keras/src/applications/mobilenet_v3.py/MobileNetV3Large
def MobileNetV3Large(
    input_shape=None,
    alpha=1.0,
    minimalistic=False,
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    classes=1000,
    pooling=None,
    dropout_rate=0.2,
    classifier_activation="softmax",
    include_preprocessing=True,
    name="MobileNetV3Large",
):
    def stack_fn(x, kernel, activation, se_ratio):
        def depth(d):
            return _depth(d * alpha)

        x = _inverted_res_block(x, 1, depth(16), 3, 1, None, relu, 0)
        x = _inverted_res_block(x, 4, depth(24), 3, 2, None, relu, 1)
        x = _inverted_res_block(x, 3, depth(24), 3, 1, None, relu, 2)
        x = _inverted_res_block(x, 3, depth(40), kernel, 2, se_ratio, relu, 3)
        x = _inverted_res_block(x, 3, depth(40), kernel, 1, se_ratio, relu, 4)
        x = _inverted_res_block(x, 3, depth(40), kernel, 1, se_ratio, relu, 5)
        x = _inverted_res_block(x, 6, depth(80), 3, 2, None, activation, 6)
        x = _inverted_res_block(x, 2.5, depth(80), 3, 1, None, activation, 7)
        x = _inverted_res_block(x, 2.3, depth(80), 3, 1, None, activation, 8)
        x = _inverted_res_block(x, 2.3, depth(80), 3, 1, None, activation, 9)
        x = _inverted_res_block(
            x, 6, depth(112), 3, 1, se_ratio, activation, 10
        )
        x = _inverted_res_block(
            x, 6, depth(112), 3, 1, se_ratio, activation, 11
        )
        x = _inverted_res_block(
            x, 6, depth(160), kernel, 2, se_ratio, activation, 12
        )
        x = _inverted_res_block(
            x, 6, depth(160), kernel, 1, se_ratio, activation, 13
        )
        x = _inverted_res_block(
            x, 6, depth(160), kernel, 1, se_ratio, activation, 14
        )
        return x

    return MobileNetV3(
        stack_fn,
        1280,
        input_shape,
        alpha,
        "large",
        minimalistic,
        include_top,
        weights,
        input_tensor,
        classes,
        pooling,
        dropout_rate,
        classifier_activation,
        include_preprocessing,
        name=name,
    )