Question

我正在研究单词预测问题。我有职业发展道路的例子，我希望能够使用他们的前2个工作来预测下一个人的工作。我已经建立了一个LSTM模型来执行

我打算从keras model.predict_classes函数中获取多个结果时遇到问题。它仅返回1个结果。我想获得多个结果，并按其概率排序。

这是代码：

from numpy import array
from keras.preprocessing.text import Tokenizer
from keras.utils import to_categorical
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.layers import Embedding

# generate a sequence from a language model
def generate_seq(model, tokenizer, max_length, seed_text, n_words):
    in_text = seed_text
    # generate a fixed number of words
    for _ in range(n_words):
        # encode the text as integer
        encoded = tokenizer.texts_to_sequences([in_text])[0]
        # pre-pad sequences to a fixed length
        encoded = pad_sequences([encoded], maxlen=max_length, padding='pre')
        # predict probabilities for each word
        yhat = model.predict_classes(encoded, verbose=1)
        print('yhat = ' + yhat)
        #print('yhat : ' + str(yhat))
        # map predicted word index to word
        out_word = ''
        for word, index in tokenizer.word_index.items():
            if index == yhat:
                out_word = word
                break
        # append to input
        in_text += ' ' + out_word
    return in_text


# source text
data = """apprenti electricien chefOdeOprojet \n
soudeur chefOdeOsection directeurOusine\n
mecanicien chefOdeOsection directeurOadjoint\n
ingenieur chefOdeOprojet directeurOadjoint directeurOusine\n
ingenieur chefOdeOprojet \n
apprenti soudeur chefOdeOsection chefOdeOprojet\n
ingenieurOetude chefOdeOprojet\n
ingenieurOetude manager chefOdeOprojet directeurOdepartement\n
apprenti gestionOproduction manager directeurOdepartement\n
ingenieurOetude commercial\n
soudeur ingenieurOetude manager directeurOadjoint\n
ingenieurOetude directeurOdepartement directeurOusine\n
apprenti soudeur\n
agentOsecurite chefOsecurite\n
apprenti mecanicien ouvrier manager\n
commercial directeurOadjoint\n
agentOsecurite chefOsecurite\n
directeurOusine retraite\n
ouvrier manager\n
ingenieur vente\n
secretaire comptable\n
comptable chefOcomptable\n
chefOcomptable directeurOdepartement\n
assistant secretaire comptable\n
assistant comptable\n
assistant secretaire commercial\n
commercial chefOdeOprojet\n
commercial vente chefOdeOprojet\n
electricien chefOdeOsection\n
apprenti ouvrier chefOdeOsection\n"""
# integer encode sequences of words
tokenizer = Tokenizer()
tokenizer.fit_on_texts([data])
encoded = tokenizer.texts_to_sequences([data])[0]
# retrieve vocabulary size
vocab_size = len(tokenizer.word_index) + 1
print('Vocabulary Size: %d' % vocab_size)
# encode 2 words -> 1 word    
sequences = list()
for line in data.split('\n'):
    encoded = tokenizer.texts_to_sequences([line])[0]
    for i in range(2, len(encoded)):
        sequence = encoded[i-2:i+1]
        sequences.append(sequence)    

print('Total Sequences: %d' % len(sequences))
# pad sequences
max_length = max([len(seq) for seq in sequences])
sequences = pad_sequences(sequences, maxlen=max_length, padding='pre')
print('Max Sequence Length: %d' % max_length)
# split into input and output elements
sequences = array(sequences)
X, y = sequences[:,:-1],sequences[:,-1]
y = to_categorical(y, num_classes=vocab_size)
# define model
model = Sequential()
model.add(Embedding(vocab_size, 10, input_length=max_length-1))
model.add(LSTM(50))
model.add(Dropout(0.2))
#model.add(Dense(units = 3,  activation = 'relu'))
model.add(Dense(vocab_size, activation='softmax'))
print(model.summary())
# compile network
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# fit network
model.fit(X, y, epochs=500, verbose=0)
# evaluate model
print(generate_seq(model, tokenizer, max_length-1, 'electricien secretaire', 1))

，其中有控制台显示：

Vocabulary Size: 24
Total Sequences: 20
Max Sequence Length: 3
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_2 (Embedding)      (None, 2, 10)             240       
_________________________________________________________________
lstm_2 (LSTM)                (None, 50)                12200     
_________________________________________________________________
dropout_2 (Dropout)          (None, 50)                0         
_________________________________________________________________
dense_2 (Dense)              (None, 24)                1224      
=================================================================
Total params: 13,664
Trainable params: 13,664
Non-trainable params: 0
_________________________________________________________________
None
1/1 [==============================] - 0s 86ms/step
yhat = [1]
electricien secretaire chefodeoprojet

Answer 1

如果我正确理解了您的问题，您想查看与多分类问题的每个类别相关的概率吗？

代码对我来说似乎很正确，但是我建议尝试其他评估步骤。我获得了带有以下代码段的多分类器输出：

 # Fit the model
    print "Fitting model..."
    model.fit(np.asarray(self.X), np.asarray(self.Y), epochs=200, batch_size=10)
    print "Model fitting complete."

    self.TEST = np.asarray(self.TEST).reshape(( test_data.shape[0], 1, 128))
    print "Predicting on Test (unseen) data..."
    predictions = model.predict(self.TEST)

    # Sigmoid predictions
    labels = np.zeros(predictions.shape)
    labels[predictions>0.5] = 1
    print "Prediction labels for unseen: " + str(labels)

输出：

 Prediction labels for unseen: 
 [[ 0.  1.  0.  0.]
 [ 0.  1.  0.  0.]
 [ 0.  1.  0.  0.]
 [ 0.  1.  0.  0.]
 [ 0.  1.  0.  0.]
 [ 0.  0.  1.  0.]
 [ 0.  0.  1.  0.]
 [ 0.  0.  1.  0.]]

每一行表示一个样本的分类； 1的索引表示样本属于哪一类（A，B，C，D）。

用于多个类别预测的多个命题

1 个答案: