mirror of
https://github.com/zebrajr/pytorch.git
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8286ce1e3a
Summary: Closes https://github.com/caffe2/caffe2/pull/1260 Differential Revision: D5906739 Pulled By: Yangqing fbshipit-source-id: e482ba9ba60b5337d9165f28f7ec68d4518a0902
78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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##############################################################################
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## @package fc_without_bias
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# Module caffe2.python.layers.fc_without_bias
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import schema
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from caffe2.python.layers.layers import ModelLayer
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from caffe2.python.layers.sampling_trainable_mixin import SamplingTrainableMixin
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import math
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import numpy as np
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class FCWithoutBias(SamplingTrainableMixin, ModelLayer):
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def __init__(
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self,
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model,
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input_record,
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output_dims,
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weight_init=None,
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weight_optim=None,
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name='fc_without_bias',
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**kwargs
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):
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super(FCWithoutBias, self).__init__(model, name, input_record, **kwargs)
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assert isinstance(input_record, schema.Scalar), "Incorrect input type"
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assert len(input_record.field_types()[0].shape) > 0, (
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"FCWithoutBias expects limited dimensions of the input tensor"
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)
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input_dims = input_record.field_types()[0].shape[0]
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assert input_dims > 0, (
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"FCWithoutBias expects input dimensions > 0, got {}".format(input_dims)
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)
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self.output_schema = schema.Scalar(
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(np.float32, (output_dims, )),
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self.get_next_blob_reference('output')
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)
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scale = math.sqrt(1.0 / input_dims)
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weight_init = weight_init if weight_init else (
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'UniformFill', {'min': -scale,
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'max': scale}
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)
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self.w = self.create_param(param_name='w',
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shape=[output_dims, input_dims],
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initializer=weight_init,
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optimizer=weight_optim)
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def _add_ops(self, net, params):
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net.MatMul(
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self.input_record.field_blobs() + params,
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self.output_schema.field_blobs(), trans_b=1, **self.kwargs
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)
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@property
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def param_blobs(self):
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return [self.w]
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