Building a Deep Learning Model From Scratch in PyTorch

Background A neural network in 6 steps

In Building a Simple Neural Network From Scratch in PyTorch, we described a recipe with 6 functions as follows:

  1. train_model(epochs=30, lr=0.1): This function acts as the outer wrapper of our training process. It requires access to the training data, trainingIn and trainingOut, which should be defined in the environment. train_model orchestrates the training process by calling the execute_epoch function for a specified number of epochs.
  2. execute_epoch(coeffs, lr): Serving as the inner wrapper, this function carries out one complete training epoch. It takes the current coefficients (weights and biases) and a learning rate as input. Within an epoch, it calculates the loss and updates the coefficients. To estimate the loss, it calls calc_loss, which compares the predicted output generated by calc_preds with the target output. After this, execute_epoch performs a backward pass to compute the gradients of the loss, storing these gradients in the grad attribute of each coefficient tensor.
  3. calc_loss(coeffs, indeps, deps): This function calculates the loss using the given coefficients, input predictors indeps, and target output deps. It relies on calc_preds to obtain the predicted output, which is then compared to the target output to compute the loss. The backward pass is subsequently invoked to compute the gradients, which are stored within the grad attribute of the coefficient tensors for further optimization.
  4. calc_preds(coeffs, indeps): Responsible for computing the predicted output based on the given coefficients and input predictors indeps. This function follows the forward pass logic and applies activation functions where necessary to produce the output.
  5. update_coeffs(coeffs, lr): This function plays a pivotal role in updating the coefficients. It iterates through the coefficient tensors, applying gradient descent with the specified learning rate lr. After each update, it resets the gradients to zero using the zero_ function, ensuring the gradients are fresh for the next iteration.
  6. init_coeffs(n_hidden=20): The initialization function is responsible for setting up the initial coefficients. It shapes each coefficient tensor based on the number of neurons specified for the sole hidden layer.
  7. model_accuracy(coeffs): An optional function that evaluates the prediction accuracy on the validation set, providing insights into how well the trained model generalizes to unseen data.

In this blog post, we’ll take a deep dive into constructing a powerful deep learning neural network from the ground up using PyTorch. Building upon the foundations of the previous simple neural network, we’ll refactor some of these functions for deep learning.

Deep Learning Refactor code for multiple hidden layers

Initializing Weights and Biases

To prepare our neural network for deep learning, we’ve revamped the weight and bias initialization process. The init_coeffs function now allows for specifying the number of neurons in each hidden layer, making it flexible for different network configurations. We generate weight matrices and bias vectors for each layer while ensuring they are equipped to handle the deep learning challenges.

	def init_coeffs(hiddens=[10, 10]):
    sizes = [trainingIn.shape[1]] + hiddens + [1]
    n = len(sizes)
    weights = [(torch.rand(sizes[i], sizes[i+1]) - 0.3) / sizes[i+1] * 4 for i in range(n-1)]  # Weight initialization
    biases = [(torch.rand(1)[0] - 0.5) * 0.1 for i in range(n-1)]  # Bias initialization
    for wt in weights: wt.requires_grad_()
    for bs in biases: bs.requires_grad_()
    return weights, biases

We define the architecture’s structure using sizes, where hiddens specifies the number of neurons in each hidden layer. We ensure that weight and bias initialization is suitable for deep networks.

Forward Propagation With Multiple Hidden Layers

Our revamped calc_preds function accommodates multiple hidden layers in the network. It iterates through the layers, applying weight matrices and biases at each step and introducing non-linearity using the ReLU activation function in the hidden layers and the sigmoid activation in the output layer. This enables our deep learning network to capture complex patterns in the data.

	def calc_preds(coeffs, indeps):
    weights, biases = coeffs
    res = indeps
    n = len(weights)
    for i, wt in enumerate(weights):
        res = res @ wt + biases[i]
        if (i != n-1):
            res = F.relu(res)  # Apply ReLU activation in hidden layers
    return torch.sigmoid(res)  # Sigmoid activation in the output layer

Note that weights is now a list of tensors containing layer-wise weights and correspondingly, biases is the the list of tensors containing layer-wise biases. 

Backward Propagation With Multiple Hidden Layers

Loss calculation and gradient descent remain consistent with the simple neural network implementation. We use the mean absolute error (MAE) for loss as before and tweak the update_coeffs function to apply gradient descent to update the weights and biases in each hidden layer.

	def update_coeffs(coeffs, lr):
  	weights, biases = coeffs
  	for layer in weights+biases:
    		layer.sub_(layer.grad * lr)

Putting It All Together in Wrapper Functions

Our train_model function can be used ‘as is’ to orchestrate the raining process using the execute_epoch wrapper function to help as before. The model_accuracy function also does not change.

Summary Conclusion and Takeaways

With these modifications, we’ve refactored our simple neural network into a deep learning model that has greater capacity for learning. The beauty of it is we have retained the same set of functions and interfaces that we implemented in a simple neural network, refactoring the code to scale with multiple hidden layers. 

  1. train_model(epochs=30, lr=0.1): No change!
  2. execute_epoch(coeffs, lr): No change!
  3. calc_loss(coeffs, indeps, deps): No change!
  4. calc_preds(coeffs, indeps): Tweak to use the set of weights and corresponding set of biases in each hidden layer, iterating over all layers from input to output.
  5. update_coeffs(coeffs, lr): Tweak to iterate over the set of weights and accompanying set of biases in each layer.
  6. init_coeffs(hiddens=[10, 10]): Tweak for compatibility with an architecture that can potentially have any number of hidden layers of any size.
  7. model_accuracy(coeffs): No change!

Such a deep learning model has greater capacity for learning. However, it is is more hungry for training data! In subsequent posts, we will examine the breakthroughs that have made it possible to make deep learning models practically feasible and reliable. These include advancements such as:

  1. Batch Normalization
  2. Residual Connections
  3. Dropouts

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