it is the second model that allows/denies to open trades
i.e. there are two models in the production.
I see. I have a discriminator. Some time ago I successfully implemented a script that collects the best models on various attributes and parses them into one EA.
Maybe a meta-model should be applied to several optimal generative models at once?
I see. I have a discriminator. Some time ago I've realized a script that collects the best models on different attributes and parses them into one Expert Advisor.
Maybe the meta-model should be applied to several optimal generative models at once?
It is also a discriminator, i.e. a bunch of 2 or more models is simply retrained.
But I have not implemented the loop yet, all f-and-s have to be redesigned there.
A classifier for TensorFlow DNN models. Inherits From: , Used in the notebooks Used in the tutorials Example: Input of and should have following features, otherwise there will be a : if is not , a feature with whose value is a . for each in : if is a , a feature with whose is a . if is a , two features: the first with the id column name, the...
by the way, python 3.9 is significantly faster than 3.8 in the console, switched to it
I have no such fierce tasks as counting tons of profits with high frequency, so for now 3.7))
it is the second model that allows/denies to open trades
i.e. there are two models in the production.
I see. I have a discriminator. Some time ago I successfully implemented a script that collects the best models on various attributes and parses them into one EA.
Maybe a meta-model should be applied to several optimal generative models at once?
I'll have to try itI see. I have a discriminator. Some time ago I've realized a script that collects the best models on different attributes and parses them into one Expert Advisor.
Maybe the meta-model should be applied to several optimal generative models at once?
It is also a discriminator, i.e. a bunch of 2 or more models is simply retrained.
But I have not implemented the loop yet, all f-and-s have to be redesigned there.
maybe even a few, I don't know yet.
it is also a discriminator, i.e. it simply retrains a bunch of 2 models
But I haven't implemented the loop yet, I have to redo all the functions there.
I can send you my scripts, maybe help what.
I can send my scripts, maybe help what.
It seems to be clear how to do
Maxim Dmitrievsky
I can write an article
That would be great) I'm always happy to see articles from you.
That would be wonderful) I am always glad to see articles from you.
I always enjoy something new myself, but it gets harder and harder to come up with every time )
As long as there are no qualitative changes compared to the previous one, i don't see the point of writing
I always enjoy something new myself, but it gets harder and harder to come up with every time )
As long as there are no qualitative changes compared to the previous one, I don't see the point of writing
Can you share an example how to use this deep neural network instead of GMM?
https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier
it should be replaced
to this: tf.estimator.DNNClassifier
Can you share an example of how to use this deep neural network instead of GMM?
https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier
you need to replace
to this: tf.estimator.DNNClassifier
no way, they are absolutely different things
Is MLPClassifier not suitable for this problem too?
There is a method for estimating the probability of which class the sample belongs to.
https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html