预先训练Keras Xception和InceptionV3模型 [英] Pre-training Keras Xception and InceptionV3 models

问题描述

我正在尝试使用Keras及其预建的ImageNet CNN架构来做一个简单的二进制分类问题.

I'm trying to do a simple binary classification problem using Keras and its pre-built ImageNet CNN architecture.

对于VGG16，我采用了以下方法，

For VGG16, I took the following approach,

vgg16_model = keras.application.vgg16.VGG16()

'''Rebuild the vgg16 using an empty sequential model'''
model = Sequential()
for layer in vgg16_model.layers:
    model.add(layer)

'''Since the problem is binary, I got rid of the output layer and added a more appropriate output layer.'''
model.pop()

'''Freeze other pre-trained weights'''
for layer in model.layers:
    layer.trainable = False

'''Add the modified final layer'''
model.add(Dense(2, activation = 'softmax'))

与我自定义的CNN相比，此方法的工作精度更高.但是花了一些时间训练，我想使用Xception和InceptionV3采取类似的方法，因为它们是更轻便，精度更高的模型.

And this worked marvelously with higher accuracy than my custom built CNN. But it took a while to train and I wanted to take a similar approach using Xception and InceptionV3 since they were lighter models with higher accuracy.

xception_model = keras.applicaitons.xception.Xception()
model = Sequential()
for layer in xception_model.layers:
    model_xception.add(layer)

运行上面的代码时，出现以下错误:

When I run the above code, I get the following error:

ValueError: Input 0 is incompatible with layer conv2d_193: expected axis -1 of input shape to have value 64 but got shape (None, None, None, 128)

基本上，我想做与VGG16模型相同的事情；保持其他预训练的权重不变，只需将输出层修改为二进制分类输出，而不是将输出层具有1000个结果即可.我可以看到，与具有相对简单的卷积层结构的VGG16不同，Xception和InceptionV3具有一些我并不100％熟悉的时髦节点，我假设它们是引起问题的原因.如果有人可以帮助您解决问题，将不胜感激！

Basically, I would like to do the same thing as I did with VGG16 model; keep the other pretrained weights as they are and simply modify the output layer to a binary classification output instead of an output layer with 1000 outcomes. I can see that unlike VGG16, which has relatively straightforward convolution layer structure, Xception and InceptionV3 have some funky nodes that I'm not 100% familiar with and I'm assuming those are causing issues. If anyone can help out sort the problem, it'd be much appreciated!

谢谢！

推荐答案

您的代码失败，因为InceptionV3和Xception不是Sequential模型(即，它们包含分支").因此，您不能只是将图层添加到Sequential容器中.

Your code fails because InceptionV3 and Xception are not Sequential models (i.e., they contain "branches"). So you can't just add the layers into a Sequential container.

现在，由于InceptionV3和Xception的顶层都由GlobalAveragePooling2D层和最后的Dense(1000)层组成，

Now since the top layers of both InceptionV3 and Xception consist of a GlobalAveragePooling2D layer and the final Dense(1000) layer,

if include_top:
    x = GlobalAveragePooling2D(name='avg_pool')(x)
    x = Dense(classes, activation='softmax', name='predictions')(x)

如果要删除最终的致密层，则可以在创建这些模型时设置include_top=False加pooling='avg'.

if you want to remove the final dense layer, you can just set include_top=False plus pooling='avg' when creating these models.

base_model = InceptionV3(include_top=False, pooling='avg')
for layer in base_model.layers:
    layer.trainable = False
output = Dense(2, activation='softmax')(base_model.output)
model = Model(base_model.input, output)

这篇关于预先训练Keras Xception和InceptionV3模型的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！