Hi! Welcome to the RStudio Community and happy coding with Tensorflow!
SVMs
Things should be relatively easy (crossing fingers ). The usual place where to find all the "nonmainstream" Tensorflow stuff is tf.contrib
. Unfortunately, the SVM classes in tf.contrib.learn
are being deprecated, so probably it's not a good idea to use them. However, a SVM is basically a (L^2regularized) linear model (exactly the same model used in linear regression), where however instead of using the mean_squared_error
loss, you use hinge_loss
. You can take one of the numerous linear regression examples in Tensorflow, e.g.
https://tensorflow.rstudio.com/tensorflow/articles/examples/linear_regression_multiple.html
and then basically all you have to do is to substitute the crossentropy loss with a call to the hinge loss:
https://www.tensorflow.org/api_docs/python/tf/losses/hinge_loss
Note that the above script doesn't call the Tensorflow mean squared error loss
https://www.tensorflow.org/api_docs/python/tf/losses/mean_squared_error
(which is the way to go for production code) because it's a example thought for learners, thus the loss function is explicitly computed as
cost < tf$reduce_mean(tf$square(Y_hat  Y))
Random forests
Things get more dire with random forests! I've never implemented the Breitman algorithm from scratch myself...again, the usual place to find all these "nonmainstream" Tensorflow models is tf.contrib
, but in this case I could only find some Python code:
you may try to use the R reticulate to run Python code from R, but I don't know if it supports Tensorflow...or you could try to convert the above code to R code, but it's fairly complicated and I wouldn't suggest that you do that, if this is the beginning of your Tensorflow journey. Chances are the code calls some other class/module which you should then dig out...let's try a different angle of attack:

why do you need SVMs and random forests in Tensorflow for R? Do you have access to a powerful GPU cluster, or to cloud instances with GPUs? If you're going to run on locale, you have extremely efficient implementations of these models in R. For example https://cran.rproject.org/web/packages/ranger/index.html

if you really prefer to use Tensorflow, what about using a high level API such as Keras? https://keras.rstudio.com/ Keras basically wraps Tensorflow in a very userfriendly API. Linear regression is so simple in Keras that you don't even have an example for it, the simplest regression example in Keras already uses something more advanced than a linear regression! But you can easily convert it to a linear regression, just change
model < keras_model_sequential() %>%
layer_dense(units = 64, activation = "relu",
input_shape = dim(train_data)[2]) %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dense(units = 1)
to
model < keras_model_sequential() %>%
layer_dense(units = 1, activation = "linear",
input_shape = dim(train_data)[2])
and again, once you can do linear regression, building a (linear) SVM is only a matter of substituting the MSE loss with the hinge loss. I can't help you with random forests in Keras, though.
Hope this helps!