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)
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