Exercise Neural Networks

The goal of this exercise is to implement a multilayer dense neural network using torch. Type,

pip install -r requirements.txt

into the terminal to install the required software.

Torch takes care of our autograd needs. The documentation is available at https://pytorch.org/docs/stable/index.html. torch.nn provides all the necessary modules for neural network. https://pytorch.org/docs/stable/nn.html hosts the documentation.

Task 1: Denoising a cosine

To get a notion of how function learning of a dense layer network works on given data, we will first have a look at the example from the lecture. In the following task you will implement gradient descent learning of a dense neural network using torch and use it to learn a function, e.g. a cosine.

• As a first step, create a cosine function in torch and add some noise with torch.randn. Use, for example, a signal length of $n = 200$ samples and a period of your choosing. This will be the noisy signal that the model is supposed to learn the underlaying cosine from.

• Recall the definition of the sigmoid function $\sigma$

$$\sigma(x) = \frac{1}{1 + e^{-x}}$$

• Implement the sigmoid function in src/denoise_cosine.py.

• Implement a dense layer in the net function of src/denoise_cosine.py. The function should return

$$\mathbf{o} = \mathbf{W}_2 \sigma(\mathbf{W}_1 \mathbf{x} + \mathbf{b})$$

where $\mathbf{W}_1\in \mathbb{R}^{m,n}, \mathbf{x}\in\mathbb{R}^n, \mathbf{b}\in\mathbb{R}^m$ and $m$ denotes the number of neurons and $n$ the input signal length. Suppose that the input parameters are stored in a python dictonary with the keys W_1, W_2 and b. Use numpys @ notation for the matrix product.

• Use torch.randn to initialize your weights. For a signal length of $200$ the $W_2$ matrix should have e.g. have the shape [200, hidden_neurons] and $W_1$ a shape of [hidden_neurons, 200].

• Implement and test a squared error cost

$$C_{\text{se}} = \frac{1}{2} \sum_{k=1}^{n} (\mathbf{y}_k - \mathbf{o}_k)^2$$

• ** denotes squares in Python, torch.sum allows you to sum up all terms.

• Define the forward pass in the net_cost function. The forward pass evaluates the network and the cost function.

• Train your network to denoise a cosine. To do so, implement gradient descent on the noisy input signal and use e.g. torch.grad_and_value to gradient and compute cost at the same time. Remember the gradient descent update rule

$$\mathbf{W}_{\tau + 1} = \mathbf{W}_\tau - \epsilon \cdot \delta\mathbf{W}_{\tau}.$$

• In the equation above $\mathbf{W} \in \mathbb{R}$ holds for weight matrices and biases $\epsilon$ denotes the step size and $\delta$ the gradient operation with respect to the following weight. Use a loop to repeat weight updates for multiple operations. Try to train for one hundred updates.

• At last, compute the network output y_hat on the final values to see if the network learned the underlying cosine function. Use matplotlib.pyplot.plot to plot the noisy signal and the network output $\mathbf{o}$.

• Test your code with nox -r -s test and run the script with python ./src/denoise_cosine.py or by pressing Ctrl + F5 in Vscode.

Task 2: MNIST

In this task we will go one step further. Instead of a cosine function, our neural network will learn how to identify handwritten digits from the MNSIT dataset. For that, we will be using the torch.nn module. To get started familiarize yourself with the torch.nn to train a fully connected network in src/mnist.py. In this script, some functions are already implemented and can be reused. Broadcasting is an elegant way to deal with data batches (Torch takes care of this for us). This task aims to compute gradients and update steps for all batches in the list. If you are coding on bender the function matplotlib.pyplot.show doesn't work if you are not connected to the X server of bender. Use e.g. plt.savefig to save the figure and view it in vscode.

• Implement the normalize_batch function to ensure approximate standard-normal inputs. Make use of handy torch inbuilt methods. Normalization requires subtraction of the mean and division by the standard deviation with $i = 1, \dots w$ and $j = 1, \dots h$ with $w$ the image width and $h$ the image height and $k$ running through the batch dimension:

$$\tilde{{x}}_{ijk} = \frac{x_{ijk} - \mu}{\sigma}$$

• The forward step requires the Net object from its class. It is your fully connected neural network model. Implement a dense network in Net of your choosing using a combination of torch.nn.Linear and th.nn.ReLU or th.nn.Sigmoid

• In Net class additionally, implement the forward function to compute the network forwad pass.

• Write a cross_entropy cost function with $n_o$ the number of labels and $n_b$ in the batched case using

$$C_{\text{ce}}(\mathbf{y},\mathbf{o})=-\frac{1}{n_b}\sum_{i=1}^{n_b}\sum_{k=1}^{n_o}[(\mathbf{y}_{i,k}\ln\mathbf{o}_{i,k})+(\mathbf{1}-\mathbf{y}_{i,k})\ln(\mathbf{1}-\mathbf{o}_{i,k})].$$

• If you have chosen to work with ten output neurons. Use torch.nn.functional.one_hot to encode the labels.

• Next we want to be able to do an optimization step with stochastic gradient descent (sgd). Implement sgd_step. One way to do this is to iterate over model.parameters() and update each parameter individually with its gradient. One can access the gradient for each parameter with <param>.grad.

• To evaluate the network we calculate the accuracy of the network output. Implement get_acc to calculate the accuracy given a dataloader containing batches of images and corresponding labels. More about dataloaders is available here.

• Now is the time to move back to the main procedure. First, the train data is fetched via the torchvision torchvision.MNIST. To be able to evaluate the network while it is being trained, we use a validation set. Here the train set is split into two disjoint sets: the training and the validation set using torch.utils.data.random_split.

• Initialize the network with the Net object (see the torch documentation for help).

• Train your network for a fixed number of EPCOHS over the entire dataset. Major steps in trianing loop include normalize inputs, model prediction, loss calculation, .backward() over loss to compute gradients, sgd_step and zero_grad. Validate model once per epoch.

• When model is trained, load the test data with test_loader and calculate the test accuracy.

• Optional: Plot the training and validation accuracies and add the test accuracy in the end.