#?coding:?utf-8

import?tensorflow?as?tf


class?TCNNConfig（object）:
????“““CNN配置參數(shù)“““

????embedding_dim?=?64??#?字的特征是64
????seq_length?=?600??#?序列長度????句子長度600
????num_classes?=?10??#?類別數(shù)
????num_filters?=?256??#?卷積核數(shù)目
????kernel_size?=?5??#?卷積核尺寸
????vocab_size?=?5000??#?字數(shù)

????hidden_dim?=?128??#?全連接層神經(jīng)元

????dropout_keep_prob?=?0.5??#?dropout保留比例
????learning_rate?=?1e-3??#?學習率0.001

????batch_size?=?64??#?每批訓練大小
????num_epochs?=?10000??#?總迭代輪次

????print_per_batch?=?100??#?每多少輪輸出一次結(jié)果
????save_per_batch?=?10??#?每多少輪存入tensorboard


class?TextCNN（object）:
????“““文本分類，CNN模型“““

????def?__init__（self?config）:
????????self.config?=?config

????????#?三個待輸入的數(shù)據(jù)
????????self.input_x?=?tf.placeholder（tf.int32?[None?self.config.seq_length]?name=‘input_x‘）#句子長度（句子數(shù)600）
????????self.input_y?=?tf.placeholder（tf.float32?[None?self.config.num_classes]?name=‘input_y‘）#標簽類別（110）
????????self.keep_prob?=?tf.placeholder（tf.float32?name=‘keep_prob‘）#設(shè)置的dropout

????????self.cnn（）

????def?cnn（self）:
????????“““CNN模型“““
????????#?字向量映射
????????with?tf.device（‘/cpu:0‘）:#5000行64列代表一個字
????????????embedding?=?tf.get_variable（‘embedding‘?[self.config.vocab_size?self.config.embedding_dim]）#（500064）5000個字
????????????embedding_inputs?=?tf.nn.embedding_lookup（embedding?self.input_x）?#選取一個張量里面索引對應(yīng)的元素?shape=（句子數(shù)?600?64）

????????with?tf.name_scope（“cnn“）:
????????????#?CNN?layer??embedding_inputs?是三維（hw）
????????????conv?=?tf.layers.conv1d（embedding_inputs?self.config.num_filters?self.config.kernel_size?name=‘conv‘）
????????????#1*5的個卷積核，256個核?一維計算?輸入的（??600?64）??????輸出?shape=（??596?256）??（600-5）/1+1=596??
????????????#?global?max?pooling?layer?reduce_max計算張量的各個維度上的元素的最大值??64個句子，每個句子是600?每個字是256維（每個維度是1*5卷積）
????????????gmp?=?tf.reduce_max（conv?reduction_indices=[1]?name=‘gmp‘）#shape=（??256）?按1維去取最大[[12][34]]指定按行列，不指定按均值

????????with?tf.name_scope（“score“）:
????????????#?全連接層，后面接dropout以及relu激活gmp輸入的數(shù)據(jù)，hidden_dim輸出的維度大小
????????????fc?=?tf.layers.dense（gmp?self.config.hidden_dim?name=‘fc1‘）#shape=（64?128）
????????????fc?=?tf.contrib.layers.dropout（fc?self.keep_prob）#shape=（64?128）
????????????fc?=?tf.nn.relu（fc）#shape=（64?128）

????????????#?分類器
????????????self.logits?=?tf.layers.dense（fc?self.config.num_classes?name=‘fc2‘）#shape=（??10）
????????????self.y_pred_cls?=?tf.argmax（tf.nn.softmax（self.logits）?1）??#?預(yù)測類別?shape=（?）按列取

????????with?tf.name_scope（“optimize“）:
????????????#?損失函數(shù)，交叉熵
????????????cross_entropy?=?tf.nn.softmax_cross_entropy_with_logits（logits=self.logits?labels=self.input_y）#?shape=（?）
????????????self.loss?=?tf.reduce_mean（cross_entropy）#shape=（）?
????????????#?優(yōu)化器
????????????self.optim?=?tf.train.AdamOptimizer（learning_rate=self.config.learning_rate）.minimize（self.loss）

????????with?tf.name_scope（“accuracy“）:
????????????#?準確率
????????????correct_pred?=?tf.equal（tf.argmax（self.input_y?1）?self.y_pred_cls）#shape=（?）
????????????self.acc?=?tf.reduce_mean（tf.cast（correct_pred?tf.float32））#shape=（）

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----

?????文件?????????96??2018-04-25?15:32??text-classification-cnn-rnn\.gitignore

?????文件????6127232??2018-08-30?10:55??text-classification-cnn-rnn\checkpoints\textcnn\best_validation.data-00000-of-00001

?????文件???????1554??2018-08-30?10:55??text-classification-cnn-rnn\checkpoints\textcnn\best_validation.index

?????文件?????474291??2018-08-30?10:55??text-classification-cnn-rnn\checkpoints\textcnn\best_validation.meta

?????文件?????????87??2018-08-30?10:55??text-classification-cnn-rnn\checkpoints\textcnn\checkpoint

?????文件???????3518??2018-08-29?18:38??text-classification-cnn-rnn\cnn_model.py

?????文件???27508648??2018-04-30?23:12??text-classification-cnn-rnn\data\cnews\cnews.test.txt

?????文件??130089129??2018-04-30?23:13??text-classification-cnn-rnn\data\cnews\cnews.train.txt

?????文件???11788178??2018-04-30?23:15??text-classification-cnn-rnn\data\cnews\cnews.val.txt

?????文件??????24784??2018-08-29?13:59??text-classification-cnn-rnn\data\cnews\cnews.vocab.txt

?????文件??????19782??2018-04-30?23:12??text-classification-cnn-rnn\data\cnews\cnews.vocab1.txt

?????文件???????5437??2018-08-29?17:02??text-classification-cnn-rnn\data\cnews_loader.py

?????文件??????????0??2018-04-25?15:32??text-classification-cnn-rnn\data\__init__.py

?????文件???????4167??2018-08-29?17:05??text-classification-cnn-rnn\data\__pycache__\cnews_loader.cpython-36.pyc

?????文件????????150??2018-05-07?23:57??text-classification-cnn-rnn\data\__pycache__\__init__.cpython-36.pyc

?????文件???????1745??2018-04-25?15:32??text-classification-cnn-rnn\helper\cnews_group.py

?????文件????????440??2018-04-25?15:32??text-classification-cnn-rnn\helper\copy_data.sh

?????文件??????????0??2018-04-25?15:32??text-classification-cnn-rnn\helper\__init__.py

?????文件?????185646??2018-04-25?15:32??text-classification-cnn-rnn\images\acc_loss.png

?????文件?????137770??2018-04-25?15:32??text-classification-cnn-rnn\images\acc_loss_rnn.png

?????文件??????60095??2018-04-25?15:32??text-classification-cnn-rnn\images\cnn_architecture.png

?????文件??????57085??2018-04-25?15:32??text-classification-cnn-rnn\images\rnn_architecture.png

?????文件????????586??2018-05-12?23:29??text-classification-cnn-rnn\New?Project?#2.wpr

?????文件??????38443??2018-07-14?01:10??text-classification-cnn-rnn\New?Project?#2.wpu

?????文件???????1890??2018-04-25?15:32??text-classification-cnn-rnn\predict.py

?????文件?????????24??2018-04-25?15:32??text-classification-cnn-rnn\requirements.txt

?????文件???????4344??2018-08-31?18:05??text-classification-cnn-rnn\rnn_model.py

?????文件???????7396??2018-08-30?09:48??text-classification-cnn-rnn\run_cnn.py

?????文件??????81109??2018-08-29?16:51??text-classification-cnn-rnn\tensorboard\textcnn\events.out.tfevents.1535532668.LAPTOP-1V3PUQTB

?????文件?????100376??2018-08-29?17:29??text-classification-cnn-rnn\tensorboard\textcnn\events.out.tfevents.1535533727.LAPTOP-1V3PUQTB

............此處省略17個文件信息