What is the process of computing R^2 using Tensorflow?

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The process of computing R^2 using Tensorflow involves the following steps:

1. Define the input placeholders for the features X and labels Y.
2. Define the model architecture using Tensorflow operations and variables.
3. Define the loss function, which could be the mean squared error or any other appropriate loss function.
4. Define the train operation using an optimizer like stochastic gradient descent, Adam, or RMSProp.
5. Initialize the Tensorflow session and run the training operation with the input data.
6. Evaluate the model's performance on a validation or test set by computing the R^2 score using the prediction and true labels.

To compute R^2, the formula is:

R^2 = 1 - (sum of squared errors / total sum of squares)

where the sum of squared errors is the sum of the squared difference between the predicted and true labels, and the total sum of squares is the sum of the squared difference between the true labels and their mean.

In Tensorflow, you can calculate R^2 by first computing the sum of squared errors and the total sum of squares using Tensorflow operations. Then, you can divide the difference between the total sum of squares and the sum of squared errors by the total sum of squares to get the R^2 value.