提交 68c68ec9 编写于 作者: E emailweixu 提交者: GitHub

Merge pull request #2218 from luotao1/rename

rename AggregateLevel and ExpandLevel
......@@ -28,17 +28,17 @@ pooling 的使用示例如下,详细见 :ref:`api_v2.layer_pooling` 配置API
seq_pool = pooling(input=layer,
pooling_type=pooling.Max(),
agg_level=AggregateLevel.EACH_SEQUENCE)
agg_level=AggregateLevel.TO_SEQUENCE)
- `pooling_type` 目前支持两种,分别是:pooling.Max()和pooling.Avg()。
- `agg_level=AggregateLevel.EACH_TIMESTEP` 时(默认值):
- `agg_level=AggregateLevel.TO_NO_SEQUENCE` 时(默认值):
- 作用:双层序列经过运算变成一个0层序列,或单层序列经过运算变成一个0层序列
- 输入:一个双层序列,或一个单层序列
- 输出:一个0层序列,即整个输入序列(单层或双层)的平均值(或最大值)
- `agg_level=AggregateLevel.EACH_SEQUENCE` 时:
- `agg_level=AggregateLevel.TO_SEQUENCE` 时:
- 作用:一个双层序列经过运算变成一个单层序列
- 输入:必须是一个双层序列
......@@ -52,15 +52,15 @@ last_seq 的使用示例如下( :ref:`api_v2.layer_first_seq` 类似),详
.. code-block:: bash
last = last_seq(input=layer,
agg_level=AggregateLevel.EACH_SEQUENCE)
agg_level=AggregateLevel.TO_SEQUENCE)
- `agg_level=AggregateLevel.EACH_TIMESTEP` 时(默认值):
- `agg_level=AggregateLevel.TO_NO_SEQUENCE` 时(默认值):
- 作用:一个双层序列经过运算变成一个0层序列,或一个单层序列经过运算变成一个0层序列
- 输入:一个双层序列或一个单层序列
- 输出:一个0层序列,即整个输入序列(双层或者单层)最后一个,或第一个元素。
- `agg_level=AggregateLevel.EACH_SEQUENCE` 时:
- `agg_level=AggregateLevel.TO_SEQUENCE` 时:
- 作用:一个双层序列经过运算变成一个单层序列
- 输入:必须是一个双层序列
- 输出:一个单层序列,其中每个元素是双层序列中每个subseq最后一个(或第一个)元素。
......@@ -74,9 +74,9 @@ expand 的使用示例如下,详细见 :ref:`api_v2.layer_expand` 配置API。
ex = expand(input=layer1,
expand_as=layer2,
expand_level=ExpandLevel.FROM_TIMESTEP)
expand_level=ExpandLevel.FROM_NO_SEQUENCE)
- `expand_level=ExpandLevel.FROM_TIMESTEP` 时(默认值):
- `expand_level=ExpandLevel.FROM_NO_SEQUENCE` 时(默认值):
- 作用:一个0层序列经过运算扩展成一个单层序列,或者一个双层序列
- 输入:layer1必须是一个0层序列,是待扩展的数据;layer2 可以是一个单层序列,或者是一个双层序列,提供扩展的长度信息
......
......@@ -81,7 +81,7 @@
* 在本例中,我们将原始数据的每一组,通过\ :code:`recurrent_group`\ 进行拆解,拆解成的每一句话再通过一个LSTM网络。这和单层RNN的配置是等价的。
* 与单层RNN的配置类似,我们只需要使用LSTM encode成的最后一个向量。所以对\ :code:`recurrent_group`\ 进行了\ :code:`last_seq`\ 操作。但和单层RNN不同,我们是对每一个子序列取最后一个元素,因此\ :code:`agg_level=AggregateLevel.EACH_SEQUENCE`\ 。
* 与单层RNN的配置类似,我们只需要使用LSTM encode成的最后一个向量。所以对\ :code:`recurrent_group`\ 进行了\ :code:`last_seq`\ 操作。但和单层RNN不同,我们是对每一个子序列取最后一个元素,因此\ :code:`agg_level=AggregateLevel.TO_SEQUENCE`\ 。
* 至此,\ :code:`lstm_last`\ 便和单层RNN配置中的\ :code:`lstm_last`\ 具有相同的结果了。
......
......@@ -59,7 +59,7 @@ lstm_nest_group = recurrent_group(
input=SubsequenceInput(emb_group), step=lstm_group, name="lstm_nest_group")
# hasSubseq ->(seqlastins) seq
lstm_last = last_seq(
input=lstm_nest_group, agg_level=AggregateLevel.EACH_SEQUENCE)
input=lstm_nest_group, agg_level=AggregateLevel.TO_SEQUENCE)
# seq ->(expand) hasSubseq
lstm_expand = expand_layer(
......@@ -71,7 +71,7 @@ lstm_expand = expand_layer(
lstm_average = pooling_layer(
input=lstm_expand,
pooling_type=AvgPooling(),
agg_level=AggregateLevel.EACH_SEQUENCE)
agg_level=AggregateLevel.TO_SEQUENCE)
with mixed_layer(
size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
......
......@@ -237,16 +237,19 @@ class AggregateLevel(object):
Accordingly, AggregateLevel supports two modes:
- :code:`AggregateLevel.EACH_TIMESTEP` means the aggregation acts on each
- :code:`AggregateLevel.TO_NO_SEQUENCE` means the aggregation acts on each
timestep of a sequence, both :code:`SUB_SEQUENCE` and :code:`SEQUENCE` will
be aggregated to :code:`NO_SEQUENCE`.
- :code:`AggregateLevel.EACH_SEQUENCE` means the aggregation acts on each
- :code:`AggregateLevel.TO_SEQUENCE` means the aggregation acts on each
sequence of a nested sequence, :code:`SUB_SEQUENCE` will be aggregated to
:code:`SEQUENCE`.
"""
EACH_TIMESTEP = 'non-seq'
EACH_SEQUENCE = 'seq'
TO_NO_SEQUENCE = 'non-seq'
TO_SEQUENCE = 'seq'
# compatible with previous configuration
EACH_TIMESTEP = TO_NO_SEQUENCE
EACH_SEQUENCE = TO_SEQUENCE
class LayerOutput(object):
......@@ -1083,7 +1086,7 @@ def pooling_layer(input,
pooling_type=None,
name=None,
bias_attr=None,
agg_level=AggregateLevel.EACH_TIMESTEP,
agg_level=AggregateLevel.TO_NO_SEQUENCE,
layer_attr=None):
"""
Pooling layer for sequence inputs, not used for Image.
......@@ -1094,10 +1097,10 @@ def pooling_layer(input,
seq_pool = pooling_layer(input=layer,
pooling_type=AvgPooling(),
agg_level=AggregateLevel.EACH_SEQUENCE)
agg_level=AggregateLevel.TO_NO_SEQUENCE)
:param agg_level: AggregateLevel.EACH_TIMESTEP or
AggregateLevel.EACH_SEQUENCE
:param agg_level: AggregateLevel.TO_NO_SEQUENCE or
AggregateLevel.TO_SEQUENCE
:type agg_level: AggregateLevel
:param name: layer name.
:type name: basestring
......@@ -1367,7 +1370,7 @@ def grumemory(input,
@layer_support()
def last_seq(input,
name=None,
agg_level=AggregateLevel.EACH_TIMESTEP,
agg_level=AggregateLevel.TO_NO_SEQUENCE,
stride=-1,
layer_attr=None):
"""
......@@ -1402,7 +1405,7 @@ def last_seq(input,
" series information at all. Maybe you want to use"
" first_seq instead.")
if agg_level == AggregateLevel.EACH_SEQUENCE:
if agg_level == AggregateLevel.TO_SEQUENCE:
assert stride == -1
Layer(
......@@ -1423,7 +1426,7 @@ def last_seq(input,
@layer_support()
def first_seq(input,
name=None,
agg_level=AggregateLevel.EACH_TIMESTEP,
agg_level=AggregateLevel.TO_NO_SEQUENCE,
stride=-1,
layer_attr=None):
"""
......@@ -1459,7 +1462,7 @@ def first_seq(input,
' time series information at all. Maybe you want to use'
' last_seq instead.')
if agg_level == AggregateLevel.EACH_SEQUENCE:
if agg_level == AggregateLevel.TO_SEQUENCE:
assert stride == -1
Layer(
......@@ -1482,16 +1485,18 @@ class ExpandLevel(object):
ExpandLevel supports two modes:
- :code:`ExpandLevel.FROM_TIMESTEP` means the expandation acts on each
timestep of a sequence, :code:`NO_SEQUENCE` will be expanded to
- :code:`ExpandLevel.FROM_NO_SEQUENCE` means the expansion acts on
:code:`NO_SEQUENCE`, which will be expanded to
:code:`SEQUENCE` or :code:`SUB_SEQUENCE`.
- :code:`ExpandLevel.FROM_SEQUENCE` means the expandation acts on each
sequence of a nested sequence, :code:`SEQUENCE` will be expanded to
- :code:`ExpandLevel.FROM_SEQUENCE` means the expansion acts on
:code:`SEQUENCE`, which will be expanded to
:code:`SUB_SEQUENCE`.
"""
FROM_TIMESTEP = AggregateLevel.EACH_TIMESTEP
FROM_SEQUENCE = AggregateLevel.EACH_SEQUENCE
FROM_NO_SEQUENCE = AggregateLevel.TO_NO_SEQUENCE
FROM_SEQUENCE = AggregateLevel.TO_SEQUENCE
# compatible with previous configuration
FROM_TIMESTEP = FROM_NO_SEQUENCE
@wrap_name_default()
......@@ -1500,7 +1505,7 @@ def expand_layer(input,
expand_as,
name=None,
bias_attr=False,
expand_level=ExpandLevel.FROM_TIMESTEP,
expand_level=ExpandLevel.FROM_NO_SEQUENCE,
layer_attr=None):
"""
A layer for "Expand Dense data or (sequence data where the length of each
......@@ -1512,7 +1517,7 @@ def expand_layer(input,
expand = expand_layer(input=layer1,
expand_as=layer2,
expand_level=ExpandLevel.FROM_TIMESTEP)
expand_level=ExpandLevel.FROM_NO_SEQUENCE)
:param input: Input layer
:type input: LayerOutput
......
......@@ -6,7 +6,7 @@ din = data_layer(name='data', size=30)
seq_op = [first_seq, last_seq]
agg_level = [AggregateLevel.EACH_SEQUENCE, AggregateLevel.EACH_TIMESTEP]
agg_level = [AggregateLevel.TO_SEQUENCE, AggregateLevel.TO_NO_SEQUENCE]
opts = []
......@@ -15,6 +15,7 @@ for op in seq_op:
opts.append(op(input=din, agg_level=al))
for op in seq_op:
opts.append(op(input=din, agg_level=AggregateLevel.EACH_TIMESTEP, stride=5))
opts.append(
op(input=din, agg_level=AggregateLevel.TO_NO_SEQUENCE, stride=5))
outputs(opts)
......@@ -9,4 +9,6 @@ outputs(
expand_layer(
input=din, expand_as=data_seq, expand_level=ExpandLevel.FROM_SEQUENCE),
expand_layer(
input=din, expand_as=data_seq, expand_level=ExpandLevel.FROM_TIMESTEP))
input=din,
expand_as=data_seq,
expand_level=ExpandLevel.FROM_NO_SEQUENCE))
......@@ -6,7 +6,7 @@ din = data_layer(name='dat_in', size=100)
POOL_TYPE = [MaxPooling, AvgPooling, SumPooling]
AGG_LEVEL = [AggregateLevel.EACH_SEQUENCE, AggregateLevel.EACH_TIMESTEP]
AGG_LEVEL = [AggregateLevel.TO_SEQUENCE, AggregateLevel.TO_NO_SEQUENCE]
opts = []
......
......@@ -73,7 +73,7 @@ class AggregateLayerTest(unittest.TestCase):
pool = layer.pooling(
input=pixel,
pooling_type=pooling.Avg(),
agg_level=layer.AggregateLevel.EACH_SEQUENCE)
agg_level=layer.AggregateLevel.TO_SEQUENCE)
last_seq = layer.last_seq(input=pixel)
first_seq = layer.first_seq(input=pixel)
concat = layer.concat(input=[last_seq, first_seq])
......@@ -109,7 +109,7 @@ class ReshapeLayerTest(unittest.TestCase):
expand = layer.expand(
input=weight,
expand_as=pixel,
expand_level=layer.ExpandLevel.FROM_TIMESTEP)
expand_level=layer.ExpandLevel.FROM_NO_SEQUENCE)
repeat = layer.repeat(input=pixel, num_repeats=4)
reshape = layer.seq_reshape(input=pixel, reshape_size=4)
rotate = layer.rotate(input=pixel, height=16, width=49)
......
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