When I attempt to train a CreateML model, I get the following screen after inputting my training data:
Create ML error message
I am then unable to add my test data or train the model. Any ideas on what is going on here?
[EDIT] As mentioned in my comment below, this issue went away when I removed some of my training data. Any newcomers who are running into this issue are encouraged to try some of the solutions below and comment on whether it worked for them. I'm happy to accept an answer if it seems like it's working for people.
This happens when the first picture in the dataset has no label. If you place a labeled photo as the first in the dataset and in the coreML json, you shouldn't get that issue.
Correct:
[{"annotations":[{"label":"Enemy","coordinates":{"y":156,"x":302,"width":26,"height":55}}],"imagefilename":"Enemy1.png"},{"annotations":[{"label":"Enemy","coordinates":{"y":213,"x":300,"width":69,"height":171}}],"imagefilename":"Enemy7.png"},{"annotations":
Incorrect:
[{"annotations":[],"imagefilename":"Enemy_v40.png"},{"annotations":[],"imagefilename":"Enemy_v41.png"},{"annotations":[],"imagefilename":"Enemy_v42.png"},{"annotations":
At the minimum you should check for these 2 situations, which triggered the same generic error for me (data analysis stopped), in the context of an Object Detection Model:
One or more of the image names referenced in annotations.json is incorrect (e.g. typo in image name)
The first entry in annotations.json has an empty annotations array (i.e. an image that does not contain any of the objects to be detected)
If you are using any random Split or something similar, make sure, its parsing the data correctly. you can test this easily by debugging.
I suggest you check to see if your training data is consistent and all entries have all needed values. The error is likely in the section of data you removed.
That would cause the error Nate commented he is seeing when he gets that pop up.
Getting the log would be the next step in any other evaluation.
Related
I am trying to export a table or a regression analysis summary. using the export_summs function keeps returning the error message: Error in [<-(*tmp*, , names(coef(fm)), value = coef(fm)) :
subscript out of bounds
Note that this issue did not happen with the exact same data before. I just ran it again with more efficient and statistically better model. everything else is the same. I have tried changing the coefficient names in several ways and omitting the missing values. summary() is not an issue. only when I try running export_summs(). another issue is that I have used na.action=na.omit in the regression formula and na.omit("regression formula") and NA still shows up as a value in my regression table.
How do I fix those two issues?
Appreciate the help.
I'm using TF-Agents library for reinforcement learning,
and I would like to take into account that, for a given state,
some actions are invalid.
How can this be implemented?
Should I define a "observation_and_action_constraint_splitter" function when
creating the DqnAgent?
If yes: do you know any tutorial on this?
Yes you need to define the function, pass it to the agent and also appropriately change the environment output so that the function can work with it. I am not aware on any tutorials on this, however you can look at this repo I have been working on.
Note that it is very messy and a lot of the files in there actually are not being used and the docstrings are terrible and often wrong (I forked this and didn't bother to sort everything out). However it is definetly working correctly. The parts that are relevant to your question are:
rl_env.py in the HanabiEnv.__init__ where the _observation_spec is defined as a dictionary of ArraySpecs (here). You can ignore game_obs, hand_obs and knowledge_obs which are used to run the environment verbosely, they are not fed to the agent.
rl_env.py in the HanabiEnv._reset at line 110 gives an idea of how the timestep observations are constructed and returned from the environment. legal_moves are passed through a np.logical_not since my specific environment marks legal_moves with 0 and illegal ones with -inf; whilst TF-Agents expects a 1/True for a legal move. My vector when cast to bool would therefore result in the exact opposite of what it should be for TF-agents.
These observations will then be fed to the observation_and_action_constraint_splitter in utility.py (here) where a tuple containing the observations and the action constraints is returned. Note that game_obs, hand_obs and knowledge_obs are implicitly thrown away (and not fed to the agent as previosuly mentioned.
Finally this observation_and_action_constraint_splitter is fed to the agent in utility.py in the create_agent function at line 198 for example.
I am getting a Duplicate tag error when I try to write out histogram summaries for a multi-layer network that I generate procedurally. I think that the problem might be related to naming. Imagine code like the following:
with tf.name_scope(some_unique_name):
...
_ = tf.histogram_summary('weights', kernel_weights)
I'd naively assumed that 'weights' would be scoped to some_unique_name but I'm suspecting that it is not. Are summary names independent of name_scope?
As Dave points out, the tag argument to tf.histogram_summary(tag, ...) is indeed independent of the current name scope. Part of the reason for this is that the tag may be a string Tensor (i.e. computed by part of your graph), whereas name scopes are a purely client-side construct (i.e. Python-only), so there's no good way to make the scoping work consistently across the two modes of use.
However, if you're using TensorFlow build from source (and should be available in the next release, 0.8.0), you can use the following recipe to scope your tags (using Graph.unique_name(..., mark_as_used=False)):
with tf.name_scope(some_unique_name):
# ...
tf.histogram_summary(
tf.get_default_graph().unique_name('weights', mark_as_used=False),
kernel_weights)
Alternatively, you can do the following in the current version:
with tf.name_scope(some_unique_name) as scope:
# ...
tf.histogram_summary(scope + 'weights', kernel_weights)
They are.
I'm with you in thinking this is a bug, but I haven't run it past the designers of the op yet. Go ahead and open an issue for it on GitHub!
(I've run into this also and found it terribly annoying -- it prevents reuse of the model without deliberately parameterizing the summary op invocations.)
I have two different structures (DATA and orient). They both are build up like this: DATA.S(1).M(1).A(1).var1 etc...
When I want to save them with the following lines:
text='data1';
filename=strcat('save/',text,'.mat');
save(filename, 'DATA', 'orient');
I get the error "Index exceeds matrix dimensions." for the save line.
When I stop the Programm before the line and try to save it manually over the Workspace matlab is crashing but using all the processing power.
I'm using MAtlab R2014a an the structs are between 2000-4000Bytes.
I hope anyone has a idea what it could be about. Thanks for your help!
Ok, I found a solution. I simply used the following code(http://www.mathworks.com/matlabcentral/fileexchange/39721-save-mat-files-more-quickly/content/savefast.m) from Tim Holy. Now it works without any problems. Thanks Tim Holy!!! :)
First, i admit all the things i will ask are about our homework but i assure you i am not asking without struggling at least two hours.
Description: We are supposed to add a field called max_cpu_percent to task_struct data type and manipulate process scheduling algorithm so that processes can not use an higher percentage of the cpu.
for example if i set max_cpu_percent field as 20 for the process firefox, firefox will not be able to use more than 20% of the cpu.
We wrote a system call to set max_cpu_percent field. Now we need to see if the system call works or not but we could not get the value of the max_cpu_percent field from a user-spaced program.
Can we do this? and how?
We tried proc/pid/ etc can we get the value using this util?
By the way, We may add additional questions here if we could not get rid of something else
Thanks All
Solution:
The reason was we did not modify the code block writing the output to the proc queries.
There are some methods in array.c file (fs/proc/array.c) we modified the function so that also print the newly added fields value. kernel is now compiling we'll see the result after about an hour =)
It Worked...
(If you simply extended getrlimit/setrlimit, then you'd be done by now…)
There's already a mechanism where similar parts of task_struct are exposed: /proc/$PID/stat (and /proc/$PID/$TID/stat). Look for functions proc_tgid_stat and proc_tid_stat. You can add new fields to the ends of these files.