利用keras搭建AlexNet神经网络识别kaggle猫狗图片

AlexNet结构

keras代码

from PIL import Image
import numpy as np
from keras.utils import to_categorical

path="F:\\kaggle\\dog_vs_cat\\" train_X=np.empty((2000,227,227,3),dtype="float16") train_Y=np.empty((2000,),dtype="int") for i in range(1000): file_path=path+"cat."+str(i)+".jpg" image=Image.open(file_path) resized_image = image.resize((227, 227), Image.ANTIALIAS) img=np.array(resized_image) train_X[i,:,:,:]=img
    train_Y[i]=0 for i in range(1000): file_path=path+"dog."+str(i)+".jpg" image = Image.open(file_path) resized_image = image.resize((227, 227), Image.ANTIALIAS) img = np.array(resized_image) train_X[i+1000, :, :, :] = img
    train_Y[i+1000] = 1 train_X /= 255 train_Y = to_categorical(train_Y, 2) index = np.arange(2000) np.random.shuffle(index) train_X = train_X[index, :, :, :] train_Y = train_Y[index] print(train_X.shape) print(train_Y.shape) from keras.layers import BatchNormalization, Dropout
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense,Activation # AlexNet model = Sequential() # 第一段
model.add(Conv2D(filters=96, kernel_size=(11, 11), strides=(4, 4), padding='valid', input_shape=(227, 227, 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']) batch_size = 32 epochs = 20 model.fit(train_X, train_Y, batch_size=batch_size, epochs=epochs) 

其中数据集为2000张猫狗图片，1000张猫，1000张狗，图片名为cat.0.jpg,dog.1.jpg等，即cat(dog).i.jpg格式，读取图像后resize为227x227x3作为AlexNet的输入，这里用BN代替LRN，batch_size取为32，训练20轮(实在太慢，20轮就算了很久),最后得到如下结果：