用python实现头部姿态估计算法WHEnet
时间: 2024-02-20 18:56:22 浏览: 21
要用Python实现头部姿态估计算法WHEnet,需要先安装相应的深度学习库,如TensorFlow或PyTorch。
然后,可以按照以下步骤进行实现:
1.导入所需的库
```python
import tensorflow as tf
import numpy as np
```
2.定义WHEnet网络结构
```python
def WHEnet(input_shape):
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(256, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(3)
])
return model
```
3.定义损失函数和优化器
```python
model.compile(optimizer='adam',
loss=tf.keras.losses.MeanSquaredError(),
metrics=['accuracy'])
```
4.读取数据集并进行预处理
```python
# 读取数据集
X_train, Y_train, X_test, Y_test = load_dataset()
# 数据预处理
X_train = X_train / 255.
X_test = X_test / 255.
# 训练数据集维度
m_train = X_train.shape[0]
n_H, n_W, n_C = X_train.shape[1:]
# 测试数据集维度
m_test = X_test.shape[0]
```
5.训练模型
```python
model.fit(X_train, Y_train, epochs=10, batch_size=32)
```
6.测试模型
```python
loss, accuracy = model.evaluate(X_test, Y_test)
print("Test accuracy = ", accuracy)
```
以上是一个简单的WHEnet头部姿态估计算法的Python实现。