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