Categorical Crossentropy is a loss function that is used in multi-class classification tasks. These are tasks where an example can belong to one of many possible categories, and the model must decide which one.
For example, let’s say you have a model that’s trying to recognize different types of animals in images. The categories might be “cat”, “dog”, “bird”, “fish”, etc. When you train your model, you give it a bunch of images and tell it what the correct animal is for each one. The model makes a guess, and then the Categorical Crossentropy loss function measures how good or bad that guess was.
Now, what does “Sparse” mean in this context? Well, in machine learning, we often represent categories as “one-hot” vectors. For our animal example, we might represent “cat” as [1, 0, 0, 0], “dog” as [0, 1, 0, 0], “bird” as [0, 0, 1, 0], and “fish” as [0, 0, 0, 1]. This is a very clear way to represent categories, but it can be inefficient if we have a lot of categories.
That’s where “Sparse” comes in. Sparse Categorical Crossentropy allows us to represent the categories as integers instead. So “cat” might be 0, “dog” might be 1, “bird” might be 2, and “fish” might be 3. This is much more efficient, especially when dealing with a large number of categories.
Here’s a simple Python example using TensorFlow and Keras:
import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense # Let's say we have 4 categories num_categories = 4 # Create a simple model model = Sequential([ Dense(10, activation='relu', input_shape=(32,)), Dense(num_categories, activation='softmax'), ]) # Compile the model with Sparse Categorical Crossentropy loss model.compile(optimizer='adam', loss='sparse_categorical_crossentropy') # Let's say we have some input data and labels data = tf.random.normal((100, 32)) labels = tf.random.uniform((100,), minval=0, maxval=num_categories, dtype=tf.int32) # Train the model model.fit(data, labels, epochs=10)
In this example, we create a simple model with one hidden layer and a final layer with a size of
num_categories (4 in this case). We compile the model with the ‘adam’ optimizer and the ‘sparse_categorical_crossentropy’ loss function. Then we generate some random data and labels and train the model on this data. The labels are integers representing the categories, which is why we can use ‘sparse_categorical_crossentropy’.