如何测试 Keras 自定义层的正确性？

Question

创建具有训练权重的 Keras 自定义层后，如何测试代码的正确性？ Keras 的手册中似乎没有描述。

例如，要测试函数的预期行为，可以编写单元测试。我们如何为 Keras 自定义层做到这一点？

Answer 1

您仍然可以通过获取给定输入的自定义层的输出并根据手动计算的输出验证它来执行单元测试之类的操作，

假设您的自定义层 Custom 将 (None, 3, 200) 作为输入形状并返回 (None, 3)

from keras.layers import Input
from keras.models import Model

inp = Input(shape=(3, 200))
out = Custom()(inp)
model = Model(inp, out)

output = model.predict(your_input)

您可以使用已知输入your_input 的预期输出来验证层输出output。

Answer 2

layer_test 在 keras 实用程序中。 https://github.com/keras-team/keras/blob/master/keras/utils/test_utils.py

他们提供以下代码，测试形状、实际结果、序列化和训练：

def layer_test(layer_cls, kwargs={}, input_shape=None, input_dtype=None,
               input_data=None, expected_output=None,
               expected_output_dtype=None, fixed_batch_size=False):
    """Test routine for a layer with a single input tensor
    and single output tensor.
    """
    # generate input data
    if input_data is None:
        assert input_shape
        if not input_dtype:
            input_dtype = K.floatx()
        input_data_shape = list(input_shape)
        for i, e in enumerate(input_data_shape):
            if e is None:
                input_data_shape[i] = np.random.randint(1, 4)
        input_data = (10 * np.random.random(input_data_shape))
        input_data = input_data.astype(input_dtype)
    else:
        if input_shape is None:
            input_shape = input_data.shape
        if input_dtype is None:
            input_dtype = input_data.dtype
    if expected_output_dtype is None:
        expected_output_dtype = input_dtype

    # instantiation
    layer = layer_cls(**kwargs)

    # test get_weights , set_weights at layer level
    weights = layer.get_weights()
    layer.set_weights(weights)

    expected_output_shape = layer.compute_output_shape(input_shape)

    # test in functional API
    if fixed_batch_size:
        x = Input(batch_shape=input_shape, dtype=input_dtype)
    else:
        x = Input(shape=input_shape[1:], dtype=input_dtype)
    y = layer(x)
    assert K.dtype(y) == expected_output_dtype

    # check with the functional API
    model = Model(x, y)

    actual_output = model.predict(input_data)
    actual_output_shape = actual_output.shape
    for expected_dim, actual_dim in zip(expected_output_shape,
                                        actual_output_shape):
        if expected_dim is not None:
            assert expected_dim == actual_dim

    if expected_output is not None:
        assert_allclose(actual_output, expected_output, rtol=1e-3)

    # test serialization, weight setting at model level
    model_config = model.get_config()
    recovered_model = model.__class__.from_config(model_config)
    if model.weights:
        weights = model.get_weights()
        recovered_model.set_weights(weights)
        _output = recovered_model.predict(input_data)
        assert_allclose(_output, actual_output, rtol=1e-3)

    # test training mode (e.g. useful when the layer has a
    # different behavior at training and testing time).
    if has_arg(layer.call, 'training'):
        model.compile('rmsprop', 'mse')
        model.train_on_batch(input_data, actual_output)

    # test instantiation from layer config
    layer_config = layer.get_config()
    layer_config['batch_input_shape'] = input_shape
    layer = layer.__class__.from_config(layer_config)

    # for further checks in the caller function
    return actual_output