文章目录
- 代码展示
- 代码解读
- 双向LSTM介绍（BiLSTM）
代码展示
import pandas as pd
import tensorflow as tf
tf.random.set_seed(1)
df = pd.read_csv("../data/Clothing Reviews.csv")
print(df.info())
df['Review Text'] = df['Review Text'].astype(str)
x_train = df['Review Text']
y_train = df['Rating']
print(y_train.unique())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 23486 entries, 0 to 23485
Data columns (total 11 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Unnamed: 0 23486 non-null int64
1 Clothing ID 23486 non-null int64
2 Age 23486 non-null int64
3 Title 19676 non-null object
4 Review Text 22641 non-null object
5 Rating 23486 non-null int64
6 Recommended IND 23486 non-null int64
7 Positive Feedback Count 23486 non-null int64
8 Division Name 23472 non-null object
9 Department Name 23472 non-null object
10 Class Name 23472 non-null object
[4 5 3 2 1]
from tensorflow.keras.preprocessing.text import Tokenizer
dict_size = 14848
tokenizer = Tokenizer(num_words=dict_size)
tokenizer.fit_on_texts(x_train)
print(len(tokenizer.word_index),tokenizer.index_word)
x_train_tokenized = tokenizer.texts_to_sequences(x_train)
from tensorflow.keras.preprocessing.sequence import pad_sequences
max_comment_length = 120
x_train = pad_sequences(x_train_tokenized,maxlen=max_comment_length)
for v in x_train[:10]:
print(v,len(v))
# 构建RNN神经网络
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,SimpleRNN,Embedding,LSTM,Bidirectional
import tensorflow as tf
rnn = Sequential()
# 对于rnn来说首先进行词向量的操作
rnn.add(Embedding(input_dim=dict_size,output_dim=60,input_length=max_comment_length))
# RNN：simple_rnn (SimpleRNN) (None, 100) 16100
# LSTM：simple_rnn (SimpleRNN) (None, 100) 64400
rnn.add(Bidirectional(LSTM(units=100))) # 第二层构建了100个RNN神经元
rnn.add(Dense(units=10,activation=tf.nn.relu))
rnn.add(Dense(units=6,activation=tf.nn.softmax)) # 输出分类的结果
rnn.compile(loss='sparse_categorical_crossentropy',optimizer="adam",metrics=['accuracy'])
print(rnn.summary())
result = rnn.fit(x_train,y_train,batch_size=64,validation_split=0.3,epochs=10)
print(result)
print(result.history)
代码解读
首先，我们来总结这段代码的流程：
- 导入了必要的TensorFlow Keras模块。
- 初始化了一个Sequential模型，这表示我们的模型会按顺序堆叠各层。
- 添加了一个Embedding层，用于将整数索引（对应词汇）转换为密集向量。
- 添加了一个双向LSTM层，其中包含100个神经元。
- 添加了两个Dense全连接层，分别包含10个和6个神经元。
- 使用
sparse_categorical_crossentropy损失函数编译了模型。 - 打印了模型的摘要。
- 使用给定的训练数据和验证数据对模型进行了训练。
- 打印了训练的结果。
现在，让我们逐行解读代码：
- 导入依赖:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,SimpleRNN,Embedding,LSTM,Bidirectional
import tensorflow as tf
你导入了创建和训练RNN模型所需的TensorFlow Keras库。
- 初始化模型:
rnn = Sequential()
你选择了一个顺序模型，这意味着你可以简单地按顺序添加层。
- 添加Embedding层:
rnn.add(Embedding(input_dim=dict_size,output_dim=60,input_length=max_comment_length))
此层将整数索引转换为固定大小的向量。dict_size是词汇表的大小，max_comment_length是输入评论的最大长度。
- 添加LSTM层:
rnn.add(Bidirectional(LSTM(units=100)))
你选择了双向LSTM，这意味着它会考虑过去和未来的信息。它有100个神经元。
- 添加全连接层:
rnn.add(Dense(units=10,activation=tf.nn.relu))
rnn.add(Dense(units=6,activation=tf.nn.softmax))
这两个Dense层用于模型的输出，最后一层使用softmax激活函数进行6类的分类。
- 编译模型:
rnn.compile(loss='sparse_categorical_crossentropy',optimizer="adam",metrics=['accuracy'])
你选择了一个适合分类问题的损失函数，并选择了adam优化器。
- 显示模型摘要:
print(rnn.summary())
这将展示模型的结构和参数数量。
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 120, 60) 890880
bidirectional (Bidirectiona (None, 200) 128800
l)
dense (Dense) (None, 10) 2010
dense_1 (Dense) (None, 6) 66
=================================================================
Total params: 1,021,756
Trainable params: 1,021,756
Non-trainable params: 0
_________________________________________________________________
None
- 训练模型:
result = rnn.fit(x_train,y_train,batch_size=64,validation_split=0.3,epochs=10)
你用训练数据集训练了模型，其中30%的数据用作验证，训练了10个周期。
Epoch 1/10
257/257 [==============================] - 74s 258ms/step - loss: 1.2142 - accuracy: 0.5470 - val_loss: 1.0998 - val_accuracy: 0.5521
Epoch 2/10
257/257 [==============================] - 57s 221ms/step - loss: 0.9335 - accuracy: 0.6293 - val_loss: 0.9554 - val_accuracy: 0.6094
Epoch 3/10
257/257 [==============================] - 59s 229ms/step - loss: 0.8363 - accuracy: 0.6616 - val_loss: 0.9321 - val_accuracy: 0.6168
Epoch 4/10
257/257 [==============================] - 61s 236ms/step - loss: 0.7795 - accuracy: 0.6833 - val_loss: 0.9812 - val_accuracy: 0.6089
Epoch 5/10
257/257 [==============================] - 56s 217ms/step - loss: 0.7281 - accuracy: 0.7010 - val_loss: 0.9559 - val_accuracy: 0.6043
Epoch 6/10
257/257 [==============================] - 56s 219ms/step - loss: 0.6934 - accuracy: 0.7156 - val_loss: 1.0197 - val_accuracy: 0.5999
Epoch 7/10
257/257 [==============================] - 57s 220ms/step - loss: 0.6514 - accuracy: 0.7364 - val_loss: 1.1192 - val_accuracy: 0.6080
Epoch 8/10
257/257 [==============================] - 57s 222ms/step - loss: 0.6258 - accuracy: 0.7486 - val_loss: 1.1350 - val_accuracy: 0.6100
Epoch 9/10
257/257 [==============================] - 57s 220ms/step - loss: 0.5839 - accuracy: 0.7749 - val_loss: 1.1537 - val_accuracy: 0.6019
Epoch 10/10
257/257 [==============================] - 57s 222ms/step - loss: 0.5424 - accuracy: 0.7945 - val_loss: 1.1715 - val_accuracy: 0.5744
<keras.callbacks.History object at 0x00000244DCE06D90>
- 显示训练结果:
print(result)
<keras.callbacks.History object at 0x0000013AEAAE1A30>
print(result.history)
{'loss': [1.2142471075057983, 0.9334620833396912, 0.8363043069839478, 0.7795010805130005, 0.7280740141868591, 0.693393349647522, 0.6514003872871399, 0.6257606744766235, 0.5839114189147949, 0.5423741340637207],
'accuracy': [0.5469586253166199, 0.6292579174041748, 0.6616179943084717, 0.6833333373069763, 0.7010340690612793, 0.7156326174736023, 0.7363746762275696, 0.748600959777832, 0.7748783230781555, 0.7944647073745728],
'val_loss': [1.0997602939605713, 0.9553984999656677, 0.932131290435791, 0.9812102317810059, 0.9558586478233337, 1.019730806350708, 1.11918044090271, 1.1349923610687256, 1.1536787748336792, 1.1715185642242432],
'val_accuracy': [0.5520862936973572, 0.609423816204071, 0.6168038845062256, 0.6088560819625854, 0.6043145060539246, 0.5999148488044739, 0.6080045700073242, 0.6099914908409119, 0.6019017696380615, 0.574368417263031]
}
这将展示训练过程中的损失和准确性等信息。
双向LSTM介绍（BiLSTM）
例子：
![四、[mysql]索引优化-1](https://img-blog.csdnimg.cn/a41260e4fc72448487e2223f071d334a.png)
