Created
June 30, 2020 05:44
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from keras.layers.convolutional import Conv1D | |
from keras.layers.convolutional import MaxPooling1D | |
preprocessed_question_title_input = Input(shape=(max_length,), name = "preprocessed_question_title") | |
emb = Embedding(vocab_size, 300, weights=[embedding_matrix], input_length=max_length,trainable=False)(preprocessed_question_title_input) | |
conv1 = Conv1D(filters=32, kernel_size=4, activation='relu')(emb) | |
pool1 = MaxPooling1D(pool_size=2)(conv1) | |
lstm_CNN= LSTM(128,return_sequences=True)(pool1) | |
dropout_CNN_LSTM = Dropout(0.2)(lstm_CNN) | |
flat_title = Flatten()(dropout_CNN_LSTM) | |
concat_layers = [] | |
concat_layers.append(flat_title) | |
concat_layers.append(flat_question) | |
concat_layers.append(flat_answer) | |
concat_layers.append(category_flat) | |
concat_layers.append(host_flat) | |
concat_layers.append(num_field_dense) | |
concat_layers = Concatenate()(concat_layers) | |
concat_layers= Dense(512,activation='relu',kernel_initializer='he_normal',kernel_regularizer=l2(0.001))(concat_layers) | |
concat_layers = BatchNormalization()(concat_layers) | |
concat_layers= Dropout(0.4)(concat_layers) | |
concat_layers= Dense(64,activation='relu',kernel_initializer='he_normal',kernel_regularizer=l2(0.001))(concat_layers) | |
concat_layers = BatchNormalization()(concat_layers) | |
concat_layers= Dropout(0.4)(concat_layers) | |
concat_layers = BatchNormalization()(concat_layers) | |
output=Dense(len(target_columns), activation='sigmoid')(concat_layers) | |
model_4 = Model(inputs=[preprocessed_question_title_input,preprocessed_question_body_input,preprocessed_question_answer_input,category_input,host_input,num_field_input], outputs=output) | |
model_4.summary() |
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