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

Using grid search for parameter tuning

Tuning is a time-consuming and computation-expensive task. Throughout this book, we've paid limited attention to tuning hyperparameters. Most were obtained with pre-chosen values. To choose the right values, we can use heuristics or an extensive grid search. Grid search is a popular method for parameter tuning in machine learning.

In the following recipe, we will demonstrate how you can apply grid search when building a deep learning model. For this, we will be using Hyperopt. 

How to do it...

  1. We start by importing the libraries used in this recipe:
import sys
import numpy as np

from hyperopt import fmin, tpe, hp, STATUS_OK, Trials

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 sklearn.model_selection import train_test_split
from keras.utils import to_categorical
from keras.callbacks import EarlyStopping, TensorBoard...