Hello! Not having the computational resources to train a seq2seq transformer-based model, I’m trying to do that by fine-tuning BERT as an encoder and GPT2 as a decoder. Has anyone tried something similar? How can I condition GPT2 on the encoder’s output?

In the AlphaGo Zero paper figure 6b shows the performance of a raw network which directly takes the action with the highest q-value(?) versus an MCTS approach which gets 5 seconds of thinking time. The MCTS approach has a large performance gain over the raw network approach.

Now I have trained a network with a policy and value head that uses the first approach and does not have a tree structure with accompanying data (such as times visited per node). I’m wondering if I can skip training using MCTS but just use the network to build a tree in the simulation phase and if there’s any precedent for this technique.

The problem is a deterministic RL problem with only one goal state and no other rewards. The same state can be reached twice and this often happens when I use the raw network approach. The agent then gets stuck in a loop. In a previous post, someone suggested taking the next best option once a certain state is reached more than once. This worked like a charm. But for real-world application, I would like to keep the number of actions taken as low as possible. This is why I think MCTS mightbe an improvement.

To be precise, I am talking about image classification, not general computer vision, and by traditional approaches I mean approaches based on features like Fisher, BoW, … even if they use multi-layered classifiers.

Sometimes when I’m doing machine learning experiments, I often end up collecting my own data- image, audio, video, etc. Would other people find an app that lets you collect and label image/audio/video data right from your phone? Auto-export CSVs, labeled folders, etc.

Hi folks,

I was recently dealing with some performance issues related to the keras image preprocessing. After several experiments, I thought it would might be helpful to share my insights. There are several possible fixes:

• update all packages, especially keras-preprocessing.
• Deactivate your virus scanner (whitelist your data folder) and check if you have an internal SSD.
• Try to tweak the configuration on fit_generator (workers and queue_size). If you are using linux try out multiprocessing and a thread-safe generator.
• Convert your dataset to TFrecords and use it with keras or directly move to tensorflow. If you already using tensorflow 2.0, you can directly fit keras models on TFRecord datasets.

Furthermore the tensorflow implementaion was always (slightly) faster.

Here is a more detailed explaination.

Cheers