p3_cnn.py 文件源码

def trainCNN(obj, dataset_headLines, dataset_body):
    embedding_dim = 300
    LSTM_neurons = 50
    dense_neuron = 16
    dimx = 100
    dimy = 200
    lamda = 0.0
    nb_filter = 100
    filter_length = 4
    vocab_size = 10000
    batch_size = 50
    epochs = 5
    ntn_out = 16
    ntn_in = nb_filter 
    state = False


    train_head,train_body,embedding_matrix = obj.process_data(sent_Q=dataset_headLines,
                                                     sent_A=dataset_body,dimx=dimx,dimy=dimy,
                                                     wordVec_model = wordVec_model)    
    inpx = Input(shape=(dimx,),dtype='int32',name='inpx')
    #x = Embedding(output_dim=embedding_dim, input_dim=vocab_size, input_length=dimx)(inpx)
    x = word2vec_embedding_layer(embedding_matrix)(inpx)  
    inpy = Input(shape=(dimy,),dtype='int32',name='inpy')
    #y = Embedding(output_dim=embedding_dim, input_dim=vocab_size, input_length=dimy)(inpy)
    y = word2vec_embedding_layer(embedding_matrix)(inpy)
    ques = Convolution1D(nb_filter=nb_filter, filter_length=filter_length,
                         border_mode='valid', activation='relu',
                         subsample_length=1)(x)

    ans = Convolution1D(nb_filter=nb_filter, filter_length=filter_length,
                        border_mode='valid', activation='relu',
                        subsample_length=1)(y)

    #hx = Lambda(max_1d, output_shape=(nb_filter,))(ques)
    #hy = Lambda(max_1d, output_shape=(nb_filter,))(ans)
    hx = GlobalMaxPooling1D()(ques)
    hy = GlobalMaxPooling1D()(ans)
    #wordVec_model = []
    #h =  Merge(mode="concat",name='h')([hx,hy])

    h1 = Multiply()([hx,hy])
    h2 = Abs()([hx,hy])

    h =  Merge(mode="concat",name='h')([h1,h2])
    #h = NeuralTensorLayer(output_dim=1,input_dim=ntn_in)([hx,hy])
    #h = ntn_layer(ntn_in,ntn_out,activation=None)([hx,hy])
    #score = h
    wrap = Dense(dense_neuron, activation='relu',name='wrap')(h)
    #score = Dense(1,activation='sigmoid',name='score')(h)
    #wrap = Dense(dense_neuron,activation='relu',name='wrap')(h)
    score = Dense(4,activation='softmax',name='score')(wrap)

    #score=K.clip(score,1e-7,1.0-1e-7)
    #corr = CorrelationRegularization(-lamda)([hx,hy])
    #model = Model( [inpx,inpy],[score,corr])
    model = Model( [inpx,inpy],score)
    model.compile( loss='categorical_crossentropy',optimizer="adadelta",metrics=['accuracy'])    
    return model,train_head,train_body