deep text match 模型融合 xgboost 特征

def model_conv1D_(emb_matrix):

    # The embedding layer containing the word vectors
    emb_layer = Embedding(
        input_dim=emb_matrix.shape[0],
        output_dim=emb_matrix.shape[1],
        weights=[emb_matrix],
        input_length=60,
        trainable=False
    )

    # 1D convolutions that can iterate over the word vectors
    conv1 = Conv1D(filters=128, kernel_size=1, padding='same', activation='relu')
    conv2 = Conv1D(filters=128, kernel_size=2, padding='same', activation='relu')
    conv3 = Conv1D(filters=128, kernel_size=3, padding='same', activation='relu')
    conv4 = Conv1D(filters=128, kernel_size=4, padding='same', activation='relu')
    conv5 = Conv1D(filters=32, kernel_size=5, padding='same', activation='relu')
    conv6 = Conv1D(filters=32, kernel_size=6, padding='same', activation='relu')

    # Define inputs
    seq1 = Input(shape=(60,))
    seq2 = Input(shape=(60,))

    # Run inputs through embedding
    emb1 = emb_layer(seq1)
    emb2 = emb_layer(seq2)

    # Run through CONV + GAP layers
    conv1a = conv1(emb1)
    glob1a = GlobalAveragePooling1D()(conv1a)
    conv1b = conv1(emb2)
    glob1b = GlobalAveragePooling1D()(conv1b)

    conv2a = conv2(emb1)
    glob2a = GlobalAveragePooling1D()(conv2a)
    conv2b = conv2(emb2)
    glob2b = GlobalAveragePooling1D()(conv2b)

    conv3a = conv3(emb1)
    glob3a = GlobalAveragePooling1D()(conv3a)
    conv3b = conv3(emb2)
    glob3b = GlobalAveragePooling1D()(conv3b)

    conv4a = conv4(emb1)
    glob4a = GlobalAveragePooling1D()(conv4a)
    conv4b = conv4(emb2)
    glob4b = GlobalAveragePooling1D()(conv4b)

    conv5a = conv5(emb1)
    glob5a = GlobalAveragePooling1D()(conv5a)
    conv5b = conv5(emb2)
    glob5b = GlobalAveragePooling1D()(conv5b)

    conv6a = conv6(emb1)
    glob6a = GlobalAveragePooling1D()(conv6a)
    conv6b = conv6(emb2)
    glob6b = GlobalAveragePooling1D()(conv6b)

    mergea = concatenate([glob1a, glob2a, glob3a, glob4a, glob5a, glob6a])
    mergeb = concatenate([glob1b, glob2b, glob3b, glob4b, glob5b, glob6b])

    # We take the explicit absolute difference between the two sentences
    # Furthermore we take the multiply different entries to get a different measure of equalness
    diff = Lambda(lambda x: K.abs(x[0] - x[1]), output_shape=(4 * 128 + 2*32,))([mergea, mergeb])
    mul = Lambda(lambda x: x[0] * x[1], output_shape=(4 * 128 + 2*32,))([mergea, mergeb])

    # Add the magic features
    magic_input = Input(shape=(5,))
    magic_dense = BatchNormalization()(magic_input)
    magic_dense = Dense(64, activation='relu')(magic_dense)

    # Add the distance features (these are now TFIDF (character and word), Fuzzy matching, 
    # nb char 1 and 2, word mover distance and skew/kurtosis of the sentence vector)
    distance_input = Input(shape=(20,))
    distance_dense = BatchNormalization()(distance_input)
    distance_dense = Dense(128, activation='relu')(distance_dense)

    # Merge the Magic and distance features with the difference layer
    merge = concatenate([diff, mul, magic_dense, distance_dense])

    # The MLP that determines the outcome
    x = Dropout(0.2)(merge)
    x = BatchNormalization()(x)
    x = Dense(300, activation='relu')(x)

    x = Dropout(0.2)(x)
    x = BatchNormalization()(x)
    pred = Dense(1, activation='sigmoid')(x)

    model = Model(inputs=[seq1, seq2, magic_input, distance_input], outputs=pred)
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])

    return model

转自 https://www.kaggle.com/rethfro/1d-cnn-single-model-score-0-14-0-16-or-0-23/notebook