CNN | convnet for EEG analysis | Dataset library

I did some research on the topic and now I'm confused why you want a proxy for HTTPS.

While it is understandable to want a proxy for HTTP, (HTTP is unencrypted) HTTPS is secure.

Could it be possible your proxy is not connecting because you don't need one?

I am not a proxy expert, so I apologize if I'm putting out something completely stupid.

I don't want to leave you completely empty-handed though. If you are looking for complete privacy, I would suggest a VPN. Both Windscribe and RiseUpVPN are free and encrypt all your data on your computer. (The desktop version, not the browser extension.)

While this is not a fully automated process, it is still very effective.

Your input images have a shape of (32,32,3) whil you first conv2D layer sets the inputshape to (32,32,1). Most likely your numbers have only 1 channel since they are grayscale, while you face images have 3 color channels.

change:

If the hyperspectral dataset is given to you as a large image with many channels, I suppose that the classification of each pixel should depend on the pixels around it (otherwise I would not format the data as an image, i.e. without grid structure). Given this assumption, breaking up the input picture into 1x1 parts is not a good idea as you are loosing the grid structure.

I further suppose that the order of the channels is arbitrary, which implies that convolution over the channels is probably not meaningful (which you however did not plan to do anyways).

Instead of reformatting the data the way you did, you may want to create a model that takes an image as input and also outputs an "image" containing the classifications for each pixel. I.e. if you have 10 classes and take a (145, 145, 200) image as input, your model would output a (145, 145, 10) image. In that architecture you would not have any fully-connected layers. Your output layer would also be a convolutional layer.

That however means that you will not be able to keep your current architecture. That is because the tasks for MNIST/CIFAR10 and your hyperspectral dataset are not the same. For MNIST/CIFAR10 you want to classify an image in it's entirety, while for the other dataset you want to assign a class to each pixel (while most likely also using the pixels around each pixel).

Some further ideas:

• If you want to turn the pixel classification task on the hyperspectral dataset into a classification task for an entire image, maybe you can reformulate that task as "classifying a hyperspectral image as the class of it's center (or top-left, or bottom-right, or (21th, 104th), or whatever) pixel". To obtain the data from your single hyperspectral image, for each pixel, I would shift the image such that the target pixel is at the desired location (e.g. the center). All pixels that "fall off" the border could be inserted at the other side of the image.
• If you want to stick with a pixel classification task but need more data, maybe split up the single hyperspectral image you have into many smaller images (e.g. 10x10x200). You may even want to use images of many different sizes. If you model only has convolution and pooling layers and you make sure to maintain the sizes of the image, that should work out.

As @jodag suggests, using DataLoaders is a good idea.

I have a snippet of that I use for some of my CNN in Pytorch

First, call model.compile() if you really miss it.

Second, check x.shape. I made mock data generator, and it works fine.

Usually, neural networks will have a very hard time to predict exact coordinates of landmarks. A better approach is probably a fully convolutional network. This would work as follows:

1. You omit the dense layers at the end and thus end up with an output of (m, n, n_filters) with m and n being the dimensions of your downsampled feature maps (since you use maxpooling at some earlier stage in the network they will be lower resolution than your input image).
2. You set n_filters for the last (output-)layer to the number of different landmarks you want to detect plus one more to indicate no landmark.
3. You remove some of the max pooling such that your final output has a fairly high resolution (so the earlier referenced m and n are bigger). Now your output has shape mxnx(n_landmarks+1) and each of the nxm (n_landmark+1)-dimensional vectors indicate which landmark is present as the position in the image that corresponds to the position in the mxn grid. So the activation for your last output convolutional layer needs to be a softmax to represent probabilities.
4. Now you can train your network to predict the landmarks locally without having to use dense layers.

This is a very simple architecture and for optimal results a more sophisticated architecture might be needed, but I think this should give you a first idea of a better approach than using the dense layers for the prediction.

And for the explanation why your network does predict the same values every time: This is probably, because your network is just not able to learn what you want it to learn because it is not suited to do so. If this is the case, the network will just learn to predict a value, that is fairly good for most of the images (so basically the "average" position of each landmark for all of your images).

Now you will have to excuse my answer, as this can be a bit complicated, but here we go:

So developers do have options to add in CSS properties such as 'text-size-adjust' to control the text in a way where it can be easy to read. Now that attribute may not work with some browsers such as: Firefox, Internet Explorer, and Safari.

Now I did take a look at both sites and it looks like they have 'text-size-adjust' and the webkit version of that too. So I would also come to a conclusion that the mobile responsiveness of text varies on the browser itself too.

It is supposed to run in single gpu (probably the first gpu, GPU:0) for any codes that are outside of mirrored_strategy.run(). Also, as you want to have the gradients returned from replicas, mirrored_strategy.gather() is needed as well.

Besides these, a distributed dataset must be created by using mirrored_strategy.experimental_distribute_dataset. Distributed dataset tries to distribute single batch of data across replicas evenly. An example about these points is included below.

model.fit(), model.predict(),and etc... run in distributed manner automatically just because they've already handled everything mentioned above for you.

Example codes:

Use from tensorflow.keras. instead of from keras.