Mnist demo (#162)
* added mnist demo * modified .gitignore for .project files * normalize pixel in mnist_provider.py and set use_gpu=0avx_docs
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6f0d634e02
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data/raw_data
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data/*.list
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mnist_vgg_model
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plot.png
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train.log
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*pyc
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# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
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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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o = open("./" + "train.list", "w")
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o.write("./data/raw_data/train" +"\n")
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o.close()
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o = open("./" + "test.list", "w")
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o.write("./data/raw_data/t10k" +"\n")
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o.close()
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#!/usr/bin/env sh
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# This scripts downloads the mnist data and unzips it.
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DIR="$( cd "$(dirname "$0")" ; pwd -P )"
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rm -rf "$DIR/raw_data"
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mkdir "$DIR/raw_data"
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cd "$DIR/raw_data"
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echo "Downloading..."
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for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
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do
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if [ ! -e $fname ]; then
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wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
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gunzip ${fname}.gz
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fi
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done
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cd $DIR
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rm -f *.list
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python generate_list.py
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from paddle.trainer.PyDataProvider2 import *
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# Define a py data provider
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@provider(input_types=[
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dense_vector(28 * 28),
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integer_value(10)
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])
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def process(settings, filename): # settings is not used currently.
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imgf = filename + "-images-idx3-ubyte"
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labelf = filename + "-labels-idx1-ubyte"
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f = open(imgf, "rb")
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l = open(labelf, "rb")
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f.read(16)
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l.read(8)
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# Define number of samples for train/test
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if "train" in filename:
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n = 60000
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else:
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n = 10000
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for i in range(n):
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label = ord(l.read(1))
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pixels = []
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for j in range(28*28):
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pixels.append(float(ord(f.read(1))) / 255.0)
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yield { "pixel": pixels, 'label': label }
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f.close()
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l.close()
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#!/bin/bash
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# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
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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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set -e
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config=vgg_16_mnist.py
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output=./mnist_vgg_model
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log=train.log
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paddle train \
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--config=$config \
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--dot_period=10 \
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--log_period=100 \
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--test_all_data_in_one_period=1 \
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--use_gpu=0 \
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--trainer_count=1 \
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--num_passes=100 \
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--save_dir=$output \
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2>&1 | tee $log
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python -m paddle.utils.plotcurve -i $log > plot.png
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# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
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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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from paddle.trainer_config_helpers import *
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is_predict = get_config_arg("is_predict", bool, False)
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####################Data Configuration ##################
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if not is_predict:
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data_dir='./data/'
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define_py_data_sources2(train_list= data_dir + 'train.list',
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test_list= data_dir + 'test.list',
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module='mnist_provider',
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obj='process')
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######################Algorithm Configuration #############
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settings(
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batch_size = 128,
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learning_rate = 0.1 / 128.0,
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learning_method = MomentumOptimizer(0.9),
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regularization = L2Regularization(0.0005 * 128)
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)
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#######################Network Configuration #############
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data_size=1*28*28
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label_size=10
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img = data_layer(name='pixel', size=data_size)
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# small_vgg is predined in trainer_config_helpers.network
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predict = small_vgg(input_image=img,
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num_channels=1,
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num_classes=label_size)
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if not is_predict:
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lbl = data_layer(name="label", size=label_size)
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outputs(classification_cost(input=predict, label=lbl))
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else:
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outputs(predict)
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