chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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{
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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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"import torch.nn.functional as F\n",
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"\n",
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"class TextCNN(nn.Module):\n",
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" def __init__(self):\n",
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" super(TextCNN, self).__init__()\n",
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" self.num_filters_total = num_filters * len(filter_sizes)\n",
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" self.W = nn.Embedding(vocab_size, embedding_size)\n",
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" self.Weight = nn.Linear(self.num_filters_total, num_classes, bias=False)\n",
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" self.Bias = nn.Parameter(torch.ones([num_classes]))\n",
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" self.filter_list = nn.ModuleList([nn.Conv2d(1, num_filters, (size, embedding_size)) for size in filter_sizes])\n",
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"\n",
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" def forward(self, X):\n",
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" embedded_chars = self.W(X) # [batch_size, sequence_length, sequence_length]\n",
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" embedded_chars = embedded_chars.unsqueeze(1) # add channel(=1) [batch, channel(=1), sequence_length, embedding_size]\n",
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"\n",
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" pooled_outputs = []\n",
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" for i, conv in enumerate(self.filter_list):\n",
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" # conv : [input_channel(=1), output_channel(=3), (filter_height, filter_width), bias_option]\n",
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" h = F.relu(conv(embedded_chars))\n",
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" # mp : ((filter_height, filter_width))\n",
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" mp = nn.MaxPool2d((sequence_length - filter_sizes[i] + 1, 1))\n",
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" # pooled : [batch_size(=6), output_height(=1), output_width(=1), output_channel(=3)]\n",
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" pooled = mp(h).permute(0, 3, 2, 1)\n",
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" pooled_outputs.append(pooled)\n",
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"\n",
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" h_pool = torch.cat(pooled_outputs, len(filter_sizes)) # [batch_size(=6), output_height(=1), output_width(=1), output_channel(=3) * 3]\n",
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" h_pool_flat = torch.reshape(h_pool, [-1, self.num_filters_total]) # [batch_size(=6), output_height * output_width * (output_channel * 3)]\n",
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" model = self.Weight(h_pool_flat) + self.Bias # [batch_size, num_classes]\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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" embedding_size = 2 # embedding size\n",
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" sequence_length = 3 # sequence length\n",
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" num_classes = 2 # number of classes\n",
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" filter_sizes = [2, 2, 2] # n-gram windows\n",
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" num_filters = 3 # number of filters\n",
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"\n",
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" # 3 words sentences (=sequence_length is 3)\n",
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" sentences = [\"i love you\", \"he loves me\", \"she likes baseball\", \"i hate you\", \"sorry for that\", \"this is awful\"]\n",
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" labels = [1, 1, 1, 0, 0, 0] # 1 is good, 0 is not good.\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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" vocab_size = len(word_dict)\n",
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"\n",
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" model = TextCNN()\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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" inputs = torch.LongTensor([np.asarray([word_dict[n] for n in sen.split()]) for sen in sentences])\n",
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" targets = torch.LongTensor([out for out in labels]) # To using Torch Softmax Loss function\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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" output = model(inputs)\n",
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"\n",
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" # output : [batch_size, num_classes], target_batch : [batch_size] (LongTensor, not one-hot)\n",
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" loss = criterion(output, targets)\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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" # Test\n",
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" test_text = 'sorry hate you'\n",
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" tests = [np.asarray([word_dict[n] for n in test_text.split()])]\n",
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" test_batch = torch.LongTensor(tests)\n",
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"\n",
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" # Predict\n",
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" predict = model(test_batch).data.max(1, keepdim=True)[1]\n",
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" if predict[0][0] == 0:\n",
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" print(test_text,\"is Bad Mean...\")\n",
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" else:\n",
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" print(test_text,\"is Good Mean!!\")"
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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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}
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@@ -0,0 +1,84 @@
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# %%
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# code by Tae Hwan Jung @graykode
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import torch.nn.functional as F
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class TextCNN(nn.Module):
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def __init__(self):
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super(TextCNN, self).__init__()
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self.num_filters_total = num_filters * len(filter_sizes)
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self.W = nn.Embedding(vocab_size, embedding_size)
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self.Weight = nn.Linear(self.num_filters_total, num_classes, bias=False)
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self.Bias = nn.Parameter(torch.ones([num_classes]))
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self.filter_list = nn.ModuleList([nn.Conv2d(1, num_filters, (size, embedding_size)) for size in filter_sizes])
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def forward(self, X):
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embedded_chars = self.W(X) # [batch_size, sequence_length, sequence_length]
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embedded_chars = embedded_chars.unsqueeze(1) # add channel(=1) [batch, channel(=1), sequence_length, embedding_size]
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pooled_outputs = []
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for i, conv in enumerate(self.filter_list):
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# conv : [input_channel(=1), output_channel(=3), (filter_height, filter_width), bias_option]
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h = F.relu(conv(embedded_chars))
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# mp : ((filter_height, filter_width))
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mp = nn.MaxPool2d((sequence_length - filter_sizes[i] + 1, 1))
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# pooled : [batch_size(=6), output_height(=1), output_width(=1), output_channel(=3)]
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pooled = mp(h).permute(0, 3, 2, 1)
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pooled_outputs.append(pooled)
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h_pool = torch.cat(pooled_outputs, len(filter_sizes)) # [batch_size(=6), output_height(=1), output_width(=1), output_channel(=3) * 3]
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h_pool_flat = torch.reshape(h_pool, [-1, self.num_filters_total]) # [batch_size(=6), output_height * output_width * (output_channel * 3)]
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model = self.Weight(h_pool_flat) + self.Bias # [batch_size, num_classes]
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return model
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if __name__ == '__main__':
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embedding_size = 2 # embedding size
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sequence_length = 3 # sequence length
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num_classes = 2 # number of classes
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filter_sizes = [2, 2, 2] # n-gram windows
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num_filters = 3 # number of filters
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# 3 words sentences (=sequence_length is 3)
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sentences = ["i love you", "he loves me", "she likes baseball", "i hate you", "sorry for that", "this is awful"]
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labels = [1, 1, 1, 0, 0, 0] # 1 is good, 0 is not good.
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word_list = " ".join(sentences).split()
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word_list = list(set(word_list))
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word_dict = {w: i for i, w in enumerate(word_list)}
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vocab_size = len(word_dict)
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model = TextCNN()
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(model.parameters(), lr=0.001)
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inputs = torch.LongTensor([np.asarray([word_dict[n] for n in sen.split()]) for sen in sentences])
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targets = torch.LongTensor([out for out in labels]) # To using Torch Softmax Loss function
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# Training
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for epoch in range(5000):
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optimizer.zero_grad()
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output = model(inputs)
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# output : [batch_size, num_classes], target_batch : [batch_size] (LongTensor, not one-hot)
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loss = criterion(output, targets)
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if (epoch + 1) % 1000 == 0:
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print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
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loss.backward()
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optimizer.step()
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# Test
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test_text = 'sorry hate you'
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tests = [np.asarray([word_dict[n] for n in test_text.split()])]
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test_batch = torch.LongTensor(tests)
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# Predict
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predict = model(test_batch).data.max(1, keepdim=True)[1]
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if predict[0][0] == 0:
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print(test_text,"is Bad Mean...")
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else:
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print(test_text,"is Good Mean!!")
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