def test_uneven_batch_size():

    learning_rate = 1e-3
    batch_size = 5

    dim = 3
    m1, m2, m3 = 10, 15, 22

    rng = np.random.RandomState([25, 9, 2012])

    dataset1 = DenseDesignMatrix(X=rng.randn(m1, dim))
    dataset2 = DenseDesignMatrix(X=rng.randn(m2, dim))
    dataset3 = DenseDesignMatrix(X=rng.randn(m3, dim))

    def train_with_monitoring_datasets(train_dataset,
                                       monitoring_datasets,
                                       model_force_batch_size,
                                       train_iteration_mode,
                                       monitor_iteration_mode):

        model = SoftmaxModel(dim)
        if model_force_batch_size:
            model.force_batch_size = model_force_batch_size

        cost = DummyCost()

        algorithm = SGD(learning_rate, cost,
                        batch_size=batch_size,
                        train_iteration_mode=train_iteration_mode,
                        monitor_iteration_mode=monitor_iteration_mode,
                        monitoring_dataset=monitoring_datasets,
                        termination_criterion=EpochCounter(2))

        train = Train(train_dataset,
                      model,
                      algorithm,
                      save_path=None,
                      save_freq=0,
                      extensions=None)

        train.main_loop()

    no_monitoring_datasets = None
    even_monitoring_datasets = {'valid': dataset2}
    uneven_monitoring_datasets = {'valid': dataset2, 'test': dataset3}

    # without monitoring datasets
    train_with_monitoring_datasets(
        train_dataset=dataset1,
        monitoring_datasets=no_monitoring_datasets,
        model_force_batch_size=False,
        train_iteration_mode='sequential',
        monitor_iteration_mode='sequential')

    train_with_monitoring_datasets(
        train_dataset=dataset1,
        monitoring_datasets=no_monitoring_datasets,
        model_force_batch_size=batch_size,
        train_iteration_mode='sequential',
        monitor_iteration_mode='sequential')

    # with uneven training datasets
    train_with_monitoring_datasets(
        train_dataset=dataset3,
        monitoring_datasets=no_monitoring_datasets,
        model_force_batch_size=False,
        train_iteration_mode='sequential',
        monitor_iteration_mode='sequential')

    try:
        train_with_monitoring_datasets(
            train_dataset=dataset3,
            monitoring_datasets=no_monitoring_datasets,
            model_force_batch_size=batch_size,
            train_iteration_mode='sequential',
            monitor_iteration_mode='sequential')

        assert False
    except ValueError:
        pass

    train_with_monitoring_datasets(
        train_dataset=dataset3,
        monitoring_datasets=no_monitoring_datasets,
        model_force_batch_size=batch_size,
        train_iteration_mode='even_sequential',
        monitor_iteration_mode='sequential')

    # with even monitoring datasets
    train_with_monitoring_datasets(
        train_dataset=dataset1,
        monitoring_datasets=even_monitoring_datasets,
        model_force_batch_size=False,
        train_iteration_mode='sequential',
        monitor_iteration_mode='sequential')

    train_with_monitoring_datasets(
        train_dataset=dataset1,
        monitoring_datasets=even_monitoring_datasets,
        model_force_batch_size=batch_size,
        train_iteration_mode='sequential',
        monitor_iteration_mode='sequential')

    # with uneven monitoring datasets
    train_with_monitoring_datasets(
        train_dataset=dataset1,
        monitoring_datasets=uneven_monitoring_datasets,
        model_force_batch_size=False,
        train_iteration_mode='sequential',
        monitor_iteration_mode='sequential')

    try:
        train_with_monitoring_datasets(
            train_dataset=dataset1,
            monitoring_datasets=uneven_monitoring_datasets,
            model_force_batch_size=batch_size,
            train_iteration_mode='sequential',
            monitor_iteration_mode='sequential')

        assert False
    except ValueError:
        pass

    train_with_monitoring_datasets(
        train_dataset=dataset1,
        monitoring_datasets=uneven_monitoring_datasets,
        model_force_batch_size=batch_size,
        train_iteration_mode='sequential',
        monitor_iteration_mode='even_sequential')
Testing extensively sgd parametrisations for datasets with a number of
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/training_algorithms/tests/test_sgd.py/test_uneven_batch_size