# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
FullyConnectedNet.
"""
import mindspore.nn as nn
[docs]class FullyConnectedNet(nn.Cell):
"""
A basic fully connected neural network.
Args:
input_size(int): numbers of input size.
hidden_size(int): numbers of hidden layers.
output_size(int): numbers of output size.
Examples:
>>> input = Tensor(np.ones([2, 4]).astype(np.float32))
>>> net = FullyConnectedNet(4, 10, 2)
>>> output = net(input)
>>> print(output.shape)
(2, 2)
"""
def __init__(self, input_size, hidden_size, output_size):
super(FullyConnectedNet, self).__init__()
self.linear1 = nn.Dense(
input_size,
hidden_size,
weight_init="XavierUniform")
self.linear2 = nn.Dense(
hidden_size,
output_size,
weight_init="XavierUniform")
self.relu = nn.ReLU()
[docs] def construct(self, x):
"""
Returns output of Dense layer.
Args:
x (Tensor): Tensor as the input of network.
Returns:
The output of the Dense layer.
"""
x = self.relu(self.linear1(x))
x = self.linear2(x)
return x