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Vince Camuto Womens Ejella Slide Sandal Beaming Blush r33njrEQVh
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Vince Camuto Womens Ejella Slide Sandal Beaming Blush r33njrEQVh Vince Camuto Womens Ejella Slide Sandal Beaming Blush r33njrEQVh Vince Camuto Womens Ejella Slide Sandal Beaming Blush r33njrEQVh Vince Camuto Womens Ejella Slide Sandal Beaming Blush r33njrEQVh Vince Camuto Womens Ejella Slide Sandal Beaming Blush r33njrEQVh

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The tricks we used, boosting grads for less likely events, and diversifying training examples have improved the quality of results. That being said though, the final results still contain some bad results occasionally. As a final filter, I threw out results that exceeded the size of the writing area and had them start over until they get it all within a specified box area.

I included in sketch-rnn github repo a smaller pre-trained net so if you like, you can try to run sketch-rnn on your machine by just running python .

The smaller pre-trained net generates 24 gaussian mixture distributions for each time step, and uses 2 layers of 256 LSTM nodes, with dropout keep probability of 80% employed at the outputs of each layer.

I scaled the data down in size by a factor of 15. This is an interesting problem, as the typical training examples have sizes around 80 to 160 units on each axis. I found a good rule of thumb is to scale the data down so that the average dimension of the data is in the order of 10×10, and typically for Chinese characters, the offsets of each successful step is in the order of 1×1 size.

Using minibatches of 50-100 examples seemed to work well. I tried to have a relatively larger initial learning rate, and have that learning rate decrease proportionally by 1% after each subsequent epoch. Sometimes having a learning rate too large will crash the training, and the part of the training that crashes is to do with the estimation of end-of-character likelihood. It is a bit tricky when we need to estimate the probability of unlikely events using the gradient boosting method above, and that may lead to numerical instability.

I’m quite happy with the results. sketch-rnn was able to generate a variety of Kanji that does not exist, but resembles somewhat the way Kanji are supposed to be writen. Many radicals and basic parts of Kanji are placed and configured in locations that makes sense in terms of forming the structure of a Kanji. It seems to resemble a child struggling to pass a Chinese dictation test and trying to wing it by desperately making up answers.

Other notable examples below I couldn’t really describe, could you?

Some examples reminds me of some Cantonese Puma Womens R698 Blocks and St hG1Co
converted to new Chinese characters (like 𨳊 or 撚).

I have also looked at this online handwriting database by LOSLANDIFEN Womens Double Criss Cross Strap Dance Shoes Weave Style Buckle Salsa Tango Latin Sandals Reda 4xADgg
. It will be really easy to apply this algorithm on that data and possibly train the recurrent net to generate fake cursive Chinese handwriting. Personally I don’t find that as interesting as this stroke-based dataset, because I wanted to see if the algorithm can generate distinct structures inside Chinese characters, rather than squigly handwritten characters that have already been done with the previous handwriting example.

As an additional rant, as a designer, I’m not a big fan of post 1956 Simplied Chinese as I feel that the PRC has done too much to simplify Chinese in an Orwellian New Speak sense of the language. Compare original beautiful traditional Chinese characters, their accepted simplied form pre-1900 for handwriting across Asia, and post-1956 Simplied Chinese forms in the above example.

Even in a graduate-level class like this one, we made lots of simplifying assumptions. This was the header text that appeared at the top of our problem sets:

For this assignment (and generally in this class) you may disregard measure-theoretic niceties about conditioning on measure-zero sets. Of course, you can feel free to write up the problem rigorously if you want, and I am happy to field questions or chat about how we could make the treatment fully rigorous (Piazza is a great forum for that kind of question as long as you can pose it without spoilers!)

Well, I shouldn’t say I was disappointed, since I was more relieved if anything. If I had to formalize everything with measure theory, I think I would get bogged down with trying to understand measure this, measure that, the integral is with respect to X measure, etc.

Even with the “measure-theoretically naive” simplification, I had to spend lots of time working on the problem sets. In general, I could usually get about 50-80 percent of a problem set done by myself, but for the remainder of it, I needed to consult with other students, the GSI, or the professor. Since problem sets were usually released Thursday afternoons and due the following Thursday, I made sure to set aside either Saturday or Sunday as a day where I worked solely on the problem set, which allowed me to get huge chunks of it done and LaTeX-ed up right away.

The bad news is that it wasn’t easy to find student collaborators. I had collaborators for some (but not all of) the problem sets, and we stopped meeting later in the semester. Man, I wish I were more popular. I also attended a few office hours, and it could sometimes be hard to understand the professor or the GSI if there were a lot of other students there. The GSI office hours were a major problem, since another class had office hours in the same room . Think of overlapping voices from two different subjects. Yeah, it’s not the best setting for the acoustics and I could not understand or follow what people were discussing apart from scraps of information I could get from the chalkboard. The good news is that the GSI, apart from obviously knowing the material very well, was generous with partial credit and created solutions to all the problem sets, which were really helpful to me when I prepared for the three-hour final exam.

The day of the final was pretty hectic for me. I had to gave a talk at Grazie Womens Mangoes Wedge Pump Turquoise cvsXkm
in San Francisco that morning, then almost immediately board BART to get back to Berkeley by 1:00PM to take the exam. (The exam was originally scheduled at 8:00AM 1 (!!) but Professor Fithian kindly allowed me to take it at 1:00PM.) I did not have any time to change clothes, so for the first time in my life, I took an exam wearing business attire .

Business attire or not, the exam was tough. Professor Fithian made it four broad questions, each with multiple parts in it. I tried doing all four, and probably did half of all four correct, roughly. We were allowed a one-sided, one-page cheat sheet, and I crammed mine in with A LOT of information, but I almost never used it during the exam. This is typical, by the way. Most of the time, simply creating the cheat sheet actually serves as my studying. As I was also preoccupied with research, I only spent a day and a half studying for the final.

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