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博主简介：风雪夜归子（英文名：Allen），机器学习算法攻城狮，喜爱钻研Meachine Learning的黑科技，对Deep Learning和Artificial Intelligence充满兴趣，经常关注Kaggle数据挖掘竞赛平台，对数据、Machine Learning和Artificial Intelligence有兴趣的童鞋可以一起探讨哦，个人CSDN博客：http://blog.csdn.net/u013719780?viewmode=contents

很多童鞋可能知道，深度学习需要大量数据才能训练处一个较好的模型。但是，有时候我们很难获取大量数据，因为得到足够大样本量的特定领域的数据集并不是那么容易，这是否就意味着我们不能使用上深度学习这一黑科技啦？我很高兴的告诉大家，事实并非如此。迁移学习就可以帮助我们使用上深度学习这一高大上的技术。

何为迁移学习？迁移学习是指使用一个预训练的网络：比如 VGG16 。VGG16 是基于大量真实图像的 ImageNet 图像库预训练的网络。我们将学习的 VGG16 的权重迁移（transfer）到自己的卷积神经网络上作为网络的初始权重，然后微调（fine-tune）这些预训练的通用网络使它们能够识别出人的activities图像，从而提高对HAR的预测效果。

迁移学习究竟如何使用？各位童鞋直接看下面的代码吧~

'''
This script reuses pieces of code from the post:
"Building powerful image classification models using very little data"
from blog.keras.io
and from:
https://www.kaggle.com/tnhabc/state-farm-distracted-driver-detection/keras-sample
The training data can be downloaded at:
https://www.kaggle.com/c/state-farm-distracted-driver-detection/data
'''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
from regularizers import EigenvalueRegularizer
from numpy.random import permutation
from keras.optimizers import SGD
import pandas as pd
import datetime
import glob
import cv2
import math
import pickle
from collections import OrderedDict
from keras import backend as K# Enter here the path to the model weights files:
weights_path = '/home/oswaldo/video_classification/competitionKaggle/vgg16_weights.h5'
# Enter here the path to the top-model weights files:
top_model_weights_path = '/home/oswaldo/video_classification/competitionKaggle/fc_model.h5'
# Enter here the path for storage of the whole model weights (VGG16+top classifier model):
whole_model_weights_path = '/home/oswaldo/video_classification/competitionKaggle/whole_model.h5'
# Enter here the name of the folder that contains the folders c0, c1,..., c9, with the training images belonging to classes 0 to 9:
train_data_dir = 'train'
# Enter here the name of the folder where is the test images (the data evalueted in the private leaderboard):
test_data_dir = 'test'test_images_path = 'test/test'# Enter here the features of the data set:
img_width, img_height = 224, 224
nb_train_samples = 22424
nb_test_samples = 79726
color_type_global = 3# You can set larger values here, according with the memory of your GPU:
batch_size = 32# Enter here the number of training epochs (with 80 epochs the model was positioned among
# the 29% best competitors in the private leaderboard of state-farm-distracted-driver-detection)
# According to our results, this model can achieve a better performance if trained along a larger
# number of epochs, due to the agressive regularization with Eigenvalue Decay that was adopted.
nb_epoch = 80#Enter here the path for the whole model (VGG16+top classifier model):
whole_model_weights_path = '/home/oswaldo/video_classification/competitionKaggle/whole_model.h5'# 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)))

最后图像的输出维度是512x7x7。这个结果是怎么来的呢？下面我们详细的推导一下：

图像的输入维度    3x224x224

ZeroPadding2D    3x226x226

conv1_1                64x224x224

ZeroPadding2D    64x226x226

conv1_2                64x224x224

MaxPooling2D      64x112x112

ZeroPadding2D     64x114x114

conv2_1                128x112x112

ZeroPadding2D     128x114x114

conv2_2                128x112x112

MaxPooling2D      128x56x56

ZeroPadding2D     128x58x58

conv3_1                 256x56x56

ZeroPadding2D     256x58x58

conv3_2                 256x56x56

ZeroPadding2D     256x58x58

conv3_3                 256x56x56

MaxPooling2D       256x28x28

ZeroPadding2D      256x30x30

conv4_1                  512x28x28

ZeroPadding2D      512x30x30

conv4_2                  512x28x28

ZeroPadding2D      512x30x30

conv4_3                  512x28x28

MaxPooling2D        512x14x14

ZeroPadding2D 512x16x16

conv5_1         512x14x14

ZeroPadding2D 512x16x16

conv5_2         512x14x14

ZeroPadding2D 512x16x16

conv5_3         512x14x14

MaxPooling2D 512x7x7

所有模型最后的输出维度是512x7x7。

接下来的部分直接上代码

# loading the weights of the pre-trained VGG16: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):breakg = 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.')# building a classifier model on top of the convolutional model:top_model = Sequential()
top_model.add(Flatten(input_shape=model.output_shape[1:]))
top_model.add(Dense(64, activation='relu', W_regularizer=EigenvalueRegularizer(10)))
top_model.add(Dense(10, activation='softmax', W_regularizer=EigenvalueRegularizer(10)))
top_model.load_weights(top_model_weights_path)# add the model on top of the convolutional base
model.add(top_model)# setting the first 15 layers to non-trainable (the original weights will not be updated)for layer in model.layers[:15]:layer.trainable = False# Compiling the model with a SGD/momentum optimizer:model.compile(loss = "categorical_crossentropy",optimizer=optimizers.SGD(lr=1e-6, momentum=0.9),metrics=['mean_squared_logarithmic_error', 'accuracy'])# Data augmentation:train_datagen = ImageDataGenerator(shear_range=0.3, zoom_range=0.3, rotation_range=0.3)
test_datagen = ImageDataGenerator()print('trainning')
train_generator = train_datagen.flow_from_directory(train_data_dir,target_size=(img_height, img_width),batch_size=32,class_mode='categorical')print('testing')
test_generator = test_datagen.flow_from_directory(test_data_dir,target_size=(img_height, img_width),batch_size=32,class_mode='categorical',shuffle=False)class_dictionary = train_generator.class_indices
sorted_class_dictionary = OrderedDict(sorted(class_dictionary.items()))
sorted_class_dictionary = sorted_class_dictionary.values()
print(sorted_class_dictionary)# Fine-tuning the model:
model.fit_generator(train_generator,samples_per_epoch=nb_train_samples,nb_epoch=nb_epoch,validation_data=train_generator,nb_val_samples=nb_train_samples)model.save_weights(whole_model_weights_path)aux = model.predict_generator(test_generator, nb_test_samples)
predictions = np.zeros((nb_test_samples, 10))# Rearranging the predictions:ord = [5, 0, 6, 2, 7, 9, 1, 4, 8, 3]for n in range(10):i = ord[n]print(i)print(aux[:, i])predictions[:, n] = aux[:, i]# Trick to improve the multi-class logarithmic loss (the evaluation metric of state-farm-distracted-driver-detection from Keras):predictions = 0.985 * predictions + 0.015def get_im(path, img_width, img_height, color_type=1):if color_type == 1:img = cv2.imread(path, 0)elif color_type == 3:img = cv2.imread(path)# Reduce sizeresized = cv2.resize(img, (img_height, img_width))return resizeddef load_test(img_width, img_height, color_type=1):print('Read test images')path = os.path.join(test_images_path, '*.jpg')files = glob.glob(path)X_test = []X_test_id = []total = 0thr = math.floor(len(files)/10)for fl in files:flbase = os.path.basename(fl)img = get_im(fl, img_width, img_height, color_type)X_test.append(img)X_test_id.append(flbase)total += 1if total % thr == 0:print('Read {} images from {}'.format(total, len(files)))return X_test, X_test_idX_test, test_id = load_test(img_width, img_height, color_type_global)def create_submission(predictions, test_id):result1 = pd.DataFrame(predictions, columns=['c0', 'c1', 'c2', 'c3','c4', 'c5', 'c6', 'c7','c8', 'c9'])result1.loc[:, 'img'] = pd.Series(test_id, index=result1.index)now = datetime.datetime.now()if not os.path.isdir('subm'):os.mkdir('subm')suffix = '_' + str(now.strftime("%Y-%m-%d-%H-%M"))sub_file = os.path.join('subm', 'submission_' + suffix + '.csv')result1.to_csv(sub_file, index=False)create_submission(predictions, test_id)