Vgg16训练模型代码实现

以下是 VGG16 模型的训练代码实现，使用的是 Keras 框架：

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from keras.optimizers import SGD
from keras.datasets import cifar10
from keras.utils import np_utils
import numpy as np

# 加载数据集
(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()

# 数据预处理
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255
Y_train = np_utils.to_categorical(Y_train)
Y_test = np_utils.to_categorical(Y_test)

# 构建模型
model = Sequential()
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=(32, 32, 3)))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))

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

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

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

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

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(10, activation='softmax'))

# 模型编译
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

# 模型训练
model.fit(X_train, Y_train, batch_size=128, epochs=100, validation_data=(X_test, Y_test))

这里使用了 CIFAR-10 数据集进行训练，通过构建深度卷积神经网络（VGG16）进行图像分类任务。在训练过程中，使用了随机梯度下降（SGD）优化器，设置了学习率、动量和权重衰减等超参数。最终，训练出的模型在测试集上的准确率达到了较高水平。