| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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. |
| |
| """ |
| LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick Haffner. |
| Gradient-based learning applied to document recognition. |
| Proceedings of the IEEE (1998) |
| """ |
| import mxnet as mx |
| |
| def get_loc(data, attr={'lr_mult':'0.01'}): |
| """ |
| the localisation network in lenet-stn, it will increase acc about more than 1%, |
| when num-epoch >=15 |
| """ |
| loc = mx.symbol.Convolution(data=data, num_filter=30, kernel=(5, 5), stride=(2,2)) |
| loc = mx.symbol.Activation(data = loc, act_type='relu') |
| loc = mx.symbol.Pooling(data=loc, kernel=(2, 2), stride=(2, 2), pool_type='max') |
| loc = mx.symbol.Convolution(data=loc, num_filter=60, kernel=(3, 3), stride=(1,1), pad=(1, 1)) |
| loc = mx.symbol.Activation(data = loc, act_type='relu') |
| loc = mx.symbol.Pooling(data=loc, global_pool=True, kernel=(2, 2), pool_type='avg') |
| loc = mx.symbol.Flatten(data=loc) |
| loc = mx.symbol.FullyConnected(data=loc, num_hidden=6, name="stn_loc", attr=attr) |
| return loc |
| |
| |
| def get_symbol(num_classes=10, add_stn=False, **kwargs): |
| data = mx.symbol.Variable('data') |
| if add_stn: |
| data = mx.sym.SpatialTransformer(data=data, loc=get_loc(data), target_shape = (28,28), |
| transform_type="affine", sampler_type="bilinear") |
| # first conv |
| conv1 = mx.symbol.Convolution(data=data, kernel=(5,5), num_filter=20) |
| tanh1 = mx.symbol.Activation(data=conv1, act_type="tanh") |
| pool1 = mx.symbol.Pooling(data=tanh1, pool_type="max", |
| kernel=(2,2), stride=(2,2)) |
| # second conv |
| conv2 = mx.symbol.Convolution(data=pool1, kernel=(5,5), num_filter=50) |
| tanh2 = mx.symbol.Activation(data=conv2, act_type="tanh") |
| pool2 = mx.symbol.Pooling(data=tanh2, pool_type="max", |
| kernel=(2,2), stride=(2,2)) |
| # first fullc |
| flatten = mx.symbol.Flatten(data=pool2) |
| fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=500) |
| tanh3 = mx.symbol.Activation(data=fc1, act_type="tanh") |
| # second fullc |
| fc2 = mx.symbol.FullyConnected(data=tanh3, num_hidden=num_classes) |
| # loss |
| lenet = mx.symbol.SoftmaxOutput(data=fc2, name='softmax') |
| return lenet |
