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The Deep Learning with Keras Workshop

You're reading from   The Deep Learning with Keras Workshop Learn how to define and train neural network models with just a few lines of code

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800562967
Length 496 pages
Edition 1st Edition
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Authors (3):
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Matthew Moocarme Matthew Moocarme
Author Profile Icon Matthew Moocarme
Matthew Moocarme
Mahla Abdolahnejad Mahla Abdolahnejad
Author Profile Icon Mahla Abdolahnejad
Mahla Abdolahnejad
Ritesh Bhagwat Ritesh Bhagwat
Author Profile Icon Ritesh Bhagwat
Ritesh Bhagwat
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Table of Contents (11) Chapters Close

Preface
1. Introduction to Machine Learning with Keras 2. Machine Learning versus Deep Learning FREE CHAPTER 3. Deep Learning with Keras 4. Evaluating Your Model with Cross-Validation Using Keras Wrappers 5. Improving Model Accuracy 6. Model Evaluation 7. Computer Vision with Convolutional Neural Networks 8. Transfer Learning and Pre-Trained Models 9. Sequential Modeling with Recurrent Neural Networks Appendix

7. Computer Vision with Convolutional Neural Networks

Activity 7.01: Amending Our Model with Multiple Layers and the Use of softmax

Let's try and improve the performance of our image classification algorithm. There are many ways to improve its performance, and one of the most straightforward ways is by adding multiple ANN layers to the model, which we will learn about in this activity. We will also change the activation from sigmoid to softmax. Then, we can compare the result with that of the previous exercise. Follow these steps to complete this activity:

  1. Import the numpy library and the necessary Keras libraries and classes:
    # Import the Libraries 
    from keras.models import Sequential
    from keras.layers import Conv2D, MaxPool2D, Flatten, Dense
    import numpy as np
    from tensorflow import random
  2. Now, initiate the model with the Sequential class:
    # Initiate the classifier
    seed = 1
    np.random.seed(seed)
    random.set_seed(seed)
    classifier=Sequential()
  3. Add the first layer...
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