So I am following the deep learning course in deep lizard and in the course they told me to use steps_per_epoch when training the model. I understand what steps_per_epoch does but I do not understand why we need it. Can anyone please explain it.
Thanks in advance!
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I am sorry I am asking this question, it seems so stupid, but I have no other way to look for it. If you have some parallel documentation that easily compares this kind of questions, please let me know.
Good people of SO, the SparseCategoricalCrossentropy is equivalent to it in TF2.0 : https://www.tensorflow.org/api_docs/python/tf/keras/losses/SparseCategoricalCrossentropy
It's SparseCategoricalCrossentropy and not CategoricalCrossentropy, because torch's cross entropy does not use one hot encoding as CategoricalCrossentropy in Tensorflow does.
I am developing an encoder-decoder model in order to predict titles for lecture transcripts. but the model is predicting the same title no matter what the input is. Any idea what may have caused such a problem?
If you would like to solve this, I will strongly recommend you to provide your code as an example, better including your loss, accuracy or something people will be more familiar about your problem. However, here are some conditions that will run into that problem: 1) your code was not doing the things you would like to do somehow. 2) LSTM sometimes experience gradient explode or gradient vanish problem, although it was said to fix those problem that a RNN structure will face, it still get into that problem form time to time anyway. 3) forget to shuffle your dataset before training, which makes your model learn the same pattern of one kind all the time. If all the things that mentioned above did not fit in your case, try to provide your code and dataset information to make it clear.
I have been reading some Keras codes on Seq2Seq and come to wonder whether the teacher forcing is used or not during its training? I couldn't find any parameters? I might have missed though.
Is the teacher forcing on by default in Keras? Or Keras doesn't support it natively yet?
Thank you in advance!
I am new to neural networks so I tried my first neural network which is pretty close to one at keras learn page,given below:
https://github.com/aakarsh1011/Neural-Network/blob/master/MNSIT%20classification.ipynb
Kindlly look at the ending where I red a random image and tried to predict it which comes out as a bag, and when trained at epocs=5 it predicted it as a sandal.
Is something wrong with my code or labeling.
UPDATE - Being new to the field I didn't know the importance of epochs so I asked this question, I was afraid that I don't over-fit the model or train train too much. But there is no definite way to do this, it's all try and error. GOOD LUCK!
First of all, as far as I can see, your code is correct. Your model predicting the wrong item can be caused by the model not being trained for long enough. I would highly recommend you to set epochs=100 and you will be able to see the model's accuracy rise. You should generally always try to give your model as many epochs as possible for training. It will simply take some time. Try out some different numbers of epochs to find the one not taking too long, but still giving an acceptable result.
I want to get a deep idea about how this keras layers works in a model. What does each layer doing in the model etc. I followed kers documentation and information isn't enough. If any of you know place to get more knowledge let me know.Thanks in advance
Keras layers are widely used CNN, DNN and RNN layers. There is atleast one research paper for each of them and there is a lot of educational material out there. If you are really curious you could look at keras' code. Some links for you:
https://github.com/keras-team/keras/tree/master/keras/layers
http://cs231n.github.io/convolutional-networks/
https://leonardoaraujosantos.gitbooks.io/artificial-inteligence
http://www.jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf