如何将Mobilenet的最后一层的输出提供给Unet模型

问题描述

我正在尝试使用在imagenet数据集上预先训练的Keras mobilenet模型构建图像分割模型。如何进一步训练模型，我想将U-net层添加到现有模型中，而仅使用移动网模型作为骨干来训练u-net体系结构的层。

I am trying to build an image segmentation model with a Keras mobilenet model pre-trained on imagenet dataset. How ever to train the model further, I want to add the U-net layers to the existing model and only train the layers of u-net architecture with mobilenet model helping as a backbone.

问题：mobilenet模型的最后一层是（RelU）层，尺寸为（7x7x1024），我希望将其重塑为（256x256x3），U-net输入层可以理解。 / p>

Problem: The last layer of mobilenet model is of dimensions (7x7x1024), which is a RelU layer, I wish want to re-shape this to (256x256x3) which can be understood by the U-net input layer.

推荐答案

不是最后一层，但是可以使用以下代码在移动网络上创建unet：

not the last layer, but creating a unet on mobilenet can be done using the below code:

ALPHA = 1 # Width hyper parameter for MobileNet (0.25, 0.5, 0.75, 1.0). Higher width means more accurate but slower

IMAGE_HEIGHT = 224
IMAGE_WIDTH = 224

HEIGHT_CELLS = 28
WIDTH_CELLS = 28

def create_model(trainable=True):
    model = MobileNet(input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3), include_top=False, alpha=ALPHA, weights="imagenet")

    block0 = model.get_layer("conv_pw_1_relu").output
    block = model.get_layer("conv_pw_1_relu").output
    block1 = model.get_layer("conv_pw_3_relu").output
    block2 = model.get_layer("conv_pw_5_relu").output
    block3 = model.get_layer("conv_pw_11_relu").output
    block4 = model.get_layer("conv_pw_13_relu").output

    x = Concatenate()([UpSampling2D()(block4), block3])
    x = Concatenate()([UpSampling2D()(x), block2])
    x = Concatenate()([UpSampling2D()(x), block1])
    x = Concatenate()([UpSampling2D()(x), block])
 #   x = Concatenate()([UpSampling2D()(x), block0])
    x = UpSampling2D()(x)
    x = Conv2D(1, kernel_size=1, activation="sigmoid")(x)

    x = Reshape((IMAGE_HEIGHT, IMAGE_HEIGHT))(x)

    return Model(inputs=model.input, outputs=x)

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