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

Implementing a convolutional autoencoder

In the previous chapter, we how to implement an autoencoder for the Street View House Numbers dataset. We got some decent results, but the output could definitely be improved. In the following recipe, we will show how a convolutional autoencoder produces better outputs.

How to do it...

  1. Let's start with importing the libraries as follows:
import numpy as np

from matplotlib import pyplot as plt
from keras.utils import np_utils
from keras.models import Sequential, Input, Model
from keras.layers.core import Dense, Dropout, Activation, Reshape, Flatten, Lambda
from keras.layers import Conv2D, MaxPooling2D, UpSampling2D
from keras.callbacks import EarlyStopping
  1. Next, we load the dataset and extract the data we will use in this recipe:
mat ='Data/train_32x32.mat')
mat = mat['X']
b, h, d, n = mat.shape
  1. Before feeding the data to our network, we pre-process the data:
#Convert all RGB-Images to greyscale
img_gray = np.zeros(shape...