Keras: A Python Deep Learning Tutorial in Its Entirely

Keras is a Python-based, open-source deep learning library that makes use of the TensorFlow machine learning framework. Keras was developed by Francois Chollet, a Google AI researcher, and it is being used by major corporations such as Google, Square, Netflix, Huawei, and Uber as an integral part of their systems.

This book provides an overview of deep learning and step-by-step instructions on installing the Keras library. It also covers the fundamentals of Keras, including its models, layers, modules, and applications.

What is Keras?

Keras is an API (application programming interface) that is designed to ease the experimentation process and prioritise user needs. To reduce cognitive load on users, it follows standards such as providing simple and standardised APIs, minimising the number of steps required for common tasks, and generating helpful feedback when errors occur. To assist programmers, Keras provides extensive documentation and guidelines to ensure a successful implementation.

Why is it called “Keras?”

The name “Keras” is derived from the ancient Greek and Latin literary image of a horn, which was first mentioned in Homer’s Odyssey. This image represents a dream-like vision that can be either deceitful or revealing. Deceitful dreams are symbolised by the Oneiroi, or dream spirits, who enter through an ivory gate, while revealing dreams are symbolised by dream spirits who enter through a horn gate and provide insight into the future. Keras is now being used by researchers involved in the ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System) project.

Characteristics of Keras

One reason Keras is a favourite among deep-learning APIs is due to its numerous useful features.

  • Keras is characterised by its consistency, usability, and scalability.
  • Keras has the ability to iterate at the speed of thought.
  • The possibility of exascale machine learning is now achievable with Keras.
  • Keras has a wide range of applications.
  • The Keras environment is vast.
  • Keras has the potential to facilitate groundbreaking research.
  • Keras framework can be used with both central processing units (CPUs) and graphics processing units (GPUs).

Architecture of Keras

Keras can be divided into three main components: the model, layers, and core modules.

  1. Models

    There are two ways to classify models created with Keras:

    Sequential Modelling

    Keras has specific layers that follow a predetermined pattern. The sequential model can represent all known neural networks.

    Functional API

    Creating complex models is easier using this approach.
  2. Layers

    There is a direct relationship between the layers in a Keras model and the underlying neural network model. To further clarify this notion, the following is a detailed breakdown of the different layers of a Keras model:
    • Constructing from lower levels
    • Quantity of convolution layers
    • Crucial layers
    • Recurring layers
  3. Key components

    Keras provides the following functionalities for working with neural networks:
    • Activation module:

      This module includes activation procedures such as relu, softmax, and others.
    • Optimiser module:

      This package includes a range of optimisers such as sgd and adm.
    • Regularisation:

      This module includes tools such as L1 Regulariser and L2 Regulariser.
    • Loss functions module:

      This module includes loss functions like Poisson, mean absolute error, and others.

Keras for Deep Learning

Deep Learning is a sub-field of Machine Learning that emphasises on the use of layered analysis of input data. The approach is distinguished by employing key principles such as:

  1. Artificial Neural Networks (ANN)

    ANNs are the most commonly used deep learning methods owing to their brain-like model. The nodes in the hidden layers are organised to mimic neurons in the human brain, forming connections in the same manner. This is what makes ANNs so widely adapted and effective.

    The data is initially processed by the nodes in the input layer which then transmit it to subsequent hidden layers. From there, the anticipated outcome is computed. For instance, the output layer is tasked to generate the expected result.
  2. “Multi-Layer Perceptron” (MLP)

    A multi-layer perceptron is a form of artificial neural network (ANN) that commences with a single input layer, progresses through multiple hidden layers, and terminates in a solitary output layer. Each hidden layer handles a portion of the input and transmits it to the subsequent layer. Each of these internal layers can hold one neuron or numerous neurons, depending on the application requirements. The last hidden layer transfers the processed information to the layer responsible for the output generation. Finally, the output layer produces the desired output.
  3. Convolutional Neural Network (CNN)

    Convolutional Neural Networks (CNNs) are a widely recognised type of Artificial Neural Networks (ANNs). They are based on the mathematical concept of convolution, which is extensively used in applications dealing with video and image analysis. Essentially, CNNs are similar to the most fundamental type of ANN- Multilayer Perceptron (MLP), except that the neurons within the network are organised in a three-dimensional structure. This three-dimensional structure enables the network to recognise patterns in both spatial and temporal contexts. The main components of a CNN will be discussed in more detail below.
    • “Convolution Layer”:

      This fundamental building block utilises the convolution function to execute arithmetic operations.
    • Pooling Layer:

      This layer follows the convolution layer and is designed to reduce the input’s size by removing redundant information.
    • Fully Connected Layer:

      This layer, in addition to the convolution and pooling layers, categorises data into multiple classes.
  4. Introducing Recurrent Neural Networks (RNN)

    Recurrent Neural Networks (RNNs) are Artificial Neural Networks (ANNs) that are effective in pattern detection within data. They are often utilised in image classification systems to identify and monitor user preferences and behaviours. Furthermore, Bidirectional RNNs may be utilised to forecast future patterns based on previous data.

Instructions for Implementing Keras

If you have installed Anaconda, then the package management system ‘pip’ is automatically configured for you. To verify this, type ‘$ pip’ in the command line; this should provide you with a list of options and commands to execute.

A Beginner’s Guide to Keras

  1. Obtain the Source Code and Modules

    import numpy as np
    np.random.seed(123) # for reproducibility

    from keras.models import Sequential
    from keras.layers import Dense, Dropout, Activation, Flatten
    from keras.layers import Convolution2D, MaxPooling2D
    from keras.utils import np_utils
    from keras.datasets import mnist
  2. Prepare Training and Testing Datasets by Loading MNIST Data in Random Order.

    (X_train, y_train), (X_test, y_test) = mnist.load_data()
  3. Set Up the Foundation to Receive Feedback

    X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
    X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)
    X_train = X_train.astype('float32')
    X_test = X_test.astype('float32')
    X_train /= 255
    X_test /= 255
  4. Set Up Labeling for Classes

    Y_train = np_utils.to_categorical(y_train, 10)
    Y_test = np_utils.to_categorical(y_test, 10)
  5. Define the Model Structure.

    model = Sequential()

    model.add(Convolution2D(32,3,3,activation='relu', input_shape=(1,28,28)))
    model.add(Convolution2D(32,3,3, activation='relu'))

    model.add(Dense(128, activation='relu'))
    model.add(Dense(10, activation='softmax'))
  6. Compiling the Model

  7. The Model was Trained on the Training Data., Y_train,
    batch_size=32, nb_epoch=10, verbose=1)
  8. Evaluating the Model Performance

    score = model.evaluate(X_test, Y_test, verbose=0)

Using Keras

Keras is an advanced deep learning platform that empowers developers to swiftly and effectively generate pre-trained models for various applications such as prediction, feature extraction, and customization. One of the primary benefits of using Keras is its ability to automatically download weights during model creation, enabling developers to leverage the platform’s ultimate speed and scalability. This eliminates the need for developers to spend significant time and effort on process-oriented tasks, allowing them to focus on critical elements of their work. Moreover, Keras provides an uncomplicated and easy-to-follow workflow, making complex tasks achievable and simple tasks quick and convenient. Ultimately, Keras is an invaluable resource for developers to implement in their projects.

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