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/fc_without_bias.py (1954B)

## @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]

## @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]