'Accuracy' Metrics in model.compile of Tensor Flow

 In the context of model.compile in TensorFlow/Keras, 'accuracy' is a metric used to evaluate the performance of a model during training and testing. It measures how often the model's predictions match the true labels.

What Does 'accuracy' Mean?

• 'accuracy' is a general metric that calculates the proportion of correct predictions over the total number of samples.
• It is useful for classification problems, where you want to know how many predictions the model got right out of all the predictions made.

Formula for Accuracy

$$\text{Accuracy} = \frac{\text{Number of Correct Predictions}}{\text{Total Number of Predictions}}$$

How 'accuracy' Works in model.compile

When you specify 'accuracy' as a metric in model.compile, TensorFlow/Keras automatically computes the accuracy during each epoch of training and evaluation. The accuracy is displayed in the training log, helping you monitor how well your model is learning.

Example Usage in model.compile

python

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

How 'accuracy' Works Internally

1. For Binary Classification: If the model outputs probabilities (e.g., using a sigmoid activation function), the predictions are thresholded (commonly at 0.5) to determine the class label (0 or 1). Accuracy is calculated based on how many predictions match the actual labels.

   • Example: If the true labels are [1, 0, 1] and the model predicts [0.8, 0.2, 0.9], the predicted labels are [1, 0, 1], resulting in an accuracy of 100%.

2. For Multi-Class Classification: If the model outputs class probabilities (e.g., using a softmax activation function), the class with the highest probability is chosen as the predicted label. Accuracy is then calculated based on how many predicted labels match the true labels.

   • Example: If the true labels are [2, 0, 1] and the model predicts classes [2, 0, 1], the accuracy is 100%.

3. One-Hot Encoded Labels: If your labels are one-hot encoded (e.g., [0, 1, 0]), you should use CategoricalAccuracy. However, 'accuracy' automatically handles one-hot encoded labels by treating them as categorical accuracy.

   • Sparse vs. Categorical:
     • sparse_categorical_crossentropy loss: Use 'accuracy' when labels are integers.
     • categorical_crossentropy loss: Use 'accuracy' or CategoricalAccuracy for one-hot encoded labels.

Variants of Accuracy

Depending on your use case, you may choose a more specific accuracy metric:

• 'binary_accuracy': Used for binary classification problems. It calculates accuracy using a threshold to determine the class labels.
• 'categorical_accuracy': Used for multi-class classification problems with one-hot encoded labels. It checks if the class with the highest probability is the same as the true label.
• 'sparse_categorical_accuracy': Used for multi-class classification problems with integer labels (not one-hot encoded). It compares the predicted class with the integer-encoded true label.

Summary

• 'accuracy' is a general-purpose metric for evaluating the proportion of correct predictions.
• It is suitable for binary or multi-class classification problems.
• TensorFlow/Keras automatically handles different types of labels and calculates accuracy appropriately.

Example for Multi-Class Classification

python

model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']  # Uses sparse categorical accuracy for integer labels
)

If you have one-hot encoded labels, you would specify:

python

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

Note

• 'accuracy' is an alias that Keras interprets as the appropriate form of accuracy (e.g., BinaryAccuracy, CategoricalAccuracy, or SparseCategoricalAccuracy) based on the loss function you are using.
• It simplifies the specification of metrics, so you don’t have to worry about choosing the exact metric for most common use cases.