main.py

@@ -1,65 +0,0 @@
-from gensim.test.utils import common_texts
-from gensim.models import Word2Vec
-
-from sklearn import preprocessing
-
-import pandas as pd
-import numpy as np
-import gensim
-import torch
-
-class NeuralNetworkModel(torch.nn.Module):
-
-    def __init__(self):
-        super(NeuralNetworkModel, self).__init__()
-        self.fc1 = torch.nn.Linear(maxim, 500)
-        self.fc2 = torch.nn.Linear(500, 1)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = torch.relu(x)
-        x = self.fc2(x)
-        x = torch.sigmoid(x)
-        return x
-
-with open("train/in.tsv") as xd:
-    x1 = xd.readlines()
-    with open("train/expected.tsv") as xdd:
-        y1 = xdd.readlines() 
-        with open("test-A/in.tsv") as xddd:
-            x = xddd.readlines() 
-
-maxim = 500
-bLen = 5
-
-exp = Word2Vec(x1, min_count = 1, vector_size = 500, workers = 3, window = 3, sg = 1)
-exp1 = Word2Vec(x, min_count = 1, vector_size = 500, workers = 3, window = 3, sg = 1)
-exp2 = NeuralNetworkModel()
-
-x1 = exp.wv
-x = exp1.wv
-
-crt = torch.nn.BCELoss()
-miz = torch.optim.SGD(exp2.parameters(), lr = 0.1)
-
-for each in range(10):
-    lossScore, accScore, sums = 0
-    exp2.train()
-    for i in range(0, y1.shape[0], bLen):
-        x = x1[i : i + bLen]
-        x = torch.tensor(x.astype(np.float32).todense())
-        y = y1[i : i + bLen]
-        y = torch.tensor(y.astype(np.float32)).reshape(-1, 1)
-        y2 = exp2(x)
-        accScore += torch.sum((y2 > 0.5) == y).item()
-        sums += y.shape[0] 
-
-        miz.zero_grad()
-        loss = crt(y2, Y)
-        loss.backward()
-        miz.step()
-
-        lossScore += loss.item() * Y.shape[0] 
-        with open('test-A/out.tsv', 'w') as file:
-            for each in y2:
-                file.write("%f\n" % each)