人工智能 | CIFAR10卷积神经网络实践

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一、实验目的

二、算法步骤

三、实验结果

一、实验目的

采用CIFAR10图像数据集中的猫狗图像集或kaggle的猫狗图像数据集对经典深度卷积神经网络模型VGG16进行分类识别训练和测试。

二、算法步骤

1、加载数据集：

从本地地址path加载数据集(共有25000张jpg猫和狗的图像)中的图片，其中,取5000张作为训练集，再取5000张作为测试集，将所有加载的图片的大小格式化为 224 * 224像素：

imgs = os.listdir(path)

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 range(5000):

if i % 2:

train_data[i] = cv2.resize(cv2.imread(path + '/' + 'dog.' + str(i) + '.jpg'), (resize, resize))

train_label[i] = 1

else:

train_data[i] = cv2.resize(cv2.imread(path + '/' + 'cat.' + str(i) + '.jpg'), (resize, resize))

train_label[i] = 0

#设置训练集和测试集的标签，偶数命名为猫,标签=0；奇数命名为狗,标签=1.

for i in range(5000, 10000):

j = i - 5000

if i % 2:

test_data[j] = cv2.resize(cv2.imread(path + '/' + 'dog.' + str(i) + '.jpg'), (resize, resize))

test_label[j] = 1

else:

test_data[j] = cv2.resize(cv2.imread(path + '/' + 'cat.' + str(i) + '.jpg'), (resize, resize))

test_label[j] = 0

构建并训练VGG数据模型：

构建VGG模型(代码)：

# layer1

model = Sequential()

model.add(Conv2D(64, (3, 3), padding='same',input_shape=(224, 224, 3), kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu'));model.add(BatchNormalization())

model.add(Dropout(0.3))

# layer2

model.add(Conv2D(64, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu')); model.add(BatchNormalization())

model.add(MaxPooling2D(pool_size=(2, 2)))

# layer3

model.add(Conv2D(128, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu')); model.add(BatchNormalization())

model.add(Dropout(0.4))

# layer4

model.add(Conv2D(128, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu'))

model.add(BatchNormalization()); model.add(MaxPooling2D(pool_size=(2, 2)))

# layer5

model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu'))

model.add(BatchNormalization()); model.add(Dropout(0.4))

# layer6

model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu'))

model.add(BatchNormalization()); model.add(Dropout(0.4))

# layer7

model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu'))

model.add(BatchNormalization()); model.add(MaxPooling2D(pool_size=(2, 2)))

# layer8