COMP9444 : Neural Networks and Deep Learning Characters, Spirals and Hidden Unit Dynamics - IT/Computer Science Assignment Help

Assignment Task:

Task:

In this assignment, you will be implementing and training various neural network models for four different tasks, and analysing the results. You are to submit three Python files kuzu.py, rect.py and encoder.py, as well as a written report hw1.pdf (in pdf format). Provided Files Copy the archive hw1.zip into your own filespace and unzip it. This should create a directory hw1 with the data file rect.csv, subdirectories plot and net, as well as eleven Python files kuzu.py, rect.py, encoder.py, kuzu_main.py, rect_main.py, endoder_main.py, encoder_model.py, seq_train.py, seq_plot.py, reber.py and anbn.py. Your task is to complete the skeleton files kuzu.py, rect.py, encoder.py and submit them, along with your report.

Part 1: Japanese Character Recognition For Part 1 of the assignment you will be implementing networks to recognize handwritten Hiragana symbols. The dataset to be used is Kuzushiji-MNIST or KMNIST for short. The paper describing the dataset is available here. It is worth reading, but in short: significant changes occurred to the language when Japan reformed their education system in 1868, and the majority of Japanese today cannot read texts published over 150 years ago. This paper presents a dataset of handwritten, labeled examples of this old-style script (Kuzushiji). Along with this dataset, however, they also provide a much simpler one, containing 10 Hiragana characters with 7000 samples per class. This is the dataset we will be using. Text from 1772 (left) compared to 1900 showing the standardization of written Japanese.

1. [1 mark] Implement a model NetLin which computes a linear function of the pixels in the image, followed by log softmax. Run the code by typing: python3 kuzu_main.py --net lin Copy the final accuracy and confusion matrix into your report. The final accuracy should be around 70%. Note that the rows of the confusion matrix indicate the target character, while the columns indicate the one chosen by the network. (0="o", 1="ki", 2="su", 3="tsu", 4="na", 5="ha", 6="ma", 7="ya", 8="re", 9="wo"). More examples of each character can be found here.

2. [1 mark] Implement a fully connected 2-layer network NetFull (i.e. one hidden layer, plus the output layer), using tanh at the hidden nodes and log softmax at the output node. Run the code by typing: python3 kuzu_main.py --net full Try different values (multiples of 10) for the number of hidden nodes and try to determine a value that achieves high accuracy (at least 84%) on the test set. Copy the final accuracy and confusion matrix into your report.

3. [1 marks] Implement a convolutional network called NetConv, with two convolutional layers plus one fully connected layer, all using relu activation function, followed by the output layer, using log softmax. You are free to choose for yourself the number and size of the filters, metaparameter values (learning rate and momentum), and whether to use max pooling or a fully convolutional architecture. Run the code by typing: python3 kuzu_main.py --net conv Your network should consistently achieve at least 93% accuracy on the test set after 10 training epochs. Copy the final accuracy and confusion matrix into your report.

4. [3 marks] Briefly discuss the following points:

a. the relative accuracy of the three models, b. the confusion matrix for each model: which characters are most likely to be mistaken for which other characters, and why?

Part 2: Rectangular Spirals Task For Part 2 you will be training a network to distinguish two intertwined rectangular spirals. The supplied code rect_main.py loads the training data from rect.csv, applies the specified model and produces a graph of the resulting function, along with the data. For this task there is no test set as such, but we instead judge the generalization by plotting the function computed by the network and making a visual assessment.

1. [2 marks] Provide code for a Pytorch Module called Network which is initialized with two parameters layer and hid. If layer == 1 the network should only have one hidden layer, with hid units. If layer == 2 it should have two (fully connected) hidden layers, each with hid units. The tanh activation function should be applied at each hidden layer, and sigmoid at the output layer.

2. [2 marks] Using graph_output() as a guide, write a method called graph_hidden(net, layer, node) which plots the activation (after applying the tanh function) of the hidden node with the specified number (node) in the specified layer (1 or 2). Specifically, it should show where the activation is positive and where it is negative. Hint: you might need to modify forward() so that the hidden unit activations are retained, i.e. replace hid1 = torch.tanh(...) with self.hid1 = torch.tanh(...)

3. [1 mark] Train a network with one hidden layer by typing: python3 rect_main.py --layer 1 --hid 10 Try to determine a number of hidden nodes close to the mininum required for the network to be trained successfully (although, it need not be the absolute minimum). You may need to run the network several times before finding a set of initial weights which allows it to converge. (If it trains for a minute or so and seems to be stuck in a local minimum, kill it with ⟨cntrl⟩-c and run it again).

You are free to adjust the learning rate and initial weight size, if you want to. The graph_output() method will generate a picture of the function computed by your Network and store it in the plot subdirectory with a name like out?_?.png. You should include this picture in your report. Your graph_hidden() method should generate plots of all the hidden nodes, which you should also include in your report.

4. [1 mark] Train a network with two hidden layers by typing:

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