Luo Tao
7 years ago
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#!/usr/bin/env 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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from paddle.trainer_config_helpers import *
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######################## data source ################################
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dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
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dict_file = dict()
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for line_count, line in enumerate(open(dict_path, "r")):
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dict_file[line.strip()] = line_count
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define_py_data_sources2(
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train_list='gserver/tests/Sequence/train.list',
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test_list=None,
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module='sequenceGen',
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obj='process',
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args={"dict_file": dict_file})
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settings(batch_size=5)
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######################## network configure ################################
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dict_dim = len(open(dict_path, 'r').readlines())
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word_dim = 128
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hidden_dim = 256
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label_dim = 3
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sparse_update = get_config_arg("sparse_update", bool, False)
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data = data_layer(name="word", size=dict_dim)
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emb = embedding_layer(
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input=data,
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size=word_dim,
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param_attr=ParamAttr(sparse_update=sparse_update))
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with mixed_layer(size=hidden_dim * 4) as lstm_input:
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lstm_input += full_matrix_projection(input=emb)
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lstm = lstmemory(
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input=lstm_input,
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act=TanhActivation(),
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gate_act=SigmoidActivation(),
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state_act=TanhActivation())
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lstm_last = last_seq(input=lstm)
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with mixed_layer(
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size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
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output += full_matrix_projection(input=lstm_last)
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outputs(
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classification_cost(
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input=output, label=data_layer(
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name="label", size=1)))
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@ -1,154 +0,0 @@
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#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 = 999
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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_sparse.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 = 32
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layer2_dim = 16
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layer3_dim = 16
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hidden_dim = 32
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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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