cnn_test.py

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


test_file_path="./data//test.txt"
train_data_path="./data//cnn//training_params.pickle"
embedding_model_path="./data//embedding_64.bin"

class config():
    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_cnn_result():
    if not os.path.exists(embedding_model_path):
        print("word2vec model is not found")

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

    params = readdata.loadDict(train_data_path)
    train_length = int(params['max_sentences_length'])



    test_sample_lists = readdata.get_cleaned_list(test_file_path)
    test_sample_lists,max_sentences_length = readdata.padding_sentences(test_sample_lists,padding_token='<PADDING>',padding_sentence_length=train_length)
    test_sample_arrays=np.array(word2vec.get_embedding_vector(test_sample_lists,embedding_model_path))
    testconfig=config()
    testconfig.max_sentences_length=max_sentences_length

    sess=tf.InteractiveSession()
    cnn=cnn_model.TextCNN(config=testconfig)

    #加载参数
    saver = tf.train.Saver()
    saver.restore(sess, "./data/cnn/text_model")

    #定义测试函数
    def test_step(x_batch):
        feed_dict={
            cnn.input_x:x_batch,
            cnn.dropout_keep_prob:1.0
            }
        predictions,scores=sess.run(
            [cnn.predictions,cnn.softmax_result],
            feed_dict=feed_dict
            )
        return (predictions,scores)


    #拿到结果
    predictions,scores=test_step(test_sample_arrays)
    return np.array(predictions)
    #print("(0->neg & 1->pos)the result is:")
    #print(predictions)
    #print("********************************")
    #print("the scores is:")
    #print(scores)
    #print(scores.shape)