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question:Por favor, configúrame el código para usar Wikitext y ajústame los siguientes parámetros para que genere una buena predicción, así se demore el proceso de entrenamiento: vocab_size = len(vocab) d_model = 768 nhead = 8 # Ajuste de hparámetro num_layers = 6 # Ajuste de hiperparámetro num_epochs = 30 # Increase the number of epochs learning_rate = 1e-3 # Experiment with different learning rates optimizer_choice = “Adam” # Choose the optimizer: “Adam”, “RMSprop”, or “Adagrad” batch_size = 64 # Ajuste de hiperparámetro max_seq_len = 50 # Limitar la longitud máxima de la secuencia para evitar problemas de memoria
answer:He actualizado el código para que utilice el conjunto de datos Wikitext-2 y ajusté algunos hiperparámetros según tus requerimientos. Ten en cuenta que entrenar un modelo durante muchas épocas o en largas secuencias de texto puede aumentar el tiempo de entrenamiento y el uso de la memoria. ```python import torch import torch.nn as nn from torchtext.datasets import WikiText2 from torchtext.data.functional import to_map_style_dataset from torchtext.data.utils import get_tokenizer from torchtext.vocab import build_vocab_from_iterator from torch.utils.data import DataLoader # 1. Define the model class TransformerModel(nn.Module): def __init__(self, vocab_size, d_model, nhead, num_layers, dropout=0.1): super(TransformerModel, self).__init__() self.embedding = nn.Embedding(vocab_size, d_model) self.transformer = nn.Transformer(d_model, nhead, num_layers, dropout=dropout) self.fc = nn.Linear(d_model, vocab_size) def forward(self, src, tgt): src = self.embedding(src) tgt = self.embedding(tgt) x = self.transformer(src, tgt) x = self.fc(x) return x # 2. Preparar datos train_data_raw, valid_data_raw, _ = WikiText2(split=('train', 'valid', 'test')) train_data_raw = to_map_style_dataset(train_data_raw) valid_data_raw = to_map_style_dataset(valid_data_raw) tokenizer = get_tokenizer("spacy", "en_core_web_sm") # Agregar tokens especiales (UNK y PAD) al vocabulario specials = ['<unk>','<pad>'] vocab = build_vocab_from_iterator((tokenizer(y) for y in train_data_raw), specials=specials) vocab.set_default_index(vocab['<unk>']) vocab_size = len(vocab) d_model = 768 nhead = 8 num_layers = 6 num_epochs = 30 learning_rate = 1e-3 optimizer_choice = 'Adam' batch_size = 64 max_seq_len = 50 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Rest of the code remains unchanged ... ``` Este código utiliza el conjunto de datos Wikitext-2 al cargarlo mediante `torchtext.datasets.WikiText2`. Los hiperparámetros se han mantenido igual que los de tus requerimientos. Sin embargo, podrías ajustar `num_epochs`, `max_seq_len`, `learning_rate`, `optimizer_choice` y otros hiperparámetros según la calidad de las predicciones y la capacidad de tu equipo. Ten en cuenta que aunque he cambiado el código para usar Wikitext-2 en lugar de Penn Treebank, los detalles de cómo se tokeniza, se crea el vocabulario y los lotes siguen siendo los mismos que en tu código original. Dependiendo de la adecuación de los datos y la arquitectura del modelo, la calidad de las predicciones puede variar. Recuerda que es posible que debas experimentar con distintos hiperparámetros y ajustar el preprocesamiento de los datos para obtener una predicción deseable. Estos cambios pueden requerir tiempos más largos de entrenamiento y ajuste, así que ten paciencia al experimentar con distintas configuraciones y conjuntos de datos.
question:Por favor, dame una buena configuración en los hiperparámetros para evitar esto: Epoch: 1, Loss: 9.567360877990723 Epoch: 1, Average Loss: 3.7003726646474107, Time taken: 292.0087857246399 seconds Epoch: 2, Loss: 3.559448480606079 Epoch: 2, Average Loss: 3.618738709293844, Time taken: 292.34765434265137 seconds Epoch: 3, Loss: 3.5392792224884033 Epoch: 3, Average Loss: 3.605547330225375, Time taken: 292.08597803115845 seconds Epoch: 4, Loss: 3.522873640060425 Epoch: 4, Average Loss: 3.5965943490597687, Time taken: 292.3419635295868 seconds Epoch: 5, Loss: 3.5057291984558105 Epoch: 5, Average Loss: 3.5902317744244185, Time taken: 291.74957275390625 seconds Epoch: 6, Loss: 3.5161614418029785 Epoch: 6, Average Loss: 3.587415994346822, Time taken: 292.1076292991638 seconds Epoch: 7, Loss: 3.511284589767456 Epoch: 7, Average Loss: 3.5886631560416276, Time taken: 291.77211713790894 seconds Epoch: 8, Loss: 3.5088226795196533 Epoch: 8, Average Loss: 3.5876666606605734, Time taken: 291.85539841651917 seconds Epoch: 9, Loss: 3.505660057067871 Epoch: 9, Average Loss: 3.586097168831771, Time taken: 291.7574985027313 seconds Epoch: 10, Loss: 3.5023460388183594 Epoch: 10, Average Loss: 3.584595971687665, Time taken: 291.9380223751068 seconds Epoch: 11, Loss: 3.5048999786376953 Epoch: 11, Average Loss: 3.583113308641847, Time taken: 292.0109279155731 seconds Epoch: 12, Loss: 3.5031940937042236 Epoch: 12, Average Loss: 3.580842248840477, Time taken: 291.76497888565063 seconds Epoch: 13, Loss: 3.50352144241333 Epoch: 13, Average Loss: 3.5786439665370113, Time taken: 291.74229407310486 seconds Epoch: 14, Loss: 3.497807025909424 Epoch: 14, Average Loss: 3.5781566240035083, Time taken: 291.3640627861023 seconds Epoch: 15, Loss: 3.4952096939086914 Epoch: 15, Average Loss: 3.5777785469823917, Time taken: 291.2954170703888 seconds Epoch: 16, Loss: 3.497789144515991 Epoch: 16, Average Loss: 3.577572054282794, Time taken: 291.54339694976807 seconds Epoch: 17, Loss: 3.4942948818206787 Epoch: 17, Average Loss: 3.5770502394143167, Time taken: 291.37605381011963 seconds Epoch: 18, Loss: 3.4956154823303223 Epoch: 18, Average Loss: 3.576229152117392, Time taken: 290.98668670654297 seconds Epoch: 19, Loss: 3.492783546447754 Epoch: 19, Average Loss: 3.5748239583388934, Time taken: 290.95834708213806 seconds Epoch: 20, Loss: 3.4896273612976074 Epoch: 20, Average Loss: 3.572025065639626, Time taken: 290.86392188072205 seconds Epoch: 21, Loss: 3.4918222427368164 Epoch: 21, Average Loss: 3.5701150871501675, Time taken: 290.33885979652405 seconds Epoch: 22, Loss: 3.485771894454956 Epoch: 22, Average Loss: 3.568570154701349, Time taken: 289.31501388549805 seconds Epoch: 23, Loss: 3.4859931468963623 Epoch: 23, Average Loss: 3.567728873894695, Time taken: 289.6293840408325 seconds Epoch: 24, Loss: 3.4873950481414795 Epoch: 24, Average Loss: 3.567380349898973, Time taken: 289.5343222618103 seconds Epoch: 25, Loss: 3.4872074127197266 Epoch: 25, Average Loss: 3.5652468594308133, Time taken: 289.4293782711029 seconds Epoch: 26, Loss: 3.4843451976776123 Epoch: 26, Average Loss: 3.5643289098268225, Time taken: 289.92592668533325 seconds Epoch: 27, Loss: 3.4866209030151367 Epoch: 27, Average Loss: 3.563500219878135, Time taken: 289.3788197040558 seconds Epoch: 28, Loss: 3.481735944747925 Epoch: 28, Average Loss: 3.563423510740012, Time taken: 289.35951137542725 seconds Epoch: 29, Loss: 3.4829232692718506 Epoch: 29, Average Loss: 3.5658165968869575, Time taken: 289.45616269111633 seconds Epoch: 30, Loss: 3.487635374069214 Epoch: 30, Average Loss: 3.5698270244743435, Time taken: 288.84308218955994 seconds Epoch: 1, Loss: 3.4927570819854736 Epoch: 1, Average Train Loss: 3.5711315339962337, Average Valid Loss: 3.6290017239975207, Time taken: 312.00482964515686 seconds Epoch: 2, Loss: 3.489135503768921 Epoch: 2, Average Train Loss: 3.571447698788951, Average Valid Loss: 3.6297632943500173, Time taken: 312.0168857574463 seconds Epoch: 3, Loss: 3.4861600399017334 Epoch: 3, Average Train Loss: 3.5712698911079435, Average Valid Loss: 3.6287157372994856, Time taken: 311.7727360725403 seconds Epoch: 4, Loss: 3.481497049331665 Epoch: 4, Average Train Loss: 3.5710239242691504, Average Valid Loss: 3.625587866161809, Time taken: 311.834326505661 seconds Epoch: 5, Loss: 3.4813199043273926 Epoch: 5, Average Train Loss: 3.5704481733162594, Average Valid Loss: 3.6241414601152595, Time taken: 311.65282440185547 seconds Epoch: 6, Loss: 3.4821066856384277 Epoch: 6, Average Train Loss: 3.5700396979263074, Average Valid Loss: 3.624173494902524, Time taken: 311.28872323036194 seconds Epoch: 7, Loss: 3.4856691360473633 Epoch: 7, Average Train Loss: 3.569556606586442, Average Valid Loss: 3.6256951039487664, Time taken: 311.3145525455475 seconds Epoch: 8, Loss: 3.485272169113159 Epoch: 8, Average Train Loss: 3.5691312433648927, Average Valid Loss: 3.6258612672487893, Time taken: 311.2050988674164 seconds Epoch: 9, Loss: 3.483003616333008 Epoch: 9, Average Train Loss: 3.5687793647381745, Average Valid Loss: 3.62409432367845, Time taken: 311.68029260635376 seconds Epoch: 10, Loss: 3.4820141792297363 Epoch: 10, Average Train Loss: 3.5684012791049797, Average Valid Loss: 3.6230082620273936, Time taken: 311.22282123565674 seconds Epoch: 11, Loss: 3.4847073554992676 Epoch: 11, Average Train Loss: 3.567918264367281, Average Valid Loss: 3.624123398101691, Time taken: 310.99913930892944 seconds Epoch: 12, Loss: 3.4879515171051025 Epoch: 12, Average Train Loss: 3.567450798056425, Average Valid Loss: 3.625333491599921, Time taken: 310.98185873031616 seconds Epoch: 13, Loss: 3.485262393951416 Epoch: 13, Average Train Loss: 3.5671426356971945, Average Valid Loss: 3.6250820737896543, Time taken: 311.36055850982666 seconds Epoch: 14, Loss: 3.483027935028076 Epoch: 14, Average Train Loss: 3.565806134572047, Average Valid Loss: 3.622268743587263, Time taken: 311.6248605251312 seconds Epoch: 15, Loss: 3.4813599586486816 Epoch: 15, Average Train Loss: 3.5641346063903985, Average Valid Loss: 3.6236986445658133, Time taken: 311.5210154056549 seconds Epoch: 16, Loss: 3.4823060035705566 Epoch: 16, Average Train Loss: 3.5628339691307156, Average Valid Loss: 3.6223301923636235, Time taken: 311.5788004398346 seconds Epoch: 17, Loss: 3.482267141342163 Epoch: 17, Average Train Loss: 3.5627272088264785, Average Valid Loss: 3.6255278822147483, Time taken: 311.63774728775024 seconds Epoch: 18, Loss: 3.484595775604248 Epoch: 18, Average Train Loss: 3.5622248064882402, Average Valid Loss: 3.624577581882477, Time taken: 311.4593484401703 seconds Epoch: 19, Loss: 3.4822275638580322 Epoch: 19, Average Train Loss: 3.5611390410267356, Average Valid Loss: 3.624938767967802, Time taken: 311.02139925956726 seconds Epoch: 20, Loss: 3.4826202392578125 Epoch: 20, Average Train Loss: 3.5610133885430746, Average Valid Loss: 3.6282201156471716, Time taken: 311.3768696784973 seconds Epoch: 21, Loss: 3.479175090789795 Epoch: 21, Average Train Loss: 3.561582879875096, Average Valid Loss: 3.6201832077719946, Time taken: 310.644433259964 seconds Epoch: 22, Loss: 3.4808602333068848 Epoch: 22, Average Train Loss: 3.564162126965396, Average Valid Loss: 3.6246202606143374, Time taken: 310.5687975883484 seconds Epoch: 23, Loss: 3.4795570373535156 Epoch: 23, Average Train Loss: 3.5652912070995955, Average Valid Loss: 3.6242916367270728, Time taken: 310.62603640556335 seconds Epoch: 24, Loss: 3.481790065765381 Epoch: 24, Average Train Loss: 3.5656078845375845, Average Valid Loss: 3.623452957832452, Time taken: 311.1524329185486 seconds Epoch: 25, Loss: 3.4807748794555664 Epoch: 25, Average Train Loss: 3.565567792595113, Average Valid Loss: 3.6228528998114844, Time taken: 310.44076776504517 seconds Epoch: 26, Loss: 3.4812822341918945 Epoch: 26, Average Train Loss: 3.565412968737091, Average Valid Loss: 3.622271534168359, Time taken: 310.4774465560913 seconds Epoch: 27, Loss: 3.4829905033111572 Epoch: 27, Average Train Loss: 3.5651702132968395, Average Valid Loss: 3.621801087350556, Time taken: 310.73558282852173 seconds Epoch: 28, Loss: 3.4825916290283203 Epoch: 28, Average Train Loss: 3.5650173851745666, Average Valid Loss: 3.621133531584884, Time taken: 310.61714482307434 seconds Epoch: 29, Loss: 3.4815187454223633 Epoch: 29, Average Train Loss: 3.5648914144972883, Average Valid Loss: 3.620044368686098, Time taken: 310.6605079174042 seconds Epoch: 30, Loss: 3.4797537326812744 Epoch: 30, Average Train Loss: 3.564734306172273, Average Valid Loss: 3.6189178192254268, Time taken: 310.59446597099304 seconds Predicted output: <pad> <pad>
answer:Entiendo que los resultados actuales no son ideales para generar una predicción coherente. En este caso, puedes probar ajustando los hiperparámetros para mejorar el rendimiento del modelo. Adjustaremos algunos hiperparámetros, como el número de capas, la tasa de aprendizaje y el tamaño del lote. Ten en cuenta que estos cambios pueden afectar el tiempo de entrenamiento, pero podrían mejorar la calidad de las predicciones. Aquí hay una configuración de hiperparámetros sugerida: ```python vocab_size = len(vocab) d_model = 512 # Reduced to 512 nhead = 8 num_layers = 4 # Reduced to 4 num_epochs = 50 # Increased to 50 learning_rate = 5e-4 # Reduced to 5e-4 optimizer_choice = 'Adam' batch_size = 128 # Increased to 128 max_seq_len = 100 # Increased to 100 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") ``` Asegúrate de entrenar el modelo nuevamente con esta nueva configuración. No dudes en ajustar estos hiperparámetros aún más si no se ajustan a tus necesidades o limitaciones de hardware. El ajuste de hiperparámetros a menudo es una tarea experimental y puede requerir varias iteraciones para obtener resultados deseables. También puedes considerar el uso de herramientas como [Optuna](https://optuna.org/) para automatizar la búsqueda de hiperparámetros óptimos.
question:NameError Traceback (most recent call last) <ipython-input-2-c53e9b318f67> in <cell line: 47>() 45 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 46 ---> 47 train_batches = create_batches(train_data, batch_size, max_seq_len=max_seq_len) 48 valid_batches = create_batches(valid_data, batch_size, max_seq_len=max_seq_len) 49 NameError: name 'create_batches' is not defined
answer:Disculpa por la confusión. La variable `train_data` no existe en el código actual. En su lugar, se debe utilizar `train_data_raw` y `valid_data_raw` (por la nueva línea) al llamar a la función `create_batches()`. Vamos a hacer los ajustes necesarios en el código: ```python # Replace these two lines train_batches = create_batches(train_data_raw, batch_size, max_seq_len=max_seq_len) valid_batches = create_batches(valid_data_raw, batch_size, max_seq_len=max_seq_len) ``` Asegúrate de que cualquier referencia a `train_data` o `valid_data` sea actualizada para utilizar `train_data_raw` y `valid_data_raw` como se muestra arriba. Esta corrección resolverá el error "`NameError: name 'train_data' is not defined`" y permitirá que se ejecute el entrenamiento correctamente.
question:NameError: name 'create_batches' is not defined
answer:Mis disculpas nuevamente. Cometí un error y copié solo la parte modificada del código, olvidando incluir las funciones. A continuación, se muestra el código completo actualizado, incluidas las definiciones de funciones, cambios de hiperparámetros y el uso correcto de las variables `train_data_raw` y `valid_data_raw`. Ejecuta todo el código de una vez: ```python import torch import torch.nn as nn import time from torchtext.datasets import WikiText2 from torchtext.data.functional import to_map_style_dataset from torchtext.data.utils import get_tokenizer from torchtext.vocab import build_vocab_from_iterator from torch.utils.data import DataLoader # 1. Define the model class TransformerModel(nn.Module): def __init__(self, vocab_size, d_model, nhead, num_layers, dropout=0.1): super(TransformerModel, self).__init__() self.embedding = nn.Embedding(vocab_size, d_model) self.transformer = nn.Transformer(d_model, nhead, num_layers, dropout=dropout) self.fc = nn.Linear(d_model, vocab_size) def forward(self, src, tgt): src = self.embedding(src) tgt = self.embedding(tgt) x = self.transformer(src, tgt) x = self.fc(x) return x # 2. Preparar datos train_data_raw, valid_data_raw, _ = WikiText2(split=('train', 'valid', 'test')) train_data_raw = to_map_style_dataset(train_data_raw) valid_data_raw = to_map_style_dataset(valid_data_raw) tokenizer = get_tokenizer("spacy", "en_core_web_sm") # Agregar tokens especiales (UNK y PAD) al vocabulario specials = ['<unk>','<pad>'] vocab = build_vocab_from_iterator((tokenizer(y) for y in train_data_raw), specials=s