整理代码

network.py

@@ -16,11 +16,11 @@ def build_model(want_answer_size, infact_answer_size):
     inputs_infact_answer = Input(shape=(infact_answer_size, ), name='infact_answer_input')
     x_1 = Embedding(want_answer_size, 128, name='want_answer_embedding', embeddings_initializer='he_normal', embeddings_regularizer=keras.regularizers.l2(0.01))(inputs_want_answer)
     x_2 = Embedding(infact_answer_size, 128, name='infact_answer_embedding', embeddings_initializer='he_normal', embeddings_regularizer=keras.regularizers.l2(0.01))(inputs_infact_answer)
-    x_1 = GRU(128, dropout=0.4, return_sequences=True, recurrent_initializer='he_normal', recurrent_regularizer=keras.regularizers.l2(0.01))(x_1)
+    x_1 = GRU(128, dropout=0.2, return_sequences=True, recurrent_initializer='he_normal', recurrent_regularizer=keras.regularizers.l2(0.01))(x_1)
-    x_2 = GRU(128, dropout=0.4, return_sequences=True, recurrent_initializer='he_normal', recurrent_regularizer=keras.regularizers.l2(0.01))(x_2)
+    x_2 = GRU(128, dropout=0.2, return_sequences=True, recurrent_initializer='he_normal', recurrent_regularizer=keras.regularizers.l2(0.01))(x_2)
     x = keras.layers.concatenate([x_1, x_2])
     x = Flatten()(x)
-    x = Dropout(0.3)(x)
+    x = Dropout(0.4)(x)
     x = Dense(64, activation='relu')(x)
     predictions = Dense(2, activation='softmax')(x)
     model = Model(inputs=[inputs_want_answer, inputs_infact_answer], outputs=predictions)

utils.py

@@ -1,13 +1,6 @@
 import jieba
-import pandas as pd
 from sortedcontainers import SortedSet
 import numpy as np
-from sklearn.model_selection import train_test_split
-from keras.layers import Dense, Embedding, Input, Flatten
-from keras.layers import LSTM, GRU, Dropout
-from keras.models import Model
-import keras
-from keras.utils import plot_model
 
 
 def stopwordslist(filepath):