What is Keras: Python For AI Explained

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A python coiled around a symbolic ai brain with keras modules integrated into it

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

With the rise of artificial intelligence (AI), the need for user-friendly and efficient programming tools has never been greater. Python, a versatile and popular programming language, has become a go-to for many AI developers, thanks to its simplicity and wide range of libraries. One such library is Keras, a high-level neural networks API that is designed to facilitate the creation and training of AI models.

Introduction to Keras

Keras was developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System). Its primary author and maintainer is François Chollet, a Google engineer. Keras’ user-friendly, modular, and extensible interface makes it a great tool for building neural networks.

While Keras can use Theano or Microsoft Cognitive Toolkit as backend, its default backend is TensorFlow, another popular library for creating AI models. This means that Keras acts as a user-friendly interface for the powerful, but complex, TensorFlow library.

Why Use Keras?

Keras is designed to be user-friendly, modular, and extensible. It provides clear and concise API, which avoids unnecessary complexity and makes it easier to debug and explore. This makes Keras a great choice for both beginners and experts in AI development.

Another advantage of Keras is its flexibility. It allows you to design any architecture of neural network, from simple linear models to complex multi-output models. It also supports convolutional networks, recurrent networks, and any combination of the two. This makes Keras suitable for a wide range of AI tasks.

Key Features of Keras

One of the key features of Keras is its model-building APIs. It provides two ways to build models: the Sequential model API and the Functional API. The Sequential model API allows you to create models layer-by-layer, which is great for simple neural networks. The Functional API, on the other hand, provides more flexibility, allowing you to create complex models with multiple inputs or outputs, shared layers, and non-sequential data flows.

Keras also provides a wide range of pre-processing functions and utilities to work with image and text data. These utilities make it easier to load and preprocess data, augment data, and generate batches of tensor image data with real-time data augmentation.

Using Keras for AI Development

Now that we have a basic understanding of what Keras is and its key features, let’s delve into how it can be used for AI development. The first step in using Keras is to install it. Keras can be installed via pip, a package manager for Python. Once installed, you can import it in your Python script with the command ‘import keras’.

The next step is to define your model. As mentioned earlier, Keras provides two APIs for this: the Sequential model API and the Functional API. The choice of API depends on the complexity of your model. For simple models, the Sequential API is often sufficient. For more complex models with multiple inputs or outputs, or models with shared layers, the Functional API is usually the better choice.

Building a Simple Model with the Sequential API

To build a model with the Sequential API, you start by creating an instance of the Sequential class. Then, you can add layers to the model using the ‘add’ method. Each layer is added on top of the previous layer. The first layer in the model needs to receive information about its input shape, which can be done by passing the ‘input_shape’ argument to it.

Here is an example of how to create a simple model with the Sequential API:


from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(32, input_shape=(500,)))
model.add(Dense(10, activation='softmax'))

In this example, we first import the necessary modules. Then, we create a Sequential model and add two layers to it. The first layer is a dense layer with 32 units and an input shape of (500,). The second layer is a dense layer with 10 units and a softmax activation function. The softmax function is often used in the output layer of a model for multi-class classification problems.

Training and Evaluating the Model

Once the model is defined, the next step is to compile it. During compilation, you need to define the loss function and the optimizer. The loss function is a measure of how well the model is doing on the training data. The optimizer is the algorithm used to update the weights of the model based on the loss function.

After compiling the model, you can train it on your data using the ‘fit’ method. The ‘fit’ method takes three main arguments: the training data, the training labels, and the number of epochs. An epoch is one complete pass through the entire training dataset.

Here is an example of how to compile, train, and evaluate a model:


from keras.optimizers import SGD

model.compile(loss='categorical_crossentropy',
              optimizer=SGD(lr=0.01, momentum=0.9),
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=10, batch_size=32)

loss_and_metrics = model.evaluate(x_test, y_test, batch_size=128)

In this example, we first compile the model with a categorical crossentropy loss function, a stochastic gradient descent (SGD) optimizer with a learning rate of 0.01 and a momentum of 0.9, and accuracy as the metric. Then, we train the model on the training data ‘x_train’ and ‘y_train’ for 10 epochs with a batch size of 32. Finally, we evaluate the model on the test data ‘x_test’ and ‘y_test’ with a batch size of 128.

Advanced Usage of Keras

While Keras is designed to be user-friendly and easy to use, it also offers a lot of flexibility and control for more advanced users. In this section, we will explore some of the advanced features of Keras, including custom layers, callbacks, and multi-GPU and distributed training.

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One of the strengths of Keras is its extensibility. If the built-in layers and functions do not meet your needs, you can create your own. Custom layers in Keras are created by subclassing the ‘Layer’ class and implementing the methods ‘build’, ‘call’, and optionally ‘compute_output_shape.

Creating Custom Layers

Creating a custom layer in Keras involves subclassing the ‘Layer’ class and implementing the following methods:

  • ‘build’: This is where you will define your weights. This method must set ‘self.built = True’, which can be done by calling ‘super([Layer], self).build()’.
  • ‘call’: This is where you will define the layer’s logic.
  • ‘compute_output_shape’: This is where you will define how to compute the output shape of the layer given the input shape. This method is not needed for layers where the output shape is the same as the input shape.

Here is an example of a custom layer that computes the element-wise square of its input:


from keras import backend as K
from keras.layers import Layer

class Square(Layer):
    def build(self, input_shape):
        super(Square, self).build(input_shape)

    def call(self, x):
        return K.square(x)

    def compute_output_shape(self, input_shape):
        return input_shape

In this example, we first import the necessary modules. Then, we define a new class ‘Square’ that inherits from the ‘Layer’ class. In the ‘build’ method, we simply call the ‘build’ method of the parent class. In the ‘call’ method, we define the logic of the layer, which is to compute the square of its input. In the ‘compute_output_shape’ method, we return the input shape, since the output shape is the same as the input shape.

Using Callbacks

Callbacks in Keras are functions that are applied at certain stages of the training process. You can use callbacks to get a view on internal states and statistics of the model during training. You can pass a list of callbacks to the ‘fit’ method of your model. The relevant methods of the callbacks will then be called at each stage of the training.

There are many built-in callbacks in Keras, such as ‘ModelCheckpoint’ for saving the model after each epoch, ‘EarlyStopping’ for stopping the training when the validation loss is no longer improving, and ‘TensorBoard’ for visualizing the training process. You can also create your own custom callbacks by subclassing the ‘Callback’ class and implementing the methods you need.

Here is an example of how to use the ‘ModelCheckpoint’ callback to save the model after each epoch:


from keras.callbacks import ModelCheckpoint

model = Sequential()
model.add(Dense(32, input_shape=(500,)))
model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

checkpoint = ModelCheckpoint('model-{epoch:03d}.h5',
                             monitor='val_loss',
                             verbose=0,
                             save_best_only=True,
                             mode='auto')

model.fit(x_train, y_train,
          epochs=10,
          callbacks=[checkpoint])

In this example, we first create a Sequential model and compile it. Then, we create a ‘ModelCheckpoint’ callback that saves the model to a file ‘model-{epoch:03d}.h5’ after each epoch. The ‘monitor’ argument is set to ‘val_loss’, which means that the model is saved only if the validation loss of the current epoch is lower than that of the previous epoch. The ‘mode’ argument is set to ‘auto’, which means that the decision to save the model is made automatically based on the monitored quantity. Finally, we pass the callback to the ‘fit’ method of the model.

Multi-GPU and Distributed Training

Keras supports multi-GPU and distributed training, which can be useful when dealing with large datasets or complex models. Multi-GPU training can be achieved by using the ‘multi_gpu_model’ function, which replicates a model on different GPUs and merges their outputs. This allows you to train your model on multiple GPUs and thus speed up the training process.

Distributed training, on the other hand, involves training the model on multiple machines. This can be achieved by using the ‘tf.distribute’ API, which is part of TensorFlow. This API provides several strategies for distributed training, such as ‘MirroredStrategy’ for synchronous training on multiple GPUs, and ‘TPUStrategy’ for training on Google’s Tensor Processing Units (TPUs).

Here is an example of how to use the ‘multi_gpu_model’ function to train a model on multiple GPUs:


from keras.utils import multi_gpu_model

model = Sequential()
model.add(Dense(32, input_shape=(500,)))
model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

parallel_model = multi_gpu_model(model, gpus=2)
parallel_model.compile(loss='categorical_crossentropy',
                       optimizer='adam',
                       metrics=['accuracy'])

parallel_model.fit(x_train, y_train, epochs=10)

In this example, we first create a Sequential model and compile it. Then, we create a parallel model by calling the ‘multi_gpu_model’ function with the original model and the number of GPUs as arguments. We compile the parallel model with the same loss function, optimizer, and metrics as the original model. Finally, we train the parallel model on the training data.

Conclusion

Keras is a powerful and flexible library for building and training AI models. Its user-friendly, modular, and extensible design makes it a great choice for both beginners and experts in AI development. Whether you are building a simple linear model or a complex multi-output model, Keras has the tools you need to make the process easy and efficient.

While this article provides a comprehensive overview of Keras, it only scratches the surface of what is possible with this library. Keras provides many more features and utilities that can help you in your AI development journey. So, if you are interested in AI and Python, I encourage you to explore Keras further.

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