深度学习基础系列：AlexNet

深度学习基础系列：AlexNet

本文用keras实现AlexNet网络,用于猫狗分类项目，没有使用数据增强

参考资料
代码参考地址：https://github.com/ShuaiGuo95/DeepLearning 下的dogs_vs_cats_CNN.ipynb文件。
数据下载地址：https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/data

数据介绍
数据包含两部分，训练集和测试集，训练集有25000张图片，测试集有12500张图片，在训练集的图片名称中包含了图片的标签信息，而测试集的图片名称代表的图片的id。

Alex的模型结构如下图：

AlexNet的模型的代码如下，使用keras框架

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D, BatchNormalization

resize = 224
def model_alexnet():
    # AlexNet
    model = Sequential()
    # 第一段
    model.add(Conv2D(filters=96, kernel_size=(11, 11),
                     strides=(4, 4), padding='valid',
                     input_shape=(resize, resize, 3),
                     activation='relu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(3, 3),
                           strides=(2, 2),
                           padding='valid'))
    # 第二段
    model.add(Conv2D(filters=256, kernel_size=(5, 5),
                     strides=(1, 1), padding='same',
                     activation='relu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(3, 3),
                           strides=(2, 2),
                           padding='valid'))
    # 第三段
    model.add(Conv2D(filters=384, kernel_size=(3, 3),
                     strides=(1, 1), padding='same',
                     activation='relu'))
    model.add(Conv2D(filters=384, kernel_size=(3, 3),
                     strides=(1, 1), padding='same',
                     activation='relu'))
    model.add(Conv2D(filters=256, kernel_size=(3, 3),
                     strides=(1, 1), padding='same',
                     activation='relu'))
    model.add(MaxPooling2D(pool_size=(3, 3),
                           strides=(2, 2), padding='valid'))
    # 第四段
    model.add(Flatten())
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))

    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))

    model.add(Dense(1000, activation='relu'))
    model.add(Dropout(0.5))

    # Output Layer
    model.add(Dense(2))
    model.add(Activation('softmax'))

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

    return model

模型的输出如下:

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 54, 54, 96)        34944     
_________________________________________________________________
batch_normalization_1 (Batch (None, 54, 54, 96)        384       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 26, 26, 96)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 26, 26, 256)       614656    
_________________________________________________________________
batch_normalization_2 (Batch (None, 26, 26, 256)       1024      
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 12, 12, 256)       0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 12, 12, 384)       885120    
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 12, 12, 384)       1327488   
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 12, 12, 256)       884992    
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 5, 5, 256)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 6400)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 4096)              26218496  
_________________________________________________________________
dropout_1 (Dropout)          (None, 4096)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 4096)              16781312  
_________________________________________________________________
dropout_2 (Dropout)          (None, 4096)              0         
_________________________________________________________________
dense_3 (Dense)              (None, 1000)              4097000   
_________________________________________________________________
dropout_3 (Dropout)          (None, 1000)              0         
_________________________________________________________________
dense_4 (Dense)              (None, 2)                 2002      
_________________________________________________________________
activation_1 (Activation)    (None, 2)                 0         
=================================================================

训练模型的代码如下：

import os
import numpy as np
import keras
from tqdm import tqdm
import matplotlib.pyplot as plt
from model_AlexNet import *
import datetime
import cv2

DATA_DIR = "J:/dataset/CatVsDog/train/"
resize = 224
def load_data():
    imgs = os.listdir(DATA_DIR)
    num = len(imgs)
    train_data = np.empty((5000, resize, resize, 3), dtype="int32")
    train_label = np.empty((5000, ), dtype="int32")
    test_data = np.empty((5000, resize, resize, 3), dtype="int32")
    test_label = np.empty((5000, ), dtype="int32")
    for i in tqdm(range(5000)):
        if i % 2:
            train_data[i] = cv2.resize(cv2.imread(DATA_DIR + 'dog.' + str(i) + '.jpg'), (resize, resize))
            train_label[i] = 1
        else:
            train_data[i] = cv2.resize(cv2.imread(DATA_DIR + 'cat.' + str(i) + '.jpg'), (resize, resize))
            train_label[i] = 0
    for i in tqdm(range(5000, 10000)):
        if i % 2:
            test_data[i-5000] = cv2.resize(cv2.imread(DATA_DIR + 'dog.' + str(i) + '.jpg'), (resize, resize))
            test_label[i-5000] = 1
        else:
            test_data[i-5000] = cv2.resize(cv2.imread(DATA_DIR + 'cat.' + str(i) + '.jpg'), (resize, resize))
            test_label[i-5000] = 0
    return train_data, train_label, test_data, test_label

train_data, train_label, test_data, test_label = load_data()
train_data, test_data = train_data.astype('float32'), test_data.astype('float32')
train_data, test_data = train_data/255, test_data/255

train_label = keras.utils.to_categorical(train_label, 2)
test_label = keras.utils.to_categorical(test_label, 2)

start=datetime.datetime.now()

model = model_alexnet()
his = model.fit(train_data, train_label,
          batch_size = 64,
          epochs = 50,
          validation_split = 0.2,
          shuffle = True)

end=datetime.datetime.now()
print('Running time: %s Seconds'%(end-start))

print(his.history.keys())

plt.plot(his.history['acc'])
plt.plot(his.history['val_acc'])
plt.title('model_accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
plt.plot(his.history['loss'])
plt.plot(his.history['val_loss'])
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

最后一次训练结果如下：
loss: 0.0756 - acc: 0.9785 - val_loss: 0.6687 - val_acc: 0.8070
acc还行，但是val_acc不高，有过拟合的情况