I am reading data from tfrecords with dataset api. I am converting string data to dummy data with following code.
SFR1 = tf.feature_column.indicator_column(
tf.feature_column.categorical_column_with_vocabulary_list("SFR1 ",
vocabulary_list=("1", "2")))
But when i run my code, tensorflow is throwing following error.
tensorflow.python.framework.errors_impl.FailedPreconditionError: Table
already initialized. [[Node:
Generator/input_layer/SFR1 _indicator/SFR1 _lookup/hash_table/table_init
= InitializeTableV2[Tkey=DT_STRING, Tval=DT_INT64](Generator/input_layer/SFR1 _indicator/SFR1 _lookup/hash_table,
Generator/input_layer/SFR1 _indicator/SFR1 _lookup/Const,
Generator/input_layer/SFR1 _indicator/SFR1 _lookup/ToInt64)]]
[[Node: Generator2/IteratorGetNext =
IteratorGetNextoutput_shapes=[[?,10000,160]],
output_types=[DT_FLOAT],
_device="/job:localhost/replica:0/task:0/device:CPU:0"]]
I have tried many combinations to determine the source of problem. I understood that this problem occurs when model includes both tf.feature_column.categorical_column_with_vocabulary_list and dataset api. If i choose TFRecordReader instead of dataset, code is running.
When i search stackoverflow, I noticed that there is a similar issue. I am adding issue link below. As both problem are same, I didn't copy all my code. Below link includes enough data to explain my problem
Tensorflow feature columns in Dataset map Table already initialized issue
Thanks.
I came across the same issue. Then modified my code following the warning from Tensorflow and it works:
Creating lookup tables inside a function passed to Dataset.map() is not supported. Create each table outside the function, and capture it inside the function to use it.
Hope it would help.
This is issue with earlier version of TensorFlow, updating to TF2.0 should resolve this.
pip install --upgrade tensorflow
Related
I tried to make graphs for my csv dataset in Jupyter Notebook, using this line of code:
bank['marital'].value_counts().plot(kind='pie',autopct='%.2f')
plt.show()
However, the system return, "string indices must be integers".
I have tried to use many different methods like changing the string to a number,... but nothing really worked
I tried to reproduce it and it worked fine. So it's not something wrong with the code itself.
I suggest experimenting with:
restart Jupyter Notebook
play with a tiny synthetic dataset
cut the real dataset till it works
attach failing dataset contents to the question
Attaching my results:
[input.csv]
name,smth
Maria,12
Anton,2
Maria,3
...
df = pd.read_csv('input.csv')
df['name'].value_counts().plot(kind='pie',autopct='%.2f')
Short version:
I am trying to upload US LCI database to Brightway2 and I am failing miserably. Has anyone succeeded? If so, could you share it with me? :D
Long version:
I am following the notebook IO - Importing the US LCI database notebook and I am having a lot of problems. I am aware that, as the notebook indicates, it is a work in progress. Anyhow, I wanted to give it a try:
I tried uploading every ecospold version database found here, following the method from the notebook. The only one that gave me a similar results was version FY20.Q3.02. However, right off the bat I get the following differences/errors:
Same as the notebook, I get this error: Couldn't apply strategy link_technosphere_by_activity_hash: Object in source database can't be uniquely linked to target database. And two activities that are linked. When I follow the instructions of ignoring these datasets, it throws me that error over and over again.
Trying to move on with the tutorial, I get more errors and at the end I end up with all exchanges unlinked:
633 datasets
37513 exchanges
37505 unlinked exchanges
Finally, after running the code in line [15]:
import functools
f = functools.partial(link_iterable_by_fields,
other=Database(config.biosphere),
kind='biosphere'
)
sp.apply_strategy(f)
sp.statistics(f)
I end up with:
0 datasets
0 exchanges
0 unlinked exchanges
Which is hilarious and sad at the same time. Since I am new with Python and BW, my troubleshooting is clumpsy and probably erroneous (I promise I googled a lot and went through the code). And concluded I am failing and it is time to ask questions:
Has anybody succeeded uploading the US LCI database to Brightway2?
If so, how? Which file did you use?
Thank you!!!!
This is an excellent question. I have added text to the offending notebook to note that it is obsolete.
In general, I think trying to import the ecospold files is a fools errand, as though they are labeled ecospold2, they are actually ecospold1 (which is a totally different format):
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<ecoSpold xmlns="http://www.EcoInvent.org/EcoSpold01">
The most recent export also raises an error when I try the ecospold1 importer:
AttributeError: no such child: {http://www.EcoInvent.org/EcoSpold01}modellingAndValidation
This is a required attribute in ecospold1.
I think the best way forward would be to consume the JSON-LD directly. Note that it is important not to run bw2setup(), as you would also want to use their list of elementary flows and LCIA methods. Currently the experimental JSON-LD importer fails because the provided datasets need allocation, but don't provide a set of consistent allocation methods. When I use the git checkout of bw2io and do the following:
uslci = JSONLDImporter(
"/Users/cmutel/Downloads/National_Renewable_Energy_Laboratory-USLCI_Database/",
"US LCI",
preferred_allocation="CAUSAL_ALLOCATION"
)
uslci.apply_strategies()
I get the following error:
UnallocatableDataset: We currently only support exchange-specific CAUSAL_ALLOCATION
This is fixable, but someone would need to step through this and fix the allocation procedure, and I don't have the time to do that now.
I'm new with NLP and the rasa api. I'm trying to prepare the data so that it can be used as training data for intent recognition. The function that I'm trying to use is:
from rasa_nlu.training_data import load_data #Import function
train_data_rasa=load_data('/content/data_file.json') #Json file
However the next error pop ups:
AttributeError: 'str' object has no attribute 'get'
The json file is the result of using pandas.to_json() function. The original dataset, is the ATIS flight intent dataframe in which there are two columns: The text and the intent.
Here is a preview of the json file:
{"Intent":{"0":"atis_flight","1":"atis_flight_time","2":"atis_airfare","3":"atis_airfare","4":"atis_flight","5":"atis_aircraft","6" ........
I don't really know what is going on as the dataset seems to be clean. I have also tried multiple alternatives such as markdown (md) type of file but it does not seem to work.
Thank you in advance !!
I would suggest to try the rasa data convert command (that converts your training data from json to yml format) and then try to train your data (with command rasa train from the cli) to see if you get the same error. Also, the Training Data format page in the docs might be a useful resource for you since it explains the types of training data and their expected structure. Another idea would be to post your question also on the Rasa forum where there might be more people that have encountered the same error like here. That way you might get more ideas on how to solve your issue or more people will jump in and help.
I'm getting this error when i use the tensorboard callback while training.
I tried looking for answers from posts related to tensorboard errors but this exact error was not found in any stackoverflow posts or github issues.
Please let know.
The following versions are installed in my pc:
Tensorflow and Tensorflow GPU : 2.0.0
Tensorboard: 2.0.0
I had the same problem and fixed it with this hack
model._get_distribution_strategy = lambda: None
It seems to be a bug on tensorflow's side.
https://github.com/tensorflow/tensorflow/pull/34870
Remove tensorboard callback for now.
This error mostly happens because of mixed imports from keras and tf.keras. Make sure that throughout the code exact referencing of libraries is maintained. For example instead of model.add(Conv2d()) try model.add(tf.keras.layers.Conv2D()) , applying this for all layers solved the problem for me.
I wanna use the LDA in gensim for topic modeling over a few thousand documents.
Therefore I´m using a csv-File as Input in the format of a term-document-matrix.
Currently it occurs an error when running the following code:
from gensim import corpora
import_path ="TDM.csv"
dictionary = corpora.csvcorpus(import_path, labels='true')
The error is the following:
dictionary = corpora.csvcorpus(import_path, labels='true')
AttributeError: module 'gensim.corpora' has no attribute 'csvcorpus'
Am I using the module correctly and if so, where is my mistake?
Thanks in advance.
This also bugged me for quite awhile.
It looks like csvcorpus is actually in the experimental stage as you can see in their github issue, https://github.com/RaRe-Technologies/gensim/issues/1583
I would recommend going by the old fashioned way of using the csv package to read your csv file instead.
Cheers.