提交 cbcd53af 编写于 作者: Y Yu Yang

Merge branch 'develop' of github.com:baidu/Paddle into feature/clean_mnist_v2

......@@ -139,24 +139,12 @@ lstmemory
:members: lstmemory
:noindex:
lstm_step_layer
---------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: lstm_step_layer
:noindex:
grumemory
---------
.. automodule:: paddle.trainer_config_helpers.layers
:members: grumemory
:noindex:
gru_step_layer
---------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: gru_step_layer
:noindex:
Recurrent Layer Group
=====================
......@@ -172,6 +160,18 @@ recurrent_group
:members: recurrent_group
:noindex:
lstm_step_layer
---------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: lstm_step_layer
:noindex:
gru_step_layer
---------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: gru_step_layer
:noindex:
beam_search
------------
.. automodule:: paddle.trainer_config_helpers.layers
......@@ -308,6 +308,12 @@ repeat_layer
:members: repeat_layer
:noindex:
rotate_layer
------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: rotate_layer
:noindex:
seq_reshape_layer
-----------------
.. automodule:: paddle.trainer_config_helpers.layers
......@@ -462,6 +468,12 @@ ctc_layer
:members: ctc_layer
:noindex:
warp_ctc_layer
--------------
.. automodule:: paddle.trainer_config_helpers.layers
:members: warp_ctc_layer
:noindex:
nce_layer
-----------
.. automodule:: paddle.trainer_config_helpers.layers
......
......@@ -112,6 +112,8 @@ __all__ = [
'priorbox_layer',
'spp_layer',
'pad_layer',
'eos_layer',
'layer_support',
]
......@@ -708,6 +710,7 @@ class MixedLayerType(LayerOutput):
# update the size which might be computed inside MixedLayer
# according to the operator's output size
self.size = ml.config.size
self.finalized = True
@wrap_name_default("mixed")
......@@ -1287,6 +1290,12 @@ def last_seq(input,
"""
Get Last Timestamp Activation of a sequence.
The simple usage is:
.. code-block:: python
seq = last_seq(input=layer)
:param agg_level: Aggregated level
:param name: Layer name.
:type name: basestring
......@@ -1325,6 +1334,12 @@ def first_seq(input,
"""
Get First Timestamp Activation of a sequence.
The simple usage is:
.. code-block:: python
seq = first_seq(input=layer)
:param agg_level: aggregation level
:param name: Layer name.
:type name: basestring
......@@ -1425,7 +1440,7 @@ def repeat_layer(input, num_repeats, name=None, layer_attr=None):
.. code-block:: python
expand = repeat_layer(layer, 4)
expand = repeat_layer(input=layer, num_repeats=4)
:param input: Input layer
:type input: LayerOutput
......@@ -1797,6 +1812,12 @@ def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None):
Note that the above computation is for one sample. Multiple samples are
processed in one batch.
The example usage is:
.. code-block:: python
cos = cos_sim(a=layer1, b=layer2, size=3)
:param name: layer name
:type name: basestring
:param a: input layer a
......@@ -1958,6 +1979,16 @@ def img_conv_layer(input,
pieces. First 256/4 = 64 channels will process by first 32 filters. The
rest channels will be processed by rest group of filters.
The example usage is:
.. code-block:: python
conv = img_conv_layer(input=data, filter_size=1, filter_size_y=1,
num_channels=8,
num_filters=16, stride=1,
bias_attr=False,
act=ReluActivation())
:param name: Layer name.
:type name: basestring
:param input: Layer Input.
......@@ -2097,6 +2128,34 @@ def img_pool_layer(input,
.. _pooling: http://ufldl.stanford.edu/tutorial/supervised/Pooling/
- ceil_mode=True:
.. math::
w = 1 + int(ceil(input\_width + 2 * padding - pool\_size) / float(stride))
h = 1 + int(ceil(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y))
- ceil_mode=False:
.. math::
w = 1 + int(floor(input\_width + 2 * padding - pool\_size) / float(stride))
h = 1 + int(floor(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y))
The example usage is:
.. code-block:: python
maxpool = img_pool_layer(input=conv,
pool_size=3,
pool_size_y=5,
num_channels=8,
stride=1,
stride_y=2,
padding=1,
padding_y=2,
pool_type=MaxPooling())
:param padding: pooling padding width.
:type padding: int
:param padding_y: pooling padding height. It's equal to padding by default.
......@@ -2123,19 +2182,6 @@ def img_pool_layer(input,
:param ceil_mode: Wether to use ceil mode to calculate output height and with.
Defalut is True. If set false, Otherwise use floor.
- ceil_mode=True:
.. math::
w = 1 + int(ceil(input_width + 2 * padding - pool_size) / float(stride))
h = 1 + int(ceil(input_height + 2 * padding_y - pool_size_y) / float(stride_y))
- ceil_mode=False:
.. math::
w = 1 + int(floor(input_width + 2 * padding - pool_size) / float(stride))
h = 1 + int(floor(input_height + 2 * padding_y - pool_size_y) / float(stride_y))
:type ceil_mode: bool
:return: LayerOutput object.
:rtype: LayerOutput
......@@ -2197,6 +2243,15 @@ def spp_layer(input,
The details please refer to
`Kaiming He's paper <https://arxiv.org/abs/1406.4729>`_.
The example usage is:
.. code-block:: python
spp = spp_layer(input=data,
pyramid_height=2,
num_channels=16,
pool_type=MaxPooling())
:param name: layer name.
:type name: basestring
:param input: layer's input.
......@@ -2285,6 +2340,12 @@ def img_cmrnorm_layer(input,
The details please refer to
`Alex's paper <http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf>`_.
The example usage is:
.. code-block:: python
norm = img_cmrnorm_layer(input=net, size=5)
:param name: layer name.
:type name: None|basestring
:param input: layer's input.
......@@ -2340,6 +2401,12 @@ def batch_norm_layer(input,
The details of batch normalization please refer to this
`paper <http://arxiv.org/abs/1502.03167>`_.
The example usage is:
.. code-block:: python
norm = batch_norm_layer(input=net, act=ReluActivation())
:param name: layer name.
:type name: basestring
:param input: batch normalization input. Better be linear activation.
......@@ -3903,13 +3970,13 @@ def conv_shift_layer(a, b, name=None, layer_attr=None):
.. code-block:: python
conv_shift = conv_shift_layer(input=[layer1, layer2])
conv_shift = conv_shift_layer(a=layer1, b=layer2)
:param name: layer name
:type name: basestring
:param a: Input layer a.
:type a: LayerOutput
:param b: input layer b
:param b: input layer b.
:type b: LayerOutput
:param layer_attr: layer's extra attribute.
:type layer_attr: ExtraLayerAttribute
......@@ -4001,8 +4068,8 @@ def tensor_layer(a,
@wrap_act_default()
@layer_support()
def selective_fc_layer(input,
select,
size,
select=None,
act=None,
name=None,
pass_generation=False,
......@@ -4029,6 +4096,7 @@ def selective_fc_layer(input,
:type input: LayerOutput|list|tuple
:param select: The select layer. The output of select layer should be a
sparse binary matrix, and treat as the mask of selective fc.
If is None, acts exactly like fc_layer.
:type select: LayerOutput
:param size: The layer dimension.
:type size: int
......@@ -4257,7 +4325,7 @@ def block_expand_layer(input,
.. code-block:: python
block_expand = block_expand_layer(input,
block_expand = block_expand_layer(input=layer,
num_channels=128,
stride_x=1,
stride_y=1,
......@@ -4594,6 +4662,13 @@ def crf_decoding_layer(input,
this layer will also calculate error. output.value[i] is 1 for incorrect
decoding or 0 for correct decoding.
The simple usage:
.. code-block:: python
crf_decoding = crf_decoding_layer(input=input,
size=label_dim)
:param input: The first input layer.
:type input: LayerOutput
:param size: size of this layer.
......
......@@ -22,11 +22,12 @@ import data_feeder
from . import dataset
from . import reader
import attr
import pooling
import py_paddle.swig_paddle as api
__all__ = [
'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer',
'event', 'data_type', 'attr', 'data_feeder', 'dataset', 'reader'
'event', 'data_type', 'attr', 'pooling', 'data_feeder', 'dataset', 'reader'
]
......
......@@ -71,19 +71,37 @@ import collections
import paddle.trainer_config_helpers as conf_helps
from paddle.trainer_config_helpers.config_parser_utils import \
parse_network_config as __parse__
from paddle.trainer_config_helpers.default_decorators import wrap_name_default
from paddle.trainer_config_helpers.default_decorators import wrap_act_default
from paddle.trainer_config_helpers.default_decorators import wrap_bias_attr_default
from paddle.trainer_config_helpers.layers import layer_support
import data_type
import activation
import attr
__all__ = [
'parse_network', 'data', 'fc', 'max_id', 'classification_cost',
'cross_entropy_cost', 'cross_entropy_with_selfnorm_cost', 'regression_cost',
'parse_network', 'data', 'fc', 'conv_shift', 'img_conv', 'img_pool', 'spp',
'maxout', 'img_cmrnorm', 'batch_norm', 'sum_to_one_norm', 'recurrent',
'lstmemory', 'grumemory', 'pool', 'last_seq', 'first_seq', 'concat',
'seq_concat', 'block_expand', 'expand', 'repeat', 'seq_reshape', 'addto',
'linear_comb', 'interpolation', 'bilinear_interp', 'power', 'scaling',
'slope_intercept', 'tensor', 'cos_sim', 'trans', 'max_id', 'sampling_id',
'pad', 'classification_cost', 'cross_entropy_cost',
'cross_entropy_with_selfnorm_cost', 'regression_cost',
'multi_binary_label_cross_entropy_cost', 'rank_cost', 'lambda_cost',
'sum_cost', 'huber_cost'
'sum_cost', 'huber_cost', 'crf', 'crf_decoding', 'ctc', 'warp_ctc', 'nce',
'hsigmoid', 'eos'
]
__projection_names__ = filter(lambda x: x.endswith('_projection'),
dir(conf_helps))
__all__ += __projection_names__
__operator_names__ = filter(lambda x: x.endswith('_operator'), dir(conf_helps))
__all__ += __operator_names__
def parse_network(*outputs):
"""
......@@ -101,9 +119,8 @@ def parse_network(*outputs):
class Layer(object):
def __init__(self, name, parent_layers):
def __init__(self, name=None, parent_layers=None):
assert isinstance(parent_layers, dict)
assert isinstance(name, basestring)
self.name = name
self.__parent_layers__ = parent_layers
......@@ -122,22 +139,25 @@ class Layer(object):
self.__parent_layers__[layer_name])
kwargs[layer_name] = v1_layer
if self.name not in context:
if self.name is None:
return self.to_proto_impl(**kwargs)
elif self.name not in context:
context[self.name] = self.to_proto_impl(**kwargs)
return context[self.name]
def to_proto_impl(self, **kwargs):
raise NotImplementedError()
def __convert_to_v2__(method_name, name_prefix, parent_names):
if name_prefix is not None:
wrapper = wrap_name_default(name_prefix=name_prefix)
def __convert_to_v2__(method_name, parent_names, is_default_name=True):
if is_default_name:
wrapper = wrap_name_default(name_prefix=method_name)
else:
wrapper = None
class V2LayerImpl(Layer):
def __init__(self, name=None, **kwargs):
def __init__(self, **kwargs):
parent_layers = dict()
other_kwargs = dict()
for pname in parent_names:
......@@ -148,6 +168,7 @@ def __convert_to_v2__(method_name, name_prefix, parent_names):
if key not in parent_names:
other_kwargs[key] = kwargs[key]
name = kwargs.get('name', None)
super(V2LayerImpl, self).__init__(name, parent_layers)
self.__other_kwargs__ = other_kwargs
......@@ -160,7 +181,7 @@ def __convert_to_v2__(method_name, name_prefix, parent_names):
args[each] = kwargs[each]
for each in self.__other_kwargs__:
args[each] = self.__other_kwargs__[each]
return getattr(conf_helps, method_name)(name=self.name, **args)
return getattr(conf_helps, method_name)(**args)
return V2LayerImpl
......@@ -191,67 +212,171 @@ class DataLayerV2(Layer):
return getattr(conf_helps, self.__method_name__)(name=self.name, **args)
class MixedLayerV2(Layer):
"""
This class is use to support `with` grammar. If not, the following code
could convert mixed_layer simply.
mixed = __convert_to_v2__(
'mixed_layer', name_prefix='mixed', parent_names=['input'])
"""
class AddToSealedMixedLayerExceptionV2(Exception):
pass
def __init__(self,
size=0,
input=None,
name=None,
act=None,
bias_attr=None,
layer_attr=None):
self.__method_name__ = 'mixed_layer'
self.finalized = False
self.__inputs__ = []
if input is not None:
self.__inputs__ = input
other_kwargs = dict()
other_kwargs['name'] = name
other_kwargs['size'] = size
other_kwargs['act'] = act
other_kwargs['bias_attr'] = bias_attr
other_kwargs['layer_attr'] = layer_attr
parent_layers = {"input": self.__inputs__}
super(MixedLayerV2, self).__init__(name, parent_layers)
self.__other_kwargs__ = other_kwargs
def __iadd__(self, other):
if not self.finalized:
self.__inputs__.append(other)
return self
else:
raise MixedLayerTypeV2.AddToSealedMixedLayerExceptionV2()
def __enter__(self):
assert len(self.__inputs__) == 0
return self
def __exit__(self, *args, **kwargs):
self.finalized = True
def to_proto_impl(self, **kwargs):
args = dict()
for each in kwargs:
args[each] = kwargs[each]
for each in self.__other_kwargs__:
args[each] = self.__other_kwargs__[each]
return getattr(conf_helps, self.__method_name__)(**args)
@wrap_name_default("mixed")
@wrap_act_default(act=activation.Linear())
@wrap_bias_attr_default(has_bias=False)
@layer_support(conf_helps.layers.ERROR_CLIPPING, conf_helps.layers.DROPOUT)
def mixed(size=0,
name=None,
input=None,
act=None,
bias_attr=False,
layer_attr=None):
return MixedLayerV2(size, input, name, act, bias_attr, layer_attr)
data = DataLayerV2
fc = __convert_to_v2__('fc_layer', name_prefix='fc', parent_names=['input'])
max_id = __convert_to_v2__(
'maxid_layer', name_prefix='maxid', parent_names=['input'])
classification_cost = __convert_to_v2__(
'classification_cost',
name_prefix='classification_cost',
parent_names=['input', 'label', 'weight'])
regression_cost = __convert_to_v2__(
'regression_cost',
name_prefix='regression_cost',
parent_names=['input', 'label', 'weight'])
cross_entropy_cost = __convert_to_v2__(
'cross_entropy',
name_prefix='cross_entropy',
parent_names=['input', 'label'])
cross_entropy_with_selfnorm_cost = __convert_to_v2__(
'cross_entropy_with_selfnorm',
name_prefix='cross_entropy_with_selfnorm',
parent_names=['input', 'label'])
multi_binary_label_cross_entropy_cost = __convert_to_v2__(
'multi_binary_label_cross_entropy',
name_prefix='multi_binary_label_cross_entropy',
parent_names=['input', 'label'])
rank_cost = __convert_to_v2__(
'rank_cost',
name_prefix='rank_cost',
parent_names=['left', 'right', 'label', 'weight'])
lambda_cost = __convert_to_v2__(
'lambda_cost', name_prefix='lambda_cost', parent_names=['input', 'score'])
sum_cost = __convert_to_v2__(
'sum_cost', name_prefix='sum_cost', parent_names=['input'])
huber_cost = __convert_to_v2__(
'huber_cost', name_prefix='huber_cost', parent_names=['input', 'label'])
if __name__ == '__main__':
pixel = data(name='pixel', type=data_type.dense_vector(784))
label = data(name='label', type=data_type.integer_value(10))
weight = data(name='weight', type=data_type.dense_vector(10))
score = data(name='score', type=data_type.dense_vector(1))
hidden = fc(input=pixel,
size=100,
act=activation.Sigmoid(),
param_attr=attr.Param(name='hidden'))
inference = fc(input=hidden, size=10, act=activation.Softmax())
maxid = max_id(input=inference)
cost1 = classification_cost(input=inference, label=label)
cost2 = classification_cost(input=inference, label=label, weight=weight)
cost3 = cross_entropy_cost(input=inference, label=label)
cost4 = cross_entropy_with_selfnorm_cost(input=inference, label=label)
cost5 = regression_cost(input=inference, label=label)
cost6 = regression_cost(input=inference, label=label, weight=weight)
cost7 = multi_binary_label_cross_entropy_cost(input=inference, label=label)
cost8 = rank_cost(left=score, right=score, label=score)
cost9 = lambda_cost(input=inference, score=score)
cost10 = sum_cost(input=inference)
cost11 = huber_cost(input=score, label=label)
print parse_network(cost1, cost2)
print parse_network(cost3, cost4)
print parse_network(cost5, cost6)
print parse_network(cost7, cost8, cost9, cost10, cost11)
print parse_network(inference, maxid)
AggregateLevel = conf_helps.layers.AggregateLevel
ExpandLevel = conf_helps.layers.ExpandLevel
layer_list = [
# [V2LayerImpl, V1_method_name, parent_names]
# fully connected layers
['fc', 'fc_layer', ['input']],
# conv layers
['conv_shift', 'conv_shift_layer', ['a', 'b']],
['img_conv', 'img_conv_layer', ['input']],
# image pooling layers
['img_pool', 'img_pool_layer', ['input']],
['spp', 'spp_layer', ['input']],
['maxout', 'maxout_layer', ['input']],
# norm layers
['img_cmrnorm', 'img_cmrnorm_layer', ['input']],
['batch_norm', 'batch_norm_layer', ['input']],
['sum_to_one_norm', 'sum_to_one_norm_layer', ['input']],
# recurrent layers
['recurrent', 'recurrent_layer', ['input']],
['lstmemory', 'lstmemory', ['input']],
['grumemory', 'grumemory', ['input']],
# aggregate layers
['pool', 'pooling_layer', ['input']],
['last_seq', 'last_seq', ['input']],
['first_seq', 'first_seq', ['input']],
['concat', 'concat_layer', ['input']],
['seq_concat', 'seq_concat_layer', ['a', 'b']],
# reshaping layers
['block_expand', 'block_expand_layer', ['input']],
['expand', 'expand_layer', ['input', 'expand_as']],
['repeat', 'repeat_layer', ['input']],
['rotate', 'rotate_layer', ['input']],
['seq_reshape', 'seq_reshape_layer', ['input']],
# math layers
['addto', 'addto_layer', ['input']],
['linear_comb', 'linear_comb_layer', ['weights', 'vectors']],
['interpolation', 'interpolation_layer', ['input', 'weight']],
['bilinear_interp', 'bilinear_interp_layer', ['input']],
['power', 'power_layer', ['input', 'weight']],
['scaling', 'scaling_layer', ['input', 'weight']],
['slope_intercept', 'slope_intercept_layer', ['input']],
['tensor', 'tensor_layer', ['a', 'b']],
['cos_sim', 'cos_sim', ['a', 'b']],
['trans', 'trans_layer', ['input']],
# sampling layers
['max_id', 'maxid_layer', ['input']],
['sampling_id', 'sampling_id_layer', ['input']],
# slicing and joining layers
['pad', 'pad_layer', ['input']],
# cost layers
[
'classification_cost', 'classification_cost',
['input', 'label', 'weight']
],
['regression_cost', 'regression_cost', ['input', 'label', 'weight']],
['cross_entropy_cost', 'cross_entropy', ['input', 'label']],
[
'cross_entropy_with_selfnorm_cost', 'cross_entropy_with_selfnorm',
['input', 'label']
],
[
'multi_binary_label_cross_entropy_cost',
'multi_binary_label_cross_entropy', ['input', 'label']
],
['rank_cost', 'rank_cost', ['left', 'right', 'label', 'weight']],
['lambda_cost', 'lambda_cost', ['input', 'score']],
['sum_cost', 'sum_cost', ['input']],
['huber_cost', 'huber_cost', ['input', 'label']],
['crf', 'crf_layer', ['input', 'label']],
['crf_decoding', 'crf_decoding_layer', ['input']],
['ctc', 'ctc_layer', ['input', 'label']],
['warp_ctc', 'warp_ctc_layer', ['input', 'label']],
['nce', 'nce_layer', ['input', 'label']],
['hsigmoid', 'hsigmoid', ['input', 'label']],
# check layers
['eos', 'eos_layer', ['input']]
]
for l in layer_list:
globals()[l[0]] = __convert_to_v2__(l[1], l[2])
# convert projection
for prj in __projection_names__:
globals()[prj] = __convert_to_v2__(
prj, parent_names=['input'], is_default_name=False)
# convert operator
operator_list = [
# [V1_method_name, parent_names],
['dotmul_operator', ['a', 'b']],
['conv_operator', ['img', 'filter']]
]
for op in operator_list:
globals()[op[0]] = __convert_to_v2__(
op[0], parent_names=op[1], is_default_name=False)
# 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.poolings import *
__all__ = ["Max", "CudnnMax", "Avg", "CudnnAvg", "Sum", "SquareRootN"]
Max = MaxPooling
CudnnMax = CudnnMaxPooling
Avg = AvgPooling
CudnnAvg = CudnnAvgPooling
Sum = SumPooling
SquareRootN = SquareRootNPooling
......@@ -19,18 +19,106 @@ import paddle.v2.activation as activation
import paddle.v2.attr as attr
import paddle.v2.data_type as data_type
import paddle.v2.layer as layer
import paddle.v2.pooling as pooling
from paddle.trainer_config_helpers.config_parser_utils import \
parse_network_config as parse_network
pixel = layer.data(name='pixel', type=data_type.dense_vector(784))
pixel = layer.data(name='pixel', type=data_type.dense_vector(128))
label = layer.data(name='label', type=data_type.integer_value(10))
weight = layer.data(name='weight', type=data_type.dense_vector(10))
score = layer.data(name='score', type=data_type.dense_vector(1))
hidden = layer.fc(input=pixel,
size=100,
act=activation.Sigmoid(),
param_attr=attr.Param(name='hidden'))
inference = layer.fc(input=hidden, size=10, act=activation.Softmax())
conv = layer.img_conv(
input=pixel,
filter_size=1,
filter_size_y=1,
num_channels=8,
num_filters=16,
act=activation.Linear())
class ImageLayerTest(unittest.TestCase):
def test_conv_layer(self):
conv_shift = layer.conv_shift(a=pixel, b=score)
print layer.parse_network(conv, conv_shift)
def test_pooling_layer(self):
maxpool = layer.img_pool(
input=conv,
pool_size=2,
num_channels=16,
padding=1,
pool_type=pooling.Max())
spp = layer.spp(input=conv,
pyramid_height=2,
num_channels=16,
pool_type=pooling.Max())
maxout = layer.maxout(input=conv, num_channels=16, groups=4)
print layer.parse_network(maxpool, spp, maxout)
def test_norm_layer(self):
norm1 = layer.img_cmrnorm(input=conv, size=5)
norm2 = layer.batch_norm(input=conv)
norm3 = layer.sum_to_one_norm(input=conv)
print layer.parse_network(norm1, norm2, norm3)
class AggregateLayerTest(unittest.TestCase):
def test_aggregate_layer(self):
pool = layer.pool(
input=pixel,
pooling_type=pooling.Avg(),
agg_level=layer.AggregateLevel.EACH_SEQUENCE)
last_seq = layer.last_seq(input=pixel)
first_seq = layer.first_seq(input=pixel)
concat = layer.concat(input=[last_seq, first_seq])
seq_concat = layer.seq_concat(a=last_seq, b=first_seq)
print layer.parse_network(pool, last_seq, first_seq, concat, seq_concat)
class MathLayerTest(unittest.TestCase):
def test_math_layer(self):
addto = layer.addto(input=[pixel, pixel])
linear_comb = layer.linear_comb(weights=weight, vectors=hidden, size=10)
interpolation = layer.interpolation(
input=[hidden, hidden], weight=score)
bilinear = layer.bilinear_interp(input=conv, out_size_x=4, out_size_y=4)
power = layer.power(input=pixel, weight=score)
scaling = layer.scaling(input=pixel, weight=score)
slope = layer.slope_intercept(input=pixel)
tensor = layer.tensor(a=pixel, b=pixel, size=1000)
cos_sim = layer.cos_sim(a=pixel, b=pixel)
trans = layer.trans(input=tensor)
print layer.parse_network(addto, linear_comb, interpolation, power,
scaling, slope, tensor, cos_sim, trans)
class ReshapeLayerTest(unittest.TestCase):
def test_reshape_layer(self):
block_expand = layer.block_expand(
input=conv, num_channels=4, stride_x=1, block_x=1)
expand = layer.expand(
input=weight,
expand_as=pixel,
expand_level=layer.ExpandLevel.FROM_TIMESTEP)
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)
print layer.parse_network(block_expand, expand, repeat, reshape, rotate)
class RecurrentLayerTest(unittest.TestCase):
def test_recurrent_layer(self):
word = layer.data(name='word', type=data_type.integer_value(12))
recurrent = layer.recurrent(input=word)
lstm = layer.lstmemory(input=word)
gru = layer.grumemory(input=word)
print layer.parse_network(recurrent, lstm, gru)
class CostLayerTest(unittest.TestCase):
......@@ -51,12 +139,120 @@ class CostLayerTest(unittest.TestCase):
cost10 = layer.sum_cost(input=inference)
cost11 = layer.huber_cost(input=score, label=label)
print dir(layer)
layer.parse_network(cost1, cost2)
print dir(layer)
#print layer.parse_network(cost3, cost4)
#print layer.parse_network(cost5, cost6)
#print layer.parse_network(cost7, cost8, cost9, cost10, cost11)
print layer.parse_network(cost1, cost2)
print layer.parse_network(cost3, cost4)
print layer.parse_network(cost5, cost6)
print layer.parse_network(cost7, cost8, cost9, cost10, cost11)
crf = layer.crf(input=inference, label=label)
crf_decoding = layer.crf_decoding(input=inference, size=3)
ctc = layer.ctc(input=inference, label=label)
warp_ctc = layer.warp_ctc(input=pixel, label=label)
nce = layer.nce(input=inference, label=label, num_classes=3)
hsigmoid = layer.hsigmoid(input=inference, label=label, num_classes=3)
print layer.parse_network(crf, crf_decoding, ctc, warp_ctc, nce,
hsigmoid)
class OtherLayerTest(unittest.TestCase):
def test_sampling_layer(self):
maxid = layer.max_id(input=inference)
sampling_id = layer.sampling_id(input=inference)
eos = layer.eos(input=maxid, eos_id=5)
print layer.parse_network(maxid, sampling_id, eos)
def test_slicing_joining_layer(self):
pad = layer.pad(input=conv, pad_c=[2, 3], pad_h=[1, 2], pad_w=[3, 1])
print layer.parse_network(pad)
class ProjOpTest(unittest.TestCase):
def test_projection(self):
input = layer.data(name='data', type=data_type.dense_vector(784))
word = layer.data(
name='word', type=data_type.integer_value_sequence(10000))
fc0 = layer.fc(input=input, size=100, act=activation.Sigmoid())
fc1 = layer.fc(input=input, size=200, act=activation.Sigmoid())
mixed0 = layer.mixed(
size=256,
input=[
layer.full_matrix_projection(input=fc0),
layer.full_matrix_projection(input=fc1)
])
with layer.mixed(size=200) as mixed1:
mixed1 += layer.full_matrix_projection(input=fc0)
mixed1 += layer.identity_projection(input=fc1)
table = layer.table_projection(input=word)
emb0 = layer.mixed(size=512, input=table)
with layer.mixed(size=512) as emb1:
emb1 += table
scale = layer.scaling_projection(input=fc0)
scale0 = layer.mixed(size=100, input=scale)
with layer.mixed(size=100) as scale1:
scale1 += scale
dotmul = layer.dotmul_projection(input=fc0)
dotmul0 = layer.mixed(size=100, input=dotmul)
with layer.mixed(size=100) as dotmul1:
dotmul1 += dotmul
context = layer.context_projection(input=fc0, context_len=5)
context0 = layer.mixed(size=100, input=context)
with layer.mixed(size=100) as context1:
context1 += context
conv = layer.conv_projection(
input=input,
filter_size=1,
num_channels=1,
num_filters=128,
stride=1,
padding=0)
conv0 = layer.mixed(input=conv, bias_attr=True)
with layer.mixed(bias_attr=True) as conv1:
conv1 += conv
print layer.parse_network(mixed0)
print layer.parse_network(mixed1)
print layer.parse_network(emb0)
print layer.parse_network(emb1)
print layer.parse_network(scale0)
print layer.parse_network(scale1)
print layer.parse_network(dotmul0)
print layer.parse_network(dotmul1)
print layer.parse_network(conv0)
print layer.parse_network(conv1)
def test_operator(self):
ipt0 = layer.data(name='data', type=data_type.dense_vector(784))
ipt1 = layer.data(name='word', type=data_type.dense_vector(128))
fc0 = layer.fc(input=ipt0, size=100, act=activation.Sigmoid())
fc1 = layer.fc(input=ipt0, size=100, act=activation.Sigmoid())
dotmul_op = layer.dotmul_operator(a=fc0, b=fc1)
dotmul0 = layer.mixed(input=dotmul_op)
with layer.mixed() as dotmul1:
dotmul1 += dotmul_op
conv = layer.conv_operator(
img=ipt0,
filter=ipt1,
filter_size=1,
num_channels=1,
num_filters=128,
stride=1,
padding=0)
conv0 = layer.mixed(input=conv)
with layer.mixed() as conv1:
conv1 += conv
print layer.parse_network(dotmul0)
print layer.parse_network(dotmul1)
print layer.parse_network(conv0)
print layer.parse_network(conv1)
if __name__ == '__main__':
......
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