def evaluate_lenet5(learning_rate=0.1, n_epochs=200, dataset='mnist.pkl.gz', nkerns=[2,5], batch_size=50):

    evaluate_lenet5

    original_image = cv2.imread('dataset')



    rng = numpy.random.RandomState(23455)

    datasets = load_data(dataset)

    train_set_x, train_set_y = datasets[0]
    valid_set_x, valid_set_y = datasets[1]
    test_set_x, test_set_y = datasets[2]


    n_train_batches = train_set_x.get_value(borrow=True).shape[0]
    n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
    n_test_batches = test_set_x.get_value(borrow=True).shape[0]
    n_train_batches //= batch_size
    n_valid_batches //= batch_size
    n_test_batches //= batch_size


    index = T.lscalar()


    x = T.matrix('x')
    y = T.ivector('y')


    print('... building the model')

    layer0_input = x.reshape((batch_size, 1, 28, 28))

    layer0 = LeNetConvPoolLayer(
        rng,
        input=layer0_input,
        image_shape=(batch_size, 1, 28, 28),
        filter_shape=(nkerns[0], 1, 5, 5),
        poolsize=(2, 2)
    )

    layer1 = LeNetConvPoolLayer(
        rng,
        input=layer0.output,
        image_shape=(batch_size, nkerns[0], 12, 12),
        filter_shape=(nkerns[1], nkerns[0], 5, 5),
        poolsize=(2, 2)
    )

    layer00_input = x.reshape((batch_size, 1, 28, 28))

    layer00 = LeNetConvPoolLayer(
        rng,
        input=layer00_input,
        image_shape=(batch_size, 1, 28, 28),
        filter_shape=(nkerns[0], 1, 5, 5),
        poolsize=(2, 2)
    )

    layer11 = LeNetConvPoolLayer(
        rng,
        input=layer00.output,
        image_shape=(batch_size, nkerns[0], 12, 12),
        filter_shape=(nkerns[1], nkerns[0], 5, 5),
        poolsize=(2, 2)
    )

    pdb.set_trace()

    layer2_input1=layer1.output.flatten(2)
    layer2_input2=layer11.output.flatten(2)



    input1=numpy.asarray(layer2_input1)
    input2=numpy.asarray(layer2_input2)