CNN | pytorch implementation of several CNNs | Computer Vision library

I have a NET like (exemple from here)

I have a dataset of 100000 binary 3D arrays of shape (6, 4, 4) so the shape of my input is (10000, 6, 4, 4). I'm trying to set up a 3D Convolutional Neural Network (CNN) using Keras; however, there seems to be a problem with the input_shape that I enter. My first layer is:

I have the below JSON in a Google Sheet cell that I would like to split into multiple rows. Can anyone suggest a way to do this via a formula?

I found this nice code Pytorch mobilenet which I cant get running on a CPU. https://github.com/rdroste/unisal

I am new to Pytorch so I am not shure what to do.

In line 174 of the module train.py the device is set:

I have created and trained one very simple network in pytorch as shown below:

The data is basically in CSV format, which is a fasta/genome sequence, basically the whole sequence is a string. To pass this data into a CNN model I convert the data into numeric. The genome/fasta sequence, which I want to change into tensor acceptable format so I convert this string into float e.g., "AACTG,...,AAC.." to [[0.25,0.25,0.50,1.00,0.75],....,[0.25,0.25,0.50.....]]. But the conversion data shows like this (see #data show 2). But, when I run tf.convert_to_tensor(train_data) it gives me an error of Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray). But in order to pass the data into CNN model, it has to be a tensor, but I don't know why it gives an error! What will be the solution to it?

I know it's basic and too easy for you people, but I'm a beginner who needs your help. I'm struggling to make binary classifier with CNN. My final goal is to check accuracy over 0.99

I import both MNIST and FASHION_MNIST to identify if it's number or clothing. So there are 2 category. I want to categorize 0-60000 as 0, and 60001-120000 as 1. I will use binary_crossentropy.

but I dont know how to start from the beginning. How can I use vstack hstack at first to combine MNIST and FASHION_MNIST?

This is how I tried so far

i have created a CNN with Keras and Tensorflow as my backend and my data consists of 2D images which represent EEG (Electroencephalography)-data from the preprocessed DEAP-Dataset.

I have considered to use SHAP as the model explainer but since there are several shap-explainers (kernel, deep, linear, gradient...) I am not sure which one fits my needs the most or if even SHAP could be helpful in my case. Since my images (Dimensions:40x100x1, the third dimension comes from np.expand_dims, since keras needs 3D images) have no colors, is SHAP even a considerable approach?

Snippet of one item in my dataset

The bigquery code below provided by Mikhail Berlyant (thank you again!) works well on left-to-right languages such as Russian. However, it fails on right-to-left languages such as Arabic and Hebrew whenever there is a double quotation mark <" "> inside the text to be translated. The expected result should show all input text-to-be-translated without unicode letters inside the translation. Thanks!