neural-network

问题 While usually people tend to simply resize any image into a square while training a CNN (for example resnet takes a 224x224 square image), that looks ugly to me, especially when the aspect ratio is not around 1. (In fact that might change ground truth eg the label that an expert might give the distorted image could be different than the original one). So now I resize the image to,say, 224x160 , keeping the original ratio, and then I pad the image with 0s (paste it into a random location in a

问题 While usually people tend to simply resize any image into a square while training a CNN (for example resnet takes a 224x224 square image), that looks ugly to me, especially when the aspect ratio is not around 1. (In fact that might change ground truth eg the label that an expert might give the distorted image could be different than the original one). So now I resize the image to,say, 224x160 , keeping the original ratio, and then I pad the image with 0s (paste it into a random location in a

问题 word2vec is a open source tool by Google: For each word it provides a vector of float values, what exactly do they represent? There is also a paper on paragraph vector can anyone explain how they are using word2vec in order to obtain fixed length vector for a paragraph. 回答1: TLDR : Word2Vec is building word projections ( embeddings ) in a latent space of N dimensions, (N being the size of the word vectors obtained). The float values represents the coordinates of the words in this N

问题 word2vec is a open source tool by Google: For each word it provides a vector of float values, what exactly do they represent? There is also a paper on paragraph vector can anyone explain how they are using word2vec in order to obtain fixed length vector for a paragraph. 回答1: TLDR : Word2Vec is building word projections ( embeddings ) in a latent space of N dimensions, (N being the size of the word vectors obtained). The float values represents the coordinates of the words in this N

问题 word2vec is a open source tool by Google: For each word it provides a vector of float values, what exactly do they represent? There is also a paper on paragraph vector can anyone explain how they are using word2vec in order to obtain fixed length vector for a paragraph. 回答1: TLDR : Word2Vec is building word projections ( embeddings ) in a latent space of N dimensions, (N being the size of the word vectors obtained). The float values represents the coordinates of the words in this N

问题 I want to fine-tune BERT on texts that are related to a specific domain (in my case related to engineering). The training should be unsupervised since I don't have any labels or anything. Is this possible? 回答1: What you in fact want to is continue pre-training BERT on text from your specific domain. What you do in this case is to continue training the model as masked language model, but on your domain-specific data. You can use the run_mlm.py script from the Huggingface's Transformers. 来源：

问题 I want to fine-tune BERT on texts that are related to a specific domain (in my case related to engineering). The training should be unsupervised since I don't have any labels or anything. Is this possible? 回答1: What you in fact want to is continue pre-training BERT on text from your specific domain. What you do in this case is to continue training the model as masked language model, but on your domain-specific data. You can use the run_mlm.py script from the Huggingface's Transformers. 来源：

问题 I have trained a GAN to reproduce CIFAR10 like images. Initially I notice all images cross one batch produced by the generator look always the same, like the picture below: After hours of debugging and comparison to the tutorial which is a great learning source for beginners (https://machinelearningmastery.com/how-to-develop-a-generative-adversarial-network-for-a-cifar-10-small-object-photographs-from-scratch/), I just add only one letter on my original code and the generated images start

问题 I have trained a GAN to reproduce CIFAR10 like images. Initially I notice all images cross one batch produced by the generator look always the same, like the picture below: After hours of debugging and comparison to the tutorial which is a great learning source for beginners (https://machinelearningmastery.com/how-to-develop-a-generative-adversarial-network-for-a-cifar-10-small-object-photographs-from-scratch/), I just add only one letter on my original code and the generated images start

问题 I have trained a GAN to reproduce CIFAR10 like images. Initially I notice all images cross one batch produced by the generator look always the same, like the picture below: After hours of debugging and comparison to the tutorial which is a great learning source for beginners (https://machinelearningmastery.com/how-to-develop-a-generative-adversarial-network-for-a-cifar-10-small-object-photographs-from-scratch/), I just add only one letter on my original code and the generated images start