HW2: Multi-Layer Neural Network Conceptual Questions: due Monday, 10/03/2022 at 11:59 PM EST 

Programming Assignment: due Monday, 10/10/2022 at 11:59 PM EST This homework is intended to give you an introduction to building, training, and testing neural network models. You will not only be exposed to using Python packages to build a neural network from scratch, but also the mathematical aspects of backpropagation and gradient descent. While in practical scenarios, you wont necessarily have to implement neural networks from scratch (as you will see in future labs and assignments), this assignment aims at giving you a rudimentary idea of what goes on under the hood in packages such as TensorFlow and Keras. In this assignment, you will use the MNIST handwritten digits dataset to train a simple classification neural network using batch learning and evaluate your model. Conceptual Questions Please submit the Conceptuals Questions on Gradescope under hw2-mlp conceptual. You must type your submissions and upload a PDF. We recommend using LaTeX. Getting the Stencil Github Classroom Link with Stencil Code! Guide on GitHub and GitHub Classroom Getting the Data You can use download.sh for downloading the data. You can run a bash script with the command ./script_name.sh (ex: bash ./download.sh). This is similar to HW1.Setup Work off of the stencil code provided, but do not change the stencil except where specified. Doing so may cause incompatibility with the autograder. Dont change any method signatures! This assignment requires NumPy and Matplotlib. You should already have this from HW1. Also check the virtual environment guide to set up TensorFlow 2.5 on your local machine. Feel free to reference this guide if you have to work through colab. Assignment Overview In this assignment, you will be constructing a Keras mimic, Beras (haha funny name), and will make a sequential model specification that mimics the Tensorflow/Keras API. The Python notebook associated with this homework is meant for you to explore an example implementation so that you can build on it yourself! There are no TODOs for you to work on in the notebook; rather, the testing is done by running the main method of assignment.py. Our stencil provides a model class with several methods and hyperparameters you need to use for your network. You will also answer conceptual questions related to the assignment and class material (dont forget to answer the 2470-only questions if youre a 2470 student!). You should include a brief README with your model's accuracy and any known bugs. Before Starting This homework assignment is due two weeks from release. Labs 1-3 provide great practice for this assignment, so you can wait a little for them if you get stuck. Specifically: - Implementing Callable/Diffable Components: The skills you need in order to do this can be found by working through Lab 1. This includes comfort with mathematical notation, matrix operations, and the logic behind call and gradient methods. - Implementing Optimizers: You can implement the BasicOptimizer class just by following the logic from the gradient_descent method in lab 1. The other optimizers (i.e. Adam, RMSProp) will be covered in Lab 2: Optimizers. - Using batch_step and GradientTape: You can figure out how to use these to train your model based on the assignment instructions and your implementations of these. With that said, they do mimic the Keras API. Youll learn about all this in Lab 3: Intro to Tensorflow. If your lab is after the due date, it should be fine; just skim over the complementary notebook associated with the lab. Feel free to start off by doing what you can and then add onto it as you learn more about deep learning and realize that the same concepts you learn in the class can actually be used here! Do not get discouraged, and try to have fun! Roadmap For this assignment, we'll walk you through the pipeline of training a neural net, including the structure of the model class and the methods you will have to fill in. 1. Preprocessing the Data Note: Code for preprocessing should be pulled in from HW1. Before training a network, you will need to clean your data. This includes retrieving, altering, and formatting the data into the inputs for your network. For this assignment, you will be working on the MNIST dataset. It can be downloaded through the download.sh script, but its also linked here (ignore that it says hw1; were using this dataset for hw2 this time!). The original data source is here. You should train your network using only the training data and then test your network's accuracy on the testing data. Your program should print its accuracy over the test dataset upon completion. 2. One Hot Encoding Before training or testing your model, you will need to one-hot encode your class labels so that the model can optimize towards predicting any desired class. Note that the class labels by themselves are simply categories and do not mean anything numerically. In the absence of one-hot encoding, your model might learn some natural ordering between the different class labels based on the labels (which are arbitrary). For example, lets say theres a data point A which corresponds to label 2 and a data point B which corresponds to label 7. We dont want the model to somehow learn that B has a higher weightage than A simply because, numerically speaking, 7 > 2. : 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To one-hot encode your class labels, you will have to convert your 1-dimensional label vector into a vector of size num_classes (where num_classes is the total number of classes in your dataset). For the MNIST dataset, it looks something like the matrix on the right: You have to fill out the following method in Beras/onehot.py. fit(): [TODO] In this function you need to fetch all the unique labels in the data (store this in self.uniq) and create a dictionary with labels as the keys and their corresponding one hot encodings as values. Hint: You might want to look at np.eye() to get the one-hot encodings. Ultimately, you will store this dictionary in self.uniq2oh. forward(): In this function, we pass a vector of all the actual labels in the training set and call fit() to populate the uniq2oh dictionary with unique labels and their corresponding one-hot encoding and then use it to return an array of one-hot encoded labels for each label in the training set. This function has already been filled out for you! inverse(): In the function, we reverse the one-hot encoding back to the actual label. This has already been done for you. For example, if we have labels X and Y with one-hot encodings of [1,0] and [0,1], wed want to create a dictionary as follows: {X: [1,0], Y: [0,1]}. As shown in the image above, for MNIST, you will have 10 labels, so your dictionary should have ten entries! You may notice that some classes inherit from Callableor Diffable. More on this in the next section! 3. Core Abstractions Consider the following abstract classes of modules. Be sure to play around with the Python notebook associated with this homework to get a good grip of the core abstraction modules defined for you in Beras/core.py! The notebook is exploratory in nature (it is NOT required and all of the code is given) and will provide you with lots of insights into understanding and using these class abstractions! Note that these modules are very similar to the Tensorflow/Keras API. Callable: A function with a well-defined forward function. These are the ones youll need to implement: CategoricalAccuracy (./metrics.py): Computes the accuracy of predicted probabilities against a list of ground-truth labels. As accuracy is not optimized for, there is no need to compute its gradient. Furthermore, categorical accuracy is piecewise discontinuous, so the gradient would technically be 0 or undefined. OneHotEncoder (./onehot.py): You can one-hot encode a class instance into a probability distribution to optimize for classifying into discrete options. e.g. the -th logit with has OHE Youve already implemented this in Step 2! Diffable: A callable which can also be differentiated. We can use these in our pipeline and optimize through them! Thus, most of these classes are made for use in your neural network layers. These are the ones youll need to implement: Dense (./layers.py) Alias for linear layer or fully-connected layer. LeakyReLU(./activations.py) Rectified Linear; when input is negative, lower its magnitude. More details provided in section 5 Note that the implementation for ReLU has already been given to you as its just a special case of LeakyReLU (where =0). Softmax (./activations.py) 2470 ONLY For a given vector of logits, convert it into a vector of probabilities such that the sum of the probabilities for all the logits sums up to 1. More details provided in section 5 MeanSquaredError (./losses.py) Given the predicted and actual labels, compute the average of the squares of the difference between predicted and actual values. More details provided in section 7 Example: Consider a Dense layer instance. Let srepresents the input size (source), d represents the output size (destination), and b represents the batch size. Then: Forward function: (x) or Input gradient function: (can be a batch) Weight gradient function: (can be a batch) Let be the transposed Jacobian matrix (matrix of partials) : - self.weights would be a list containing the layer weights and bias. - More formally, we could define self.weights , which consists of weights of dimension [s,d] and bias of dimension [1,d]. - The forward function would model and would do the following: - Store the input of shape [b,s] - Compute, store, and return the output of shape [b,d] - Inputs and outputs are stored to help compute gradients - Using input_gradient, compose_to_input computes a batch of , the gradients of the loss w.r.t the input (for each entry). Specifically, compose_to_input would take in upstream jacobian and compose it with input_gradients to get . - Each has same dims as . - Using weight_gradient, compose_to_weights computes , the batch-average gradient of loss w.r.t. the weights. - Each pair has same dims as , ]. GradientTape: This class will function exactly like tf.GradientTape() (See lab 3). You can think of a GradientTape as a logger. Every time an operation is performed within the scope of a GradientTape, it records which operation occurred. Then, during backprop, we can compute the gradient for all of the operations. This allows us to differentiate our final output with respect to any intermediate step. When operations are computed outside the scope of GradientTape, they arent recorded, so your code will have no record of them and cannot compute the gradients. You can check out how this is implemented in core! Of course, Tensorflows gradient tape implementation is a lot more complicated and involves constructing a graph. [TODO] Implement the gradient method, which returns a list of gradients corresponding to the list of trainable weights in the network. Details in the code. 4. Layers For the purposes of this assignment, you will implement the Dense layer to use in your sequential model in Beras/layers.py. You have to fill in the following methods. forward(): [TODO] Implement the forward pass and return the outputs. weight_gradients() : [TODO] Calculate the gradients with respect to the weights and the biases. This will be used to optimize the layer. input_gradients() : [TODO] Calculate the gradients with respect to the layer inputs. This will be used to propagate the gradient to previous layers. _initialize_weight() : [TODO] Initialize the dense layers weight values. By default, initialize all the weights to zero (usually a bad idea, by the way). You are also required to allow for more sophisticated options (when the initializer is set to normal, xavier, and kaiming). Follow Keras math assumptions! Normal: Pretty self-explanatory, a unit normal distribution. Xavier Normal: Based on keras.GlorotNormal. Kaiming He Normal: Based on Keras.HeNormal. You may find np.random.normal helpful while implementing these. The TODOs provide some justification for why these different initialization methods are necessary but for more detail, check out this website! Feel free to add more initializer options! 5. Activation Functions In this assignment, you will be implementing two major activation functions, namely, LeakyReLU and Softmaxin Beras/activations.py. Since ReLU is a special case of LeakyReLU, we have already provided you with the code for it. LeakyReLU() forward(): [TODO] Given input x, compute & return LeakyReLU(x). input_gradients(): [TODO] Compute & return the partial with respect to inputs by differentiating LeakyReLU. Softmax(): (2470 ONLY) forward(): [TODO] Given input x, compute & return Softmax(x). Make sure you use stable softmax where you subtract max of all entries to prevent overflow/undvim erflow issues. input_gradients(): [TODO] Partial w.r.t. inputs of Softmax(). 6. Filling in the model With these abstractions in mind, lets create a pipeline for our sequential deep learning model. You can find the SequentialModel class in assignment.py where you will initialize your neural networks layers, parameters (weights and biases), and hyperparameters (optimizer, loss function, learning rate, accuracy function, etc.). The SequentialModel class inherits from Beras/model.py, where youll find many useful methods. This will also contain functions that fit the model to your data and evaluate the performance of your model: compile(): Initialize the model optimizer, loss function, & accuracy function, which are fed in as arguments, for your SequentialModel instance to use. fit(): Trains your model to associate input to outputs. Training is repeated for each epoch, and the data is batched based on argument. It also computes batch_metrics, epoch_metrics, and the aggregated agg_metrics that can be used to track the training progress of your model. evaluate(): [TODO] Evaluate the performance of the final model using the metrics mentioned above during the testing phase. Its almost identical to the fit() function; think about what would change between training and testing). call(): [TODO] Hint: what does it mean to call a sequential model? Remember that a sequential model is a stack of layers where each layer has exactly one input vector and one output vector. You can find this function within the SequentialModel class in assignment.py. batch_step(): [TODO] You will observe that fit() calls this function for each batch. You will first compute your model predictions for the input batch. In the training phase, you will need to compute gradients and update your weights according to the optimizer you are using. For backpropagation during training, you will use GradientTape from the core abstractions (core.py) to record operations and intermediate values. You will then use the model's optimizer to apply the gradients to your model's trainable variables. Finally, compute and return the loss and accuracy for the batch. You can find this function within the SequentialModel class in assignment.py. We encourage you to check out keras.SequentialModel in the intro notebook (under Exploring a possible modular implementation: TensorFlow/Keras) and refer to Lab 3 to get a feel for how we can work with gradient tapes in deep learning. 7. Loss Function This is one of the most crucial aspects of model training. In this assignment, we will implement the MSE or mean-squared error loss function. You can find your loss function in Beras/losses.py. forward(): [TODO] Write a function that computes and returns the mean squared error given the predicted and actual labels. Hint: What is MSE? Given the predicted and actual labels, MSE is the average of the squares of the differences between predicted and actual values. input_gradients(): [TODO] Compute and return the gradients. Use differentiation to derive the formula for these gradients. 8. Optimizers In the Beras/optimizers.py file make sure to implement the optimization for each of the different types of optimizers. Lab 2 should help with this, so good luck! BasicOptimizer: [TODO] A simple optimizer strategy as seen in Lab 1. RMSProp: [TODO] Root mean squared error propagation. Adam: [TODO] A common adaptive motion estimation-based optimizer. 9. Accuracy metrics Finally, to evaluate the performance of your model, you need to use appropriate accuracy metrics. In this assignment, you will implement categorical accuracy in Beras/metrics.py: forward(): [TODO] Return the categorical accuracy of your model given the predicted probabilities and true labels. You should be returning the proportion of predicted labels equal to the true labels, where the predicted label for an image is the label corresponding to the highest probability. Refer to the internet or lecture slides for categorical accuracy math! 10. Train and Test Finally, using all the above primitives, you are required to build two models in assignment.py: A simple model in get_simple_model() with at most one Diffable layer (e.g., Dense - ./layers.py) and one activation function (look for them in ./activation.py). This one is provided for you by default, though you can change it if youd like. The autograder will evaluate the original one though! A slightly more complex model in get_advanced_model() with two or more Diffable layers and two or more activation functions. We recommend using Adam optimizer for this model with a decently low learning rate. You will need to set simple = False under if __name__ == __main__ to use the advanced model. For any hyperparameters you use (layer sizes, learning rate, epoch size, batch size, etc.), please hardcode these values in the get_simple_model() andget_advanced_model() functions. Do NOT store them under the main handler. Once everything is implemented, you can use python3 assignment.py to run your model and see the loss/accuracy! 11. Visualizing Results We provided the visualize_metrics method for you to visualize how your loss and accuracy changes after each batch using matplotlib. DO NOT EDIT THIS FUNCTION. You should call this function in your main method after you store the loss and accuracy per batch in an array, which would be passed into this function. This should plot line graphs where the horizontal axis is the i'th batch and the vertical axis is the loss/accuracy value of the batch. Calling this is OPTIONAL! We've also provided the visualize_images method for you to visualize your predictions against the true labels with matplotlib. This method is currently written with the labels having a shape of [number of images, 1]. DO NOT EDIT THIS FUNCTION. You should call this function with all your inputs and labels after training your model. The function will randomly pick 500 samples from your input and will plot 10 correct and 10 incorrect classifications to help you visually interpret your models predictions! You should do this last, after you have met the benchmark for test accuracy. CS1470 Students - Complete and Submit HW2 Conceptual - Implement Beras per specifications and make a SequentialModel in assignment.py - Test the model inside of main - Get test accuracy >85% on MNIST with default get_simple_model_components. - Complete the Exploration notebook and export it to a PDF. - The HW2 Intro to Beras notebook is just for your reference. CS2470 Students - Same as 1470 except: - Implement Softmax activation function (forward pass and input_gradients) - Get testing accuracy >95% on MNIST model from get_advanced_model_components. - You will need to specify a multi-layered model, will have to explore hyperparameter options, and may want to add additional features. - Additional features may include regularization, other weight initialization schemes, aggregation layers, dropout, rate scheduling, or skip connections. If you have other ideas, feel free to ask publicly on Ed and well let you know if they are also ok. - When implementing these features, try to mimic the Keras API as much as possible. This will help significantly with your Exploration notebook. - Finish 2470 components for Exploration notebook and conceptual questions. Grading and Autograder Compatibility Conceptual: You will be primarily graded on correctness, thoughtfulness, and clarity. Code: You will be primarily graded on functionality. Your model should have an accuracy that is at least greater than the threshold on the testing data. For 1470, this can be achieved with the simple model parameterization provided. For 2470, you may need to experiment with hyperparameters or develop some custom components. Although you will not be graded on code style, you should nothave an excessive number of print statements in your final submission. IMPORTANT! Please use vectorized operations when possible and limit the number of for loops you use. While there is no strict time limit for running this assignment, it should typically be less than 3 minutes. The autograder will automatically time out after 10 minutes. You will not receive any credit for methods that use Tensorflow or Keras functions within them. Notebook: The exploration notebook will be graded manually and should be submitted as a pdf file. Feel free to use the Notebooks to Latex PDFs.ipynb notebook! Handing In You should submit the assignment via Gradescope under the corresponding project assignment by zipping up your hw2 folder (the path on Gradescope MUST be hw2/code/filename.py) or through GitHub (recommended). To submit through GitHub, commit and push all changes to your repository to GitHub. You can do this by running the following three commands (this is a good resource for learning more about them): : 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?accessToken=eyJhbGciOiJIUzI1NiIsImtpZCI6ImRlZmF1bHQiLCJ0eXAiOiJKV1QifQ.eyJleHAiOjE2NjUzMTg3MzUsImZpbGVHVUlEIjoiMndBbFhEUnIxYlRleVdBUCIsImlhdCI6MTY2NTMxODQzNSwiaXNzIjoidXBsb2FkZXJfYWNjZXNzX3Jlc291cmNlIiwidXNlcklkIjo0OTE2NTYxfQ.Uv6xM41_Qb5ed56MFH92p8DDxEe8CDoXEr5phHkj_To 1. git add file1 file2 file3 (or -A) 2. git commit -m commit message 3. git push After committing and pushing your changes to your repo (which you can check online if you're unsure if it worked), you can now just upload the repo to Gradescope! If youre testing out code on multiple branches, you have the option to pick whichever one you want. IMPORTANT! 1. Please make sure all your files are in hw2/code. Otherwise, the autograder will fail! 2. Delete the data folder before zipping up your code. 3. 2470 STUDENTS: Add a blank file named 2470studentin the hw2/code directory! The file should have no extension, and is used as a flag to grade 2470-specific requirements. If you dont do this, YOU WILL LOSE POINTS!