Python 文本分类RNN-LSTM-错误检查目标

我正在用Keras开发LSTM-RNN文本分类 这是我的密码

import numpy as np
import csv
import keras
import sklearn
import gensim
import random
import scipy
from keras.preprocessing import text
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
from keras.models import Sequential
from keras.layers.core import Dense , Dropout , Activation  , Flatten
from keras.layers.convolutional import Convolution1D, MaxPooling1D
from keras.layers import Embedding , LSTM
from sklearn import preprocessing
from sklearn.base import BaseEstimator
from sklearn.svm import LinearSVC , SVC
from sklearn.naive_bayes import MultinomialNB
from gensim.models.word2vec import Word2Vec
from gensim.models.doc2vec import Doc2Vec , TaggedDocument

# size of the word embeddings
embeddings_dim = 300

# maximum number of words to consider in the representations
max_features = 30000

# maximum length of a sentence
max_sent_len = 50

# percentage of the data used for model training
percent = 0.75

# number of classes
num_classes = 2

print ("")
print ("Reading pre-trained word embeddings...")
embeddings = dict( )
embeddings = gensim.models.KeyedVectors.load_word2vec_format("GoogleNews-vectors-negative300.bin.gz" , binary=True) 

print ("Reading text data for classification and building representations...")
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(num_words=max_features, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', 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 )
print("Classes that are considered in the problem : " + repr( le.classes_ ))


print("-----WEIGHTS-----")
print(embedding_weights.shape)

print ("Method = Stack of two LSTMs")
np.random.seed(0)
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(activation="sigmoid", units=embeddings_dim, recurrent_activation="hard_sigmoid", return_sequences=True))
model.add(Dropout(0.25))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', class_mode='binary')
else: model.compile(Adam(lr=0.04),'categorical_crossentropy',metrics=['accuracy'])

model.summary()


model.fit(train_sequences, train_labels , epochs=30, batch_size=32)

我的错误是： 检查目标时出错：预期激活_1有3个维度，但得到了形状为（750，1）的数组

我试图重塑我所有的阵列，但我没有找到解决方案。 有人能帮我吗？？？谢谢：D

---

对不起，我的英语不好。

您需要对标签进行热编码。您可以使用Keras

to_category

方法转换标签编码的整数。

最后，我的模型是

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(activation='sigmoid', units=embeddings_dim, recurrent_activation='hard_sigmoid', return_sequences=False))
model.add(Dropout(0.25))
model.add(Dense(num_classes))
model.add(Activation('sigmoid'))

adam=keras.optimizers.Adam(lr=0.04)
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])

---

但是准确度很差！！！：（

train\u序列的形状是什么？和

train\u标签的形状是什么？（750,50）e（750，）谢谢，但我已经有一个错误：value错误：检查目标时出错：预期激活1有3个维度，但得到了形状为（750,2）的数组。我使用train\u标签=to\u分类（train\u标签，num\u类）test_labels=to_category（test_labels，num_classes）…你能帮我吗？
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(activation='sigmoid', units=embeddings_dim, recurrent_activation='hard_sigmoid', return_sequences=False))
model.add(Dropout(0.25))
model.add(Dense(num_classes))
model.add(Activation('sigmoid'))

adam=keras.optimizers.Adam(lr=0.04)
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])