, 1),
                param.discrete("nunits", 128, 1024, step.ARITHMETIC, 128),
                optimizer,
                param.discrete("skip_top", 0,30, step.ARITHMETIC, 1)
        ]

        self.params = [param.name for param in self.space]
        self.starting_point = ['relu', 32, 0.5, 5, 1000, 512, 'adam', 0]
github deephyper / deephyper / deephyper / benchmarks_hps / mnistmlp / space.py View on Github external
def __init__(self):
        self.space = [
                param.discrete('batch_size', 8, 512, step.GEOMETRIC, 2),
                param.discrete('epochs', 5, 60, step.ARITHMETIC, 1),
                param.discrete('nhidden', 1, 20, step.ARITHMETIC, 1),
                param.discrete('nunits', 1, 1000, step.ARITHMETIC, 1),
                activation,
                activation1,
                activation2,
                dropout,
                optimizer
        ]

        self.params = [param.name for param in self.space]
        self.starting_point = [128, 20, 1, 512, 'relu', 0.2, 'adam']
github deephyper / deephyper / deephyper / benchmarks_hps / mnist_siamese / space.py View on Github external
def __init__(self):
        self.space = [
                param.discrete('margin', 1, 3, step.ARITHMETIC, 2),
                param.discrete('units', 32, 512, step.ARITHMETIC, 1),
                param.discrete('batch_size', 32, 512, step.ARITHMETIC, 1),
                param.discrete('epochs', 10, 45, step.ARITHMETIC, 1),
                activation,
                dropout,
                optimizer
        ]

        self.params = [param.name for param in self.space]
        self.starting_point = [1, 128, 128, 20, 'relu', 0.3, 'adam']
github deephyper / deephyper / deephyper / benchmarks_hps / cifar10cnn / space.py View on Github external
def __init__(self):
        self.space = [
            activation1,
            activation2,
            activation3,
            activation4,
            activation5,
            param.discrete("batch_size", 8, 1024, step.GEOMETRIC, 2),
            param.non_ordinal("data_augmentation", [False, True]),
            dropout,
            param.discrete("epochs", 50, 200, step.ARITHMETIC, 1),
            param.discrete("f1_size", 1, 5, step.ARITHMETIC, 1),
            param.discrete("f2_size", 1, 5, step.ARITHMETIC, 1),
            param.discrete("f1_units", 8, 64, step.GEOMETRIC, 2),
            param.discrete("f2_units", 8, 64, step.GEOMETRIC, 2),
            param.continuous("nunits", 1, 1000),
            optimizer,
            param.discrete("p_size", 1, 4, step.ARITHMETIC, 1),
            param.non_ordinal("padding_c1", ["same", "valid"]),
            param.non_ordinal("padding_c2", ["same", "valid"]),
            param.non_ordinal("padding_p1", ["same", "valid"]),
            param.non_ordinal("padding_p2", ["same", "valid"]),
            param.discrete("stride1", 1, 4, step.ARITHMETIC, 1),
            param.discrete("stride2", 1, 4, step.ARITHMETIC, 1)
        ]

        self.params = [param.name for param in self.space]
        self.starting_point = ['relu', 32, True, 0.25, 100, 3, 3, 16, 16, 512, 'adam', 2, 'valid', 'valid', 'same', 'same', 1, 1]
github deephyper / deephyper / deephyper / benchmarks_hps / mnistcnn / space.py View on Github external
def __init__(self):
        self.space = [
                activation1,
                activation2,
                activation3,
                activation4,
                activation5,
                param.discrete("batch_size", 8, 1024, step.GEOMETRIC, 2),
                dropout,
                param.discrete("epochs", 5, 35, step.ARITHMETIC, 1),
                param.discrete("f1_size", 1, 7, step.ARITHMETIC, 1),
                param.discrete("f2_size", 1, 7, step.ARITHMETIC, 1),
                param.discrete("f1_units", 8, 64, step.GEOMETRIC, 2),
                param.discrete("f2_units", 8, 64, step.GEOMETRIC, 2),
                param.non_ordinal("max_pool", [True, False]),
                param.discrete("nunits", 1, 1000, step.ARITHMETIC, 1),
                optimizer,
                param.non_ordinal("padding_c1", ["valid", "same"]),
                param.non_ordinal("padding_c2", ["valid", "same"]),
                param.non_ordinal("padding_p1", ["valid", "same"]),
                param.non_ordinal("padding_p2", ["valid", "same"]),
                param.discrete('p_size', 1, 5, step.ARITHMETIC, 1)
        ]

        self.params = [param.name for param in self.space]
        self.starting_point = ['relu', 128, 0.3, 12, 3, 3, 16, 16, True, 512, 'adam', 'valid', 'valid', 'same', 'same', 3]
github deephyper / deephyper / deephyper / benchmarks_hps / cifar10_resnet_v2 / space.py View on Github external
def __init__(self):
        self.space = [
            activation,
            param.discrete('batch_size', 8, 128, step.GEOMETRIC, 2),
            param.continuous('base_lr', 1e-3, 1e-1),
            param.discrete('epochs', 100, 300),
            param.continuous('lr80', 1e-3, 1e-1),
            param.continuous('lr120', 1e-4, 1e-1),
            param.continuous('lr160', 1e-4, 1e-2),
            param.continuous('lr180', 1e-5, 1e-2),
            param.discrete('num_filters', 16, 64, step.GEOMETRIC, 2),
            param.discrete('num_filters_in', 16, 64, step.GEOMETRIC, 2),
            optimizer
        ]

        self.params = [param.name for param in self.space]
        self.starting_point = ['relu', 32, 1e-3, 200, 1e-1, 1e-2, 1e-3, 0.5e-3, 16, 16, 'adam']
github deephyper / deephyper / deephyper / benchmarks_hps / imdbcnn / space.py View on Github external
def __init__(self):
        self.space = [
            activation1,
            activation2,
            param.discrete("batch_size", 8, 1024, step.GEOMETRIC, 2),
            dropout,
            param.discrete("embedding_dims", 40, 60, step.ARITHMETIC, 1),
            param.discrete("epochs", 1, 5, step.ARITHMETIC, 1),
            param.discrete("filters", 200, 300, step.ARITHMETIC, 1),
            param.discrete("hidden_dims", 20, 30, step.ARITHMETIC, 1),
            param.discrete("kernel_size", 1, 5, step.ARITHMETIC, 1),
            param.discrete("max_features", 3000, 7000, step.ARITHMETIC, 1),
            param.discrete("maxlen", 300, 500, step.ARITHMETIC, 1),
            optimizer,
            padding,
            param.discrete("strides", 1, 4, step.ARITHMETIC, 1)
        ]

        self.params = [param.name for param in self.space]
        self.starting_point = ['relu', 32, 0.2, 50, 3, 250, 25, 3, 5000, 400, 'adam', 'same', 1]
github deephyper / deephyper / deephyper / benchmarks_hps / mnistcnn / space.py View on Github external
def __init__(self):
        self.space = [
                activation1,
                activation2,
                activation3,
                activation4,
                activation5,
                param.discrete("batch_size", 8, 1024, step.GEOMETRIC, 2),
                dropout,
                param.discrete("epochs", 5, 35, step.ARITHMETIC, 1),
                param.discrete("f1_size", 1, 7, step.ARITHMETIC, 1),
                param.discrete("f2_size", 1, 7, step.ARITHMETIC, 1),
                param.discrete("f1_units", 8, 64, step.GEOMETRIC, 2),
                param.discrete("f2_units", 8, 64, step.GEOMETRIC, 2),
                param.non_ordinal("max_pool", [True, False]),
                param.discrete("nunits", 1, 1000, step.ARITHMETIC, 1),
                optimizer,
                param.non_ordinal("padding_c1", ["valid", "same"]),
                param.non_ordinal("padding_c2", ["valid", "same"]),
                param.non_ordinal("padding_p1", ["valid", "same"]),
                param.non_ordinal("padding_p2", ["valid", "same"]),
                param.discrete('p_size', 1, 5, step.ARITHMETIC, 1)
        ]

        self.params = [param.name for param in self.space]
        self.starting_point = ['relu', 128, 0.3, 12, 3, 3, 16, 16, True, 512, 'adam', 'valid', 'valid', 'same', 'same', 3]
github deephyper / deephyper / deephyper / benchmarks_hps / mnistcnn / space.py View on Github external
def __init__(self):
        self.space = [
                activation1,
                activation2,
                activation3,
                activation4,
                activation5,
                param.discrete("batch_size", 8, 1024, step.GEOMETRIC, 2),
                dropout,
                param.discrete("epochs", 5, 35, step.ARITHMETIC, 1),
                param.discrete("f1_size", 1, 7, step.ARITHMETIC, 1),
                param.discrete("f2_size", 1, 7, step.ARITHMETIC, 1),
                param.discrete("f1_units", 8, 64, step.GEOMETRIC, 2),
                param.discrete("f2_units", 8, 64, step.GEOMETRIC, 2),
                param.non_ordinal("max_pool", [True, False]),
                param.discrete("nunits", 1, 1000, step.ARITHMETIC, 1),
                optimizer,
                param.non_ordinal("padding_c1", ["valid", "same"]),
                param.non_ordinal("padding_c2", ["valid", "same"]),
                param.non_ordinal("padding_p1", ["valid", "same"]),
                param.non_ordinal("padding_p2", ["valid", "same"]),
                param.discrete('p_size', 1, 5, step.ARITHMETIC, 1)
        ]

        self.params = [param.name for param in self.space]
        self.starting_point = ['relu', 128, 0.3, 12, 3, 3, 16, 16, True, 512, 'adam', 'valid', 'valid', 'same', 'same', 3]
github deephyper / deephyper / deephyper / benchmarks_hps / reutersmlp / space.py View on Github external
def __init__(self):
        self.space = [
                activation1,
                activation2,
                activation3,
                activation4,
                activation5,
                param.discrete("batch_size", 8, 128, step.GEOMETRIC, 2),
                dropout,
                param.discrete("epochs", 2, 20, step.ARITHMETIC, 1),
                param.discrete("max_words", 800, 1300, step.ARITHMETIC, 1),
                param.discrete("nunits", 128, 1024, step.ARITHMETIC, 128),
                optimizer,
                param.discrete("skip_top", 0,30, step.ARITHMETIC, 1)
        ]

        self.params = [param.name for param in self.space]
        self.starting_point = ['relu', 32, 0.5, 5, 1000, 512, 'adam', 0]