What is the best neural network architecture?

What is the best neural network architecture?

Popular Neural Network Architectures

• LeNet5. LeNet5 is a neural network architecture that was created by Yann LeCun in the year 1994.
• Dan Ciresan Net.
• AlexNet.
• Overfeat.
• VGG.
• Network-in-network.
• GoogLeNet and Inception.
• Bottleneck Layer.

What is the architecture of the neural network?

Usually, a Neural Network consists of an input and output layer with one or multiple hidden layers within. In a Neural Network, all the neurons influence each other, and hence, they are all connected.

How do you create a neural network architecture?

5 Guidelines for Building a Neural Network Architecture

1. KISS; yes, keep it simple.
2. Build, train, and test for robustness rather than preciseness.
3. Don’t over-train your network.
4. Keep track of your results with different network designs to see which characteristics work better for your problem domain.

How do you choose the number of hidden neurons?

The number of hidden neurons should be between the size of the input layer and the size of the output layer. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. The number of hidden neurons should be less than twice the size of the input layer.

How to choose the best neural network architecture?

According to the previous tutorial, the first step for deducing the best number of hidden layers and neurons is to visualize the samples in a 2D graph. Below is the Python code used for plotting the data. The data inputs are stored in the input_data variable, while the output_data variable stores their outputs.

Which is the most common type of neural network?

Generally, these architectures can be put into 3 specific categories: These are the commonest type of neural network in practical applications. The first layer is the input and the last layer is the output.

What do we call a deep neural network?

If there is more than one hidden layer, we call them “deep” neural networks. They compute a series of transformations that change the similarities between cases. The activities of the neurons in each layer are a non-linear function of the activities in the layer below.

Why are there only 2 hidden layers in a neural network?

Because there are just 2 lines, then there will be just a single connection (i.e. just single hidden neuron in the second hidden layer). We can use the output neuron to connect the lines created by the neurons of the first hidden layer. Thus, we avoided creating a second hidden layer.