Auto_code_complete is a auto word-completetion program which allows you to customize it on your needs

Related tags

Text Data & NLPnlpdeep-learning
Overview

auto_code_complete v1.3

purpose and usage

auto_code_complete is a auto word-completetion program which allows you to customize it on your needs. the model for this program is a combined model of a deep-learning NLP(Natural Language Process) model structure called 'GRU(gated recurrent unit)' and 'LSTM(Long Short Term Memory)'.

the model for this program is one of the deep-learning NLP(Natural Language Process) model structure called 'GRU(gated recurrent unit)'.

how to use (terminal)

  • first, download the repository on your local environment.
  • install the neccessary libraries on your dependent environment.

pip install -r requirements.txt

  • change your working directory to auto-complete/ and execute the line below

python -m auto_complete_model

  • it will require for you to enter the data you want to train with the model
ENTER THE CODE YOU WANT TO TRAIN IN YOUR MODEL : tensorflow tf.keras tf.keras.layers LSTM
==== TRAINING START ====
2022-01-08 18:24:14.308919: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
Epoch 1/100
3/3 [==============================] - 1s 59ms/step - loss: 4.7865 - acc: 0.0532
Epoch 2/100
3/3 [==============================] - 0s 62ms/step - loss: 3.9297 - acc: 0.2872
Epoch 3/100
3/3 [==============================] - 0s 58ms/step - loss: 2.9941 - acc: 0.5532
...
Epoch 31/100
3/3 [==============================] - 0s 75ms/step - loss: 0.2747 - acc: 0.8617
Epoch 32/100
3/3 [==============================] - 0s 65ms/step - loss: 0.2700 - acc: 0.8298
==== TRAINING DONE ====
Now, Load the best weights on your model.
  • if you input your dataset successfully, it will ask for any uncompleted word to be entered.
ENTER THE UNCOMPLETED CODE YOU WANT TO COMPLETE : t tf te l la li k ke tf.kera tf.keras.l
t  - best recommendation : tensorflow
		 - all recommendations :  ['tensorflow']
tf  - best recommendation : tf.keras
		 - all recommendations :  ['tfkeras', 'tf.keras']
te  - best recommendation : tensorflow
		 - all recommendations :  ['tensorflow']
l  - best recommendation : list
		 - all recommendations :  ['list', 'layers']
la  - best recommendation : lange
		 - all recommendations :  ['layers', 'lange']
li  - best recommendation : list
		 - all recommendations :  ['list']
k  - best recommendation : keras
		 - all recommendations :  ['keras']
ke  - best recommendation : keras
		 - all recommendations :  ['keras']
tf.kera  - best recommendation : tf.keras
		 - all recommendations :  []
tf.keras.l  - best recommendation : tf.keras.layers
		 - all recommendations :  ['tf.keras.layers']
  • it will return the best matched word to complete and other recommendations
Do you want to check only the recommendations? (y/n) : y
['tensorflow'], 
['tfkeras', 'tf.keras'], 
['tensorflow'], 
['list', 'layers'], 
['layers', 'lange'], 
['list'], 
['keras'], 
['keras'], 
[], 
['tf.keras.layers']

version update & issues

v1.2 update

2022.01.08

  • change deep-learning model from GRU to GRU+LSTM to improve the performance

By adding the same structrue of new LSTM layers to concatenate before the output layer to an existing model, it shows faster learning and better accuracies in predicting matched recommendations for given incomplete words.

v1.3.1 update

2022.01.09

  • fix the glitches in data preprocessing

We solved the problem that it wouldn't add a new dataset on an existing dataset.

  • add plot_history function in a model class

v1.3.2 update

2022.01.09

  • add model_save,model_load mode in order that users can save and load their model while training a customized model
# Load text data
tf_filepath = "../data/text_data/tf_all_symbols.txt"
with open(tf_filepath, 'r') as f:
    tf_code_text = f.read()

# split the data into 10 parts
total_length = len(tf_code_text)
tf_code_ls = []
for i in range(10):
    globals()[f'tf_code_text_{i}'] = tf_code_text[int(total_length*0.1)*i:int(total_length*0.1)]
    tf_code_ls.append(globals()[f'tf_code_text_{i}'])

# train each dataset with a model setting up arguments 'model_save=True, model_name='mymodel', model_load=True' 
for tf_code in tf_code_ls:
    my_model = auto_coding(new_code=tf_code,
                          # verbose=0,
                           batch_size=100,
                           epochs=200,
                           patience=12,
                           model_summary=True,
                           model_save=True,
                           model_name='tf_model', # 'tf_model/tf_model.h5'
                           model_load=True
                          )
Owner
RUO
RUO
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