diff --git a/README.md b/README.md
index 8658f37..aa78829 100644
--- a/README.md
+++ b/README.md
@@ -27,3 +27,4 @@ To add your own project below, just [edit](https://github.com/wwdc/2022/edit/mai
|[Peter Yaacoub](https://github.com/Yaacoub)|[GitHub](https://github.com/Yaacoub/Swift-Student-Challenge/tree/main/WWDC%202022)|[YouTube](https://youtu.be/t4NQSHLIbaw)|AVFoundation, CoreGraphics, SwiftUI, UIKit|Accepted|
|[Riccardo Persello](https://github.com/persello)|[GitHub](https://github.com/persello/ssc22)||Accelerate, AVFoundation, SwiftUI|Accepted|
|[Yunho Oh](https://github.com/Helloyunho)|[GitHub](https://github.com/Helloyunho/about_computer_bits)|[YouTube](https://youtu.be/V8Zhc-dDbVI)|SwiftUI|Rejected|
+|[Yaoyao Wu](https://github.com/wyy511511)|[GitHub](https://github.com/wyy511511/HockeyTourWithBingDwenDwen)|[YouTube]()|SwiftUI,ARKit,ARQuickLook|Accepted|
diff --git a/feedforward_LM_post.ipynb b/feedforward_LM_post.ipynb
new file mode 100644
index 0000000..3933abe
--- /dev/null
+++ b/feedforward_LM_post.ipynb
@@ -0,0 +1,439 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "ceZgvB4HW-qc"
+ },
+ "outputs": [],
+ "source": [
+ "'''\n",
+ "First, we will import everything we need. We will also define a couple of useful functions.\n",
+ "'''\n",
+ "import torch\n",
+ "from torch import nn\n",
+ "from torch import optim\n",
+ "\n",
+ "import random\n",
+ "\n",
+ "# This is a function that prints the number of trainable parameters \n",
+ "# of a model.\n",
+ "def count_parameters(model):\n",
+ " return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
+ "\n",
+ "# This functions prints all parameters (and their gradients) of a model.\n",
+ "def print_parameters(model):\n",
+ " for name, param in model.named_parameters():\n",
+ " print(name)\n",
+ " print(param.data)\n",
+ " print(param.grad)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "2I6uUdg9W-qe"
+ },
+ "outputs": [],
+ "source": [
+ "'''\n",
+ "Then, we need to define our model. Remember, we want to build a bigram language model.\n",
+ "'''\n",
+ "class FeedforwardLM(nn.Module):\n",
+ " \n",
+ " def __init__(self, vocab_size, embedding_dim, hidden_dim):\n",
+ " super().__init__()\n",
+ " \n",
+ " self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
+ " self.hidden_layer = nn.Linear(embedding_dim, hidden_dim)\n",
+ " self.output_layer = nn.Linear(hidden_dim, vocab_size)\n",
+ " \n",
+ " self.relu = nn.ReLU()\n",
+ " \n",
+ " def forward(self, input):\n",
+ "\n",
+ " embedding = self.embedding(input)\n",
+ " hidden_rep = self.relu(self.hidden_layer(embedding))\n",
+ " output = self.output_layer(hidden_rep)\n",
+ " \n",
+ " return output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "B5xgbVvqW-qe"
+ },
+ "outputs": [],
+ "source": [
+ "def load_data(filename, old_vocab=False):\n",
+ " \n",
+ " # TODO: Load data, convert text into tensors, construct vocabulary, return data and vocab\n",
+ " if not old_vocab:\n",
+ " vocab = {'': 0}\n",
+ " else:\n",
+ " vocab = old_vocab\n",
+ " data = list()\n",
+ " file = open(filename)\n",
+ " for line in file:\n",
+ " line_text = line.split()\n",
+ " line_text = [''] + line_text + ['']\n",
+ " \n",
+ " if not old_vocab:\n",
+ " # form vocabulary\n",
+ " for word in line_text:\n",
+ " if word not in vocab:\n",
+ " vocab[word] = len(vocab)\n",
+ " \n",
+ " # add words to data\n",
+ " for i, word in enumerate(line_text):\n",
+ " if i < len(line_text) - 1:\n",
+ " if word in vocab:\n",
+ " idx1 = vocab[word]\n",
+ " else: \n",
+ " idx1 = vocab['']\n",
+ " if line_text[i + 1] in vocab:\n",
+ " idx2 = vocab[line_text[i + 1]]\n",
+ " else: \n",
+ " idx2 = vocab['']\n",
+ " data.append((torch.tensor(idx1), torch.tensor(idx2)))\n",
+ " \n",
+ " return vocab, data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive') \n",
+ "\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "data_dir = 'drive/MyDrive/Colab Notebooks/'"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "v7ka5nCTq-Ji",
+ "outputId": "a75849a3-7a9f-4003-ed20-ecb165c46c53"
+ },
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Mounted at /content/drive\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "vocab, train_data = load_data(data_dir+'bible.train.txt')\n",
+ "print(len(vocab))"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "-u_E7jeHrGUh",
+ "outputId": "2e73a625-8d93-4b9c-ed93-7d83b263ef6d"
+ },
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "7050\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "OV6xzqYEW-qf",
+ "outputId": "d21e7997-a762-445e-b22e-b8e530349c5b"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "7050\n",
+ "7050\n",
+ "7050\n",
+ "(tensor(1), tensor(2))\n",
+ "(tensor(205), tensor(4))\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Let's put it all together. \n",
+ "\n",
+ "# 1) Load the data, and shuffle the training data.\n",
+ "# TODO\n",
+ "vocab, train_data = load_data(data_dir+'bible.train.txt')\n",
+ "print(len(vocab))\n",
+ "_, dev_data = load_data(data_dir+'bible.dev.txt', vocab)\n",
+ "print(len(vocab))\n",
+ "_, test_data = load_data(data_dir+'bible.test.txt', vocab)\n",
+ "print(len(vocab))\n",
+ "\n",
+ "print(train_data[0])\n",
+ "random.shuffle(train_data)\n",
+ "print(train_data[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "NR1QqTsnW-qg",
+ "outputId": "e6e53249-89b4-4430-cfae-e7e98d697f4b"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "183465"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 7
+ }
+ ],
+ "source": [
+ "# 2) Initialize our model.\n",
+ "# TODO\n",
+ "\n",
+ "our_lm = FeedforwardLM(len(vocab), 10, 15)\n",
+ "count_parameters(our_lm)\n",
+ "#print_parameters(our_lm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Saqt81EAW-qh",
+ "outputId": "b3c79ad5-7bed-42f0-a6f8-798edc5d6ec3"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "### Epoch: 1 ###\n",
+ "Average loss: tensor(6.4752, grad_fn=)\n",
+ "tensor(5.7503, grad_fn=)\n",
+ "### Epoch: 2 ###\n",
+ "Average loss: tensor(6.4417, grad_fn=)\n",
+ "tensor(5.4675, grad_fn=)\n",
+ "### Epoch: 3 ###\n",
+ "Average loss: tensor(6.5658, grad_fn=)\n",
+ "tensor(5.2705, grad_fn=)\n",
+ "### Epoch: 4 ###\n",
+ "Average loss: tensor(6.7042, grad_fn=)\n",
+ "tensor(5.1239, grad_fn=)\n",
+ "### Epoch: 5 ###\n",
+ "Average loss: tensor(6.7930, grad_fn=)\n",
+ "tensor(5.0167, grad_fn=)\n",
+ "### Epoch: 6 ###\n",
+ "Average loss: tensor(6.8785, grad_fn=)\n",
+ "tensor(4.9195, grad_fn=)\n",
+ "### Epoch: 7 ###\n",
+ "Average loss: tensor(6.9361, grad_fn=)\n",
+ "tensor(4.8433, grad_fn=)\n",
+ "### Epoch: 8 ###\n",
+ "Average loss: tensor(6.9608, grad_fn=)\n",
+ "tensor(4.7808, grad_fn=)\n",
+ "### Epoch: 9 ###\n",
+ "Average loss: tensor(7.0621, grad_fn=)\n",
+ "tensor(4.7264, grad_fn=)\n",
+ "### Epoch: 10 ###\n",
+ "Average loss: tensor(7.1504, grad_fn=)\n",
+ "tensor(4.6751, grad_fn=)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 3) Now we train our model. \n",
+ "# TODO\n",
+ "\n",
+ "epochs = 10\n",
+ "ce = nn.CrossEntropyLoss()\n",
+ "softmax = nn.Softmax(dim=0)\n",
+ "optimizer = optim.SGD(our_lm.parameters(), lr=0.1)\n",
+ "\n",
+ "for i in range(epochs):\n",
+ " print('### Epoch: ' + str(i+1) + ' ###')\n",
+ " av_loss = 0\n",
+ " our_lm.train()\n",
+ " for (x, y) in train_data[:10000]:\n",
+ " optimizer.zero_grad()\n",
+ " \n",
+ " # a) calculate probs / get an output\n",
+ " y_raw = our_lm(x)\n",
+ " y_hat = softmax(y_raw)\n",
+ " \n",
+ " # b) compute loss\n",
+ " loss = ce(y_raw.unsqueeze(0),y.unsqueeze(0))\n",
+ " av_loss += loss\n",
+ " \n",
+ " # c) get the gradient\n",
+ " loss.backward()\n",
+ "\n",
+ " # d) update the weights\n",
+ " optimizer.step()\n",
+ " validate(our_lm, dev_data)\n",
+ " print(av_loss/len(train_data[:10000]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "1atkuorSW-qh",
+ "outputId": "91399cd8-264f-4f39-9881-900b9e4e8575"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "walk as\n",
+ "to him\n",
+ "as the\n",
+ "him ,\n",
+ "the same\n",
+ ", but\n",
+ "same people\n",
+ "but ,\n",
+ "people ,\n",
+ ", but\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Use the model to predict some words!\n",
+ "# TODO\n",
+ "\n",
+ "words = ['walk', 'to']\n",
+ "\n",
+ "for i in range(10):\n",
+ " word = words[i]\n",
+ " idx = vocab[word]\n",
+ " tensor_idx = torch.tensor(idx)\n",
+ " \n",
+ " raw_output = our_lm(tensor_idx)\n",
+ " probs = softmax(raw_output)\n",
+ " \n",
+ " pred = torch.argmax(probs)\n",
+ " \n",
+ " # Print prediction.\n",
+ " for w, v in vocab.items():\n",
+ " if v == pred:\n",
+ " print(word + ' ' + w)\n",
+ " words.append(w)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "id": "u5v16264W-qi"
+ },
+ "outputs": [],
+ "source": [
+ "# Note: Perplexity is just exp(2, cross-entropy). So we just use the loss here.\n",
+ "def validate(model, data):\n",
+ " \n",
+ " model.eval()\n",
+ " \n",
+ " # TODO: Implement validation function\n",
+ " av_loss = 0\n",
+ " for (x, y) in data[:1000]:\n",
+ " \n",
+ " # a) calculate probs / get an output\n",
+ " y_raw = model(x)\n",
+ " \n",
+ " # b) compute loss\n",
+ " loss = ce(y_raw.unsqueeze(0),y.unsqueeze(0))\n",
+ " av_loss += loss\n",
+ "\n",
+ " av_loss = av_loss/len(data[:1000])\n",
+ " \n",
+ " print(\"Average loss: \" + str(av_loss))\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "H6kl55XHW-qj"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.8"
+ },
+ "colab": {
+ "provenance": [],
+ "include_colab_link": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file