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- ## @package add_bias
- # Module caffe2.python.layers.add_bias
- from caffe2.python import schema
- from caffe2.python.layers.layers import ModelLayer
- import math
- class AddBias(ModelLayer):
- def __init__(self, model, input_record, bias_init=None,
- bias_optim=None, name='add_bias'):
- super(AddBias, self).__init__(model, name, input_record)
- assert isinstance(input_record, schema.Scalar), "Incorrect input type"
- assert len(input_record.field_type().shape) > 0, (
- "AddBias expects limited dimensions of the input tensor")
- input_dims = input_record.field_type().shape[0]
- assert input_dims > 0, (
- "AddBias expects input dimensions > 0, got {}".format(input_dims))
- scale = math.sqrt(1.0 / input_dims)
- bias_init = bias_init if bias_init else (
- 'UniformFill', {'min': -scale, 'max': scale})
- self.b = self.create_param(
- param_name='b',
- shape=[input_dims, ],
- initializer=bias_init,
- optimizer=bias_optim,
- )
- self.output_schema = schema.Scalar(
- (input_record.field_type().base, (input_dims, )),
- self.get_next_blob_reference('output')
- )
- def add_ops(self, net):
- net.Add(self.input_record.field_blobs() + [self.b],
- self.output_schema.field_blobs(), broadcast=1)
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