Change cos_sim to use CosSimLayer layer when size=1 and rename convex_comb_layer to linear_comb_layer

doc/ui/api/trainer_config_helpers/layers.rst

@@ -245,10 +245,10 @@ addto_layer
     :members: addto_layer
     :noindex:
 
-convex_comb_layer
+linear_comb_layer
 -----------------
 ..  automodule:: paddle.trainer_config_helpers.layers
-    :members: convex_comb_layer
+    :members: linear_comb_layer
     :noindex:
 
 interpolation_layer
@@ -280,7 +280,13 @@ tensor_layer
 ..  automodule:: paddle.trainer_config_helpers.layers
     :members: tensor_layer
     :noindex:
-
+
+cos_sim
+-------
+..  automodule:: paddle.trainer_config_helpers.layers
+    :members: cos_sim
+    :noindex:
+
 trans_layer
 ------------
 ..  automodule:: paddle.trainer_config_helpers.layers
@@ -341,12 +347,6 @@ rank_cost
     :members: rank_cost
     :noindex:
 
-cos_sim
--------
-..  automodule:: paddle.trainer_config_helpers.layers
-    :members: cos_sim
-    :noindex:
-
 crf_layer
 -----------------
 ..  automodule:: paddle.trainer_config_helpers.layers

paddle/gserver/layers/ConvexCombinationLayer.cpp

@@ -21,18 +21,20 @@ limitations under the License. */
 namespace paddle {
 
 /**
- * @brief A layer for convex weighted average of vectors,
+ * @brief A layer for weighted sum of vectors,
  * which is used in NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND
  * TRANSLATE
- * - Input: the first input contains the convex weights (batchSize x weightDim),
- *          and the shape of second input is (batchSize x (weightdim*dataDim)).
- * - Output: the shape of output is (batchSize x dataDim).
+ * - Input: the the size of the first input is weightDim,
+ *          and the size of the second input is weightdim * dataDim.
+ * - Output: the sizeof the output is dataDim
  * \f[
- *   out[i][j] = \sum_{j}(in0(i, j) * in1(i,j + i * dataDim)),
- *               i = 0,1,...,(batchSize-1); j = 0, 1,...,(dataDim-1)
+ *   out(j) = \sum_{i}(in0(i) * in1(i,j + i * dataDim)),
+ *               i = 0,1,...,(weightDim-1); j = 0, 1,...,(dataDim-1)
  * \f]
+ * Note that the above computation is for one sample. Multiple samples are
+ * processed in one batch.
  *
- * The config file api is convex_comb_layer.
+ * The config file api is linear_comb_layer.
  */
 class ConvexCombinationLayer : public Layer {
 protected:

paddle/gserver/layers/CosSimLayer.cpp

@@ -48,7 +48,7 @@ void CosSimLayer::forward(PassType passType) {
     REGISTER_TIMER_INFO("CosFwAtvTimer", getName().c_str());
     MatrixPtr prevOut1 = getInputValue(0);
     MatrixPtr prevOut2 = getInputValue(1);
-    outV->cosSim(*prevOut1, *prevOut2, kCosSimScale_);
+    outV->cosSim(*prevOut1, *prevOut2, config_.cos_scale());
   }
 }
 
@@ -59,7 +59,7 @@ void CosSimLayer::backward(const UpdateCallback& callback) {
 
     outG->cosSimDerivative(*this->getOutputValue(), *getInputValue(0),
                            *getInputValue(1), *getInputGrad(0),
-                           *getInputGrad(1), kCosSimScale_);
+                           *getInputGrad(1), config_.cos_scale());
   }
 }
 

paddle/gserver/layers/CosSimLayer.h

@@ -36,16 +36,14 @@ namespace paddle {
 class CosSimLayer : public Layer {
 public:
   explicit CosSimLayer(const LayerConfig& config)
-      : Layer(config), kCosSimScale_(5.0f) {}
+      : Layer(config) {}
 
   ~CosSimLayer() {}
 
   bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
 
   void forward(PassType passType);
   void backward(const UpdateCallback& callback = nullptr);
-
-  const real kCosSimScale_;
 };
 
 }  // namespace paddle

python/CMakeLists.txt

@@ -22,6 +22,8 @@ find_python_module(pip REQUIRED)
 find_python_module(wheel REQUIRED)
 find_python_module(google.protobuf REQUIRED)
 
+add_subdirectory(paddle/trainer_config_helpers/tests)
+
 install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/dist/
     DESTINATION opt/paddle/share/wheels
 )

python/paddle/trainer/config_parser.py

@@ -2264,6 +2264,9 @@ def __init__(
            name, 'convex_comb', size, inputs=inputs, device=device)
         config_assert(len(self.inputs) == 2,
           'ConvexCombinationLayer must have 2 inputs')
+        config_assert(
+            size * self.get_input_layer(0).size == self.get_input_layer(1).size,
+            'Wrong input size for ConvexCombinationLayer')
         self.set_layer_size(size)
 
 @config_layer('interpolation')
@@ -2313,6 +2316,9 @@ def __init__(
         self.config.cos_scale = cos_scale
         config_assert(len(self.inputs) == 2,
           'CosSimVecMatLayer must have 2 inputs')
+        config_assert(
+            size * self.get_input_layer(0).size == self.get_input_layer(1).size,
+            'Wrong input size for CosSimVecMatLayer')
 
 @config_layer('sampling_id')
 class SamplingIdLayer(LayerBase):
@@ -2361,13 +2367,15 @@ def __init__(
             self,
             name,
             inputs,
+            cos_scale=5,
             device=None):
         super(CosSimLayer, self).__init__(
             name, 'cos', 1, inputs=inputs, device=device)
         config_assert(len(self.inputs) == 2, 'CosSimLayer must have 2 inputs')
         config_assert(
             self.get_input_layer(0).size == self.get_input_layer(1).size,
             'inputs of CosSimLayer must have same dim')
+        self.config.cos_scale = cos_scale
 
 
 @config_layer('tensor')

python/paddle/trainer_config_helpers/layers.py

@@ -47,6 +47,7 @@
            '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', 'crf_layer', 'crf_decoding_layer',
            'cross_entropy_with_selfnorm', 'cross_entropy',
            'multi_binary_label_cross_entropy',
@@ -70,7 +71,8 @@ class LayerType(object):
     POOLING_AVG = 'average'
     FC_LAYER = "fc"
     COST = 'cost'
-    COSINE_SIM = 'cos_vm'
+    COSINE_SIM_VEC = 'cos_vm'
+    COSINE_SIM = 'cos'
     HSIGMOID = 'hsigmoid'
     CONV_LAYER = "conv"
     POOL_LAYER = "pool"
@@ -102,7 +104,7 @@ class LayerType(object):
     SEL_FC_LAYER = "selective_fc"
     SAMPLING_ID_LAYER = "sampling_id"
     SLOPE_INTERCEPT_LAYER = "slope_intercept"
-    CONVEX_COMBINATION_LAYER = "convex_comb"
+    LINEAR_COMBINATION_LAYER = "convex_comb"
     BLOCK_EXPAND = "blockexpand"
 
     CTC_LAYER = "ctc"
@@ -1171,13 +1173,16 @@ def power_layer(input, weight, name=None, layer_attr=None):
 @layer_support()
 def scaling_layer(input, weight, name=None, layer_attr=None):
     """
-    A layer for each row of a matrix, multiplying with a element of a vector.
+    A layer for multiplying input vector by weight scalar.
 
     .. math::
-       y.row[i] = w[i] * x.row[i]
+       y  = w x
 
-    where :math:`x` is (batchSize x dataDim) input, :math:`w` is
-    (batchSize x 1) weight vector, and :math:`y` is (batchSize x dataDim) output.
+    where :math:`x` is size=dataDim input, :math:`w` is size=1 weight,
+    and :math:`y` is size=dataDim output.
+
+    Note that the above computation is for one sample. Multiple samples are
+    processed in one batch.
 
     The example usage is:
 
@@ -1251,11 +1256,14 @@ def cos_sim(a, b, scale=5, size=1, name=None, layer_attr=None):
 
     ..  math::
         similarity = cos(\\theta) = {\\mathbf{a} \\cdot \\mathbf{b}
-        \\over \\|\\mathbf{b}\\| \\|\\mathbf{b}\\|}
+        \\over \\|\\mathbf{a}\\| \\|\\mathbf{b}\\|}
+
+    The size of a is M, size of b is M*N,
+    Similarity will be calculated N times by step M. The output size is
+    N. The scale will be multiplied to similarity.
 
-    And the input dimension is :math:`a \in R^M`, :math:`b \in R^{MN}`. The
-    similarity will be calculated N times by step M. The output dimension is
-    :math:`R^N`. The scale will be multiplied to similarity.
+    Note that the above computation is for one sample. Multiple samples are
+    processed in one batch.
 
     :param name: layer name
     :type name: basestring
@@ -1272,14 +1280,23 @@ def cos_sim(a, b, scale=5, size=1, name=None, layer_attr=None):
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
-    Layer(
-        name=name,
-        type=LayerType.COSINE_SIM,
-        size=size,
-        cos_scale=scale,
-        inputs=[a.name, b.name],
-        **ExtraLayerAttribute.to_kwargs(layer_attr)
-    )
+    if size == 1:
+        Layer(
+            name=name,
+            type=LayerType.COSINE_SIM,
+            cos_scale=scale,
+            inputs=[a.name, b.name],
+            **ExtraLayerAttribute.to_kwargs(layer_attr)
+        )
+    else:
+        Layer(
+            name=name,
+            type=LayerType.COSINE_SIM_VEC,
+            size=size,
+            cos_scale=scale,
+            inputs=[a.name, b.name],
+            **ExtraLayerAttribute.to_kwargs(layer_attr)
+        )
     return LayerOutput(name, LayerType.COSINE_SIM, parents=[a, b])
 
 @wrap_name_default()
@@ -2911,29 +2928,37 @@ def slope_intercept_layer(input, name=None, slope=1.0, intercept=0.0):
 
 
 @wrap_name_default()
-def convex_comb_layer(input, size, name=None):
+def linear_comb_layer(weights, vectors, size, name=None):
     """
-    A layer for convex weighted average of vectors takes two inputs.
-      - Input: a vector containing the convex weights (batchSize x weightdim),
-               and a matrix in a vector form (batchSize x (weightdim * datadim)).
-      - Output: a vector (batchSize * datadim).
+    A layer for weighted sum of vectors takes two inputs.
+      - Input: size of weights is M
+               size of vectors is M*N
+      - Output: a vector of size=N
 
     .. math::
 
-       y[i][j] = \sum_{j}(x_{1}(i, j) * x_{2}(i,j + i * dataDim)),
+       z(i) = \sum_{j=0}^{M-1} x(j) y(i+Nj)
+    where :math:`0 \le i \le N-1`
+
+    Or in the matrix notation:
+
+    .. math::
 
-                   i = 0,1,...,(batchSize-1); j = 0, 1,...,(dataDim-1)
+       z = x^T Y
 
     In this formular:
-      - :math:`x_{1}`: the first input.
-      - :math:`x_{2}`: the second input.
-      - :math:`y`: the output.
+      - :math:`x`: weights
+      - :math:`y`: vectors.
+      - :math:`z`: the output.
+
+    Note that the above computation is for one sample. Multiple samples are
+    processed in one batch.
 
     The simple usage is:
 
     .. code-block:: python
 
-       convex_comb = convex_comb_layer(input=inputs,
+       linear_comb = linear_comb_layer(weighs=weight, vectors=vectors,
                                        size=elem_dim)
 
     :param input: The input layers.
@@ -2946,15 +2971,16 @@ def convex_comb_layer(input, size, name=None):
     :rtype: LayerOutput
     """
 
-    assert isinstance(input, list) or isinstance(input, tuple)
-    assert len(input) == 2
     Layer(
         name=name,
-        type=LayerType.CONVEX_COMBINATION_LAYER,
+        type=LayerType.LINEAR_COMBINATION_LAYER,
         size=size,
-        inputs=[Input(input[0].name), Input(input[1].name)],
+        inputs=[Input(weights.name), Input(vectors.name)],
     )
-    return LayerOutput(name, LayerType.CONVEX_COMBINATION_LAYER, input, size=size)
+    return LayerOutput(name, LayerType.LINEAR_COMBINATION_LAYER,
+                       [weights, vectors], size=size)
+
+convex_comb_layer = linear_comb_layer
 
 @wrap_name_default()
 def block_expand_layer(input,

python/paddle/trainer_config_helpers/tests/CMakeLists.txt

@@ -0,0 +1,5 @@
+#################### test_config_parser #########################
+add_test(NAME layers_test
+  COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/
+        python ${PROJ_ROOT}/python/paddle/trainer_config_helpers/tests/layers_test.py
+    WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle)

python/paddle/trainer_config_helpers/tests/layers_test.py

@@ -0,0 +1,19 @@
+# Copyright (c) 2016 Baidu, Inc. 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_parser import parse_config_and_serialize
+
+if __name__ == '__main__':
+    parse_config_and_serialize(
+        'trainer_config_helpers/tests/layers_test_config.py', '')

python/paddle/trainer_config_helpers/tests/layers_test_config.py

@@ -0,0 +1,43 @@
+# Copyright (c) 2016 Baidu, Inc. 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 import *
+
+num_classes = 5
+
+x = data_layer(name="input1", size=3)
+y = data_layer(name="input2", size=5)
+
+x1 = fc_layer(input=x, size=5)
+y1 = fc_layer(input=y, size=5)
+y2 = fc_layer(input=y, size=15)
+
+cos1 = cos_sim(a=x1, b=y1)
+cos3 = cos_sim(a=x1, b=y2, size=3)
+
+linear_comb = linear_comb_layer(weights=x1, vectors=y2, size=3)
+
+out = fc_layer(input=[cos1, cos3, linear_comb],
+               size=num_classes,
+               act=SoftmaxActivation())
+
+outputs(classification_cost(out, data_layer(name="label", size=num_classes)))
+
+settings(
+    batch_size=10,
+    learning_rate=2e-3,
+    learning_method=AdamOptimizer(),
+    regularization=L2Regularization(8e-4),
+    gradient_clipping_threshold=25
+)