Data_Processor | 数据algorithm & 分析算法 - 六元 : 爱念 , 劳动 , 创新 , 学习 , 安全 , 帮助 | Computer Vision library

You are setting up the classifier wrongly, this is giving you the error and you can easily try this before going to the pipeline:

But it seems that I have to choose between retrieving the output (result) of the function called by the executor and being able to run several subprocesses in parallel.

Luckily this is not the case, you can also use asyncio.gather() to wait for multiple items at once. But you obtain data items one by one from the queue, so you don't have a batch of items to process. The simplest solution is to just start multiple consumers. Replace

It's likely you're including some Windows libraries, where CreateFile is a #define which points to a different function, namely CreateFileW. I think the easiest solution is to just rename your function to just Create or something, or MakeFile or something else. I think you'll find by renaming it it will probably solve your issue.

A very similar issue exists where Windows has a #define called min which screws up uses of std::min

In a general case if you want to create your own BiLSTM network, you need to create two regular LSTMs, and feed one with the regular input sequence, and the other with inverted input sequence. After you finish feeding both sequences, you just take the last states from both nets and somehow tie them together (sum or concatenate).

As I understand, you are using built-in BiLSTM as in this example (setting bidirectional=True in nn.LSTM constructor). Then you get the concatenated output after feeding the batch, as PyTorch handles all the hassle for you.

If it is the case, and you want to sum the hidden states, then you have to

In custom_implementation.jl , I tried replacing function load_data with function Provider.load_data because I was using the statement import Provider instead of using Provider as I wanted to override the definition. With this change, it works fine and the overriding function gets called!

The updated custom_implementation.jl is now:

This error is shown because you are sending a NaN value to your database. Please check the values like this.

If you run the maps one after the other then everything should pipeline nicely.

There is some tension between two desired objectives:

1. Tasks should pipeline, as you like
2. Tasks that are submitted first should have higher priority

To balance between these two objectives Dask assigns policies based on the delay between calls. If two map calls happen right after each other then Dask assumes that they're part of the same computation, however if they are separated by a significant amount of time then Dask assumes that they are different computations, and so prioritizes the earlier tasks. You can control this with the fifo_timeout keyword

Your X placeholder expects an input with shape=(None, n_inputs) and X_batch has the shape of n_inputs so the shapes don't match.

You can solve the problem by putting n_inputs into a list making its shape (1, n_inputs):