Running the model out of the box generates these files in the data dir :
ls
dev-v2.tgz newstest2013.en
giga-fren.release2.fixed.en newstest2013.en.ids40000
giga-fren.release2.fixed.en.gz newstest2013.fr
giga-fren.release2.fixed.en.ids40000 newstest2013.fr.ids40000
giga-fren.release2.fixed.fr training-giga-fren.tar
giga-fren.release2.fixed.fr.gz vocab40000.from
giga-fren.release2.fixed.fr.ids40000 vocab40000.to
Reading the src of translate.py :
https://github.com/tensorflow/models/blob/master/tutorials/rnn/translate/translate.py
tf.app.flags.DEFINE_string("from_train_data", None, "Training data.")
tf.app.flags.DEFINE_string("to_train_data", None, "Training data.")
To utilize my own training data I created dirs my-from-train-data & to-from-train-data and add my own training data to each of these dirs, training data is contained in the files mydata.from & mydata.to
my-to-train-data contains mydata.from
my-from-train-data contains mydata.to
I could not find documentation as to using own training data or what format it should take so I inferred this from the translate.py src and contents of data dir created when executing translate model out of the box.
Contents of mydata.from :
Is this a question
Contents of mydata.to :
Yes!
I then attempt to train the model using :
python translate.py --from_train_data my-from-train-data --to_train_data my-to-train-data
This returns with an error :
tensorflow.python.framework.errors_impl.NotFoundError: my-from-train-data.ids40000
Appears I need to create file my-from-train-data.ids40000 , what should it's contents be ? Is there an example of how to train this model using custom data ?
blue-sky
Great question, training a model on your own data is way more fun than using the standard data. An example of what you could put in the terminal is:
python translate.py --from_train_data mydatadir/to_translate.in --to_train_data mydatadir/to_translate.out --from_dev_data mydatadir/test_to_translate.in --to_dev_data mydatadir/test_to_translate.out --train_dir train_dir_model --data_dir mydatadir
What goes wrong in your example is that you are not pointing to a file, but to a folder. from_train_data should always point to a plaintext file, whose rows should be aligned with those in the to_train_data file.
Also: as soon as you run this script with sensible data (more than one line ;) ), translate.py will generate your ids (40.000 if from_vocab_size and to_vocab_size are not set). Important to know is that this file is created in the folder specified by data_dir... if you do not specify one this means they are generated in /tmp (I prefer them at the same place as my data).
Hope this helps!
Quick answer to :
Appears I need to create file my-from-train-data.ids40000 , what should it's contents be ? Is there an example of how to train this model using custom data ?
Yes, that's the vocab/ word-id file missing, which is generated when preparing to create the data.
Here is a tutorial from the Tesnorflow documentation.
quick over-view of the files and why you might be confused by the files outputted vs what to use:
python/ops/seq2seq.py: >> Library for building sequence-to-sequence models.
models/rnn/translate/seq2seq_model.py: >> Neural translation sequence-to-sequence model.
models/rnn/translate/data_utils.py: >> Helper functions for preparing translation data.
models/rnn/translate/translate.py: >> Binary that trains and runs the translation model.
The Tensorflow translate.py file requires several files to be generated when using your own corpus to translate.
It needs to be aligned, meaning: language line 1 in file 1. <> language line 1 file 2. This
allows the model to do encoding and decoding.
You want to make sure the Vocabulary have been generated from the dataset using this file:
Check these steps:
python translate.py
--data_dir [your_data_directory] --train_dir [checkpoints_directory]
--en_vocab_size=40000 --fr_vocab_size=40000
Note! If the Vocab-size is lower, then change that value.
There is a longer discussion here tensorflow/issues/600
If all else fails, check out this ByteNet implementation in Tensorflow which does translation task as well.
Related
I was using Google Colab to train Yolo-v3 to detect custom objects. I'm new to Colab, and darknet.
I used the following command for training:
!./darknet detector train "/content/gdrive/My Drive/darknet/obj.data" "/content/gdrive/My Drive/darknet/cfg/yolov3-PID.cfg" "/content/gdrive/My Drive/darknet/backup/yolov3-PID_final.weights" -dont_show
The training finished as follows, and it didn't display any details of the epochs (I don't know how many epochs actually run). Actually, it took very short time until it displayed Done!, and saved the weights as shown in the above image
Then, I tried to detect a test image with the following command:
!./darknet detect "/content/gdrive/My Drive/darknet/cfg/yolov3-PID.cfg" "/content/gdrive/My Drive/darknet/backup/yolov3-PID_final.weights" "/content/gdrive/My Drive/darknet/img/MN 111-0-515 (45).jpg" -dont-show
However, I got the following error:
Error: in the file data/coco.names number of names 80 that isn't equal to classes=13 in the file /content/gdrive/My Drive/darknet/cfg/yolov3-PID.cfg
Even, the resulting image didn't contain any bounding boxes, so I don't know if the training worked or not.
Could you pls advise what might be wrong with the training, and why the error is referring to coco.names, while I'm using other files for names, and configuration?
You did not share the yolov3-PID.cfg, obj.data and coco.names. I am assuming coco.names contain 80 classes as in the repo.
The error likely is in obj.data, where it seems your goal here is to detect 13 custom objects. If this is the case, then set classes=13, also replace names=data/coco.names with names=data/obj.names. Here, obj.names file should contain 13 lines for the custom class names. Also modify yolov3-PID.cfg to contain same amount of classes.
I suggest using this repo below if you are not already using this. It contains google colab training and inference script for yolov3, yolov4.
Here are the instructions for custom object detection training.
Nice work!!! coming this far. Well, everything is fine, you just need to edit the data folder of the darknet. By default it's using coco label, go to darknet folder --> find data folder --> coco.names file --> edit the file by removing 80 classes(in colab just double click to edit and ctrl+s to save) --> Put down your desired class and it's done!!!
i was having the same problem when i was training custom model in colab.
i just cloned darknet again in another folder and edited coco.name and moved it to my training folder. and it worked!!
I am trying to use spacy's 'pre-train' feature for a NER task, so here is what I tried doing(I am still trying to use it),
Step 1: I started by initializing the model with 'en_core_web_lg' next I saved this model to disk and tested its NER capability on few lines to see if it recognizes the tags in those test lines. (Made a note of ignored tags)
Step 2: Next I created a .jsonl file with new data to train on (about 20 new lines, I wanted to see the model's capability given new data around an entity(ignored tags found earlier) will it be able to correctly identify tags after doing transfer learning). So using this .jsonl and the model I saved earlier file I used 'spacy pre-train' command to train, this created a token2vec .bin file for me (model999.bin).
Step 3: Next I created a function that takes the location of an earlier saved model(model saved in step 1) and location of token2vec (model999.bin file obtained in step 2). Inside the function it loads the model>creates/gets pipe>disables rest of the files>uses (pipe_name).model.tok2vec.from_bytes(file_.read()) to read from model999.bin and broadcast the learned vectors to base model.
But when I run this function, I get this error:
ValueError: could not broadcast input array from shape (96,3,384) into shape (96,3,480)
(I have uploaded the entire notebook here: [https://github.com/pratikdk/ner_test/blob/master/base_model_contextual_TF.ipynb ]).
In order to pre-train I used this function
python -m spacy pre-train ub.jsonl model_saves w2s
Here are the 20 lines I tried training on top of the base model
[ https://github.com/pratikdk/ner_test/blob/master/ub.jsonl ]
What am I doing wrong here exactly? Please can you also point the fix, I am sure many would need insight on this.
Environment
Operating System: CentOS
Python Version Used: 3.7.3
spaCy Version Used: 2.1.3
Environment Information: Anaconda Jupyter Lab
So I was able to fix this, the developer(on github) answered my question.
Here is the answer:
https://github.com/explosion/spaCy/issues/3616
In tensorflow, the training produced the following files:
checkpoint
model.ckpt-10000.meta
model.ckpt-10000.data-00000-of-00001
model.ckpt-10000.index
model.ckpt-11000.meta
model.ckpt-11000.data-00000-of-00001
model.ckpt-11000.index
model.ckpt-12000.meta
model.ckpt-12000.data-00000-of-00001
model.ckpt-12000.index
model.ckpt-8000.meta
model.ckpt-8000.data-00000-of-00001
model.ckpt-8000.index
model.ckpt-9000.meta
model.ckpt-9000.data-00000-of-00001
model.ckpt-9000.index
I am interested in creating a .pb file from the output generated training; however, from the examples I have seen, it requires one set of intermediate output files. How do I merge all the output set files into a single .pb?
What you are trying to do does not make sense (at least to me). I recommend you to read about these checkpoint files here and here at first.
In short, checkpoint file just tells you what is the latest model. The .meta file stores info about your graph structure, .data stores values for variables and .index stores key/value pairs which have info where the values for each parameter can be found in .data files.
All your files look like model.ckpt-xxxx. This xxxx is the step number. So you have snapshots of training at different steps. And this is why it does not make sense to combine the value of the variable at step 9000 with the value at step 11000. Also .meta files are probably all the same.
I am trying to do a Deep Learning project by using Tensorflow.
Each of my data sets contains 2 files( PNGimage file + TXTvectors file ), where are put in different folders as follow:
./data/image/ #Folders contains different size of images
./data/vector/ #Folders contains vectors of corresponding image
#For example: apple.png + apple.txt
The example content of vector shows as follow:
10.0,2.5,5,13
And since image size are different, the resize and some transformation apply on vectors are required. It is important to make sure that I can do these processing during Tensorflow is running. Is there any good way to manage this kind of datasets?
I referred to a lot of basic tutorial however most of them are not so many details about arrange customized data input and output. Please give me some advice!
I recommend you to take a look at TFRecords and queues. Basically the idea is the following: you resize all your images to the same format and store them together with your txt vectors in one TFRecord file. This is done separately before you run your model.
When you create your model you create a queue which reads data from the TFRecord file and feeds it to your model.
im getting started with tensorflow und using retrain.py to teach it some new categories - this works well - however i have some questions:
In the comments of retrain.py it says:
"This produces a new model file that can be loaded and run by any TensorFlow
program, for example the label_image sample code"
however I havent found where this new model file is saved to ?
also: it does contain the whole model, right ? not just the retrained part ?
Thanks for clearing this up
1)I think you may want to save the new model.
When you want to save a model after some process, you can use
saver.save(sess, 'directory/model-name', *optional-arg).
Check out https://www.tensorflow.org/api_docs/python/tf/train/Saver
If you change model-name by epoch or any measure you would like to use, you can save the new model(otherwise, it may overlap with previous models saved).
You can find the model saved by searching 'checkpoint', '.index', '.meta'.
2)Saving the whole model or just part of it?
It's the part you need to learn bunch of ideas on tf.session and savers. You can save either the whole or just part, it's up to you. Again, start from the above link. The moral is that you put the variables you would like to save in a list quoted as 'var_list' in the link, and you can save only for them. When you call them back, you now also need to specify which variables in your model correspond to the variables in the loaded variables.
While running retrain.py you can give --output_graph and --output_labels parameters which specify the location to save graph (default is /tmp/output_graph.pb) and the labels as well. You can change those as per your requirements.