Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Visualizing training with TensorBoard and Keras

Analyzing results (during or after training) is much more if we can visualize the metrics. A great tool for this is TensorBoard. Originally developed for TensorFlow, it can also be used with other frameworks such as Keras and PyTorch. TensorBoard gives us the ability to follow loss, metrics, weights, outputs, and more. In the following recipe, we'll show you how to use TensorBoard with Keras and leverage it to visualize training data interactively. 

How to do it...

  1. First, we import all the libraries in Python, as follows:
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping, TensorBoard, ModelCheckpoint
from keras.utils import to_categorical
  1. Let's load the cifar10 dataset for this example...