generator

问题 I was trying to train my siamese network with fit_generator() ,I learned from this answer: Keras: How to use fit_generator with multiple inputs that the best way to do this was to create your own generator that yield the multiple data points, my problem was that I retrieve my data with flow_from_directory() function and I didn't know if that was possible. This is my attempt to readapt a generator for my problem: from keras.models import load_model from keras import optimizers from keras

问题 Here is code I am running: def infinite_Third() -> Generator: num = 1 while True: if num % 3 ==0: i = (yield num) if i is not None: num = i num += 1 if __name__=='__main__': third_gen = infinite_Third() for i in third_gen: print(f"it is {i}") if i>1000: break third_gen.send(10*i+1) I am expecting to see results as: it is 3 it is 33 it is 333 it is 3333 However, what I really get is: it is 3 it is 36 it is 366 it is 3666 I think this might be related to using send in the main code, but couldn

问题 Just came across this awesome __length_hint__() method for iterators from PEP 424 (https://www.python.org/dev/peps/pep-0424/). Wow! A way to get the iterator length without exhausting the iterator. My questions: Is there a simple explanation how does this magic work? I'm just curious. Are there limitations and cases where it wouldn't work? ("hint" just sounds a bit suspicious). Is there a way to get the hint for zips and generators as well? Or is it something fundamental only to iterators?

问题 For example given ['a', 'b'] (as a generator) and 2 as a length the function would output a generator that would yield: '', 'a', 'b', 'ab' 'ba' 'aa' 'bb' or given ['a'] and a length of 3: '', 'a', 'aa', 'aaa', As you could imagine this set would get a lot larger if more letters were added or length was increased, it should list all permutations of the given characters up until the length 回答1: Here's a fairly self-explanatory solution. //Returns all permuations of a certain length. function

问题 I have a question, which very likely has been asked like this before, because I think what I want is something that a considerable amount of people would want. However I could not come up with any way of expressing it that would return what I wanted in search (not google, not here). So maybe the answer here is just a single term used to describe what I mean. What I want to implement is something that roughly does the following: It can take a functor struct/class and generate a sequence of

问题 I am trying to run a generator process in parallel by child processes. But when I tried to do this, I see the function with generator was processed by the parent process!!! from multiprocessing import Process import os import time class p(Process): def __init__(self): Process.__init__(self) def run(self): print('PID:', os.getpid()) def genfunc(self): time.sleep(1) yield os.getpid() p1 = p() p2 = p() p1.start() p2.start() print('Iterators:') print('Ran by:',next(p1.genfunc())) print('Ran by:'

问题 What is the difference between iterators and generators? Some examples for when you would use each case would be helpful. 回答1: iterator is a more general concept: any object whose class has a __next__ method ( next in Python 2) and an __iter__ method that does return self . Every generator is an iterator, but not vice versa. A generator is built by calling a function that has one or more yield expressions ( yield statements, in Python 2.5 and earlier), and is an object that meets the previous

问题 What is the difference between iterators and generators? Some examples for when you would use each case would be helpful. 回答1: iterator is a more general concept: any object whose class has a __next__ method ( next in Python 2) and an __iter__ method that does return self . Every generator is an iterator, but not vice versa. A generator is built by calling a function that has one or more yield expressions ( yield statements, in Python 2.5 and earlier), and is an object that meets the previous

问题 What is the difference between iterators and generators? Some examples for when you would use each case would be helpful. 回答1: iterator is a more general concept: any object whose class has a __next__ method ( next in Python 2) and an __iter__ method that does return self . Every generator is an iterator, but not vice versa. A generator is built by calling a function that has one or more yield expressions ( yield statements, in Python 2.5 and earlier), and is an object that meets the previous

问题 I'd like to train a convolutional network to solve a multi-class, multi-label problem on image data. Due to the nature of the data, and for reasons I'll spare you, it would be best if I could use a custom R generator function to feed to the fit_generator command, instead of its built-in image_data_generator and flow_images_from_directory commands (which I was successfully able to get working, just not for this particular problem). Here (https://www.rdocumentation.org/packages/keras/versions/2