If not os.path.exists(os.path.abspath('.') + image_folder): If not os.path.exists(os.path.abspath('.') + annotation_folder):Īnnotation_zip = tf._file('captions.zip',Īnnotation_file = os.path.dirname(annotation_zip)+'/annotations/captions_train2014.json' You'll use the training set, which is a 13GB file.
#Tutorial surfer 9 bahasa indonesia code
The code below downloads and extracts the dataset automatically. The dataset contains over 82,000 images, each of which has at least 5 different caption annotations.
You will use the MS-COCO dataset to train your model.
# your model focuses on during captioning # You'll generate plots of attention in order to see which parts of an image In this example, you will train a model on a relatively small amount of data-the first 30,000 captions for about 20,000 images (because there are multiple captions per image in the dataset). When you run the notebook, it downloads the MS-COCO dataset, preprocesses and caches a subset of images using Inception V3, trains an encoder-decoder model, and generates captions on new images using the trained model. The model architecture is similar to Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. To accomplish this, you'll use an attention-based model, which enables us to see what parts of the image the model focuses on as it generates a caption. Given an image like the example below, your goal is to generate a caption such as "a surfer riding on a wave".
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