{"action":"closed","issue":{"url":"https://api.github.com/repos/siddharth-agrawal/Neural-Tensor-Network/issues/1","labels_url":"https://api.github.com/repos/siddharth-agrawal/Neural-Tensor-Network/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/siddharth-agrawal/Neural-Tensor-Network/issues/1/comments","events_url":"https://api.github.com/repos/siddharth-agrawal/Neural-Tensor-Network/issues/1/events","html_url":"https://github.com/siddharth-agrawal/Neural-Tensor-Network/issues/1","id":92741515,"number":1,"title":"Activation function for calculating the score of a tripple is missing","user":{"login":"c0der1337","id":6539636,"avatar_url":"https://avatars.githubusercontent.com/u/6539636?v=3","gravatar_id":"","url":"https://api.github.com/users/c0der1337","html_url":"https://github.com/c0der1337","followers_url":"https://api.github.com/users/c0der1337/followers","following_url":"https://api.github.com/users/c0der1337/following{/other_user}","gists_url":"https://api.github.com/users/c0der1337/gists{/gist_id}","starred_url":"https://api.github.com/users/c0der1337/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/c0der1337/subscriptions","organizations_url":"https://api.github.com/users/c0der1337/orgs","repos_url":"https://api.github.com/users/c0der1337/repos","events_url":"https://api.github.com/users/c0der1337/events{/privacy}","received_events_url":"https://api.github.com/users/c0der1337/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"milestone":null,"comments":1,"created_at":"2015-07-02T21:40:08Z","updated_at":"2015-07-03T04:40:37Z","closed_at":"2015-07-03T04:40:37Z","body":"Hello,\r\n\r\nI think the acitviation function for calculating the score of a tripple after the training is missing.\r\nhttps://github.com/siddharth-agrawal/Neural-Tensor-Network/blob/master/neuralTensorNetwork.py#L428-L434 and also in getPredictions().\r\n\r\ndev_scores[i, 0] += U[rel][k, 0] * (np.dot(entity_vector_e1.T, np.dot(W[rel][:, :, k], entity_vector_e2)) +\r\n                    np.dot(V[rel][:, k].T, entity_stack) + b[rel][0, k])\r\n\r\nI think it should be like this:\r\ntest_score += U[rel][k, 0] * self.activationFunction((np.dot(entity_vector_e1.T, np.dot(W[rel][:, :, k], entity_vector_e2)) +\r\n                    np.dot(V[rel][:, k].T, entity_stack) + b[rel][0, k]))\r\n\r\nWhat do you think?\r\n"}}