TensorFlow for RNN

The basic workflow for creating RNN models in low-level TensorFlow library is almost the same as MLP:

• First create the input and output placeholders of shape (None, # TimeSteps, # Features) or (Batch Size, # TimeSteps, # Features)
• From the input placeholder, create a list of length # TimeSteps, containing Tensors of Shape (None, #Features) or (Batch Size, # Features)
• Create a cell of the desired RNN type from the tf.rnn.rnn_cell module
• Use the cell and the input tensor list created previously to create a static or dynamic RNN
• Create the output weights and bias variables, and define the loss and optimizer functions
• For the required number of epochs, train the model using the loss and optimizer functions

Let us look at the various classes available to support the previous workflow.