95 lines
2.3 KiB
Python
95 lines
2.3 KiB
Python
import gensim.downloader as gensim
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import numpy as np
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import pandas as pd
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import torch
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from nltk.tokenize import word_tokenize
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class NeuralNetworkModel(torch.nn.Module):
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def __init__(self):
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super(NeuralNetworkModel, self).__init__()
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self.1 = torch.nn.Linear(300, 300)
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self.2 = torch.nn.Linear(300, 1)
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def forward(self, x):
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x = self.1(x)
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x = torch.relu(x)
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x = self.2(x)
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x = torch.sigmoid(x)
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return x
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nm = NeuralNetworkModel()
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dev_train = []
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test_train = []
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word2vec = gensim.load('word2vec-google-news-300')
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np.mean([word2vec[word] for word in doc if word in word2vec] or [np.zeros(300)], axis=0)
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def model_train():
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train = pd.read_table('train/in.tsv.xz', compression='xz', sep='\t', quoting=3)
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trainy = pd.read_table('train/expected.tsv', sep='\t', quoting=3)
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trainy = trainy[0]
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def model_prepare():
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dev = pd.read_table('dev-0/in.tsv.xz', compression='xz', sep='\t', quoting=3)
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test = pd.read_table('test-A/in.tsv.xz', compression='xz', sep='\t', quoting=3)
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train = [word_tokenize(x) for x in train]
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dev = [word_tokenize(x) for x in dev]
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test = [word_tokenize(x) for x in test]
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def word_2_voc():
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train = [d2v(doc) for doc in train]
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dev = [d2v(doc) for doc in dev]
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test = [d2v(doc) for doc in test]
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criterion = torch.nn.BCELoss()
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optimizer = torch.optim.Adam(model.parameters())
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print ("1")
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for epoch in range(5):
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model.train()
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for i in range(0, y_train.shape[0], 5):
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X = train[i:i + 5]
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X = torch.tensor(X)
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y = trainy[i:i + 5]
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y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
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optimizer.zero_grad()
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outputs = nm(X.float())
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loss = criterion(outputs, y)
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loss.backward()
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optimizer.step()
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print ("2")
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with torch.no_grad():
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for i in range(0, len(dev), 5):
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X = dev[i:i + 5]
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X = torch.tensor(X)
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outputs = nm(X.float())
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y = (outputs > 0.5)
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dev_train.extend(y)
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for i in range(0, len(test), 5):
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X = test[i:i + 5]
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X = torch.tensor(X)
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outputs = nm(X.float())
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y = (outputs >= 0.5)
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testy.extend(y)
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dev_train.to_csv(r'dev-0/out.tsv', sep='\t', index=False, header=False)
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test_train.to_csv(r'test-A/out.tsv', sep='\t', index=False, header=False)
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