liaogang
8 years ago
commit
fd4eeaf59c
@ -0,0 +1,6 @@
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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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@ -0,0 +1,21 @@
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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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@ -0,0 +1,22 @@
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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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set -e
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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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@ -0,0 +1,32 @@
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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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'pixel': dense_vector(28 * 28),
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'label': 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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@ -0,0 +1,31 @@
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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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@ -0,0 +1,53 @@
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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
|
||||
# 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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inputs(img, lbl)
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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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@ -0,0 +1,62 @@
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# edit-mode: -*- python -*-
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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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# 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.
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||||
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from paddle.trainer_config_helpers import *
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dict_file = "./data/dict.txt"
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word_dict = dict()
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with open(dict_file, 'r') as f:
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for i, line in enumerate(f):
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w = line.strip().split()[0]
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word_dict[w] = i
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is_predict = get_config_arg('is_predict', bool, False)
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trn = 'data/train.list' if not is_predict else None
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tst = 'data/test.list' if not is_predict else 'data/pred.list'
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process = 'process' if not is_predict else 'process_predict'
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define_py_data_sources2(train_list=trn,
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test_list=tst,
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module="dataprovider_emb",
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obj=process,
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args={"dictionary": word_dict})
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batch_size = 128 if not is_predict else 1
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settings(
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batch_size=batch_size,
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learning_rate=2e-3,
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learning_method=AdamOptimizer(),
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regularization=L2Regularization(8e-4),
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gradient_clipping_threshold=25
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)
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bias_attr = ParamAttr(initial_std=0.,l2_rate=0.)
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data = data_layer(name="word", size=len(word_dict))
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emb = embedding_layer(input=data, size=128)
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bi_lstm = bidirectional_lstm(input=emb, size=128)
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dropout = dropout_layer(input=bi_lstm, dropout_rate=0.5)
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output = fc_layer(input=dropout, size=2,
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bias_attr=bias_attr,
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act=SoftmaxActivation())
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if is_predict:
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maxid = maxid_layer(output)
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outputs([maxid, output])
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else:
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label = data_layer(name="label", size=2)
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cls = classification_cost(input=output, label=label)
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outputs(cls)
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@ -0,0 +1,73 @@
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# edit-mode: -*- python -*-
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|
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# 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 *
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dict_file = "./data/dict.txt"
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word_dict = dict()
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with open(dict_file, 'r') as f:
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for i, line in enumerate(f):
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w = line.strip().split()[0]
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word_dict[w] = i
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is_predict = get_config_arg('is_predict', bool, False)
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trn = 'data/train.list' if not is_predict else None
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tst = 'data/test.list' if not is_predict else 'data/pred.list'
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process = 'process' if not is_predict else 'process_predict'
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define_py_data_sources2(train_list=trn,
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test_list=tst,
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module="dataprovider_emb",
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obj=process,
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args={"dictionary": word_dict})
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|
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batch_size = 128 if not is_predict else 1
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settings(
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batch_size=batch_size,
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learning_rate=2e-3,
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learning_method=AdamOptimizer(),
|
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regularization=L2Regularization(8e-4),
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gradient_clipping_threshold=25
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)
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|
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bias_attr = ParamAttr(initial_std=0.,l2_rate=0.)
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|
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data = data_layer(name="word", size=len(word_dict))
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emb = embedding_layer(input=data, size=128)
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hidden_0 = mixed_layer(size=128, input=[full_matrix_projection(input=emb)])
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lstm_0 = lstmemory(input=hidden_0, layer_attr=ExtraAttr(drop_rate=0.1))
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input_layers = [hidden_0, lstm_0]
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|
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for i in range(1,8):
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fc = fc_layer(input=input_layers, size=128)
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lstm = lstmemory(input=fc, layer_attr=ExtraAttr(drop_rate=0.1),
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reverse=(i % 2) == 1,)
|
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input_layers = [fc, lstm]
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|
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lstm_last = pooling_layer(input=lstm, pooling_type=MaxPooling())
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|
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output = fc_layer(input=lstm_last, size=2,
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bias_attr=bias_attr,
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act=SoftmaxActivation())
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|
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if is_predict:
|
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maxid = maxid_layer(output)
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outputs([maxid, output])
|
||||
else:
|
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label = data_layer(name="label", size=2)
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||||
cls = classification_cost(input=output, label=label)
|
||||
outputs(cls)
|
||||
@ -0,0 +1,21 @@
|
||||
#!/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
|
||||
|
||||
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
|
||||
cd $DIR
|
||||
|
||||
wget http://www.cnts.ua.ac.be/conll2000/chunking/train.txt.gz
|
||||
wget http://www.cnts.ua.ac.be/conll2000/chunking/test.txt.gz
|
||||
@ -0,0 +1 @@
|
||||
data/test.txt.gz
|
||||
@ -0,0 +1 @@
|
||||
data/train.txt.gz
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,84 @@
|
||||
# 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 *
|
||||
|
||||
import math
|
||||
|
||||
define_py_data_sources2(train_list="data/train.list",
|
||||
test_list="data/test.list",
|
||||
module="dataprovider",
|
||||
obj="process")
|
||||
|
||||
|
||||
batch_size = 1
|
||||
settings(
|
||||
learning_method=MomentumOptimizer(),
|
||||
batch_size=batch_size,
|
||||
regularization=L2Regularization(batch_size * 1e-4),
|
||||
average_window=0.5,
|
||||
learning_rate=1e-1,
|
||||
learning_rate_decay_a=1e-5,
|
||||
learning_rate_decay_b=0.25,
|
||||
)
|
||||
|
||||
num_label_types=23
|
||||
|
||||
def get_simd_size(size):
|
||||
return int(math.ceil(float(size) / 8)) * 8
|
||||
|
||||
# Currently, in order to use sparse_update=True,
|
||||
# the size has to be aligned.
|
||||
num_label_types = get_simd_size(num_label_types)
|
||||
|
||||
features = data_layer(name="features", size=76328)
|
||||
word = data_layer(name="word", size=6778)
|
||||
pos = data_layer(name="pos", size=44)
|
||||
chunk = data_layer(name="chunk",
|
||||
size=num_label_types)
|
||||
|
||||
crf_input = fc_layer(
|
||||
input=features,
|
||||
size=num_label_types,
|
||||
act=LinearActivation(),
|
||||
bias_attr=False,
|
||||
param_attr=ParamAttr(initial_std=0, sparse_update=True))
|
||||
|
||||
crf=crf_layer(
|
||||
input=crf_input,
|
||||
label=chunk,
|
||||
param_attr=ParamAttr(name="crfw", initial_std=0),
|
||||
)
|
||||
|
||||
crf_decoding=crf_decoding_layer(
|
||||
size=num_label_types,
|
||||
input=crf_input,
|
||||
label=chunk,
|
||||
param_attr=ParamAttr(name="crfw"),
|
||||
)
|
||||
|
||||
sum_evaluator(
|
||||
name="error",
|
||||
input=crf_decoding,
|
||||
)
|
||||
|
||||
chunk_evaluator(
|
||||
name="chunk_f1",
|
||||
input =[crf_decoding, chunk],
|
||||
chunk_scheme="IOB",
|
||||
num_chunk_types=11,
|
||||
)
|
||||
|
||||
inputs(word, pos, chunk, features)
|
||||
outputs(crf)
|
||||
@ -0,0 +1,45 @@
|
||||
# Sequence Tagging
|
||||
|
||||
This demo is a sequence model for assigning tags to each token in a sentence. The task is described at <a href = "http://www.cnts.ua.ac.be/conll2000/chunking">CONLL2000 Text Chunking</a> task.
|
||||
|
||||
## Download data
|
||||
```bash
|
||||
cd demo/sequence_tagging
|
||||
./data/get_data.sh
|
||||
```
|
||||
|
||||
## Train model
|
||||
```bash
|
||||
cd demo/sequence_tagging
|
||||
./train.sh
|
||||
```
|
||||
|
||||
## Model description
|
||||
|
||||
We provide two models. One is a linear CRF model (linear_crf.py) with is equivalent to the one at <a href="http://leon.bottou.org/projects/sgd#stochastic_gradient_crfs">leon.bottou.org/projects/sgd</a>. The second one is a stacked bidirectional RNN and CRF model (rnn_crf.py).
|
||||
<center>
|
||||
<table border="2" cellspacing="0" cellpadding="6" rules="all" frame="border">
|
||||
|
||||
<thead>
|
||||
<th scope="col" class="left">Model name</th>
|
||||
<th scope="col" class="left">Number of parameters</th>
|
||||
<th scope="col" class="left">F1 score</th>
|
||||
</thead>
|
||||
|
||||
<tbody>
|
||||
<tr>
|
||||
<td class="left">linear_crf</td>
|
||||
<td class="left"> 1.8M </td>
|
||||
<td class="left"> 0.937</td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td class="left">rnn_crf</td>
|
||||
<td class="left"> 960K </td>
|
||||
<td class="left">0.941</td>
|
||||
</tr>
|
||||
|
||||
</tbody>
|
||||
</table>
|
||||
</center>
|
||||
<br>
|
||||
@ -0,0 +1,130 @@
|
||||
# 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 *
|
||||
|
||||
import math
|
||||
|
||||
define_py_data_sources2(train_list="data/train.list",
|
||||
test_list="data/test.list",
|
||||
module="dataprovider",
|
||||
obj="process")
|
||||
|
||||
batch_size = 16
|
||||
settings(
|
||||
learning_method=MomentumOptimizer(),
|
||||
batch_size=batch_size,
|
||||
regularization=L2Regularization(batch_size * 1e-5),
|
||||
average_window=0.5,
|
||||
learning_rate = 2e-3,
|
||||
learning_rate_decay_a = 5e-7,
|
||||
learning_rate_decay_b = 0.5,
|
||||
)
|
||||
|
||||
word_dim=128
|
||||
hidden_dim = 128
|
||||
with_rnn = True
|
||||
|
||||
initial_std=1/math.sqrt(hidden_dim)
|
||||
param_attr=ParamAttr(initial_std=initial_std)
|
||||
cpu_layer_attr=ExtraLayerAttribute(device=-1)
|
||||
|
||||
default_device(0)
|
||||
|
||||
num_label_types=23
|
||||
|
||||
features = data_layer(name="features", size=76328)
|
||||
word = data_layer(name="word", size=6778)
|
||||
pos = data_layer(name="pos", size=44)
|
||||
chunk = data_layer(name="chunk",
|
||||
size=num_label_types,
|
||||
layer_attr=cpu_layer_attr)
|
||||
|
||||
emb = embedding_layer(
|
||||
input=word, size=word_dim, param_attr=ParamAttr(initial_std=0))
|
||||
|
||||
hidden1 = mixed_layer(
|
||||
size=hidden_dim,
|
||||
act=STanhActivation(),
|
||||
bias_attr=True,
|
||||
input=[full_matrix_projection(emb),
|
||||
table_projection(pos, param_attr=param_attr)]
|
||||
)
|
||||
|
||||
if with_rnn:
|
||||
rnn1 = recurrent_layer(
|
||||
act=ReluActivation(),
|
||||
bias_attr=True,
|
||||
input=hidden1,
|
||||
param_attr=ParamAttr(initial_std=0),
|
||||
)
|
||||
|
||||
hidden2 = mixed_layer(
|
||||
size=hidden_dim,
|
||||
act=STanhActivation(),
|
||||
bias_attr=True,
|
||||
input=[full_matrix_projection(hidden1)
|
||||
] + ([
|
||||
full_matrix_projection(rnn1, param_attr=ParamAttr(initial_std=0))
|
||||
] if with_rnn else []),
|
||||
)
|
||||
|
||||
if with_rnn:
|
||||
rnn2=recurrent_layer(
|
||||
reverse=True,
|
||||
act=ReluActivation(),
|
||||
bias_attr=True,
|
||||
input=hidden2,
|
||||
param_attr=ParamAttr(initial_std=0),
|
||||
)
|
||||
|
||||
crf_input = mixed_layer(
|
||||
size=num_label_types,
|
||||
bias_attr=False,
|
||||
input=[
|
||||
full_matrix_projection(hidden2),
|
||||
] + ([
|
||||
full_matrix_projection(rnn2, param_attr=ParamAttr(initial_std=0))
|
||||
] if with_rnn else []),
|
||||
)
|
||||
|
||||
crf = crf_layer(
|
||||
input=crf_input,
|
||||
label=chunk,
|
||||
param_attr=ParamAttr(name="crfw", initial_std=0),
|
||||
layer_attr=cpu_layer_attr,
|
||||
)
|
||||
|
||||
crf_decoding = crf_decoding_layer(
|
||||
size=num_label_types,
|
||||
input=crf_input,
|
||||
label=chunk,
|
||||
param_attr=ParamAttr(name="crfw"),
|
||||
layer_attr=cpu_layer_attr,
|
||||
)
|
||||
|
||||
sum_evaluator(
|
||||
name="error",
|
||||
input=crf_decoding,
|
||||
)
|
||||
|
||||
chunk_evaluator(
|
||||
name="chunk_f1",
|
||||
input =[crf_decoding, chunk],
|
||||
chunk_scheme="IOB",
|
||||
num_chunk_types=11,
|
||||
)
|
||||
|
||||
inputs(word, pos, chunk, features)
|
||||
outputs(crf)
|
||||
@ -0,0 +1,10 @@
|
||||
#!/bin/bash
|
||||
|
||||
paddle train \
|
||||
--config rnn_crf.py \
|
||||
--parallel_nn=1 \
|
||||
--use_gpu=1 \
|
||||
--dot_period=10 \
|
||||
--log_period=1000 \
|
||||
--test_period=0 \
|
||||
--num_passes=10
|
||||
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Reference in new issue