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155 lines
4.5 KiB
155 lines
4.5 KiB
#edit-mode: -*- python -*-
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
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# Note: when making change to this file, please make sure
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# sample_trainer_config_rnn.conf is changed accordingly so that the uniitest
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# for comparing these two nets can pass (test_CompareTwoNets)
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default_initial_std(0.1)
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default_device(0)
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word_dim = 1451594
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l1 = 0
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l2 = 0
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model_type("nn")
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sparse_update = get_config_arg("sparse_update", bool, False)
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TrainData(ProtoData(
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type = "proto_sequence",
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files = ('trainer/tests/train.list'),
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))
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Settings(
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algorithm='sgd',
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batch_size=100,
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learning_rate=0.0001,
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learning_rate_decay_a=4e-08,
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learning_rate_decay_b=0.0,
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learning_rate_schedule='poly',
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)
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wordvec_dim = 128
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layer2_dim = 96
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layer3_dim = 96
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hidden_dim = 128
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slot_names = ["qb", "qw", "tb", "tw"]
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def ltr_network(network_name,
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word_dim=word_dim,
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wordvec_dim=wordvec_dim,
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layer2_dim=layer2_dim,
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layer3_dim=layer3_dim,
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hidden_dim=hidden_dim,
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slot_names=slot_names,
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l1=l1,
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l2=l2):
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slotnum = len(slot_names)
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for i in xrange(slotnum):
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Inputs(slot_names[i] + network_name)
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for i in xrange(slotnum):
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Layer(
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name = slot_names[i] + network_name,
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type = "data",
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size = word_dim,
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device = -1,
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)
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Layer(
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name = slot_names[i] + "_embedding_" + network_name,
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type = "mixed",
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size = wordvec_dim,
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bias = False,
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device = -1,
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inputs = TableProjection(slot_names[i] + network_name,
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parameter_name = "embedding.w0",
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decay_rate_l1=l1,
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sparse_remote_update = True,
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sparse_update = sparse_update,
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),
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)
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Layer(
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name = slot_names[i] + "_rnn1_" + network_name,
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type = "recurrent",
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active_type = "tanh",
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bias = Bias(initial_std = 0,
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parameter_name = "rnn1.bias"),
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inputs = Input(slot_names[i] + "_embedding_" + network_name,
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parameter_name = "rnn1.w0")
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)
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Layer(
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name = slot_names[i] + "_rnnlast_" + network_name,
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type = "seqlastins",
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inputs = [
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slot_names[i] + "_rnn1_" + network_name,
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],
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)
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Layer(
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name = "layer2_" + network_name,
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type = "fc",
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active_type = "tanh",
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size = layer2_dim,
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bias = Bias(parameter_name = "layer2.bias"),
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inputs = [Input(slot_name + "_rnnlast_" + network_name,
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parameter_name = "_layer2_" + slot_name + ".w",
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decay_rate = l2,
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initial_smart = True) for slot_name in slot_names]
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)
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Layer(
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name = "layer3_" + network_name,
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type = "fc",
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active_type = "tanh",
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size = layer3_dim,
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bias = Bias(parameter_name = "layer3.bias"),
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inputs = [
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Input("layer2_" + network_name,
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parameter_name = "_layer3.w",
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decay_rate = l2,
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initial_smart = True),
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]
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)
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Layer(
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name = "output_" + network_name,
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type = "fc",
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size = 1,
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bias = False,
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inputs = [
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Input("layer3_" + network_name,
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parameter_name = "_layerO.w"),
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],
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)
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ltr_network("left")
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ltr_network("right")
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Inputs("label")
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Layer(
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name = "label",
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type = "data",
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size = 1,
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)
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Outputs("cost", "qb_rnnlast_left")
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Layer(
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name = "cost",
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type = "rank-cost",
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inputs = ["output_left", "output_right", "label"],
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)
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