In [1]:

    

import numpy as np
import theano
import pickle


    

In [8]:

    

pkl_fname = '471000.pkl'


    

In [9]:

    

with open(pkl_fname, 'rb') as f:
    d = pickle.load(f)


    

In [10]:

    

for wb in d['allwts']:
    if len(wb) == 0: continue
    w, b = wb
    print w.shape, w.dtype
    print b.shape, b.dtype


    

    




(6, 1, 5, 5) float32
(6,) float32
(60, 6, 2, 2) float32
(60,) float32
(11760, 1000) float32
(1000,) float32
(1000, 460) float32
(460,) float32

In [11]:

    

d['training_params']['SEED'] = 471000


    

In [12]:

    

d['training_params']


    

    
Out[12]:





{'CUR_EPOCH': 20,
 'DEFORM': 'parallel',
 'DFM_PRMS': {'cval': 1, 'ncpus': 4, 'scale': 64, 'sigma': 8},
 'EPOCHS_TO_HALF_RATE': 1,
 'EPOCHS_TO_TEST': 4,
 'INIT_LEARNING_RATE': 0.1,
 'LAMBDA1': 0.0,
 'LAMBDA2': 0.001,
 'MAXNORM': 4,
 'MOMENTUM': 0.95,
 'NUM_EPOCHS': 201,
 'SEED': 471000,
 'TEST_SAMP_SZ': 5000,
 'TRAIN_ON_FRACTION': 0.75}

In [13]:

    

with open(pkl_fname, 'wb') as f:
    pickle.dump(d, f, -1)


    

In [ ]: