def do(self, callback_name, *args):
"""calculate PER and monitor it's mean, min and max """
per = []
for batch in self.data_stream.get_epoch_iterator(as_dict=True):
inputs=batch[conf.input_theano]
input_masks = batch[conf.input_mask_theano]
targets = batch[conf.target_theano]
target_masks = batch[conf.target_mask_theano]
outputs = self.getOutput(inputs, input_masks)
decoded = self.decodeSequences(outputs, input_masks)
targets = self.maskTargets(targets,target_masks)
if conf.mapTo39Phonemes_Decoding:
scoreMap = getPhonemeMapForScoring()
decoded = self.mapForScoring(decoded, scoreMap)
targets = self.mapForScoring(targets, scoreMap)
for t,d in zip(targets,decoded):
per.append(np.min([self.per(t,d),1]))
per=np.asarray(per)
accuracies_dict={'meanPER':np.mean(per), # mean accuracy over epoch
'minPER':np.min(per),
'maxPER':np.max(per),
}
self.add_records(self.main_loop.log, accuracies_dict.items())
def do(self, callback_name, *args):
"""calculate PER and monitor it's mean, min and max """
per = []
for batch in self.data_stream.get_epoch_iterator(as_dict=True):
inputs = batch[conf.input_theano]
input_masks = batch[conf.input_mask_theano]
targets = batch[conf.target_theano]
target_masks = batch[conf.target_mask_theano]
outputs = self.getOutput(inputs, input_masks)
decoded = self.decodeSequences(outputs, input_masks)
targets = self.maskTargets(targets, target_masks)
if conf.mapTo39Phonemes_Decoding:
scoreMap = getPhonemeMapForScoring()
decoded = self.mapForScoring(decoded, scoreMap)
targets = self.mapForScoring(targets, scoreMap)
for t, d in zip(targets, decoded):
per.append(np.min([self.per(t, d), 1]))
per = np.asarray(per)
accuracies_dict = {
'meanPER': np.mean(per), # mean accuracy over epoch
'minPER': np.min(per),
'maxPER': np.max(per),
}
self.add_records(self.main_loop.log, accuracies_dict.items())
def do(self, callback_name, *args):
"""Write the values of monitored variables to the log."""
per = []
for batch in self.data_stream.get_epoch_iterator(as_dict=True):
inputs=batch[conf.input_theano]
masks = batch[conf.input_mask_theano]
targets = batch[conf.target_theano]
outputs = self.getOutput(inputs, masks)
decoded = self.decodeSequences(outputs)
if conf.mapTo39Phonemes_Decoding:
scoreMap = getPhonemeMapForScoring()
decoded = self.mapForScoring(decoded, scoreMap)
targets = self.mapForScoring(targets, scoreMap)
per.append(np.min([self.per(targets,decoded,masks),1]))
per=np.asarray(per)
accuracies_dict={'meanPER':np.mean(per), # mean accuracy over epoch
'varPER':np.var(per),
'minPER':np.min(per),
'maxPER':np.max(per),
#'decoded':d,
#'taget':t,
#'orig outputs':outputs,
}
self.add_records(self.main_loop.log, accuracies_dict.items())
def do(self, callback_name, *args):
"""Write the values of monitored variables to the log."""
per = []
for batch in self.data_stream.get_epoch_iterator(as_dict=True):
inputs = batch[conf.input_theano]
masks = batch[conf.input_mask_theano]
targets = batch[conf.target_theano]
outputs = self.getOutput(inputs, masks)
decoded = self.decodeSequences(outputs)
if conf.mapTo39Phonemes_Decoding:
scoreMap = getPhonemeMapForScoring()
decoded = self.mapForScoring(decoded, scoreMap)
targets = self.mapForScoring(targets, scoreMap)
per.append(np.min([self.per(targets, decoded, masks), 1]))
per = np.asarray(per)
accuracies_dict = {
'meanPER': np.mean(per), # mean accuracy over epoch
'varPER': np.var(per),
'minPER': np.min(per),
'maxPER': np.max(per),
#'decoded':d,
#'taget':t,
#'orig outputs':outputs,
}
self.add_records(self.main_loop.log, accuracies_dict.items())
