模型升级，准确度89%，添加调用方法。

README.md

@@ -169,6 +169,20 @@ Time usage: 0:00:05
 
 输出在目录文件夹，名称为`predicted_data.txt`
 
+### 功能调用
+
+调用方法为:
+
+```python
+from run_cnn import name2subcategory
+
+name_list = ['安赛瑞 消防斧 应急斧头破拆斧工具 消防器材斧子消防站配置 消防检查斧子 25847'] 
+a = name2subcategory()
+category = a.namelyst_predict(name_list)
+```
+
+输入一个含有多个产品名称的列表，返回一个各名称子类的列表。
+
 ## RNN循环神经网络
 
 ### 和上面类似，代码尚未修改，可以根据CNN代码自行修改为训练自己模型的格式。
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cnn_model.py

@@ -5,21 +5,23 @@ import tensorflow as tf
 
 class TCNNConfig(object):
     """CNN配置参数"""
-
-    embedding_dim = 64  # 词向量维度
+    def __init__(self, num_classes):
+        self.num_classes = num_classes
+        
+    embedding_dim = 128  # 词向量维度
     seq_length = 300  # 序列长度
-    num_classes = 1078  # 类别数
-    num_filters = 256  # 卷积核数目
-    kernel_size = 5  # 卷积核尺寸
+    #num_classes = 668  # 类别数
+    num_filters = 1024  # 卷积核数目
+    kernel_size = 3  # 卷积核尺寸
     vocab_size = 5000  # 词汇表大小
 
-    hidden_dim = 128  # 全连接层神经元
+    hidden_dim = 256  # 全连接层神经元
 
     dropout_keep_prob = 0.5  # dropout保留比例
     learning_rate = 1e-3  # 学习率
 
     batch_size = 64  # 每批训练大小
-    num_epochs = 10  # 总迭代轮次
+    num_epochs = 20  # 总迭代轮次
 
     print_per_batch = 100  # 每多少轮输出一次结果
     save_per_batch = 10  # 每多少轮存入tensorboard
@@ -46,19 +48,19 @@ class TextCNN(object):
             embedding_inputs = tf.nn.embedding_lookup(embedding, self.input_x)
 
         with tf.name_scope("cnn"):
-            # CNN layer
-            conv = tf.layers.conv1d(embedding_inputs, self.config.num_filters, self.config.kernel_size, name='conv')
+            # CNN layer 3*3
+            conv_1 = tf.layers.conv1d(embedding_inputs, self.config.num_filters, self.config.kernel_size, name='conv_1')
             # global max pooling layer
-            gmp = tf.reduce_max(conv, reduction_indices=[1], name='gmp')
+            gmp_1 = tf.reduce_max(conv_1, reduction_indices=[1], name='gmp_1')
 
         with tf.name_scope("score"):
             # 全连接层，后面接dropout以及relu激活
-            fc = tf.layers.dense(gmp, self.config.hidden_dim, name='fc1')
-            fc = tf.contrib.layers.dropout(fc, self.keep_prob)
-            fc = tf.nn.relu(fc)
+            fc_1 = tf.layers.dense(gmp_1, self.config.hidden_dim, name='fc_1')
+            fc_1 = tf.contrib.layers.dropout(fc_1, self.keep_prob)
+            fc_1 = tf.nn.relu(fc_1)
 
             # 分类器
-            self.logits = tf.layers.dense(fc, self.config.num_classes, name='fc2')
+            self.logits = tf.layers.dense(fc_1, self.config.num_classes, name='fc_2')
             self.y_pred_cls = tf.argmax(tf.nn.softmax(self.logits), 1)  # 预测类别
 
         with tf.name_scope("optimize"):

cnn_model2.py

+# coding: utf-8
+
+import tensorflow as tf
+
+
+class TCNNConfig(object):
+    """RNN配置参数"""
+    def __init__(self, num_classes):
+        self.num_classes = num_classes
+    # 模型参数
+    embedding_dim = 64      # 词向量维度
+    seq_length = 600        # 序列长度
+    #num_classes = 8        # 类别数
+    vocab_size = 5000       # 词汇表达小
+
+    num_layers= 2           # 隐藏层层数
+    hidden_dim = 128        # 隐藏层神经元
+    rnn = 'gru'             # lstm 或 gru
+
+    dropout_keep_prob = 0.8 # dropout保留比例
+    learning_rate = 1e-3    # 学习率
+
+    batch_size = 128         # 每批训练大小
+    num_epochs = 10          # 总迭代轮次
+
+    print_per_batch = 100    # 每多少轮输出一次结果
+    save_per_batch = 10      # 每多少轮存入tensorboard
+
+
+class TextCNN(object):
+    """文本分类，RNN模型"""
+    def __init__(self, config):
+        self.config = config
+
+        # 三个待输入的数据
+        self.input_x = tf.placeholder(tf.int32, [None, self.config.seq_length], name='input_x')
+        self.input_y = tf.placeholder(tf.float32, [None, self.config.num_classes], name='input_y')
+        self.keep_prob = tf.placeholder(tf.float32, name='keep_prob')
+
+        self.rnn()
+
+    def rnn(self):
+        """rnn模型"""
+
+        def lstm_cell():   # lstm核
+            return tf.contrib.rnn.BasicLSTMCell(self.config.hidden_dim, state_is_tuple=True)
+
+        def gru_cell():  # gru核
+            return tf.contrib.rnn.GRUCell(self.config.hidden_dim)
+
+        def dropout(): # 为每一个rnn核后面加一个dropout层
+            if (self.config.rnn == 'lstm'):
+                cell = lstm_cell()
+            else:
+                cell = gru_cell()
+            return tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=self.keep_prob)
+
+        # 词向量映射
+        with tf.device('/cpu:0'):
+            embedding = tf.get_variable('embedding', [self.config.vocab_size, self.config.embedding_dim])
+            embedding_inputs = tf.nn.embedding_lookup(embedding, self.input_x)
+
+        with tf.name_scope("rnn"):
+            # 多层rnn网络
+            cells = [dropout() for _ in range(self.config.num_layers)]
+            rnn_cell = tf.contrib.rnn.MultiRNNCell(cells, state_is_tuple=True)
+
+            _outputs, _ = tf.nn.dynamic_rnn(cell=rnn_cell, inputs=embedding_inputs, dtype=tf.float32)
+            last = _outputs[:, -1, :]  # 取最后一个时序输出作为结果
+
+        with tf.name_scope("score"):
+            # 全连接层，后面接dropout以及relu激活
+            fc = tf.layers.dense(last, self.config.hidden_dim, name='fc1')
+            fc = tf.contrib.layers.dropout(fc, self.keep_prob)
+            fc = tf.nn.relu(fc)
+
+            # 分类器
+            self.logits = tf.layers.dense(fc, self.config.num_classes, name='fc2')
+            self.y_pred_cls = tf.argmax(tf.nn.softmax(self.logits), 1)  # 预测类别
+
+        with tf.name_scope("optimize"):
+            # 损失函数，交叉熵
+            cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=self.logits, labels=self.input_y)
+            self.loss = tf.reduce_mean(cross_entropy)
+            # 优化器
+            self.optim = tf.train.AdamOptimizer(learning_rate=self.config.learning_rate).minimize(self.loss)
+
+        with tf.name_scope("accuracy"):
+            # 准确率
+            correct_pred = tf.equal(tf.argmax(self.input_y, 1), self.y_pred_cls)
+            self.acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
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data/cnews_loader.py

@@ -4,7 +4,7 @@ import sys
 from collections import Counter
 
 import numpy as np
-import tensorflow.keras as kr
+from tensorflow import keras as kr
 import pandas as pd
 
 if sys.version_info[0] > 2:
@@ -83,8 +83,9 @@ def read_vocab(vocab_dir):
 
 def read_category():
     """读取分类目录，固定"""
-    table = pd.read_excel('VW_ProductALLState.xlsx')
+    table = pd.read_excel('Product_Api_Data.xlsx')
     categories = list(set(table['SubCategoryName'].tolist()))
+    categories.sort(key = table['SubCategoryName'].tolist().index)
     #categories = ['0', '1']
 
     categories = [native_content(x) for x in categories]

data_prepare.py

 import pandas as pd
 from function import Index
 
-table = pd.read_excel('VW_ProductALLState.xlsx')
-#category_set = list(set(table['SubCategoryName'].tolist()))
+table = pd.read_excel('Product_Api_Data.xlsx')
+#category_set = list(set(table['categoryCodeName'].tolist()))
 #len(list(category_set))
 f = open('D:/Users/86183/Desktop/text-classification-cnn-rnn/data/name2category/name2category.val.txt','w',encoding='utf-8')
 g = open('D:/Users/86183/Desktop/text-classification-cnn-rnn/data/name2category/name2category.train.txt','w',encoding='utf-8')
@@ -12,6 +12,7 @@ len_table = len(table)
 index = Index()
 for category,name in zip(table['SubCategoryName'],table['ProductName']):
     name = str(name).replace('\t',' ')
+    category = str(category)
     if '错误子类' not in category:
         if m%13 == 1:
             f.write(f'{category}\t{name}\n')

predicted_data.txt

-迷你植物	格林王 GREENKING 英国原装进口 IPA印度麦啤 精酿啤酒印度淡色艾尔啤酒 500ML*6瓶 整箱装
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+迷你植物	格林王 GREENKING 英国原装进口 IPA印度麦啤 精酿啤酒印度淡色艾尔啤酒 500ML*6瓶
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run_cnn.py

@@ -15,6 +15,7 @@ from sklearn import metrics
 from cnn_model import TCNNConfig, TextCNN
 from data.cnews_loader import read_vocab, read_category, batch_iter, process_file, build_vocab
 import pandas as pd
+from function import Index
 
 base_dir = 'data/name2category'
 train_dir = os.path.join(base_dir, 'name2category.train.txt')
@@ -182,12 +183,59 @@ def test():
     time_dif = get_time_dif(start_time)
     print("Time usage:", time_dif)
 
+class name2subcategory():
+
+    def __init__(self):
+        print('Configuring CNN model...')
+        if not os.path.exists(vocab_dir):  # 如果不存在词汇表，重建
+            build_vocab(train_dir, vocab_dir, config.vocab_size)
+        self.categories, cat_to_id = read_category()
+        words, self.word_to_id = read_vocab(vocab_dir)
+        self.table = pd.read_excel('Product_Api_Data.xlsx')
+        category_set = list(set(self.table['SubCategoryName'].tolist()))
+        self.config = TCNNConfig(len(list(category_set)))
+        self.config.vocab_size = len(words)
+        self.model = TextCNN(self.config)
+        self.categories = list(set(self.table['SubCategoryName'].tolist()))
+        self.categories.sort(key = self.table['SubCategoryName'].tolist().index)
+
+    def namelyst_predict(self, contents):
+        from tensorflow import keras as kr
+
+        print("Loading predicted data...")
+        data_id = []
+        for i in range(len(contents)):
+            data_id.append([self.word_to_id[x] for x in contents[i] if x in self.word_to_id])
+        x_pred = kr.preprocessing.sequence.pad_sequences(data_id, self.config.seq_length)
+        session = tf.Session()
+        session.run(tf.global_variables_initializer())
+        saver = tf.train.Saver()
+        saver.restore(sess=session, save_path=save_path)  # 读取保存的模型
+        batch_size = 128
+        data_len = len(x_pred)
+        num_batch = int((data_len - 1) / batch_size) + 1
+        y_pred_cls = np.zeros(shape=len(x_pred), dtype=np.int32)  # 保存预测结果
+        for i in range(num_batch):  # 逐批次处理
+            start_id = i * batch_size
+            end_id = min((i + 1) * batch_size, data_len)
+            feed_dict = {
+                self.model.input_x: x_pred[start_id:end_id],
+                self.model.keep_prob: 1.0
+            }
+            y_pred_cls[start_id:end_id] = session.run(self.model.y_pred_cls, feed_dict=feed_dict)    # y_pred_cls为预测的list。
+        y_pred_list = []
+        for m in range(len(y_pred_cls)):
+            y_pred_list.append(self.categories[y_pred_cls[m]])
+
+        return y_pred_list
+
 
 def predict():
-    import tensorflow.keras as kr
+    from tensorflow import keras as kr
 
-    table = pd.read_excel('VW_ProductALLState.xlsx')
+    table = pd.read_excel('Product_Api_Data.xlsx')
     categories = list(set(table['SubCategoryName'].tolist()))
+    categories.sort(key = table['SubCategoryName'].tolist().index)
     print("Loading predicted data...")
     f = open(pred_dir, 'r', encoding='utf-8', errors='ignore')
     contents = []
@@ -235,10 +283,14 @@ def predict():
 if __name__ == '__main__':
     
     if len(sys.argv) != 2 or sys.argv[1] not in ['train', 'test', 'predict']:
-        raise ValueError("""usage: python run_cnn.py [train / test]""")
+        raise ValueError("""usage: python run_cnn.py [train / test / predict]""")
 
     print('Configuring CNN model...')
-    config = TCNNConfig()
+    table = pd.read_excel('Product_Api_Data.xlsx')
+    SubCategoryName_list = table['SubCategoryName'].tolist()
+    category_set = list(set(SubCategoryName_list))
+    category_set.sort(key = SubCategoryName_list.index)
+    config = TCNNConfig(len(category_set))
     if not os.path.exists(vocab_dir):  # 如果不存在词汇表，重建
         build_vocab(train_dir, vocab_dir, config.vocab_size)
     categories, cat_to_id = read_category()

示例.py

+from run_cnn import name2subcategory
+
+name_list = ['安赛瑞 消防斧 应急斧头破拆斧工具 消防器材斧子消防站配置 消防检查斧子 25847'] 
+a = name2subcategory()
+category = a.namelyst_predict(name_list)
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