1-2.Word2Vec/Word2Vec_pytorch.ipynb

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    "# %%\n",
    "# code by Tae Hwan Jung @graykode\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def random_batch():\n",
    "    random_inputs = []\n",
    "    random_labels = []\n",
    "    random_index = np.random.choice(range(len(skip_grams)), batch_size, replace=False)\n",
    "\n",
    "    for i in random_index:\n",
    "        random_inputs.append(np.eye(voc_size)[skip_grams[i][0]])  # target\n",
    "        random_labels.append(skip_grams[i][1])  # context word\n",
    "\n",
    "    return random_inputs, random_labels\n",
    "\n",
    "# Model\n",
    "class Word2Vec(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Word2Vec, self).__init__()\n",
    "        # W and WT is not Traspose relationship\n",
    "        self.W = nn.Linear(voc_size, embedding_size, bias=False) # voc_size > embedding_size Weight\n",
    "        self.WT = nn.Linear(embedding_size, voc_size, bias=False) # embedding_size > voc_size Weight\n",
    "\n",
    "    def forward(self, X):\n",
    "        # X : [batch_size, voc_size]\n",
    "        hidden_layer = self.W(X) # hidden_layer : [batch_size, embedding_size]\n",
    "        output_layer = self.WT(hidden_layer) # output_layer : [batch_size, voc_size]\n",
    "        return output_layer\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    batch_size = 2 # mini-batch size\n",
    "    embedding_size = 2 # embedding size\n",
    "\n",
    "    sentences = [\"apple banana fruit\", \"banana orange fruit\", \"orange banana fruit\",\n",
    "                 \"dog cat animal\", \"cat monkey animal\", \"monkey dog animal\"]\n",
    "\n",
    "    word_sequence = \" \".join(sentences).split()\n",
    "    word_list = \" \".join(sentences).split()\n",
    "    word_list = list(set(word_list))\n",
    "    word_dict = {w: i for i, w in enumerate(word_list)}\n",
    "    voc_size = len(word_list)\n",
    "\n",
    "    # Make skip gram of one size window\n",
    "    skip_grams = []\n",
    "    for i in range(1, len(word_sequence) - 1):\n",
    "        target = word_dict[word_sequence[i]]\n",
    "        context = [word_dict[word_sequence[i - 1]], word_dict[word_sequence[i + 1]]]\n",
    "        for w in context:\n",
    "            skip_grams.append([target, w])\n",
    "\n",
    "    model = Word2Vec()\n",
    "\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "    # Training\n",
    "    for epoch in range(5000):\n",
    "        input_batch, target_batch = random_batch()\n",
    "        input_batch = torch.Tensor(input_batch)\n",
    "        target_batch = torch.LongTensor(target_batch)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        output = model(input_batch)\n",
    "\n",
    "        # output : [batch_size, voc_size], target_batch : [batch_size] (LongTensor, not one-hot)\n",
    "        loss = criterion(output, target_batch)\n",
    "        if (epoch + 1) % 1000 == 0:\n",
    "            print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))\n",
    "\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    for i, label in enumerate(word_list):\n",
    "        W, WT = model.parameters()\n",
    "        x, y = W[0][i].item(), W[1][i].item()\n",
    "        plt.scatter(x, y)\n",
    "        plt.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points', ha='right', va='bottom')\n",
    "    plt.show()"
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