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

Before Change

    // model.add(nn.Dropout(0.2))
    // model.add(nn.Dense(1000, activation="softmax"))

    model = mobilenet_wd4()
    model.compile(
        optimizer="rmsprop",
        loss="categorical_crossentropy",
        metrics=["accuracy"])
    data = np.random.random((20, 3, 224, 224)).astype(np.float32)
    labels = keras.utils.to_categorical(np.random.randint(1000, size=(20, 1)), num_classes=1000)
    model.fit(data, labels, epochs=2, batch_size=10)
    model.summary()
    weight_count = model.count_params()
    print("{}".format(weight_count))

    // models = [

After Change

    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__":

Instances