Keras LSTM多类分类

我有这个代码适用于二进制分类.我已经为keras imdb数据集测试了它.

    model = Sequential()
    model.add(Embedding(5000, 32, input_length=500))
    model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])        
    print(model.summary())
    model.fit(X_train, y_train, epochs=3, batch_size=64)
    # Final evaluation of the model
    scores = model.evaluate(X_test, y_test, verbose=0)

我需要将上面的代码转换为多类别分类,总共有7个类别.在阅读了几篇文章以转换上面的代码之后,我理解了什么,我必须改变

model.add(Dense(7, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])  

显然,改变两行以上是行不通的.我还需要做些什么来使代码适用于多类分类.另外我认为我必须将类更改为一个热编码但不知道如何在keras中.

是的,您需要一个热门目标,您可以使用它to_categorical来编码您的目标或一个简短的方法:

model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

这是完整的代码:

from keras.models import Sequential
from keras.layers import *

model = Sequential()
model.add(Embedding(5000, 32, input_length=500))
model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(7, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

model.summary()

摘要

Using TensorFlow backend.
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_1 (Embedding)      (None, 500, 32)           160000    
_________________________________________________________________
lstm_1 (LSTM)                (None, 100)               53200     
_________________________________________________________________
dense_1 (Dense)              (None, 7)                 707       
=================================================================
Total params: 213,907
Trainable params: 213,907
Non-trainable params: 0
_________________________________________________________________