The output format of a Neural network is largely determined by the specific task it is designed to perform.
Classification
Binary Classification
Single Output Node: This involves a single output node with a value between 0 and 1, representing the probability of the input belonging to the positive class.
Example: A spam classifier might output a value close to 1 for a spam email and a value close to 0 for a legitimate email.
Multiclass Classification
Multiple Output Nodes: Each class has its own output node, with values typically between 0 and 1, representing the probability of the input belonging to that class. These probabilities often sum to 1.
Example: An image classifier for different types of animals (cat, dog, bird) might output a vector like [0.2, 0.7, 0.1], indicating a 70% probability of the image being a dog.
Regression
Single Output Node: This involves a single output node representing a continuous value.
Example: A neural network predicting house prices would output a single value representing the predicted price.
SequencetoSequence Tasks
Sequence of Outputs: The output is often represented as a list or a tensor.
Example: A neural machine translation model would output a sequence of words or subword units in the target language.
Example Applications
- Machine Translation: Converts a sentence from one language to another.
- Text Summarization: Generates a concise summary from a longer text.
- Speech Recognition: Transcribes spoken language into written text.
Generative Tasks (e.g., Image Generation, Music Composition)
Data in the Same Format as the Input: The output is typically in the same format as the input data.
Example: An image generation model might output a tensor representing a generated image.
See Generative AI
Key Considerations
Activation Function: The choice of activation function in the output layer can significantly influence the output format.
Loss Functions: The loss function used during training also guides the output format. For example, binary crossentropy is commonly used for binary classification, while mean squared error is often used for regression.