Convolutional-Neural-Network | Convolutional Neural Network exercise in the Stanford UFLDL

I am new to PyTorch and Machine Learning so I try to follow the tutorial from here: https://medium.com/@nutanbhogendrasharma/pytorch-convolutional-neural-network-with-mnist-dataset-4e8a4265e118

By copying the code step by step I got the following error for no reason. I tried the program on another computer and it gives syntax error. However, my IDE didn't warn my anything about syntax. I am really confused how I can fix the issue. Any help is appreciated.

I am working on this dataset (the image part), normalized to 1103 images of size 1396 x 1676. I am trying to feed it to a CNN (taken from here) of this shape:

I'm trying to view every channel for all of the intermediate activations for my model for an image, following this tutorial.

I wrote this:

How to use Keras Applications for a multi-output model?

I am trying to use VGG16 for a multi-output model but get an value error ValueError: Input 0 of layer block1_conv1 is incompatible with the layer: expected axis -1 of input shape to have value 3 but received input with shape (None, 64, 64, 1). I am a bit lost and can't seem to find anything on multioutputs for VGG16 or any other Keras pre-trained application.

I am using this as a guide: Transfer Learning in Keras with Computer Vision

I have a code (from here) to classify the MINST digits. The code is working fine. Here they used CrossEntropyLoss and Adam optimizer.

The model code is given below

I am trying to build an image classification neural network using Keras to identify if a picture of a square on a chessboard contains either a black piece or a white piece. I created 256 pictures with size 45 x 45 of all chess pieces of a single chess set for both white and black by flipping them and rotating them. Since the number of training samples is relatively low and I am a newbie in Keras, I am having difficulties creating a model.

The structure of the images folders looks as follows:
-Data
---Training Data
--------black
--------white
---Validation Data
--------black
--------white

The zip file is linked here (Only 1.78 MB)

The code I have tried is based off this and can be seen here:

I'm trying to train a CoreML sound classifier on device, on iOS, and I have been struggling to find learning resources on the topic. The sound classifier is used to determine whether a snippet of music is similar to a collection of other songs. Hence the output of the classifier is just a label of either "match" / "no match".

It is so simple to train with the CreateML app workflow. I am simply trying to get the same kind of training on device in iOS, but as far as I know (please correct me if I'm wrong) iOS doesn't support createML.

I have been trying to adapt code from various source to get this to work in an iOS playground. I can only find resources on training image classifiers, these two have been the most helpful (1, 2).

Please see the code that I have come up with so far below.

I am new to CNNs, so I am guessing I am making an elementary error here. I am trying to do age estimation and gender classification on the UTKFace dataset. I have made a dataframe which looks like this:

I've split the data using Sklearn train_test_split

I have a homework about Neural Networks. I need to develop a flower recognition application.

First of all, i tried to learn how to classify cat-dog photos from that link and apply myself. After that, i started to adapt that model to flower recognition with 5 labels.

Although model runs properly, training isn't successful and accuracy is %16.

Here accuracy plot is.