路径规划的迁移学习代码
时间: 2024-06-02 11:06:38 浏览: 10
路径规划的迁移学习是指,将已有的路径规划模型在不同场景下进行微调,以适应新的环境。以下是一个简单的路径规划迁移学习代码示例,仅供参考:
```
import tensorflow as tf
import tensorflow.contrib.slim as slim
# 定义基础模型
def base_model(inputs, reuse=False):
with tf.variable_scope('base_model', reuse=reuse):
net = slim.fully_connected(inputs, 128, activation_fn=tf.nn.relu)
net = slim.fully_connected(net, 128, activation_fn=tf.nn.relu)
net = slim.fully_connected(net, 64, activation_fn=tf.nn.relu)
return net
# 定义微调模型
def fine_tune_model(inputs, reuse=False):
with tf.variable_scope('fine_tune_model', reuse=reuse):
net = base_model(inputs, reuse=True) # 调用基础模型
net = slim.fully_connected(net, 32, activation_fn=tf.nn.relu)
net = slim.fully_connected(net, 16, activation_fn=tf.nn.relu)
net = slim.fully_connected(net, 2, activation_fn=None) # 输出为2维向量表示目标点坐标
return net
# 定义损失函数和优化器
x = tf.placeholder(tf.float32, [None, 10])
y = tf.placeholder(tf.float32, [None, 2])
base_loss = tf.reduce_mean(tf.square(base_model(x) - y))
fine_tune_loss = tf.reduce_mean(tf.square(fine_tune_model(x) - y))
train_op_base = tf.train.AdamOptimizer(learning_rate=1e-3).minimize(base_loss)
train_op_fine_tune = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(fine_tune_loss)
# 进行迁移学习
with tf.Session() as sess:
# 预训练基础模型
sess.run(tf.global_variables_initializer())
for i in range(1000):
batch_x, batch_y = generate_data()
sess.run(train_op_base, feed_dict={x: batch_x, y: batch_y})
# 在新的环境下微调模型
for i in range(1000):
batch_x, batch_y = generate_new_data()
sess.run(train_op_fine_tune, feed_dict={x: batch_x, y: batch_y})
```