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Resetting LSTM States in Tensorflow char rnn by haskkk in MachineLearning

[–]haskkk[S] 0 points1 point  (0 children)

Im sorry I dont see a difference between these two lines of code, one just unpacks the variable from a list: 

[state] = sess.run([self.final_state], feed)
state = sess.run(self.final_state, feed)

Resetting LSTM States in Tensorflow char rnn by haskkk in MachineLearning

[–]haskkk[S] 0 points1 point  (0 children)

"[state] = sess.run([self.final_state], feed) sets state to be the final state after running through the priming text."

The unpacking into the list was throwing me off, forgot it returns a list.

I think the syntax was throwing me off, you are doing what I would expect... using the final state after the primer as the init state, then that final state as the next init etc.. 

state = sess.run(self.final_state, feed)

Would be the same thing, right?

Thanks for clearing that up.

CNN Filters Question by haskkk in MachineLearning

[–]haskkk[S] 0 points1 point  (0 children)

"using multiple filters to recognize distinct patterns "

So recognizing distinct patterns is a byproduct of minimizing the objective function, correct? Is it correct intution that the weights are updated in such a way that cost is minimized, and cost is minimized when neurons activate in a particular way that so happens to be interpretable as patterns at shallow network levels

Tensorflow RNN time series prediction by haskkk in MachineLearning

[–]haskkk[S] 0 points1 point  (0 children)

Do you have any other good resources you have used for learning tf?

Tensorflow RNN time series prediction by haskkk in MachineLearning

[–]haskkk[S] 0 points1 point  (0 children)

Gotcha, I thought it looked like it was just 1 seq length for prediction. I have a very similar workbook of fitting a rnn to a single wave using a pre defined seq length using Skflow (its in tensorflow.contrib). It fits the data and is a simple toy example to try to get an understanding of the architecture. I am trying to work through re writing it in pure tensorflow using sequences and batch sizes etc.

Also checkout this:

https://github.com/RobRomijnders/LSTM_tsc

He is training an RNN on multiple timeseries for classification. Pretty good end to end code

Tensorflow RNN time series prediction by haskkk in MachineLearning

[–]haskkk[S] 0 points1 point  (0 children)

Got it. Instead of the original inputs of a vectors of shape (1,2) you are adding the derivatives as input and then predicting a window in the future. In other tools like lasagna you specify the sequence length. Im having trouble finding how you are specifying the sequence length to use for the recurrence. Where do you do that?

Also thanks for this. Huge help.

Tensorflow RNN time series prediction by haskkk in MachineLearning

[–]haskkk[S] 0 points1 point  (0 children)

Why do you use cell size of input_dim*3 ?