misc updates
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@@ -15,7 +15,7 @@ advantages such as improved learning feedback and generalization, as we'll outli
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In contrast to physics-informed loss functions, it also enables handling more complex
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solution manifolds instead of single inverse problems. Thus instead of using deep learning
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to solve single inverse problems, we'll show how to train ANNs that solve
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to solve single inverse problems, we'll show how to train NNs that solve
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larger classes of inverse problems very quickly.
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```{figure} resources/diffphys-shortened.jpg
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+7
-2
@@ -11,8 +11,13 @@ fte = re.compile(r"👋")
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# TODO , replace phi symbol w text in phiflow
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# TODO , filter tensorflow warnings?
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# also torch "UserWarning:"
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# TODO , filter tensorflow warnings? "WARNING:tensorflow:" eg in physloss-code
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# also torch "UserWarning:" eg in supervised-airfoils
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# from PINN burgers:
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# u = np.asarray( [0.008612174447657694, 0.02584669669548606, 0.043136357266407785, 0.060491074685516746, 0.07793926183951633, 0.0954779141740818, 0.11311894389663882, 0.1308497114054023, 0.14867023658641343, 0.1665634396808965, 0.18452263429574314, 0.20253084411376132, 0.22057828799835133, 0.23865132431365316, 0.25673879161339097, 0.27483167307082423, 0.2929182325574904, 0.3109944766354339, 0.3290477753208284, 0.34707880794585116, 0.36507311960102307, 0.38303584302507954, 0.40094962955534186, 0.4188235294008765, 0.4366357052408043, 0.45439856841363885, 0.4720845505219581, 0.4897081943759776, 0.5072391070000235, 0.5247011051514834, 0.542067187709797, 0.5593576751669057, 0.5765465453632126, 0.5936507311857876, 0.6106452944663003, 0.6275435911624945, 0.6443221318186165, 0.6609900633731869, 0.67752574922899, 0.6939334022562877, 0.7101938106059631, 0.7263049537163667, 0.7422506131457406, 0.7580207366534812, 0.7736033721649875, 0.7889776974379873, 0.8041371279965555, 0.8190465276590387, 0.8337064887158392, 0.8480617965162781, 0.8621229412131242, 0.8758057344502199, 0.8891341984763013, 0.9019806505391214, 0.9143881632159129, 0.9261597966464793, 0.9373647624856912, 0.9476871303793314, 0.9572273019669029, 0.9654367940878237, 0.9724097482283165, 0.9767381835635638, 0.9669484658390122, 0.659083299684951, -0.659083180712816, -0.9669485121167052, -0.9767382069792288, -0.9724097635533602, -0.9654367970450167, -0.9572273263645859, -0.9476871280825523, -0.9373647681120841, -0.9261598056102645, -0.9143881718456056, -0.9019807055316369, -0.8891341634240081, -0.8758057205293912, -0.8621229450911845, -0.8480618138204272, -0.833706571569058, -0.8190466131476127, -0.8041372124868691, -0.7889777195422356, -0.7736033858767385, -0.758020740007683, -0.7422507481169578, -0.7263049162371344, -0.7101938950789042, -0.6939334061553678, -0.677525822052029, -0.6609901538934517, -0.6443222327338847, -0.6275436932970322, -0.6106454472814152, -0.5936507836778451, -0.5765466491708988, -0.5593578078967361, -0.5420672759411125, -0.5247011730988912, -0.5072391580614087, -0.4897082914472909, -0.47208460952428394, -0.4543985995006753, -0.4366355580500639, -0.41882350871539187, -0.40094955631843376, -0.38303594105786365, -0.36507302109186685, -0.3470786936847069, -0.3290476440540586, -0.31099441589505206, -0.2929180880304103, -0.27483158663081614, -0.2567388003912687, -0.2386513127155433, -0.22057831776499126, -0.20253089403524566, -0.18452269630486776, -0.1665634500729787, -0.14867027528284874, -0.13084990929476334, -0.1131191325854089, -0.09547794429803691, -0.07793928430794522, -0.06049114408297565, -0.0431364527809777, -0.025846763281087953, -0.00861212501518312] );
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# ->
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# u = np.asarray( [0.008612174447657694, 0.02584669669548606, ... ] )
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path = "tmp2.txt" # simple
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path = "tmp.txt" # utf8
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@@ -10,7 +10,7 @@ As much as possible, the algorithms will come with hands-on code examples to qui
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Beyond standard _supervised_ learning from data, we'll look at _physical loss_ constraints,
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more tightly coupled learning algorithms with _differentiable simulations_, as well as extensions such
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as reinforcement learning and uncertainty modeling.
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These methods have a huge potential to fundamentally change what we can achieve
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We live in exciting times: these methods have a huge potential to fundamentally change what we can achieve
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with simulations.
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+219
-219
@@ -1,29 +1,4 @@
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "supervised-airfoils-may16a.ipynb",
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"provenance": []
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},
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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": "3.8.5"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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@@ -92,6 +67,7 @@
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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@@ -99,6 +75,18 @@
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"id": "JwZudtWauiGa",
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"outputId": "802dc68e-ac0d-4a0e-ffb0-30153433b98b"
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},
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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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"Torch version 1.8.1+cu101\n",
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"Downloading training data (300MB), this can take a few minutes the first time...\n",
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"Loaded data, 320 training, 80 validation samples\n",
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"Size of the inputs array: (320, 3, 128, 128)\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import os.path, random\n",
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@@ -116,19 +104,6 @@
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"print(\"Loaded data, {} training, {} validation samples\".format(len(npfile[\"inputs\"]),len(npfile[\"vinputs\"])))\n",
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"\n",
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"print(\"Size of the inputs array: \"+format(npfile[\"inputs\"].shape))\n"
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],
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Torch version 1.8.1+cu101\n",
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"Downloading training data (300MB), this can take a few minutes the first time...\n",
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"Loaded data, 320 training, 80 validation samples\n",
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"Size of the inputs array: (320, 3, 128, 128)\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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@@ -144,6 +119,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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@@ -152,6 +128,21 @@
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"id": "6y6YGxMeNECD",
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"outputId": "91a5b3c7-4b87-4e31-d6e8-105d09d08f7e"
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},
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 1152x360 with 2 Axes>"
|
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]
|
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},
|
||||
"metadata": {
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||||
"needs_background": "light",
|
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"tags": []
|
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},
|
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"output_type": "display_data"
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}
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],
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"source": [
|
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"import pylab\n",
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"\n",
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@@ -173,22 +164,6 @@
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"\n",
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"NUM=72\n",
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"showSbs(npfile[\"inputs\"][NUM],npfile[\"targets\"][NUM], stats=False, bottom=\"Target Output\", top=\"Inputs\", title=\"3 inputs are shown at the top (mask, in-ux, in-uy), with the 3 output channels (p,ux,uy) at the bottom\")\n"
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],
|
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"execution_count": 2,
|
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"outputs": [
|
||||
{
|
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"output_type": "display_data",
|
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"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x360 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"tags": [],
|
||||
"needs_background": "light"
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -206,6 +181,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
@@ -213,6 +189,15 @@
|
||||
"id": "BGN4GqxkIueM",
|
||||
"outputId": "b97ce299-8c12-402a-d90a-c583224c8971"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Training & validation batches: 32 , 8\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# some global training constants\n",
|
||||
"\n",
|
||||
@@ -241,16 +226,6 @@
|
||||
"valiLoader = torch.utils.data.DataLoader(vdata, batch_size=BATCH_SIZE, shuffle=False, drop_last=True) \n",
|
||||
"\n",
|
||||
"print(\"Training & validation batches: {} , {}\".format(len(trainLoader),len(valiLoader) ))"
|
||||
],
|
||||
"execution_count": 3,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Training & validation batches: 32 , 8\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -272,9 +247,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"id": "PVNjz4FjGhdY"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os, sys, random\n",
|
||||
"import numpy as np\n",
|
||||
@@ -367,9 +344,7 @@
|
||||
" m.weight.data.normal_(1.0, 0.02)\n",
|
||||
" m.bias.data.fill_(0)\n",
|
||||
"\n"
|
||||
],
|
||||
"execution_count": 4,
|
||||
"outputs": []
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -384,6 +359,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
@@ -391,6 +367,15 @@
|
||||
"id": "N1uV8k5VIoqT",
|
||||
"outputId": "ceb8fb79-6154-423a-9d17-d6e4dd0c2718"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Trainable params: 147555 -> crucial! always keep in view... \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# channel exponent to control network size\n",
|
||||
"EXPO = 3\n",
|
||||
@@ -412,16 +397,6 @@
|
||||
"\n",
|
||||
"targets = torch.autograd.Variable(torch.FloatTensor(BATCH_SIZE, 3, 128, 128))\n",
|
||||
"inputs = torch.autograd.Variable(torch.FloatTensor(BATCH_SIZE, 3, 128, 128))\n"
|
||||
],
|
||||
"execution_count": 5,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Trainable params: 147555 -> crucial! always keep in view... \n"
|
||||
],
|
||||
"name": "stdout"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -446,6 +421,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
@@ -453,6 +429,37 @@
|
||||
"id": "kkOYvwxqKa8n",
|
||||
"outputId": "a973331c-9e8f-401f-dfd7-9b7177faca0f"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Training from scratch\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3458: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
|
||||
" \"See the documentation of nn.Upsample for details.\".format(mode)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch: 0, L1 train: 0.20192, L1 vali: 0.17916\n",
|
||||
"Epoch: 1, L1 train: 0.18912, L1 vali: 0.18013\n",
|
||||
"Epoch: 2, L1 train: 0.17917, L1 vali: 0.17145\n",
|
||||
"Epoch: 20, L1 train: 0.08637, L1 vali: 0.08432\n",
|
||||
"Epoch: 40, L1 train: 0.03682, L1 vali: 0.03222\n",
|
||||
"Epoch: 60, L1 train: 0.03176, L1 vali: 0.02710\n",
|
||||
"Epoch: 80, L1 train: 0.02772, L1 vali: 0.02522\n",
|
||||
"Training done, saved model\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"history_L1 = []\n",
|
||||
"history_L1val = []\n",
|
||||
@@ -502,38 +509,6 @@
|
||||
"\n",
|
||||
" torch.save(net.state_dict(), \"model\" )\n",
|
||||
" print(\"Training done, saved model\")\n"
|
||||
],
|
||||
"execution_count": 6,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Training from scratch\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
},
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3458: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
|
||||
" \"See the documentation of nn.Upsample for details.\".format(mode)\n"
|
||||
],
|
||||
"name": "stderr"
|
||||
},
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch: 0, L1 train: 0.20192, L1 vali: 0.17916\n",
|
||||
"Epoch: 1, L1 train: 0.18912, L1 vali: 0.18013\n",
|
||||
"Epoch: 2, L1 train: 0.17917, L1 vali: 0.17145\n",
|
||||
"Epoch: 20, L1 train: 0.08637, L1 vali: 0.08432\n",
|
||||
"Epoch: 40, L1 train: 0.03682, L1 vali: 0.03222\n",
|
||||
"Epoch: 60, L1 train: 0.03176, L1 vali: 0.02710\n",
|
||||
"Epoch: 80, L1 train: 0.02772, L1 vali: 0.02522\n",
|
||||
"Training done, saved model\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -549,6 +524,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
@@ -557,6 +533,21 @@
|
||||
"id": "Hlki3bU8JMTT",
|
||||
"outputId": "2a36d013-5af2-4580-f782-86b65f6f8c11"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light",
|
||||
"tags": []
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
@@ -567,22 +558,6 @@
|
||||
"plt.plot(np.arange(l1vali.shape[0] ),l1vali ,'g',label='Validation loss')\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()\n"
|
||||
],
|
||||
"execution_count": 7,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "display_data",
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"tags": [],
|
||||
"needs_background": "light"
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -607,15 +582,39 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 386
|
||||
},
|
||||
"id": "N6OONK0bL4ev",
|
||||
"scrolled": true,
|
||||
"outputId": "240ad666-e266-42d1-c13f-3573eddc7221"
|
||||
"outputId": "240ad666-e266-42d1-c13f-3573eddc7221",
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3458: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
|
||||
" \"See the documentation of nn.Upsample for details.\".format(mode)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x360 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light",
|
||||
"tags": []
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"net.eval()\n",
|
||||
"for i, validata in enumerate(valiLoader, 0):\n",
|
||||
@@ -626,30 +625,6 @@
|
||||
" outputs = net(inputs)\n",
|
||||
" outputs_curr = outputs.data.cpu().numpy()\n",
|
||||
" if i<1: showSbs(targets_curr[0] , outputs_curr[0], title=\"Validation sample %d\"%(i*BATCH_SIZE))\n"
|
||||
],
|
||||
"execution_count": 8,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3458: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
|
||||
" \"See the documentation of nn.Upsample for details.\".format(mode)\n"
|
||||
],
|
||||
"name": "stderr"
|
||||
},
|
||||
{
|
||||
"output_type": "display_data",
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x360 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"tags": [],
|
||||
"needs_background": "light"
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -678,6 +653,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
@@ -686,6 +662,72 @@
|
||||
"id": "dD5O58L9HLE-",
|
||||
"outputId": "9ea61f54-2bc2-4881-f6bf-e37502e48383"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Downloading test data, this should be fast...\n",
|
||||
"Loaded 10/10 test samples\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3458: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
|
||||
" \"See the documentation of nn.Upsample for details.\".format(mode)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"Average test error: 0.029000501427799464\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x360 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light",
|
||||
"tags": []
|
||||
},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x360 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light",
|
||||
"tags": []
|
||||
},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x360 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light",
|
||||
"tags": []
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"if not os.path.isfile('data-airfoils-test.npz'):\n",
|
||||
" import urllib.request\n",
|
||||
@@ -714,73 +756,6 @@
|
||||
" if i<3: showSbs(targets_curr[0] , outputs_curr[0], title=\"Test sample %d\"%(i))\n",
|
||||
"\n",
|
||||
"print(\"\\nAverage test error: {}\".format( L1t_accum/len(testLoader) ))"
|
||||
],
|
||||
"execution_count": 9,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Downloading test data, this should be fast...\n",
|
||||
"Loaded 10/10 test samples\n",
|
||||
"\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
},
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3458: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
|
||||
" \"See the documentation of nn.Upsample for details.\".format(mode)\n"
|
||||
],
|
||||
"name": "stderr"
|
||||
},
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"Average test error: 0.029000501427799464\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
},
|
||||
{
|
||||
"output_type": "display_data",
|
||||
"data": {
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x360 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"tags": [],
|
||||
"needs_background": "light"
|
||||
}
|
||||
},
|
||||
{
|
||||
"output_type": "display_data",
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x360 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"tags": [],
|
||||
"needs_background": "light"
|
||||
}
|
||||
},
|
||||
{
|
||||
"output_type": "display_data",
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x360 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"tags": [],
|
||||
"needs_background": "light"
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -818,5 +793,30 @@
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"name": "supervised-airfoils-may16a.ipynb",
|
||||
"provenance": []
|
||||
},
|
||||
"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": "3.8.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
Reference in new issue
Block a user