wehub-resource-sync
115 lines
4.5 KiB
Plaintext
115 lines
4.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"# code by Tae Hwan Jung @graykode\n",
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"import numpy as np\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.optim as optim\n",
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"\n",
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"def make_batch():\n",
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" input_batch = []\n",
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" target_batch = []\n",
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"\n",
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" for sen in sentences:\n",
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" word = sen.split() # space tokenizer\n",
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" input = [word_dict[n] for n in word[:-1]] # create (1~n-1) as input\n",
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" target = word_dict[word[-1]] # create (n) as target, We usually call this 'casual language model'\n",
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"\n",
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" input_batch.append(np.eye(n_class)[input])\n",
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" target_batch.append(target)\n",
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"\n",
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" return input_batch, target_batch\n",
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"\n",
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"class TextRNN(nn.Module):\n",
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" def __init__(self):\n",
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" super(TextRNN, self).__init__()\n",
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" self.rnn = nn.RNN(input_size=n_class, hidden_size=n_hidden)\n",
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" self.W = nn.Linear(n_hidden, n_class, bias=False)\n",
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" self.b = nn.Parameter(torch.ones([n_class]))\n",
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"\n",
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" def forward(self, hidden, X):\n",
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" X = X.transpose(0, 1) # X : [n_step, batch_size, n_class]\n",
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" outputs, hidden = self.rnn(X, hidden)\n",
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" # outputs : [n_step, batch_size, num_directions(=1) * n_hidden]\n",
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" # hidden : [num_layers(=1) * num_directions(=1), batch_size, n_hidden]\n",
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" outputs = outputs[-1] # [batch_size, num_directions(=1) * n_hidden]\n",
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" model = self.W(outputs) + self.b # model : [batch_size, n_class]\n",
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" return model\n",
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"\n",
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"if __name__ == '__main__':\n",
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" n_step = 2 # number of cells(= number of Step)\n",
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" n_hidden = 5 # number of hidden units in one cell\n",
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"\n",
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" sentences = [\"i like dog\", \"i love coffee\", \"i hate milk\"]\n",
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"\n",
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" word_list = \" \".join(sentences).split()\n",
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" word_list = list(set(word_list))\n",
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" word_dict = {w: i for i, w in enumerate(word_list)}\n",
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" number_dict = {i: w for i, w in enumerate(word_list)}\n",
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" n_class = len(word_dict)\n",
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" batch_size = len(sentences)\n",
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"\n",
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" model = TextRNN()\n",
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"\n",
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" criterion = nn.CrossEntropyLoss()\n",
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" optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
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"\n",
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" input_batch, target_batch = make_batch()\n",
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" input_batch = torch.FloatTensor(input_batch)\n",
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" target_batch = torch.LongTensor(target_batch)\n",
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"\n",
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" # Training\n",
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" for epoch in range(5000):\n",
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" optimizer.zero_grad()\n",
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"\n",
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" # hidden : [num_layers * num_directions, batch, hidden_size]\n",
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" hidden = torch.zeros(1, batch_size, n_hidden)\n",
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" # input_batch : [batch_size, n_step, n_class]\n",
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" output = model(hidden, input_batch)\n",
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"\n",
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" # output : [batch_size, n_class], target_batch : [batch_size] (LongTensor, not one-hot)\n",
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" loss = criterion(output, target_batch)\n",
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" if (epoch + 1) % 1000 == 0:\n",
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" print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))\n",
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"\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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"\n",
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" input = [sen.split()[:2] for sen in sentences]\n",
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"\n",
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" # Predict\n",
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" hidden = torch.zeros(1, batch_size, n_hidden)\n",
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" predict = model(hidden, input_batch).data.max(1, keepdim=True)[1]\n",
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" print([sen.split()[:2] for sen in sentences], '->', [number_dict[n.item()] for n in predict.squeeze()])"
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],
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"outputs": [],
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"execution_count": null
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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} |