Keras: A Python Deep Learning Tutorial in Its Entirely

Keras is an open-source, deep learning library written in the Python programming language and is built on the foundation of TensorFlow, which is a machine learning framework. Google AI researcher Francois Chollet is the developer of Keras, and several major companies, such as Google, Square, Netflix, Huawei, and Uber, are integrating Keras into their systems.

Within this book, readers will be presented with an introduction to deep learning, as well as instructions on how to install the Keras library and gain an understanding of its models, layers, modules, and uses.

Who or what is Keras?

Keras is an application programming interface (API) designed to prioritise the needs of humans and simplify the process of experimentation. It implements standards to reduce the cognitive load on users, such as standardising and simplifying APIs, minimising the number of steps required to perform common tasks, and providing helpful feedback when errors occur. To ensure successful implementation, Keras also provides comprehensive documentation and guidelines for programmers.

Exactly what’s up with the name “Keras?”

The ancient Greek and Latin literatures have long employed the literary image of a horn, which originates from Homer’s Odyssey, to refer to the word “keras”. This image symbolises a dream-like vision which is manifested in two ways: Oneiroi, or dream spirits, who enter through an ivory gate and deceive dreamers, and dream spirits who enter through a horn gate and reveal a future that will come to pass. Recently, researchers involved in the ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System) project have implemented the use of Keras as part of their work.

Keras Characteristics

Keras is a popular choice among deep-learning APIs because to its many useful features.

  • Consistency, usability, and scalability are its defining characteristics.
  • As fast as you can think, it can iterate.
  • Exascale machine learning is now possible.
  • It has universal applicability.
  • The environment there is enormous.
  • It may pave the way for innovative studies.
  • This framework may be used with either a central processing unit or a graphics processing unit.

Keras Architecture

The three primary parts of Keras are the model, the layers, and the core modules.

  1. Models

    It is possible to classify models built using Keras in two ways:

    Modelling in sequence

    The keras has distinct layers that follow a certain pattern. The sequential model may be used to represent any and all known neural networks.

    Provider Interface (API) that Actually Works

    Complex models may be constructed using this method more easily.
  2. Layers

    It has been established that there is a direct correlation between the layers of a Keras model and the underlying neural network model. In order to further explain this concept, the following provides a comprehensive breakdown of the various layers of a Keras model:
    • Building up from lower levels
    • Number of convolution layers
    • Essential tiers
    • Continually reappearing layers
  3. Fundamental parts

    Keras offers the following capabilities for working with neural networks:
    • Module for activations: Activation procedures like relu, softmax, and others are available in this module.
    • Modulus optimizer: This package includes many optimizers like sgd and adm.
    • Corrections, or Regularizers: The L 1 Regularizer and L 2 Regularizer tools are available to you here.
    • – the Grief unit: Poisson, mean absolute error, and other loss functions are included here.

Deep learning using Keras

Deep learning is a sub-discipline of the wider field of machine learning, which focuses on the use of layered analysis of input data. This approach is characterised by the use of several core principles, such as:

  1. A Network of Synthetic Neurons (ANN)

    Artificial Neural Networks (ANNs) are by far the most widely used deep learning methods due to their model of the human brain. The nodes in their hidden layers are structured to resemble neurons in the human brain, connecting them in the same way. This is what makes ANNs so popular and effective.

    The data that is initially entered is handled by the nodes in the internal layer, which then transmit it to additional hidden layers. From there, the anticipated result is calculated. To provide a specific illustration, the output layer is responsible for producing the expected outcome.
  2. “multi-layer perceptron” (MLP)

    A multilayer perceptron is a type of artificial neural network (ANN) that starts with a single input layer, followed by multiple hidden layers, and culminating in a single output layer. Each hidden layer processes some of the input and passes it on to the next layer. Each of these subsurface layers can contain either a single neuron or several neurons, depending on the specific application. The last hidden layer then transfers the information to the layer responsible for producing an output. Finally, the output layer generates the desired output.
  3. Synaptic convolutional neural network (CNN)

    Convolutional Neural Networks (CNNs) are one of the most widely-recognised types of Artificial Neural Networks (ANNs). They are based on the mathematical concept of convolution, which is used extensively in applications relating to video and image recognition. In essence, CNNs are identical to a Multilayer Perceptron (MLP), the most basic form of ANN, in every way except for the fact that the layers of neurons are organised in a three-dimensional structure. This three-dimensional structure enables the network to detect patterns in both spatial and temporal contexts. Below, we will examine the main components of a CNN in more detail.
    • “Convolution layer”: The basic building block that uses the convolution function to carry out arithmetic operations.
    • Thickness of the Layer Where Water collects: This layer follows the convolution layer and serves to reduce the size of the input by discarding unnecessary information.
    • Layer with complete connectivity: Layer that, in addition to the pooling and convolution layers, organises data into several classes.
  4. For the time being, let’s call it a recurrent neural network (RNN)

    Recurrent Neural Networks (RNNs) are a type of Artificial Neural Network (ANN) that are useful for recognising patterns in data. They are commonly employed in image classification systems to help identify and track user preferences and behaviours. In addition, Bidirectional RNNs may be used to generate future predictions based on past data.

Guidelines for Putting in Keras

If you have installed Anaconda, the package management system pip will already be configured for you. To quickly verify this, you can type ‘$ pip’ into the command line; this should provide you with a list of options and commands to work with.

An Introduction to Keras for the Ignorant

  1. Acquire 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 sets by loading randomly-shuffled MNIST data.

    (X_train, y_train), (X_test, y_test) = mnist.load_data()
  3. Prepare the groundwork for receiving 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. Prepare labelling for classes

    Y_train = np_utils.to_categorical(y_train, 10)
    Y_test = np_utils.to_categorical(y_test, 10)
  5. Specify the structure of the model.

    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(MaxPooling2D(pool_size=(2,2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(10, activation='softmax'))
  6. Model compilation

    model.compile(loss='categorical_crossentropy',
    optimizer='adam',
    metrics=['accuracy'])
  7. A model was fitted to the training data.

    model.fit(X_train, Y_train,
    batch_size=32, nb_epoch=10, verbose=1)
  8. Putting the model to the test

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

Usage of Keras

Keras is a powerful deep learning platform that enables developers to quickly and efficiently create pre-trained models for use in a variety of applications, such as prediction, feature extraction, and tweaking. One of the key advantages of Keras is the automatic downloading of weights when a model is created, enabling developers to benefit from the platform’s top-tier speed and scalability. This technology eliminates the need for developers to spend time and energy on process-oriented tasks, freeing their minds to focus on the most important aspects of their work. Additionally, Keras provides a straightforward and uncomplicated workflow, making complex tasks achievable and simple tasks fast and convenient. Ultimately, Keras provides an invaluable tool for developers to make use of in their projects.

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