mixed_cnn_lstm_test.py

import os
import readdata
import word2vec
import lstm_model
import cnn_model
import numpy as np
import tensorflow as tf



#文件路径
current_path=os.path.abspath(os.curdir)
test_file_path="./data//test.txt"
embedding_model_path="./data//embedding_64.bin"
lstm_train_data_path="./data//lstm//training_params.pickle"
cnn_train_data_path="./data//cnn//training_params.pickle"


#模型超参
class lstmconfig():
    test_sample_percentage=0.03
    num_labels=2
    embedding_size=64
    dropout_keep_prob=1
    batch_size=64
    num_epochs=80
    max_sentences_length=40
    num_layers=2
    max_grad_norm=5
    l2_rate=0.0001

class cnnconfig():
    test_sample_percentage=0.03
    num_labels=2
    embedding_size=64
    filter_sizes=[2,3,4]
    num_filters=128
    dropout_keep_prob=1
    l2_reg_lambda=0.1
    batch_size=32
    num_epochs=15
    max_sentences_length=0
    lr_rate=1e-3

def get_mixed_result():
    if not os.path.exists(embedding_model_path):
        print("word2vec model is not found")

    if not os.path.exists(lstm_train_data_path):
        print("lstm train params is not found")

    lstm_params = readdata.loadDict(lstm_train_data_path)
    lstm_train_length = int(lstm_params['max_sentences_length'])

    if not os.path.exists(cnn_train_data_path):
        print("cnn train params is not found")

    cnn_params = readdata.loadDict(cnn_train_data_path)
    cnn_train_length = int(cnn_params['max_sentences_length'])


    test_sample_lists = readdata.get_cleaned_list(test_file_path)
    lstm_test_sample_lists,lstm_max_sentences_length = readdata.padding_sentences(test_sample_lists,padding_token='<PADDING>',padding_sentence_length=lstm_train_length)
    cnn_test_sample_lists,cnn_max_sentences_length = readdata.padding_sentences(test_sample_lists,padding_token='<PADDING>',padding_sentence_length=cnn_train_length)
    lstm_test_sample_arrays=np.array(word2vec.get_embedding_vector(lstm_test_sample_lists,embedding_model_path))
    cnn_test_sample_arrays=np.array(word2vec.get_embedding_vector(cnn_test_sample_lists,embedding_model_path))
    lstm_config=lstmconfig()
    cnn_config=cnnconfig()
    lstm_config.max_sentences_length=lstm_max_sentences_length
    cnn_config.max_sentences_length=cnn_max_sentences_length



    lstm_graph=tf.Graph()
    cnn_graph=tf.Graph()
    lstm_sess=tf.Session(graph=lstm_graph)
    cnn_sess=tf.Session(graph=cnn_graph)


    with lstm_sess.as_default():
        with lstm_graph.as_default():
            lstm = lstm_model.TextLSTM(config=lstm_config)
            lstm_saver = tf.train.Saver()
            lstm_saver.restore(lstm_sess, "./data/lstm/text_model")
            def lstm_test_step(x_batch):
                feed_dict={
                    lstm.input_x:x_batch,
                    lstm.dropout_keep_prob:lstm_config.dropout_keep_prob
                }
                scores=lstm_sess.run(
                    [lstm.softmax_result],
                    feed_dict=feed_dict
                )
                return scores


            lstm_scores = lstm_test_step(lstm_test_sample_arrays)


    with cnn_sess.as_default():
        with cnn_graph.as_default():
            cnn = cnn_model.TextCNN(config=cnn_config)
            cnn_saver = tf.train.Saver()
            cnn_saver.restore(cnn_sess, "./data/cnn/text_model")
            def cnn_test_step(x_batch):
                feed_dict={
                    cnn.input_x:x_batch,
                    cnn.dropout_keep_prob:cnn_config.dropout_keep_prob
                }
                scores=cnn_sess.run(
                    [cnn.softmax_result],
                    feed_dict=feed_dict
                )
                return scores


            cnn_scores = cnn_test_step(cnn_test_sample_arrays)

    lstm_sess.close()
    cnn_sess.close()
    mixed_scores=np.sum([lstm_scores,cnn_scores],axis=0)
    predictions=np.argmax(mixed_scores,axis=2)
    return np.array(predictions)