comparison keras_deep_learning.py @ 31:64b771b1471a draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"

30:18b39ada6f35

            "constraint_type":
                "MinMaxNorm"
            }
    """
    constraint_type = config['constraint_type']
    if constraint_type == 'None':
        return None

    klass = getattr(keras.constraints, constraint_type)
    options = config.get('constraint_options', {})
    if 'axis' in options:

def _handle_layer_parameters(params):
    """Access to handle all kinds of parameters
    """
    for key, value in six.iteritems(params):
        if value == 'None':
            params[key] = None
            continue

        if type(value) in [int, float, bool]\
                or (type(value) is str and value.isalpha()):

    Parameters
    -----------
    config : dictionary, galaxy tool parameters loaded by JSON
    """
    generator_type = config.pop('generator_type')
    klass = try_get_attr('galaxy_ml.preprocessors', generator_type)

    if generator_type == 'GenomicIntervalBatchGenerator':
        config['ref_genome_path'] = 'to_be_determined'
        config['intervals_path'] = 'to_be_determined'

        model = get_functional_model(layers_config)

    json_string = model.to_json()

    with open(outfile, 'w') as f:
        f.write(json_string)


def build_keras_model(inputs, outfile, model_json, infile_weights=None,
                      batch_mode=False, outfile_params=None):
    """ for `keras_model_builder` tool

31:64b771b1471a

            "constraint_type":
                "MinMaxNorm"
            }
    """
    constraint_type = config['constraint_type']
    if constraint_type in ('None', ''):
        return None

    klass = getattr(keras.constraints, constraint_type)
    options = config.get('constraint_options', {})
    if 'axis' in options:

def _handle_layer_parameters(params):
    """Access to handle all kinds of parameters
    """
    for key, value in six.iteritems(params):
        if value in ('None', ''):
            params[key] = None
            continue

        if type(value) in [int, float, bool]\
                or (type(value) is str and value.isalpha()):

    Parameters
    -----------
    config : dictionary, galaxy tool parameters loaded by JSON
    """
    generator_type = config.pop('generator_type')
    if generator_type == 'none':
        return None

    klass = try_get_attr('galaxy_ml.preprocessors', generator_type)

    if generator_type == 'GenomicIntervalBatchGenerator':
        config['ref_genome_path'] = 'to_be_determined'
        config['intervals_path'] = 'to_be_determined'

        model = get_functional_model(layers_config)

    json_string = model.to_json()

    with open(outfile, 'w') as f:
        json.dump(json.loads(json_string), f, indent=2)


def build_keras_model(inputs, outfile, model_json, infile_weights=None,
                      batch_mode=False, outfile_params=None):
    """ for `keras_model_builder` tool