![]() ![]() Extremely flexible Bayesian probabilistic models can be implemented via Probabilistic Programming Languages (PPLs), which provide automatic inference via practical and efficient Markov Chain Monte Carlo (MCMC) sampling. We present a concrete implementation that translates probabilistic programs to interactive graphical representations and show illustrative examples for a variety of Bayesian probabilistic models.īayesian probabilistic modeling has many advantages it accounts for and represents uncertainty systematically it allows precise incorporation of prior expert knowledge and the intrinsic structure of models is well-defined in terms of relations among random variables: the mathematical and statistical dependencies are explicitly stated. The interpretability of Bayesian probabilistic programming models is enhanced through the interactive graphical representations, which provide human users with more informative, transparent, and explainable probabilistic models. This interactive graphical representation supports the exploration of the prior and posterior distribution of MCMC samples. We propose the automatic transformation of Bayesian probabilistic models, expressed in a probabilistic programming language, into an interactive graphical representation of the model's structure at varying levels of granularity, with seamless integration of uncertainty visualization. ![]() ![]() To support this, we see a need for visualization tools that make probabilistic programs interpretable to reveal the interdependencies in probabilistic models and their inherent uncertainty. This enables better assessment of the risk overall possible outcomes compatible with observations and thus more informed decisions. Decision-makers need simultaneous insight into both the model's structure and its predictions, including uncertainty in inferred parameters. However, the results of Bayesian inference are challenging for users to interpret in tasks like decision-making under uncertainty or model refinement. School of Computing Science, University of Glasgow, Glasgow, United Kingdomīayesian probabilistic modeling is supported by powerful computational tools like probabilistic programming and efficient Markov Chain Monte Carlo (MCMC) sampling.Test_results = trainer.Evdoxia Taka *, Sebastian Stein and John H. But -0.00 if loaded from saved or new model. Model.load_state_dict(torch.load(model_path_pt)) Model = DoodleTransformer.load_from_checkpoint(model_path, num_labels=340) # I tried with both lines below as well as each one separately. # not require weight_decay but just using AdamW out-of-the-box works fine # We could make the optimizer more fancy by adding a scheduler and specifying which parameters do Self.log("validation_accuracy", accuracy) Loss, accuracy = mon_step(batch, batch_idx) Self.log("accuracy", accuracy, on_step=True, on_epoch=False, prog_bar=True, logger=True)ĭef validation_step(self, batch, batch_idx): ![]() Logits = self.classifier(outputs.last_hidden_state)Ĭorrect = (predictions = labels).sum().item()ĭef training_step(self, batch, batch_idx): Outputs = self.vit(pixel_values=input_data) Self.classifier = nn.Linear(_size, num_labels) # Model Class #Ĭlass DoodleTransformer(LightningModule): Please find the necessary parts of my code here: from torch import nnįrom pytorch_lightning import LightningModuleįrom transformers import ViTModel, ViTConfigįrom transformers import ViTForImageClassification, AdamWįrom pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint I was using Google's Quick Draw dataset downloaded from Google's Kaggle Competition Could someone help me figure out what I am doing wrong? However, when I saved and later loaded the model, test accuracy had fallen back to 0. After 5 hours of training, test accuracy increased from 0.0 to 0.75. I'm trying to train a model with google's ViT model and an extra layer on a doodle dataset. ![]()
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