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这篇文章主要介绍如何解决Keras TensorFlow混编中trainable=False设置无效问题，文中示例代码介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们一定要看完！
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 = False似乎也是可以的 for layer in base_mode.layers: layer.trainable = False
设置好trainable=False后，再次查看可训练的变量，发现并没有变化，也就是说设置无效
# 再次查看可训练的变量
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>]
解决的办法
解决的办法就是在导入模型的时候建立一个variable_scope，将需要训练的变量放在另一个variable_scope,然后通过tf.get_collection获取需要训练的变量，最后通过tf的优化器中var_list指定需要训练的变量
from keras import models
with tf.variable_scope('base_model'):
base_model = VGG16(include_top=False, input_shape=(224,224,3))
with tf.variable_scope('xxx'):
model = models.Sequential()
model.add(base_model)
model.add(layers.Flatten())
model.add(layers.Dense(10))# 获取需要训练的变量 trainable_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'xxx') trainable_var
[<tf.Variable 'xxx_2/dense_1/kernel:0' shape=(25088, 10) dtype=float32_ref>,
<tf.Variable 'xxx_2/dense_1/bias:0' shape=(10,) dtype=float32_ref>]
# 定义tf优化器进行训练，这里假设有一个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)
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
以上是如何解决Keras TensorFlow混编中trainable=False设置无效问题的所有内容，感谢各位的阅读！希望分享的内容对大家有帮助，更多相关知识，欢迎关注亿速云行业资讯频道！
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