## @package batch_sigmoid_cross_entropy_loss
# Module caffe2.python.layers.batch_sigmoid_cross_entropy_loss
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
from caffe2.python.layers.tags import Tags
import numpy as np
class BatchSigmoidCrossEntropyLoss(ModelLayer):
def __init__(
self,
model,
input_record,
name='batch_sigmoid_cross_entropy_loss',
**kwargs
):
super(BatchSigmoidCrossEntropyLoss, self).__init__(
model, name, input_record, **kwargs)
assert schema.is_schema_subset(
schema.Struct(
('label', schema.Scalar(np.float32)),
('prediction', schema.Scalar(np.float32)),
),
input_record
)
assert input_record.prediction.field_type().shape == \
input_record.label.field_type().shape, \
"prediction and label must have the same shape"
self.tags.update([Tags.EXCLUDE_FROM_PREDICTION])
self.output_schema = schema.Scalar(
(np.float32, tuple()), self.get_next_blob_reference('loss')
)
def add_ops(self, net):
sigmoid_cross_entropy = net.SigmoidCrossEntropyWithLogits(
[self.input_record.prediction(), self.input_record.label()],
net.NextScopedBlob('sigmoid_cross_entropy')
)
net.AveragedLoss(
sigmoid_cross_entropy, self.output_schema.field_blobs())