What are feedforward artificial neural networks?

Artificial neural networks (ANNs) are the foundation of many modern machine learning techniques, mimicking the learning abilities of the human brain. One of the most basic types of ANNs is the feedforward artificial neural network (FANN).

In this Answer, we will delve into the world of FANNs, understand their structure, working, and see them in action with some Python code.

What is a feedforward artificial neural network?

In a feedforward artificial neural network, node connections are organized without forming cycles, distinguishing it from recurrent neural networks that incorporate cycles among the nodes. The term 'feedforward' comes from how information travels through the network - it moves forward, from the input layer, through the hidden layers, to the output layer, without looping back.

Anatomy of a feedforward artificial neural network

A feedforward artificial neural network comprises multiple layers of nodes or 'neurons.' These layers include:

Input layer: The network's starting point. Each node in this layer corresponds to one feature in the dataset. The number of nodes equals the number of features.

Note: To learn more about input layers in Keras , refer to this Answer.

Hidden layer(s): Hidden layers, situated between the input and output layers, constitute an essential part of a feedforward artificial neural network (FANN). The network can include one or multiple hidden layers, and each neuron within these layers employs a nonlinear activation function.
Output layer: The network's endpoint. The output layer transforms the values from the last hidden layer into a format suitable for the task at hand.

Let's visualize this with a simple diagram:

# Import necessary libraries
from keras.models import Sequential
from keras.layers import Dense
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
# Generate a dataset
data_features, data_labels = make_classification(n_samples=200, n_features=25, n_informative=20, n_redundant=5, random_state=42)
# Split the dataset into training set and test set
train_features, test_features, train_labels, test_labels = train_test_split(data_features, data_labels, stratify=data_labels, random_state=42)
# Define the model
neural_network = Sequential()
neural_network.add(Dense(50, input_dim=25, activation='relu'))
neural_network.add(Dense(50, activation='relu'))
neural_network.add(Dense(50, activation='relu'))
neural_network.add(Dense(1, activation='sigmoid'))
# Compile the model
neural_network.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Train the model
neural_network.fit(train_features, train_labels, epochs=200, batch_size=20)
# Evaluate the model
_, model_accuracy = neural_network.evaluate(test_features, test_labels)
print('Model Accuracy: %.2f' % (model_accuracy*100))

Code explanation

Lines 1–5: Import necessary libraries. Sequential and Dense are imported from keras for creating the neural network model. make_classification is imported from sklearn.datasets for generating a random n-class classification problem. train_test_split is imported from sklearn.model_selection for splitting arrays or matrices into random train and test subsets.

Line 7: Generate a dataset with 200 samples, 25 features, 20 informative features, and 5 redundant features. The generated dataset is stored in data_features and data_labels.

Line 11: Split the dataset into a training set and a test set. 75% of the data is used for training and 25% of the data is used for testing.

Line 14: Initialize a Sequential model. This is a linear stack of layers that you can add to sequentially.

Lines 15–18: Add layers to the model. The Dense function is used to create fully connected layers.

Note: To learn more about Keras dense layers, refer to this Answer.

The first layer has 50 neurons and expects 25 input features. The activation function used is ‘relu’. The second and third layers also have 50 neurons each. The final layer has 1 neuron (for binary classification) and uses the ‘sigmoid’ activation function.

Line 21: Compile the model. The loss function used is ‘binary_crossentropy’, which is suitable for binary classification problems. The optimizer used is ‘adam’, a popular choice for many machine learning models. The model will also measure ‘accuracy’ during training and testing.

Line 24: Train the model using the training data. The model will run through the data 200 times (epochs), and update the model weights after every 20 samples (batch_size).

Line 27: Evaluate the model using the test data. The model’s accuracy is printed out. The _ is a common convention for a variable that we won’t use (the evaluate function returns the loss value and the metrics value, but we only care about the latter).

Applications

Feedforward ANNs have a wide range of applications in real-world problems:

Image recognition: They are used in computer vision tasks for object detection, face recognition, and more.
Natural language processing: They are used for language translation, sentiment analysis, and other language-related tasks.
Medical diagnosis: They predict diseases based on symptoms and medical history.
Financial forecasting: They predict stock prices and other financial indicators.

Conclusion

Feedforward ANNs are fundamental in the field of artificial intelligence. They are the stepping stone to understanding more complex networks. The understanding of their structure, the mathematical formulas involved, and their visual representation are crucial in the journey of learning artificial intelligence and machine learning.

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