09e4a76102ff4a06a835180237ea171eb475985c,keras_/models/mobilenet.py,,_test,#,370

Before Change

    model.fit(data, labels, epochs=2, batch_size=10)
    model.summary()
    weight_count = model.count_params()
    print("{}".format(weight_count))

    // models = [
    //     mobilenet_w1,
    //     mobilenet_w3d4,

After Change

    pretrained = False

    models = [
        mobilenet_w1,
        mobilenet_w3d4,
        mobilenet_wd2,
        mobilenet_wd4,
        fdmobilenet_w1,
        fdmobilenet_w3d4,
        fdmobilenet_wd2,
        fdmobilenet_wd4,
    ]

    for model in models:

        net = model(pretrained=pretrained)
        //net.summary()
        weight_count = keras.utils.layer_utils.count_params(net.trainable_weights)
        print("m={}, {}".format(model.__name__, weight_count))
        assert (model != mobilenet_w1 or weight_count == 4231976)
        assert (model != mobilenet_w3d4 or weight_count == 2585560)
        assert (model != mobilenet_wd2 or weight_count == 1331592)
        assert (model != mobilenet_wd4 or weight_count == 470072)
        assert (model != fdmobilenet_w1 or weight_count == 2901288)
        assert (model != fdmobilenet_w3d4 or weight_count == 1833304)
        assert (model != fdmobilenet_wd2 or weight_count == 993928)
        assert (model != fdmobilenet_wd4 or weight_count == 383160)

        x = np.zeros((1, 224, 224, 3), np.float32)
        y = net.predict(x)
        assert (y.shape == (1, 1000))


if __name__ == "__main__":
    _test()

Instances