How to use the deephyper.benchmarks_hps.util.get_activation_instance function in deephyper

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deephyper / deephyper / deephyper / benchmarks_hps / reutersmlp / main.py View on Github

def run(param_dict=None, verbose=2):
    """Run a param_dict on the reutersmlp benchmark."""
    # Read in values from CLI if no param dict was specified and clean up the param dict.
    param_dict = util.handle_cli(param_dict, build_parser())

    # Display the parsed param dict.
    if verbose:
        print("PARAM_DICT_CLEAN=")
        pprint(param_dict)

    # Get values from param_dict.
    # Hyperparameters
    ACTIVATION1    = util.get_activation_instance(param_dict["activation1"], param_dict["alpha1"])
    ACTIVATION2    = util.get_activation_instance(param_dict["activation2"], param_dict["alpha2"])
    ACTIVATION3    = util.get_activation_instance(param_dict["activation3"], param_dict["alpha3"])
    ACTIVATION4    = util.get_activation_instance(param_dict["activation4"], param_dict["alpha4"])
    ACTIVATION5    = util.get_activation_instance(param_dict["activation5"], param_dict["alpha5"])
    BATCH_SIZE    = param_dict["batch_size"]
    DROPOUT       = param_dict["dropout"]
    EPOCHS        = param_dict["epochs"]
    MAX_WORDS     = param_dict["max_words"]
    NHIDDEN       = param_dict['nhidden']
    NUNITS        = param_dict["nunits"]
    OPTIMIZER     = util.get_optimizer_instance(param_dict)
    SKIP_TOP      = param_dict["skip_top"]

    # Other
    model_path    = param_dict["model_path"]

deephyper / deephyper / deephyper / benchmarks_hps / mnist_siamese / main.py View on Github

'''Compute classification accuracy with a fixed threshold on distances.
        '''
        return K.mean(K.equal(y_true, K.cast(y_pred &lt; 0.5, y_true.dtype)))


    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    timer.end()

    num_classes = 10
    BATCH_SIZE      = param_dict['batch_size']
    EPOCHS          = param_dict['epochs']
    DROPOUT         = param_dict['dropout']
    ACTIVATION1      = util.get_activation_instance(param_dict['activation1'], param_dict['alpha1'])
    ACTIVATION2      = util.get_activation_instance(param_dict['activation2'], param_dict['alpha2'])
    ACTIVATION3      = util.get_activation_instance(param_dict['activation3'], param_dict['alpha3'])
    UNITS           = param_dict['units']
    OPTIMIZER       = util.get_optimizer_instance(param_dict)
    patience  = math.ceil(EPOCHS/2)
    callbacks = [
        EarlyStopping(monitor="val_acc", min_delta=0.0001, patience=patience, verbose=verbose, mode="auto"),
        TerminateOnNaN()]

    timer.start('preprocessing')

    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255
    x_test /= 255
    input_shape = x_train.shape[1:]

    digit_indices = [np.where(y_train == i)[0] for i in range(num_classes)]

deephyper / deephyper / deephyper / benchmarks_hps / mnistcnn / main.py View on Github

"""Run a param_dict on the MNISTCNN benchmark."""
    # Read in values from CLI if no param_dict was specified and clean up the param dict.
    param_dict = util.handle_cli(param_dict, build_parser())

    # Display the filled in param dict.
    if verbose:
        print("PARAM_DICT_CLEAN=")
        pprint(param_dict)

    # Get values from param_dict.
    # Hyperparameters
    ACTIVATION1    = util.get_activation_instance(param_dict['activation1'], param_dict['alpha1'])
    ACTIVATION2    = util.get_activation_instance(param_dict['activation2'], param_dict['alpha2'])
    ACTIVATION3    = util.get_activation_instance(param_dict['activation3'], param_dict['alpha3'])
    ACTIVATION4    = util.get_activation_instance(param_dict['activation4'], param_dict['alpha4'])
    ACTIVATION5    = util.get_activation_instance(param_dict['activation5'], param_dict['alpha5'])
    BATCH_SIZE    = param_dict["batch_size"]
    DROPOUT       = param_dict["dropout"]
    EPOCHS        = param_dict["epochs"]
    F1_SIZE       = param_dict["f1_size"]
    F2_SIZE       = param_dict["f2_size"]
    F1_UNITS      = param_dict["f1_units"]
    F2_UNITS      = param_dict["f2_units"]
    MAX_POOL      = param_dict["max_pool"]
    NUNITS        = param_dict["nunits"]
    OPTIMIZER     = util.get_optimizer_instance(param_dict)
    PADDING_C1    = param_dict["padding_c1"]
    PADDING_C2    = param_dict["padding_c2"]
    PADDING_P1    = param_dict["padding_p1"]
    PADDING_P2    = param_dict["padding_p2"]
    P_SIZE        = param_dict["p_size"]

deephyper / deephyper / deephyper / benchmarks_hps / cifar10cnn / main.py View on Github

"""Run a param_dict on the cifar10 benchmark."""
    # Read in values from CLI if no param dict was specified and clean up the param dict.
    param_dict = util.handle_cli(param_dict, build_parser())

    # Display the parsed param dict.
    if verbose:
        print("PARAM_DICT_CLEAN=")
        pprint(param_dict)

    # Get values from param_dict.
    # Hyperparameters
    ACTIVATION1       = util.get_activation_instance(param_dict['activation1'], param_dict['alpha1'])
    ACTIVATION2       = util.get_activation_instance(param_dict['activation2'], param_dict['alpha2'])
    ACTIVATION3       = util.get_activation_instance(param_dict['activation3'], param_dict['alpha3'])
    ACTIVATION4       = util.get_activation_instance(param_dict['activation4'], param_dict['alpha4'])
    ACTIVATION5       = util.get_activation_instance(param_dict['activation5'], param_dict['alpha5'])
    BATCH_SIZE        = param_dict["batch_size"]
    DATA_AUGMENTATION = param_dict["data_augmentation"]
    DROPOUT           = param_dict["dropout"]
    EPOCHS            = param_dict["epochs"]
    F1_SIZE           = param_dict["f1_size"]
    F2_SIZE           = param_dict["f2_size"]
    F1_UNITS          = param_dict["f1_units"]
    F2_UNITS          = param_dict["f2_units"]
    NUNITS            = param_dict["nunits"]
    OPTIMIZER         = util.get_optimizer_instance(param_dict)
    P_SIZE            = param_dict["p_size"]
    PADDING_C1        = param_dict["padding_c1"]
    PADDING_C2        = param_dict["padding_c2"]
    PADDING_P1        = param_dict["padding_p1"]
    PADDING_P2        = param_dict["padding_p2"]
    STRIDE1           = param_dict["stride1"]

deephyper / deephyper / deephyper / benchmarks_hps / cifar10cnn / main.py View on Github

def run(param_dict=None, verbose=2):
    """Run a param_dict on the cifar10 benchmark."""
    # Read in values from CLI if no param dict was specified and clean up the param dict.
    param_dict = util.handle_cli(param_dict, build_parser())

    # Display the parsed param dict.
    if verbose:
        print("PARAM_DICT_CLEAN=")
        pprint(param_dict)

    # Get values from param_dict.
    # Hyperparameters
    ACTIVATION1       = util.get_activation_instance(param_dict['activation1'], param_dict['alpha1'])
    ACTIVATION2       = util.get_activation_instance(param_dict['activation2'], param_dict['alpha2'])
    ACTIVATION3       = util.get_activation_instance(param_dict['activation3'], param_dict['alpha3'])
    ACTIVATION4       = util.get_activation_instance(param_dict['activation4'], param_dict['alpha4'])
    ACTIVATION5       = util.get_activation_instance(param_dict['activation5'], param_dict['alpha5'])
    BATCH_SIZE        = param_dict["batch_size"]
    DATA_AUGMENTATION = param_dict["data_augmentation"]
    DROPOUT           = param_dict["dropout"]
    EPOCHS            = param_dict["epochs"]
    F1_SIZE           = param_dict["f1_size"]
    F2_SIZE           = param_dict["f2_size"]
    F1_UNITS          = param_dict["f1_units"]
    F2_UNITS          = param_dict["f2_units"]
    NUNITS            = param_dict["nunits"]
    OPTIMIZER         = util.get_optimizer_instance(param_dict)
    P_SIZE            = param_dict["p_size"]
    PADDING_C1        = param_dict["padding_c1"]
    PADDING_C2        = param_dict["padding_c2"]

deephyper / deephyper / deephyper / benchmarks_hps / reutersmlp / main.py View on Github

def run(param_dict=None, verbose=2):
    """Run a param_dict on the reutersmlp benchmark."""
    # Read in values from CLI if no param dict was specified and clean up the param dict.
    param_dict = util.handle_cli(param_dict, build_parser())

    # Display the parsed param dict.
    if verbose:
        print("PARAM_DICT_CLEAN=")
        pprint(param_dict)

    # Get values from param_dict.
    # Hyperparameters
    ACTIVATION1    = util.get_activation_instance(param_dict["activation1"], param_dict["alpha1"])
    ACTIVATION2    = util.get_activation_instance(param_dict["activation2"], param_dict["alpha2"])
    ACTIVATION3    = util.get_activation_instance(param_dict["activation3"], param_dict["alpha3"])
    ACTIVATION4    = util.get_activation_instance(param_dict["activation4"], param_dict["alpha4"])
    ACTIVATION5    = util.get_activation_instance(param_dict["activation5"], param_dict["alpha5"])
    BATCH_SIZE    = param_dict["batch_size"]
    DROPOUT       = param_dict["dropout"]
    EPOCHS        = param_dict["epochs"]
    MAX_WORDS     = param_dict["max_words"]
    NHIDDEN       = param_dict['nhidden']
    NUNITS        = param_dict["nunits"]
    OPTIMIZER     = util.get_optimizer_instance(param_dict)
    SKIP_TOP      = param_dict["skip_top"]

    # Other
    model_path    = param_dict["model_path"]

    # Constants
    patience  = math.ceil(EPOCHS/2)
    callbacks = [

deephyper / deephyper / deephyper / benchmarks_hps / mnistmlp / main.py View on Github

#     data_source = os.path.dirname(os.path.abspath(__file__))
    #     data_source = os.path.join(data_source, 'data')

    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    
    timer.end()



    #hyperparameters
    BATCH_SIZE = param_dict['batch_size']
    EPOCHS = param_dict['epochs']
    DROPOUT = param_dict['dropout']
    ACTIVATION = util.get_activation_instance(param_dict['activation'], param_dict['alpha'])
    ACTIVATION1 = util.get_activation_instance(param_dict['activation1'], param_dict['alpha1'])
    ACTIVATION2 = util.get_activation_instance(param_dict['activation2'], param_dict['alpha2'])
    NHIDDEN = param_dict['nhidden']
    NUNITS = param_dict['nunits']
    OPTIMIZER      = util.get_optimizer_instance(param_dict)
    
    #other
    LOSS_FUNCTION = param_dict['loss_function']
    METRICS = param_dict['metrics']
    model_path = ''

    #constants
    num_classes = 10
    patience  = math.ceil(EPOCHS/2)
    callbacks = [
        EarlyStopping(monitor="val_acc", min_delta=0.0001, patience=patience, verbose=verbose, mode="auto"),
        TerminateOnNaN()]

deephyper / deephyper / deephyper / benchmarks / reutersmlp_hps / main.py View on Github

def run(param_dict=None, verbose=2):
    """Run a param_dict on the reutersmlp benchmark."""
    # Read in values from CLI if no param dict was specified and clean up the param dict.
    param_dict = util.handle_cli(param_dict, build_parser())

    # Display the parsed param dict.
    if verbose:
        print("PARAM_DICT_CLEAN=")
        pprint(param_dict)

    # Get values from param_dict.
    # Hyperparameters
    ACTIVATION    = util.get_activation_instance(param_dict)
    BATCH_SIZE    = param_dict["batch_size"]
    DROPOUT       = param_dict["dropout"]
    EPOCHS        = param_dict["epochs"]
    MAX_WORDS     = param_dict["max_words"]
    NUNITS        = param_dict["nunits"]
    OPTIMIZER     = util.get_optimizer_instance(param_dict)
    SKIP_TOP      = param_dict["skip_top"]

    # Other
    model_path    = param_dict["model_path"]

    # Constants
    patience  = math.ceil(EPOCHS/2)
    callbacks = [
        EarlyStopping(monitor="val_acc", min_delta=0.0001, patience=patience, verbose=verbose, mode="auto"),
        TerminateOnNaN()]

deephyper / deephyper / deephyper / benchmarks_hps / mnistcnn / main.py View on Github

def run(param_dict=None, verbose=2):
    """Run a param_dict on the MNISTCNN benchmark."""
    # Read in values from CLI if no param_dict was specified and clean up the param dict.
    param_dict = util.handle_cli(param_dict, build_parser())

    # Display the filled in param dict.
    if verbose:
        print("PARAM_DICT_CLEAN=")
        pprint(param_dict)

    # Get values from param_dict.
    # Hyperparameters
    ACTIVATION1    = util.get_activation_instance(param_dict['activation1'], param_dict['alpha1'])
    ACTIVATION2    = util.get_activation_instance(param_dict['activation2'], param_dict['alpha2'])
    ACTIVATION3    = util.get_activation_instance(param_dict['activation3'], param_dict['alpha3'])
    ACTIVATION4    = util.get_activation_instance(param_dict['activation4'], param_dict['alpha4'])
    ACTIVATION5    = util.get_activation_instance(param_dict['activation5'], param_dict['alpha5'])
    BATCH_SIZE    = param_dict["batch_size"]
    DROPOUT       = param_dict["dropout"]
    EPOCHS        = param_dict["epochs"]
    F1_SIZE       = param_dict["f1_size"]
    F2_SIZE       = param_dict["f2_size"]
    F1_UNITS      = param_dict["f1_units"]
    F2_UNITS      = param_dict["f2_units"]
    MAX_POOL      = param_dict["max_pool"]
    NUNITS        = param_dict["nunits"]
    OPTIMIZER     = util.get_optimizer_instance(param_dict)
    PADDING_C1    = param_dict["padding_c1"]
    PADDING_C2    = param_dict["padding_c2"]

deephyper / deephyper / deephyper / benchmarks_hps / imdb_fasttext / main.py View on Github

# else:
    #     data_source = os.path.dirname(os.path.abspath(__file__))
    #     data_source = os.path.join(data_source, 'data')

    ngram_range = 1 
    MAX_FEATURES = param_dict['max_features'] # = 20000
    MAXLEN = param_dict['maxlen'] # = 400
    ENBEDDING_DIMS = param_dict['embedding_dims'] # = 50

    (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=MAX_FEATURES)

    timer.end()

    BATCH_SIZE = param_dict['batch_size']
    EPOCHS = param_dict['epochs']
    ACTIVATION     = util.get_activation_instance(param_dict)
    OPTIMIZER      = util.get_optimizer_instance(param_dict)

    #constants
    patience = math.ceil(EPOCHS/2)
    callbacks = [
        EarlyStopping(monitor="val_acc", min_delta=0.0001, patience=patience, verbose=verbose, mode="auto"),
        TerminateOnNaN()]
    

    timer.start('preprocessing')

    if ngram_range > 1:
        print('Adding {}-gram features'.format(ngram_range))

        ngram_set = set()
        for input_list in x_train:

How to use the deephyper.benchmarks_hps.util.get_activation_instance function in deephyper

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