diff --git a/cw/08_regresja_logistyczna.ipynb b/cw/08_regresja_logistyczna.ipynb
index efcec1b..f7eca63 100644
--- a/cw/08_regresja_logistyczna.ipynb
+++ b/cw/08_regresja_logistyczna.ipynb
@@ -777,6 +777,7 @@
     "\n",
     "    def forward(self, x):\n",
     "        x = self.fc1(x)\n",
+    "        x = torch.relu(x)\n",
     "        x = self.fc2(x)\n",
     "        x = torch.sigmoid(x)\n",
     "        return x"
diff --git a/cw/08_regresja_logistyczna_ODPOWIEDZI.ipynb b/cw/08_regresja_logistyczna_ODPOWIEDZI.ipynb
index dba395f..5d01226 100644
--- a/cw/08_regresja_logistyczna_ODPOWIEDZI.ipynb
+++ b/cw/08_regresja_logistyczna_ODPOWIEDZI.ipynb
@@ -402,11 +402,11 @@
     {
      "data": {
       "text/plain": [
-       "tensor([[0.4978],\n",
-       "        [0.5009],\n",
-       "        [0.4998],\n",
-       "        [0.4990],\n",
-       "        [0.5018]], grad_fn=<SigmoidBackward>)"
+       "tensor([[0.4989],\n",
+       "        [0.4985],\n",
+       "        [0.4970],\n",
+       "        [0.4968],\n",
+       "        [0.5007]], grad_fn=<SigmoidBackward>)"
       ]
      },
      "execution_count": 20,
@@ -449,10 +449,10 @@
      "data": {
       "text/plain": [
        "[Parameter containing:\n",
-       " tensor([[-0.0059,  0.0035,  0.0021,  ..., -0.0042, -0.0057, -0.0049]],\n",
+       " tensor([[ 0.0006, -0.0076,  0.0002,  ...,  0.0051,  0.0034, -0.0004]],\n",
        "        requires_grad=True),\n",
        " Parameter containing:\n",
-       " tensor([-0.0023], requires_grad=True)]"
+       " tensor([-0.0099], requires_grad=True)]"
       ]
      },
      "execution_count": 22,
@@ -556,10 +556,10 @@
     {
      "data": {
       "text/plain": [
-       "tensor([[0.5667],\n",
-       "        [0.5802],\n",
-       "        [0.5757],\n",
-       "        [0.5670]], grad_fn=<SigmoidBackward>)"
+       "tensor([[0.5657],\n",
+       "        [0.5827],\n",
+       "        [0.5727],\n",
+       "        [0.5672]], grad_fn=<SigmoidBackward>)"
       ]
      },
      "execution_count": 28,
@@ -604,7 +604,7 @@
     {
      "data": {
       "text/plain": [
-       "452"
+       "453"
       ]
      },
      "execution_count": 30,
@@ -645,7 +645,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "accuracy: 0.5587144622991347\n"
+      "accuracy: 0.5599505562422744\n"
      ]
     }
    ],
@@ -664,7 +664,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "BCE loss: 0.6745463597170355\n"
+      "BCE loss: 0.6745760098965412\n"
      ]
     }
    ],
@@ -717,7 +717,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.6443227143826974, 0.622991347342398)"
+       "(0.6443268107837445, 0.6254635352286774)"
       ]
      },
      "execution_count": 35,
@@ -737,7 +737,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.6369243131743537, 0.6037037037037037)"
+       "(0.6371536641209213, 0.6074074074074074)"
       ]
      },
      "execution_count": 36,
@@ -757,7 +757,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.6323775731785694, 0.6499302649930265)"
+       "(0.6322633745447529, 0.6485355648535565)"
       ]
      },
      "execution_count": 37,
@@ -785,10 +785,10 @@
      "data": {
       "text/plain": [
        "[Parameter containing:\n",
-       " tensor([[ 0.0314, -0.0375,  0.0131,  ..., -0.0057, -0.0008, -0.0089]],\n",
+       " tensor([[ 0.0379, -0.0485,  0.0113,  ...,  0.0035,  0.0083, -0.0044]],\n",
        "        requires_grad=True),\n",
        " Parameter containing:\n",
-       " tensor([0.0563], requires_grad=True)]"
+       " tensor([0.0556], requires_grad=True)]"
       ]
      },
      "execution_count": 38,
@@ -808,7 +808,7 @@
     {
      "data": {
       "text/plain": [
-       "tensor([ 0.0314, -0.0375,  0.0131,  ..., -0.0057, -0.0008, -0.0089],\n",
+       "tensor([ 0.0379, -0.0485,  0.0113,  ...,  0.0035,  0.0083, -0.0044],\n",
        "       grad_fn=<SelectBackward>)"
       ]
      },
@@ -830,11 +830,11 @@
      "data": {
       "text/plain": [
        "torch.return_types.topk(\n",
-       "values=tensor([0.3753, 0.2305, 0.2007, 0.2006, 0.1993, 0.1952, 0.1930, 0.1898, 0.1831,\n",
-       "        0.1731, 0.1649, 0.1647, 0.1543, 0.1320, 0.1314, 0.1303, 0.1296, 0.1261,\n",
-       "        0.1245, 0.1243], grad_fn=<TopkBackward>),\n",
-       "indices=tensor([8942, 6336, 1852, 9056, 1865, 4039, 7820, 5002, 8208, 1857, 9709,  803,\n",
-       "        1046,  130, 4306, 6481, 4370, 4259, 4285, 1855]))"
+       "values=tensor([0.3804, 0.2315, 0.2033, 0.2026, 0.2014, 0.1993, 0.1942, 0.1890, 0.1868,\n",
+       "        0.1818, 0.1727, 0.1542, 0.1474, 0.1458, 0.1360, 0.1359, 0.1260, 0.1204,\n",
+       "        0.1184, 0.1174], grad_fn=<TopkBackward>),\n",
+       "indices=tensor([8942, 6336, 1865, 1852, 8208, 9056, 7820, 4039, 5002, 1857, 9709,  803,\n",
+       "         130, 1046, 4370, 4259, 4306, 1855, 4285, 6481]))"
       ]
      },
      "execution_count": 40,
@@ -857,24 +857,24 @@
      "text": [
       "the\n",
       "of\n",
-      "christ\n",
-      "to\n",
       "church\n",
-      "god\n",
-      "rutgers\n",
-      "jesus\n",
+      "christ\n",
       "sin\n",
+      "to\n",
+      "rutgers\n",
+      "god\n",
+      "jesus\n",
       "christians\n",
       "we\n",
       "and\n",
-      "athos\n",
       "1993\n",
-      "hell\n",
-      "our\n",
+      "athos\n",
       "his\n",
       "he\n",
+      "hell\n",
+      "christian\n",
       "heaven\n",
-      "christian\n"
+      "our\n"
      ]
     }
    ],
@@ -892,11 +892,11 @@
      "data": {
       "text/plain": [
        "torch.return_types.topk(\n",
-       "values=tensor([-0.3478, -0.2578, -0.2455, -0.2347, -0.2330, -0.2265, -0.2205, -0.2050,\n",
-       "        -0.2044, -0.1979, -0.1876, -0.1790, -0.1747, -0.1745, -0.1734, -0.1647,\n",
-       "        -0.1639, -0.1617, -0.1601, -0.1592], grad_fn=<TopkBackward>),\n",
-       "indices=tensor([5119, 8096, 5420, 4436, 6194, 1627, 6901, 5946, 9970, 3116, 1036, 9906,\n",
-       "        5654, 8329, 7869, 1039, 1991, 4926, 5035, 4925]))"
+       "values=tensor([-0.3464, -0.2578, -0.2372, -0.2307, -0.2300, -0.2259, -0.2227, -0.2107,\n",
+       "        -0.2054, -0.1949, -0.1919, -0.1767, -0.1767, -0.1749, -0.1747, -0.1739,\n",
+       "        -0.1715, -0.1633, -0.1567, -0.1562], grad_fn=<TopkBackward>),\n",
+       "indices=tensor([5119, 8096, 5420, 1627, 6194, 6901, 4436, 9970, 5946, 3116, 1036, 9906,\n",
+       "        7869, 5654, 1991, 8329, 4925, 4926, 6373, 1039]))"
       ]
      },
      "execution_count": 42,
@@ -922,23 +922,23 @@
       "keith\n",
       "sgi\n",
       "livesey\n",
-      "host\n",
-      "nntp\n",
       "caltech\n",
+      "nntp\n",
       "posting\n",
-      "morality\n",
+      "host\n",
       "you\n",
+      "morality\n",
       "edu\n",
       "atheism\n",
       "wpd\n",
-      "mathew\n",
-      "solntze\n",
       "sandvik\n",
-      "atheists\n",
+      "mathew\n",
       "com\n",
+      "solntze\n",
+      "islam\n",
       "islamic\n",
-      "jon\n",
-      "islam\n"
+      "okcforum\n",
+      "atheists\n"
      ]
     }
    ],
@@ -969,6 +969,7 @@
     "\n",
     "    def forward(self, x):\n",
     "        x = self.fc1(x)\n",
+    "        x = torch.relu(x)\n",
     "        x = self.fc2(x)\n",
     "        x = torch.sigmoid(x)\n",
     "        return x"
@@ -1029,7 +1030,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.6605833534551934, 0.5908529048207664)"
+       "(0.6734723948651398, 0.5636588380716935)"
       ]
      },
      "metadata": {},
@@ -1038,7 +1039,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.6379233609747004, 0.6481481481481481)"
+       "(0.6606645694485417, 0.5777777777777777)"
       ]
      },
      "metadata": {},
@@ -1056,7 +1057,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.4341224195120214, 0.896168108776267)"
+       "(0.5035873688342987, 0.8677379480840544)"
       ]
      },
      "metadata": {},
@@ -1065,7 +1066,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.3649017943276299, 0.9074074074074074)"
+       "(0.43131878033832266, 0.8851851851851852)"
       ]
      },
      "metadata": {},
@@ -1083,7 +1084,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.18619558424660096, 0.9765142150803461)"
+       "(0.22238253315332793, 0.9678615574783683)"
       ]
      },
      "metadata": {},
@@ -1092,7 +1093,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.16293201995668588, 0.9888888888888889)"
+       "(0.18925935278336206, 0.9814814814814815)"
       ]
      },
      "metadata": {},
@@ -1110,7 +1111,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.09108264647580784, 0.9962917181705809)"
+       "(0.10367853983509158, 0.9913473423980222)"
       ]
      },
      "metadata": {},
@@ -1119,7 +1120,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.08985773311858927, 0.9962962962962963)"
+       "(0.09969225936327819, 0.9962962962962963)"
       ]
      },
      "metadata": {},
@@ -1137,7 +1138,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.053487053708540566, 0.9987639060568603)"
+       "(0.0588170926504491, 0.9987639060568603)"
       ]
      },
      "metadata": {},
@@ -1146,7 +1147,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.05794332528279887, 1.0)"
+       "(0.06267384567332489, 1.0)"
       ]
      },
      "metadata": {},
@@ -1189,7 +1190,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.16834938257537793, 0.9428172942817294)"
+       "(0.17201613383874234, 0.9414225941422594)"
       ]
      },
      "execution_count": 50,