forked from filipg/aitech-eks-pub
Merge branch 'master' of git.wmi.amu.edu.pl:filipg/aitech-eks
This commit is contained in:
commit
517fb0dae8
@ -777,6 +777,7 @@
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"\n",
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"\n",
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" def forward(self, x):\n",
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" def forward(self, x):\n",
|
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" x = self.fc1(x)\n",
|
" x = self.fc1(x)\n",
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|
" x = torch.relu(x)\n",
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" x = self.fc2(x)\n",
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" x = self.fc2(x)\n",
|
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" x = torch.sigmoid(x)\n",
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" x = torch.sigmoid(x)\n",
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" return x"
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" return x"
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@ -402,11 +402,11 @@
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{
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"tensor([[0.4978],\n",
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"tensor([[0.4989],\n",
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" [0.5009],\n",
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" [0.4985],\n",
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" [0.4998],\n",
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" [0.4970],\n",
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" [0.4990],\n",
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" [0.4968],\n",
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" [0.5018]], grad_fn=<SigmoidBackward>)"
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" [0.5007]], grad_fn=<SigmoidBackward>)"
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]
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]
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},
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"execution_count": 20,
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"data": {
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"text/plain": [
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"[Parameter containing:\n",
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"[Parameter containing:\n",
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" tensor([[-0.0059, 0.0035, 0.0021, ..., -0.0042, -0.0057, -0.0049]],\n",
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" tensor([[ 0.0006, -0.0076, 0.0002, ..., 0.0051, 0.0034, -0.0004]],\n",
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" requires_grad=True),\n",
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" requires_grad=True),\n",
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" Parameter containing:\n",
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" Parameter containing:\n",
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" tensor([-0.0023], requires_grad=True)]"
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" tensor([-0.0099], requires_grad=True)]"
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"execution_count": 22,
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"data": {
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"text/plain": [
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"tensor([[0.5667],\n",
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" [0.5802],\n",
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" [0.5827],\n",
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" [0.5757],\n",
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" [0.5727],\n",
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" [0.5670]], grad_fn=<SigmoidBackward>)"
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" [0.5672]], grad_fn=<SigmoidBackward>)"
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]
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},
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"execution_count": 28,
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{
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"452"
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"453"
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]
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]
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},
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},
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"execution_count": 30,
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"execution_count": 30,
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@ -645,7 +645,7 @@
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"name": "stdout",
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"name": "stdout",
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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"text": [
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"accuracy: 0.5587144622991347\n"
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"accuracy: 0.5599505562422744\n"
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]
|
]
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}
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}
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],
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],
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@ -664,7 +664,7 @@
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"name": "stdout",
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"name": "stdout",
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"output_type": "stream",
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"output_type": "stream",
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"text": [
|
"text": [
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||||||
"BCE loss: 0.6745463597170355\n"
|
"BCE loss: 0.6745760098965412\n"
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||||||
]
|
]
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}
|
}
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],
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],
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@ -717,7 +717,7 @@
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{
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"(0.6443227143826974, 0.622991347342398)"
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"(0.6443268107837445, 0.6254635352286774)"
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]
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]
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},
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},
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"execution_count": 35,
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"execution_count": 35,
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@ -737,7 +737,7 @@
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{
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"(0.6369243131743537, 0.6037037037037037)"
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"(0.6371536641209213, 0.6074074074074074)"
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]
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]
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},
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"execution_count": 36,
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{
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{
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"data": {
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"data": {
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"text/plain": [
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"text/plain": [
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"(0.6323775731785694, 0.6499302649930265)"
|
"(0.6322633745447529, 0.6485355648535565)"
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]
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]
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},
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},
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"execution_count": 37,
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"execution_count": 37,
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@ -785,10 +785,10 @@
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"data": {
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"data": {
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"text/plain": [
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"text/plain": [
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"[Parameter containing:\n",
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"[Parameter containing:\n",
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||||||
" tensor([[ 0.0314, -0.0375, 0.0131, ..., -0.0057, -0.0008, -0.0089]],\n",
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" tensor([[ 0.0379, -0.0485, 0.0113, ..., 0.0035, 0.0083, -0.0044]],\n",
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" requires_grad=True),\n",
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" requires_grad=True),\n",
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" Parameter containing:\n",
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" Parameter containing:\n",
|
||||||
" tensor([0.0563], requires_grad=True)]"
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" tensor([0.0556], requires_grad=True)]"
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]
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]
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},
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},
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"execution_count": 38,
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"execution_count": 38,
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@ -808,7 +808,7 @@
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{
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{
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"data": {
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"data": {
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"text/plain": [
|
"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",
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||||||
" grad_fn=<SelectBackward>)"
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" grad_fn=<SelectBackward>)"
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||||||
]
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]
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},
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},
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@ -830,11 +830,11 @@
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"data": {
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"data": {
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"text/plain": [
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"text/plain": [
|
||||||
"torch.return_types.topk(\n",
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"torch.return_types.topk(\n",
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||||||
"values=tensor([0.3753, 0.2305, 0.2007, 0.2006, 0.1993, 0.1952, 0.1930, 0.1898, 0.1831,\n",
|
"values=tensor([0.3804, 0.2315, 0.2033, 0.2026, 0.2014, 0.1993, 0.1942, 0.1890, 0.1868,\n",
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||||||
" 0.1731, 0.1649, 0.1647, 0.1543, 0.1320, 0.1314, 0.1303, 0.1296, 0.1261,\n",
|
" 0.1818, 0.1727, 0.1542, 0.1474, 0.1458, 0.1360, 0.1359, 0.1260, 0.1204,\n",
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||||||
" 0.1245, 0.1243], grad_fn=<TopkBackward>),\n",
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" 0.1184, 0.1174], grad_fn=<TopkBackward>),\n",
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||||||
"indices=tensor([8942, 6336, 1852, 9056, 1865, 4039, 7820, 5002, 8208, 1857, 9709, 803,\n",
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"indices=tensor([8942, 6336, 1865, 1852, 8208, 9056, 7820, 4039, 5002, 1857, 9709, 803,\n",
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||||||
" 1046, 130, 4306, 6481, 4370, 4259, 4285, 1855]))"
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" 130, 1046, 4370, 4259, 4306, 1855, 4285, 6481]))"
|
||||||
]
|
]
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},
|
},
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||||||
"execution_count": 40,
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"execution_count": 40,
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@ -857,24 +857,24 @@
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|||||||
"text": [
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"text": [
|
||||||
"the\n",
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"the\n",
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||||||
"of\n",
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"of\n",
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||||||
"christ\n",
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||||||
"to\n",
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||||||
"church\n",
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"church\n",
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||||||
"god\n",
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"christ\n",
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"rutgers\n",
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||||||
"jesus\n",
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||||||
"sin\n",
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"sin\n",
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"to\n",
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"rutgers\n",
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||||||
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"god\n",
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||||||
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"jesus\n",
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||||||
"christians\n",
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"christians\n",
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||||||
"we\n",
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"we\n",
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||||||
"and\n",
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"and\n",
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||||||
"athos\n",
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"1993\n",
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"1993\n",
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||||||
"hell\n",
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"athos\n",
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"our\n",
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"his\n",
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"his\n",
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||||||
"he\n",
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"he\n",
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||||||
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"hell\n",
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||||||
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"christian\n",
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||||||
"heaven\n",
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"heaven\n",
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||||||
"christian\n"
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"our\n"
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||||||
]
|
]
|
||||||
}
|
}
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],
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],
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@ -892,11 +892,11 @@
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"data": {
|
"data": {
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"text/plain": [
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"text/plain": [
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||||||
"torch.return_types.topk(\n",
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"torch.return_types.topk(\n",
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||||||
"values=tensor([-0.3478, -0.2578, -0.2455, -0.2347, -0.2330, -0.2265, -0.2205, -0.2050,\n",
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"values=tensor([-0.3464, -0.2578, -0.2372, -0.2307, -0.2300, -0.2259, -0.2227, -0.2107,\n",
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||||||
" -0.2044, -0.1979, -0.1876, -0.1790, -0.1747, -0.1745, -0.1734, -0.1647,\n",
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" -0.2054, -0.1949, -0.1919, -0.1767, -0.1767, -0.1749, -0.1747, -0.1739,\n",
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||||||
" -0.1639, -0.1617, -0.1601, -0.1592], grad_fn=<TopkBackward>),\n",
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" -0.1715, -0.1633, -0.1567, -0.1562], grad_fn=<TopkBackward>),\n",
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||||||
"indices=tensor([5119, 8096, 5420, 4436, 6194, 1627, 6901, 5946, 9970, 3116, 1036, 9906,\n",
|
"indices=tensor([5119, 8096, 5420, 1627, 6194, 6901, 4436, 9970, 5946, 3116, 1036, 9906,\n",
|
||||||
" 5654, 8329, 7869, 1039, 1991, 4926, 5035, 4925]))"
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" 7869, 5654, 1991, 8329, 4925, 4926, 6373, 1039]))"
|
||||||
]
|
]
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},
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},
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"execution_count": 42,
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"execution_count": 42,
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@ -922,23 +922,23 @@
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|||||||
"keith\n",
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"keith\n",
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||||||
"sgi\n",
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"sgi\n",
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||||||
"livesey\n",
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"livesey\n",
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||||||
"host\n",
|
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||||||
"nntp\n",
|
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||||||
"caltech\n",
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"caltech\n",
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||||||
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"nntp\n",
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||||||
"posting\n",
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"posting\n",
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||||||
"morality\n",
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"host\n",
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||||||
"you\n",
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"you\n",
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||||||
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"morality\n",
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||||||
"edu\n",
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"edu\n",
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||||||
"atheism\n",
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"atheism\n",
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||||||
"wpd\n",
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"wpd\n",
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||||||
"mathew\n",
|
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||||||
"solntze\n",
|
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||||||
"sandvik\n",
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"sandvik\n",
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||||||
"atheists\n",
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"mathew\n",
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||||||
"com\n",
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"com\n",
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||||||
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"solntze\n",
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||||||
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"islam\n",
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||||||
"islamic\n",
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"islamic\n",
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||||||
"jon\n",
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"okcforum\n",
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||||||
"islam\n"
|
"atheists\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
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@ -969,6 +969,7 @@
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"\n",
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"\n",
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" def forward(self, x):\n",
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" def forward(self, x):\n",
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" x = self.fc1(x)\n",
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" x = self.fc1(x)\n",
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" x = torch.relu(x)\n",
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" x = self.fc2(x)\n",
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" x = self.fc2(x)\n",
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||||||
" x = torch.sigmoid(x)\n",
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" x = torch.sigmoid(x)\n",
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||||||
" return x"
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" return x"
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@ -1029,7 +1030,7 @@
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{
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{
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"data": {
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"data": {
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"text/plain": [
|
"text/plain": [
|
||||||
"(0.6605833534551934, 0.5908529048207664)"
|
"(0.6734723948651398, 0.5636588380716935)"
|
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]
|
]
|
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},
|
},
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"metadata": {},
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"metadata": {},
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@ -1038,7 +1039,7 @@
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{
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{
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"data": {
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"text/plain": [
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"text/plain": [
|
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"(0.6379233609747004, 0.6481481481481481)"
|
"(0.6606645694485417, 0.5777777777777777)"
|
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]
|
]
|
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},
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},
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"metadata": {},
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@ -1056,7 +1057,7 @@
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{
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"(0.4341224195120214, 0.896168108776267)"
|
"(0.5035873688342987, 0.8677379480840544)"
|
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]
|
]
|
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|
},
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"metadata": {},
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@ -1065,7 +1066,7 @@
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{
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{
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"data": {
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"text/plain": [
|
"text/plain": [
|
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"(0.3649017943276299, 0.9074074074074074)"
|
"(0.43131878033832266, 0.8851851851851852)"
|
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|
]
|
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},
|
},
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"metadata": {},
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@ -1083,7 +1084,7 @@
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{
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"(0.18619558424660096, 0.9765142150803461)"
|
"(0.22238253315332793, 0.9678615574783683)"
|
||||||
]
|
]
|
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},
|
},
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"metadata": {},
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@ -1092,7 +1093,7 @@
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{
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{
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"data": {
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"text/plain": [
|
"text/plain": [
|
||||||
"(0.16293201995668588, 0.9888888888888889)"
|
"(0.18925935278336206, 0.9814814814814815)"
|
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]
|
]
|
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},
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},
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"metadata": {},
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@ -1110,7 +1111,7 @@
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{
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{
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"data": {
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"text/plain": [
|
"text/plain": [
|
||||||
"(0.09108264647580784, 0.9962917181705809)"
|
"(0.10367853983509158, 0.9913473423980222)"
|
||||||
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]
|
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"metadata": {},
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{
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"data": {
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"text/plain": [
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|
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"(0.08985773311858927, 0.9962962962962963)"
|
"(0.09969225936327819, 0.9962962962962963)"
|
||||||
]
|
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|
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|
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"metadata": {},
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@ -1137,7 +1138,7 @@
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{
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{
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"data": {
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|
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"text/plain": [
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|
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"(0.053487053708540566, 0.9987639060568603)"
|
"(0.0588170926504491, 0.9987639060568603)"
|
||||||
]
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|
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|
},
|
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"metadata": {},
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@ -1146,7 +1147,7 @@
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{
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"data": {
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|
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"(0.05794332528279887, 1.0)"
|
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|
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|
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|
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"metadata": {},
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@ -1189,7 +1190,7 @@
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{
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{
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"data": {
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|
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"text/plain": [
|
"text/plain": [
|
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"(0.16834938257537793, 0.9428172942817294)"
|
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||||||
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|
||||||
},
|
},
|
||||||
"execution_count": 50,
|
"execution_count": 50,
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user