def _SummarizeTensor(self, t_name):
min_var = self._GetQStateVar(t_name, 'min')
max_var = self._GetQStateVar(t_name, 'max')
# foo/q/somet_min:0 -> foo/q/somet_min
summary_name_min = min_var.name.split(':')[0]
summary_name_max = max_var.name.split(':')[0]
summary_utils.scalar(summary_name_min, min_var)
summary_utils.scalar(summary_name_max, max_var)
def AddSummary(self, lr, optimizer, var_grad):
p = self.params
summary_utils.scalar('adagrad_lr', lr)
for v, _ in var_grad.Flatten():
slot = optimizer.get_slot(v, 'accumulator')
assert slot is not None
summary_utils.scalar('optimizer/adagrad_accum_%s' % v.name,
tf.reduce_mean(slot))
def PostTrainingStepUpdate(self, global_step):
summary_utils.scalar('cap', self._Value(global_step))
return tf.no_op()
def AddSummary(self, lr, optimizer, var_grad):
summary_utils.scalar('adam_lr', lr)
