方式1：静态获取,通过直接解析checkpoint文件获取变量名及变量值

通过

reader = tf.train.NewCheckpointReader(model_path)

或者通过：

from tensorflow.python import pywrap_tensorflowreader = pywrap_tensorflow.NewCheckpointReader(model_path)

代码：

model_path = "./checkpoints/model.ckpt-75000"## 下面两个reader作用等价#reader = pywrap_tensorflow.NewCheckpointReader(model_path)reader = tf.train.NewCheckpointReader(model_path) ## 用reader获取变量字典,key是变量名,value是变量的shapevar_to_shape_map = reader.get_variable_to_shape_map()for var_name in var_to_shape_map.keys(): #用reader获取变量值 var_value = reader.get_tensor(var_name) print("var_name",var_name) print("var_value",var_value)

方式2：动态获取,先加载checkpoint模型,然后用graph.get_tensor_by_name()获取变量值

代码 (注意：要先在脚本中构建model中对应的变量及scope):

model_path = "./checkpoints/model.ckpt-75000" config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: ## 获取待加载的变量列表 trainable_vars = tf.trainable_variables() g_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,scope="generator") d_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,scope='discriminator') flow_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,scope='flow_net') var_restore = g_vars + d_vars ## 仅加载目标变量 loader = tf.train.Saver(var_restore) loader.restore(sess,model_path) ## 显示加载的变量值 graph = tf.get_default_graph() for var in var_restore: tensor = graph.get_tensor_by_name(var.name) print("=======变量名=======",tensor) print("-------变量值-------",sess.run(tensor))

以上这篇tensorflow 获取checkpoint中的变量列表实例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。