{"action":"created","issue":{"url":"https://api.github.com/repos/keras-team/keras/issues/1597","repository_url":"https://api.github.com/repos/keras-team/keras","labels_url":"https://api.github.com/repos/keras-team/keras/issues/1597/labels{/name}","comments_url":"https://api.github.com/repos/keras-team/keras/issues/1597/comments","events_url":"https://api.github.com/repos/keras-team/keras/issues/1597/events","html_url":"https://github.com/keras-team/keras/issues/1597","id":129950004,"node_id":"MDU6SXNzdWUxMjk5NTAwMDQ=","number":1597,"title":" acc and val_acc don't change?","user":{"login":"talentlei","id":3364689,"node_id":"MDQ6VXNlcjMzNjQ2ODk=","avatar_url":"https://avatars3.githubusercontent.com/u/3364689?v=4","gravatar_id":"","url":"https://api.github.com/users/talentlei","html_url":"https://github.com/talentlei","followers_url":"https://api.github.com/users/talentlei/followers","following_url":"https://api.github.com/users/talentlei/following{/other_user}","gists_url":"https://api.github.com/users/talentlei/gists{/gist_id}","starred_url":"https://api.github.com/users/talentlei/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/talentlei/subscriptions","organizations_url":"https://api.github.com/users/talentlei/orgs","repos_url":"https://api.github.com/users/talentlei/repos","events_url":"https://api.github.com/users/talentlei/events{/privacy}","received_events_url":"https://api.github.com/users/talentlei/received_events","type":"User","site_admin":false},"labels":[{"id":611595073,"node_id":"MDU6TGFiZWw2MTE1OTUwNzM=","url":"https://api.github.com/repos/keras-team/keras/labels/stale","name":"stale","color":"ffffff","default":false}],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":26,"created_at":"2016-01-30T06:41:44Z","updated_at":"2018-10-11T02:04:08Z","closed_at":"2017-06-22T20:12:20Z","author_association":"NONE","body":"I use LSTM to do a sequence labeling task, but I got the same acc and cal_acc for each epoch.\nhere is my code:\n\ndef moduleRnn(self):\n        model = Sequential()\n        model.add(LSTM(output_dim=64,input_length=self.seq_len,batch_input_shape=(16,1,200),input_dim=self.embed_length,return_sequences=True,stateful=False ))\n        #model.add(LSTM(output_dim=16,return_sequences=True,stateful=False ))\n        model.add(Dropout(0.2))\n        model.add(TimeDistributedDense(output_dim=self.labs_len))\n        model.add(Activation('softmax'))\n        model.compile(loss=\"categorical_crossentropy\" , optimizer='rmsprop' , class_mode='categorical')\n        #model.fit(self.train,self.train_lab,batch_size=16,nb_epoch=3,verbose=1, validation_split=0.1,show_accuracy=True)\n        model.fit(self.X_train,self.Y_train,batch_size=16,nb_epoch=15,verbose=1,show_accuracy=True,validation_split=0.2)\n        score = model.evaluate(self.X_test,self.Y_test,batch_size=16)\n        print score\n\nAnyone meets the same problem?  please help me\n"},"comment":{"url":"https://api.github.com/repos/keras-team/keras/issues/comments/428792681","html_url":"https://github.com/keras-team/keras/issues/1597#issuecomment-428792681","issue_url":"https://api.github.com/repos/keras-team/keras/issues/1597","id":428792681,"node_id":"MDEyOklzc3VlQ29tbWVudDQyODc5MjY4MQ==","user":{"login":"timtimtimab","id":36322949,"node_id":"MDQ6VXNlcjM2MzIyOTQ5","avatar_url":"https://avatars3.githubusercontent.com/u/36322949?v=4","gravatar_id":"","url":"https://api.github.com/users/timtimtimab","html_url":"https://github.com/timtimtimab","followers_url":"https://api.github.com/users/timtimtimab/followers","following_url":"https://api.github.com/users/timtimtimab/following{/other_user}","gists_url":"https://api.github.com/users/timtimtimab/gists{/gist_id}","starred_url":"https://api.github.com/users/timtimtimab/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/timtimtimab/subscriptions","organizations_url":"https://api.github.com/users/timtimtimab/orgs","repos_url":"https://api.github.com/users/timtimtimab/repos","events_url":"https://api.github.com/users/timtimtimab/events{/privacy}","received_events_url":"https://api.github.com/users/timtimtimab/received_events","type":"User","site_admin":false},"created_at":"2018-10-11T02:04:08Z","updated_at":"2018-10-11T02:04:08Z","author_association":"NONE","body":"I used to face the same result before. I found that using smaller neural network architecture. Reason behind should be due to vanishing gradient. In some situation, your input might not carry as much information as the neural network expects, and therefore, the weights are gonna vanish to zeros even after several layers. Such problem is more serious when you are doing ConvNet, and it's the reason why we got residual network. Hope this help.  "}}
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