训练多个分类器,如何将每个分类器训练时的参数初始化代码
时间: 2024-02-21 20:58:24 浏览: 20
以下是几种常见的分类器参数初始化代码:
1. 随机初始化:
```python
import tensorflow as tf
# 初始化分类器1的参数
with tf.variable_scope('classifier_1'):
w1 = tf.get_variable('w1', shape=[input_size, hidden_size], initializer=tf.random_normal_initializer())
b1 = tf.get_variable('b1', shape=[hidden_size], initializer=tf.zeros_initializer())
w2 = tf.get_variable('w2', shape=[hidden_size, num_classes], initializer=tf.random_normal_initializer())
b2 = tf.get_variable('b2', shape=[num_classes], initializer=tf.zeros_initializer())
# 初始化分类器2的参数
with tf.variable_scope('classifier_2'):
w1 = tf.get_variable('w1', shape=[input_size, hidden_size], initializer=tf.random_normal_initializer())
b1 = tf.get_variable('b1', shape=[hidden_size], initializer=tf.zeros_initializer())
w2 = tf.get_variable('w2', shape=[hidden_size, num_classes], initializer=tf.random_normal_initializer())
b2 = tf.get_variable('b2', shape=[num_classes], initializer=tf.zeros_initializer())
```
2. 预训练模型初始化:
```python
import tensorflow as tf
# 加载预训练模型
pretrained_model = 'path/to/pretrained_model.ckpt'
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, pretrained_model)
# 使用预训练模型初始化分类器1的参数
with tf.variable_scope('classifier_1', reuse=True):
w1 = tf.get_variable('w1')
b1 = tf.get_variable('b1')
w2 = tf.get_variable('w2')
b2 = tf.get_variable('b2')
# 使用预训练模型初始化分类器2的参数
with tf.variable_scope('classifier_2', reuse=True):
w1 = tf.get_variable('w1')
b1 = tf.get_variable('b1')
w2 = tf.get_variable('w2')
b2 = tf.get_variable('b2')
```
3. 共享参数初始化:
```python
import tensorflow as tf
# 共享参数的初始化
w1 = tf.get_variable('w1', shape=[input_size, hidden_size], initializer=tf.random_normal_initializer())
b1 = tf.get_variable('b1', shape=[hidden_size], initializer=tf.zeros_initializer())
w2 = tf.get_variable('w2', shape=[hidden_size, num_classes], initializer=tf.random_normal_initializer())
b2 = tf.get_variable('b2', shape=[num_classes], initializer=tf.zeros_initializer())
# 初始化分类器1的参数
with tf.variable_scope('classifier_1'):
classifier_1_w1 = w1
classifier_1_b1 = b1
classifier_1_w2 = w2
classifier_1_b2 = b2
# 初始化分类器2的参数
with tf.variable_scope('classifier_2'):
classifier_2_w1 = w1
classifier_2_b1 = b1
classifier_2_w2 = w2
classifier_2_b2 = b2
```
以上是三种常见的分类器参数初始化代码,需要根据具体的需求进行选择和修改。
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