在Keras中训练多类图像分类器 [英] Train multi-class image classifier in Keras

问题描述

我正在遵循一个教程，学习使用Keras训练分类器

I was following a tutorial to learn train a classifier using Keras

https://blog. keras.io/building-powerful-image-classification-models-using-very-little-data.html

具体来说，从作者提供的第二个脚本中，我想将该脚本转换为一个可以训练多分类器的分类器(是猫和狗的二进制).我的火车文件夹中有5个班级，所以做了以下更改:

Specifically, from the second script given by the author, I wanted to transform the script into a one that can train multi-class classifier(was a binary for cat and dog). I have 5 classes in my train folder so I did the following change:

在 train_top_model()函数中:

我改变了

model = Sequential()
model.add(Flatten(input_shape=train_data.shape[1:]))
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])

进入

model = Sequential()
model.add(Flatten(input_shape=train_data.shape[1:]))
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(5, activation='sigmoid'))

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

train_labels = to_categorical(train_labels, 5)
validation_labels = to_categorical(validation_labels, 5)

完成训练后，模型达到了接近99％的训练准确度，但仅达到验证准确度的70％左右.因此，我开始考虑将2节课的训练转换为5节课并不是那么简单.标记类时也许需要使用一键编码(但我不知道如何)

After done the training, the model reached training accuracy of near 99% but only for like 70% accuracy of the validation accuracy. Thus I started thinking maybe it's not that simple to convert 2 classes training to 5 classes. Maybe I need to use one-hot encoding when labeling the classes(but I don't know how)

我还附上了运行良好的脚本.另一个问题:开始微调时，精度没有有效提高.

I attached my fine-tunning script as well. Another problem: the accuracy did not effectively increase when fine-tunning starts.

import os
import h5py
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.layers import Activation, Dropout, Flatten, Dense

# path to the model weights files.
weights_path = 'D:/Users/EJLTZ/Desktop/vgg16_weights.h5'
top_model_weights_path = 'bottleneck_weights_2.h5'
# dimensions of our images.
img_width, img_height = 150, 150

train_data_dir = 'D:/Users/EJLTZ/Desktop/BodyPart-full/train_new'
validation_data_dir = 'D:/Users/EJLTZ/Desktop/BodyPart-full/validation_new'
nb_train_samples = 500
nb_validation_samples = 972
nb_epoch = 50

# build the VGG16 network
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=(3, img_width, img_height)))

model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

# load the weights of the VGG16 networks
# (trained on ImageNet, won the ILSVRC competition in 2014)
# note: when there is a complete match between your model definition
# and your weight savefile, you can simply call model.load_weights(filename)
assert os.path.exists(weights_path), 'Model weights not found (see "weights_path" variable in script).'
f = h5py.File(weights_path)
for k in range(f.attrs['nb_layers']):
    if k >= len(model.layers):
        # we don't look at the last (fully-connected) layers in the savefile
        break
    g = f['layer_{}'.format(k)]
    weights = [g['param_{}'.format(p)] for p in range(g.attrs['nb_params'])]
    model.layers[k].set_weights(weights)
f.close()
print('Model loaded.')

# build a classifier model to put on top of the convolutional model
top_model = Sequential()
top_model.add(Flatten(input_shape=model.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(5, activation='softmax'))

# note that it is necessary to start with a fully-trained
# classifier, including the top classifier,
# in order to successfully do fine-tuning
top_model.load_weights(top_model_weights_path)

# add the model on top of the convolutional base
model.add(top_model)

# set the first 25 layers (up to the last conv block)
# to non-trainable (weights will not be updated)
for layer in model.layers[:25]:
    layer.trainable = False

# compile the model with a SGD/momentum optimizer
# and a very slow learning rate.
model.compile(loss='categorical_crossentropy',
          optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
          metrics=['accuracy'])

# prepare data augmentation configuration
train_datagen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_height, img_width),
    batch_size=32,
    class_mode= 'categorical')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_height, img_width),
    batch_size=32,
    class_mode= 'categorical')

# fine-tune the model
model.fit_generator(
    train_generator,
    samples_per_epoch=nb_train_samples,
    nb_epoch=nb_epoch,
    validation_data=validation_generator,
    nb_val_samples=nb_validation_samples)

model.save_weights("fine-tune_weights.h5")
model.save("fine-tune_model.h5", True)

推荐答案

1. 将softmax用作输出层的激活函数，它是多类情况下逻辑函数的概括.在此处了解更多信息.

1. Use softmax as activation function of the output layer, it is a generalization of the logistic function for a multi class case. Read more about it here.

如果您的情况下验证错误比训练错误大得多，则表明安装过度.您应该进行一些正则化，这被定义为学习算法的任何变化，目的是减少测试错误，而不是训练错误.您可以尝试诸如数据增强，提早停止，噪声注入，更具攻击性的辍学等操作.

If validation error is much greater than the training one, as in your case, it is an indicator of overfitting. You should do some regularization, which is defined as any changes of the learning algorithm, that are intended to reduce the test error but not the training one. You can try things like data augmentation, early stopping, noise injection, more aggressive dropout, etc.

如果您具有与链接教程中相同的设置，请将train_generator和validation_generator的class_mode更改为categorical，它将对您的类进行一次热编码.

If you have the same set-up as in the linked tutorial, change the class_modeof the train_generatorand validation_generator to categorical and it will one-hot encode your classes.

这篇关于在Keras中训练多类图像分类器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！