提交 b6ca314c 编写于 作者: D dangqingqing

update

......@@ -4,22 +4,14 @@ cache:
- $HOME/third_party
- $HOME/.ccache
- $HOME/.cache/pip
- $HOME/Library/Caches/Homebrew
sudo: required
dist: trusty
os:
- linux
- osx
env:
- JOB=DOCS
- JOB=BUILD_AND_TEST
- JOB=PRE_COMMIT
matrix:
exclude:
- os: osx
env: JOB=DOCS # Only generate documentation in linux.
- os: osx
env: JOB=PRE_COMMIT # Only check pre-commit hook in linux
addons:
apt:
......@@ -53,7 +45,6 @@ before_install:
fi
fi
fi
- if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then paddle/scripts/travis/before_install.osx.sh; fi
- if [[ "$JOB" == "PRE_COMMIT" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
# Paddle is using protobuf 3.1 currently. Protobuf 3.2 breaks the compatibility. So we specify the python
# protobuf version.
......
......@@ -14,46 +14,50 @@
INCLUDE(ExternalProject)
SET(PROTOBUF_SOURCES_DIR ${THIRD_PARTY_PATH}/protobuf)
SET(PROTOBUF_INSTALL_DIR ${THIRD_PARTY_PATH}/install/protobuf)
SET(PROTOBUF_INCLUDE_DIR "${PROTOBUF_INSTALL_DIR}/include" CACHE PATH "protobuf include directory." FORCE)
FIND_PACKAGE(Protobuf)
INCLUDE_DIRECTORIES(${PROTOBUF_INCLUDE_DIR})
IF(NOT PROTOBUF_FOUND)
SET(PROTOBUF_SOURCES_DIR ${THIRD_PARTY_PATH}/protobuf)
SET(PROTOBUF_INSTALL_DIR ${THIRD_PARTY_PATH}/install/protobuf)
SET(PROTOBUF_INCLUDE_DIR "${PROTOBUF_INSTALL_DIR}/include" CACHE PATH "protobuf include directory." FORCE)
IF(WIN32)
SET(PROTOBUF_LITE_LIBRARY
"${PROTOBUF_INSTALL_DIR}/lib/libprotobuf-lite.lib" CACHE FILEPATH "protobuf lite library." FORCE)
SET(PROTOBUF_LIBRARY
"${PROTOBUF_INSTALL_DIR}/lib/libprotobuf.lib" CACHE FILEPATH "protobuf library." FORCE)
SET(PROTOBUF_PROTOC_LIBRARY
"${PROTOBUF_INSTALL_DIR}/lib/libprotoc.lib" CACHE FILEPATH "protoc library." FORCE)
SET(PROTOBUF_PROTOC_EXECUTABLE "${PROTOBUF_INSTALL_DIR}/bin/protoc.exe" CACHE FILEPATH "protobuf executable." FORCE)
ELSE(WIN32)
SET(PROTOBUF_LITE_LIBRARY
"${PROTOBUF_INSTALL_DIR}/lib/libprotobuf-lite.a" CACHE FILEPATH "protobuf lite library." FORCE)
SET(PROTOBUF_LIBRARY
"${PROTOBUF_INSTALL_DIR}/lib/libprotobuf.a" CACHE FILEPATH "protobuf library." FORCE)
SET(PROTOBUF_PROTOC_LIBRARY
"${PROTOBUF_INSTALL_DIR}/lib/libprotoc.a" CACHE FILEPATH "protoc library." FORCE)
SET(PROTOBUF_PROTOC_EXECUTABLE "${PROTOBUF_INSTALL_DIR}/bin/protoc" CACHE FILEPATH "protobuf executable." FORCE)
ENDIF(WIN32)
IF(WIN32)
SET(PROTOBUF_LITE_LIBRARY
"${PROTOBUF_INSTALL_DIR}/lib/libprotobuf-lite.lib" CACHE FILEPATH "protobuf lite library." FORCE)
SET(PROTOBUF_LIBRARY
"${PROTOBUF_INSTALL_DIR}/lib/libprotobuf.lib" CACHE FILEPATH "protobuf library." FORCE)
SET(PROTOBUF_PROTOC_LIBRARY
"${PROTOBUF_INSTALL_DIR}/lib/libprotoc.lib" CACHE FILEPATH "protoc library." FORCE)
SET(PROTOBUF_PROTOC_EXECUTABLE "${PROTOBUF_INSTALL_DIR}/bin/protoc.exe" CACHE FILEPATH "protobuf executable." FORCE)
ELSE(WIN32)
SET(PROTOBUF_LITE_LIBRARY
"${PROTOBUF_INSTALL_DIR}/lib/libprotobuf-lite.a" CACHE FILEPATH "protobuf lite library." FORCE)
SET(PROTOBUF_LIBRARY
"${PROTOBUF_INSTALL_DIR}/lib/libprotobuf.a" CACHE FILEPATH "protobuf library." FORCE)
SET(PROTOBUF_PROTOC_LIBRARY
"${PROTOBUF_INSTALL_DIR}/lib/libprotoc.a" CACHE FILEPATH "protoc library." FORCE)
SET(PROTOBUF_PROTOC_EXECUTABLE "${PROTOBUF_INSTALL_DIR}/bin/protoc" CACHE FILEPATH "protobuf executable." FORCE)
ENDIF(WIN32)
ExternalProject_Add(
protobuf
${EXTERNAL_PROJECT_LOG_ARGS}
PREFIX ${PROTOBUF_SOURCES_DIR}
UPDATE_COMMAND ""
DEPENDS zlib
GIT_REPOSITORY "https://github.com/google/protobuf.git"
GIT_TAG "9f75c5aa851cd877fb0d93ccc31b8567a6706546"
CONFIGURE_COMMAND
${CMAKE_COMMAND} ${PROTOBUF_SOURCES_DIR}/src/protobuf/cmake
-Dprotobuf_BUILD_TESTS=OFF
-DZLIB_ROOT:FILEPATH=${ZLIB_ROOT}
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_INSTALL_PREFIX=${PROTOBUF_INSTALL_DIR}
-DCMAKE_INSTALL_LIBDIR=lib
)
ExternalProject_Add(
protobuf
${EXTERNAL_PROJECT_LOG_ARGS}
PREFIX ${PROTOBUF_SOURCES_DIR}
UPDATE_COMMAND ""
DEPENDS zlib
GIT_REPOSITORY "https://github.com/google/protobuf.git"
GIT_TAG "9f75c5aa851cd877fb0d93ccc31b8567a6706546"
CONFIGURE_COMMAND
${CMAKE_COMMAND} ${PROTOBUF_SOURCES_DIR}/src/protobuf/cmake
-Dprotobuf_BUILD_TESTS=OFF
-DZLIB_ROOT:FILEPATH=${ZLIB_ROOT}
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_INSTALL_PREFIX=${PROTOBUF_INSTALL_DIR}
-DCMAKE_INSTALL_LIBDIR=lib
)
LIST(APPEND external_project_dependencies protobuf)
ENDIF(NOT PROTOBUF_FOUND)
LIST(APPEND external_project_dependencies protobuf)
INCLUDE_DIRECTORIES(${PROTOBUF_INCLUDE_DIR})
......@@ -221,7 +221,3 @@ ENDIF(PYTHONLIBS_FOUND AND PYTHONINTERP_FOUND)
INCLUDE_DIRECTORIES(${PYTHON_INCLUDE_DIR})
INCLUDE_DIRECTORIES(${PYTHON_NUMPY_INCLUDE_DIR})
MESSAGE("[Paddle] Python Executable: ${PYTHON_EXECUTABLE}")
MESSAGE("[Paddle] Python Include: ${PYTHON_INCLUDE_DIRS}")
MESSAGE("[Paddle] Python Libraries: ${PYTHON_LIBRARIES}")
......@@ -30,10 +30,11 @@ def main():
result = trainer.test(reader=paddle.reader.batched(
paddle.dataset.mnist.test(), batch_size=256))
print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics,
result.metrics)
print "Pass %d, Batch %d, Cost %.2f, %s\n" \
"Testing cost %.2f metrics %s" % (
event.pass_id, event.batch_id, event.cost,
event.metrics,
result.cost, result.metrics)
else:
pass
......
import sys
import math
import numpy as np
import paddle.v2 as paddle
import paddle.v2.dataset.conll05 as conll05
def db_lstm():
word_dict, verb_dict, label_dict = conll05.get_dict()
word_dict_len = len(word_dict)
label_dict_len = len(label_dict)
pred_len = len(verb_dict)
mark_dict_len = 2
word_dim = 32
mark_dim = 5
hidden_dim = 512
depth = 8
#8 features
def d_type(size):
return paddle.data_type.integer_value_sequence(size)
word = paddle.layer.data(name='word_data', type=d_type(word_dict_len))
predicate = paddle.layer.data(name='verb_data', type=d_type(pred_len))
ctx_n2 = paddle.layer.data(name='ctx_n2_data', type=d_type(word_dict_len))
ctx_n1 = paddle.layer.data(name='ctx_n1_data', type=d_type(word_dict_len))
ctx_0 = paddle.layer.data(name='ctx_0_data', type=d_type(word_dict_len))
ctx_p1 = paddle.layer.data(name='ctx_p1_data', type=d_type(word_dict_len))
ctx_p2 = paddle.layer.data(name='ctx_p2_data', type=d_type(word_dict_len))
mark = paddle.layer.data(name='mark_data', type=d_type(mark_dict_len))
target = paddle.layer.data(name='target', type=d_type(label_dict_len))
default_std = 1 / math.sqrt(hidden_dim) / 3.0
emb_para = paddle.attr.Param(name='emb', initial_std=0., learning_rate=0.)
std_0 = paddle.attr.Param(initial_std=0.)
std_default = paddle.attr.Param(initial_std=default_std)
predicate_embedding = paddle.layer.embedding(
size=word_dim,
input=predicate,
param_attr=paddle.attr.Param(
name='vemb', initial_std=default_std))
mark_embedding = paddle.layer.embedding(
size=mark_dim, input=mark, param_attr=std_0)
word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
emb_layers = [
paddle.layer.embedding(
size=word_dim, input=x, param_attr=emb_para) for x in word_input
]
emb_layers.append(predicate_embedding)
emb_layers.append(mark_embedding)
hidden_0 = paddle.layer.mixed(
size=hidden_dim,
bias_attr=std_default,
input=[
paddle.layer.full_matrix_projection(
input=emb, param_attr=std_default) for emb in emb_layers
])
mix_hidden_lr = 1e-3
lstm_para_attr = paddle.attr.Param(initial_std=0.0, learning_rate=1.0)
hidden_para_attr = paddle.attr.Param(
initial_std=default_std, learning_rate=mix_hidden_lr)
lstm_0 = paddle.layer.lstmemory(
input=hidden_0,
act=paddle.activation.Relu(),
gate_act=paddle.activation.Sigmoid(),
state_act=paddle.activation.Sigmoid(),
bias_attr=std_0,
param_attr=lstm_para_attr)
#stack L-LSTM and R-LSTM with direct edges
input_tmp = [hidden_0, lstm_0]
for i in range(1, depth):
mix_hidden = paddle.layer.mixed(
size=hidden_dim,
bias_attr=std_default,
input=[
paddle.layer.full_matrix_projection(
input=input_tmp[0], param_attr=hidden_para_attr),
paddle.layer.full_matrix_projection(
input=input_tmp[1], param_attr=lstm_para_attr)
])
lstm = paddle.layer.lstmemory(
input=mix_hidden,
act=paddle.activation.Relu(),
gate_act=paddle.activation.Sigmoid(),
state_act=paddle.activation.Sigmoid(),
reverse=((i % 2) == 1),
bias_attr=std_0,
param_attr=lstm_para_attr)
input_tmp = [mix_hidden, lstm]
feature_out = paddle.layer.mixed(
size=label_dict_len,
bias_attr=std_default,
input=[
paddle.layer.full_matrix_projection(
input=input_tmp[0], param_attr=hidden_para_attr),
paddle.layer.full_matrix_projection(
input=input_tmp[1], param_attr=lstm_para_attr)
], )
crf_cost = paddle.layer.crf(size=label_dict_len,
input=feature_out,
label=target,
param_attr=paddle.attr.Param(
name='crfw',
initial_std=default_std,
learning_rate=mix_hidden_lr))
crf_dec = paddle.layer.crf_decoding(
name='crf_dec_l',
size=label_dict_len,
input=feature_out,
label=target,
param_attr=paddle.attr.Param(name='crfw'))
return crf_cost, crf_dec
def load_parameter(file_name, h, w):
with open(file_name, 'rb') as f:
f.read(16) # skip header.
return np.fromfile(f, dtype=np.float32).reshape(h, w)
def main():
paddle.init(use_gpu=False, trainer_count=1)
# define network topology
crf_cost, crf_dec = db_lstm()
# create parameters
parameters = paddle.parameters.create([crf_cost, crf_dec])
# create optimizer
optimizer = paddle.optimizer.Momentum(
momentum=0,
learning_rate=2e-2,
regularization=paddle.optimizer.L2Regularization(rate=8e-4),
model_average=paddle.optimizer.ModelAverage(
average_window=0.5, max_average_window=10000), )
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics)
trainer = paddle.trainer.SGD(cost=crf_cost,
parameters=parameters,
update_equation=optimizer)
parameters.set('emb', load_parameter(conll05.get_embedding(), 44068, 32))
trn_reader = paddle.reader.batched(
paddle.reader.shuffle(
conll05.test(), buf_size=8192), batch_size=10)
reader_dict = {
'word_data': 0,
'ctx_n2_data': 1,
'ctx_n1_data': 2,
'ctx_0_data': 3,
'ctx_p1_data': 4,
'ctx_p2_data': 5,
'verb_data': 6,
'mark_data': 7,
'target': 8
}
trainer.train(
reader=trn_reader,
event_handler=event_handler,
num_passes=10000,
reader_dict=reader_dict)
if __name__ == '__main__':
main()
import sys
from os.path import join as join_path
import paddle.trainer_config_helpers.attrs as attrs
from paddle.trainer_config_helpers.poolings import MaxPooling
import paddle.v2.layer as layer
import paddle.v2.activation as activation
import paddle.v2.data_type as data_type
import paddle.v2.dataset.imdb as imdb
import paddle.v2 as paddle
def sequence_conv_pool(input,
input_size,
context_len,
hidden_size,
name=None,
context_start=None,
pool_type=None,
context_proj_layer_name=None,
context_proj_param_attr=False,
fc_layer_name=None,
fc_param_attr=None,
fc_bias_attr=None,
fc_act=None,
pool_bias_attr=None,
fc_attr=None,
context_attr=None,
pool_attr=None):
"""
Text convolution pooling layers helper.
Text input => Context Projection => FC Layer => Pooling => Output.
:param name: name of output layer(pooling layer name)
:type name: basestring
:param input: name of input layer
:type input: LayerOutput
:param context_len: context projection length. See
context_projection's document.
:type context_len: int
:param hidden_size: FC Layer size.
:type hidden_size: int
:param context_start: context projection length. See
context_projection's context_start.
:type context_start: int or None
:param pool_type: pooling layer type. See pooling_layer's document.
:type pool_type: BasePoolingType.
:param context_proj_layer_name: context projection layer name.
None if user don't care.
:type context_proj_layer_name: basestring
:param context_proj_param_attr: context projection parameter attribute.
None if user don't care.
:type context_proj_param_attr: ParameterAttribute or None.
:param fc_layer_name: fc layer name. None if user don't care.
:type fc_layer_name: basestring
:param fc_param_attr: fc layer parameter attribute. None if user don't care.
:type fc_param_attr: ParameterAttribute or None
:param fc_bias_attr: fc bias parameter attribute. False if no bias,
None if user don't care.
:type fc_bias_attr: ParameterAttribute or None
:param fc_act: fc layer activation type. None means tanh
:type fc_act: BaseActivation
:param pool_bias_attr: pooling layer bias attr. None if don't care.
False if no bias.
:type pool_bias_attr: ParameterAttribute or None.
:param fc_attr: fc layer extra attribute.
:type fc_attr: ExtraLayerAttribute
:param context_attr: context projection layer extra attribute.
:type context_attr: ExtraLayerAttribute
:param pool_attr: pooling layer extra attribute.
:type pool_attr: ExtraLayerAttribute
:return: output layer name.
:rtype: LayerOutput
"""
# Set Default Value to param
context_proj_layer_name = "%s_conv_proj" % name \
if context_proj_layer_name is None else context_proj_layer_name
with layer.mixed(
name=context_proj_layer_name,
size=input_size * context_len,
act=activation.Linear(),
layer_attr=context_attr) as m:
m += layer.context_projection(
input=input,
context_len=context_len,
context_start=context_start,
padding_attr=context_proj_param_attr)
fc_layer_name = "%s_conv_fc" % name \
if fc_layer_name is None else fc_layer_name
fl = layer.fc(name=fc_layer_name,
input=m,
size=hidden_size,
act=fc_act,
layer_attr=fc_attr,
param_attr=fc_param_attr,
bias_attr=fc_bias_attr)
return layer.pooling(
name=name,
input=fl,
pooling_type=pool_type,
bias_attr=pool_bias_attr,
layer_attr=pool_attr)
def convolution_net(input_dim,
class_dim=2,
emb_dim=128,
hid_dim=128,
is_predict=False):
data = layer.data("word", data_type.integer_value_sequence(input_dim))
emb = layer.embedding(input=data, size=emb_dim)
conv_3 = sequence_conv_pool(
input=emb, input_size=emb_dim, context_len=3, hidden_size=hid_dim)
conv_4 = sequence_conv_pool(
input=emb, input_size=emb_dim, context_len=4, hidden_size=hid_dim)
output = layer.fc(input=[conv_3, conv_4],
size=class_dim,
act=activation.Softmax())
lbl = layer.data("label", data_type.integer_value(2))
cost = layer.classification_cost(input=output, label=lbl)
return cost
def stacked_lstm_net(input_dim,
class_dim=2,
emb_dim=128,
hid_dim=512,
stacked_num=3,
is_predict=False):
"""
A Wrapper for sentiment classification task.
This network uses bi-directional recurrent network,
consisting three LSTM layers. This configure is referred to
the paper as following url, but use fewer layrs.
http://www.aclweb.org/anthology/P15-1109
input_dim: here is word dictionary dimension.
class_dim: number of categories.
emb_dim: dimension of word embedding.
hid_dim: dimension of hidden layer.
stacked_num: number of stacked lstm-hidden layer.
is_predict: is predicting or not.
Some layers is not needed in network when predicting.
"""
assert stacked_num % 2 == 1
layer_attr = attrs.ExtraLayerAttribute(drop_rate=0.5)
fc_para_attr = attrs.ParameterAttribute(learning_rate=1e-3)
lstm_para_attr = attrs.ParameterAttribute(initial_std=0., learning_rate=1.)
para_attr = [fc_para_attr, lstm_para_attr]
bias_attr = attrs.ParameterAttribute(initial_std=0., l2_rate=0.)
relu = activation.Relu()
linear = activation.Linear()
data = layer.data("word", data_type.integer_value_sequence(input_dim))
emb = layer.embedding(input=data, size=emb_dim)
fc1 = layer.fc(input=emb, size=hid_dim, act=linear, bias_attr=bias_attr)
lstm1 = layer.lstmemory(
input=fc1, act=relu, bias_attr=bias_attr, layer_attr=layer_attr)
inputs = [fc1, lstm1]
for i in range(2, stacked_num + 1):
fc = layer.fc(input=inputs,
size=hid_dim,
act=linear,
param_attr=para_attr,
bias_attr=bias_attr)
lstm = layer.lstmemory(
input=fc,
reverse=(i % 2) == 0,
act=relu,
bias_attr=bias_attr,
layer_attr=layer_attr)
inputs = [fc, lstm]
fc_last = layer.pooling(input=inputs[0], pooling_type=MaxPooling())
lstm_last = layer.pooling(input=inputs[1], pooling_type=MaxPooling())
output = layer.fc(input=[fc_last, lstm_last],
size=class_dim,
act=activation.Softmax(),
bias_attr=bias_attr,
param_attr=para_attr)
lbl = layer.data("label", data_type.integer_value(2))
cost = layer.classification_cost(input=output, label=lbl)
return cost
if __name__ == '__main__':
# init
paddle.init(use_gpu=True, trainer_count=4)
# network config
print 'load dictionary...'
word_dict = imdb.word_dict()
dict_dim = len(word_dict)
class_dim = 2
# Please choose the way to build the network
# by uncommenting the corresponding line.
cost = convolution_net(dict_dim, class_dim=class_dim)
# cost = stacked_lstm_net(dict_dim, class_dim=class_dim, stacked_num=3)
# create parameters
parameters = paddle.parameters.create(cost)
# create optimizer
adam_optimizer = paddle.optimizer.Adam(
learning_rate=2e-3,
regularization=paddle.optimizer.L2Regularization(rate=8e-4),
model_average=paddle.optimizer.ModelAverage(average_window=0.5))
# End batch and end pass event handler
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "\nPass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics)
else:
sys.stdout.write('.')
sys.stdout.flush()
if isinstance(event, paddle.event.EndPass):
result = trainer.test(
reader=paddle.reader.batched(
lambda: imdb.test(word_dict), batch_size=128),
reader_dict={'word': 0,
'label': 1})
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
# create trainer
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=adam_optimizer)
trainer.train(
reader=paddle.reader.batched(
paddle.reader.shuffle(
lambda: imdb.train(word_dict), buf_size=1000),
batch_size=100),
event_handler=event_handler,
reader_dict={'word': 0,
'label': 1},
num_passes=10)
......@@ -4,9 +4,10 @@ At training and testing time, PaddlePaddle programs need to read data. To ease t
- A *reader* is a function that reads data (from file, network, random number generator, etc) and yields data items.
- A *reader creator* is a function that returns a reader function.
- A *reader* decorator is a function, which accepts one or more readers, and returns a reader.
- A *reader decorator* is a function, which accepts one or more readers, and returns a reader.
- A *batch reader* is a function that reads data (from *reader*, file, network, random number generator, etc) and yields a batch of data items.
and provide frequently used reader creators and reader decorators.
and provide function which converts reader to batch reader, frequently used reader creators and reader decorators.
## Data Reader Interface
......@@ -37,9 +38,54 @@ def reader_creator_random_imageand_label(widht, height, label):
return reader
```
## Batch Reader Interface
*batch reader* can be any function with no parameter that creates a iterable (anything can be used in `for x in iterable`). The output of the iterable should be a batch (list) of data items. Each item inside the list must be a tuple.
Here are valid outputs:
```python
# a mini batch of three data items. Each data item consist three columns of data, each of which is 1.
[(1, 1, 1),
(2, 2, 2),
(3, 3, 3)]
# a mini batch of three data items, each data item is a list (single column).
[([1,1,1],),
([2,2,2],),
([3,3,3],),
```
Please note that each item inside the list must be a tuple, below is an invalid output:
```python
# wrong, [1,1,1] needs to be inside a tuple: ([1,1,1],).
# Otherwise it's ambiguous whether [1,1,1] means a single column of data [1, 1, 1],
# or three column of datas, each of which is 1.
[[1,1,1],
[2,2,2],
[3,3,3]]
```
It's easy to convert from reader to batch reader:
```python
mnist_train = paddle.dataset.mnist.train()
mnist_train_batch_reader = paddle.batch(mnist_train, 128)
```
Also easy to create custom batch reader:
```python
def custom_batch_reader():
while True:
batch = []
for i in xrange(128):
batch.append((numpy.random.uniform(-1, 1, 28*28),)) # note that it's a tuple being appended.
yield batch
mnist_random_image_batch_reader = custom_batch_reader
```
## Usage
data reader, mapping from item(s) read to data layer, batch size and number of total pass will be passed into `paddle.train`:
batch reader, mapping from item(s) read to data layer, batch size and number of total pass will be passed into `paddle.train`:
```python
# two data layer is created:
......@@ -47,8 +93,8 @@ image_layer = paddle.layer.data("image", ...)
label_layer = paddle.layer.data("label", ...)
# ...
paddle.train(paddle.dataset.mnist, {"image":0, "label":1}, 128, 10, ...)
batch_reader = paddle.batch(paddle.dataset.mnist.train(), 128)
paddle.train(batch_reader, {"image":0, "label":1}, 128, 10, ...)
```
## Data Reader Decorator
......@@ -64,7 +110,7 @@ Since reading data may take time and training can not proceed without data. It i
Use `paddle.reader.buffered` to prefetch data:
```python
buffered_reader = paddle.reader.buffered(paddle.dataset.mnist, 100)
buffered_reader = paddle.reader.buffered(paddle.dataset.mnist.train(), 100)
```
`buffered_reader` will try to buffer (prefetch) `100` data entries.
......@@ -91,10 +137,10 @@ def reader_creator_bool(t):
true_reader = reader_creator_bool(True)
false_reader = reader_creator_bool(False)
reader = paddle.reader.compose(paddle.dataset.mnist, data_reader_creator_random_image(20, 20), true_reader, false_reader)
# Skipped 1 because paddle.dataset.mnist produces two items per data entry.
reader = paddle.reader.compose(paddle.dataset.mnist.train(), data_reader_creator_random_image(20, 20), true_reader, false_reader)
# Skipped 1 because paddle.dataset.mnist.train() produces two items per data entry.
# And we don't care second item at this time.
paddle.train(reader, {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...)
paddle.train(paddle.batch(reader, 128), {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...)
```
### Shuffle
......@@ -103,16 +149,20 @@ Given shuffle buffer size `n`, `paddle.reader.shuffle` will return a data reader
Example:
```python
reader = paddle.reader.shuffle(paddle.dataset.mnist, 512)
reader = paddle.reader.shuffle(paddle.dataset.mnist.train(), 512)
```
## Q & A
### Why return only a single entry, but not a mini batch?
### Why reader return only a single entry, but not a mini batch?
Always returning a single entry make reusing existing data readers much easier (e.g., if existing reader return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2).
We provide function `paddle.batch` to turn (single entry) reader into batch reader.
If a mini batch is returned, data reader need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`.
### Why do we need batch reader, isn't train take reader and batch_size as arguments sufficient?
Practically, always return a single entry make reusing existing data readers much easier (e.g., if existing reader return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2).
In most of the case, train taking reader and batch_size as arguments would be sufficent. However sometimes user want to customize order of data entries inside a mini batch. Or even change batch size dynamically.
### Why use a dictionary but not a list to provide mapping?
......@@ -137,7 +187,7 @@ def image_reader_creator(image_path, label_path, n):
# images_reader_creator creates a reader
reader = image_reader_creator("/path/to/image_file", "/path/to/label_file", 1024)
paddle.train(reader, {"image":0, "label":1}, ...)
paddle.train(paddle.batch(reader, 128), {"image":0, "label":1}, ...)
```
### How is `paddle.train` implemented
......@@ -145,17 +195,8 @@ paddle.train(reader, {"image":0, "label":1}, ...)
An example implementation of paddle.train could be:
```python
def make_minibatch(reader, minibatch_size):
def ret():
r = reader()
buf = [r.next() for x in xrange(minibatch_size)]
while len(buf) > 0:
yield buf
buf = [r.next() for x in xrange(minibatch_size)]
return ret
def train(reader, mapping, batch_size, total_pass):
def train(batch_reader, mapping, batch_size, total_pass):
for pass_idx in range(total_pass):
for mini_batch in make_minibatch(reader): # this loop will never end in online learning.
for mini_batch in batch_reader(): # this loop will never end in online learning.
do_forward_backward(mini_batch, mapping)
```
......@@ -132,7 +132,8 @@ def startPaddle(idMap={}, train_args_dict=None):
logDir = JOB_PATH_OUTPUT + "/node_" + str(trainerId)
if not os.path.exists(JOB_PATH_OUTPUT):
os.makedirs(JOB_PATH_OUTPUT)
os.mkdir(logDir)
if not os.path.exists(logDir):
os.mkdir(logDir)
copyCommand = 'cp -rf ' + JOB_PATH + \
"/" + str(trainerId) + "/data/*" + " ./data/"
os.system(copyCommand)
......
#!/bin/bash
brew update
brew tap homebrew/science
brew install openblas swig md5sha1sum
......@@ -2,18 +2,11 @@
source ./common.sh
NPROC=1
if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then
export PYTHONPATH=/opt/python/2.7.12/lib/python2.7/site-packages
export PYTHONHOME=/opt/python/2.7.12
export PATH=/opt/python/2.7.12/bin:${PATH}
cmake .. -DCMAKE_Fortran_COMPILER=/usr/bin/gfortran-4.8 -DON_TRAVIS=ON -DON_COVERALLS=ON -DCOVERALLS_UPLOAD=ON ${EXTRA_CMAKE_OPTS}
NRPOC=`nproc`
make -j $NPROC
make coveralls
sudo make install
elif [[ "$TRAVIS_OS_NAME" == "osx" ]]; then
export PYTHONPATH=/usr/local/lib/python2.7/site-packages
cmake .. -DON_TRAVIS=ON -DON_COVERALLS=ON -DCOVERALLS_UPLOAD=ON ${EXTRA_CMAKE_OPTS}
NPROC=`sysctl -n hw.ncpu`
make -j $NPROC
fi
export PYTHONPATH=/opt/python/2.7.12/lib/python2.7/site-packages
export PYTHONHOME=/opt/python/2.7.12
export PATH=/opt/python/2.7.12/bin:${PATH}
cmake .. -DCMAKE_Fortran_COMPILER=/usr/bin/gfortran-4.8 -DON_TRAVIS=ON -DON_COVERALLS=ON -DCOVERALLS_UPLOAD=ON ${EXTRA_CMAKE_OPTS}
NRPOC=`nproc`
make -j $NPROC
make coveralls
sudo make install
......@@ -17,6 +17,9 @@ import imikolov
import imdb
import cifar
import movielens
import conll05
import uci_housing
__all__ = ['mnist', 'imikolov', 'imdb', 'cifar', 'movielens', 'uci_housing']
__all__ = [
'mnist', 'imikolov', 'imdb', 'cifar', 'movielens', 'conll05', 'uci_housing'
]
......@@ -12,10 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.v2.dataset.common
import tarfile
import gzip
import itertools
from common import download
__all__ = ['test, get_dict', 'get_embedding']
"""
......@@ -160,7 +160,6 @@ def reader_creator(corpus_reader,
ctx_p2 = 'eos'
word_idx = [word_dict.get(w, UNK_IDX) for w in sentence]
pred_idx = [predicate_dict.get(predicate)] * sen_len
ctx_n2_idx = [word_dict.get(ctx_n2, UNK_IDX)] * sen_len
ctx_n1_idx = [word_dict.get(ctx_n1, UNK_IDX)] * sen_len
......@@ -168,38 +167,30 @@ def reader_creator(corpus_reader,
ctx_p1_idx = [word_dict.get(ctx_p1, UNK_IDX)] * sen_len
ctx_p2_idx = [word_dict.get(ctx_p2, UNK_IDX)] * sen_len
pred_idx = [predicate_dict.get(predicate)] * sen_len
label_idx = [label_dict.get(w) for w in labels]
yield word_idx, pred_idx, ctx_n2_idx, ctx_n1_idx, \
ctx_0_idx, ctx_p1_idx, ctx_p2_idx, mark, label_idx
yield word_idx, ctx_n2_idx, ctx_n1_idx, \
ctx_0_idx, ctx_p1_idx, ctx_p2_idx, pred_idx, mark, label_idx
return reader()
return reader
def get_dict():
word_dict = load_dict(
common.download(WORDDICT_URL, 'conll05st', WORDDICT_MD5))
verb_dict = load_dict(
common.download(VERBDICT_URL, 'conll05st', VERBDICT_MD5))
label_dict = load_dict(
common.download(TRGDICT_URL, 'conll05st', TRGDICT_MD5))
word_dict = load_dict(download(WORDDICT_URL, 'conll05st', WORDDICT_MD5))
verb_dict = load_dict(download(VERBDICT_URL, 'conll05st', VERBDICT_MD5))
label_dict = load_dict(download(TRGDICT_URL, 'conll05st', TRGDICT_MD5))
return word_dict, verb_dict, label_dict
def get_embedding():
return common.download(EMB_URL, 'conll05st', EMB_MD5)
return download(EMB_URL, 'conll05st', EMB_MD5)
def test():
word_dict, verb_dict, label_dict = get_dict()
reader = corpus_reader(
common.download(DATA_URL, 'conll05st', DATA_MD5),
download(DATA_URL, 'conll05st', DATA_MD5),
words_name='conll05st-release/test.wsj/words/test.wsj.words.gz',
props_name='conll05st-release/test.wsj/props/test.wsj.props.gz')
return reader_creator(reader, word_dict, verb_dict, label_dict)
if __name__ == '__main__':
print get_embedding()
for f in test():
print f
......@@ -116,3 +116,8 @@ def test(word_idx):
return reader_creator(
re.compile("aclImdb/test/pos/.*\.txt$"),
re.compile("aclImdb/test/neg/.*\.txt$"), word_idx, 1000)
def word_dict():
return build_dict(
re.compile("aclImdb/((train)|(test))/((pos)|(neg))/.*\.txt$"), 150)
......@@ -34,8 +34,9 @@ class WithMetric(object):
class TestResult(WithMetric):
def __init__(self, evaluator):
def __init__(self, evaluator, cost):
super(TestResult, self).__init__(evaluator)
self.cost = cost
class BeginPass(object):
......
......@@ -108,9 +108,6 @@ class SGD(ITrainer):
pass_evaluator.start()
updater.startPass()
for batch_id, data_batch in enumerate(reader()):
pass_type = updater.startBatch(len(data_batch))
self.__gradient_machine__.forwardBackward(
feeder(data_batch), out_args, pass_type)
batch_evaluator.start()
event_handler(
v2_event.BeginIteration(
......@@ -123,10 +120,8 @@ class SGD(ITrainer):
for each_param in self.__gradient_machine__.getNonStaticParameters(
):
updater.update(each_param)
# Get cost. We use numpy to calculate total cost for this batch.
cost_vec = out_args.getSlotValue(0)
cost_vec = cost_vec.copyToNumpyMat()
cost = cost_vec.sum() / len(data_batch)
cost_sum = out_args.sumCosts()
cost = cost_sum / len(data_batch)
updater.finishBatch(cost)
batch_evaluator.finish()
event_handler(
......@@ -155,13 +150,18 @@ class SGD(ITrainer):
evaluator = self.__gradient_machine__.makeEvaluator()
out_args = api.Arguments.createArguments(0)
evaluator.start()
total_cost = 0
num_samples = 0.0
for data_batch in reader():
num_samples += len(data_batch)
self.__gradient_machine__.forward(
feeder(data_batch), out_args, api.PASS_TEST)
total_cost += out_args.sumCosts()
self.__gradient_machine__.eval(evaluator)
evaluator.finish()
return v2_event.TestResult(evaluator=evaluator)
return v2_event.TestResult(
evaluator=evaluator, cost=total_cost / num_samples)
def __check_train_args__(reader, event_handler, **kwargs):
......
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