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## @package fc_without_bias
# Module caffe2.python.layers.fc_without_bias
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
from caffe2.python.layers.sampling_trainable_mixin import SamplingTrainableMixin
import math
import numpy as np
class FCWithoutBias(SamplingTrainableMixin, ModelLayer):
def __init__(
self,
model,
input_record,
output_dims,
weight_init=None,
weight_optim=None,
name='fc_without_bias',
uniform_weight_init_scale_numerator=1.0,
**kwargs
):
super(FCWithoutBias, self).__init__(model, name, input_record, **kwargs)
assert isinstance(input_record, schema.Scalar), "Incorrect input type"
assert len(input_record.field_types()[0].shape) > 0, (
"FCWithoutBias expects limited dimensions of the input tensor"
)
input_dims = input_record.field_types()[0].shape[0]
assert input_dims > 0, (
"FCWithoutBias expects input dimensions > 0, got {}".format(input_dims)
)
self.output_schema = schema.Scalar(
(np.float32, (output_dims, )),
self.get_next_blob_reference('output')
)
scale = math.sqrt(uniform_weight_init_scale_numerator / input_dims)
weight_init = weight_init if weight_init else (
'UniformFill', {'min': -scale,
'max': scale}
)
self.w = self.create_param(param_name='w',
shape=[output_dims, input_dims],
initializer=weight_init,
optimizer=weight_optim)
def _add_ops(self, net, params):
net.MatMul(
self.input_record.field_blobs() + params,
self.output_schema.field_blobs(), trans_b=1, **self.kwargs
)
@property
def param_blobs(self):
return [self.w]
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