kandi X-RAY | training Summary
kandi X-RAY | training Summary
This repository contains various training materials that accompany the sections and chapters in O'Reilly's Cloud Native Java: Building Resilient Systems with Spring Boot, Spring Cloud, and Cloud Foundry. To find more information on the book, please visit cloudnativejava.io.
Top functions reviewed by kandi - BETA
- Applies authorization to your HttpSecurityFilter
- The REST template bean .
- Gets profile .
- Any resource limiter that should be used for routing requests .
- Configure the client details .
- Runs all reservations .
- Registers a new user .
- Returns a String representation of this User object .
- Returns the set of reservations .
- Lookup a user by username .
training Key Features
training Examples and Code Snippets
Trending Discussions on training
I have a large dataset (~5 Mio rows) with results from a Machine Learning training. Now I want to check to see if the results hit the "target range" or not. Lets say this range contains all values between
+0.25. If it's inside this range, it's a
Hit, if it's below
Low and on the other side
I now would create this three columns Hit, Low, High and calculate for each row which condition applies and put a
1 into this col, the other two would become
0. After that I would group the values and sum them up. But I suspect there must be a better and faster way, such as calculate it directly while grouping. I'm happy for any idea.
ANSWERAnswered 2022-Feb-10 at 16:13
I am getting an error when trying to save a model with data augmentation layers with Tensorflow version 2.7.0.
Here is the code of data augmentation:...
ANSWERAnswered 2022-Feb-04 at 17:25
This seems to be a bug in Tensorflow 2.7 when using
model.save combined with the parameter
save_format="tf", which is set by default. The layers
RandomContrast are causing the problems, since they are not serializable. Interestingly, the
Rescaling layer can be saved without any problems. A workaround would be to simply save your model with the older Keras H5 format
I am trying code from this page. I ran up to the part
LR (tf-idf) and got the similar results
After that I decided to try
GridSearchCV. My questions below:
ANSWERAnswered 2021-Dec-09 at 23:12
You end up with the error with precision because some of your penalization is too strong for this model, if you check the results, you get 0 for f1 score when C = 0.001 and C = 0.01
I wrote a unit-test in order to safe a model after noticing that I am not able to do so (anymore) during training....
ANSWERAnswered 2021-Sep-06 at 13:25
Your issue is not related to
The error code tells you this element is referring to a non trackable element. It seems the non-trackable object is not directly assigned to an attribute of this conv2d/kernel:0.
To solve your issue, we need to localize
Tensor("77040:0", shape=(), dtype=resource) from this error code:
Okay, so Google is telling us "Background location access not declared" and not letting us publish our app. We have no use for background location, so we're trying to elimiate it completely.
Of course my manifest doesn't have it:...
ANSWERAnswered 2021-Mar-12 at 11:42
I had this issue a few weeks ago, what a pain! In my case I had one dependency that was requiring background location without me noticing. Secondly, I had a wrong permission declaration on Google Play so my builds kept being rejected.1. Find the evil dependency
To do this I used the Merged Manifest inspector in Android Studio. This shows you what your manifest looks like after all project dependencies have been taken into account. Find
ACCESS_BACKGROUND_LOCATION and double click on it, this will bring you to the actual manifest where it's requested. Scroll to the top of this file and the
package=some.package.name should help you identify what it is. In my case the permission was requested by an old dependency I didn't use anymore so I just uninstalled it.
Note: if you're often working on different branches, make sure you have the correct dependencies installed and make a clean build before checking the merged manifest:
I'm learning DRL with the book Deep Reinforcement Learning in Action. In chapter 3, they present the simple game Gridworld (instructions here, in the rules section) with the corresponding code in PyTorch.
I've experimented with the code and it takes less than 3 minutes to train the network with 89% of wins (won 89 of 100 games after training).
As an exercise, I have migrated the code to tensorflow. All the code is here.
The problem is that with my tensorflow port it takes near 2 hours to train the network with a win rate of 84%. Both versions are using the only CPU to train (I don't have GPU)
Training loss figures seem correct and also the rate of a win (we have to take into consideration that the game is random and can have impossible states). The problem is the performance of the overall process.
I'm doing something terribly wrong, but what?
The main differences are in the training loop, in torch is this:...
ANSWERAnswered 2021-May-13 at 12:42
TensorFlow has 2 execution modes: eager execution, and graph mode.
TensorFlow default behavior, since version 2, is to default to eager execution. Eager execution is great as it enables you to write code close to how you would write standard python. It's easier to write, and it's easier to debug. Unfortunately, it's really not as fast as graph mode.
So the idea is, once the function is prototyped in eager mode, to make TensorFlow execute it in graph mode. For that you can use
tf.function compiles a callable into a TensorFlow graph. Once the function is compiled into a graph, the performance gain is usually quite important. The recommended approach when developing in
TensorFlow is the following:
- Debug in eager mode, then decorate with
- Don't rely on Python side effects like object mutation or list appends.
tf.functionworks best with TensorFlow ops; NumPy and Python calls are converted to constants.
I would add: think about the critical parts of your program, and which ones should be converted first into graph mode. It's usually the parts where you call a model to get a result. It's where you will see the best improvements.
You can find more information in the following guides:
tf.functionto your code
So, there are at least two things you can change in your code to make it run quite faster:
- The first one is to not use
model.predicton a small amount of data. The function is made to work on a huge dataset or on a generator. (See this comment on Github). Instead, you should call the model directly, and for performance enhancement, you can wrap the call to the model in a
Model.predict is a top-level API designed for batch-predicting outside of any loops, with the fully-features of the Keras APIs.
- The second one is to make your training step a separate function, and to decorate that function with
So, I would declare the following things before your training loop:
I have the following code which works normally but got a...
ANSWERAnswered 2021-May-01 at 13:10
Remove roc_auc if it is multi class. They do not play well together. Use default scoring or choose something else.
I'm following this guide on saving and loading checkpoints. However, something is not right. My model would train and the parameters would correctly update during the training phase. However, there seem to be a problem when I load the checkpoints. That is, the parameters are not being updated anymore.
ANSWERAnswered 2021-Apr-22 at 12:51
The way you are loading your data is not the recommended way to load your parameters because you're overwriting the graph connections (or something along those lines...). You even save the model state_dict, so why not use it!
I changed the load function to:
I want to create a Tensorflow neural network model using the Functional API, but I'm not sure how to separate the input into two. I wanted to do something like: given an input, its first half goes to the first part of the neural network, its second half goes to the second part, and each input is passed through the layers until they concatenate, go through another layer and finally reach the output. I thought of something like the snippet of code below, along with a quick sketch....
ANSWERAnswered 2021-Apr-20 at 20:07
There is some issue with your code. I will try to answer the main issue here and discard some side questions such as whether you should use
BatchNormalization layers in your model or not because that's totally out of the scope of your main question and also irrelevant.
If you try to build your model, using
m = define_model(), I'm pretty sure you will encounter the following error:
Please add a minimum comment on your thoughts so that I can improve my query. Thanks. :)
I'm trying to train a
tf.keras model with Gradient Accumulation (GA). But I don't want to use it in the custom training loop (like) but customizing the
.fit() method by overriding the
train_step.Is it possible? How to accomplish this? The reason is if we want to get the benefit of
keras built-in functionality like
callbacks, we don't want to use the custom training loop but at the same time if we want to override
train_step for some reason (like GA or else) we can customize the
fit method and still get the leverage of using those built-in functions.
And also, I know the pros of using GA but what are the major cons of using it? Why it's not come as a default but an optional feature with the framework?...
ANSWERAnswered 2021-Mar-10 at 09:47
Thanks to @Mr.For Example for his convenient answer.
Usually, I also observed that using Gradient Accumulation, won't speed up training since we are doing
forward pass and compute all the gradients. But it will speed up the convergence of our model. And I found that using the
mixed_precision technique here can be really helpful here. Details here.
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