{"action":"opened","issue":{"url":"https://api.github.com/repos/keras-team/keras/issues/10624","repository_url":"https://api.github.com/repos/keras-team/keras","labels_url":"https://api.github.com/repos/keras-team/keras/issues/10624/labels{/name}","comments_url":"https://api.github.com/repos/keras-team/keras/issues/10624/comments","events_url":"https://api.github.com/repos/keras-team/keras/issues/10624/events","html_url":"https://github.com/keras-team/keras/issues/10624","id":339223816,"node_id":"MDU6SXNzdWUzMzkyMjM4MTY=","number":10624,"title":"accuracy issue (using the 20 newsgroups example model)","user":{"login":"mikkokotila","id":7943188,"node_id":"MDQ6VXNlcjc5NDMxODg=","avatar_url":"https://avatars1.githubusercontent.com/u/7943188?v=4","gravatar_id":"","url":"https://api.github.com/users/mikkokotila","html_url":"https://github.com/mikkokotila","followers_url":"https://api.github.com/users/mikkokotila/followers","following_url":"https://api.github.com/users/mikkokotila/following{/other_user}","gists_url":"https://api.github.com/users/mikkokotila/gists{/gist_id}","starred_url":"https://api.github.com/users/mikkokotila/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mikkokotila/subscriptions","organizations_url":"https://api.github.com/users/mikkokotila/orgs","repos_url":"https://api.github.com/users/mikkokotila/repos","events_url":"https://api.github.com/users/mikkokotila/events{/privacy}","received_events_url":"https://api.github.com/users/mikkokotila/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-07-08T12:56:17Z","updated_at":"2018-07-08T12:56:17Z","closed_at":null,"author_association":"NONE","body":"The post in Keras blog [here](https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html) yields a 95%  validation accuracy after 2 epochs. Given the example code, I can't reproduce this (in fact the validation accuracy will not go over 75% and loss is very high as well). \r\n\r\nI'm following the example code here:\r\n\r\nhttps://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html\r\n\r\n...with the exception that I open the GloVe vector file with encoding='utf-8' as it fails otherwise. The link is provided in the example blog post itself. "}}
{"actor":{"display_login":"mikkokotila"}}