FM / layers

/usr/local/lib64/python3.6/site-packages/caffe2/python/layers/

/usr/local/lib64/python3.6/site-packages/caffe2/python/layers

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
adaptive_weight.py	5687	0644	edit dl rm
add_bias.py	1396	0644	edit dl rm
arc_cosine_feature_map.py	7345	0644	edit dl rm
batch_huber_loss.py	3523	0644	edit dl rm
batch_lr_loss.py	11577	0644	edit dl rm
batch_mse_loss.py	2333	0644	edit dl rm
batch_normalization.py	3823	0644	edit dl rm
batch_sigmoid_cross_entropy_loss.py	1483	0644	edit dl rm
batch_softmax_loss.py	4580	0644	edit dl rm
blob_weighted_sum.py	2219	0644	edit dl rm
bpr_loss.py	1499	0644	edit dl rm
bucket_weighted.py	2355	0644	edit dl rm
build_index.py	1937	0644	edit dl rm
concat.py	4849	0644	edit dl rm
constant_weight.py	1208	0644	edit dl rm
conv.py	5050	0644	edit dl rm
dropout.py	1410	0644	edit dl rm
fc.py	9296	0644	edit dl rm
fc_without_bias.py	1954	0644	edit dl rm
fc_with_bootstrap.py	12788	0644	edit dl rm
feature_sparse_to_dense.py	14361	0644	edit dl rm
functional.py	4875	0644	edit dl rm
gather_record.py	3260	0644	edit dl rm
homotopy_weight.py	4306	0644	edit dl rm
label_smooth.py	3507	0644	edit dl rm
last_n_window_collector.py	2392	0644	edit dl rm
layers.py	17412	0644	edit dl rm
layer_normalization.py	4291	0644	edit dl rm
margin_rank_loss.py	1951	0644	edit dl rm
merge_id_lists.py	1500	0644	edit dl rm
pairwise_similarity.py	3549	0644	edit dl rm
position_weighted.py	2066	0644	edit dl rm
random_fourier_features.py	3187	0644	edit dl rm
reservoir_sampling.py	3013	0644	edit dl rm
sampling_train.py	2210	0644	edit dl rm
sampling_trainable_mixin.py	1366	0644	edit dl rm
select_record_by_context.py	2381	0644	edit dl rm
semi_random_features.py	5809	0644	edit dl rm
sparse_dropout_with_replacement.py	3943	0644	edit dl rm
sparse_feature_hash.py	4618	0644	edit dl rm
sparse_itemwise_dropout_with_replacement.py	3944	0644	edit dl rm
sparse_lookup.py	22170	0644	edit dl rm
split.py	2257	0644	edit dl rm
tags.py	4114	0644	edit dl rm
uniform_sampling.py	2779	0644	edit dl rm
__init__.py	943	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/layers/add_bias.py (1396B)

## @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)

## @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)