Book Image

Python: Advanced Guide to Artificial Intelligence

By : Giuseppe Bonaccorso, Rajalingappaa Shanmugamani
Book Image

Python: Advanced Guide to Artificial Intelligence

By: Giuseppe Bonaccorso, Rajalingappaa Shanmugamani

Overview of this book

This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: • Mastering Machine Learning Algorithms by Giuseppe Bonaccorso • Mastering TensorFlow 1.x by Armando Fandango • Deep Learning for Computer Vision by Rajalingappaa Shanmugamani
Table of Contents (31 chapters)
Title Page
About Packt
Tensor Processing Units

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.

TensorFlow RNN Cell Classes

The tf.nn.rnn_cell module contains the following classes for creating different kinds of cells in TensorFlow:




Provides simple RNN cell