Disclaimer: I’m fairly new to deep learning

I’m trying to implement a model that I read about in a paper in Keras. I understand the Conv2D and MaxPool2D layers up until that point but I’m slightly confused about the fully connected layers. The following is my code for the last 4 layers:

model.add(MaxPool2D(pool_size = (3, 3), strides = (2, 2)))

model.add(Dropout(0.1))

model.add(Dense(256)) (First Dense Layer)

model.add(LeakyReLU(alpha = 0.3))

model.add(Dropout(0.1))

model.add(Dense(256)) (Second Dense Layer)

model.add(LeakyReLU(alpha = 0.3))

model.add(Dropout(0.1))

model.add(Dense(5, activation = ‘softmax’)) (Last Dense Layer)

A) Should the dropout come before or after the Dense/Fully Connected Layers (I’m pretty sure they’re the same thing – correct me if I’m wrong)? The paper says they used dropout in the fully connected layers but it doesn’t mention whether it came before or after.

B) Is my code for the Dense layers correct? The MaxPool layer at the top of my code above takes a 16x16x128 feature map and reduces it to 7x7x128, resulting in an input size of 6272 to the first Dense/FC layer. The first Dense/FC layer should have an output of 256 FC units and the second Dense/FC layer should take this input and output 256 FC units. The last dense layer should take the input of 256 FC units from the second dense layer and output 5 probabilities using a softmax function.

If anything is unclear, please tell me and I will try to clarify. Responses are much appreciated.

i have a collection of about 30,000 images that i have a script tweeting out every ten mins. the images are generally ‘aesthetically pleasing’ but theres a bunch of random images in there thats making me have to moderate the tweets, so this process is less autonomous than id like.

is there a way using TS or otherwise to feed a model say ~100 images that i handpick and it be able to tell me how ‘alike’ the image is to the other images? it seems hard bc im not classifying nude images (for example) so training the model would be difficult, right? im classifying a bunch of different things – fashion, landscapes, plants, etc. is determining if an image is ‘aesthetic’ too abstract for ML? If you have any resources please let me know! thanks!

I would love to make something like the animations and graphs from facebook reserach

https://twitter.com/facebookai/status/1185266949032275968

https://twitter.com/facebookai/status/1164938451055042561

This is a vague and philosophical question, looking for your ideas.

Should the decoder of an autoencoder be injective?

On one hand, if it is a deterministic AE, then it must be so, but I am not asking that.

It seems strange that there should be a mapping between hugely varying dimension spaces and still be injective. If the AE can then also encode/decode arbitrary data, it must implement some complicated scramble of the input space down to latent.

In PCA some dimensions are dropped in going to the “latent” space, but I guess the inverse direction can be still injective.

Hi guys, could you point me in the right directions, or at least tell me something like this isn’t (easily) doable.

I have data containing several months worth of langitude/longitude coordinates of signals, all within a relatively small area. Data is such that time component has granularity of 10 minutes, meaning that if a signal is in that area and moving, it’s location will be reported every 10 minutes so I can kinda see some moving patterns with simulation, where a particular signals enters the area and where it exits it.

There aren’t many signals in general, however it can sometime occur that 2 or more signals shows up in different parts of the area, with their own moving patterns.

I would like to create several clusters without having any prior labels, such that I could group together similar signals, for example every Monday morning some signals enters at approximately northwest, makes a clockwise circle, and exits at southeast.

Hey ML’ers.

For anyone unaware, here is the link to the competition: https://www.kaggle.com/c/data-science-bowl-2019

The competition is coming to an end in a couple of weeks and I’ve come as far as I could on my own. I’ve hit a plateau and I’m stuck at around 150th place.

If anyone would like to share ideas and work on the final predictions, I’m open to collaboration.

You can consider this thread as an open discussion on this topic, I’m happy to answer any questions if someone will start on their own 🙂

So I am making a project based on this paper:

https://arxiv.org/ftp/arxiv/papers/1901/1901.07761.pdf

In this paper, a U-Net is used to generate optimized mechanical structures. I am trying to recreate the model and use it on my own generated data.

Now I have two questions:

1. In 7.1 a pixelwise accuracy is mentioned. Right now I am using the default Keras metric “accuracy”, which isn’t reaching even close to the accuracy in the paper. (it starts at 0.3ish and goes to like 0.45). What I always do is to manually compare the generated structures to the ground truth in the training set. There are often models which have better accuracy, but the structures make less sense. What accuracy metric did they use in the paper?
2. In the paper under 4.2.1, the KL Divergence is mentioned. My problem was, that the KL Divergence turned negative after an epoch or two (an Indicator that I don’t work with probability distributions?), so I switched to binary cross-entropy, which provides good results, but it is still bothering me , that I cant use the proposed loss method. Another point is the L2 Regularization: I get the best results using 1e-7 or lower as the l2 value, which is really low compared to the normally used values. What does that indicate?

Another point I wanted to mention: the dimensions of my data is a little bit different from the ones used in the paper: I use 65×49 as my Input Dimension.

I would really appreciate if someone can help me in fixing the problems.

In this piece, we’ll look at some of the AI and machine learning conferences that took place in 2019 and highlight some of the best speakers and presentations. These events form a fundamental part of the machine learning and AI communities because they bring people together to learn from each other as well as forge meaningful collaborations.

https://heartbeat.fritz.ai/2019s-top-ai-machine-learning-conferences-157826706308

I’m a cs student currently learning machine learning (focussing mostly on deep learning scaffolding and computational techniques to make models efficient) I have prior experience in server-side coding, with an academic project which focussed on large scale API deployments and CI/CD integration to an existing analytics API.

I am planning on building an easy to use service which makes it easy to expose a machine learning model to the web via an API endpoint. While I know this has been done before by bigger players, I found most of the solutions to have cumbersome setups and slow speeds.

I would really appreciate it if you guys could provide me some insight as to what improvements you would like to see in such a service as compared to your current workflow.