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Author SHA1 Message Date
udlbook
d101aa428b Merge pull request #236 from aleksandrskoselevs/patch-1
Update 13_4_Graph_Attention_Networks.ipynb
2024-10-15 17:24:40 -04:00
aleksandrskoselevs
8c6e40daee Update 13_4_Graph_Attention_Networks.ipynb
`phi` is defined in the book as a column vector
2024-10-11 10:54:05 +02:00
udlbook
efafb942eb Add files via upload 2024-10-01 15:14:01 -04:00
udlbook
b10a2b6940 Delete UDL_Answer_Booklet.pdf 2024-10-01 15:13:35 -04:00
udlbook
ede7247a0c Add files via upload 2024-10-01 15:13:14 -04:00
udlbook
c3b97af456 Created using Colab 2024-09-16 09:21:22 -04:00
udlbook
e1df2156a3 Created using Colab 2024-09-16 09:19:49 -04:00
udlbook
f887835646 Created using Colab 2024-09-16 09:18:12 -04:00
udlbook
e9c8d846f2 Created using Colab 2024-09-16 07:36:27 -04:00
7 changed files with 37 additions and 39 deletions

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@@ -130,7 +130,8 @@
"\n",
" print('Iter %d, a=%3.3f, b=%3.3f, c=%3.3f, d=%3.3f'%(n_iter, a,b,c,d))\n",
"\n",
" # Rule #1 If the HEIGHT at point A is less than the HEIGHT at points B, C, and D then halve values of B, C, and D\n",
" # Rule #1 If the HEIGHT at point A is less than the HEIGHT at points B, C, and D then move them to they are half\n",
" # as far from A as they start\n",
" # i.e. bring them closer to the original point\n",
" # TODO REPLACE THE BLOCK OF CODE BELOW WITH THIS RULE\n",
" if (0):\n",

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@@ -1,18 +1,16 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap06/6_2_Gradient_Descent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "el8l05WQEO46"
@@ -111,7 +109,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "QU5mdGvpTtEG"
@@ -140,7 +137,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "eB5DQvU5hYNx"
@@ -162,7 +158,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "F3trnavPiHpH"
@@ -218,7 +213,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "s9Duf05WqqSC"
@@ -252,7 +246,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "RS1nEcYVuEAM"
@@ -290,7 +283,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "5EIjMM9Fw2eT"
@@ -333,11 +325,11 @@
" print('Iter %d, a=%3.3f, b=%3.3f, c=%3.3f, d=%3.3f'%(n_iter, a,b,c,d))\n",
" print('a %f, b%f, c%f, d%f'%(lossa,lossb,lossc,lossd))\n",
"\n",
" # Rule #1 If point A is less than points B, C, and D then halve points B,C, and D\n",
" # Rule #1 If point A is less than points B, C, and D then halve distance from A to points B,C, and D\n",
" if np.argmin((lossa,lossb,lossc,lossd))==0:\n",
" b = b/2\n",
" c = c/2\n",
" d = d/2\n",
" b = a+ (b-a)/2\n",
" c = a+ (c-a)/2\n",
" d = a+ (d-a)/2\n",
" continue;\n",
"\n",
" # Rule #2 If point b is less than point c then\n",
@@ -412,8 +404,8 @@
],
"metadata": {
"colab": {
"include_colab_link": true,
"provenance": []
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
@@ -425,4 +417,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}

View File

@@ -1,18 +1,16 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "el8l05WQEO46"
@@ -122,7 +120,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "QU5mdGvpTtEG"
@@ -150,7 +147,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "eB5DQvU5hYNx"
@@ -172,7 +168,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "F3trnavPiHpH"
@@ -228,7 +223,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "s9Duf05WqqSC"
@@ -279,7 +273,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "RS1nEcYVuEAM"
@@ -316,7 +309,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "5EIjMM9Fw2eT"
@@ -359,11 +351,11 @@
" print('Iter %d, a=%3.3f, b=%3.3f, c=%3.3f, d=%3.3f'%(n_iter, a,b,c,d))\n",
" print('a %f, b%f, c%f, d%f'%(lossa,lossb,lossc,lossd))\n",
"\n",
" # Rule #1 If point A is less than points B, C, and D then halve points B,C, and D\n",
" # Rule #1 If point A is less than points B, C, and D then change B,C,D so they are half their current distance from A\n",
" if np.argmin((lossa,lossb,lossc,lossd))==0:\n",
" b = b/2\n",
" c = c/2\n",
" d = d/2\n",
" b = a+ (b-a)/2\n",
" c = a+ (c-a)/2\n",
" d = a+ (d-a)/2\n",
" continue;\n",
"\n",
" # Rule #2 If point b is less than point c then\n",
@@ -577,9 +569,8 @@
],
"metadata": {
"colab": {
"authorship_tag": "ABX9TyNk5FN4qlw3pk8BwDVWw1jN",
"include_colab_link": true,
"provenance": []
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
@@ -591,4 +582,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}

View File

@@ -4,7 +4,7 @@
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyNAcc98STMeyQgh9SbVHWG+",
"authorship_tag": "ABX9TyNELb86uz5qbhEKH81UqFKT",
"include_colab_link": true
},
"kernelspec": {
@@ -65,6 +65,11 @@
"source": [
"# Run this once to load the train and test data straight into a dataloader class\n",
"# that will provide the batches\n",
"\n",
"# (It may complain that some files are missing because the files seem to have been\n",
"# reorganized on the underlying website, but it still seems to work). If everything is working\n",
"# properly, then the whole notebook should run to the end without further problems\n",
"# even before you make changes.\n",
"batch_size_train = 64\n",
"batch_size_test = 1000\n",
"train_loader = torch.utils.data.DataLoader(\n",
@@ -91,6 +96,15 @@
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "YGwbxJDEm88i"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [

View File

@@ -109,7 +109,7 @@
"# Choose random values for the parameters\n",
"omega = np.random.normal(size=(D,D))\n",
"beta = np.random.normal(size=(D,1))\n",
"phi = np.random.normal(size=(1,2*D))"
"phi = np.random.normal(size=(2*D,1))"
],
"metadata": {
"id": "79TSK7oLMobe"
@@ -210,4 +210,4 @@
}
}
]
}
}

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