import numpy as np
test=np.load('./bvlc_alexnet.npy',encoding = "latin1")  #加載文件
doc = open('1.txt', 'a')  #打開一個存儲文件，并依次寫入
print(test, file=doc)  #將打印內(nèi)容寫入文件中

#顯示字典
print(train_dataset.class_to_idx)
idx_to_labels = {y:x for x,y in train_dataset.class_to_idx.items()}#獲取dict:train_dataset.class_to_idx的keys和values
print(idx_to_labels)

np.save('idx_to_labels.npy',idx_to_labels)
np.save('labels_to index.npy',train_dataset.class_to_idx)

import numpy as np
from numpy import *  #使用numpy的屬性且不需要在前面加上numpy
import tensorflow as tf
 
#模型文件（.npy）部分內(nèi)容如下：由一個字典組成，字典中的每一個鍵對應(yīng)一層網(wǎng)絡(luò)模型參數(shù)。（包括權(quán)重w和偏置b）
a = {'conv1':[array([[1,2],[3,4]],dtype=float32),array([5,6],dtype=float32)],'conv2':[array([[1,2],[3,4]],dtype=float32),array([5,6],dtype=float32)]}
 
conv1_w = a['conv1'][0]
conv1_b = a['conv1'][1]
conv2_w = a['conv2'][0]
conv2_b = a['conv2'][1]
 
print(conv1_w)
print(tf.Variable(conv1_w))
print(conv1_b)
print(tf.Variable(conv1_b))