File size: 1,480 Bytes
6a4f823 a27d55f 6a4f823 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
import numpy as np
import ddmr.utils.constants as C
class SummaryDictionary:
def __init__(self, model, batch_size, accumulative_gradients_step=None):
self.train_names = model.metrics_names
self.val_names = ['val_'+n for n in self.train_names]
self.batch_size = batch_size
self.acc_grad_step = accumulative_gradients_step
self._reset()
def _reset(self):
self.summary_dict = {'size': self.batch_size}
if self.acc_grad_step is not None:
self.summary_dict = {'accumulative_grad_step': self.acc_grad_step}
for k in self.train_names + self.val_names:
self.summary_dict[k] = list()
def on_train_batch_end(self, values):
for k, v in zip(self.train_names, values):
self.summary_dict[k].append(v)
def on_validation_batch_end(self, values):
for k, v in zip(self.val_names, values):
self.summary_dict[k].append(v)
def on_epoch_end(self):
for k, v in self.summary_dict.items():
self.summary_dict[k] = np.asarray(v).mean()
ret_val = self.summary_dict.copy()
self._reset()
return ret_val
def named_logs(model, logs, validation=False):
result = {'size': C.BATCH_SIZE} # https://gist.github.com/erenon/91f526302cd8e9d21b73f24c0f9c4bb8#gistcomment-3041181
for l in zip(model.metrics_names, logs):
k = ('val_' if validation else '') + l[0]
result[k] = l[1]
return result
|