如何在Keras中微调ResNet50? [英] How to fine-tune ResNet50 in Keras?

问题描述

我正在尝试微调Keras中的现有模型以对我自己的数据集进行分类.到目前为止，我已经尝试了以下代码(摘自Keras文档: https://keras.io/applications/)，其中Inception V3在一组新的类上进行了微调.

Im trying to finetune the existing models in Keras to classify my own dataset. Till now I have tried the following code (taken from Keras docs: https://keras.io/applications/) in which Inception V3 is fine-tuned on a new set of classes.

from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K

# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)

# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(1024, activation='relu')(x)
# and a logistic layer -- let's say we have 200 classes
predictions = Dense(200, activation='softmax')(x)

# this is the model we will train
model = Model(inputs=base_model.input, outputs=predictions)

# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional InceptionV3 layers
for layer in base_model.layers:
    layer.trainable = False

# compile the model (should be done *after* setting layers to non-trainable)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')

# train the model on the new data for a few epochs
model.fit_generator(...)

# at this point, the top layers are well trained and we can start fine-tuning
# convolutional layers from inception V3. We will freeze the bottom N layers
# and train the remaining top layers.

# let's visualize layer names and layer indices to see how many layers
# we should freeze:
for i, layer in enumerate(base_model.layers):
   print(i, layer.name)

# we chose to train the top 2 inception blocks, i.e. we will freeze
# the first 172 layers and unfreeze the rest:
for layer in model.layers[:172]:
   layer.trainable = False
for layer in model.layers[172:]:
   layer.trainable = True

# we need to recompile the model for these modifications to take effect
# we use SGD with a low learning rate
from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy')

# we train our model again (this time fine-tuning the top 2 inception blocks
# alongside the top Dense layers
model.fit_generator(...)

任何人都可以指导我对以上代码进行哪些更改，以便微调Keras中存在的ResNet50模型.

Can anyone plz guide me what changes should I do in the above code so as to fine-tune ResNet50 model present in Keras.

谢谢.

推荐答案

很难找出具体的问题，您除了尝试复制代码而不进行任何更改外，还尝试了其他什么吗?

It is difficult to make out a specific question, have you tried anything more than just copying the code without any changes?

也就是说，代码中存在很多问题:这是来自keras.io的简单复制/粘贴，不起作用，并且需要在完全工作之前要进行一些调整(无论使用ResNet50还是InceptionV3 ):

That said, there is an abundance of problems in the code: It is a simple copy/paste from keras.io, not functional as it is, and needs some adaption before working at all (regardless of using ResNet50 or InceptionV3):

1):加载InceptionV3时需要定义input_shape，特别是将base_model = InceptionV3(weights='imagenet', include_top=False)替换为base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(299,299,3))

1): You need to define the input_shape when loading InceptionV3, specifically replace base_model = InceptionV3(weights='imagenet', include_top=False) with base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(299,299,3))

2):此外，您需要调整最后添加的层中的类数，例如如果您只有2个课程可以:predictions = Dense(2, activation='softmax')(x)

2): Further, you need to adapt the number of the classes in the last added layer, e.g. if you have only 2 classes to: predictions = Dense(2, activation='softmax')(x)

3):将模型编译时的损失函数从categorical_crossentropy更改为sparse_categorical_crossentropy

3): Change the loss-function when compiling your model from categorical_crossentropy to sparse_categorical_crossentropy

4):最重要的是，您需要在调用model.fit_generator()并添加steps_per_epoch之前定义fit_generator.如果您将训练图像放在 ./data/train 中，并且每个类别都位于不同的子文件夹中，则可以例如像这样:

4): Most importantly, you need to define the fit_generator before calling model.fit_generator() and add steps_per_epoch. If you have your training images in ./data/train with every category in a different subfolder, this can be done e.g. like this:

from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator()
train_generator = train_datagen.flow_from_directory(
     "./data/train",
    target_size=(299, 299),
    batch_size=50,
    class_mode='binary')
model.fit_generator(train_generator, steps_per_epoch=100)

这当然仅是基本训练，例如，您需要定义保存呼叫以保持训练后的体重.仅当您获得适用于上述更改的InceptionV3的代码时，我才建议继续为ResNet50实施此代码:首先，您可以将InceptionV3()替换为ResNet50()(当然，仅在from keras.applications.resnet50 import ResNet50之后)，然后进行更改input_shape至(224,224,3)和target_size至(224,244).

This of course only does basic training, you will for example need to define save calls to hold on to the trained weights. Only if you get the code working for InceptionV3 with the changes above I suggest to proceed to work on implementing this for ResNet50: As a start you can replace InceptionV3() with ResNet50() (of course only after from keras.applications.resnet50 import ResNet50), and change the input_shape to (224,224,3) and target_size to (224,244).

上述代码更改应在 Python 3.5.3/Keras 2.0/Tensorflow 后端上起作用.

The above mentioned code-changes should work on Python 3.5.3 / Keras 2.0 / Tensorflow backend.

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