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more content for more exmaples #103

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65 changes: 39 additions & 26 deletions tutorial-contents-notebooks/303_build_nn_quickly.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -15,22 +15,19 @@
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn.functional as F"
"import torch.nn.functional as F\n",
"from collections import OrderedDict"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"# replace following class code with an easy sequential network\n",
Expand All @@ -49,20 +46,16 @@
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"net1 = Net(1, 10, 1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# easy and fast way to build your network\n",
Expand All @@ -75,28 +68,48 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# network with specific layer names\n",
"net3 = torch.nn.Sequential(OrderedDict([\n",
" ('fc1', torch.nn.Linear(1, 10)),\n",
" ('relu', torch.nn.ReLU()),\n",
" ('output', torch.nn.Linear(10, 1))\n",
"]))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Net (\n",
" (hidden): Linear (1 -> 10)\n",
" (predict): Linear (10 -> 1)\n",
"Net(\n",
" (hidden): Linear(in_features=1, out_features=10, bias=True)\n",
" (predict): Linear(in_features=10, out_features=1, bias=True)\n",
")\n",
"Sequential(\n",
" (0): Linear(in_features=1, out_features=10, bias=True)\n",
" (1): ReLU()\n",
" (2): Linear(in_features=10, out_features=1, bias=True)\n",
")\n",
"Sequential (\n",
" (0): Linear (1 -> 10)\n",
" (1): ReLU ()\n",
" (2): Linear (10 -> 1)\n",
"Sequential(\n",
" (fc1): Linear(in_features=1, out_features=10, bias=True)\n",
" (relu): ReLU()\n",
" (output): Linear(in_features=10, out_features=1, bias=True)\n",
")\n"
]
}
],
"source": [
"print(net1) # net1 architecture\n",
"print(net2) # net2 architecture"
"print(net2) # net2 architecture\n",
"print(net3) # net3 architecture"
]
},
{
Expand All @@ -111,7 +124,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -125,7 +138,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
"version": "3.9.7"
}
},
"nbformat": 4,
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