# Copyright (c) 2020 Sony Corporation. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import nnabla.functions as F
from nnabla.initializer import ConstantInitializer
from nnabla.initializer import UniformInitializer
from nnabla.initializer import calc_uniform_lim_glorot
from .module import Module
from .parameter import Parameter
[docs]
class Linear(Module):
r"""Linear layer.
Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
Args:
in_features (int): The size of each input sample.
in_features (int): The size of each output sample.
base_axis (int, optional): Dimensions up to `base_axis` are treated as
the sample dimensions. Defaults to 1.
w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`):
Initializer for weight. By default, it is initialized with
:obj:`nnabla.initializer.UniformInitializer` within the range
determined by :obj:`nnabla.initializer.calc_uniform_lim_glorot`.
b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`):
Initializer for bias. By default, it is initialized with zeros if
`with_bias` is `True`.
rng (numpy.random.RandomState): Random generator for Initializer.
with_bias (bool): Specify whether to include the bias term.
name (string): the name of this module
"""
def __init__(self, in_features, out_features, base_axis=1, w_init=None,
b_init=None, rng=None, bias=True, name=''):
Module.__init__(self, name=name)
self._scope_name = f'<linear at {hex(id(self))}>'
if w_init is None:
w_init = UniformInitializer(
calc_uniform_lim_glorot(in_features, out_features), rng=rng)
self._W = Parameter((in_features, out_features), initializer=w_init,
scope=self._scope_name)
self._b = None
if bias:
if b_init is None:
b_init = ConstantInitializer()
self._b = Parameter((out_features, ), initializer=b_init,
scope=self._scope_name)
self._base_axis = base_axis
self._in_features = in_features
self._out_features = out_features
[docs]
def call(self, input):
return F.affine(input, self._W, self._b, self._base_axis)