未验证 提交 33300cd1 编写于 作者: S Suven 提交者: GitHub

Case - Update new case for NeuronBlocks (#637)

* Case - Update new case for neuronblocks
上级 0b51bde9
{
"license": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT license.",
"tool_version": "1.1.0",
"model_description": "This model is used for model compression",
"language": "Chinese",
"inputs": {
"use_cache": false,
"dataset_type": "classification",
"data_paths": {
"train_data_path": "./dataset/chinese_nli/cnli_train.txt",
"valid_data_path": "./dataset/chinese_nli/cnli_dev.txt",
"test_data_path": "./dataset/chinese_nli/cnli_test.txt",
"predict_data_path": "./dataset/chinese_nli/cnli_test.txt",
"pre_trained_emb": "./dataset/sogou_embed/sgns.sogou.word"
},
"add_start_end_for_seq": true,
"file_header": {
"premise_text": 0,
"hypothesis_text": 1,
"label": 2
},
"predict_file_header": {
"premise_text": 0,
"hypothesis_text": 1,
"label": 2
},
"model_inputs": {
"premise": [
"premise_text"
],
"hypothesis": [
"hypothesis_text"
]
},
"target": [
"label"
]
},
"outputs": {
"save_base_dir": "./models/chinese_nli/",
"model_name": "model.nb",
"train_log_name": "train.log",
"test_log_name": "test.log",
"predict_log_name": "predict.log",
"predict_fields": [
"prediction"
],
"predict_output_name": "predict.txt",
"cache_dir": ".cache.chinese_nli/"
},
"training_params": {
"vocabulary": {
"min_word_frequency": 1
},
"optimizer": {
"name": "SGD",
"params": {
"lr": 0.2,
"momentum": 0.9,
"nesterov": true
}
},
"lr_decay": 0.95,
"minimum_lr": 0.005,
"epoch_start_lr_decay": 1,
"use_gpu": false,
"batch_size": 64,
"batch_num_to_show_results": 100,
"max_epoch": 6,
"steps_per_validation": 1000,
"max_lengths": {
"premise": 32,
"hypothesis": 32
}
},
"architecture": [
{
"layer": "Embedding",
"conf": {
"word": {
"cols": [
"premise_text",
"hypothesis_text"
],
"dim": 300
}
}
},
{
"layer_id": "premise_dropout",
"layer": "Dropout",
"conf": {
"dropout": 0
},
"inputs": [
"premise"
]
},
{
"layer_id": "hypothesis_dropout",
"layer": "Dropout",
"conf": {
"dropout": 0
},
"inputs": [
"hypothesis"
]
},
{
"layer_id": "premise_bigru",
"layer": "BiGRU",
"conf": {
"hidden_dim": 128,
"dropout": 0.3,
"num_layers": 2
},
"inputs": [
"premise_dropout"
]
},
{
"layer_id": "hypothesis_bigru",
"layer": "premise_bigru",
"inputs": [
"hypothesis_dropout"
]
},
{
"layer_id": "premise_attn",
"layer": "BiAttFlow",
"conf": {},
"inputs": [
"premise_bigru",
"hypothesis_bigru"
]
},
{
"layer_id": "hypothesis_attn",
"layer": "BiAttFlow",
"conf": {},
"inputs": [
"hypothesis_bigru",
"premise_bigru"
]
},
{
"layer_id": "premise_bigru_final",
"layer": "BiGRU",
"conf": {
"hidden_dim": 128,
"num_layers": 1
},
"inputs": [
"premise_attn"
]
},
{
"layer_id": "hypothesis_bigru_final",
"layer": "BiGRU",
"conf": {
"hidden_dim": 128,
"num_layers": 1
},
"inputs": [
"hypothesis_attn"
]
},
{
"layer_id": "premise_pooling",
"layer": "Pooling",
"conf": {
"pool_axis": 1,
"pool_type": "max"
},
"inputs": [
"premise_bigru_final"
]
},
{
"layer_id": "hypothesis_pooling",
"layer": "Pooling",
"conf": {
"pool_axis": 1,
"pool_type": "max"
},
"inputs": [
"hypothesis_bigru_final"
]
},
{
"layer_id": "comb",
"layer": "Combination",
"conf": {
"operations": [
"origin",
"difference",
"dot_multiply"
]
},
"inputs": [
"premise_pooling",
"hypothesis_pooling"
]
},
{
"output_layer_flag": true,
"layer_id": "output",
"layer": "Linear",
"conf": {
"hidden_dim": [
128,
3
],
"activation": "PReLU",
"batch_norm": true,
"last_hidden_activation": false
},
"inputs": [
"comb"
]
}
],
"loss": {
"losses": [
{
"type": "CrossEntropyLoss",
"conf": {
"size_average": true
},
"inputs": [
"output",
"label"
]
}
]
},
"metrics": [
"accuracy"
]
}
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......@@ -48,7 +48,8 @@
5|[中级案例-生成对抗网络GAN](./B15-生成对抗网络/README.md)|GAN简介;动手实现并训练生成对抗网络 |必选
6|[计算机视觉中级案例-图像超分辨率](https://github.com/microsoft/ai-edu/tree/master/B-实践案例/复旦大学城市声音分类-图像超分辨率)|数据预处理;使用GAN、CNN和ResNet的组合构建超分辨率模型|必选
7|[中级案例-智慧城市之声音分类](https://github.com/microsoft/ai-edu/tree/master/B-实践案例/复旦大学城市声音分类-图像超分辨率)|数据分析;特征工程;TensorFlow 框架下构建多种深度学习模型(多层感知机、LSTM、GRU 和 CNN 等) |必选
8|[扩展阅读-机器学习平台建设](./B10-扩展阅读-机器学习平台建设/readme.md)|机器学习平台的架构;机器学习平台的功能;微软开源机器学习平台OpenPAI |可选
8|[扩展阅读-机器学习平台建设](./B10-扩展阅读-机器学习平台建设/readme.md)|机器学习平台的架构;机器学习平台的功能;微软开源机器学习平台OpenPAI | 可选
本部分内容也可以结合 **[神经网络进阶](https://github.com/microsoft/ai-edu/tree/master/A-基础教程#2-神经网络简明原理)** 以及 **[深度网络基础](https://github.com/microsoft/ai-edu/tree/master/A-基础教程#2-神经网络简明原理)** 的理论知识完成理论加实践的AI进阶学习
......@@ -66,3 +67,4 @@
3|[高级实战项目-Open Platform for AI (OpenPAI)](https://github.com/Microsoft/pai)|微软开源GPU管理利器
4|[高级实战项目-LightGBM](https://github.com/Microsoft/LightGBM)|boosting框架
5|[高级实战项目-基于近邻图的向量搜索案例](./B16-基于近邻图的向量搜索案例/README.md)|在大规模向量中快速搜索最近邻
6| [高级实战案例-中文文本蕴含深度学习模型](./B17-快速构建中文文本蕴含深度学习模型/Readme.md) | 数据预处理; 微软开源NLP深度学习建模工具包Neuronblocks
......@@ -12,6 +12,10 @@
# <font size=5>新闻</font>
**<font size=3>2021-04-20:</font>**
更新[中文文本蕴含](./B-实践案例/B17-快速构建中文文本蕴含深度学习模型/Readme.md)案例
**<font size=3>2021-03-07:</font>**
A7-强化学习,准备中。
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