别总是抱怨生活不够幸运，是你欠生活一份努力。未来美不美，取决于你现在拼不拼！
实例如下所示：
#coding=gbk
import numpy as np
import tensorflow as tf
from tensorflow.python import pywrap_tensorflow
checkpoint_path='model.ckpt-5000'#your ckpt path
reader=pywrap_tensorflow.NewCheckpointReader(checkpoint_path)
var_to_shape_map=reader.get_variable_to_shape_map()
alexnet={}
alexnet_layer = ['conv1','conv2','conv3','conv4','conv5','fc6','fc7','fc8']
add_info = ['weights','biases']
alexnet={'conv1':[[],[]],'conv2':[[],[]],'conv3':[[],[]],'conv4':[[],[]],'conv5':[[],[]],'fc6':[[],[]],'fc7':[[],[]],'fc8':[[],[]]}
for key in var_to_shape_map:
#print ("tensor_name",key)
str_name = key
# 因为模型使用Adam算法优化的，在生成的ckpt中，有Adam后缀的tensor
if str_name.find('Adam') > -1:
continue
print('tensor_name:' , str_name)
if str_name.find('/') > -1:
names = str_name.split('/')
# first layer name and weight, bias
layer_name = names[0]
layer_add_info = names[1]
else:
layer_name = str_name
layer_add_info = None
if layer_add_info == 'weights':
alexnet[layer_name][0]=reader.get_tensor(key)
elif layer_add_info == 'biases':
alexnet[layer_name][1] = reader.get_tensor(key)
else:
alexnet[layer_name] = reader.get_tensor(key)
# save npy
np.save('alexnet_pointing04.npy',alexnet)
print('save npy over...')
#print(alexnet['conv1'][0].shape)
#print(alexnet['conv1'][1].shape)
以上这篇将tensorflow的ckpt模型存储为npy的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。
