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34 lines
888 B
Python
34 lines
888 B
Python
import prepare
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import keras
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import pickle
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import numpy as np
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mxlen = 32
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token_file = open('tokenizer', 'rb')
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prepare.tokenizer = pickle.load(token_file)
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train_json = 'nlvr\\train\\train.json'
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train_img_folder = 'nlvr\\train\\images'
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test_json = 'nlvr\\test\\test.json'
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test_img_folder = 'nlvr\\test\\images'
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data = prepare.load_data(train_json)
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data = prepare.tokenize_data(data, mxlen)
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imgs, ws, labels = prepare.load_images(train_img_folder, data)
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data.clear()
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model = keras.models.load_model('model')
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test_data = prepare.load_data(test_json)
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test_data = prepare.tokenize_data(test_data, mxlen)
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test_imgs, test_ws, test_labels = prepare.load_images(test_img_folder, test_data)
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test_data.clear()
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imgs_mean = np.mean(imgs)
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imgs_std = np.std(imgs - imgs_mean)
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test_imgs = (test_imgs - imgs_mean) / imgs_std
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print(model.evaluate([test_imgs, test_ws], test_labels, batch_size=128)) |