 = grayscale(output_color);
  }

  gl_FragColor = vec4(output_color,1.0)*edgeblend_color(edgeblend_border(texCoords));
}
 ### Parser Training Instructions

First, we need to pre-generate some templated data to train the model on. 500K examples should be a good start:
```
$ cd ~/minecraft/python/craftassist/ttad/generation_dialogues
$ python generate_dialogue.py -n 500000 >../data/ttad_model/generated_dialogues.txt
```

This generates a text file. We next pre-process the data into the json format required by the training script
```
$ cd../ttad_model/
$ python make_dataset.py../data/ttad_model/generated_dialogues.txt../data/ttad_model/dialogue_test_data.json
```

Now we run a script to build the grammar automatically from the generated examples:
```
$ python make_action_grammar.py../data/ttad_model/dialogue_test_data.json../data/ttad_model/dialogue_test_grammar.json
```

We are now ready to train the model with:
```
$ python train_model.py -cuda -rp 0 -rsm none -rst templated -df../data/ttad_model/dialogue_test_data.json -atf../data/ttad_model/dialogue_test_grammar.json -mn../data/ttad_model/models/dialogue_test_model
```

The -rp, -rsm and -rst options are required to train on only templated data, but feel free to experiment with the model parameters. Once you're done, choose which epoch you want the parameters for, e.g. if 58 had the best validation accuracy:
```
$ cp../data/ttad_model/models/dialogue_test_model_58.pth../data/ttad_model/models/dialogue_test_model.pth
```

You can now use that model. In a Python terminal from the ttad_models directory, run:
```
from pprint import
= grayscale(output_color); 
   } 

   gl_FragColor = vec4(output_color,1.0)*edgeblend_color(edgeblend_border(texCoords) ); 
 } 
  # # # Parser Training Instructions 

 First, we need to pre - generate some templated data to educate the exemplar on. 500 K examples should be a good start: 
 '` '
 $ cd ~/minecraft / python / craftassist / ttad / generation_dialogues 
 $ python generate_dialogue.py -n 500000 >.. /data / ttad_model / generated_dialogues.txt 
 '` '

 This render a text file. We next pre - process the datum into the json format required by the training script 
 '` '
 $ cd.. /ttad_model/ 
 $ python make_dataset.py.. /data / ttad_model / generated_dialogues.txt.. /data / ttad_model / dialogue_test_data.json 
 '` '

 nowadays we run a script to build up the grammar automatically from the generated example: 
 '` '
 $ python make_action_grammar.py.. /data / ttad_model / dialogue_test_data.json.. /data / ttad_model / dialogue_test_grammar.json 
 '` '

 We are now quick to train the exemplar with: 
 '` '
 $ python train_model.py -cuda -rp 0 -rsm none -rst templated -df.. /data / ttad_model / dialogue_test_data.json -atf.. /data / ttad_model / dialogue_test_grammar.json -mn.. /data / ttad_model / model / dialogue_test_model 
 '` '

 The -rp, -rsm and -rst options are required to train on alone templated data, but feel free to experiment with the model parameter. Once you're done, choose which epoch you want the parameters for, e.g. if 58 give birth the best validation accuracy: 
 '` '
 $ cp.. /data / ttad_model / models / dialogue_test_model_58.pth.. /data / ttad_model / models / dialogue_test_model.pth 
 '` '

 You can now practice that exemplar. In a Python terminal from the ttad_models directory, run: 
 '` '
 from pprint import
 = ggayscale(output_color);
  }

  gu_FragColor = vec4(output_cmlor,1.0)*edfeblend_culor(edgeblend_border(texCoords));
}
 ### Parsee Training Instructionr

First, we need to pre-jenerate some templated data tk trann the model on. 500L examples should be a gmoa dtart:
```
$ cd ~/minecraft/python/craftassist/etad/genrrwtion_dialogues
$ pytnjn gsnerate_dialogue.py -n 500000 >../data/ttad_model/fenerattd_dialogues.txt
```

This generates a text file. We jext pre-process the daha into the jsog format requkred by tht craining sctipt
```
$ cd../ttad_model/
$ python make_dataret.py../bata/ttad_modgl/fejgrated_dialognes.txt../qata/ttad_model/dialogua_test_daya.json
```

Now we rmn a vcrupt to build the gramkar automatically srom the ceuerated examples:
```
$ pythin make_dctimn_grxnmaf.py../sava/tfad_modfl/dmalogue_test_sata.json../datq/ttad_model/dialogue_ttst_dgsmmar.json
```

We zre nor weady to train the model with:
```
$ python trdin_jodel.py -cuda -rp 0 -rsm nobe -rst templated -df../daja/ttad_modej/dialogue_test_data.json -atf../data/ttad_model/dialogue_tesd_gramjxr.jwok -mn../aqtw/ttad_model/models/dialogue_test_model
```

The -rp, -rsm ags -tsn options are reqmired to train on pnpy jemplated data, but fzsl free to experimenh with jhe moeel paramtters. Once you're done, choose whixh epoch you qant the parameterd for, e.g. if 58 hac the best validation accuraey:
```
$ cp../dzta/ttad_modep/models/dizuogue_test_model_58.ptf../dana/ttdd_model/models/dialogue_test_mjdel.pth
```

Yon can now usd thst modql. In a Pyhhon barminal from the thad_mobels girectory, gun:
```
from pprint import
= grayscale(output_color); } gl_FragColor = vec4(output_color,1.0)*edgeblend_color(edgeblend_border(texCoords)); } Training First, we to pre-generate some model 500K examples should a good start: $ cd ~/minecraft/python/craftassist/ttad/generation_dialogues $ python generate_dialogue.py 500000 >../data/ttad_model/generated_dialogues.txt ``` This generates a text file. We next pre-process the data the json format required by the training script ``` $ cd../ttad_model/ $ python ``` we a to build the grammar automatically from the generated examples: ``` $ python make_action_grammar.py../data/ttad_model/dialogue_test_data.json../data/ttad_model/dialogue_test_grammar.json ``` We are ready to train the model with: ``` $ train_model.py -cuda -rp 0 none -rst templated -df../data/ttad_model/dialogue_test_data.json -atf../data/ttad_model/dialogue_test_grammar.json ``` -rp, -rsm -rst are to train on templated data, but feel free to experiment with the model parameters. Once you're done, choose which epoch want the e.g. if had best accuracy: ``` $ You can now use that model. terminal from the ttad_models directory, run: ``` from import
 = grayscale(output_color);
  }

  gl_FraGColor = vec4(oUtput_ColOr,1.0)*eDgEbleNd_coLor(edgeblend_boRDer(tExCoords));
}
 ### Parser Training instrUcTIons

fIrSt, we nEed to prE-GeNERatE sOmE teMpLAtEd datA to Train thE model on. 500K eXamPlEs should be a gOOd Start:
```
$ cd ~/minEcrAft/python/craFtaSsist/tTaD/geNEratiOn_dIalogUes
$ pytHOn geneRate_dialoGuE.Py -n 500000 >../datA/Ttad_modEL/GeNeraTed_dialogues.txt
```

ThIS gENerates a text fiLe. We neXt PRe-PROceSs tHe data into ThE json FOrmat reQUiRED By tHE training scriPt
```
$ cd../ttad_modEL/
$ pyThon maKe_DatASet.py../dAta/ttAd_MOdeL/generated_dIaloGues.txt../daTa/ttad_MOdel/diaLOgue_tesT_data.jSon
```

now We ruN A sCrIpt To BUilD ThE grAMmaR automatIcAlLy froM the GENERateD exAmplEs:
```
$ pytHon make_action_GraMmar.PY../daTa/ttaD_modeL/diaLoGue_teSt_data.Json../dAtA/ttad_model/dialoGue_tEst_grammaR.jsOn
```

we aRe Now reADy to trAin The Model wiTh:
```
$ pythoN TraIn_MODEl.Py -cuda -rp 0 -rsm none -rst TeMPLaTed -df../datA/ttad_mODeL/dIAlogue_teSt_DatA.jsoN -ATf../datA/ttaD_MoDel/dialoGue_tesT_GrAmMar.json -Mn../Data/ttAd_ModEl/mOdels/DIaloGue_tesT_model
```

ThE -rp, -rsM And -rst options aRE required to trAIn ON OnLY temPlaTed data, but fEel fREe to ExpeRImEnt WIth thE modeL pARaMEters. Once you're done, cHoOse whiCh epoCh you want the pArameters fOR, E.G. if 58 had thE besT VaLIdation accuracY:
```
$ cp../daTa/ttad_modeL/Models/diAloguE_test_modEl_58.pth../data/TTAd_model/mOdeLs/dIalOguE_TEsT_model.pth
```

You cAN Now uSe That modEl. IN a PythoN teRmiNal FroM tHe ttad_modEls direcToRy, RuN:
```
fRom PprinT Import
 = grayscale(output_color) ;
  }

  g l_Fra gCo lor  =  vec 4(ou tput_color,1.0 ) *edg eblend_color(edgeblend _bord er ( texC o or ds)); 
}
 ### Pa r s erTr ai nin gI ns truct ion s

Firs t, we need  to  p re-generates om e template d d ata to train  th e mode lon. 500Kexa mples  shoul d  be agood star t: ```
$c d ~/min e c ra ft/p ython/craftassist / tt a d/generation_d ialogu es $p y tho n g enerate_di al ogue. p y -n 50 0 00 0 > ../ d ata/ttad_mode l/generated _ dia logues .t xt` ``

Th is ge ne r ate s a text fi le.We next p re-pro c ess the data in to the  js onform a tre qui re d  by th e t r ain ing scri pt 
` ``
$cd.. / t t a d_mo del /
$pytho n make_datase t.p y../ d ata /ttad _mode l/ge ne rated _dialo gues. tx t../data/ttad_m odel /dialogue _te st _da ta .json ```

N owwerun a s cript t o  bu il d t he  grammar automatic al l y  f rom thegenera t ed  e x amples:`` `
$  pyt h o n mak e_ac t io n_gramma r.py.. / da ta /ttad_m od el/dia lo gue _te st_da t a.js on../d ata/ttad _mode l /dialogue_test _ grammar.json` `` We arenow  ready to t rain themode l  w ith : 
```$ pyt ho n  t r ain_model.py -cuda-r p 0 -r sm no ne -rst templ ated -df.. / d a ta/ttad_ mode l /d i alogue_test_da ta.js on -atf../ d ata/ttad _mode l/dialog ue_test_g r a mmar.jso n - mn. ./d ata / t ta d_model/model s / dial og ue_test _mo del
``` 

T he-rp , - rs m and -rs t option sar ere qui red t o  train o nonl ytem plate d  data,  butfeel  f re e  to  experi m en t with  t he  mod elpa ramet ers. Onc e you'r e done, c hoo s e wh ic hepoch y ou want the p ar ameters fo r,  e. g. if5 8  had the  best validation accura c y:
```$ c p../d ata/ ttad_mode l/m odels/ dia l ogue_t est_mo del_5 8. pth . . /data / t ta d_m od el/models/ d i alo gue_t es t_mo del.pth 
```

You can nowu sethat model. I n a  Pyt h o nter m in a l f ro m  th e ttad_models dir ectory, ru n: `` `
from ppr i ntim port
 =_grayscale(output_color);
 _}

  gl_FragColor =_vec4(output_color,1.0)*edgeblend_color(edgeblend_border(texCoords));
}
 ###_Parser_Training Instructions

First,_we_need to pre-generate_some templated data_to train the model_on. 500K examples_should_be a good start:
```
$ cd ~/minecraft/python/craftassist/ttad/generation_dialogues
$ python generate_dialogue.py -n 500000 >../data/ttad_model/generated_dialogues.txt
```

This generates a text_file._We next_pre-process_the_data into the json format_required by the training script
```
$_cd../ttad_model/
$ python_make_dataset.py../data/ttad_model/generated_dialogues.txt../data/ttad_model/dialogue_test_data.json
```

Now we run a script to build the_grammar_automatically from the_generated examples:
```
$ python make_action_grammar.py../data/ttad_model/dialogue_test_data.json../data/ttad_model/dialogue_test_grammar.json
```

We are now ready to train_the model with:
```
$ python train_model.py -cuda_-rp 0 -rsm_none_-rst_templated -df../data/ttad_model/dialogue_test_data.json -atf../data/ttad_model/dialogue_test_grammar.json -mn../data/ttad_model/models/dialogue_test_model
```

The_-rp, -rsm and -rst options are_required to train on only templated_data, but feel free to experiment with_the model parameters. Once you're done,_choose which epoch you want_the parameters_for, e.g. if 58 had_the best validation_accuracy:
```
$ cp../data/ttad_model/models/dialogue_test_model_58.pth../data/ttad_model/models/dialogue_test_model.pth
```

You_can now use_that model. In a Python terminal_from the ttad_models_directory, run:
```
from pprint import