提交 e26f220d 编写于 作者: W wangyang59 提交者: Yu Yang

Mnist demo (#162)

* added mnist demo

* modified .gitignore for .project files

* normalize pixel in mnist_provider.py and set use_gpu=0
上级 6f0d634e
......@@ -4,3 +4,5 @@ build/
.vscode
.idea
.project
.pydevproject
data/raw_data
data/*.list
mnist_vgg_model
plot.png
train.log
*pyc
# Copyright (c) 2016 Baidu, Inc. 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.
o = open("./" + "train.list", "w")
o.write("./data/raw_data/train" +"\n")
o.close()
o = open("./" + "test.list", "w")
o.write("./data/raw_data/t10k" +"\n")
o.close()
\ No newline at end of file
#!/usr/bin/env sh
# This scripts downloads the mnist data and unzips it.
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
rm -rf "$DIR/raw_data"
mkdir "$DIR/raw_data"
cd "$DIR/raw_data"
echo "Downloading..."
for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
do
if [ ! -e $fname ]; then
wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
gunzip ${fname}.gz
fi
done
cd $DIR
rm -f *.list
python generate_list.py
from paddle.trainer.PyDataProvider2 import *
# Define a py data provider
@provider(input_types=[
dense_vector(28 * 28),
integer_value(10)
])
def process(settings, filename): # settings is not used currently.
imgf = filename + "-images-idx3-ubyte"
labelf = filename + "-labels-idx1-ubyte"
f = open(imgf, "rb")
l = open(labelf, "rb")
f.read(16)
l.read(8)
# Define number of samples for train/test
if "train" in filename:
n = 60000
else:
n = 10000
for i in range(n):
label = ord(l.read(1))
pixels = []
for j in range(28*28):
pixels.append(float(ord(f.read(1))) / 255.0)
yield { "pixel": pixels, 'label': label }
f.close()
l.close()
\ No newline at end of file
#!/bin/bash
# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
config=vgg_16_mnist.py
output=./mnist_vgg_model
log=train.log
paddle train \
--config=$config \
--dot_period=10 \
--log_period=100 \
--test_all_data_in_one_period=1 \
--use_gpu=0 \
--trainer_count=1 \
--num_passes=100 \
--save_dir=$output \
2>&1 | tee $log
python -m paddle.utils.plotcurve -i $log > plot.png
# Copyright (c) 2016 Baidu, Inc. 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.
from paddle.trainer_config_helpers import *
is_predict = get_config_arg("is_predict", bool, False)
####################Data Configuration ##################
if not is_predict:
data_dir='./data/'
define_py_data_sources2(train_list= data_dir + 'train.list',
test_list= data_dir + 'test.list',
module='mnist_provider',
obj='process')
######################Algorithm Configuration #############
settings(
batch_size = 128,
learning_rate = 0.1 / 128.0,
learning_method = MomentumOptimizer(0.9),
regularization = L2Regularization(0.0005 * 128)
)
#######################Network Configuration #############
data_size=1*28*28
label_size=10
img = data_layer(name='pixel', size=data_size)
# small_vgg is predined in trainer_config_helpers.network
predict = small_vgg(input_image=img,
num_channels=1,
num_classes=label_size)
if not is_predict:
lbl = data_layer(name="label", size=label_size)
outputs(classification_cost(input=predict, label=lbl))
else:
outputs(predict)
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