[keras实战] 小型CNN实现Cifar-10数据集84%准确率

实验环境

代码基于python2.7, Keras1（部分接口在Keras2中已经被修改，如果你使用的是Keras2请查阅文档修改接口）
个人使用的是虫数据提供的免费GPU主机，GTX1080显卡，因为是免费账号，所以显存最高只有1G。为了防止超显存进程被kill，我在开头设置了占用GPU的最大显存大小,如果在这方面没有限制，可以注释掉这些代码。整个网络参数约160万，个人测试的结果一个epoch大概需要20s，最终accuracy在84%左右。

代码

# import the modules we need
from keras.models import Sequential
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.convolutional import AveragePooling2D
from keras.layers.core import Activation
from keras.layers.core import Flatten
from keras.layers.core import Dense
from keras.layers.core import Dropout
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.normalization import BatchNormalization
from keras.datasets import cifar10
from keras.utils.np_utils import to_categorical
from keras import metrics
from keras.optimizers import SGD,RMSprop,Adam
from keras.callbacks import EarlyStopping
from keras.backend.tensorflow_backend import set_session
import tensorflow as tf
import matplotlib
matplotlib.use('agg') 
import matplotlib.pyplot as plt  

config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.09
set_session(tf.Session(config=config))

#define the Sequential model
class CNNNet:

    @staticmethod
    def createNet(input_shapes,nb_class):

        feature_layers = [
        BatchNormalization(input_shape=input_shapes),
        Conv2D(64,3,3,border_mode="same"),  
        Activation("relu"),
        BatchNormalization(),
        Conv2D(64,3,3,border_mode="same"), 
        Activation("relu"),
        MaxPooling2D(pool_size=(2,2),strides=(2,2)),
        BatchNormalization(),
        Conv2D(128,3,3,border_mode="same"),   
        Activation("relu"),
        BatchNormalization(),
        Dropout(0.5),
        Conv2D(128,3,3,border_mode="same"),
        Activation("relu"),
        MaxPooling2D(pool_size=(2,2),strides=(2,2)),
        BatchNormalization(),
        Dropout(0.5),
        Conv2D(128,3,3,border_mode="same"),
        Activation("relu"),
        Dropout(0.5),
        Conv2D(128,3,3,border_mode="same"),
        Activation("relu"),
        MaxPooling2D(pool_size=(2,2),strides=(2,2)),
        BatchNormalization()
        ]

        classification_layer=[
        Flatten(),
        Dense(512),
        Activation("relu"),
        Dropout(0.5),
        Dense(nb_class),
        Activation("softmax")
        ]

        model = Sequential(feature_layers+classification_layer)
        return model

#parameters
NB_EPOCH = 40
BATCH_SIZE = 128
VERBOSE = 1
VALIDATION_SPLIT = 0.2
IMG_ROWS=32
IMG_COLS = 32
NB_CLASSES = 10
INPUT_SHAPE =(IMG_ROWS,IMG_COLS,3)

#load cifar-10 dataset
(X_train,Y_train),(X_test,Y_test) = cifar10.load_data()

X_train = X_train.astype("float32")
X_test = X_test.astype("float32")

X_train = X_train.reshape(X_train.shape[0],IMG_ROWS,IMG_COLS,3)
X_test = X_test.reshape(X_test.shape[0],IMG_ROWS,IMG_COLS,3)

print(X_train.shape[0],"train samples")
print(Y_test.shape[0],"test samples")

#convert class vectors to binary class matrices
Y_train = to_categorical(Y_train,NB_CLASSES)
Y_test = to_categorical(Y_test,NB_CLASSES)

# init the optimizer and model
model = CNNNet.createNet(input_shapes=(32,32,3),nb_class=NB_CLASSES)
model.summary()
model.compile(loss="categorical_crossentropy",optimizer='adam',metrics=['acc'])

early_stopping = EarlyStopping(monitor='val_loss', patience=2)

history = model.fit(X_train,Y_train,
                batch_size = BATCH_SIZE,
                nb_epoch = NB_EPOCH,
                verbose=VERBOSE,
                validation_split=VALIDATION_SPLIT,
                callbacks=[early_stopping]
                )

score = model.evaluate(X_test,Y_test,verbose = VERBOSE)
print("")
print("====================================")  
print("====================================")
print(score[0])
print(score[1])
print("====================================")
print("====================================")

#save model
model.save("my_model"+str(score[1])+".h5")

#show the data in history
print(history.history.keys())

#summarize history for accuracy 
plt.plot(history.history["acc"])  
plt.plot(history.history["val_acc"])  
plt.title("Model accuracy")  
plt.ylabel("accuracy")  
plt.xlabel("epoch")  
plt.legend(["train","test"],loc="upper left")  

#summarize history for loss 
plt.plot(history.history["loss"])  
plt.plot(history.history["val_loss"])  
plt.title("Model loss")  
plt.ylabel("loss")  
plt.xlabel("epoch")  
plt.legend(["train","test"],loc="upper left")  
plt.savefig("Performance:"+str(score[1])+".jpg")