Artificial neural networks are similar to the human brain. As in the human brain, neurons are connected to each other; similarly, in ANN, neurons are connected to process data and train a model. The neuron produces an output which becomes an input of another neuron. A neuron takes data as an input process and transmits it to the next neuron connected to it.
An artificial neural network(ANN) is a popular tool used in machine learning. ANNs are used in facial recognition, image classification, object detection, weather forecasting, financial forecasting, etc. To perform such tasks, neural networks are trained by giving them thousands of input data so that a model can differentiate between similar things. To process such wide-ranging data, GPU must be used.
A neural network consists of an input layer, output layer, and hidden layer, which has many units that transform the input into something meaningful that can be used by the output layer; hidden layers are required to separate the data non-linearly.
Create an artificial neural network using Keras
To create an artificial neural network, you can install Keras. You can do so by using pip:
After installing Keras, you need to import specific libraries.
Lets’s see what these import libraries are used for,
Sequential: The sequential model is a linear stack of layers; these are used for creating model layers by layers.you can add layers by using add() one by one.
Activation: It provides an activation function to output. Here, in this example, we have used RELU and SOFTMAX activation functions.
Categorical_crossentropy: It is also known as Softmax Loss. It is both Softmax Activation as well as Cross-Entropy Loss.
After importing libraries, we have to create a Sequential model:
Input_shape: It defines the integers, or we can say arrays. In this example, we have used a single integer, which is an 1D array.
RELU: Relu stands for ‘rectified linear unit.’ It is the most commonly used activation function in neural networks.mathematically we can define it as y = max(0,x).
The advantage of using RELU is that it does not activate all the neurons at the same time, which means that the neurons will only be deactivated if the output of the linear transformation is less than 0.
Softmax: It is a combination of multiple sigmoids, used for binary classification problems.
After building a model, let’s see the summary of the model; we can do so by using the summary() function.
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