Luo Tao
8 years ago
commit
3d1f8b47ef
@ -0,0 +1 @@
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.gitignore
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# Release v0.9.0
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## New Features:
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* New Layers
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* bilinear interpolation layer.
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* spatial pyramid-pool layer.
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* de-convolution layer.
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* maxout layer.
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* Support rectangle padding, stride, window and input for Pooling Operation.
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* Add —job=time in trainer, which can be used to print time info without compiler option -WITH_TIMER=ON.
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* Expose cost_weight/nce_layer in `trainer_config_helpers`
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* Add FAQ, concepts, h-rnn docs.
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* Add Bidi-LSTM and DB-LSTM to quick start demo @alvations
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* Add usage track scripts.
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## Improvements
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* Add Travis-CI for Mac OS X. Enable swig unittest in Travis-CI. Skip Travis-CI when only docs are changed.
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* Add code coverage tools.
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* Refine convolution layer to speedup and reduce GPU memory.
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* Speed up PyDataProvider2
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* Add ubuntu deb package build scripts.
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* Make Paddle use git-flow branching model.
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* PServer support no parameter blocks.
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## Bug Fixes
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* add zlib link to py_paddle
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* add input sparse data check for sparse layer at runtime
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* Bug fix for sparse matrix multiplication
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* Fix floating-point overflow problem of tanh
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* Fix some nvcc compile options
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* Fix a bug in yield dictionary in DataProvider
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* Fix SRL hang when exit.
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# Release v0.8.0beta.1
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New features:
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* Mac OSX is supported by source code. #138
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* Both GPU and CPU versions of PaddlePaddle are supported.
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* Support CUDA 8.0
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* Enhance `PyDataProvider2`
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* Add dictionary yield format. `PyDataProvider2` can yield a dictionary with key is data_layer's name, value is features.
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* Add `min_pool_size` to control memory pool in provider.
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* Add `deb` install package & docker image for no_avx machines.
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* Especially for cloud computing and virtual machines
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* Automatically disable `avx` instructions in cmake when machine's CPU don't support `avx` instructions.
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* Add Parallel NN api in trainer_config_helpers.
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* Add `travis ci` for Github
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Bug fixes:
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* Several bugs in trainer_config_helpers. Also complete the unittest for trainer_config_helpers
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* Check if PaddlePaddle is installed when unittest.
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* Fix bugs in GTX series GPU
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* Fix bug in MultinomialSampler
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Also more documentation was written since last release.
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# Release v0.8.0beta.0
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PaddlePaddle v0.8.0beta.0 release. The install package is not stable yet and it's a pre-release version.
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# Get the latest git tag.
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set(PADDLE_VERSION $ENV{PADDLE_VERSION})
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set(tmp_version "HEAD")
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while ("${PADDLE_VERSION}" STREQUAL "")
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execute_process(
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COMMAND ${GIT_EXECUTABLE} describe --tags --abbrev=0 ${tmp_version}
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WORKING_DIRECTORY ${PROJ_ROOT}
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OUTPUT_VARIABLE GIT_TAG_NAME
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RESULT_VARIABLE GIT_RESULT
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ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
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if (NOT ${GIT_RESULT})
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# Check the tag is a correct version
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if (${GIT_TAG_NAME} MATCHES "v[0-9]+\\.[0-9]+\\.[0-9]+(\\.(a|b|rc)\\.[0-9]+)?")
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string(REPLACE "v" "" PADDLE_VERSION ${GIT_TAG_NAME})
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else() # otherwise, get the previous git tag name.
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set(tmp_version "${GIT_TAG_NAME}~1")
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endif()
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else()
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set(PADDLE_VERSION "0.0.0")
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message(WARNING "Cannot add paddle version from git tag")
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endif()
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endwhile()
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message(STATUS "Paddle version is ${PADDLE_VERSION}")
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output/
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uniform_params/
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cifar_params/
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mnist_params/
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*.png
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.pydevproject
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.project
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*.log
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*.pyc
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data/mnist_data/
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data/cifar-10-batches-py/
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# Generative Adversarial Networks (GAN)
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This demo implements GAN training described in the original GAN paper (https://arxiv.org/abs/1406.2661) and DCGAN (https://arxiv.org/abs/1511.06434).
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The general training procedures are implemented in gan_trainer.py. The neural network configurations are specified in gan_conf.py (for synthetic data) and gan_conf_image.py (for image data).
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In order to run the model, first download the corresponding data by running the shell script in ./data.
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Then you can run the command below. The flag -d specifies the training data (cifar, mnist or uniform) and flag --useGpu specifies whether to use gpu for training (0 is cpu, 1 is gpu).
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$python gan_trainer.py -d cifar --use_gpu 1
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The generated images will be stored in ./cifar_samples/
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The corresponding models will be stored in ./cifar_params/
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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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set -e
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wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
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tar zxf cifar-10-python.tar.gz
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rm cifar-10-python.tar.gz
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#!/usr/bin/env sh
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# This script 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/mnist_data"
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mkdir "$DIR/mnist_data"
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cd "$DIR/mnist_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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# 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.
|
||||
# You may obtain a copy of the License at
|
||||
#
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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,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# 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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mode = get_config_arg("mode", str, "generator")
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assert mode in set(["generator",
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"discriminator",
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"generator_training",
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"discriminator_training"])
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is_generator_training = mode == "generator_training"
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is_discriminator_training = mode == "discriminator_training"
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is_generator = mode == "generator"
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is_discriminator = mode == "discriminator"
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# The network structure below follows the ref https://arxiv.org/abs/1406.2661
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# Here we used two hidden layers and batch_norm
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print('mode=%s' % mode)
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# the dim of the noise (z) as the input of the generator network
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noise_dim = 10
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# the dim of the hidden layer
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hidden_dim = 10
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# the dim of the generated sample
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sample_dim = 2
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settings(
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batch_size=128,
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learning_rate=1e-4,
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learning_method=AdamOptimizer(beta1=0.5)
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)
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def discriminator(sample):
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"""
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discriminator ouputs the probablity of a sample is from generator
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or real data.
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The output has two dimenstional: dimension 0 is the probablity
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of the sample is from generator and dimension 1 is the probabblity
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of the sample is from real data.
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"""
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param_attr = ParamAttr(is_static=is_generator_training)
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bias_attr = ParamAttr(is_static=is_generator_training,
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initial_mean=1.0,
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initial_std=0)
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hidden = fc_layer(input=sample, name="dis_hidden", size=hidden_dim,
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bias_attr=bias_attr,
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param_attr=param_attr,
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act=ReluActivation())
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hidden2 = fc_layer(input=hidden, name="dis_hidden2", size=hidden_dim,
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bias_attr=bias_attr,
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param_attr=param_attr,
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act=LinearActivation())
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hidden_bn = batch_norm_layer(hidden2,
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act=ReluActivation(),
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name="dis_hidden_bn",
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bias_attr=bias_attr,
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param_attr=ParamAttr(is_static=is_generator_training,
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initial_mean=1.0,
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initial_std=0.02),
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use_global_stats=False)
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return fc_layer(input=hidden_bn, name="dis_prob", size=2,
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bias_attr=bias_attr,
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param_attr=param_attr,
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act=SoftmaxActivation())
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def generator(noise):
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"""
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generator generates a sample given noise
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"""
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param_attr = ParamAttr(is_static=is_discriminator_training)
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bias_attr = ParamAttr(is_static=is_discriminator_training,
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initial_mean=1.0,
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initial_std=0)
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hidden = fc_layer(input=noise,
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name="gen_layer_hidden",
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size=hidden_dim,
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bias_attr=bias_attr,
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param_attr=param_attr,
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act=ReluActivation())
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hidden2 = fc_layer(input=hidden, name="gen_hidden2", size=hidden_dim,
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bias_attr=bias_attr,
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param_attr=param_attr,
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act=LinearActivation())
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||||
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||||
hidden_bn = batch_norm_layer(hidden2,
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act=ReluActivation(),
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name="gen_layer_hidden_bn",
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bias_attr=bias_attr,
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param_attr=ParamAttr(is_static=is_discriminator_training,
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initial_mean=1.0,
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initial_std=0.02),
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use_global_stats=False)
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||||
return fc_layer(input=hidden_bn,
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name="gen_layer1",
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||||
size=sample_dim,
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bias_attr=bias_attr,
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||||
param_attr=param_attr,
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act=LinearActivation())
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||||
|
||||
if is_generator_training:
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||||
noise = data_layer(name="noise", size=noise_dim)
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||||
sample = generator(noise)
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||||
|
||||
if is_discriminator_training:
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||||
sample = data_layer(name="sample", size=sample_dim)
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||||
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||||
if is_generator_training or is_discriminator_training:
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label = data_layer(name="label", size=1)
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prob = discriminator(sample)
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cost = cross_entropy(input=prob, label=label)
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classification_error_evaluator(input=prob, label=label, name=mode+'_error')
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outputs(cost)
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|
||||
if is_generator:
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noise = data_layer(name="noise", size=noise_dim)
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outputs(generator(noise))
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||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,14 @@
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||||
ABOUT
|
||||
=======
|
||||
|
||||
PaddlPaddle is an easy-to-use, efficient, flexible and scalable deep learning platform,
|
||||
which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu.
|
||||
|
||||
PaddlePaddle is now open source but far from complete, which is intended to be built upon, improved, scaled, and extended.
|
||||
We hope to build an active open source community both by providing feedback and by actively contributing to the source code.
|
||||
|
||||
|
||||
Credits
|
||||
--------
|
||||
|
||||
We owe many thanks to `all contributors and developers <https://github.com/PaddlePaddle/Paddle/blob/develop/authors>`_ of PaddlePaddle!
|
||||
@ -1,7 +0,0 @@
|
||||
Algorithm Tutorial
|
||||
==================
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
rnn/rnn.rst
|
||||
@ -1 +0,0 @@
|
||||
../../demo/sentiment_analysis/bi_lstm.jpg
|
||||
@ -1 +0,0 @@
|
||||
../../demo/text_generation/encoder-decoder-attention-model.png
|
||||
@ -1,5 +1,5 @@
|
||||
DataProvider Introduction
|
||||
=========================
|
||||
Introduction
|
||||
==============
|
||||
DataProvider is a module that loads training or testing data into cpu or gpu
|
||||
memory for the following triaining or testing process.
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
How to use PyDataProvider2
|
||||
==========================
|
||||
PyDataProvider2
|
||||
=================
|
||||
|
||||
We highly recommand users to use PyDataProvider2 to provide training or testing
|
||||
data to PaddlePaddle. The user only needs to focus on how to read a single
|
||||
@ -0,0 +1,36 @@
|
||||
API
|
||||
====
|
||||
|
||||
DataProvider API
|
||||
----------------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
data_provider/index.rst
|
||||
data_provider/pydataprovider2.rst
|
||||
|
||||
Model Config API
|
||||
----------------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
trainer_config_helpers/index.rst
|
||||
trainer_config_helpers/optimizers.rst
|
||||
trainer_config_helpers/data_sources.rst
|
||||
trainer_config_helpers/layers.rst
|
||||
trainer_config_helpers/activations.rst
|
||||
trainer_config_helpers/poolings.rst
|
||||
trainer_config_helpers/networks.rst
|
||||
trainer_config_helpers/evaluators.rst
|
||||
trainer_config_helpers/attrs.rst
|
||||
|
||||
|
||||
Applications API
|
||||
----------------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
predict/swig_py_paddle_en.rst
|
||||
@ -1,5 +1,5 @@
|
||||
Python Prediction API
|
||||
=====================
|
||||
Python Prediction
|
||||
==================
|
||||
|
||||
PaddlePaddle offers a set of clean prediction interfaces for python with the help of
|
||||
SWIG. The main steps of predict values in python are:
|
||||
@ -0,0 +1,5 @@
|
||||
Parameter Attributes
|
||||
=======================
|
||||
|
||||
.. automodule:: paddle.trainer_config_helpers.attrs
|
||||
:members:
|
||||
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Reference in new issue