adroit | ADR/PSR-7 middleware | Runtime Evironment library
kandi X-RAY | adroit Summary
kandi X-RAY | adroit Summary
This package provides a PSR-7 compatible ADR middleware.
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Top functions reviewed by kandi - BETA
- Initialize the middleware .
- Resolve an object by identifier
- Validate resolvers .
- Get the identifier from the request .
- Get the action resolver middleware .
- Checks if the result is valid .
- Validate resolver .
- Get the responder attribute .
- Get the domain payload .
- Get action .
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QUESTION
I have a table like below and I want to return the name of the item with the greatest effect of a particular type. For example, I want the name of the ring with the best 'Shield' enchantment, in this case 'Brusef Amelion's Ring'.
Description Apparel slot Effect Type Effect Value Apron of Adroitness Chest Fortify Agility 5 pts Brusef Amelion's Ring Ring Shield 18% Cuirass of the Herald Chest Fortify Health 15 pts Fortify Magicka Pants Legs Fortify Magicka 20 pts Grand ring of Aegis Ring Shield 6%I've tried using a MAXIFS statement:
...ANSWER
Answered 2021-Jun-13 at 19:56Is this what you are looking for?
QUESTION
I'm having some difficulty with chaining together two models in an unusual way.
I am trying to replicate the following flowchart:
For clarity, at each timestep of Model[0] I am attempting to generate an entire time series from IR[i] (Intermediate Representation) as a repeated input using Model[1]. The purpose of this scheme is it allows the generation of a ragged 2-D time series from a 1-D input (while both allowing the second model to be omitted when the output for that timestep is not needed, and not requiring Model[0] to constantly "switch modes" between accepting input, and generating output).
I assume a custom training loop will be required, and I already have a custom training loop for handling statefulness in the first model (the previous version only had a single output at each timestep). As depicted, the second model should have reasonably short outputs (able to be constrained to fewer than 10 timesteps).
But at the end of the day, while I can wrap my head around what I want to do, I'm not nearly adroit enough with Keras and/or Tensorflow to actually implement it. (In fact, this is my first non-toy project with the library.)
I have unsuccessfully searched literature for similar schemes to parrot, or example code to fiddle with. And I don't even know if this idea is possible from within TF/Keras.
I already have the two models working in isolation. (As in I've worked out the dimensionality, and done some training with dummy data to get garbage outputs for the second model, and the first model is based off of a previous iteration of this problem and has been fully trained.) If I have Model[0] and Model[1] as python variables (let's call them model_a and model_b), then how would I chain them together to do this?
Edit to add:
If this is all unclear, perhaps having the dimensions of each input and output will help:
The dimensions of each input and output are:
Input: (batch_size, model_a_timesteps, input_size)
IR: (batch_size, model_a_timesteps, ir_size)
IR[i] (after duplication): (batch_size, model_b_timesteps, ir_size)
Out[i]: (batch_size, model_b_timesteps, output_size)
Out: (batch_size, model_a_timesteps, model_b_timesteps, output_size)
ANSWER
Answered 2020-Aug-03 at 03:10As this question has multiple major parts, I've dedicated a Q&A to the core challenge: stateful backpropagation. This answer focuses on implementing the variable output step length.
Description:
- As validated in Case 5, we can take a bottom-up first approach. First we feed the complete input to
model_a(A) - then, feed its outputs as input tomodel_b(B), but this time one step at a time. - Note that we must chain B's output steps per A's input step, not between A's input steps; i.e., in your diagram, gradient is to flow between
Out[0][1]andOut[0][0], but not betweenOut[2][0]andOut[0][1]. - For computing loss it won't matter whether we use a ragged or padded tensor; we must however use a padded tensor for writing to TensorArray.
- Loop logic in code below is general; specific attribute handling and hidden state passing, however, is hard-coded for simplicity, but can be rewritten for generality.
Code: at bottom.
Example:
- Here we predefine the number of iterations for B per input from A, but we can implement any arbitrary stopping logic. For example, we can take a
Denselayer's output from B as a hidden state and check if its L2-norm exceeds a threshold. - Per above, if
longest_stepis unknown to us, we can simply set it, which is common for NLP & other tasks with a STOP token.- Alternatively, we may write to separate
TensorArraysat every A's input withdynamic_size=True; see "point of uncertainty" below.
- Alternatively, we may write to separate
- A valid concern is, how do we know gradients flow correctly? Note that we've validate them for both vertical and horizontal in the linked Q&A, but it didn't cover multiple output steps per an input step, for multiple input steps. See below.
Point of uncertainty: I'm not entirely sure whether gradients interact between e.g. Out[0][1] and Out[2][0]. I did, however, verify that gradients will not flow horizontally if we write to separate TensorArrays for B's outputs per A's inputs (case 2); reimplementing for cases 4 & 5, grads will differ for both models, including lower one with a complete single horizontal pass.
Thus we must write to a unified TensorArray. For such, as there are no ops leading from e.g. IR[1] to Out[0][1], I can't see how TF would trace it as such - so it seems we're safe. Note, however, that in below example, using steps_at_t=[1]*6 will make gradient flow in the both model horizontally, as we're writing to a single TensorArray and passing hidden states.
The examined case is confounded, however, with B being stateful at all steps; lifting this requirement, we might not need to write to a unified TensorArray for all Out[0], Out[1], etc, but we must still test against something we know works, which is no longer as straightforward.
Example [code]:
QUESTION
Not able to load my 3D object(GLTF) at runtime. or i might not getting 3D object(Gltf) from server. . . i'm trying to get 3D object from a live server and try to load that object into my scenefoam. but i'm not able to load and show 3D object. when i try to load the object i got these error mention below my app is not crashing but still not able to load the 3D model (GLTF). Or i need to get some special-type of Url or anything else from my server when getting object from api ?
this is my code (java )
///ARObjectActivity.java
...ANSWER
Answered 2020-Apr-03 at 14:36Well, the error logcat speaks itself. There is no file
QUESTION
I'm building a scraper that needs to perform pretty fast, over a large amount of webpages. The results of the code below will be a csv file with a list of links (and other things). Basically, I create a list of webpages that contain several links, and for each of this pages I collect these links.
Implementing multiprocessing leads to some weird results, that I wasn't able to explain. If I run this code setting the value of the pool to 1 (hence, without multithreading) I get a final result in which I have 0.5% of duplicated links (which is fair enough). As soon as I speed it up setting the value to 8, 12 or 24, I get around 25% of duplicate links in the final results.
I suspect my mistake is in the way I write the results to the csv file or in the way I use the imap() function (same happens with imap_unordered, map etc..), which leads the threads to somehow access the same elements on the iterable passed. Any suggestion?
ANSWER
Answered 2018-Dec-11 at 10:13Are you sure the page responds with the correct response in a fast sequence of requests? I have been in situations where the scraped site responded with different responses if the requests were fast vs. if the requests were spaced in time. Menaing, everything went perfectly while debugging but as soon as the requests were fast and in sequence, the website decided to give me a different response. Beside this, I would ask if the fact you are writing in a non-thread-safe environment might have impact: To minimize interactions on the final CSV output and issues with the data, you might:
- use wr.writerows with a chunk of rows to write
- use a threading.lock like here: Multiple threads writing to the same CSV in Python
QUESTION
How to get 'syn' and 'sim' values as a string from the given arrays, as array could vary i only want to extract 'syn' and 'sim' as an example following arrays are given ,i know it might be a simple question but i am new to multidimensional array that's why can't seem to solve it.
...ANSWER
Answered 2018-May-08 at 14:57Create a function:
QUESTION
f = open("sample_flickr_response.json","r")
search_result_diction = json.loads(f.read())
print search_result_diction
f.close()
search_result_diction["id"]
for sample_photo_ids in sample_photo_ids["id"]:
print sample_photo_ids['raw']
ANSWER
Answered 2017-Mar-14 at 22:18Borrowing from @roganjosh's comment, an idiomatic way to get the ids:
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