Building Recurrent Neural Networks Using TensorFlow

Ahmet Taspinar walks us through creating a recurrent neural network topology using TensorFlow:

As we have also seen in the previous blog posts, our Neural Network consists of a tf.Graph() and a tf.Session(). The tf.Graph() contains all of the computational steps required for the Neural Network, and the tf.Session is used to execute these steps.

The computational steps defined in the tf.Graph can be divided into four main parts;

  1. We initialize placeholders which are filled with batches of training data during the run.

  2. We define the RNN model and to calculate the output values (logits)

  3. The logits are used to calculate a loss value, which then

  4. is used in an Optimizer to optimize the weights of the RNN.

As a lazy casual, I’ll probably stick with letting Keras do most of the heavy lifting.

Related Posts

Building an Image Classifier with PyTorch

Rogier van der Geer shows how you can use PyTorch to build out a Convolutional Neural Network for image classification: The tool that we are going to use to make a classifier is called a convolutional neural network, or CNN. You can find a great explanation of what these are right here on wikipedia. But we […]

Read More

xgboost and Small Numbers of Subtrees

John Mount covers an interesting issue you can run into when using xgboost: While reading Dr. Nina Zumel’s excellent note on bias in common ensemble methods, I ran the examples to see the effects she described (and I think it is very important that she is establishing the issue, prior to discussing mitigation).In doing that I ran into one more […]

Read More


July 2018
« Jun Aug »