Calculation of distance and angle between vectors in keras LSTM

Question

I use keras to detect the similarities between the question pairs. The model structure seems to work fine, but it gives me an error in the model.fit function. I even checked the data type of my input and this is numpy.ndarray. Any pointers in this regard, and I will be grateful.

Error value: error checking model input: the list of Numpy arrays that you are moving to your model is not the size expected by the model. It was expected to see 1 array, but instead received the following list of 2 arrays: [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

-------- Question1 --------

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()

# --------- question2 ------- #

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

, , , , , .

# 1 # 2 , - :

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'))