我有一个顺序学习模型,工作良好,能够预测一些输出。问题是我不知道如何将输出转换回文本序列。
这是我的密码。
from keras.preprocessing.text import Tokenizer,base_filter
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense
txt1="""What makes this problem difficult is that the sequences can vary in length,
be comprised of a very large vocabulary of input symbols and may require the model
to learn the long term context or dependencies between symbols in the input sequence."""
#txt1 is used for fitting
tk = Tokenizer(nb_words=2000, filters=base_filter(), lower=True, split=" ")
tk.fit_on_texts(txt1)
#convert text to sequence
t= tk.texts_to_sequences(txt1)
#padding to feed the sequence to keras model
t=pad_sequences(t, maxlen=10)
model = Sequential()
model.add(Dense(10,input_dim=10))
model.add(Dense(10,activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy'])
#predicting new sequcenc
pred=model.predict(t)
#Convert predicted sequence to text
pred=??
您可以直接使用反
tokenizer.sequences_to_texts
函数。text = tokenizer.sequences_to_texts(<list of the integer equivalent encodings>)
我已经测试了上面的内容,并且它能按预期工作。
注意:要特别注意使参数是整数编码的列表,而不是一个热门编码。
我找到了一个解决方案:
我必须解决同一个问题,所以这里是我如何结束它(灵感来自@Ben Usemans reversed dictionary)。
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