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提交 37806792 编写于 作者: Q qiaolongfei
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demo/image_classification/api_v2_train.py
浏览文件 @ 37806792
......@@ -13,8 +13,9 @@
# limitations under the License
import sys
import paddle.v2 as paddle
from api_v2_vgg import vgg_bn_drop
from api_v2_resnet import resnet_cifar10
......@@ -23,7 +24,7 @@ def main():
classdim = 10
# PaddlePaddle init
paddle.init(use_gpu=True, trainer_count=1)
paddle.init(use_gpu=False, trainer_count=1)
image = paddle.layer.data(
name="image", type=paddle.data_type.dense_vector(datadim))
......@@ -68,8 +69,8 @@ def main():
result = trainer.test(
reader=paddle.batch(
paddle.dataset.cifar.test10(), batch_size=128),
reader_dict={'image': 0,
'label': 1})
feeding={'image': 0,
'label': 1})
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
# Create trainer
......@@ -83,8 +84,8 @@ def main():
batch_size=128),
num_passes=5,
event_handler=event_handler,
reader_dict={'image': 0,
'label': 1})
feeding={'image': 0,
'label': 1})
if __name__ == '__main__':
......
demo/introduction/api_train_v2.py
浏览文件 @ 37806792
......@@ -30,26 +30,26 @@ def main():
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)
print "Pass %d, Batch %d, Cost %f" % (
event.pass_id, event.batch_id, event.cost)
if isinstance(event, paddle.event.EndPass):
result = trainer.test(
reader=paddle.reader.batched(
uci_housing.test(), batch_size=2),
reader_dict={'x': 0,
if (event.pass_id + 1) % 10 == 0:
result = trainer.test(
reader=paddle.batch(
uci_housing.test(), batch_size=2),
feeding={'x': 0,
'y': 1})
if event.pass_id % 10 == 0:
print "Test %d, %s" % (event.pass_id, result.metrics)
print "Test %d, %.2f" % (event.pass_id, result.cost)
# training
trainer.train(
reader=paddle.reader.batched(
reader=paddle.batch(
paddle.reader.shuffle(
uci_housing.train(), buf_size=500),
batch_size=2),
reader_dict={'x': 0,
'y': 1},
feeding={'x': 0,
'y': 1},
event_handler=event_handler,
num_passes=30)
......
demo/mnist/.gitignore
浏览文件 @ 37806792
......@@ -5,3 +5,6 @@ plot.png
train.log
*pyc
.ipynb_checkpoints
params.pkl
params.tar
params.tar.gz
demo/mnist/api_train_v2.py
浏览文件 @ 37806792
import paddle.v2 as paddle
import gzip
def softmax_regression(img):
......@@ -71,7 +72,11 @@ def main():
cost = paddle.layer.classification_cost(input=predict, label=label)
parameters = paddle.parameters.create(cost)
try:
with gzip.open('params.tar.gz', 'r') as f:
parameters = paddle.parameters.Parameters.from_tar(f)
except IOError:
parameters = paddle.parameters.create(cost)
optimizer = paddle.optimizer.Momentum(
learning_rate=0.1 / 128.0,
......@@ -86,11 +91,19 @@ def main():
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)
if isinstance(event, paddle.event.EndPass):
result = trainer.test(reader=paddle.reader.batched(
if event.batch_id % 1000 == 0:
result = trainer.test(reader=paddle.batch(
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)
with gzip.open('params.tar.gz', 'w') as f:
parameters.to_tar(f)
elif isinstance(event, paddle.event.EndPass):
result = trainer.test(reader=paddle.batch(
paddle.dataset.mnist.test(), batch_size=128))
print "Test with Pass %d, Cost %f, %s\n" % (
event.pass_id, result.cost, result.metrics)
......
demo/semantic_role_labeling/api_train_v2.py
浏览文件 @ 37806792
......@@ -163,11 +163,11 @@ def main():
update_equation=optimizer)
parameters.set('emb', load_parameter(conll05.get_embedding(), 44068, 32))
trn_reader = paddle.reader.batched(
trn_reader = paddle.batch(
paddle.reader.shuffle(
conll05.test(), buf_size=8192), batch_size=10)
reader_dict = {
feeding = {
'word_data': 0,
'ctx_n2_data': 1,
'ctx_n1_data': 2,
......@@ -183,7 +183,7 @@ def main():
reader=trn_reader,
event_handler=event_handler,
num_passes=10000,
reader_dict=reader_dict)
feeding=feeding)
if __name__ == '__main__':
......
demo/sentiment/train_v2.py
浏览文件 @ 37806792
......@@ -18,11 +18,7 @@ from paddle.trainer_config_helpers.poolings import MaxPooling
import paddle.v2 as paddle
def convolution_net(input_dim,
class_dim=2,
emb_dim=128,
hid_dim=128,
is_predict=False):
def convolution_net(input_dim, class_dim=2, emb_dim=128, hid_dim=128):
data = paddle.layer.data("word",
paddle.data_type.integer_value_sequence(input_dim))
emb = paddle.layer.embedding(input=data, size=emb_dim)
......@@ -42,8 +38,7 @@ def stacked_lstm_net(input_dim,
class_dim=2,
emb_dim=128,
hid_dim=512,
stacked_num=3,
is_predict=False):
stacked_num=3):
"""
A Wrapper for sentiment classification task.
This network uses bi-directional recurrent network,
......@@ -110,7 +105,7 @@ def stacked_lstm_net(input_dim,
if __name__ == '__main__':
# init
paddle.init(use_gpu=True, trainer_count=4)
paddle.init(use_gpu=False, trainer_count=4)
# network config
print 'load dictionary...'
......@@ -143,11 +138,11 @@ if __name__ == '__main__':
sys.stdout.flush()
if isinstance(event, paddle.event.EndPass):
result = trainer.test(
reader=paddle.reader.batched(
reader=paddle.batch(
lambda: paddle.dataset.imdb.test(word_dict),
batch_size=128),
reader_dict={'word': 0,
'label': 1})
feeding={'word': 0,
'label': 1})
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
# create trainer
......@@ -156,11 +151,11 @@ if __name__ == '__main__':
update_equation=adam_optimizer)
trainer.train(
reader=paddle.reader.batched(
reader=paddle.batch(
paddle.reader.shuffle(
lambda: paddle.dataset.imdb.train(word_dict), buf_size=1000),
batch_size=100),
event_handler=event_handler,
reader_dict={'word': 0,
'label': 1},
feeding={'word': 0,
'label': 1},
num_passes=10)
demo/seqToseq/api_train_v2.py
浏览文件 @ 37806792
......@@ -110,11 +110,12 @@ def main():
update_equation=optimizer)
# define data reader
reader_dict = {
feeding = {
'source_language_word': 0,
'target_language_word': 1,
'target_language_next_word': 2
}
wmt14_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.wmt14.train(dict_size=dict_size), buf_size=8192),
......@@ -132,7 +133,7 @@ def main():
reader=wmt14_reader,
event_handler=event_handler,
num_passes=10000,
reader_dict=reader_dict)
feeding=feeding)
if __name__ == '__main__':
......
doc/api/index_cn.rst
浏览文件 @ 37806792
API
===
\ No newline at end of file
===
模型配置 API
------------
.. toctree::
:maxdepth: 1
v2/model_configs.rst
数据 API
--------
.. toctree::
:maxdepth: 1
v2/data.rst
训练 API
--------
.. toctree::
:maxdepth: 1
v2/run_logic.rst
\ No newline at end of file
doc/api/index_en.rst
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......@@ -7,4 +7,20 @@ Model Config API
.. toctree::
:maxdepth: 1
v2/model_configs.rst
\ No newline at end of file
v2/model_configs.rst
Data API
--------
.. toctree::
:maxdepth: 1
v2/data.rst
Train API
---------
.. toctree::
:maxdepth: 1
v2/run_logic.rst
\ No newline at end of file
doc/api/v2/data.rst 0 → 100644
浏览文件 @ 37806792
================
Data Related API
================
#########
DataTypes
#########
.. automodule:: paddle.v2.data_type
:members:
##########
DataFeeder
##########
.. automodule:: paddle.v2.data_feeder
:members:
######
Reader
######
.. automodule:: paddle.v2.reader
:members:
.. automodule:: paddle.v2.reader.creator
:members:
#########
minibatch
#########
.. automodule:: paddle.v2.minibatch
:members:
#######
Dataset
#######
.. automodule:: paddle.v2.dataset
:members:
mnist
+++++
.. automodule:: paddle.v2.dataset.mnist
:members:
cifar
+++++
.. automodule:: paddle.v2.dataset.cifar
:members:
conll05
+++++++
.. automodule:: paddle.v2.dataset.conll05
:members:
imdb
++++
.. automodule:: paddle.v2.dataset.imdb
:members:
imikolov
++++++++
.. automodule:: paddle.v2.dataset.imikolov
:members:
movielens
+++++++++
.. automodule:: paddle.v2.dataset.movielens
:members:
sentiment
+++++++++
.. automodule:: paddle.v2.dataset.sentiment
:members:
uci_housing
+++++++++++
.. automodule:: paddle.v2.dataset.uci_housing
:members:
doc/api/v2/model_configs.rst
浏览文件 @ 37806792
#########################
Configuration Related API
#########################
======
Layers
======
......@@ -33,3 +37,10 @@ Networks
.. automodule:: paddle.v2.networks
:members:
==========
Optimizers
==========
.. automodule:: paddle.v2.optimizer
:members:
doc/api/v2/run_logic.rst 0 → 100644
浏览文件 @ 37806792
###########
Trainer API
###########
==========
Parameters
==========
.. automodule:: paddle.v2.parameters
:members:
=======
Trainer
=======
.. automodule:: paddle.v2.trainer
:members:
=====
Event
=====
.. automodule:: paddle.v2.event
:members:
doc/design/reader/README.md
浏览文件 @ 37806792
......@@ -23,19 +23,19 @@ An example implementation for single item data reader creator:
```python
def reader_creator_random_image(width, height):
def reader():
while True:
yield numpy.random.uniform(-1, 1, size=width*height)
return reader
def reader():
while True:
yield numpy.random.uniform(-1, 1, size=width*height)
return reader
```
An example implementation for multiple item data reader creator:
```python
def reader_creator_random_imageand_label(widht, height, label):
def reader():
while True:
yield numpy.random.uniform(-1, 1, size=width*height), label
return reader
def reader_creator_random_image_and_label(width, height, label):
def reader():
while True:
yield numpy.random.uniform(-1, 1, size=width*height), label
return reader
```
## Batch Reader Interface
......@@ -74,11 +74,11 @@ 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
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
```
......@@ -123,16 +123,16 @@ We can do:
```python
def reader_creator_random_image(width, height):
def reader():
while True:
yield numpy.random.uniform(-1, 1, size=width*height)
return reader
def reader():
while True:
yield numpy.random.uniform(-1, 1, size=width*height)
return reader
def reader_creator_bool(t):
def reader:
while True:
yield t
return reader
def reader:
while True:
yield t
return reader
true_reader = reader_creator_bool(True)
false_reader = reader_creator_bool(False)
......@@ -172,18 +172,18 @@ We decided to use dictionary (`{"image":0, "label":1}`) instead of list (`["imag
```python
def image_reader_creator(image_path, label_path, n):
def reader():
f = open(image_path)
l = open(label_path)
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 images[i, :], labels[i] # a single entry of data is created each time
f.close()
l.close()
return reader
def reader():
f = open(image_path)
l = open(label_path)
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 images[i, :], labels[i] # a single entry of data is created each time
f.close()
l.close()
return reader
# images_reader_creator creates a reader
reader = image_reader_creator("/path/to/image_file", "/path/to/label_file", 1024)
......@@ -196,7 +196,7 @@ An example implementation of paddle.train could be:
```python
def train(batch_reader, mapping, batch_size, total_pass):
for pass_idx in range(total_pass):
for mini_batch in batch_reader(): # this loop will never end in online learning.
do_forward_backward(mini_batch, mapping)
for pass_idx in range(total_pass):
for mini_batch in batch_reader(): # this loop will never end in online learning.
do_forward_backward(mini_batch, mapping)
```
doc/howto/usage/k8s/k8s_distributed_cn.md
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......@@ -43,22 +43,55 @@ docker push [YOUR_REPO]/paddle:mypaddle
注意上述命令中`[YOUR_REPO]`表示读者所使用的Docker镜像仓库地址，读者需要替换成自己使用的仓库地址。下文使用`[YOUR_REPO]/paddle:mypaddle`这个地址来表示此步骤所构建出的镜像。
### 上传训练文件
### 准备训练数据
本文使用PaddlePaddle官方的[recommendation demo](http://www.paddlepaddle.org/doc/demo/index.html#recommendation)作为这次训练的内容，我们将训练文件与数据放在一个job name命名的目录中，上传到volume所在的共享存储（使用不同分布式存储会有不同的挂载方式，需要要先挂载这个目录，然后拷贝数据）。完成后volume中的文件内容大致如下：
这里我们通过在Kubernetes集群上启动一个Job来下载并切割数据，也可以通过修改[k8s_train](./src/k8s_train/README.md)的内容来定制image.
```bash
[root@paddle-kubernetes-node0 mfs]# tree -d
在启动Job之前，需要根据不同的分布式存储来绑定一个[persistentVolumeClaim](https://kubernetes.io/docs/user-guide/persistent-volumes/),生成的数据将会存储在这个volume下.
```yaml
apiVersion: batch/v1
kind: Job
metadata:
name: paddle-data
spec:
template:
metadata:
name: pi
spec:
hostNetwork: true
containers:
- name: paddle-data
image: paddledev/paddle-tutorial:k8s_data
imagePullPolicy: Always
volumeMounts:
- mountPath: "/mnt"
name: nfs
env:
- name: OUT_DIR
value: /home/work/mfs/paddle-cluster-job
- name: SPLIT_COUNT
value: "3"
volumes:
- name: nfs
persistentVolumeClaim:
claimName: mfs
restartPolicy: Never
```
完成后volume中的文件内容大致如下：
```base
[root@paddle-kubernetes-node0 nfsdir]$ tree -d
.
└── paddle-cluster-job
├── data
│   ├── 0
│   │
│   ├── 1
│   │
│   └── 2
├── output
└── recommendation
`-- paddle-cluster-job
|-- 0
| `-- data
|-- 1
| `-- data
|-- 2
| `-- data
|-- output
|-- quick_start
```
目录中paddle-cluster-job是本次训练对应的job name，本次训练要求有3个PaddlePaddle节点，在paddle-cluster-job/data目录中存放切分好的数据，文件夹0，1，2分别代表3个节点的trainer_id。recommendation文件夹内存放训练文件，output文件夹存放训练结果与日志。
......@@ -118,15 +151,16 @@ spec:
`env`字段表示容器的环境变量，我们将`paddle`运行的一些参数通过这种方式传递到容器内。
`JOB_PATH`表示共享存储挂载的路径，`JOB_NAME`表示job名字，`TRAIN_CONFIG_DIR`表示本次训练文件所在目录，这三个变量组合就可以找到本次训练需要的文件路径。
`CONF_PADDLE_NIC`表示`paddle pserver`进程需要的`--nics`参数，即网卡名
`CONF_PADDLE_PORT`表示`paddle pserver`的`--port`参数，`CONF_PADDLE_PORTS_NUM`则表示稠密更新的端口数量，也就是`--ports_num`参数。
`CONF_PADDLE_PORTS_NUM_SPARSE`表示稀疏更新的端口数量，也就是`--ports_num_for_sparse`参数。
`CONF_PADDLE_GRADIENT_NUM`表示训练节点数量，即`--num_gradient_servers`参数
环境变量 | 说明
--- | ---
JOB_PATH | 共享存储挂在的路径
JOB_NAME | Job的名字
TRAIN_CONFIG_DIR | 本次训练文件所在目录，与JOB_PATH,JOB_NAME组合可以找到本次训练需要的文件路径
CONF_PADDLE_NIC | `paddle pserver`进程需要的`--nics`参数，即网卡名
CONF_PADDLE_PORT | `paddle paserver`的`--port`参数
CONF_PADDLE_PORTS_NUM | 稠密更新的端口数量，即`--ports_num`参数
CONF_PADDLE_PORTS_NUM_SPARSE | 稀疏更新的端口数量，即`--ports_num_for_sparse`参数
CONF_PADDLE_GRADIENT_NUM | 训练节点数量，即`--num_gradient_servers参数`
这些参数的具体描述，读者可以查看[这里](http://www.paddlepaddle.org/doc/ui/cmd_argument/detail_introduction.html#parameter-server-and-distributed-communication)。
......
python/paddle/trainer/PyDataProvider2.py
浏览文件 @ 37806792
......@@ -45,6 +45,23 @@ class CacheType(object):
class InputType(object):
"""
InputType is the base class for paddle input types.
.. note::
this is a base class, and should never be used by user.
:param dim: dimension of input. If the input is an integer, it means the
value range. Otherwise, it means the size of layer.
:type dim: int
:param seq_type: sequence type of input. 0 means it is not a sequence. 1
means it is a variable length sequence. 2 means it is a
nested sequence.
:type seq_type: int
:param type: data type of input.
:type type: int
"""
__slots__ = ['dim', 'seq_type', 'type']
def __init__(self, dim, seq_type, tp):
......@@ -54,20 +71,61 @@ class InputType(object):
def dense_slot(dim, seq_type=SequenceType.NO_SEQUENCE):
"""
Dense Vector. It means the input feature is dense float vector. For example,
if the input is an image with 28*28 pixels, the input of Paddle neural
network should be a dense vector with dimension 784.
:param dim: dimension of this vector.
:type dim: int
:param seq_type: sequence type of input.
:type seq_type: int
:return: An input type object.
:rtype: InputType
"""
return InputType(dim, seq_type, DataType.Dense)
def sparse_non_value_slot(dim, seq_type=SequenceType.NO_SEQUENCE):
"""
Sparse binary vector. It means the input feature is a sparse vector and the
every element in this vector is either zero or one.
:param dim: dimension of this vector.
:type dim: int
:param seq_type: sequence type of this input.
:type seq_type: int
:return: An input type object.
:rtype: InputType
"""
return InputType(dim, seq_type, DataType.SparseNonValue)
def sparse_value_slot(dim, seq_type=SequenceType.NO_SEQUENCE):
"""
Sparse vector. It means the input feature is a sparse vector. Most of the
elements in this vector are zero, others could be any float value.
:param dim: dimension of this vector.
:type dim: int
:param seq_type: sequence type of this input.
:type seq_type: int
:return: An input type object.
:rtype: InputType
"""
return InputType(dim, seq_type, DataType.SparseValue)
def index_slot(value_range, seq_type=SequenceType.NO_SEQUENCE):
"""Data type of integer.
"""
Data type of integer.
:param seq_type: sequence type of this input.
:type seq_type: int
:param value_range: range of this integer.
:type value_range: int
:return: An input type object
:rtype: InputType
"""
return InputType(value_range, seq_type, DataType.Index)
......@@ -76,10 +134,17 @@ dense_vector = dense_slot
sparse_binary_vector = sparse_non_value_slot
sparse_vector = sparse_value_slot
integer_value = index_slot
integer_value.__doc__ = index_slot.__doc__
def dense_vector_sequence(dim):
"""
Data type of a sequence of dense vector.
:param dim: dimension of dense vector.
:type dim: int
:return: An input type object
:rtype: InputType
"""
return dense_vector(dim, seq_type=SequenceType.SEQUENCE)
......@@ -88,6 +153,15 @@ def dense_vector_sub_sequence(dim):
def sparse_binary_vector_sequence(dim):
"""
Data type of a sequence of sparse vector, which every element is either zero
or one.
:param dim: dimension of sparse vector.
:type dim: int
:return: An input type object
:rtype: InputType
"""
return sparse_binary_vector(dim, seq_type=SequenceType.SEQUENCE)
......@@ -96,6 +170,15 @@ def sparse_binary_vector_sub_sequence(dim):
def sparse_vector_sequence(dim):
"""
Data type of a sequence of sparse vector, which most elements are zero,
others could be any float value.
:param dim: dimension of sparse vector.
:type dim: int
:return: An input type object
:rtype: InputType
"""
return sparse_vector(dim, seq_type=SequenceType.SEQUENCE)
......@@ -104,8 +187,11 @@ def sparse_vector_sub_sequence(dim):
def integer_value_sequence(value_range):
"""Data type of a sequence of integer.
"""
Data type of a sequence of integer.
:param value_range: range of each element.
:type value_range: int
"""
return integer_value(value_range, seq_type=SequenceType.SEQUENCE)
......@@ -115,7 +201,6 @@ def integer_value_sub_sequence(dim):
integer_sequence = integer_value_sequence
integer_sequence.__doc__ = integer_value_sequence.__doc__
class SingleSlotWrapper(object):
......
python/paddle/v2/data_feeder.py
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......@@ -12,13 +12,20 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from py_paddle import swig_paddle
from py_paddle import DataProviderConverter
import data_type
import paddle.trainer.PyDataProvider2 as pydp2
__all__ = ['DataFeeder']
def default_feeding_map(data_types):
reader_dict = dict()
for i, tp in enumerate(data_types):
reader_dict[tp[0]] = i
return reader_dict
class DataFeeder(DataProviderConverter):
"""
DataFeeder converts the data returned by paddle.reader into a data structure
......@@ -29,7 +36,10 @@ class DataFeeder(DataProviderConverter):
to feed it to C++ interface.
The example usage:
.. code-block:: python
data_types = [('image', paddle.data_type.dense_vector(784)),
('label', paddle.data_type.integer_value(10))]
reader_dict = {'image':0, 'label':1}
......@@ -43,26 +53,35 @@ class DataFeeder(DataProviderConverter):
# [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ] # second sample
# ]
arg = feeder(minibatch_data)
.. note::
This module is for internal use only. Users should use the `reader`
interface.
:param data_types: A list to specify data name and type. Each item is
a tuple of (data_name, data_type).
:type data_types: list
:param reader_dict: A dictionary to specify the position of each data
in the input data.
:type feeding: dict
"""
def __init__(self, data_types, reader_dict):
"""
:param data_types: A list to specify data name and type. Each item is
a tuple of (data_name, data_type). For example:
[('image', paddle.data_type.dense_vector(784)),
('label', paddle.data_type.integer_value(10))]
:type data_types: A list of tuple
:param reader_dict: A dictionary to specify the position of each data
in the input data.
:type reader_dict: dict()
"""
def __init__(self, data_types, feeding=None):
self.input_names = []
input_types = []
self.reader_dict = reader_dict
if feeding is None:
feeding = default_feeding_map(data_types)
self.feeding = feeding
for each in data_types:
self.input_names.append(each[0])
assert isinstance(each[1], data_type.InputType)
if not isinstance(each[1], pydp2.InputType):
raise TypeError("second item in each data_type should be an "
"InputType")
input_types.append(each[1])
DataProviderConverter.__init__(self, input_types)
......@@ -70,22 +89,12 @@ class DataFeeder(DataProviderConverter):
"""
:param dat: A list of mini-batch data. Each sample is a list or tuple
one feature or multiple features.
for example:
[
([0.2, 0.2], ), # first sample
([0.8, 0.3], ), # second sample
]
or,
[
[[0.2, 0.2], ], # first sample
[[0.8, 0.3], ], # second sample
]
:type dat: List
:type dat: list
:param argument: An Arguments object contains this mini-batch data with
one or multiple features. The Arguments definition is
in the API.
:type argument: swig_paddle.Arguments
:type argument: py_paddle.swig_paddle.Arguments
"""
def reorder_data(data):
......@@ -93,7 +102,7 @@ class DataFeeder(DataProviderConverter):
for each in data:
reorder = []
for name in self.input_names:
reorder.append(each[self.reader_dict[name]])
reorder.append(each[self.feeding[name]])
retv.append(reorder)
return retv
......
python/paddle/v2/data_type.py
浏览文件 @ 37806792
......@@ -12,11 +12,15 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer.PyDataProvider2 import \
InputType, DataType, dense_vector, sparse_binary_vector,\
sparse_vector, integer_value, integer_value_sequence
import paddle.trainer.PyDataProvider2 as pydp2
__all__ = [
'InputType', 'DataType', 'dense_vector', 'sparse_binary_vector',
'sparse_vector', 'integer_value', 'integer_value_sequence'
import_list = [
nm for nm in dir(pydp2)
if '_' in nm and nm[0] != '_' and ('value' in nm or 'vector' in nm)
]
import_list.extend(['InputType'])
for nm in import_list:
globals()[nm] = getattr(pydp2, nm)
__all__ = import_list
python/paddle/v2/dataset/__init__.py
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......@@ -11,6 +11,9 @@
# 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.
"""
Dataset package.
"""
import mnist
import imikolov
......
python/paddle/v2/dataset/cifar.py
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......@@ -13,6 +13,8 @@
# limitations under the License.
"""
CIFAR dataset: https://www.cs.toronto.edu/~kriz/cifar.html
TODO(yuyang18): Complete the comments.
"""
import cPickle
......
python/paddle/v2/dataset/conll05.py
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......@@ -16,15 +16,17 @@ import tarfile
import gzip
import itertools
from common import download
__all__ = ['test, get_dict', 'get_embedding']
"""
Conll 2005 dataset. Paddle semantic role labeling Book and demo use this
dataset as an example. Because Conll 2005 is not free in public, the default
downloaded URL is test set of Conll 2005 (which is public). Users can change
URL and MD5 to their Conll dataset.
TODO(yuyang18): Complete comments.
"""
__all__ = ['test, get_dict', 'get_embedding']
DATA_URL = 'http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz'
DATA_MD5 = '387719152ae52d60422c016e92a742fc'
WORDDICT_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/wordDict.txt'
......
python/paddle/v2/dataset/imdb.py
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......@@ -13,6 +13,8 @@
# limitations under the License.
"""
IMDB dataset: http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz
TODO(yuyang18): Complete comments.
"""
import paddle.v2.dataset.common
......
python/paddle/v2/dataset/imikolov.py
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......@@ -13,6 +13,8 @@
# limitations under the License.
"""
imikolov's simple dataset: http://www.fit.vutbr.cz/~imikolov/rnnlm/
Complete comments.
"""
import paddle.v2.dataset.common
import tarfile
......
python/paddle/v2/dataset/mnist.py
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......@@ -13,6 +13,9 @@
# limitations under the License.
"""
MNIST dataset.
This module will download dataset from http://yann.lecun.com/exdb/mnist/ and
parse train set and test set into paddle reader creators.
"""
import paddle.v2.dataset.common
import subprocess
......@@ -72,6 +75,15 @@ def reader_creator(image_filename, label_filename, buffer_size):
def train():
"""
MNIST train set creator.
It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 9].
:return: Train reader creator
:rtype: callable
"""
return reader_creator(
paddle.v2.dataset.common.download(TRAIN_IMAGE_URL, 'mnist',
TRAIN_IMAGE_MD5),
......@@ -80,6 +92,15 @@ def train():
def test():
"""
MNIST test set cretor.
It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 9].
:return: Test reader creator.
:rtype: callable
"""
return reader_creator(
paddle.v2.dataset.common.download(TEST_IMAGE_URL, 'mnist',
TEST_IMAGE_MD5),
......
python/paddle/v2/dataset/movielens.py
浏览文件 @ 37806792
......@@ -11,6 +11,11 @@
# 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.
"""
Movielens 1-M dataset.
TODO(yuyang18): Complete comments.
"""
import zipfile
from common import download
......
python/paddle/v2/dataset/sentiment.py
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......@@ -15,18 +15,19 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""
The script fetch and preprocess movie_reviews data set
The script fetch and preprocess movie_reviews data set that provided by NLTK
that provided by NLTK
TODO(yuyang18): Complete dataset.
"""
import common
import collections
import nltk
import numpy as np
from itertools import chain
import nltk
from nltk.corpus import movie_reviews
import common
__all__ = ['train', 'test', 'get_word_dict']
NUM_TRAINING_INSTANCES = 1600
NUM_TOTAL_INSTANCES = 2000
......
python/paddle/v2/dataset/uci_housing.py
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......@@ -11,6 +11,11 @@
# 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.
"""
UCI Housing dataset.
TODO(yuyang18): Complete comments.
"""
import numpy as np
import os
......
python/paddle/v2/event.py
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......@@ -34,6 +34,10 @@ class WithMetric(object):
class TestResult(WithMetric):
"""
Result that trainer.test return.
"""
def __init__(self, evaluator, cost):
super(TestResult, self).__init__(evaluator)
self.cost = cost
......
python/paddle/v2/inference.py
浏览文件 @ 37806792
......@@ -21,10 +21,8 @@ class Inference(object):
self.__gradient_machine__ = gm
self.__data_types__ = topo.data_type()
def iter_infer(self, reader, reader_dict=None):
if reader_dict is None:
reader_dict = self.default_reader_dict()
feeder = DataFeeder(self.__data_types__, reader_dict)
def iter_infer(self, reader, feeding=None):
feeder = DataFeeder(self.__data_types__, feeding)
self.__gradient_machine__.start()
for data_batch in reader():
yield self.__gradient_machine__.forwardTest(feeder(data_batch))
......@@ -47,13 +45,7 @@ class Inference(object):
else:
return retv
def default_reader_dict(self):
reader_dict = dict()
for i, tp in enumerate(self.__data_types__):
reader_dict[tp[0]] = i
return reader_dict
def infer(output, parameters, reader, reader_dict=None, field='value'):
def infer(output, parameters, reader, feeding=None, field='value'):
inferer = Inference(output=output, parameters=parameters)
return inferer.infer(field=field, reader=reader, reader_dict=reader_dict)
return inferer.infer(field=field, reader=reader, feeding=feeding)
python/paddle/v2/minibatch.py
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......@@ -12,24 +12,30 @@
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = ['batch']
def batch(reader, batch_size):
"""
Create a batch reader.
Create a batched reader.
:param reader: the data reader to read from.
:param batch_size: batch_size
:return: the batch reader.
:type reader: callable
:param batch_size: size of each mini-batch
:type batch_size: int
:return: the batched reader.
:rtype: callable
"""
def batch_reader():
r = reader()
batch = []
b = []
for instance in r:
batch.append(instance)
if len(batch) == batch_size:
yield batch
batch = []
if batch:
yield batch
b.append(instance)
if len(b) == batch_size:
yield b
b = []
if b:
yield b
return batch_reader
python/paddle/v2/optimizer.py
浏览文件 @ 37806792
import py_paddle.swig_paddle as swig_api
import paddle.trainer_config_helpers.optimizers as v1_optimizers
import paddle.trainer_config_helpers.config_parser_utils as config_parser_utils
import paddle.v2
import paddle.trainer_config_helpers.optimizers as v1_optimizers
"""
Optimizers(update equation) for SGD method.
TODO(yuyang18): Complete comments.
"""
__all__ = [
'Momentum', 'Adam', 'Adamax', 'AdaGrad', 'DecayedAdaGrad', 'AdaDelta',
......@@ -44,7 +49,7 @@ class Optimizer(object):
class Momentum(Optimizer):
def __init__(self, momentum=None, sparse=False, **kwargs):
learning_method = v1_optimizers.MomentumOptimizer(
momentum=None, sparse=False)
momentum=momentum, sparse=sparse)
super(Momentum, self).__init__(
learning_method=learning_method, **kwargs)
......
python/paddle/v2/parameters.py
浏览文件 @ 37806792
import numpy as np
import py_paddle.swig_paddle as api
from paddle.proto.ParameterConfig_pb2 import ParameterConfig
import struct
import tarfile
import cStringIO
from topology import Topology
__all__ = ['Parameters', 'create']
......@@ -10,6 +12,7 @@ __all__ = ['Parameters', 'create']
def create(layers):
"""
Create parameter pool by topology.
:param layers:
:return:
"""
......@@ -67,6 +70,7 @@ class Parameters(object):
def keys(self):
"""
keys are the names of each parameter.
:return: list of parameter name
:rtype: list
"""
......@@ -75,6 +79,7 @@ class Parameters(object):
def names(self):
"""
names of each parameter.
:return: list of parameter name
:rtype: list
"""
......@@ -83,6 +88,7 @@ class Parameters(object):
def has_key(self, key):
"""
has_key return true if there are such parameter name == key
:param key: Parameter name
:type key: basestring
:return: True if contains such key
......@@ -118,6 +124,12 @@ class Parameters(object):
if len(self.__gradient_machines__) == 0:
# create new parameter in python numpy.
if len(self.__tmp_params__) != 0:
ret_list = [
mat for name, mat in self.__tmp_params__ if name == key
]
if len(ret_list) == 1:
return ret_list[0]
return np.ndarray(shape=shape, dtype=np.float32)
else:
for each_gradient_machine in self.__gradient_machines__:
......@@ -136,6 +148,7 @@ class Parameters(object):
def get_shape(self, key):
"""
get shape of the parameter.
:param key: parameter name
:type key: basestring
:return: parameter's shape
......@@ -190,6 +203,7 @@ class Parameters(object):
def set(self, parameter_name, value):
"""
Set parameter by parameter name & matrix.
:param parameter_name: parameter name
:type parameter_name: basestring
:param value: parameter matrix
......@@ -222,6 +236,67 @@ class Parameters(object):
self.__gradient_machines__.append(gradient_machine)
def serialize(self, name, f):
"""
:param name:
:param f:
:type f: file
:return:
"""
param = self.get(name)
size = reduce(lambda a, b: a * b, param.shape)
f.write(struct.pack("IIQ", 0, 4, size))
param = param.astype(np.float32)
f.write(param.tobytes())
def deserialize(self, name, f):
"""
:param name:
:param f:
:type f: file
:return:
"""
f.read(16) # header
arr = np.frombuffer(f.read(), dtype=np.float32)
self.set(name, arr.reshape(self.get_shape(name)))
def to_tar(self, f):
tar = tarfile.TarFile(fileobj=f, mode='w')
for nm in self.names():
buf = cStringIO.StringIO()
self.serialize(nm, buf)
tarinfo = tarfile.TarInfo(name=nm)
buf.seek(0)
tarinfo.size = len(buf.getvalue())
tar.addfile(tarinfo, buf)
conf = self.__param_conf__[nm]
confStr = conf.SerializeToString()
tarinfo = tarfile.TarInfo(name="%s.protobuf" % nm)
tarinfo.size = len(confStr)
buf = cStringIO.StringIO(confStr)
buf.seek(0)
tar.addfile(tarinfo, fileobj=buf)
@staticmethod
def from_tar(f):
params = Parameters()
tar = tarfile.TarFile(fileobj=f, mode='r')
for finfo in tar:
assert isinstance(finfo, tarfile.TarInfo)
if finfo.name.endswith('.protobuf'):
f = tar.extractfile(finfo)
conf = ParameterConfig()
conf.ParseFromString(f.read())
params.__append_config__(conf)
for param_name in params.names():
f = tar.extractfile(param_name)
params.deserialize(param_name, f)
return params
def __get_parameter_in_gradient_machine__(gradient_machine, name):
"""
......
python/paddle/v2/reader/__init__.py
浏览文件 @ 37806792
......@@ -11,15 +11,64 @@
# 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.
"""
At training and testing time, PaddlePaddle programs need to read data. To ease
the users' work to write data reading code, we define that
# It would be too lengthy to require our users to prefix decorators with `decorator`.
# For example, we want the following line
#
# r = paddle.reader.decorator.bufferd(paddle.reader.creator.text("hello.txt"))
#
# to be a shorter version:
#
# r = paddle.reader.buffered(paddle.reader.creator.text("hello.txt"))
- 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 *batch reader* is a function that reads data (from *reader*, file, network,
random number generator, etc) and yields a batch of data items.
#####################
Data Reader Interface
#####################
Indeed, *data reader* doesn't have to be a function that reads and yields data
items. It can be any function with no parameter that creates a iterable
(anything can be used in :code:`for x in iterable`)\:
.. code-block:: python
iterable = data_reader()
Element produced from the iterable should be a **single** entry of data,
**not** a mini batch. That entry of data could be a single item, or a tuple of
items.
Item should be of `supported type <http://www.paddlepaddle.org/doc/ui/data_provider
/pydataprovider2.html?highlight=dense_vector#input-types>`_ (e.g., numpy 1d
array of float32, int, list of int)
An example implementation for single item data reader creator:
.. code-block:: python
def reader_creator_random_image(width, height):
def reader():
while True:
yield numpy.random.uniform(-1, 1, size=width*height)
return reader
An example implementation for multiple item data reader creator:
.. code-block:: python
def reader_creator_random_image_and_label(width, height, label):
def reader():
while True:
yield numpy.random.uniform(-1, 1, size=width*height), label
return reader
TODO(yuyang18): Should we add whole design doc here?
"""
import decorator
from decorator import *
import creator
__all__ = decorator.__all__ + ['creator']
python/paddle/v2/reader/creator.py
浏览文件 @ 37806792
......@@ -11,6 +11,10 @@
# 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.
"""
Creator package contains some simple reader creator, which could be used in user
program.
"""
__all__ = ['np_array', 'text_file']
......@@ -38,7 +42,7 @@ def np_array(x):
def text_file(path):
"""
Creates a data reader that outputs text line by line from given text file.
Trailing new line ('\n') of each line will be removed.
Trailing new line ('\\\\n') of each line will be removed.
:path: path of the text file.
:returns: data reader of text file
......
python/paddle/v2/reader/decorator.py
浏览文件 @ 37806792
......@@ -28,9 +28,11 @@ def map_readers(func, *readers):
Creates a data reader that outputs return value of function using
output of each data readers as arguments.
:param func: function to use.
:param *readers: readers whose outputs will be used as arguments of func.
:returns: the created data reader.
:param func: function to use. The type of func should be (Sample) => Sample
:type: callable
:param readers: readers whose outputs will be used as arguments of func.
:return: the created data reader.
:rtype: callable
"""
def reader():
......@@ -45,16 +47,19 @@ def map_readers(func, *readers):
def shuffle(reader, buf_size):
"""
Creates a data reader whose data output is suffled.
Creates a data reader whose data output is shuffled.
Output from the iterator that created by original reader will be
buffered into shuffle buffer, and then shuffled. The size of shuffle buffer
is determined by argument buf_size.
:param reader: the original reader whose output will be shuffled.
:type reader: callable
:param buf_size: shuffle buffer size.
:type buf_size: int
:returns:the new reader whose output is shuffled.
:return: the new reader whose output is shuffled.
:rtype: callable
"""
def data_reader():
......@@ -88,7 +93,8 @@ def chain(*readers):
[0, 0, 0, 1, 1, 1, 2, 2, 2]
:param readers: input readers.
:returns: the new data reader.
:return: the new data reader.
:rtype: callable
"""
def reader():
......@@ -115,12 +121,13 @@ def compose(*readers, **kwargs):
The composed reader will output:
(1, 2, 3, 4, 5)
:*readers: readers that will be composed together.
:check_alignment: if True, will check if input readers are aligned
:param readers: readers that will be composed together.
:param check_alignment: if True, will check if input readers are aligned
correctly. If False, will not check alignment and trailing outputs
will be discarded. Defaults to True.
:type check_alignment: bool
:returns: the new data reader.
:return: the new data reader.
:raises ComposeNotAligned: outputs of readers are not aligned.
Will not raise when check_alignment is set to False.
......@@ -161,7 +168,9 @@ def buffered(reader, size):
as the buffer is not empty.
:param reader: the data reader to read from.
:type reader: callable
:param size: max buffer size.
:type size: int
:returns: the buffered data reader.
"""
......@@ -196,6 +205,13 @@ def buffered(reader, size):
def firstn(reader, n):
"""
Limit the max number of samples that reader could return.
:param reader: the data reader to read from.
:type reader: callable
:param n: the max number of samples that return.
:type n: int
:return: the decorated reader.
:rtype: callable
"""
# TODO(yuyang18): Check if just drop the reader, could clean the opened
......
python/paddle/v2/tests/run_tests.sh
浏览文件 @ 37806792
......@@ -22,7 +22,7 @@ cd $SCRIPTPATH
$1 -m pip install ../../../../paddle/dist/*.whl
test_list="test_data_feeder.py"
test_list="test_data_feeder.py test_parameters.py"
export PYTHONPATH=$PWD/../../../../python/
......
python/paddle/v2/tests/test_parameters.py 0 → 100644
浏览文件 @ 37806792
import unittest
import sys
try:
import py_paddle
del py_paddle
except ImportError:
print >> sys.stderr, "It seems swig of Paddle is not installed, this " \
"unittest will not be run."
sys.exit(0)
import paddle.v2.parameters as parameters
from paddle.proto.ParameterConfig_pb2 import ParameterConfig
import random
import cStringIO
import numpy
def __rand_param_config__(name):
conf = ParameterConfig()
conf.name = name
size = 1
for i in xrange(2):
dim = random.randint(1, 1000)
conf.dims.append(dim)
size *= dim
conf.size = size
assert conf.IsInitialized()
return conf
class TestParameters(unittest.TestCase):
def test_serialization(self):
params = parameters.Parameters()
params.__append_config__(__rand_param_config__("param_0"))
params.__append_config__(__rand_param_config__("param_1"))
for name in params.names():
param = params.get(name)
param[:] = numpy.random.uniform(
-1.0, 1.0, size=params.get_shape(name))
params.set(name, param)
tmp_file = cStringIO.StringIO()
params.to_tar(tmp_file)
tmp_file.seek(0)
params_dup = parameters.Parameters.from_tar(tmp_file)
self.assertEqual(params_dup.names(), params.names())
for name in params.names():
self.assertEqual(params.get_shape(name), params_dup.get_shape(name))
p0 = params.get(name)
p1 = params_dup.get(name)
self.assertTrue(numpy.isclose(p0, p1).all())
if __name__ == '__main__':
unittest.main()
python/paddle/v2/tests/test_topology.py
浏览文件 @ 37806792
......@@ -16,6 +16,7 @@ import paddle.v2.layer as layer
import paddle.v2.topology as topology
import paddle.v2.data_type as data_type
import paddle.trainer_config_helpers as conf_helps
import paddle.trainer.PyDataProvider2 as pydp2
class TestTopology(unittest.TestCase):
......@@ -35,13 +36,13 @@ class TestTopology(unittest.TestCase):
pixel_data_type = filter(lambda type: type[0] == "pixel", data_types)
self.assertEqual(len(pixel_data_type), 1)
pixel_data_type = pixel_data_type[0]
self.assertEqual(pixel_data_type[1].type, data_type.DataType.Dense)
self.assertEqual(pixel_data_type[1].type, pydp2.DataType.Dense)
self.assertEqual(pixel_data_type[1].dim, 784)
label_data_type = filter(lambda type: type[0] == "label", data_types)
self.assertEqual(len(label_data_type), 1)
label_data_type = label_data_type[0]
self.assertEqual(label_data_type[1].type, data_type.DataType.Index)
self.assertEqual(label_data_type[1].type, pydp2.DataType.Index)
self.assertEqual(label_data_type[1].dim, 10)
def test_get_layer(self):
......
python/paddle/v2/trainer.py
浏览文件 @ 37806792
......@@ -9,6 +9,10 @@ from . import optimizer as v2_optimizer
from . import parameters as v2_parameters
__all__ = ['SGD']
"""
Trainer package
TODO(yuyang18): Complete comments.
"""
def default_event_handler(event):
......@@ -22,14 +26,20 @@ def default_event_handler(event):
pass
class SGD():
def __init__(self, cost, parameters, update_equation):
"""
Simple SGD Trainer.
class SGD(object):
"""
Simple SGD Trainer.
TODO(yuyang18): Complete comments
:param update_equation: The optimizer object.
:type update_equation: paddle.v2.optimizer.Optimizer
:param cost: Target cost that neural network should be optimized.
:type cost: paddle.v2.config_base.Layer
:param parameters: The parameters dictionary.
:type parameters: paddle.v2.parameters.Parameters
"""
:param update_equation: The optimizer object.
:type update_equation: v2_optimizer.Optimizer
"""
def __init__(self, cost, parameters, update_equation):
if not isinstance(parameters, v2_parameters.Parameters):
raise TypeError('parameters should be parameters')
......@@ -47,29 +57,26 @@ class SGD():
self.__topology_in_proto__, api.CREATE_MODE_NORMAL,
self.__optimizer__.enable_types())
assert isinstance(gm, api.GradientMachine)
parameters.append_gradient_machine(gm)
self.__gradient_machine__ = gm
self.__gradient_machine__.randParameters()
parameters.append_gradient_machine(gm)
def train(self, reader, num_passes=1, event_handler=None, reader_dict=None):
def train(self, reader, num_passes=1, event_handler=None, feeding=None):
"""
Training method. Will train num_passes of input data.
:param reader:
:param topology: Network Topology, use one or more Layers to represent it.
:param parameters: The parameter pools.
:param num_passes: The total train passes.
:param event_handler: Event handler. A method will be invoked when event
occurred.
:type event_handler: (BaseEvent) => None
:param feeding: Feeding is a map of neural network input name and array
index that reader returns.
:type feeding: dict
:return:
"""
if event_handler is None:
event_handler = default_event_handler
if reader_dict is None:
reader_dict = self.default_reader_dict()
__check_train_args__(**locals())
updater = self.__optimizer__.create_local_updater()
......@@ -81,9 +88,7 @@ class SGD():
pass_evaluator = self.__gradient_machine__.makeEvaluator()
assert isinstance(pass_evaluator, api.Evaluator)
out_args = api.Arguments.createArguments(0)
feeder = DataFeeder(self.__data_types__, reader_dict)
feeder = DataFeeder(self.__data_types__, feeding)
for pass_id in xrange(num_passes):
event_handler(v2_event.BeginPass(pass_id))
pass_evaluator.start()
......@@ -117,17 +122,8 @@ class SGD():
event_handler(v2_event.EndPass(pass_id, evaluator=pass_evaluator))
self.__gradient_machine__.finish()
def default_reader_dict(self):
reader_dict = dict()
for i, tp in enumerate(self.__data_types__):
reader_dict[tp[0]] = i
return reader_dict
def test(self, reader, reader_dict=None):
if reader_dict is None:
reader_dict = self.default_reader_dict()
feeder = DataFeeder(self.__data_types__, reader_dict)
def test(self, reader, feeding=None):
feeder = DataFeeder(self.__data_types__, feeding)
evaluator = self.__gradient_machine__.makeEvaluator()
out_args = api.Arguments.createArguments(0)
evaluator.start()
......
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