提交 76f1fd18 编写于 作者: L Luo Tao

remain mnist demo

上级 46e60761
develop 2.0.1-rocm-post Ligoml-patch-1 OliverLPH-patch-1 OliverLPH-patch-2 PaddlePM-patch-1 PaddlePM-patch-2 ZHUI-patch-1 add_default_att add_model_benchmark_ci add_some_yaml_config addfile all_new_design_exec ascendrc ascendrelease cherry_undefined_var compile_windows delete_2.0.1-rocm-post delete_add_default_att delete_all_new_design_exec delete_ascendrc delete_compile_windows delete_delete_addfile delete_disable_iterable_dataset_unittest delete_fix_dataloader_memory_leak delete_fix_imperative_dygraph_error delete_fix_retry_ci delete_fix_undefined_var delete_improve_sccache delete_incubate/lite delete_paddle_tiny_install delete_paralleltest delete_prv-disable-more-cache delete_revert-31068-fix_conv3d_windows delete_revert-31562-mean delete_revert-33630-bug-fix delete_revert-34159-add_npu_bce_logical_dev delete_revert-34910-spinlocks_for_allocator delete_revert-35069-revert-34910-spinlocks_for_allocator delete_revert-36057-dev/read_flags_in_ut dingjiaweiww-patch-1 disable_iterable_dataset_unittest dy2static enable_eager_model_test final_state_gen_python_c final_state_intermediate fix-numpy-issue fix_concat_slice fix_dataloader_memory_leak fix_imperative_dygraph_error fix_npu_ci fix_op_flops fix_retry_ci fix_rnn_docs fix_tensor_type fix_undefined_var fixiscan fixiscan1 fixiscan2 fixiscan3 github/fork/123malin/netifaces github/fork/123malin/tdm_abacus github/fork/AshburnLee/dev_unique github/fork/ForFishes/fix_memory_matmul github/fork/ForFishes/rm_fluid github/fork/LielinJiang/move-2.0-api github/fork/LielinJiang/visual-dl-cb github/fork/LiuChiachi/add-transformer-generate-square-subsequent-mask-api github/fork/LiuChiachi/fix-example-code-for-hapi-Model github/fork/LiuChiachi/remove-input-requirment-in-dygraph-Model github/fork/MrChengmo/fix_ps_profiler github/fork/MrChengmo/update_ps_heter github/fork/PWhiddy/patch-1 github/fork/Shixiaowei02/dev/save_load_upgrade github/fork/TCChenlong/fix_hapi github/fork/TCChenlong/fix_inden 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无相关合并请求
data/raw_data
data/*.list
mnist_vgg_model
plot.png
train.log
*pyc
.ipynb_checkpoints
params.pkl
params.tar
params.tar.gz
"""
A very basic example for how to use current Raw SWIG API to train mnist network.
Current implementation uses Raw SWIG, which means the API call is directly \
passed to C++ side of Paddle.
The user api could be simpler and carefully designed.
"""
import random
import numpy as np
import paddle.v2 as paddle_v2
import py_paddle.swig_paddle as api
from paddle.trainer_config_helpers import *
from py_paddle import DataProviderConverter
from mnist_util import read_from_mnist
def init_parameter(network):
assert isinstance(network, api.GradientMachine)
for each_param in network.getParameters():
assert isinstance(each_param, api.Parameter)
array_size = len(each_param)
array = np.random.uniform(-1.0, 1.0, array_size).astype('float32')
each_param.getBuf(api.PARAMETER_VALUE).copyFromNumpyArray(array)
def generator_to_batch(generator, batch_size):
ret_val = list()
for each_item in generator:
ret_val.append(each_item)
if len(ret_val) == batch_size:
yield ret_val
ret_val = list()
if len(ret_val) != 0:
yield ret_val
class BatchPool(object):
def __init__(self, generator, batch_size):
self.data = list(generator)
self.batch_size = batch_size
def __call__(self):
random.shuffle(self.data)
for offset in xrange(0, len(self.data), self.batch_size):
limit = min(offset + self.batch_size, len(self.data))
yield self.data[offset:limit]
def input_order_converter(generator):
for each_item in generator:
yield each_item['pixel'], each_item['label']
def main():
api.initPaddle("-use_gpu=false", "-trainer_count=4") # use 4 cpu cores
optimizer = paddle_v2.optimizer.Adam(
learning_rate=1e-4,
batch_size=1000,
model_average=ModelAverage(average_window=0.5),
regularization=L2Regularization(rate=0.5))
# Create Local Updater. Local means not run in cluster.
# For a cluster training, here we can change to createRemoteUpdater
# in future.
updater = optimizer.create_local_updater()
assert isinstance(updater, api.ParameterUpdater)
# define network
images = paddle_v2.layer.data(
name='pixel', type=paddle_v2.data_type.dense_vector(784))
label = paddle_v2.layer.data(
name='label', type=paddle_v2.data_type.integer_value(10))
hidden1 = paddle_v2.layer.fc(input=images, size=200)
hidden2 = paddle_v2.layer.fc(input=hidden1, size=200)
inference = paddle_v2.layer.fc(input=hidden2,
size=10,
act=paddle_v2.activation.Softmax())
cost = paddle_v2.layer.classification_cost(input=inference, label=label)
# Create Simple Gradient Machine.
model_config = paddle_v2.layer.parse_network(cost)
m = api.GradientMachine.createFromConfigProto(model_config,
api.CREATE_MODE_NORMAL,
optimizer.enable_types())
# This type check is not useful. Only enable type hint in IDE.
# Such as PyCharm
assert isinstance(m, api.GradientMachine)
# Initialize Parameter by numpy.
init_parameter(network=m)
# Initialize ParameterUpdater.
updater.init(m)
# DataProvider Converter is a utility convert Python Object to Paddle C++
# Input. The input format is as same as Paddle's DataProvider.
converter = DataProviderConverter(input_types=[images.type, label.type])
train_file = './data/raw_data/train'
test_file = './data/raw_data/t10k'
# start gradient machine.
# the gradient machine must be started before invoke forward/backward.
# not just for training, but also for inference.
m.start()
# evaluator can print error rate, etc. It is a C++ class.
batch_evaluator = m.makeEvaluator()
test_evaluator = m.makeEvaluator()
# Get Train Data.
# TrainData will stored in a data pool. Currently implementation is not care
# about memory, speed. Just a very naive implementation.
train_data_generator = input_order_converter(read_from_mnist(train_file))
train_data = BatchPool(train_data_generator, 512)
# outArgs is Neural Network forward result. Here is not useful, just passed
# to gradient_machine.forward
outArgs = api.Arguments.createArguments(0)
for pass_id in xrange(2): # we train 2 passes.
updater.startPass()
for batch_id, data_batch in enumerate(train_data()):
# data_batch is input images.
# here, for online learning, we could get data_batch from network.
# Start update one batch.
pass_type = updater.startBatch(len(data_batch))
# Start BatchEvaluator.
# batch_evaluator can be used between start/finish.
batch_evaluator.start()
# forwardBackward is a shortcut for forward and backward.
# It is sometimes faster than invoke forward/backward separately,
# because in GradientMachine, it may be async.
m.forwardBackward(converter(data_batch), outArgs, pass_type)
for each_param in m.getParameters():
updater.update(each_param)
# Get cost. We use numpy to calculate total cost for this batch.
cost_vec = outArgs.getSlotValue(0)
cost_vec = cost_vec.copyToNumpyMat()
cost = cost_vec.sum() / len(data_batch)
# Make evaluator works.
m.eval(batch_evaluator)
# Print logs.
print 'Pass id', pass_id, 'Batch id', batch_id, 'with cost=', \
cost, batch_evaluator
batch_evaluator.finish()
# Finish batch.
# * will clear gradient.
# * ensure all values should be updated.
updater.finishBatch(cost)
# testing stage. use test data set to test current network.
updater.apply()
test_evaluator.start()
test_data_generator = input_order_converter(read_from_mnist(test_file))
for data_batch in generator_to_batch(test_data_generator, 512):
# in testing stage, only forward is needed.
m.forward(converter(data_batch), outArgs, api.PASS_TEST)
m.eval(test_evaluator)
# print error rate for test data set
print 'Pass', pass_id, ' test evaluator: ', test_evaluator
test_evaluator.finish()
updater.restore()
updater.catchUpWith()
params = m.getParameters()
for each_param in params:
assert isinstance(each_param, api.Parameter)
value = each_param.getBuf(api.PARAMETER_VALUE)
value = value.copyToNumpyArray()
# Here, we could save parameter to every where you want
print each_param.getName(), value
updater.finishPass()
m.finish()
if __name__ == '__main__':
main()
# Copyright (c) 2016 PaddlePaddle Authors. 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()
#!/usr/bin/env sh
# This scripts downloads the mnist data and unzips it.
set -e
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
# Copyright (c) 2016 PaddlePaddle Authors. 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=50, learning_rate=0.001, learning_method=AdamOptimizer())
#######################Network Configuration #############
data_size = 1 * 28 * 28
label_size = 10
img = data_layer(name='pixel', size=data_size)
# light cnn
# A shallower cnn model: [CNN, BN, ReLU, Max-Pooling] x4 + FC x1
# Easier to train for mnist dataset and quite efficient
# Final performance is close to deeper ones on tasks such as digital and character classification
def light_cnn(input_image, num_channels, num_classes):
def __light__(ipt,
num_filter=128,
times=1,
conv_filter_size=3,
dropouts=0,
num_channels_=None):
return img_conv_group(
input=ipt,
num_channels=num_channels_,
pool_size=2,
pool_stride=2,
conv_padding=0,
conv_num_filter=[num_filter] * times,
conv_filter_size=conv_filter_size,
conv_act=ReluActivation(),
conv_with_batchnorm=True,
conv_batchnorm_drop_rate=dropouts,
pool_type=MaxPooling())
tmp = __light__(input_image, num_filter=128, num_channels_=num_channels)
tmp = __light__(tmp, num_filter=128)
tmp = __light__(tmp, num_filter=128)
tmp = __light__(tmp, num_filter=128, conv_filter_size=1)
tmp = fc_layer(input=tmp, size=num_classes, act=SoftmaxActivation())
return tmp
predict = light_cnn(input_image=img, num_channels=1, num_classes=label_size)
if not is_predict:
lbl = data_layer(name="label", size=label_size)
inputs(img, lbl)
outputs(classification_cost(input=predict, label=lbl))
else:
outputs(predict)
from paddle.trainer.PyDataProvider2 import *
from mnist_util import read_from_mnist
# Define a py data provider
@provider(
input_types={'pixel': dense_vector(28 * 28),
'label': integer_value(10)},
cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, filename): # settings is not used currently.
for each in read_from_mnist(filename):
yield each
import numpy
__all__ = ['read_from_mnist']
def read_from_mnist(filename):
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
images = numpy.fromfile(
f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32')
images = images / 255.0 * 2.0 - 1.0
labels = numpy.fromfile(l, 'ubyte', count=n).astype("int")
for i in xrange(n):
yield {"pixel": images[i, :], 'label': labels[i]}
f.close()
l.close()
#!/bin/bash
# Copyright (c) 2016 PaddlePaddle Authors. 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
paddle usage -l $log -e $? -n "mnist_train" >/dev/null 2>&1
python -m paddle.utils.plotcurve -i $log > plot.png
# Copyright (c) 2016 PaddlePaddle Authors. 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)
inputs(img, lbl)
outputs(classification_cost(input=predict, label=lbl))
else:
outputs(predict)
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