188 lines
3.7 KiB
Plaintext
Executable File
188 lines
3.7 KiB
Plaintext
Executable File
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Auto grad\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torchvision"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor([5., 7., 9.], grad_fn=<AddBackward0>)\n",
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"<AddBackward0 object at 0x11f573ad0>\n",
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"tensor(21., grad_fn=<SumBackward0>)\n",
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"<SumBackward0 object at 0x11f595650>\n"
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]
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}
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],
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"source": [
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"# The tensor object keeps track of how it was created if requieres_grad is True \n",
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"x = torch.tensor([1.,2.,3],requires_grad=True)\n",
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"y = torch.tensor([4.,5.,6],requires_grad=True)\n",
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"\n",
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"z = x + y\n",
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"print(z)\n",
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"\n",
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"print(z.grad_fn)\n",
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"s = z.sum()\n",
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"\n",
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"print(s)\n",
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"\n",
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"print(s.grad_fn)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor([2., 2., 2.])\n"
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]
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}
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],
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"source": [
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"# To back propagate\n",
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"s.backward()\n",
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"print(x.grad)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"False False\n",
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"None\n",
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"<AddBackward0 object at 0x11f088650>\n",
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"True\n",
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"None\n",
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"True\n",
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"True\n",
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"False\n"
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]
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}
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],
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"source": [
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"x = torch.randn(2,2)\n",
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"y = torch.randn(2,2)\n",
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"print(x.requires_grad,y.requires_grad)\n",
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"\n",
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"z = x + y\n",
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"\n",
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"print(z.grad_fn)\n",
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"\n",
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"x.requires_grad_()\n",
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"y.requires_grad_()\n",
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"\n",
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"z= x + y\n",
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"\n",
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"print(z.grad_fn)\n",
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"\n",
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"print(z.requires_grad)\n",
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"\n",
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"new_z = z.detach()\n",
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"\n",
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"print(new_z.grad_fn)\n",
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"\n",
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"print(x.requires_grad)\n",
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"print((x+10).requires_grad)\n",
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"\n",
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"with torch.no_grad():\n",
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" print((x+10).requires_grad)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor([[1., 1.],\n",
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" [1., 1.]], requires_grad=True)\n",
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"tensor([[3., 3.],\n",
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" [3., 3.]], grad_fn=<AddBackward0>)\n",
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"<AddBackward0 object at 0x11ee83b90>\n",
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"tensor([[27., 27.],\n",
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" [27., 27.]], grad_fn=<MulBackward0>) tensor(27., grad_fn=<MeanBackward0>)\n",
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"tensor([[4.5000, 4.5000],\n",
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" [4.5000, 4.5000]])\n"
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]
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}
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],
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"source": [
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"x = torch.ones(2,2,requires_grad=True)\n",
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"print(x)\n",
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"y = x + 2\n",
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"print(y)\n",
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"print(y.grad_fn)\n",
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"\n",
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"z = y*y*3\n",
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"\n",
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"out = z.mean()\n",
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"\n",
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"print(z,out)\n",
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"\n",
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"out.backward()\n",
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"print(x.grad)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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