boson | Grammar Analyzer Generator | Code Quality library

Instead of calling resize and then copy-assigning your elements, you can reserve and then copy-initialize them:

You will experience generation loss when using lossy encoders. However, if your encoding is done well your viewers may not even notice.

x264 does not re-use information from the compressed bitstream of the source (such as motion vectors and frame types). Your compression artifacts present in the source are part of the raster image and are not re-utilized for compression. It's just noise.

Don't overthink it. ffprobe is not going to provide any useful metric to optimally choose a quality.

1. Choose a -crf value. Generally choose the highest value (lowest quality) that provides an acceptable quality. Choose the value by watching the results.
2. Choose the slowest -preset you have patience for.
3. Use this -crf and -preset for the rest of your videos.

See FFmpeg Wiki: H.264 for more info on these options.

...or keep them as WTV and don't bother with re-encoding.

The first time your component renders, this.state.housingData is an empty object so this.state.housingData[0] is indeed undefined.

If I change [...] To [...] The object get printed like this [...]

You are seeing the output of the second render of the component. That "works" because the code change doesn't cause an error anymore in the first render of the component.

Here is a simplified example of your problem:

Distribution strategies were introduced as part of TF2 to help distribute training across multiple GPUs, multiple machines or TPUs with minimal code changes. I'd recommend this guide to distributed training for starters.

Specifically creating a model under the TPUStrategy will place the model in a replicated (same weights on each of the cores) manner on the TPU and will keep the replica weights in sync by adding appropriate collective communications (all reducing the gradients). For more information check the API doc on TPUStrategy as well as this intro to TPUs in TF2 colab notebook.

As per the official documentation of Land Marks Classifier,

Inputs are expected to be 3-channel RGB color images of size 321 x 321, scaled to [0, 1].

But from Your Dataset, file format is tfrecord.

When we use Transfer Learning and when we want to reuse the Models either from TF Hub or from tf.keras.applications, our data should be in the predefined-format as mentioned in the documentation.

So, please ensure that your Dataset comprises of Images and resize Image Array to (321,321,3) for the TF Hub Module to work.

I think you need to replace site with page in your second code snippet, see the handling for variables.

• site: global website (e.g. _config.yml)
• page: current page

Additional I dropped the array index [0].

The problem is with your registration of the routes in app/views.py when you register them with current_app as app. I'm not sure how you would apply the application factory pattern without using blueprints as the pattern description in the documentation implies they are mandatory for the pattern:

If you are already using packages and blueprints for your application [...]

So I adjusted your code to use a blueprint instead:

app/main/__init__.py:

here is one way to do the job. [grin] it presumes that you only want to 1st connection type found. if you want all of them [for instance, JBloggs has all 3 types listed], you will need to concatenate them.

what it does ...

• fakes reading in the CSV files
  when ready to use real data, comment out or remove the entire #region/#endregion section and use Get-Content.
• iterates thru the main collection
• uses a switch to test for membership in each connection type list
  this breaks out of the switch when it finds a match since it presumes you only want the 1st match. if you want all of them, then you will need to accumulate them instead of breaking out of the switch block.
• sets the $ConnectionType as appropriate
• builds a PSCO with all the wanted props
  this likely could be shortened by using Select-Object, a wildcard property selector, and a calculated property.
• sends it out to the $Results collection
• shows it on screen
• saves it to a CSV file

the code ...

By calling new TTree(&chain) you're trying to call a constructor for a TTree and you provide it with a TChain * (the type of chain is TChain, so &chain is a pointer to a TChain).

The constructors for TTree https://root.cern/doc/v618/classTTree.html take either no argument or two const char* (usually string literals or .Data() of a TString or .c_str() of a std::string or the output of ROOT's Form …).

It looks like you try to call the deleted constructor TTree(const TTree&tt)=delete, which doesn't work for several reasons:

• you're providing a pointer to a TTree instead of a TTree by reference (not even sure from the top of my head if casting from TChain to TTree would work if the & wasn't there.
• the constructor is deleted, so it doesn't actually exist and cannot be called.

However, from the looks of it, you want to use your TChain as a TTree*, which doesn't require any code from your side to begin with because a TChain already is a TTree by inheritance. So, you can use chain. instead of tree->, or (if having a pointer variable is really more desirable for reasons that I don't see in the example) create a TTree* (i.e. create only the pointer to a TTree, instead of creating the actual TTree on the heap and get a pointer to it from the new operator) by doing something like