[TOC]

0 前言

比賽網(wǎng)址：

https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition

參考解法：

https://www.kaggle.com/jeffd23/catdognet-keras-convnet-starter

https://www.kaggle.com/sentdex/full-classification-example-with-convnet

1 簡介

• 卷機(jī)網(wǎng)絡(luò)模型——對VGG16修改
• 基于Keras
• test loss: 0.23535
• Titan V，73epoch，訓(xùn)練時(shí)間1h+

導(dǎo)包：

import cv2                 # working with, mainly resizing, images
import numpy as np         # dealing with arrays
import os                  # dealing with directories
from random import shuffle # mixing up or currently ordered data that might lead our network astray in training.
from tqdm import tqdm      # a nice pretty percentage bar for tasks. Thanks to viewer Daniel Bühler for this suggestion
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Input, Dropout, Flatten, Conv2D, MaxPooling2D, Dense, Activation
from keras.optimizers import RMSprop
from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping, ReduceLROnPlateau
from keras.utils import np_utils
from keras import backend

%matplotlib inline

2 數(shù)據(jù)準(zhǔn)備

2.1 導(dǎo)入數(shù)據(jù)

TRAIN_DIR = './data/train'
TEST_DIR = './data/test'
IMG_SIZE = 128

MODEL_NAME = 'dogsvscats-{}-{}.model'.format(LR, '2conv-basic')

• 將標(biāo)簽變成one-hot編碼（直接用0,1做標(biāo)簽，會出問題，在測試集上表現(xiàn)奇差，換成One-hot之后解決，未研究清楚為什么）

# one-hot 編碼
def label_img(img):
    word_label = img.split('.')[-3]
    if word_label == 'cat': 
        return [1,0]
    elif word_label == 'dog': 
        return [0,1]

• 導(dǎo)入訓(xùn)練和測試數(shù)據(jù)
  • 將圖片轉(zhuǎn)為灰度圖
  • 圖片尺寸改為IMG_SIZE * IMG_SIZE
  • 將處理后的圖片保存為.npy格式，方便下次讀取
  • 使用tqdm庫，可以將處理過程用進(jìn)度條表示出來 awesome :smile:

# 處理訓(xùn)練數(shù)據(jù)
def create_train_data():
    training_data = []
    for img in tqdm(os.listdir(TRAIN_DIR)):
        label = label_img(img)
        path = os.path.join(TRAIN_DIR,img)
        img = cv2.imread(path,cv2.IMREAD_GRAYSCALE)
        img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
        training_data.append([np.array(img), label])
    shuffle(training_data)
    np.save('train_data.npy', training_data)
    return training_data

# 處理測試數(shù)據(jù)
def process_test_data():
    testing_data = []
    for img in tqdm(os.listdir(TEST_DIR)):
        path = os.path.join(TEST_DIR,img)
        img_num = img.split('.')[0]
        img = cv2.imread(path,cv2.IMREAD_GRAYSCALE)
        img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
        testing_data.append([np.array(img), img_num])
        
    #shuffle(testing_data)
    np.save('test_data.npy', testing_data)
    return testing_data

train_data = create_train_data()
# If you have already created the dataset:
#train_data = np.load('train_data.npy')

2.2 分割驗(yàn)證集

train, val = train_test_split(train_data, test_size = 0.25)

X_train = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE,IMG_SIZE,1)
Y_train = np.array([i[1] for i in train])

X_val = np.array([i[0] for i in val]).reshape(-1,IMG_SIZE,IMG_SIZE,1)
Y_val = np.array([i[1] for i in val])

3 Convolutional Neural Network

3.1 定義網(wǎng)絡(luò)模型

網(wǎng)絡(luò)結(jié)構(gòu)：

基于VGG16

conv2D(64, (3,3)) -> conv2D(64, (3,3)) -> maxpool(2,2)
-> conv2D(128, (3,3)) -> conv2D(128, (3,3)) -> maxpool(2,2)
-> conv2D(256, (3,3)) -> conv2D(256, (3,3)) -> conv2D(256, (3,3)) -> maxpool(2,2)
-> conv2D(512, (3,3)) -> conv2D(512, (3,3)) -> conv2D(512, (3,3)) -> maxpool(2,2)
-> conv2D(512, (3,3)) -> conv2D(512, (3,3)) -> conv2D(512, (3,3)) -> maxpool(2,2)
-> flatten() -> full connect(256) -> dropout(0.5) -> full connect(2)

backend.set_image_dim_ordering('tf')  # th通道在前，tf通道在后

optimizer = RMSprop(lr=1e-4)
objective = 'categorical_crossentropy'
IMG_SIZE = 128

def catdog():
    
    model = Sequential()

    model.add(Conv2D(64, (3, 3), padding='same', input_shape=(IMG_SIZE, IMG_SIZE, 1), activation='relu'))
    model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))
    model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    
    model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))
    model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))
    model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    
    model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
    model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
    model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    
    model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
    model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
    model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Flatten())
    model.add(Dense(256, activation='relu'))
    model.add(Dropout(0.5))

    model.add(Dense(2, activation='softmax'))

    model.compile(loss=objective, optimizer=optimizer, metrics=['accuracy'])
    return model

model = catdog()

• 模型參數(shù)

model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_24 (Conv2D)           (None, 128, 128, 64)      640       
_________________________________________________________________
conv2d_25 (Conv2D)           (None, 128, 128, 64)      36928     
_________________________________________________________________
max_pooling2d_10 (MaxPooling (None, 64, 64, 64)        0         
_________________________________________________________________
conv2d_26 (Conv2D)           (None, 64, 64, 128)       73856     
_________________________________________________________________
conv2d_27 (Conv2D)           (None, 64, 64, 128)       147584    
_________________________________________________________________
max_pooling2d_11 (MaxPooling (None, 32, 32, 128)       0         
_________________________________________________________________
conv2d_28 (Conv2D)           (None, 32, 32, 256)       295168    
_________________________________________________________________
conv2d_29 (Conv2D)           (None, 32, 32, 256)       590080    
_________________________________________________________________
conv2d_30 (Conv2D)           (None, 32, 32, 256)       590080    
_________________________________________________________________
max_pooling2d_12 (MaxPooling (None, 16, 16, 256)       0         
_________________________________________________________________
conv2d_31 (Conv2D)           (None, 16, 16, 512)       1180160   
_________________________________________________________________
conv2d_32 (Conv2D)           (None, 16, 16, 512)       2359808   
_________________________________________________________________
conv2d_33 (Conv2D)           (None, 16, 16, 512)       2359808   
_________________________________________________________________
max_pooling2d_13 (MaxPooling (None, 8, 8, 512)         0         
_________________________________________________________________
conv2d_34 (Conv2D)           (None, 8, 8, 512)         2359808   
_________________________________________________________________
conv2d_35 (Conv2D)           (None, 8, 8, 512)         2359808   
_________________________________________________________________
conv2d_36 (Conv2D)           (None, 8, 8, 512)         2359808   
_________________________________________________________________
max_pooling2d_14 (MaxPooling (None, 4, 4, 512)         0         
_________________________________________________________________
flatten_3 (Flatten)          (None, 8192)              0         
_________________________________________________________________
dense_5 (Dense)              (None, 256)               2097408   
_________________________________________________________________
dropout_3 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_6 (Dense)              (None, 2)                 514       
=================================================================
Total params: 16,811,458
Trainable params: 16,811,458
Non-trainable params: 0
_________________________________________________________________

3.2 數(shù)據(jù)增強(qiáng)

隨機(jī)：旋轉(zhuǎn)40；錯切變換0.3；縮放0.2；左右上下平移0.2；水平垂直翻轉(zhuǎn)

datagen = ImageDataGenerator(
        featurewise_center=False,  # set input mean to 0 over the dataset
        samplewise_center=False,  # set each sample mean to 0
        featurewise_std_normalization=False,  # divide inputs by std of the dataset
        samplewise_std_normalization=False,  # divide each input by its std
        zca_whitening=False,  # apply ZCA whitening
        rotation_range=40,  # randomly rotate images in the range (degrees, 0 to 180)
        shear_range=0.3,    #錯切變換，效果就是讓所有點(diǎn)的x坐標(biāo)(或者y坐標(biāo))保持不變，而對應(yīng)的y坐標(biāo)(或者x坐標(biāo))則按比例發(fā)生平移
        zoom_range = 0.2, # Randomly zoom image 
        width_shift_range=0.2,  # randomly shift images horizontally (fraction of total width)
        height_shift_range=0.2,  # randomly shift images vertically (fraction of total height)
        horizontal_flip=True,  # randomly flip images
        vertical_flip=True)  # randomly flip images


#datagen = ImageDataGenerator()
datagen.fit(X_train)

3.3 訓(xùn)練

• learning_rate_reduction
  • 當(dāng)val loss不再減小時(shí)，縮小LR的值
• early_stopping
  • LR減小到閾值后，且val loss不再減小時(shí)，停止訓(xùn)練
• ModelCheckpoint
  • 保存模型。每個(gè)epoch訓(xùn)練之后，若val loss比之前小，保存當(dāng)前權(quán)重

epochs = 100
batch_size = 64

learning_rate_reduction = ReduceLROnPlateau(monitor='val_loss', 
                                            patience=4, 
                                            verbose=1, 
                                            factor=0.5, 
                                            min_lr=5e-8)

early_stopping = EarlyStopping(monitor='val_loss', patience=6, min_delta=0.0002, verbose=1, mode='auto')     
filepath="./weights/weights.best.hdf5"
if not os.path.exists('./weights'):
        os.mkdir('./weights')
checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='auto')
       
        
def run_catdog():
    
    model_his = model.fit_generator(datagen.flow(X_train,Y_train, batch_size=batch_size),
                              epochs = epochs, validation_data = (X_val,Y_val), shuffle=True, 
                              verbose = 1, steps_per_epoch=X_train.shape[0] // batch_size
                              , callbacks=[learning_rate_reduction, early_stopping, checkpoint])
    '''
    model_his = model.fit(X_train, np.array(Y_train), batch_size=batch_size, epochs=epochs,
              validation_split=0.2, verbose=1, shuffle=True, callbacks=[learning_rate_reduction, early_stopping, checkpoint])

    '''
    return model_his

history = run_catdog()

4 評估模型

4.1 訓(xùn)練和交叉驗(yàn)證曲線

# Plot the loss and accuracy curves for training and validation 
fig, ax = plt.subplots(2,1)
ax[0].plot(history.history['loss'], color='b', label="Training loss")
ax[0].plot(history.history['val_loss'], color='r', label="validation loss",axes =ax[0])
legend = ax[0].legend(loc='best', shadow=True)

ax[1].plot(history.history['acc'], color='b', label="Training accuracy")
ax[1].plot(history.history['val_acc'], color='r',label="Validation accuracy")
legend = ax[1].legend(loc='best', shadow=True)

0318-conv2d13.jpg

• accuracy接近92%，loss 0.2左右
• 模型沒有過擬合
• 進(jìn)一步提高精度、減小loss，可通過增加網(wǎng)絡(luò)層數(shù)或使用遷移學(xué)習(xí)

5 生成結(jié)果

# if you need to create the data:
test_data = process_test_data()
# if you already have some saved:
#test_data = np.load('test_data.npy')

with open('submission_file.csv','w') as f:
    f.write('id,label\n')
            
with open('submission_file.csv','a') as f:
    for data in tqdm(test_data):
        img_num = data[1]
        img_data = data[0]
        orig = img_data
        data = img_data.reshape(-1, IMG_SIZE,IMG_SIZE,1)
        model_out = model.predict([data])[0]
        f.write('{},{}\n'.format(img_num,model_out[1]))

6 踩過的坑

【待完成】