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model.compile(loss='categorical_crossentropy',optimizer = tf.keras.optimizers.legacy.RMSprop(learning_rate=0.0001, decay=1e-6), metrics='accuracy')
history = model.fit(x_train, y_train, batch_size=32, epochs=20, verbose=1, validation_split=0.2, validation_data=(x_test, y_test), shuffle = True)
Can somebody explain to me what causes the training epoch running times to vary between 130 and 220 seconds? As all affecting factors are at a set value in my view. Thanks in advance!
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I have problems with certain algorithmic terms.
What is a robust algorithm ?
What is a resistant algorithm ?
What is a resilient algorithms ?
Thank you in advance.
These attributes have no exact definition. So it depends on your topic/problem what they mean.
They are all used to describe algorithms that can cope with some kind of errors (e.g. outlier or noise) in the input-data and still deliver a useful / the expected result.
So in general you define the kind of errors the algorithm is expected to handle in a defined way.
E.g 'This algorithm returns for an input with less than 5% outlier a result with an accuracy of 99%.'
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Im pretty new to Android but I have worked with Java before.
I dont understand why my algorithm doesn't work. The result I get is 0.0%.
txtInfo.setText(Double.toString((RadioProgress/255)*100)+"%");
txtInfo is a TextView.
I can see in my graphic that my RadioProgress gets the right value. But still I get 0.0% all the time.
Please help me understand :)
Thanks in advance!
If RadioProgress is of integer type and less than 255 then division will always return 0. Cast it to double and you will see values.
Another way would be to divide by 255.0 to enforce conversion
txtInfo.setText(Double.toString((RadioProgress/255.0)*100)+"%");
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On a recent project, with roughly 6,000 hours of development, a little over 1,000 hours has gone towards "debugging"/"fixes"... does this sound to be acceptable, high or low??
I also understand that this is a rather dynamic question, while also requesting a rather simply answer, however, I'm just looking for a rough estimate/average based on past project experiences : )
Grateful for any and all input~!!
Pressman (2000) gives 30-40% as the total amount of project time for integration, testing an debugging, so your figures look a little low - but it depends on how you calculate it!
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I searched to learn Backpropagation algorithm with adaptive learning rate, and find a lot of resources but it was hard for me to understand, because I'm new in neural network. I know how standard backpropagation algorithm works, very well. Is anybody here to explain me how these two algorithms are different from each other?
I think the core difference is the update function, as you could see from here
For classic EBP
w(k+1) <- w(k) - a * gradient
For adaptive learning:
w(k+1) <- w(k) - eta * gradient
where:
eta =
(w(k) - w(k-1)) / (gradient(k) - gradient(k-1)) if eta < etamax
etamax otherwise
So you only need to change the weight update function part. The above is just a simplified version, for implementation, you would have to adjust eta according to the error(k) and error(k-1). And there are many ways to do that.
The basic idea of adaptive is that
if you get a smaller error, you want to try increasing learning rate
if you get a larger error, you want to decrease learning rate to that it converges
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There should have been an initial problem to start building the set of NPC problems. Only then could problems be added to the set NPC , from the set NP by showing that the problem in NP is reducible to that first problem in NPC. So, what was the first problem to be added to NPC and how did someone conclude that it was indeed NPC.
(Note: Google searched, No answers. I'm hoping that someone's professor here had mentioned something like this in class )
It was a satisfiability or SAT problem.
History:
http://en.wikipedia.org/wiki/Boolean_satisfiability_problem
Proof:
http://www.proofwiki.org/wiki/CNF_SAT_is_NP-complete