implement more layers in v2

doc/api/trainer_config_helpers/layers.rst

@@ -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

python/paddle/trainer_config_helpers/layers.py

@@ -30,88 +30,28 @@ except ImportError:
 import copy
 
 __all__ = [
-    "full_matrix_projection",
-    "AggregateLevel",
-    "ExpandLevel",
-    "identity_projection",
-    "dotmul_projection",
-    "dotmul_operator",
-    "repeat_layer",
-    "seq_reshape_layer",
-    "table_projection",
-    "mixed_layer",
-    "data_layer",
-    "embedding_layer",
-    "fc_layer",
-    "grumemory",
-    "pooling_layer",
-    "lstmemory",
-    "last_seq",
-    "first_seq",
-    "cos_sim",
-    "hsigmoid",
-    "conv_projection",
-    "regression_cost",
-    'classification_cost',
-    "LayerOutput",
-    'img_conv_layer',
-    'img_pool_layer',
-    'batch_norm_layer',
-    'img_cmrnorm_layer',
-    'addto_layer',
-    'concat_layer',
-    'seq_concat_layer',
-    'lstm_step_layer',
-    'recurrent_group',
-    'memory',
-    'StaticInput',
-    'expand_layer',
-    'scaling_layer',
-    'scaling_projection',
-    'power_layer',
-    'interpolation_layer',
-    'bilinear_interp_layer',
-    'trans_layer',
-    'rotate_layer',
-    'sum_to_one_norm_layer',
-    'get_output_layer',
-    'LayerType',
-    'context_projection',
-    'beam_search',
-    'maxid_layer',
-    'GeneratedInput',
-    'SubsequenceInput',
-    'gru_step_layer',
-    'recurrent_layer',
-    'BaseGeneratedInput',
-    'conv_operator',
-    'conv_shift_layer',
-    'tensor_layer',
-    'selective_fc_layer',
-    'sampling_id_layer',
-    'slope_intercept_layer',
-    'trans_full_matrix_projection',
-    'linear_comb_layer',
-    'convex_comb_layer',
-    'ctc_layer',
-    'warp_ctc_layer',
-    'crf_layer',
-    'crf_decoding_layer',
-    'nce_layer',
-    'cross_entropy_with_selfnorm',
-    'cross_entropy',
-    'multi_binary_label_cross_entropy',
-    'sum_cost',
-    'rank_cost',
-    'lambda_cost',
-    'huber_cost',
-    'block_expand_layer',
-    'maxout_layer',
-    'out_prod_layer',
-    'print_layer',
-    'priorbox_layer',
-    'spp_layer',
-    'pad_layer',
+    "full_matrix_projection", "AggregateLevel", "ExpandLevel",
+    "identity_projection", "dotmul_projection", "dotmul_operator",
+    "repeat_layer", "seq_reshape_layer", "table_projection", "mixed_layer",
+    "data_layer", "embedding_layer", "fc_layer", "grumemory", "pooling_layer",
+    "lstmemory", "last_seq", "first_seq", "cos_sim", "hsigmoid",
+    "conv_projection", "regression_cost", 'classification_cost', "LayerOutput",
+    'img_conv_layer', 'img_pool_layer', 'batch_norm_layer', 'img_cmrnorm_layer',
+    'addto_layer', 'concat_layer', 'seq_concat_layer', 'lstm_step_layer',
+    'recurrent_group', 'memory', 'StaticInput', 'expand_layer', 'scaling_layer',
+    'scaling_projection', 'power_layer', 'interpolation_layer',
+    'bilinear_interp_layer', 'trans_layer', 'rotate_layer',
+    'sum_to_one_norm_layer', 'get_output_layer', 'LayerType',
+    'context_projection', 'beam_search', 'maxid_layer', 'GeneratedInput',
+    'SubsequenceInput', 'gru_step_layer', 'recurrent_layer',
+    'BaseGeneratedInput', 'conv_operator', 'conv_shift_layer', 'tensor_layer',
+    'selective_fc_layer', 'sampling_id_layer', 'slope_intercept_layer',
+    'trans_full_matrix_projection', 'linear_comb_layer', 'convex_comb_layer',
+    'ctc_layer', 'warp_ctc_layer', 'crf_layer', 'crf_decoding_layer',
+    'nce_layer', 'cross_entropy_with_selfnorm', 'cross_entropy',
+    'multi_binary_label_cross_entropy', 'sum_cost', 'rank_cost', 'lambda_cost',
+    'huber_cost', 'block_expand_layer', 'maxout_layer', 'out_prod_layer',
+    'print_layer', 'priorbox_layer', 'spp_layer', 'pad_layer', 'eos_layer'
 ]
 
 
@@ -1287,6 +1227,12 @@ 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 +1271,12 @@ 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 +1377,7 @@ 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 +1749,12 @@ 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 +1916,16 @@ 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 +2065,34 @@ 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 +2119,6 @@ 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 +2180,15 @@ 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 +2277,12 @@ 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 +2338,12 @@ 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 +3907,13 @@ 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 +4005,8 @@ 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 +4033,7 @@ 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 +4262,7 @@ 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,
@@ -4461,7 +4466,7 @@ def warp_ctc_layer(input,
         - You can set 'blank' to any value ranged in [0, num_classes], which
           should be consistent as that used in your labels.
         - As a native 'softmax' activation is interated to the warp-ctc library,
-         'linear' activation is expected instead in the 'input' layer.
+          'linear' activation is expected instead in the 'input' layer.
 
     The simple usage:
 
@@ -4594,6 +4599,13 @@ 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.

python/paddle/v2/__init__.py

@@ -19,11 +19,12 @@ import trainer
 import event
 import data_type
 import attr
+import pooling
 import py_paddle.swig_paddle as api
 
 __all__ = [
     'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer',
-    'event', 'data_type', 'attr'
+    'event', 'data_type', 'attr', 'pooling'
 ]
 
 

python/paddle/v2/layer.py

@@ -76,12 +76,20 @@ from paddle.trainer_config_helpers.default_decorators import wrap_name_default
 import data_type
 import activation
 import attr
+import pooling
 
 __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'
 ]
 
 
@@ -130,11 +138,8 @@ class Layer(object):
         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)
-    else:
-        wrapper = None
+def __convert_to_v2__(method_name, parent_names):
+    wrapper = wrap_name_default(name_prefix=method_name)
 
     class V2LayerImpl(Layer):
         def __init__(self, name=None, **kwargs):
@@ -192,44 +197,92 @@ class DataLayerV2(Layer):
 
 
 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'])
+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])
 
 if __name__ == '__main__':
-    pixel = data(name='pixel', type=data_type.dense_vector(784))
+    pixel = data(name='pixel', type=data_type.dense_vector(128))
     label = data(name='label', type=data_type.integer_value(10))
     weight = data(name='weight', type=data_type.dense_vector(10))
+    word = data(name='word', type=data_type.integer_value(12))
     score = data(name='score', type=data_type.dense_vector(1))
 
     hidden = fc(input=pixel,
@@ -237,7 +290,90 @@ if __name__ == '__main__':
                 act=activation.Sigmoid(),
                 param_attr=attr.Param(name='hidden'))
     inference = fc(input=hidden, size=10, act=activation.Softmax())
+    print parse_network(inference)
+
+    # test conv layers
+    conv1 = conv_shift(a=pixel, b=score)
+    conv2 = img_conv(
+        input=pixel,
+        filter_size=1,
+        filter_size_y=1,
+        num_channels=8,
+        num_filters=16,
+        act=activation.Linear())
+    print parse_network(conv1, conv2)
+
+    # test image pooling layers
+    maxpool = img_pool(
+        input=conv2,
+        pool_size=2,
+        num_channels=16,
+        padding=1,
+        pool_type=pooling.Max())
+    spp = spp(input=conv2,
+              pyramid_height=2,
+              num_channels=16,
+              pool_type=pooling.Max())
+    maxout = maxout(input=conv2, num_channels=16, groups=4)
+    print parse_network(maxpool, spp, maxout)
+
+    # test norm layers
+    norm1 = img_cmrnorm(input=maxpool, size=5)
+    norm2 = batch_norm(input=maxpool)
+    norm3 = sum_to_one_norm(input=maxpool)
+    print parse_network(norm1, norm2, norm3)
+
+    # test recurrent layers
+    recurrent = recurrent(input=word)
+    lstm = lstmemory(input=word)
+    gru = grumemory(input=word)
+    print parse_network(recurrent, lstm, gru)
+
+    # test aggregate layers
+    pool = pool(
+        input=pixel,
+        pooling_type=pooling.Avg(),
+        agg_level=AggregateLevel.EACH_SEQUENCE)
+    last_seq = last_seq(input=pixel)
+    first_seq = first_seq(input=pixel)
+    concat = concat(input=[last_seq, first_seq])
+    seq_concat = seq_concat(a=last_seq, b=first_seq)
+    print parse_network(pool, last_seq, first_seq, concat, seq_concat)
+
+    # test reshaping layers
+    block_expand = block_expand(
+        input=maxout, num_channels=4, stride_x=1, block_x=1)
+    expand = expand(
+        input=last_seq, expand_as=pixel, expand_level=ExpandLevel.FROM_TIMESTEP)
+    repeat = repeat(input=last_seq, num_repeats=4)
+    reshape = seq_reshape(input=last_seq, reshape_size=4)
+    rotate = rotate(input=pixel, height=16, width=49)
+    print parse_network(block_expand, expand, repeat, reshape, rotate)
+
+    # test math layers
+    addto = addto(input=[last_seq, first_seq])
+    linear_comb = linear_comb(weights=weight, vectors=hidden, size=10)
+    interpolation = interpolation(input=[hidden, hidden], weight=score)
+    bilinear = bilinear_interp(input=conv2, out_size_x=4, out_size_y=4)
+    power = power(input=conv1, weight=score)
+    scaling = scaling(input=conv1, weight=score)
+    slope = slope_intercept(input=conv1)
+    tensor = tensor(a=last_seq, b=first_seq, size=1000)
+    cos_sim = cos_sim(a=last_seq, b=first_seq)
+    trans = trans(input=tensor)
+    print parse_network(addto, linear_comb, interpolation, bilinear, power,
+                        scaling, slope, tensor, cos_sim, trans)
+
+    # test sampling layers
     maxid = max_id(input=inference)
+    sampling_id = sampling_id(input=inference)
+    print parse_network(maxid, sampling_id)
+
+    # test slicing and joining layers
+    pad = pad(input=maxpool, pad_c=[2, 3], pad_h=[1, 2], pad_w=[3, 1])
+    print parse_network(pad)
+
+    # test cost layers
     cost1 = classification_cost(input=inference, label=label)
     cost2 = classification_cost(input=inference, label=label, weight=weight)
     cost3 = cross_entropy_cost(input=inference, label=label)
@@ -249,9 +385,18 @@ if __name__ == '__main__':
     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)
+
+    crf = crf(input=inference, label=label)
+    crf_decoding = crf_decoding(input=inference, size=3)
+    ctc = ctc(input=inference, label=label)
+    warp_ctc = warp_ctc(input=pixel, label=label)
+    nce = nce(input=inference, label=label, num_classes=3)
+    hsigmoid = hsigmoid(input=inference, label=label, num_classes=3)
+    print parse_network(crf, crf_decoding, ctc, warp_ctc, nce, hsigmoid)
+
+    # test check layers
+    eos = eos(input=maxid, eos_id=5)
+    print parse_network(eos)

python/paddle/v2/pooling.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle.trainer_config_helpers.poolings import *
+
+__all__ = ["Max", "CudnnMax", "Avg", "CudnnAvg", "Sum", "SquareRootN"]
+
+Max = MaxPooling
+CudnnMax = CudnnMaxPooling
+Avg = AvgPooling
+CudnnAvg = CudnnAvgPooling
+Sum = SumPooling
+SquareRootN = SquareRootNPooling