inference

问题 I am feeding characters ( x_train ) to the RNN model defined in example 13 of this link. Here is the code corresponding to model definition, input pre-processing and training. def char_rnn_model(features, target): """Character level recurrent neural network model to predict classes.""" target = tf.one_hot(target, 15, 1, 0) #byte_list = tf.one_hot(features, 256, 1, 0) byte_list = tf.cast(tf.one_hot(features, 256, 1, 0), dtype=tf.float32) byte_list = tf.unstack(byte_list, axis=1) cell = tf

问题 So here is what I would like to do: I use std::pair , but I would surely like to do the same using tuples, or indeed pretty much any kind of template. When assigning a pair variable, I need to type something like: T1 t1; T2 t2; std::pair<T1,T2> X; X = std::pair<T1,T2> (t1, t2); Is there a way to omit the second <T1,T2> when creating the new pair, and let the compiler guess, either using X's type (I'm obviously trying to create a pair<T1,T2> ) or t1 and t2 's types (I am building a pair with a

问题 If I have a function template with typename T , where the compiler can set the type by itself, I do not have to write the type explicitly when I call the function like: template < typename T > T min( T v1, T v2 ) { return ( v1 < v2 ) ? v1: v2; } int i1 = 1, i2 = 2; int i3 = min( i1, i2 ); //no explicit <type> But if I have a function template with two different typenames like: template < typename TOut, typename TIn > TOut round( TIn v ) { return (TOut)( v + 0.5 ); } double d = 1.54; int i =

问题 I'm trying to use a custom function called Inference() as seen in the code below. There's no documentation for the function, but it is from my DASI class in Coursera. According to the feedback I have received, I am using the function properly. I'm trying to do a two-sided hypothesis test between my class variable and my wordsum variable, that is, between the two means of the categories low class and working class. So, the average wordsum for working class - average wordsum for lower class.

问题 I´ll try to be short as the question has not been very answered. For the long explanation, go after this briefing. I will show what Im trying to do. Something like this (infering the incoming type from the constructor in order to use it in another method getLeaderHerd as a return type)... : public class ZooCage{ private CageFamily<T> inhabitants; public <T>ZooCage(CageFamily<T> herd) { this.inhabitants=herd; } public T getHerdLeader() { return inhabitants.getLeader(); } } or this public class

问题 I am a beginner to SPARQL and was wondering if there was a query which could help me return transitive relations. For example the n3 file below I would want a query that would return "a is the sameas c" or something along those lines. Thanks @prefix : <http://websitename.com/links/> . @prefix owl: <http://www.w3.org/2002/07/owl#> . :a owl:sameas :b. :b owl:sameas :c. 回答1: You can use property paths if you are using a suitably enabled SPARQL 1.1 engine, you've tagged your question Jena so I

问题 I used OpenCV that I compiled and also the OpenCV that comes as a part of Intel OpenVino. I found that the OpenCV as a part of OpenVino is faster by around 10%-20% and I am wondering what flags are used by Intel to compile OpenCV? I want to recompile it so that I can create a static library instead of world version of the library. 来源： https://stackoverflow.com/questions/53692675/flags-that-used-for-compiling-opencv-part-of-openvino

问题 This is a follow-up question from SPARQL 1.1 entailment regimes and query with FROM clause I'm currently documenting/testing about SPARQL 1.1 entailment regimes and the recommendation repeatedly states that The scoping graph is graph-equivalent to the active graph... So it would seems that the inference scoping graph depends on the query. The question is: does the scoping graph stems from the query's dataset (FROM/FROM NAMED clauses) or does it refer to the real current active graph context

问题 In my ontology, I have two individuals of type abc:Invention : abc:InventionA rdf:type abc:Invention . abc:InventionB rdf:type abc:Invention . and 2 individuals of type abc:MarketSector , linked with an object property abc:includedIn : abc:MrktSctrA rdf:type abc:MarketSector . abc:MrktSctrB rdf:type abc:MarketSector . abc:MrktSctrB abc:includedIn MrktSctrA . Currently, InventionA and InventionB are linked with, respectively MrktSctrA and MrktSctrB via an object property abc:targets : abc

问题 I trained a model in keras and I'm thinking of pruning my fully connected network. I'm little bit lost on how to prune the layers. Author of 'Learning both Weights and Connections for Efficient Neural Networks', say that they add a mask to threshold weights of a layer. I can try to do the same and fine tune the trained model. But, how does it reduce model size and # of computations? 回答1: Based on the discussion in the comments, here is a way to prune a layer (a weight matrix) of your neural