11 results
- Go to itemHere we use word embedding instead of hot encoding, using a Word2Vec Learner node. The hidden layer size is set to 10, therefore …3
- Go to itemIn this workflow we look-up a GLOVE embedding vector (https://nlp.stanford.edu/projects/glove/) for each word in our caption voca…0
- Go to itemIn this workflow we create image feature vectors for each image in the training data using a pretrained InceptionV3 (https://arxi…0
- Go to itemIn the last workflow of this series, we predict the captions of the images in our test sample. This can be done using a similar i…0
- Go to itemIn this workflow we create image feature vectors for each image in the training data using a pretrained InceptionV3 (https://arxi…0
- Go to itemAfter we cleaned the training captions and pre-calculated image-/word- features, the caption network can be trained. In this exam…0
- Go to itemIn the last workflow of this series, we predict the captions of the images in our test sample. This can be done using a similar i…0
- Go to itemAfter we cleaned the training captions and pre-calculated image-/word- features, the caption network can be trained. In this exam…0
- Go to itemIn this workflow we use simple textprocessing techniques to reduce the complexity of the captions used for training. This will li…0
- Go to itemIn this workflow we look-up a GLOVE embedding vector (https://nlp.stanford.edu/projects/glove/) for each word in our caption voca…0
- Go to itemIn this workflow we use simple textprocessing techniques to reduce the complexity of the captions used for training. This will li…0