计算keras LSTM中矢量之间的距离和角度

Question

我正在使用keras来检测问题对之间的相似性。模型结构似乎工作正常，但它给我在model.fit函数上的错误。我甚至检查了输入数据的数据类型，它是numpy.ndarray。在这方面有任何指示，我将不胜感激。

  

ValueError：检查模型输入时出错：Numpy数组列表   您传递给模型的大小不是模型预期的大小。   预计会看到1个数组但是得到以下2个列表   数组：[array（[[0,0,0，...，251,46,50]，          [0,0,0，...，7,40,6935]，          [0,0,0，...，17,314,2317]，          ...          [0，...

def Angle(inputs):

    length_input_1=K.sqrt(K.sum(tf.pow(inputs[0],2),axis=1,keepdims=True))
    length_input_2=K.sqrt(K.sum(tf.pow(inputs[1],2),axis=1,keepdims=True))
    result=K.batch_dot(inputs[0],inputs[1],axes=1)/(length_input_1*length_input_2)
    angle = tf.acos(result)
    return angle

def Distance(inputs):

    s = inputs[0] - inputs[1]
    output = K.sum(s ** 2,axis=1,keepdims=True)
    return output    




y=data.is_duplicate.values
tk=text.Tokenizer()
tk.fit_on_texts(list(data.question1.values)+list(data.question2.values))


question1 = tk.texts_to_sequences(data.question1.values)
question1 = sequence.pad_sequences(question1,maxlen=MAX_LEN)

question2 = tk.texts_to_sequences(data.question2.values)
question2 = sequence.pad_sequences(question2,maxlen=MAX_LEN)


word_index = tk.word_index
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
num_features = 300
num_workers = multiprocessing.cpu_count()
context_size = 5
downsampling = 7.5e-06
seed = 1
min_word_count = 5
hs = 1
negative = 5


Quora_word2vec = gensim.models.Word2Vec(

    sg=0,
    seed=1,
    workers=num_workers,
    min_count=min_word_count,
    size=num_features,
    window=context_size,  # (2 and 5)
    hs=hs,  # (1 and 0)
    negative=negative,  # (5 and 10)
    sample=downsampling  # (range (0, 1e-5). )

)

Quora_word2vec = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin',binary=True)
embedding_matrix=np.zeros((len(word_index)+1,300))

for word , i in tqdm(word_index.items()): #i is index

    try:

        embedding_vector =  Quora_word2vec[word] #Exception is thrown if there is key error
        embedding_matrix[i] = embedding_vector

    except Exception as e:  #If word is not found continue

        continue

-------- --------问题1

model1 = Sequential()
print "Build Model"

model1.add(Embedding(
    len(word_index)+1,
    300,
    weights=[embedding_matrix],
    input_length=MAX_LEN
    ))

model1.add(SpatialDropout1D(0.2))
model1.add(TimeDistributed(Dense(300, activation='relu')))
model1.add(Lambda(lambda x: K.sum(x, axis=1), output_shape=(300,)))

print model1.summary()

＃---------问题2 -------＃

model2=Sequential()

model2.add(Embedding(
    len(word_index) + 1,
    300,
    weights=[embedding_matrix],
    input_length=MAX_LEN
    ))  # Embedding layer
model2.add(SpatialDropout1D(0.2))
model2.add(TimeDistributed(Dense(300, activation='relu')))
model2.add(Lambda(lambda x: K.sum(x, axis=1), output_shape=(300,)))


print model2.summary()


#---------Merged------#

#Here you get question embedding

#Calculate distance between vectors
Distance_merged_model=Sequential()
Distance_merged_model.add(Merge(layers=[model1, model2], mode=Distance, output_shape=(1,)))

print Distance_merged_model.summary()

#Calculate Angle between vectors

Angle_merged_model=Sequential()
Angle_merged_model.add(Merge(layers=[model1,model2],mode=Angle,output_shape=(1,)))

print Angle_merged_model.summary()


neural_network = Sequential()
neural_network.add(Dense(2,input_shape=(1,)))
neural_network.add(Dense(1))
neural_network.add(Activation('sigmoid'))

print neural_network.summary()


neural_network.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

checkpoint = ModelCheckpoint('weights.h5', monitor='val_acc', save_best_only=True, verbose=2)

print type(question1)
print type(question2)
neural_network.fit([question1,question2],y=y, batch_size=384, epochs=10,
                 verbose=1, validation_split=0.3, shuffle=True, callbacks=[checkpoint])

Answer 1

你没有将最后两层连接到密集区域，只是让你的神经网络成为唯一一个传入数据的网络，因为你正在编译和装配该层而没有距离和角度网络连接到你的最终致密的。

获取网络＃1和＃2的所有内容在合并层中都是正确的，但您需要执行类似的操作：

neural_network = Sequential()
neural_network.add(Merge([Distance_merged_model, Angle_merged_model], mode='concat'))
neural_network.add(Dense(2,input_shape=(1,)))
neural_network.add(Dense(1))
neural_network.add(Activation('sigmoid'))