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

Stacked autoencoder in TensorFlow

The steps  to build a stacked autoencoder model in TensorFlow are as follows:

  1. First, define the hyper-parameters as follows:
learning_rate = 0.001
n_epochs = 20
batch_size = 100
n_batches = int(mnist.train.num_examples/batch_size)
  1. Define the number of inputs (that is, features) and outputs (that is, targets). The number of outputs will be the same as the number of inputs:
# number of pixels in the MNIST image as number of inputs
n_inputs = 784
n_outputs = n_inputs
  1. Define the placeholders for input and output images:
x = tf.placeholder(dtype=tf.float32, name="x", shape=[None, n_inputs])
y = tf.placeholder(dtype=tf.float32, name="y", shape=[None, n_outputs])
  1. Add the number of neurons for encoder and decoder layers as [512,256,256,512]:
# number of hidden layers
n_layers = 2
# neurons in each hidden layer
n_neurons = [512,256]
# add number of decoder layers:
n_layers = n_layers * 2
  1. Define the w and b parameters: