Here is a simple example of knowledge distillation in python.
# Import the necessary libraries
import numpy as np
from keras.layers import Input, Dense
from keras.models import Model
from keras.utils import to_categorical
# Define the inputs and outputs for the model
inputs = Input(shape=(10,))
outputs = Dense(5, activation='softmax')(inputs)
# Create the large model and compile it
large_model = Model(inputs=inputs, outputs=outputs)
large_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Generate some dummy data for training
X = np.random.rand(100, 10)
y = to_categorical(np.random.randint(0, 5, 100))
# Train the large model on the dummy data
large_model.fit(X, y, epochs=10)
# Get the outputs of the large model on the training data
large_model_outputs = large_model.predict(X)
# Create the small model and compile it
small_inputs = Input(shape=(10,))
small_outputs = Dense(5, activation='softmax')(small_inputs)
small_model = Model(inputs=small_inputs, outputs=small_outputs)
small_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Train the small model using the outputs of the large model as targets
small_model.fit(X, large_model_outputs, epochs=10)In this example, we are creating a large model and a small model, both with 10 inputs and 5 outputs. The large model is trained on some dummy data, and then the outputs of the large model are used as targets for training the small model. By doing this, the small model is able to learn from the large model and achieve similar performance, but with a smaller size.