modify the test config for test_CompareSparse.cpp

paddle/gserver/tests/CMakeLists.txt

@@ -95,3 +95,15 @@ add_test(NAME test_PyDataProvider2
    COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/paddle/gserver/tests:${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_PyDataProvider2
         WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle
 )
+
+################# test_CompareSparse ##################
+add_unittest_without_exec(test_CompareSparse
+    test_CompareSparse.cpp)
+if(NOT ON_TRAVIS)
+  add_test(NAME test_CompareSparse
+    COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
+          ${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests
+              ./.set_port.sh -p port -n 6
+                  ${CMAKE_CURRENT_BINARY_DIR}/test_CompareSparse
+    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
+endif()

paddle/gserver/tests/sequence_lstm.conf

+#!/usr/bin/env python
+# 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 import *
+
+######################## data source ################################
+dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
+dict_file = dict()
+for line_count, line in enumerate(open(dict_path, "r")):
+    dict_file[line.strip()] = line_count
+
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/train.list',
+    test_list=None,
+    module='sequenceGen',
+    obj='process',
+    args={"dict_file": dict_file})
+
+settings(batch_size=5)
+######################## network configure ################################
+dict_dim = len(open(dict_path, 'r').readlines())
+word_dim = 128
+hidden_dim = 256
+label_dim = 3
+sparse_update = get_config_arg("sparse_update", bool, False)
+
+data = data_layer(name="word", size=dict_dim)
+
+emb = embedding_layer(
+    input=data,
+    size=word_dim,
+    param_attr=ParamAttr(sparse_update=sparse_update))
+
+with mixed_layer(size=hidden_dim * 4) as lstm_input:
+    lstm_input += full_matrix_projection(input=emb)
+
+lstm = lstmemory(
+    input=lstm_input,
+    act=TanhActivation(),
+    gate_act=SigmoidActivation(),
+    state_act=TanhActivation())
+
+lstm_last = last_seq(input=lstm)
+
+with mixed_layer(
+        size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
+    output += full_matrix_projection(input=lstm_last)
+
+outputs(
+    classification_cost(
+        input=output, label=data_layer(
+            name="label", size=1)))

paddle/trainer/tests/test_CompareSparse.cpp → paddle/gserver/tests/test_CompareSparse.cpp

@@ -22,8 +22,7 @@ limitations under the License. */
 using namespace paddle;  // NOLINT
 using namespace std;     // NOLINT
 
-static const string& configFile1 =
-    "trainer/tests/sample_trainer_config_compare_sparse.conf";
+static const string& configFile1 = "gserver/tests/sequence_lstm.conf";
 
 DECLARE_bool(use_gpu);
 DECLARE_string(config);

paddle/trainer/tests/CMakeLists.txt

@@ -47,16 +47,6 @@ add_test(NAME test_CompareTwoOpts
             --num_passes=1 --need_high_accuracy=0
     WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
 
-################# test_CompareSparse ##################
-add_unittest_without_exec(test_CompareSparse
-    test_CompareSparse.cpp)
-if(NOT ON_TRAVIS)
-  add_test(NAME test_CompareSparse
-    COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
-              ./.set_port.sh -p port -n 6
-                  ${CMAKE_CURRENT_BINARY_DIR}/test_CompareSparse
-    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
-endif()
 ################# test_recurrent_machine_generation ###############
 add_unittest_without_exec(test_recurrent_machine_generation
     test_recurrent_machine_generation.cpp)

paddle/trainer/tests/sample_trainer_config_compare_sparse.conf

-#edit-mode: -*- python -*-
-# 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.
-
-#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
-
-# Note: when making change to this file, please make sure
-# sample_trainer_config_rnn.conf is changed accordingly so that the uniitest
-# for comparing these two nets can pass (test_CompareTwoNets)
-
-default_initial_std(0.1)
-default_device(0)
-
-word_dim = 999
-l1 = 0
-l2 = 0
-
-model_type("nn")
-
-sparse_update = get_config_arg("sparse_update", bool, False)
-
-TrainData(ProtoData(        
-            type = "proto_sequence",
-            files = ('trainer/tests/train_sparse.list'), 
-            ))
-
-Settings(
-    algorithm='sgd',
-    batch_size=100,
-    learning_rate=0.0001,
-    learning_rate_decay_a=4e-08,
-    learning_rate_decay_b=0.0,
-    learning_rate_schedule='poly',
-)
-
-
-wordvec_dim = 32
-layer2_dim = 16
-layer3_dim = 16
-hidden_dim = 32
-
-slot_names = ["qb", "qw", "tb", "tw"]
-
-def ltr_network(network_name,
-                word_dim=word_dim,
-                wordvec_dim=wordvec_dim,
-                layer2_dim=layer2_dim,
-                layer3_dim=layer3_dim,
-                hidden_dim=hidden_dim,
-                slot_names=slot_names,
-                l1=l1,
-                l2=l2):
-
-    slotnum = len(slot_names)
-    for i in xrange(slotnum):
-        Inputs(slot_names[i] + network_name)
-    for i in xrange(slotnum):
-        Layer(
-            name = slot_names[i] + network_name,
-            type = "data",
-            size = word_dim,
-            device = -1,
-        )
-        Layer(
-            name = slot_names[i] + "_embedding_" + network_name,
-            type = "mixed",
-            size = wordvec_dim,
-            bias = False,
-            device = -1,
-            inputs = TableProjection(slot_names[i] + network_name,
-                                     parameter_name = "embedding.w0",
-                                     decay_rate_l1=l1,
-                                     sparse_remote_update = True,
-                                     sparse_update = sparse_update,
-                                     ),
-        )
-        Layer(
-            name = slot_names[i] + "_rnn1_" + network_name,
-            type = "recurrent",
-            active_type = "tanh",
-            bias = Bias(initial_std = 0,
-                        parameter_name = "rnn1.bias"),
-            inputs = Input(slot_names[i] + "_embedding_" + network_name,
-                           parameter_name = "rnn1.w0")
-        )
-        Layer(
-            name = slot_names[i] + "_rnnlast_" + network_name,
-            type = "seqlastins",
-            inputs = [
-                slot_names[i] + "_rnn1_" + network_name,
-            ],
-        )
-
-    Layer(
-        name = "layer2_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer2_dim,
-        bias = Bias(parameter_name = "layer2.bias"),
-        inputs = [Input(slot_name + "_rnnlast_" + network_name, 
-                        parameter_name = "_layer2_" + slot_name + ".w", 
-                        decay_rate = l2, 
-                        initial_smart = True) for slot_name in slot_names]
-    )
-    Layer(
-        name = "layer3_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer3_dim,
-        bias = Bias(parameter_name = "layer3.bias"),
-        inputs = [
-            Input("layer2_" + network_name, 
-                  parameter_name = "_layer3.w", 
-                  decay_rate = l2, 
-                  initial_smart = True),
-        ]
-    )
-    Layer(
-        name = "output_" + network_name,
-        type = "fc",
-        size = 1,
-        bias = False,
-        inputs = [
-                  Input("layer3_" + network_name,
-                       parameter_name = "_layerO.w"),
-                 ],
-        )
-
-
-ltr_network("left")
-ltr_network("right")
-Inputs("label")
-Layer(
-    name = "label",
-    type = "data",
-    size = 1,
-    )
-Outputs("cost", "qb_rnnlast_left")
-Layer(
-    name = "cost",
-    type = "rank-cost",
-    inputs = ["output_left", "output_right", "label"],
-    )