diff --git a/main.py b/main.py
index ae10119..15eef8d 100644
--- a/main.py
+++ b/main.py
@@ -4,12 +4,13 @@ import numpy as np
 import torch
 from gensim import downloader
 from nltk.tokenize import word_tokenize
+import csv
 
 
 class NeuralNetworkModel(torch.nn.Module):
 
     def __init__(self):
-        dim = 100
+        dim = 200
         super(NeuralNetworkModel, self).__init__()
         self.fc1 = torch.nn.Linear(dim, 500)
         self.fc2 = torch.nn.Linear(500, 1)
@@ -27,24 +28,28 @@ def read_data():
     y_labels = (pd.read_csv('out-header.tsv', sep='\t')).columns
 
     x_train = pd.read_table('train/in.tsv', error_bad_lines=False,
-                            header=None, quoting=3, names=x_labels)
+                            header=None, quoting=csv.QUOTE_NONE, names=x_labels)
     y_train = pd.read_table('train/expected.tsv', error_bad_lines=False,
-                            header=None, quoting=3, names=y_labels)
+                            header=None, quoting=csv.QUOTE_NONE, names=y_labels)
     x_dev = pd.read_table('dev-0/in.tsv', error_bad_lines=False,
-                          header=None, quoting=3, names=x_labels)
+                          header=None, quoting=csv.QUOTE_NONE, names=x_labels)
     x_test = pd.read_table('test-A/in.tsv', error_bad_lines=False,
-                           header=None, quoting=3, names=x_labels)
+                           header=None, quoting=csv.QUOTE_NONE, names=x_labels)
 
     # remove some rows for faster development
     remove_n = 200000
     drop_indices = np.random.choice(x_train.index, remove_n, replace=False)
     x_train = x_train.drop(drop_indices)
+    y_train = y_train.drop(drop_indices)
 
     return x_labels, y_labels, x_train, y_train, x_dev, x_test
 
 
 x_labels, y_labels, x_train, y_train, x_dev, x_test = read_data()
 
+print(len(y_train))
+print(len(x_train))
+
 x_train = x_train[x_labels[0]].str.lower()
 x_dev = x_dev[x_labels[0]].str.lower()
 x_test = x_test[x_labels[0]].str.lower()
@@ -57,11 +62,11 @@ x_test = [word_tokenize(x) for x in x_test]
 w2v = downloader.load('glove-wiki-gigaword-200')
 
 x_train = [np.mean([w2v[word] for word in doc if word in w2v] or [
-                   np.zeros(50)], axis=0) for doc in x_train]
+                   np.zeros(200)], axis=0) for doc in x_train]
 x_dev = [np.mean([w2v[word] for word in doc if word in w2v]
-                 or [np.zeros(50)], axis=0) for doc in x_dev]
+                 or [np.zeros(200)], axis=0) for doc in x_dev]
 x_test = [np.mean([w2v[word] for word in doc if word in w2v]
-                  or [np.zeros(50)], axis=0) for doc in x_test]
+                  or [np.zeros(200)], axis=0) for doc in x_test]
 
 nn_model = NeuralNetworkModel()
 BATCH_SIZE = 5
@@ -72,15 +77,16 @@ for epoch in range(5):
     nn_model.train()
     for i in range(0, y_train.shape[0], BATCH_SIZE):
         X = x_train[i:i+BATCH_SIZE]
-        X = torch.tensor(X.astype(np.float32).todense())
+        X = torch.tensor(X)
         Y = y_train[i:i+BATCH_SIZE]
-        Y = torch.tensor(Y.astype(np.float32)).reshape(-1, 1)
+        Y = torch.tensor(Y.astype(np.float32).to_numpy()).reshape(-1, 1)
 
-        Y_predictions = nn_model(X)
+        Y_predictions = nn_model(X.float())
 
-        optimizer.zero_grad()
         loss = criterion(Y_predictions, Y)
+        optimizer.zero_grad()
         loss.backward()
         optimizer.step()
 
+
 print(Y_predictions)