I am following an Agents.jl tutorial (https://juliadynamics.github.io/Agents.jl/stable/examples/schelling/) and get the following error when executing this piece of code. Any ideas why?
using Agents
using InteractiveDynamics
using CairoMakie
groupColor(a) = a.group == 1 ? :blue : :green
groupMarker(a) = a.group == 1 ? :circle : :rect
fig, _ = abm_plot(model, ac = groupColor, am = groupMarker, as = 10)
#Note that abm_plot is a function from InteractiveDynamics.jl which uses makie
#and model is an AgentBasedModel obj created from Agents.jl
#Out >
No backend available (GLMakie, CairoMakie, WGLMakie)!
Maybe you imported GLMakie but it didn't build correctly.
In that case, try `]build GLMakie` and watch out for any warnings.
If that's not the case, make sure to explicitely import any of the mentioned backends.
Had to update InteractiveDynamics package from 0.14.6 to 0.15.1. The answer to this problem can be found in the following thread.
https://discourse.julialang.org/t/no-backend-available-glmakie-cairomakie-wglmakie/62984/6
Related
can anybody help me with solving my problem of Hex2Bin and Bin2Hex functions?
First I was trying to make the conversion Hex2Bin. I would like to call the AddIn function from macro so I called createUNOservice:
Function fcHex2Bin(arg as variant, NumberOfBits As integer) as string
Dim oService as Object
oService = createUNOService("com.sun.star.sheet.addin.Analysis")
sArg = cStr(arg)
fcHex2Bin = oService.getHex2Bin(sArg,NumberOfBits)
End Function
but all the time ends with fault message like "The object variable is not set.". I already don't know why.
My final goal would be to make all functions of Calc running in macros, but at this moment I would be glad to have two functions Hex2Bin and Bin2Hex running - anyhow.
My LibreOffice version:
Version: 7.1.3.2 (x64) / LibreOffice Community
Build ID: 47f78053abe362b9384784d31a6e56f8511eb1c1
CPU threads: 8; OS: Windows 10.0 Build 19042; UI render: Skia/Raster; VCL: win
Locale: cs-CZ (cs_CZ); UI: cs-CZ
Calc: CL
Thank you for your help.
This way works.
Function fcHex2Bin(aNum As String, rPlaces As Any) As String
Dim oFunc As Object
oFunc = CreateUnoService("com.sun.star.sheet.FunctionAccess")
Dim aArgs(0 to 1) As Variant
aArgs(0) = aNum
aArgs(1) = rPlaces
fcHex2Bin = oFunc.callFunction("com.sun.star.sheet.addin.Analysis.getHex2Bin", aArgs())
End Function
As for why the other way does not work, many analysis functions require a hidden XPropertySet object as the first argument. The following code produces informative error messages:
REM IllegalArgumentException: expected 3 arguments, got 1
sResult = oService.getHex2Bin(ThisComponent.getPropertySetInfo())
REM IllegalArgumentException: arg no. 0 expected: "com.sun.star.beans.XPropertySet"
sResult = oService.getHex2Bin(ThisComponent.getPropertySetInfo(), "2", 4)
However I tried passing ThisComponent.getPropertySetInfo().getProperties() from a Calc spreadsheet and it still didn't work, so I'm not exactly sure what is required to do it that way.
The documentation at https://help.libreoffice.org/latest/he/text/sbasic/shared/calc_functions.html does not really explain this. You could file a bug report about missing documentation, perhaps related to https://bugs.documentfoundation.org/show_bug.cgi?id=134032.
I'm following this tutorial that codes a sentiment analysis classifier using BERT with the huggingface library and I'm having a very odd behavior. When trying the BERT model with a sample text I get a string instead of the hidden state. This is the code I'm using:
import transformers
from transformers import BertModel, BertTokenizer
print(transformers.__version__)
PRE_TRAINED_MODEL_NAME = 'bert-base-cased'
PATH_OF_CACHE = "/home/mwon/data-mwon/paperChega/src_classificador/data/hugingface"
tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME,cache_dir = PATH_OF_CACHE)
sample_txt = 'When was I last outside? I am stuck at home for 2 weeks.'
encoding_sample = tokenizer.encode_plus(
sample_txt,
max_length=32,
add_special_tokens=True, # Add '[CLS]' and '[SEP]'
return_token_type_ids=False,
padding=True,
truncation = True,
return_attention_mask=True,
return_tensors='pt', # Return PyTorch tensors
)
bert_model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME,cache_dir = PATH_OF_CACHE)
last_hidden_state, pooled_output = bert_model(
encoding_sample['input_ids'],
encoding_sample['attention_mask']
)
print([last_hidden_state,pooled_output])
that outputs:
4.0.0
['last_hidden_state', 'pooler_output']
While the answer from Aakash provides a solution to the problem, it does not explain the issue. Since one of the 3.X releases of the transformers library, the models do not return tuples anymore but specific output objects:
o = bert_model(
encoding_sample['input_ids'],
encoding_sample['attention_mask']
)
print(type(o))
print(o.keys())
Output:
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions
odict_keys(['last_hidden_state', 'pooler_output'])
You can return to the previous behavior by adding return_dict=False to get a tuple:
o = bert_model(
encoding_sample['input_ids'],
encoding_sample['attention_mask'],
return_dict=False
)
print(type(o))
Output:
<class 'tuple'>
I do not recommend that, because it is now unambiguous to select a specific part of the output without turning to the documentation as shown in the example below:
o = bert_model(encoding_sample['input_ids'], encoding_sample['attention_mask'], return_dict=False, output_attentions=True, output_hidden_states=True)
print('I am a tuple with {} elements. You do not know what each element presents without checking the documentation'.format(len(o)))
o = bert_model(encoding_sample['input_ids'], encoding_sample['attention_mask'], output_attentions=True, output_hidden_states=True)
print('I am a cool object and you can acces my elements with o.last_hidden_state, o["last_hidden_state"] or even o[0]. My keys are; {} '.format(o.keys()))
Output:
I am a tuple with 4 elements. You do not know what each element presents without checking the documentation
I am a cool object and you can acces my elements with o.last_hidden_state, o["last_hidden_state"] or even o[0]. My keys are; odict_keys(['last_hidden_state', 'pooler_output', 'hidden_states', 'attentions'])
I faced the same issue while learning how to implement Bert. I noticed that using
last_hidden_state, pooled_output = bert_model(encoding_sample['input_ids'], encoding_sample['attention_mask'])
is the issue. Use:
outputs = bert_model(encoding_sample['input_ids'], encoding_sample['attention_mask'])
and extract the last_hidden state using
output[0]
You can refer to the documentation here which tells you what is returned by the BertModel
I'm trying to use the GET function, in the R package httr, to get info from a web page like:
http://fake.web.com/fruit?type=APPLE,GREEN&number=2
so I´m ussing the code:
resp <- GET("http://fake.web.com/fruit", query = list(type = "APPLE,GREEN", number = 2))
But when I check the url of resp I'm getting:
http://fake.web.com/fruit?type=APPLE%2CGREEN&number=2 instead of
http://fake.web.com/fruit?type=APPLE,GREEN&number=2
How can I solve this?
I am following the wildml blog on text classification using tensorflow. I have changed the code to save graph def as follows :
tf.train.write_graph(sess.graph_def,'./DeepLearn/model/','train.pb', as_text=False)
Later in a separate file i am restoring the graph as follows :
with tf.gfile.FastGFile(os.path.join('./DeepLearn/model/','train.pb'), 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
with tf.Session() as sess:
t = sess.graph.get_tensor_by_name('embedding/W:0')
sess.run(t)
When i try to run the tensor and get its value, i am getting the following error :
tensorflow.python.framework.errors.FailedPreconditionError: Attempting to use uninitialized value embedding/W
What could be the possible reason for this error. The tensor should have been initialized as i am restoring it from the saved graph.
Thanks Alexandre!
Yes, i need to load both the graph (from .pb file) and weights (from checkpoints file.). Used the following sample code (taken from a blog) and it worked for me.
with tf.Session() as persisted_sess:
print("load graph")
with gfile.FastGFile("/tmp/load/test.pb",'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
persisted_sess.graph.as_default()
tf.import_graph_def(graph_def, name='')
persisted_result = persisted_sess.graph.get_tensor_by_name("saved_result:0")
tf.add_to_collection(tf.GraphKeys.VARIABLES,persisted_result)
try:
saver = tf.train.Saver(tf.all_variables())
except:pass
print("load data")
saver.restore(persisted_sess, "checkpoint.data") # now OK
print(persisted_result.eval())
print("DONE")
I am completely new to R.
I am trying to use the dist object with a custom function based on the specification here, but I was unable to pass the custom function directly by name, so I tried to add it using the registry described here, but it appears that I am missing a library.
However, I'm not sure which library I need and cannot find a reference to find the name of the library.
Here's a code sample that I'm trying to run:
library(cluster)
myfun <- function(x,y) {
numDiffs <- 0;
for (i in x) {
if (x[i] != y[i])
numDiffs <- numDiffs + 1;
}
return(numDiffs);
}
summary(pr_DB)
pr_DB$set_entry(FUN = myfun, names = c("myfun", "vectorham"))
pr_DB$get_entry("MYFUN")
Here's the error:
Error in summary(pr_DB) : object 'pr_DB' not found
Execution halted
You need to learn the conventions used by R help pages. That "{proxy}" at the top of the page you linked to is really the answer to your question. The convention for the help page construction is "topic {package_name}".