解决Keras TensorFlow 混编中 trainable=False设置无效问题

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()

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# 设置 trainable=False
# base_mode.trainable = False似乎也是可以的
for layer in base_mode.layers:
  layer.trainable = False

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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

# 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)