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Keras is a Python library which provides an API to work with Neural Networks and Deep Learning frameworks. Keras provides functions and modules which are extremely handy when dealing with various Deep Learning applications in Python.
By the end of this tutorial, you’ll have the knowledge of the following:
- What is Keras?
- Keras APIs
- Training a Neural Network in Keras
What is Keras?
To do Deep Learning, the most used library earlier was Tensorflow 1.x which was tough to deal with for beginners. It required a lot of coding to just make a basic 1 layer network. However, with Keras, the complete process of making the structure of a Neural Network and then training & tracking it became extremely easy.
Keras is a high-level API which can run on Tensorflow, Theano and CNTK backend. It gives us the ability to run experiments using neural networks using high-level and user-friendly API. It is also capable of running on CPUs and GPUs.
Keras APIs
Keras has 10 different API modules meant to handle modelling and training the neural networks. Let’s go over each of them to understand what all Keras has to offer.
Models
The Models API provides the functionality to build complex neural networks by adding/removing layers. The model can either be Sequential, meaning, that the layers will be stacked up sequentially with single input and output. The model can also be functional, meaning, with fully customizable models. This is what is used mostly in the industry.
The API also has the training module which provides methods to compile the model along with the optimizer and the loss function, a method to fit the model, a method to evaluate and to predict on new data as well. Moreover, it also has methods for training, testing and predicting on a batch data as well. Models API also has the saving and serialization of the models as well.
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Layers
Layers are the building blocks of any neural network. The Layers API offers a complete set of methods for building the neural net architecture. The Layers API has the Base Layer class which contains methods needed to build custom layers with custom weights and initializers.
It contains the Layer Activations class which consists of the various activation functions such as ReLU, Sigmoid, Tanh, Softmax, etc. The Layer Weight Initializers class offers methods to initialize weights using different ways.
It also consists of the Core Layers class which consists of classes required to add core layers like Dense layer, Activation Layer, Embedding layer, etc. The Convolution Layer class offers various methods to add different types of Convolution layers. The Pooling Layers class contains the methods required for different types of pooling such as Max Pooling, Average Pooling, Global Max Pooling and Global Average Pooling.
Callbacks
Callbacks are a way to track the training process of the model. With Callback enabled, various actions can be performed before or after the end of an epoch or a batch. With callbacks you can:
- Monitor the training metrics by logging the metrics in TensorFlow Board
- Saving the model to disk periodically
- Early stopping in case the loss is not decreasing significantly after a certain epochs
- View internal states and statistics of a model during training
Dataset Preprocessing
The data usually is in raw format and lying in arranged directories and needs to be preprocessed before feeding to the model for fitting. The Image Data Preprocessing class has many such dedicated functions. For example, the image data needs to be in the numerical array for which we can use the img_to_array function. Or if the images are present inside a directory and subfolders, we can use the image_dataset_from_directory function.
Data Preprocessing API also has classes for Time Series data and text data as well.
Optimizers
Optimizers are the backbone of any neural network. Every neural network works on optimizing a loss function to find the best weights for prediction. There are multiple kinds of optimizers which follow slightly different techniques to find optimal weights. All these optimizers are available in the Optimizers API – SGD, RMSProp, Adam, Adadelta, Adagrad, Adamax, Nadal, FTRL.
Losses
Loss functions are needed to be specified when compiling a model. This loss function would be optimized by the optimizer which was also passed as a parameter in the compile method. The three main loss classes are: Probabilistic Losses, Regression losses, Hinge losses.
Metrics
Metrics are used in every ML model to quantify its performance on test data. Metrics are similar to Loss Functions, except that they are used on test data. There are many Accuracy metrics available such as Accuracy, Binary Accuracy, Categorical Accuracy, etc. It also contains Probabilistic metrics such as Binary Cross Entropy, Categorical Cross Entropy, etc. There are metrics for checking false positives/negatives as well such as AUC, Precision, Recall, etc.
Apart from these classification metrics, it also has regression metrics such as Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, etc.
Also Read: The What’s What of Keras and TensorFlow
Keras Applications
The Keras Applications class consists of some prebuilt models along with pre-trained weights. These pre-trained models are used for the process of Transfer Learning. These pre-trained models are different depending on the architecture, number of layers, trainable weights, etc. Some of them are Xception, VGG16, Resnet50, MobileNet, etc.
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Understanding Neural Networks
Deep learning relies on neural networks, and knowing their principles is critical for efficiently training models with Keras. In this part, we will look at the fundamental ideas behind neural networks.
The structure and operation of the human brain-inspired neural networks. They are made up of linked nodes known as artificial neurons or perceptrons. These neurons are arranged in layers, with each layer executing a different set of calculations. In most cases, the layers are separated into an input layer, one or more hidden layers, and an output layer. Weights represent the connections between neurons and define the intensity and effect of the input on the output of each neuron. These weights are modified during the training phase to improve the model’s performance.
One of the neural networks’ key benefits is their capacity to learn from data. They use backpropagation, a technique in which mistakes are transmitted back through the network, allowing the model to repeatedly update the weights and improve its predictions. Keras offers a high-level, user-friendly interface for creating neural networks. It includes a variety of pre-defined layers, including dense layers, convolutional layers, and recurrent layers, that may be readily layered together to form complicated designs. Keras also enables for customization, allowing users to design their own layers and functions.
The Advantages of Using Keras for Deep Learning
Keras is extensively used in the deep learning field because of its various benefits and features that make model creation easier. Let’s look at some of the main benefits of utilizing Keras.
User-Friendly API: Keras provides a user-friendly and straightforward API that enables even novices to comprehend and create deep learning models easily. Because of its simplicity and ease of use, it is suitable for a wide spectrum of users, from academics to developers.
Modular: Keras is modular and extensible, allowing users to create and customize neural network layers simply. This modular approach supports code reusability and allows for experimentation with multiple architectures.
TensorFlow support: Keras serves as a high-level wrapper for TensorFlow, one of the most prominent deep learning frameworks. This integration combines the best of both worlds: Keras’s simplicity and TensorFlow’s processing capacity.
Keras supports other backends in addition to TensorFlow, including Theano and Microsoft Cognitive Toolkit (CNTK). This adaptability allows customers to use their preferred backend while still reaping the benefits of Keras’ simplicity and functionality.
Comprehensive Documentation and Community Support: Keras has a well-documented API that makes it simple to access relevant information and examples. It also has a thriving community that actively participates in forums, discussion boards, and open-source projects, offering assistance and information.
Seamless GPU Integration: Deep learning models often require significant computational power, which GPUs efficiently provide. Keras seamlessly integrates with GPUs, allowing users to harness their parallel processing capabilities and accelerate training and inference.
Training a Neural Network with Keras
Let’s consider a simple dataset such as MNIST which is available in the datasets class of Keras. We will make a simple sequential Convolutional Neural Network for classifying the handwritten images of digits 0-9.
| #Loading the dataset from keras.datasets import mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() |
Normalizing the dataset by dividing each pixel by 255. Also, it is needed to transform the images into 4 dimensions before feeding it to a CNN model.
| x_train = x_train.astype(‘float32’) x_test = x_test.astype(‘float32’) x_train /= 255 x_test /= 255 x_train = X_train.reshape(X_train.shape[0], 28, 28, 1) x_test = X_test.reshape(X_test.shape[0], 28, 28, 1) |
We need to label encode the classes before feeding it to the model. We’ll do that by using Keras’ Utils class.
| from keras.utils import to_categorical
|
Now, we can start creating the model using Sequential API.
| from keras.models import Sequential from keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropoutmodel = Sequential()model.add(Conv2D(filters=32, kernel_size=(5,5), activation=‘relu’, input_shape=x_train.shape[1:])) model.add(MaxPool2D(pool_size=(2, 2))) model.add(Dropout(rate=0.25)) model.add(Conv2D(filters=64, kernel_size=(3, 3), activation=‘relu’)) model.add(MaxPool2D(pool_size=(2, 2))) model.add(Dropout(rate=0.25)) model.add(Flatten()) model.add(Dense(256, activation=‘relu’)) model.add(Dropout(rate=0.5)) model.add(Dense(10, activation=‘softmax’)) |
In the above code, we declare a sequential model and then add multiple layers to it. The convolutional layer, followed by a Max Pooling layer and then a dropout layer for regularization. Later we flatten the output using the flatten layer and the last layer is a fully connected dense layer with 10 nodes.
Next, we need to compile it by passing the loss function, the optimizer and the metric.
| model.compile( loss=‘categorical_crossentropy’, optimizer=‘adam’, metrics=[‘accuracy’] ) |
Now we’d need to add augmented images from the original images to increase the training set and accuracy of the model. We will do that using the ImageDataGenerator function.
| from keras.preprocessing.image import ImageDataGenerator datagen = ImageDataGenerator( rotation_range=10, zoom_range=0.1, width_shift_range=0.1, height_shift_range=0.1 ) |
Now that the model is compiled, and images augmented, we can start the training process. As we have used an Image Data Generator above, we will use the fit_generator method rather than just fit.
Related Read: Deep Learning & Neural Networks with Keras
| epochs = 3 batch_size = 32 history = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size), epochs=epochs, validation_data=(x_test, y_test), steps_per_epoch=x_train.shape[0]//batch_size) |
The output of the training process is as follows:
3Now the model is trained and can be evaluated by running it on unseen test data.
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Before you go
In this tutorial, we saw how well Keras is structured and makes it easy for a complex neural network to be built. Keras is now wrapped under Tensorflow 2.x which gives it even more features. Try out more such examples and explore the functions and features of Keras.
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