使用迁移学习（TransferLearning）完成图像的多标签分类（MultiLabel）任务sunwq06

使用迁移学习（Transfer Learning）完成图像的多标签分类（Multi-Label）任务

本文通过迁移学习将训练好的模型应用到图像的多标签分类问题中

本文通过迁移学习将训练好的VGG16模型应用到图像的多标签分类问题中。该项目数据来自于Kaggle，每张图片可同时属于多个标签。模型的准确度使用F score进行量化，如下表所示：

例如真实标签是(1,0,1,1,0,0), 预测标签是(1,1,0,1,1,0), 则TP=2, FN=1, FP=2, TN=1。$$Precision=\frac{TP}{TP+FP},\text{  }Recall=\frac{TP}{TP+FN},\text{  }F{\_}score=\frac{(1+\beta^2)*Presicion*Recall}{Recall+\beta^2*Precision}$$其中$\beta$越小，F score中Precision的权重越大，$\beta$等于0时F score就变为Precision；$\beta$越大，F score中Recall的权重越大，$\beta$趋于无穷大时F score就变为Recall。可以在Keras中自定义该函数（y_pred表示预测概率）：

from tensorflow.keras import backend

# calculate fbeta score for multi-label classification
def fbeta(y_true, y_pred, beta=2):
# clip predictions
y_pred = backend.clip(y_pred, 0, 1)
# calculate elements for each sample
tp = backend.sum(backend.round(backend.clip(y_true * y_pred, 0, 1)), axis=1)
fp = backend.sum(backend.round(backend.clip(y_pred - y_true, 0, 1)), axis=1)
fn = backend.sum(backend.round(backend.clip(y_true - y_pred, 0, 1)), axis=1)
# calculate precision
p = tp / (tp + fp + backend.epsilon())
# calculate recall
r = tp / (tp + fn + backend.epsilon())
# calculate fbeta, averaged across samples
bb = beta ** 2
fbeta_score = backend.mean((1 + bb) * (p * r) / (bb * p + r + backend.epsilon()))
return fbeta_score

此外在损失函数的使用上多标签分类和多类别（multi-class）分类也有区别，多标签分类使用binary_crossentropy，假设一个样本的真实标签是(1,0,1,1,0,0)，预测概率是(0.2, 0.3, 0.4, 0.7, 0.9, 0.2): $$binary{\_}crossentropy\text{  }loss=-(\ln 0.2 + \ln 0.7 + \ln 0.4 + \ln 0.7 + \ln 0.1 + \ln 0.8)/6=0.96$$另外多标签分类输出层的激活函数选择sigmoid而非softmax。模型架构如下所示：

from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.models import Model
def define_model(in_shape=(128, 128, 3), out_shape=17):
# load model
base_model = VGG16(weights=\'imagenet\', include_top=False, input_shape=in_shape)
# mark loaded layers as not trainable
for layer in base_model.layers: layer.trainable = False
# make the last block trainable
tune_layers = [layer.name for layer in base_model.layers if layer.name.startswith(\'block5_\')]
for layer_name in tune_layers: base_model.get_layer(layer_name).trainable = True
# add new classifier layers
flat1 = Flatten()(base_model.layers[-1].output)
class1 = Dense(128, activation=\'relu\', kernel_initializer=\'he_uniform\')(flat1)
output = Dense(out_shape, activation=\'sigmoid\')(class1)
# define new model
model = Model(inputs=base_model.input, outputs=output)
# compile model
opt = Adam(learning_rate=1e-3)
model.compile(optimizer=opt, loss=\'binary_crossentropy\', metrics=[fbeta])
model.summary()
return model

从Kaggle网站上下载数据并解压，将其处理成可被模型读取的数据格式

from os import listdir
from numpy import zeros, asarray, savez_compressed
from pandas import read_csv
from tensorflow.keras.preprocessing.image import load_img, img_to_array
# create a mapping of tags to integers given the loaded mapping file
def create_tag_mapping(mapping_csv):
labels = set() # create a set of all known tags
for i in range(len(mapping_csv)):
tags = mapping_csv[\'tags\'][i].split(\' \') # convert spaced separated tags into an array of tags
labels.update(tags) # add tags to the set of known labels
labels = sorted(list(labels)) # convert set of labels to a sorted list
# dict that maps labels to integers, and the reverse
labels_map = {labels[i]:i for i in range(len(labels))}
inv_labels_map = {i:labels[i] for i in range(len(labels))}
return labels_map, inv_labels_map
# create a mapping of filename to a list of tags
def create_file_mapping(mapping_csv):
mapping = dict()
for i in range(len(mapping_csv)):
name, tags = mapping_csv[\'image_name\'][i], mapping_csv[\'tags\'][i]
mapping[name] = tags.split(\' \')
return mapping
# create a one hot encoding for one list of tags
def one_hot_encode(tags, mapping):
encoding = zeros(len(mapping), dtype=\'uint8\') # create empty vector
# mark 1 for each tag in the vector
for tag in tags: encoding[mapping[tag]] = 1
return encoding
# load all images into memory
def load_dataset(path, file_mapping, tag_mapping):
photos, targets = list(), list()
# enumerate files in the directory
for filename in listdir(path):
photo = load_img(path + filename, target_size=(128,128)) # load image
photo = img_to_array(photo, dtype=\'uint8\') # convert to numpy array
tags = file_mapping[filename[:-4]] # get tags
target = one_hot_encode(tags, tag_mapping) # one hot encode tags
photos.append(photo)
targets.append(target)
X = asarray(photos, dtype=\'uint8\')
y = asarray(targets, dtype=\'uint8\')
return X, y
filename = \'train_v2.csv\' # load the target file
mapping_csv = read_csv(filename)
tag_mapping, _ = create_tag_mapping(mapping_csv) # create a mapping of tags to integers
file_mapping = create_file_mapping(mapping_csv) # create a mapping of filenames to tag lists
folder = \'train-jpg/\' # load the jpeg images
X, y = load_dataset(folder, file_mapping, tag_mapping)
print(X.shape, y.shape)
savez_compressed(\'planet_data.npz\', X, y) # save both arrays to one file in compressed format

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接下来再建立两个辅助函数，第一个函数用来分割训练集和验证集，第二个函数用来画出模型在训练过程中的学习曲线

import numpy as np
from matplotlib import pyplot
from sklearn.model_selection import train_test_split
# load train and test dataset
def load_dataset():
# load dataset
data = np.load(\'planet_data.npz\')
X, y = data[\'arr_0\'], data[\'arr_1\']
# separate into train and test datasets
trainX, testX, trainY, testY = train_test_split(X, y, test_size=0.3, random_state=1)
print(trainX.shape, trainY.shape, testX.shape, testY.shape)
return trainX, trainY, testX, testY
# plot diagnostic learning curves
def summarize_diagnostics(history):
# plot loss
pyplot.subplot(121)
pyplot.title(\'Cross Entropy Loss\')
pyplot.plot(history.history[\'loss\'], color=\'blue\', label=\'train\')
pyplot.plot(history.history[\'val_loss\'], color=\'orange\', label=\'test\')
# plot accuracy
pyplot.subplot(122)
pyplot.title(\'Fbeta\')
pyplot.plot(history.history[\'fbeta\'], color=\'blue\', label=\'train\')
pyplot.plot(history.history[\'val_fbeta\'], color=\'orange\', label=\'test\')
pyplot.show()

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使用数据扩充技术（Data Augmentation）对模型进行训练

from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.callbacks import ModelCheckpoint
trainX, trainY, testX, testY = load_dataset() # load dataset
# create data generator using augmentation
# vertical flip is reasonable since the pictures are satellite images
train_datagen = ImageDataGenerator(horizontal_flip=True, vertical_flip=True, rotation_range=90, preprocessing_function=preprocess_input)
test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)
# prepare generators
train_it = train_datagen.flow(trainX, trainY, batch_size=128)
test_it = test_datagen.flow(testX, testY, batch_size=128)
# define model
model = define_model()
# fit model
# When one epoch ends, the validation generator will yield validation_steps batches, then average the evaluation results of all batches
checkpointer = ModelCheckpoint(filepath=\'./weights.best.vgg16.hdf5\', verbose=1, save_best_Only=True)
history = model.fit_generator(train_it, steps_per_epoch=len(train_it), validation_data=test_it, validation_steps=len(test_it), \
epochs=15, callbacks=[checkpointer], verbose=0)
# evaluate optimal model
# For simplicity, the validation set is used to test the model here. In fact an entirely new test set should have been used.
model.load_weights(\'./weights.best.vgg16.hdf5\') #load stored optimal coefficients
loss, fbeta = model.evaluate_generator(test_it, steps=len(test_it), verbose=0)
print(\'> loss=%.3f, fbeta=%.3f\' % (loss, fbeta)) # loss=0.108, fbeta=0.884
model.save(\'final_model.h5\')
# learning curves
summarize_diagnostics(history)

 蓝线代表训练集，黄线代表验证集