In [5]:

    

import pandas as pd
import numpy as np


    

In [ ]:

    

from keras.models import load_model
model = load_model('./resnet50_FT38_CW_STGTrain/checkpoint/weights.000-0.0327.hdf5')


    

    




Using TensorFlow backend.

In [ ]:

    

for i, layer in enumerate(model.layers):
    print(i, layer.name)


    

In [100]:

    

df = pd.DataFrame(np.random.randn(5,3),index=range(0,10,2),columns=list('ABC'))


    

In [101]:

    

df


    

    
Out[101]:






  

    

      

      
A
      
B
      
C
    
  
  

    

      
0
      
0.256054
      
-0.025630
      
-1.108797
    
    

      
2
      
1.766054
      
-1.003439
      
-0.703147
    
    

      
4
      
0.857863
      
-0.464332
      
1.225459
    
    

      
6
      
0.550564
      
0.168159
      
-0.768391
    
    

      
8
      
-1.238786
      
-0.419822
      
1.265726
    
  

In [102]:

    

df = df.loc[df['A']>1]


    

In [103]:

    

df


    

    
Out[103]:






  

    

      

      
A
      
B
      
C
    
  
  

    

      
2
      
1.766054
      
-1.003439
      
-0.703147
    
  

In [104]:

    

df.shape


    

    
Out[104]:





(1, 3)

In [105]:

    

df.index[0]


    

    
Out[105]:





2

In [106]:

    

df.at[df.index[0],'A']


    

    
Out[106]:





1.7660535685202161

In [107]:

    

df.loc[df.index[0],['B','C']]


    

    
Out[107]:





B   -1.003439
C   -0.703147
Name: 2, dtype: float64

In [108]:

    

test_pred_df = pd.DataFrame(columns=['B','C'])


    

In [109]:

    

test_pred_df.loc[len(test_pred_df)]=df.loc[df.index[0],['B','C']]


    

In [110]:

    

test_pred_df


    

    
Out[110]:






  

    

      

      
B
      
C
    
  
  

    

      
0
      
-1.003439
      
-0.703147
    
  

In [ ]: