Book Image

Keras Deep Learning Cookbook

By : Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra
Book Image

Keras Deep Learning Cookbook

By: Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

Overview of this book

Keras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy. The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Keras functional APIs


Keras functional APIs provide each layer as a function.

How to do it...

  1. To use the functional APIs, you need to import the following classes from the keras package:
from keras.layers.core import dense, Activation
  1. Let's use the preceding imported layers as part of the Sequential model:
from keras.models import Sequential
from keras.layers.core import dense, Activation
model = Sequential([
  dense(32, input_dim=784),
  Activation("sigmoid"),
  dense(10),
  Activation("softmax"),
])
model.compile(loss="categorical_crossentropy", optimizer="adam")
  1. Let's run the previous functional API-based model on MNIST:
from keras.utils import plot_model
from keras.layers import Flatten
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.datasets import mnist
import keras

num_classes = 10
batch_size = 32
epochs = 10
batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train...