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.

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']

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']

# 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
                          )