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Export fast.ai models to ONNX

fast.ai is an amazing library, allowing us to use a vast array of pre-trained models, but it also can be used as a foundation for new models.

The fast.ai tabular model is a great model that uses:

• an embedding layer for representing categorical features
• parametrized number of hidden layers modeling the continuous features
• applies batch normalization, droput and weight decay to regularize the model (prevents overfitting and allows the model to be trained faster)

The code is so simple, that it takes an small amount of time to check it out. After reading the code, I wanted to make my "own" version of the tabular model. Mainly I wanted to try a new way to compose the hidden layers. This model uses Batch Norm + Linear + Dropout + Activation Function (ReLU), but some papers claim that the Activation Function should be placed before the Batch Norm the Dropout layers (activation functions used to be zero ce

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from fastai import torch_core, layers
from fastai.basic_data import *
from fastai.core import *
from fastai.layers import embedding
from fastai.basic_train import Learner
from torch import nn, optim, as_tensor, Tensor
import torch
import logging

def tabularexperiment_learner(data:DataBunch, layers:Collection[int], emb_szs:Dict[str,int]=None, metrics=None,

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unlink('housePrices_cache', recursive = TRUE)

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```{r rf, cache=T}
# train random forest model
```

```{r cache=T, dependson=”rf”}
# predict using random forest model
```

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```{r}
eval.trainModels=T
eval.pcaOptimization=T
```

```{r eval=eval.pcaOptimization, include=eval.pcaOptimization}
# code applying PCA optimization
...

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```{r}
options(error=function() {save.image(file="error.rmd.RData")})
```

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# reset current environment (optional)
rm(list=ls())
# load environment from file
load("housePrices.rmd.RData")

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# reset current environment (optional)
rm(list=ls())
# load environment from file
load("housePrices.rmd.RData")

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```{r}
# save document's environment (replaces existing one)
save.image(file="housePrices.rmd.RData")
# archive (optional)
save.image(file=paste0("housePrices_", format(Sys.time(),"%d%m%y_%H%M"), ".rmd.RData"))
```