This assignment is a detailed approach based on the greedy layer-wise unsupervised pretraining protocol as described in section 15.1 of “Deep Learning” by Goodfellow et al. 

Pseudo-Code

  1. Initialize the Network:
    • Create an empty list for layers.
    • Define the number of layers and their dimensions.
  2. Greedy Layer-Wise Pretraining: For each layer:
    • Initialize the current layer (e.g., Restricted Boltzmann Machine (RBM) or Autoencoder).
    • Train the current layer using the output from the previous layer as input.
    • Store the learned weights and biases.
    • The output of the trained layer serves as the input for the next layer.
  3. Stack Layers:
    • Stack the layers sequentially to form the complete network.
  4. Supervised Fine-Tuning:
    • Train the entire network using supervised learning with labeled data.

Python Program

Here’s a simplified Python example demonstrating greedy layer-wise pretraining using autoencoders. This program uses scikit-learn for MLP (Multi-layer Perceptron) to represent the autoencoders.

import numpy as np
from sklearn.neural_network import MLPRegressor
from sklearn.preprocessing import StandardScaler

class Autoencoder:
    def __init__(self, input_dim, hidden_dim):
        self.model = MLPRegressor(hidden_layer_sizes=(hidden_dim,), max_iter=1, warm_start=True)
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim

    def train(self, X):
        self.model.fit(X, X)  # Train to reconstruct the input data

    def transform(self, X):
        return self.model.predict(X)

def greedy_layer_wise_pretraining(X, layer_dims):
    layers = []
    current_input = X
    
    for hidden_dim in layer_dims:
        autoencoder = Autoencoder(input_dim=current_input.shape[1], hidden_dim=hidden_dim)
        autoencoder.train(current_input)
        layers.append(autoencoder)
        current_input = autoencoder.transform(current_input)
    
    return layers

def supervised_fine_tuning(layers, X_train, y_train):
    class StackedAutoencoder:
        def __init__(self, layers):
            self.layers = layers
            self.supervised_model = None

        def forward(self, X):
            for layer in self.layers:
                X = layer.transform(X)
            return X

        def train(self, X, y):
            X_transformed = self.forward(X)
            self.supervised_model = MLPRegressor(hidden_layer_sizes=(50,), max_iter=100)
            self.supervised_model.fit(X_transformed, y)
    
    stacked_autoencoder = StackedAutoencoder(layers)
    stacked_autoencoder.train(X_train, y_train)
    return stacked_autoencoder

# Example usage
np.random.seed(0)  # For reproducibility
X_train = np.random.rand(100, 50)  # Example training data with 50 features
y_train = np.random.rand(100, 1)   # Example labels

layer_dims = [30, 20, 10]  # Dimensions for hidden layers
layers = greedy_layer_wise_pretraining(X_train, layer_dims)
stacked_autoencoder = supervised_fine_tuning(layers, X_train, y_train)

Explanation

  1. Autoencoder Class:
    • Initialization: Defines an autoencoder with a specified input and hidden dimension.
    • Train: Fits the model to reconstruct the input data (unsupervised training).
    • Transform: Uses the trained autoencoder to transform the input data.
  2. Greedy Layer-Wise Pretraining:
    • Trains each layer of the network in sequence, where each layer is an autoencoder.
    • The output of each layer serves as the input for the next layer.
  3. Supervised Fine-Tuning:
    • Stacks the pre-trained layers and then trains the entire network with supervised learning using a new regressor on top.

This example uses autoencoders for simplicity. In practice, you might use other models like RBMs, and the specific implementation details can vary depending on the architecture used.

References

  1. 15.1 Gready Layer-Wise Unsupervised Pretraining — dl 0.0.1 documentation. (2022). Readthedocs.io. https://deep-learning-study-note.readthedocs.io/en/latest/Part%203%20(Deep%20Learning%20Research)/15%20Representation%20Learning/15.1%20Gready%20Layer%20Wise%20Unsupervised%20Pretraining.html

  2. Huang, H. (2018, February 20). Representation Learning (1) — Greedy Layer-Wise Unsupervised Pretraining. Medium; Medium. https://medium.com/@andrehuang0/representation-learning-1-greedy-layer-wise-unsupervised-pretraining-de483ead2d0a