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这是最近碰到一个问题，先描述下问题：
首先我有一个训练好的模型(例如vgg16)，我要对这个模型进行一些改变，例如添加一层全连接层，用于种种原因，我只能用TensorFlow来进行模型优化,tf的优化器，默认情况下对所有tf.trainable_variables()进行权值更新，问题就出在这，明明将vgg16的模型设置为trainable=False，但是tf的优化器仍然对vgg16做权值更新
以上就是问题描述，经过谷歌百度等等，终于找到了解决办法，下面我们一点一点的来复原整个问题。
trainable=False 无效
首先，我们导入训练好的模型vgg16，对其设置成trainable=False
from keras.applications import VGG16
import tensorflow as tf
from keras import layers
# 导入模型
base_mode = VGG16(include_top=False)
# 查看可训练的变量
tf.trainable_variables()
[<tf.Variable 'block1_conv1/kernel:0' shape=(3, 3, 3, 64) dtype=float32_ref>,
<tf.Variable 'block1_conv1/bias:0' shape=(64,) dtype=float32_ref>,
<tf.Variable 'block1_conv2/kernel:0' shape=(3, 3, 64, 64) dtype=float32_ref>,
<tf.Variable 'block1_conv2/bias:0' shape=(64,) dtype=float32_ref>,
<tf.Variable 'block2_conv1/kernel:0' shape=(3, 3, 64, 128) dtype=float32_ref>,
<tf.Variable 'block2_conv1/bias:0' shape=(128,) dtype=float32_ref>,
<tf.Variable 'block2_conv2/kernel:0' shape=(3, 3, 128, 128) dtype=float32_ref>,
<tf.Variable 'block2_conv2/bias:0' shape=(128,) dtype=float32_ref>,
<tf.Variable 'block3_conv1/kernel:0' shape=(3, 3, 128, 256) dtype=float32_ref>,
<tf.Variable 'block3_conv1/bias:0' shape=(256,) dtype=float32_ref>,
<tf.Variable 'block3_conv2/kernel:0' shape=(3, 3, 256, 256) dtype=float32_ref>,
<tf.Variable 'block3_conv2/bias:0' shape=(256,) dtype=float32_ref>,
<tf.Variable 'block3_conv3/kernel:0' shape=(3, 3, 256, 256) dtype=float32_ref>,
<tf.Variable 'block3_conv3/bias:0' shape=(256,) dtype=float32_ref>,
<tf.Variable 'block4_conv1/kernel:0' shape=(3, 3, 256, 512) dtype=float32_ref>,
<tf.Variable 'block4_conv1/bias:0' shape=(512,) dtype=float32_ref>,
<tf.Variable 'block4_conv2/kernel:0' shape=(3, 3, 512, 512) dtype=float32_ref>,
<tf.Variable 'block4_conv2/bias:0' shape=(512,) dtype=float32_ref>,
<tf.Variable 'block4_conv3/kernel:0' shape=(3, 3, 512, 512) dtype=float32_ref>,
<tf.Variable 'block4_conv3/bias:0' shape=(512,) dtype=float32_ref>,
<tf.Variable 'block5_conv1/kernel:0' shape=(3, 3, 512, 512) dtype=float32_ref>,
<tf.Variable 'block5_conv1/bias:0' shape=(512,) dtype=float32_ref>,
<tf.Variable 'block5_conv2/kernel:0' shape=(3, 3, 512, 512) dtype=float32_ref>,
<tf.Variable 'block5_conv2/bias:0' shape=(512,) dtype=float32_ref>,
<tf.Variable 'block5_conv3/kernel:0' shape=(3, 3, 512, 512) dtype=float32_ref>,
<tf.Variable 'block5_conv3/bias:0' shape=(512,) dtype=float32_ref>,
<tf.Variable 'block1_conv1_1/kernel:0' shape=(3, 3, 3, 64) dtype=float32_ref>,
<tf.Variable 'block1_conv1_1/bias:0' shape=(64,) dtype=float32_ref>,
<tf.Variable 'block1_conv2_1/kernel:0' shape=(3, 3, 64, 64) dtype=float32_ref>,
<tf.Variable 'block1_conv2_1/bias:0' shape=(64,) dtype=float32_ref>,
<tf.Variable 'block2_conv1_1/kernel:0' shape=(3, 3, 64, 128) dtype=float32_ref>,
<tf.Variable 'block2_conv1_1/bias:0' shape=(128,) dtype=float32_ref>,
<tf.Variable 'block2_conv2_1/kernel:0' shape=(3, 3, 128, 128) dtype=float32_ref>,
<tf.Variable 'block2_conv2_1/bias:0' shape=(128,) dtype=float32_ref>,
<tf.Variable 'block3_conv1_1/kernel:0' shape=(3, 3, 128, 256) dtype=float32_ref>,
<tf.Variable 'block3_conv1_1/bias:0' shape=(256,) dtype=float32_ref>,
<tf.Variable 'block3_conv2_1/kernel:0' shape=(3, 3, 256, 256) dtype=float32_ref>,
<tf.Variable 'block3_conv2_1/bias:0' shape=(256,) dtype=float32_ref>,
<tf.Variable 'block3_conv3_1/kernel:0' shape=(3, 3, 256, 256) dtype=float32_ref>,
<tf.Variable 'block3_conv3_1/bias:0' shape=(256,) dtype=float32_ref>,
<tf.Variable 'block4_conv1_1/kernel:0' shape=(3, 3, 256, 512) dtype=float32_ref>,
<tf.Variable 'block4_conv1_1/bias:0' shape=(512,) dtype=float32_ref>,
<tf.Variable 'block4_conv2_1/kernel:0' shape=(3, 3, 512, 512) dtype=float32_ref>,
<tf.Variable 'block4_conv2_1/bias:0' shape=(512,) dtype=float32_ref>,
<tf.Variable 'block4_conv3_1/kernel:0' shape=(3, 3, 512, 512) dtype=float32_ref>,
<tf.Variable 'block4_conv3_1/bias:0' shape=(512,) dtype=float32_ref>,
<tf.Variable 'block5_conv1_1/kernel:0' shape=(3, 3, 512, 512) dtype=float32_ref>,
<tf.Variable 'block5_conv1_1/bias:0' shape=(512,) dtype=float32_ref>,
<tf.Variable 'block5_conv2_1/kernel:0' shape=(3, 3, 512, 512) dtype=float32_ref>,
<tf.Variable 'block5_conv2_1/bias:0' shape=(512,) dtype=float32_ref>,
<tf.Variable 'block5_conv3_1/kernel:0' shape=(3, 3, 512, 512) dtype=float32_ref>,
<tf.Variable 'block5_conv3_1/bias:0' shape=(512,) dtype=float32_ref>]
# 设置 trainable=False
# base_mode.trainable = F一个loss
loss = model.output / 2; # 随便定义的，方便演示
train_step = tf.train.AdamOptimizer().minimize(loss, var_list=trainable_var)
总结
在keras与TensorFlow混编中，keras中设置trainable=False对于TensorFlow而言并不起作用
解决的办法就是通过variable_scope对变量进行区分，在通过tf.get_collection来获取需要训练的变量，最后通过tf优化器中var_list指定训练
以上这篇解决Keras TensorFlow 混编中 trainable=False设置无效问题就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持社区。 |
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