def __call__(self, **kwargs):
name = kwargs.get('name')
kwargs.pop('name')
with tf.variable_scope(name):
loss = self.call(**kwargs)
summary.scalar(name, loss)
return loss
def l2_loss(self, tvars=None):
_l2_loss = 0.0
weight_decay = self.config['solver']['optimizer'].get(
'weight_decay', None)
if weight_decay:
logging.info(f"add L2 Loss with decay: {weight_decay}")
with tf.name_scope('l2_loss'):
tvars = tvars if tvars else tf.trainable_variables()
tvars = [v for v in tvars if 'bias' not in v.name]
_l2_loss = weight_decay * tf.add_n(
[tf.nn.l2_loss(v) for v in tvars])
summary_lib.scalar('l2_loss', _l2_loss)
return _l2_loss
def get_train_hooks(self, labels, logits, alpha=None):
nclass = self.config['data']['task']['classes']['num']
metric_tensor = {
"batch_accuracy": metrics_lib.accuracy(logits, labels),
'global_step': tf.train.get_or_create_global_step(),
}
if nclass > 100:
logging.info(
'Too many classes, disable confusion matrix in train: %d' %
(nclass))
else:
metric_tensor['batch_confusion'] = \
metrics_lib.confusion_matrix(logits, labels, nclass)
summary_lib.scalar('batch_accuracy', metric_tensor['batch_accuracy'])
if alpha:
metric_tensor.update({"alignment": alpha})
# plot PR curve
true_label_bool = tf.cast(labels, tf.bool)
softmax = tf.nn.softmax(logits)
pr_summary.op(name='pr_curve_train_batch',
labels=true_label_bool,
predictions=softmax[:, -1],
num_thresholds=16,
weights=None)
train_hooks = [
tf.train.StepCounterHook(every_n_steps=100,
every_n_secs=None,
output_dir=None,
summary_writer=None),
tf.train.FinalOpsHook(
final_ops=[tf.train.get_or_create_global_step()],
final_ops_feed_dict=None),
tf.train.LoggingTensorHook(tensors=metric_tensor,
every_n_iter=100,
every_n_secs=None,
at_end=False,
formatter=None),
]
return train_hooks
