Question

我尝试从这个github https://github.com/bgmartins/sentence-classification/blob/master/sentence-classification.py

复制一个句子分类模型

这是我的代码：

data = [ ( row["sentence"] , row["label"]  ) for row in csv.DictReader(open("./test-data.txt"), delimiter='\t', quoting=csv.QUOTE_NONE) ]
random.shuffle( data )
train_size = int(len(data) * percent)
train_texts = [ txt.lower() for ( txt, label ) in data[0:train_size] ]
test_texts = [ txt.lower() for ( txt, label ) in data[train_size:-1] ]
train_labels = [ label for ( txt , label ) in data[0:train_size] ]
test_labels = [ label for ( txt , label ) in data[train_size:-1] ]
num_classes = len( set( train_labels + test_labels ) )
tokenizer = Tokenizer(nb_words=max_features, lower=True, split=" ")
tokenizer.fit_on_texts(train_texts)
train_sequences = sequence.pad_sequences( tokenizer.texts_to_sequences( train_texts ) , maxlen=max_sent_len )
test_sequences = sequence.pad_sequences( tokenizer.texts_to_sequences( test_texts ) , maxlen=max_sent_len )
train_matrix = tokenizer.texts_to_matrix( train_texts )
test_matrix = tokenizer.texts_to_matrix( test_texts )
embedding_weights = np.zeros( ( max_features , embeddings_dim ) )
for word,index in tokenizer.word_index.items():
    if index < max_features:
        try: embedding_weights[index,:] = embeddings[word]
        except: embedding_weights[index,:] = np.random.rand( 1 , embeddings_dim )
le = preprocessing.LabelEncoder( )
le.fit( train_labels + test_labels )
train_labels = le.transform( train_labels )
test_labels = le.transform( test_labels )

但是当我尝试适应LSTM时：

model = Sequential()
model.add(Embedding(max_features, embeddings_dim, input_length=max_sent_len, mask_zero=True, weights=[embedding_weights] ))
model.add(Dropout(0.25))
model.add(LSTM(output_dim=embeddings_dim , activation='sigmoid', inner_activation='hard_sigmoid', return_sequences=True))
model.add(Dropout(0.25))
model.add(LSTM(output_dim=embeddings_dim , activation='sigmoid', inner_activation='hard_sigmoid'))
model.add(Dropout(0.25))
model.add(Dense(1))
model.add(Activation('sigmoid'))
if num_classes == 2: model.compile(loss='binary_crossentropy', optimizer='adam', class_mode='binary')
else: model.compile(loss='categorical_crossentropy', optimizer='adam')  
model.fit( train_sequences , train_labels , nb_epoch=30, batch_size=32)

我收到此错误：

TypeError: run() got an unexpected keyword argument 'class_mode'

完整错误追溯：

--------------------------------------------------------------------------- 
TypeError                                 Traceback (most recent call last) <ipython-input-9-d63cbba5b5e7> in <module>()
     12 if num_classes == 2: model.compile(loss='binary_crossentropy', optimizer='adam', class_mode='binary')
     13 else: model.compile(loss='categorical_crossentropy', optimizer='adam')
---> 14 model.fit( train_sequences , train_labels , nb_epoch=30, batch_size=32)
     15 results = model.predict_classes( test_sequences )
     16 print ("Accuracy = " + repr( sklearn.metrics.accuracy_score( test_labels , results )  ))

~\Anaconda3\lib\site-packages\keras\models.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
    961                               initial_epoch=initial_epoch,
    962                               steps_per_epoch=steps_per_epoch,
--> 963                               validation_steps=validation_steps)
    964 
    965     def evaluate(self, x=None, y=None,

~\Anaconda3\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)    1703                   initial_epoch=initial_epoch,    1704                               steps_per_epoch=steps_per_epoch,
-> 1705                               validation_steps=validation_steps)    
   1706     
   1707     def evaluate(self, x=None, y=None,

~\Anaconda3\lib\site-packages\keras\engine\training.py in _fit_loop(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)    
   1233                         ins_batch[i] = ins_batch[i].toarray()    
   1234 
-> 1235                     outs = f(ins_batch)    
   1236                     if not isinstance(outs, list):    
   1237                         outs = [outs]

~\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py __call__(self, inputs)    
   2476         session = get_session()    
   2477         updated = session.run(fetches=fetches, feed_dict=feed_dict,
-> 2478                               **self.session_kwargs)    
   2479         return updated[:len(self.outputs)]    
   2480

Answer 1

model.compile不会使用任何名为＆＃34; class_mode＆＃34;的参数，将其删除。

Answer 2

请参阅：https://keras.io/preprocessing/image/

class_mode与生成器一起使用。

Keras：得到了一个意想不到的关键字参数＆＃39; class_mode＆＃39;

2 个答案: