comparison optimise_hyperparameters.py @ 4:afec8c595124 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 65d36f271296a38deeceb0d0e8d471b2898ee8f4"

3:5b3c08710e47

class HyperparameterOptimisation:

    def __init__(self):
        """ Init method. """

    def train_model(self, config, reverse_dictionary, train_data, train_labels, test_data, test_labels, l_tool_tr_samples, class_weights):
        """
        Train a model and report accuracy
        """
        # convert items to integer
        l_batch_size = list(map(int, config["batch_size"].split(",")))

            model_fit = model.fit_generator(
                utils.balanced_sample_generator(
                    train_data,
                    train_labels,
                    batch_size,
                    l_tool_tr_samples
                ),
                steps_per_epoch=len(train_data) // batch_size,
                epochs=optimize_n_epochs,
                callbacks=[early_stopping],
                validation_data=(test_data, test_labels),

4:afec8c595124

class HyperparameterOptimisation:

    def __init__(self):
        """ Init method. """

    def train_model(self, config, reverse_dictionary, train_data, train_labels, test_data, test_labels, tool_tr_samples, class_weights):
        """
        Train a model and report accuracy
        """
        # convert items to integer
        l_batch_size = list(map(int, config["batch_size"].split(",")))

            model_fit = model.fit_generator(
                utils.balanced_sample_generator(
                    train_data,
                    train_labels,
                    batch_size,
                    tool_tr_samples,
                    reverse_dictionary
                ),
                steps_per_epoch=len(train_data) // batch_size,
                epochs=optimize_n_epochs,
                callbacks=[early_stopping],
                validation_data=(test_data, test_labels),