Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Mastering TensorFlow 1.x

You're reading from   Mastering TensorFlow 1.x Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

Arrow left icon
Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788292061
Length 474 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Toc

Table of Contents (21) Chapters Close

Preface 1. TensorFlow 101 FREE CHAPTER 2. High-Level Libraries for TensorFlow 3. Keras 101 4. Classical Machine Learning with TensorFlow 5. Neural Networks and MLP with TensorFlow and Keras 6. RNN with TensorFlow and Keras 7. RNN for Time Series Data with TensorFlow and Keras 8. RNN for Text Data with TensorFlow and Keras 9. CNN with TensorFlow and Keras 10. Autoencoder with TensorFlow and Keras 11. TensorFlow Models in Production with TF Serving 12. Transfer Learning and Pre-Trained Models 13. Deep Reinforcement Learning 14. Generative Adversarial Networks 15. Distributed Models with TensorFlow Clusters 16. TensorFlow Models on Mobile and Embedded Platforms 17. TensorFlow and Keras in R 18. Debugging TensorFlow Models 19. Tensor Processing Units
20. Other Books You May Enjoy

Naive Neural Network policy for Reinforcement Learning

We proceed with the policy as follows:

  1. Let us implement a naive neural network-based policy. Define a new policy to use the neural network based predictions to return the actions:
def policy_naive_nn(nn,obs):
return np.argmax(nn.predict(np.array([obs])))
  1. Define nn as a simple one layer MLP network that takes the observations having four dimensions as input, and produces the probabilities of the two actions:
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(8,input_dim=4, activation='relu'))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy',optimizer='adam')
model.summary()

This is what the model looks like:

Layer (type)                 Output Shape              Param #   
===================...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image