写一个xception算法
时间: 2023-05-24 10:01:05 浏览: 62
Xception(Extreme Inception)算法是由Google在2017年提出的一种深层卷积神经网络模型。它是Inception模型的进一步改良,采用了深度可分离卷积来提高模型的性能。以下是Xception算法的实现:
```python
import tensorflow.keras.layers as layers
from tensorflow.keras.models import Model
def xception(input_shape):
img_input = layers.Input(shape=input_shape)
# Entry block
x = layers.Conv2D(32, 3, strides=2, padding='same')(img_input)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(64, 3, padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
previous_block_activation = x
for size in [128, 256, 728]:
x = layers.Activation('relu')(x)
x = layers.SeparableConv2D(size, 3,padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.SeparableConv2D(size, 3,padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.MaxPooling2D(3, strides=2,padding='same')(x)
residual = layers.Conv2D(size, 1, strides=2,padding='same')(previous_block_activation)
x = layers.add([x, residual])
previous_block_activation = x
x = layers.SeparableConv2D(1024, 3,padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(1024, activation='relu')(x)
x = layers.Dropout(0.5)(x)
# Output layer
output = layers.Dense(10, activation='softmax')(x)
model = Model(img_input, output)
return model
```
这是一个使用Tensorflow实现的Xception算法,可以用于图像分类任务。