Chapter 7 Creating PyTorch classes

Last update: Thu Oct 22 16:46:28 2020 -0500 (54a46ea04)

7.1 Build a PyTorch model class

PyTorch classes cannot not directly be instantiated from R. Yet. We need an intermediate step to create a class. For this, we use reticulate functions like py_run_string() that will read the class implementation in Python code, and then assign it to an R object.

7.1.1 Example 1: One layer NN

py_run_string("import torch")
main = py_run_string(
"
import torch.nn as nn

class Net(nn.Module):
   def __init__(self):
       super(Net, self).__init__()
       self.layer = torch.nn.Linear(1, 1)

   def forward(self, x):
       x = self.layer(x)      
       return x
")


# build a Linear Rgression model
net <- main$Net()

The R object net now contains all the object in the PyTorch class Net.

7.1.2 Example 2: Logistic Regression

main <- py_run_string(
"
import torch.nn as nn

class LogisticRegressionModel(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(LogisticRegressionModel, self).__init__()
        self.linear = nn.Linear(input_dim, output_dim)

    def forward(self, x):
        out = self.linear(x)
        return out
")

# build a Logistic Rgression model
LogisticRegressionModel <- main$LogisticRegressionModel

The R object LogisticRegressionModel now contains all the objects in the PyTorch class LogisticRegressionModel.