def VGG16():
时间: 2023-07-24 11:06:17 浏览: 53
VGG16是一种深度卷积神经网络模型,用于图像分类和识别任务。以下是一个基本的VGG16实现:
```
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
def VGG16():
model = tf.keras.models.Sequential()
# Block 1
model.add(Conv2D(filters=64, kernel_size=(3, 3), padding="same", activation="relu", input_shape=(224, 224, 3)))
model.add(Conv2D(filters=64, kernel_size=(3, 3), padding="same", activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Block 2
model.add(Conv2D(filters=128, kernel_size=(3, 3), padding="same", activation="relu"))
model.add(Conv2D(filters=128, kernel_size=(3, 3), padding="same", activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Block 3
model.add(Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu"))
model.add(Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu"))
model.add(Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Block 4
model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Block 5
model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Classification block
model.add(Flatten())
model.add(Dense(units=4096, activation="relu"))
model.add(Dense(units=4096, activation="relu"))
model.add(Dense(units=1000, activation="softmax"))
return model
```
该模型使用了5个卷积块和3个全连接层,共计16层,其中每个卷积块包含两个卷积层和一个最大池化层。最后一个全连接层输出1000个分类,适用于ImageNet数据集分类任务。该模型参数量较大,需要大量的计算资源进行训练。
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