Skip to content

Added notebooks for first 3 scripts #34

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 4 commits into
base: master
Choose a base branch
from
Open

Added notebooks for first 3 scripts #34

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
9ace2ab
Added notebooks for first 3 scripts
chinmaydas96 Oct 31, 2018
d527176
Create 04_linear_regression.ipynb
chinmaydas96 Oct 31, 2018
f9ae758
Added notebook-5
chinmaydas96 Oct 31, 2018
ebe5930
Create 06_diabets_logistic.ipynb
chinmaydas96 Oct 31, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
116 changes: 116 additions & 0 deletions notebooks/01_basics.ipynb
View file
Open in desktop

Large diffs are not rendered by default.

111 changes: 111 additions & 0 deletions notebooks/02_manual_gradient.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,111 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"x_data = [1.0, 2.0, 3.0]\n",
"y_data = [2.0, 4.0, 6.0]\n",
"\n",
"w = 1.0 # a random guess: random value"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# our model forward pass\n",
"\n",
"def forward(x):\n",
" return x * w\n",
"\n",
"# Loss function\n",
"def loss(x, y):\n",
" y_pred = forward(x)\n",
" return (y_pred - y)**2\n",
"\n",
"# compute gradient\n",
"def gradient(x, y): # d_loss/d_w\n",
" return 2 * x * (x * w - y)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predict (before training) 4 , 4.0\n"
]
}
],
"source": [
"# Before training\n",
"print(\"predict (before training)\", 4, ',',forward(4))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predict (after training) 4 , 7.804863933862125\n"
]
}
],
"source": [
"# Training loop\n",
"for epoch in range(10):\n",
" for x_val, y_val in zip(x_data, y_data):\n",
" grad = gradient(x_val, y_val)\n",
" w = w - 0.01 * grad\n",
" #print(\"\\tgrad: \", x_val, y_val, round(grad, 2))\n",
" l = loss(x_val, y_val)\n",
"\n",
" #print(\"progress:\", epoch, \"w=\", round(w, 2), \"loss=\", round(l, 2))\n",
"\n",
"# After training\n",
"print(\"predict (after training)\", \"4 ,\", forward(4))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
149 changes: 149 additions & 0 deletions notebooks/03_auto_gradient.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,149 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.4.1\n"
]
}
],
"source": [
"import torch\n",
"import torchvision\n",
"from torch.autograd import Variable\n",
"\n",
"print(torch.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"x_data = [1.0, 2.0, 3.0]\n",
"y_data = [2.0, 4.0, 6.0]\n",
"\n",
"w = Variable(torch.Tensor([1.0]), requires_grad=True) # Any random value"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predict (before training) : 4 , tensor(4.)\n"
]
}
],
"source": [
"# our model forward pass\n",
"\n",
"def forward(x):\n",
" return x * w\n",
"\n",
"# Loss function\n",
"\n",
"def loss(x, y):\n",
" y_pred = forward(x)\n",
" return (y_pred - y) ** 2\n",
"\n",
"# Before training\n",
"print(\"predict (before training) : \", 4,\",\", forward(4).data[0])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"progress: 0 tensor(7.3159)\n",
"progress: 1 tensor(3.9988)\n",
"progress: 2 tensor(2.1857)\n",
"progress: 3 tensor(1.1946)\n",
"progress: 4 tensor(0.6530)\n",
"progress: 5 tensor(0.3569)\n",
"progress: 6 tensor(0.1951)\n",
"progress: 7 tensor(0.1066)\n",
"progress: 8 tensor(0.0583)\n",
"progress: 9 tensor(0.0319)\n"
]
}
],
"source": [
"# Training loop\n",
"for epoch in range(10):\n",
" for x_val, y_val in zip(x_data, y_data):\n",
" l = loss(x_val, y_val)\n",
" l.backward()\n",
" #print(\"\\tgrad: \", x_val, y_val, w.grad.data[0])\n",
" w.data = w.data - 0.01 * w.grad.data\n",
"\n",
" # Manually zero the gradients after updating weights\n",
" w.grad.data.zero_()\n",
"\n",
" print(\"progress:\", epoch, l.data[0])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predict (after training) 4 , tensor(7.8049)\n"
]
}
],
"source": [
"# After training\n",
"print(\"predict (after training)\", 4, \",\",forward(4).data[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Loading