使用FC网络来实现DoA估计的代码
时间: 2024-01-22 16:18:24 浏览: 45
以下是使用FC网络实现DoA估计的Python代码示例:
```python
import numpy as np
import tensorflow as tf
# 构建FC网络,输入为接收信号,输出为DoA估计值
def build_fc_network(input_shape, num_angles):
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(128, input_shape=input_shape, activation='relu'))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(num_angles, activation='softmax'))
return model
# 生成数据集,包括接收信号和DoA估计值
def generate_dataset(num_samples, signal_dim, num_angles):
signals = np.random.randn(num_samples, signal_dim)
angles = np.random.randint(0, num_angles, num_samples)
one_hot_angles = tf.keras.utils.to_categorical(angles, num_angles)
return signals, one_hot_angles
# 训练FC网络
def train_fc_network(model, signals, angles, batch_size, num_epochs):
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x=signals, y=angles, batch_size=batch_size, epochs=num_epochs)
# 测试FC网络
def test_fc_network(model, signals):
predictions = model.predict(signals)
predicted_angles = np.argmax(predictions, axis=1)
return predicted_angles
```
使用示例:
```python
# 生成数据集
num_samples = 1000
signal_dim = 10
num_angles = 8
signals, angles = generate_dataset(num_samples, signal_dim, num_angles)
# 构建和训练FC网络
model = build_fc_network((signal_dim,), num_angles)
train_fc_network(model, signals, angles, batch_size=32, num_epochs=10)
# 测试FC网络
test_signals = np.random.randn(5, signal_dim)
predicted_angles = test_fc_network(model, test_signals)
print(predicted_angles)
```
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