基于TensorFlow2.0 在resnet152中增加注意力机制进行人脸表情识别代码
时间: 2023-06-13 13:02:44 浏览: 165
以下是一个基于 TensorFlow 2.0 和 ResNet152 的人脸表情识别模型代码,其中包含了注意力机制的实现:
```python
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dense, Flatten, Dropout, BatchNormalization, GlobalAveragePooling2D, Multiply
from tensorflow.keras.models import Model
from tensorflow.keras.applications.resnet import ResNet152
def ResNet152_attention(input_shape, num_classes):
# 加载预训练的 ResNet152 模型
base_model = ResNet152(include_top=False, weights='imagenet', input_shape=input_shape)
# 获取 ResNet152 模型的输出层
x = base_model.output
# 添加注意力机制
attention_weights = Conv2D(filters=1, kernel_size=(1, 1), activation='sigmoid')(x)
x = Multiply()([x, attention_weights])
# 添加全局平均池化层
x = GlobalAveragePooling2D()(x)
# 添加全连接层和输出层
x = Dense(512, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(num_classes, activation='softmax')(x)
# 构建新模型
model = Model(inputs=base_model.input, outputs=x)
# 冻结 ResNet152 的所有层
for layer in base_model.layers:
layer.trainable = False
return model
```
需要注意的是,在使用该模型之前,还需要对数据进行预处理和训练。可以参考 TensorFlow 官方文档进行相关操作。
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