split_dataset | minimal package for saving and reading large HDF5 | Data Manipulation library

I have written the host code in OpenCL. But I need first to read data from a .csv file. I need to make sure that what I did in reading the file is correct. (I am not sure if this is the way of reding a file in opencl)

1- I put the read file function which is written in c++ before the main .

2 - then, I put function to mix the data. Also before the main

3- In the main function, I call the above two function to read the data and then mix it.

4- then I write the part of host code which include(platform, device, context, queue, buffers....etc)

This is my code:

We successfully trained a TensorFlow model based on five climate features and one binary (0 or 1) label. We want an output for an outside input of five new climate variable values that will be inputted into model.predict(). However, we got an error when we tried to input an array of five values. Thanks in advance!

I'm trying to create and train a Sequential model like so:

I'm writing a model that I want to use to predict 30 days ahead. My problem is that I need to split the dataset into 30 chunks and when trying I get the error "array split does not result in an equal division".

Of course, this literally tells me the problem. So yes, I know the problem. My problem is that I can't figure out how to do an equal split. I've tried several different ways to calculate and split it and all end up with this error. I'm not certain where I'm doing wrong so I presume I haven't understood the problem. I'd like some help with this, wouldn't mind a good explanation so I understand it better too.

This is the split function:

I'm using Tensorflow to train a network to predict the third item in a list of numbers.

When I train, the network appears to train quite well and do well on both the training and test set. However, when I evaluate its performance myself, it seems to be doing quite poorly.

For example, at the end of training, Tensorflow says that the validation loss is 2.1 x 10^(-5). However, when I compute it myself, I get 0.17 x 10^0. What am I doing wrong?

Here's code that can be run on Google Colab:

I am looking into using R's targets but I am struggling to have it accept multiple file outputs.

For example, I want to be able to take a dataset, create a train/test split and write each dataset to a separate file.

An MWE would be

_targets.R

I've been playing with the Tensorflow Object Detection API 2(TF OD 2) in these days, I'm using the git head commit ce3b7227. My aim is to find the most suitable model for my custom dataset, by using the existent DL Architecture present in theTensorFlow 2 Model Zoo. I've generated my TF Records with the following tutorial of Roboflow and I have been training it with my Laptop and Google Colab, in GPU Mode.

I've found this amazing Roboflow's Colab Notebook, while I've tried to reproduce the same steps with my dataset, by using the models/research/object_detection/model_main_tf2.py, unluckly for me, the training script always ends before it started to iterate. It didn't show any Python Error and also it show some warnings as usual. The complete output is in my Colab Notebook

I'm fine-tuning the model with the following commands.

I' trying to train a CNN on video sequences. My input_data has the shape (5874, 1, 10, 128, 128) which represent (n_samples, channels, frames, height , width). The error is either 4 dimensions are given but 5 expected or 6 dimensions were given. What is the correct way to manage Conv3D?

setting Input((1,10,128,128)) results to: ValueError: Error when checking input: expected input_1 to have 5 dimensions, but got array with shape (1, 128, 128, 10). but the error is generated after fitting.

setting Input((1,1,10,128,128)) results to:ValueError: Input 0 of layer conv3d_6 is incompatible with the layer: expected ndim=5, found ndim=6. Full shape received: [None, 1, 1, 128, 128, 10] after executing the model (before fitting)

I already went through all possible documentation and forums and found nothing. Any tips would be helpful.