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Keras 最新《面向小数据集构建图像分类模型》

51自学网 2020-12-08 14:36:18
  超参数

Keras 最新《面向小数据集构建图像分类模型》

 

本文地址:http://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html

本文作者:Francois Chollet

  • 按照官方的文章实现过程有一些坑,彻底理解代码细节实现,理解keras的api具体使用方法
  • 也有很多人翻译这篇文章,但是有些没有具体实现细节
  • 另外keres开发者自己有本书的jupyter:Companion Jupyter notebooks for the book "Deep Learning with Python"
  • 另外我自己实验三收敛的准确率并没有0.94+,可以参考前面这本书上的实现
  • 文章一共有三个实验:
      1. 第一个实验使用自定义的神经网络对数据集进行训练,三层卷积加两层全连接,训练并验证网络的准确率;
      2. 第二个实验使用VGG16网络对数据进行训练,为了适应自定义的数据集,将VGG16网络的全连接层去掉,作者称之为 “Feature extraction”, 再在上面添加自己实现的全连接层,然后训练并验证网络准确性;
      3. 第三个实验称为 “fine-tune” ,利用第二个实验的实验模型和weight,重新训练VGG16的最后一个卷积层和自定义的全连接层,然后验证网络准确性;
  • 实验二的代码:
复制代码
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats
- put the cat pictures index 1000-1400 in data/validation/cats
- put the dogs pictures index 12500-13499 in data/train/dogs
- put the dog pictures index 13500-13900 in data/validation/dogs
So that we have 1000 training examples for each class, and 400 validation examples for each class.
In summary, this is our directory structure:
```
data/
    train/
        dogs/
            dog001.jpg
            dog002.jpg
            ...
        cats/
            cat001.jpg
            cat002.jpg
            ...
    validation/
        dogs/
            dog001.jpg
            dog002.jpg
            ...
        cats/
            cat001.jpg
            cat002.jpg
            ...
```
'''
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense
from keras import applications

# dimensions of our images.
img_width, img_height = 150, 150

top_model_weights_path = 'bottleneck_fc_model.h5'


data_root = 'M:/dataset/dog_cat/'
train_data_dir =data_root+ 'data/train'
validation_data_dir = data_root+'data/validation'
nb_train_samples = 2000
nb_validation_samples = 800
epochs = 50
batch_size = 16


def save_bottlebeck_features():
    datagen = ImageDataGenerator(rescale=1. / 255)

    # build the VGG16 network
    model = applications.VGG16(include_top=False, weights='imagenet')

    generator = datagen.flow_from_directory(
        train_data_dir,
        target_size=(img_width, img_height),
        batch_size=batch_size,
        class_mode=None,
        shuffle=False)
    bottleneck_features_train = model.predict_generator(
        generator, nb_train_samples // batch_size) #####2000//batch_size!!!!!!!!!!
    np.save('bottleneck_features_train.npy',
            bottleneck_features_train)

    generator = datagen.flow_from_directory(
        validation_data_dir,
        target_size=(img_width, img_height),
        batch_size=batch_size,
        class_mode=None,
        shuffle=False)
    bottleneck_features_validation = model.predict_generator(
        generator, nb_validation_samples // batch_size)
    np.save('bottleneck_features_validation.npy',
            bottleneck_features_validation)


def train_top_model():
    train_data = np.load('bottleneck_features_train.npy')
    train_labels = np.array([0] * int(nb_train_samples / 2) + [1] * int(nb_train_samples / 2))

    validation_data = np.load('bottleneck_features_validation.npy')
    validation_labels = np.array([0] * int(nb_validation_samples / 2) + [1] * int(nb_validation_samples / 2))

    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'])

    model.fit(train_data, train_labels,
              epochs=epochs,
              batch_size=batch_size,
              validation_data=(validation_data, validation_labels))
    model.save_weights(top_model_weights_path)


#save_bottlebeck_features()
train_top_model()
复制代码
  • 实验三代码,自己添加了一些api使用方法,也是以后可以参考的:
复制代码
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats
- put the cat pictures index 1000-1400 in data/validation/cats
- put the dogs pictures index 12500-13499 in data/train/dogs
- put the dog pictures index 13500-13900 in data/validation/dogs
So that we have 1000 training examples for each class, and 400 validation examples for each class.
In summary, this is our directory structure:
```
data/
    train/
        dogs/
            dog001.jpg
            dog002.jpg
            ...
        cats/
            cat001.jpg
            cat002.jpg
            ...
    validation/
        dogs/
            dog001.jpg
            dog002.jpg
            ...
        cats/
            cat001.jpg
            cat002.jpg
            ...
```
'''

# thanks sove bug @http://blog.csdn.net/aggresss/article/details/78588135

from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense
from keras.models import Model
from keras.regularizers import l2

# path to the model weights files.
weights_path = '../keras/examples/vgg16_weights.h5'
top_model_weights_path = 'bottleneck_fc_model.h5'
# dimensions of our images.
img_width, img_height = 150, 150

data_root = 'M:/dataset/dog_cat/'
train_data_dir =data_root+ 'data/train'
validation_data_dir = data_root+'data/validation'

nb_train_samples = 2000
nb_validation_samples = 800
epochs = 50
batch_size = 16

# build the VGG16 network
base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(150,150,3)) # train 指定训练大小
print('Model loaded.')

# build a classifier model to put on top of the convolutional model
top_model = Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))  # base_model.output_shape[1:])
top_model.add(Dense(256, activation='relu',kernel_regularizer=l2(0.001),))
top_model.add(Dropout(0.8))
top_model.add(Dense(1, activation='sigmoid'))

# 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) # bug

model = Model(inputs=base_model.input, outputs=top_model(base_model.output))


# set the first 25 layers (up to the last conv block)
# to non-trainable (weights will not be updated)
for layer in model.layers[:15]:  # :25 bug
    layer.trainable = False

# compile the model with a SGD/momentum optimizer
# and a very slow learning rate.
model.compile(loss='binary_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=batch_size,
    class_mode='binary')

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

model.summary() # prints a summary representation of your model.
# 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)


from keras.utils import plot_model
plot_model(model, to_file='model.png')

from keras.callbacks import History
from keras.callbacks import ModelCheckpoint
import keras
history = History()
model_checkpoint = ModelCheckpoint('temp_model.hdf5', monitor='loss', save_best_only=True)
tb_cb = keras.callbacks.TensorBoard(log_dir='log', write_images=1, histogram_freq=0)
# 设置log的存储位置,将网络权值以图片格式保持在tensorboard中显示,设置每一个周期计算一次网络的
# 权值,每层输出值的分布直方图
callbacks = [
        history,
        model_checkpoint,
        tb_cb
    ]
# model.fit()


# fine-tune the model
history=model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    callbacks=callbacks,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size,
    verbose = 2)

model.save('fine_tune_model.h5')
model.save_weights('fine_tune_model_weight')
print(history.history)


from matplotlib import pyplot as plt
history=history
plt.plot()
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

import  numpy as np
accy=history.history['acc']
np_accy=np.array(accy)
np.savetxt('save_acc.txt',np_accy)

训练集准确率97%(很高),测试集准确率50%~60%(很低),解决方案探索
使用小数据集时可能会出现的问题,如何最有效地克服这些问题
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