epoche | A modern , small wiki engine without complexity | Wiki library

Lets start with a folder containing 1000 images.

Now if we use no generator and batch_size = 10 and steps_per_epoch = 100 we will have used every picture as 10 * 100 = 1000. So increasing steps_per_epoche will (rightfully) result in the error:

tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least steps_per_epoch * epochs batches (in this case, 10000 batches)

On the other hand using a generator will result in endless batches of images:

I wanted to classify images which consist five classes. I wanted to use CNN. But when I try with several models, the training accuracy will not increase than 20%. Please some one help me to overcome this. Mostly model will trained within 3 epoches and when epoches increase there is no improvement in accuracy. Can anyone suggest me a solution or model or can specify what could be the problem?

Below is one of the model i have used

I downloaded the mnist dataset (jpg) and created a model file with .hdf (.h5).
Images were recognized using the .h5 model, but the recognition rate is low..
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The accuracy is low when actual compile..
Did I do something wrong?.
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The image used the 28x28 image of the grayscale..

I have a Spark DataFrame with a timestamp column in milliseconds since the epoche. The column is a string. I now want to transform the column to a readable human time but keep the milliseconds. For example:

1614088453671 -> 23-2-2021 13:54:13.671

Every example i found transforms the timestamp to a normal human readable time without milliseconds.

What i have:

I am new to Tensorflow and wrote the following distributed training code. The code works fine.

im trying to build image classification model that will predict if you are wearing a mask.This is a first time i make my own model and when im training it the accuracy jump arround 50% and if i predict it always says "no mask" i tryied changing number of epoches, batch size, number of training data changing model code and nothing works. This is my code:

I face that error RuntimeError: multi-target not supported at /pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:15____

My input is binary vector of 340, target is binary vector of 8, For '" loss = criterion(outputs, stat_batch), I got outputs.shape= [64,8] and stat_batch.shape=[64,8]

Here is the model

I am using neural network to classify sequence of length 340 to 8 classes, I am using cross entropy as loss. I am getting very high number for the loss . I am wondering if I did mistake in calculating the loss for each epoch. Or should i use other loss function .

I'm trying to use VGG16 from keras to train a model for image detection.

Based on these articles (https://www.pyimagesearch.com/2019/06/03/fine-tuning-with-keras-and-deep-learning/ and https://learnopencv.com/keras-tutorial-fine-tuning-using-pre-trained-models/), I've put some addition Dense layer to the VGG 16 model. However, the training accuracy with 20 epoche is around 35% to 41% which doesn't match the result of these articles (above 90%).

Due to this, I would like to know, did I do something wrong with my code below.

Basic setting