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72 lines
3.1 KiB
72 lines
3.1 KiB
3 years ago
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from textgenrnn import textgenrnn
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from datetime import datetime
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import os
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model_cfg = {
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'word_level': False, # set to True if want to train a word-level model (requires more data and smaller max_length)
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'rnn_size': 256, # number of LSTM cells of each layer (128/256 recommended)
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'rnn_layers': 3, # number of LSTM layers (>=2 recommended)
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'rnn_bidirectional': True, # consider text both forwards and backward, can give a training boost
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'max_length': 30, # number of tokens to consider before predicting the next (20-40 for characters, 5-10 for words recommended)
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'max_words': 10000, # maximum number of words to model; the rest will be ignored (word-level model only)
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}
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train_cfg = {
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'line_delimited': True, # set to True if each text has its own line in the source file
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'num_epochs': 20, # set higher to train the model for longer
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'gen_epochs': 5, # generates sample text from model after given number of epochs
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'train_size': 0.8, # proportion of input data to train on: setting < 1.0 limits model from learning perfectly
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'dropout': 0.0, # ignore a random proportion of source tokens each epoch, allowing model to generalize better
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'validation': False, # If train__size < 1.0, test on holdout dataset; will make overall training slower
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'is_csv': False # set to True if file is a CSV exported from Excel/BigQuery/pandas
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}
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file_name = "classes.txt"
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model_name = '_classes' # change to set file name of resulting trained models/texts
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textgen = textgenrnn(name=model_name)
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train_function = textgen.train_from_file if train_cfg['line_delimited'] else textgen.train_from_largetext_file
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train_function(
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file_path=file_name,
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new_model=True,
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num_epochs=train_cfg['num_epochs'],
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gen_epochs=train_cfg['gen_epochs'],
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batch_size=1024,
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train_size=train_cfg['train_size'],
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dropout=train_cfg['dropout'],
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validation=train_cfg['validation'],
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is_csv=train_cfg['is_csv'],
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rnn_layers=model_cfg['rnn_layers'],
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rnn_size=model_cfg['rnn_size'],
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rnn_bidirectional=model_cfg['rnn_bidirectional'],
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max_length=model_cfg['max_length'],
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dim_embeddings=100,
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word_level=model_cfg['word_level'])
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# this temperature schedule cycles between 1 very unexpected token, 1 unexpected token, 2 expected tokens, repeat.
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# changing the temperature schedule can result in wildly different output!
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temperature = [1.0, 0.5, 0.2, 0.2]
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prefix = None # if you want each generated text to start with a given seed text
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if train_cfg['line_delimited']:
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n = 100
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max_gen_length = 60 if model_cfg['word_level'] else 300
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else:
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n = 1
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max_gen_length = 2000 if model_cfg['word_level'] else 10000
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timestring = datetime.now().strftime('%Y%m%d_%H%M%S')
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gen_file = '{}_gentext_{}.txt'.format(model_name, timestring)
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textgen.generate_to_file(gen_file,
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temperature=temperature,
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prefix=prefix,
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n=n,
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max_gen_length=max_gen_length)
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file1 = open("class_out.txt", "w")
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file1.write(gen_file)
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file1.close()
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