Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.
The diagram in documentation suggest yes, but num_fc_layers and fc_layers are not listed as available parameters as they are for e.g., parallel cnn or stacked cnn.
It does not seem like it is supported based on a few experiments however I am using the RNN encoder inside a sequence combiner, so possibly this is causing problems.
for example, this does not seem to add any fc_layers:
co
To host model on our own server in readme.md you've provided link to https://github.com/infinitered/nsfwjs/tree/master/example/nsfw_demo/public/model
I was trying to host these on my server and was facing issues but working absolutely fine with the default files then figured out that model.json from demo folder is different from the one you're using as default here `https://s3.amazonaws.com
Hi,
I am trying to load a CSV with no header using
df = vaex.open('data/star0000-1.csv',sep=",", header=None, error_bad_lines=False)
but I get
could not convert column 0, error: TypeError('getattr(): attribute name must be string'), will try to convert it to string
Giving up column 0, error: TypeError('getattr(): attribute name must be string')
could not convert column
How to use Watcher / WatcherClient over tcp/ip network?
Watcher seems to ZMQ server, and WatcherClient is ZMQ Client, but there is no API/Interface to config server IP address.
Do I need to implement a class that inherits from WatcherClient?
In the xlearning job web UI, the metrics graph can not be seen for these is no js for it.
We should create some wiki pages on how contributors can locally do different things that are automated in this repository (with corresponding scripts). I often push stuff to check on my changes, bcs I don't know how to do it locally, which is of course not optimal.
Enable the ability to ask if we should preload weights/start from original model etc.
This would be much nicer.
Use some good UI stuff like https://github.com/Mckinsey666/bullet
Environment:
Checklist: