From a9df511a89b141ff1ba10dbfd41fa9eb2a066173 Mon Sep 17 00:00:00 2001
From: John Carter <jac202@ic.ac.uk>
Date: Wed, 20 Apr 2022 16:37:32 +0100
Subject: [PATCH] early stop v fixed epochs, GPU added to UCB1
---
MetaAugment/Baseline_JC.ipynb | 3326 +++------------------------------
MetaAugment/UCB1_JC.ipynb | 353 ++--
2 files changed, 406 insertions(+), 3273 deletions(-)
diff --git a/MetaAugment/Baseline_JC.ipynb b/MetaAugment/Baseline_JC.ipynb
index 69d10a94..f1783cd5 100644
--- a/MetaAugment/Baseline_JC.ipynb
+++ b/MetaAugment/Baseline_JC.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {
"id": "U_ZJ2LqDiu_v"
},
@@ -21,7 +21,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {
"id": "4ksS_duLFADW"
},
@@ -63,11 +63,6 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "id": "ukf2-C94UWzs"
- },
- "outputs": [],
"source": [
"\"\"\"Define internal NN module that trains on the dataset\"\"\"\n",
"class EasyNet(nn.Module):\n",
@@ -85,15 +80,15 @@
" y = self.fc2(y)\n",
" y = self.relu2(y)\n",
" return y"
- ]
+ ],
+ "metadata": {
+ "id": "ukf2-C94UWzs"
+ },
+ "execution_count": null,
+ "outputs": []
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "id": "Fd9_36R3zx5B"
- },
- "outputs": [],
"source": [
"\"\"\"Define internal NN module that trains on the dataset\"\"\"\n",
"class SimpleNet(nn.Module):\n",
@@ -107,11 +102,16 @@
" y = self.fc1(y)\n",
" y = self.relu1(y)\n",
" return y"
- ]
+ ],
+ "metadata": {
+ "id": "Fd9_36R3zx5B"
+ },
+ "execution_count": null,
+ "outputs": []
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {
"id": "xujQtvVWBgMH"
},
@@ -142,7 +142,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {
"id": "vu_4I4qkbx73"
},
@@ -207,7 +207,7 @@
" # train model\n",
" model.train()\n",
" for idx, (train_x, train_label) in enumerate(train_loader):\n",
- " train_x, train_label = train_x.to(device), train_label.to(device)\n",
+ " train_x, train_label = train_x.to(device), train_label.to(device) # new code\n",
" label_np = np.zeros((train_label.shape[0], num_labels))\n",
" sgd.zero_grad()\n",
" predict_y = model(train_x.float())\n",
@@ -254,426 +254,403 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "source": [
+ "%%time\n",
+ "\n",
+ "batch_size = 32 # size of batch the inner NN is trained with\n",
+ "learning_rate = 1e-1 # fix learning rate\n",
+ "ds = \"CIFAR10\" # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)\n",
+ "toy_size = 1 # total propeortion of training and test set we use\n",
+ "max_epochs = 100 # max number of epochs that is run if early stopping is not hit\n",
+ "early_stop_num = 10 # max number of worse validation scores before early stopping is triggered\n",
+ "early_stop_flag = True # implement early stopping or not\n",
+ "average_validation = [15,25] # if not implementing early stopping, what epochs are we averaging over\n",
+ "num_iterations = 10 # how many iterations are we averaging over\n",
+ "IsLeNet = \"LeNet\" # using LeNet or EasyNet or SimpleNet\n",
+ "\n",
+ "# run using early stopping\n",
+ "best_accuracies_1 = []\n",
+ "for baselines in trange(num_iterations):\n",
+ " best_acc = run_baseline(batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, early_stop_flag, average_validation, IsLeNet)\n",
+ " best_accuracies_1.append(best_acc)\n",
+ " if baselines % 10 == 0:\n",
+ " print(\"{}\\tBest accuracy: {:.2f}%\".format(baselines, best_acc*100))\n",
+ "print(\"Average best accuracy: {:.2f}%\\n\".format(np.mean(best_accuracies_1)*100))\n",
+ "\n",
+ "file = open(f\"{ds}_v1.txt\", \"w\")\n",
+ "content = ','.join(str(e) for e in best_accuracies_1)\n",
+ "file.write(content)\n",
+ "file.close()\n",
+ "\n",
+ "# run using average validation losses\n",
+ "early_stop_flag = False\n",
+ "best_accuracies_2 = []\n",
+ "for baselines in trange(num_iterations):\n",
+ " best_acc = run_baseline(batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, early_stop_flag, average_validation, IsLeNet)\n",
+ " best_accuracies_2.append(best_acc)\n",
+ " if baselines % 10 == 0:\n",
+ " print(\"{}\\tBest accuracy: {:.2f}%\".format(baselines, best_acc*100))\n",
+ "print(\"Average average accuracy: {:.2f}%\\n\".format(np.mean(best_accuracies_2)*100))\n",
+ "\n",
+ "file = open(f\"{ds}_v2.txt\", \"w\")\n",
+ "content = ','.join(str(e) for e in best_accuracies_2)\n",
+ "file.write(content)\n",
+ "file.close()"
+ ],
"metadata": {
"colab": {
- "base_uri": "https://localhost:8080/",
- "height": 1000,
- "referenced_widgets": [
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- ]
+ "base_uri": "https://localhost:8080/"
},
"id": "KVhYheLfBP33",
- "outputId": "acd383d5-52f4-43bd-a36e-103bc70f54cc"
+ "outputId": "4858df7d-ee29-4e3b-ac2f-e33a9a4bbf32"
},
+ "execution_count": null,
"outputs": [
{
- "name": "stderr",
"output_type": "stream",
+ "name": "stderr",
"text": [
"\r 0%| | 0/10 [00:00<?, ?it/s]"
]
},
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
- "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
- "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./MetaAugment/train/MNIST/raw/train-images-idx3-ubyte.gz\n"
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "bcbc913a6c3642dbb96dff550f5d66e6",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/9912422 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r 10%|â–ˆ | 1/10 [07:27<1:07:04, 447.20s/it]"
+ ]
},
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
- "Extracting ./MetaAugment/train/MNIST/raw/train-images-idx3-ubyte.gz to ./MetaAugment/train/MNIST/raw\n",
- "\n",
- "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
- "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./MetaAugment/train/MNIST/raw/train-labels-idx1-ubyte.gz\n"
+ "0\tBest accuracy: 54.32%\n",
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "ec2cbf2353b14d688e177602f0165abd",
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r 20%|██ | 2/10 [12:32<48:32, 364.01s/it] "
+ ]
},
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
- "Extracting ./MetaAugment/train/MNIST/raw/train-labels-idx1-ubyte.gz to ./MetaAugment/train/MNIST/raw\n",
- "\n",
- "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
- "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./MetaAugment/train/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "2e161aa0ca2349bf8dbe65649fb4ca56",
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- },
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r 30%|███ | 3/10 [17:55<40:16, 345.25s/it]"
+ ]
},
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
- "Extracting ./MetaAugment/train/MNIST/raw/t10k-images-idx3-ubyte.gz to ./MetaAugment/train/MNIST/raw\n",
- "\n",
- "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
- "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./MetaAugment/train/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "f0bd92d5abbf4dbb9a90d131eb5bf4b1",
- "version_major": 2,
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- },
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- " 0%| | 0/4542 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
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+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r 40%|████ | 4/10 [23:17<33:35, 335.87s/it]"
+ ]
},
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
- "Extracting ./MetaAugment/train/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MetaAugment/train/MNIST/raw\n",
- "\n",
- "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
- "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./MetaAugment/test/MNIST/raw/train-images-idx3-ubyte.gz\n"
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "0396266d973248bdad617b8781e5d275",
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- " 0%| | 0/9912422 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r 50%|█████ | 5/10 [27:43<25:53, 310.76s/it]"
+ ]
},
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
- "Extracting ./MetaAugment/test/MNIST/raw/train-images-idx3-ubyte.gz to ./MetaAugment/test/MNIST/raw\n",
- "\n",
- "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
- "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./MetaAugment/test/MNIST/raw/train-labels-idx1-ubyte.gz\n"
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "8286d534cbe14527a46aa7a0be632661",
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+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r 60%|██████ | 6/10 [32:27<20:06, 301.69s/it]"
+ ]
},
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
- "Extracting ./MetaAugment/test/MNIST/raw/train-labels-idx1-ubyte.gz to ./MetaAugment/test/MNIST/raw\n",
- "\n",
- "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
- "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./MetaAugment/test/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "10213db38b8246a3822f24c33415df5e",
- "version_major": 2,
- "version_minor": 0
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- " 0%| | 0/1648877 [00:00<?, ?it/s]"
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- "metadata": {},
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+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r 70%|███████ | 7/10 [38:03<15:38, 312.93s/it]"
+ ]
},
{
+ "output_type": "stream",
"name": "stdout",
+ "text": [
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
+ ]
+ },
+ {
"output_type": "stream",
+ "name": "stderr",
"text": [
- "Extracting ./MetaAugment/test/MNIST/raw/t10k-images-idx3-ubyte.gz to ./MetaAugment/test/MNIST/raw\n",
- "\n",
- "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
- "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./MetaAugment/test/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
+ "\r 80%|████████ | 8/10 [46:02<12:11, 365.57s/it]"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
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- "version_major": 2,
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- },
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- " 0%| | 0/4542 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r 90%|█████████ | 9/10 [51:08<05:47, 347.04s/it]"
+ ]
},
{
+ "output_type": "stream",
"name": "stdout",
+ "text": [
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
+ ]
+ },
+ {
"output_type": "stream",
+ "name": "stderr",
"text": [
- "Extracting ./MetaAugment/test/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MetaAugment/test/MNIST/raw\n",
+ "100%|██████████| 10/10 [57:26<00:00, 344.66s/it]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Average best accuracy: 55.38%\n",
"\n"
]
},
{
- "name": "stderr",
"output_type": "stream",
+ "name": "stderr",
"text": [
- "\r 10%|â–ˆ | 1/10 [00:17<02:34, 17.17s/it]"
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+ "output_type": "stream",
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+ "text": [
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
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+ },
+ {
"output_type": "stream",
+ "name": "stderr",
"text": [
- "0\tBest accuracy: 62.00%\n"
+ "\r 10%|â–ˆ | 1/10 [07:50<1:10:35, 470.60s/it]"
]
},
{
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "0\tBest accuracy: 52.04%\n",
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
+ ]
+ },
+ {
+ "output_type": "stream",
"name": "stderr",
+ "text": [
+ "\r 20%|██ | 2/10 [15:37<1:02:27, 468.42s/it]"
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+ {
"output_type": "stream",
+ "name": "stdout",
"text": [
- "100%|██████████| 10/10 [01:13<00:00, 7.40s/it]\n"
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
]
},
{
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+ "\r 30%|███ | 3/10 [23:15<54:05, 463.58s/it] "
+ ]
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+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
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+ {
"output_type": "stream",
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"text": [
- "Average best accuracy: 78.95%\n",
- "\n"
+ "\r 40%|████ | 4/10 [30:56<46:16, 462.78s/it]"
]
},
{
+ "output_type": "stream",
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+ "Files already downloaded and verified\n"
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+ "output_type": "stream",
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+ "text": [
+ "\r 50%|█████ | 5/10 [38:38<38:31, 462.39s/it]"
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+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
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"text": [
- " 10%|â–ˆ | 1/10 [00:07<01:03, 7.02s/it]"
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+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
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+ {
"output_type": "stream",
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+ "text": [
+ "\r 70%|███████ | 7/10 [53:57<23:02, 460.88s/it]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
"text": [
- "0\tAverage accuracy: 84.05%\n"
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
]
},
{
+ "output_type": "stream",
"name": "stderr",
+ "text": [
+ "\r 80%|████████ | 8/10 [1:01:38<15:21, 460.87s/it]"
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+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
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"output_type": "stream",
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"text": [
- "100%|██████████| 10/10 [01:09<00:00, 6.95s/it]"
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]
},
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+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
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+ "name": "stderr",
"text": [
- "Average average accuracy: 81.36%\n",
+ "100%|██████████| 10/10 [1:16:55<00:00, 461.54s/it]"
+ ]
+ },
+ {
+ "output_type": "stream",
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+ "text": [
+ "Average average accuracy: 52.48%\n",
"\n",
- "CPU times: user 2min 12s, sys: 3.91 s, total: 2min 16s\n",
- "Wall time: 2min 23s\n"
+ "CPU times: user 2h 12min 17s, sys: 2min 26s, total: 2h 14min 43s\n",
+ "Wall time: 2h 14min 22s\n"
]
},
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- ],
- "source": [
- "%%time\n",
- "\n",
- "batch_size = 32 # size of batch the inner NN is trained with\n",
- "learning_rate = 1e-1 # fix learning rate\n",
- "ds = \"FashionMNIST\" # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10,...)\n",
- "toy_size = 1 # total propeortion of training and test set we use\n",
- "max_epochs = 100 # max number of epochs that is run if early stopping is not hit\n",
- "early_stop_num = 10 # max number of worse validation scores before early stopping is triggered\n",
- "early_stop_flag = True # implement early stopping or not\n",
- "average_validation = [15,25] # if not implementing early stopping, what epochs are we averaging over\n",
- "num_iterations = 10 # how many iterations are we averaging over\n",
- "IsLeNet = \"SimpleNet\" # using LeNet or EasyNet or SimpleNet\n",
- "\n",
- "# run using early stopping\n",
- "best_accuracies_1 = []\n",
- "for baselines in trange(num_iterations):\n",
- " best_acc = run_baseline(batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, early_stop_flag, average_validation, IsLeNet)\n",
- " best_accuracies_1.append(best_acc)\n",
- " if baselines % 10 == 0:\n",
- " print(\"{}\\tBest accuracy: {:.2f}%\".format(baselines, best_acc*100))\n",
- "print(\"Average best accuracy: {:.2f}%\\n\".format(np.mean(best_accuracies_1)*100))\n",
- "\n",
- "file = open(f\"{ds}_v1.txt\", \"w\")\n",
- "content = ','.join(str(e) for e in best_accuracies_1)\n",
- "file.write(content)\n",
- "file.close()\n",
- "\n",
- "# run using average validation losses\n",
- "early_stop_flag = False\n",
- "best_accuracies_2 = []\n",
- "for baselines in trange(num_iterations):\n",
- " best_acc = run_baseline(batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, early_stop_flag, average_validation, IsLeNet)\n",
- " best_accuracies_2.append(best_acc)\n",
- " if baselines % 10 == 0:\n",
- " print(\"{}\\tBest accuracy: {:.2f}%\".format(baselines, best_acc*100))\n",
- "print(\"Average average accuracy: {:.2f}%\\n\".format(np.mean(best_accuracies_2)*100))\n",
- "\n",
- "file = open(f\"{ds}_v2.txt\", \"w\")\n",
- "content = ','.join(str(e) for e in best_accuracies_2)\n",
- "file.write(content)\n",
- "file.close()\n"
]
}
],
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+ "accelerator": "GPU"
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"nbformat": 4,
"nbformat_minor": 0
-}
+}
\ No newline at end of file
diff --git a/MetaAugment/UCB1_JC.ipynb b/MetaAugment/UCB1_JC.ipynb
index aed95670..5710a128 100644
--- a/MetaAugment/UCB1_JC.ipynb
+++ b/MetaAugment/UCB1_JC.ipynb
@@ -1,12 +1,24 @@
{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "UCB1.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ },
+ "accelerator": "GPU"
+ },
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "id": "U_ZJ2LqDiu_v"
- },
- "outputs": [],
"source": [
"import numpy as np\n",
"import torch\n",
@@ -21,15 +33,15 @@
"from matplotlib import pyplot as plt\n",
"from numpy import save, load\n",
"from tqdm import trange"
- ]
+ ],
+ "metadata": {
+ "id": "U_ZJ2LqDiu_v"
+ },
+ "execution_count": 1,
+ "outputs": []
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "id": "4ksS_duLFADW"
- },
- "outputs": [],
"source": [
"\"\"\"Define internal NN module that trains on the dataset\"\"\"\n",
"class LeNet(nn.Module):\n",
@@ -63,15 +75,15 @@
" y = self.fc3(y)\n",
" y = self.relu5(y)\n",
" return y"
- ]
+ ],
+ "metadata": {
+ "id": "4ksS_duLFADW"
+ },
+ "execution_count": 2,
+ "outputs": []
},
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "id": "LckxnUXGfxjW"
- },
- "outputs": [],
"source": [
"\"\"\"Define internal NN module that trains on the dataset\"\"\"\n",
"class EasyNet(nn.Module):\n",
@@ -89,15 +101,15 @@
" y = self.fc2(y)\n",
" y = self.relu2(y)\n",
" return y"
- ]
+ ],
+ "metadata": {
+ "id": "LckxnUXGfxjW"
+ },
+ "execution_count": 3,
+ "outputs": []
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "id": "enaD2xbw5hew"
- },
- "outputs": [],
"source": [
"\"\"\"Define internal NN module that trains on the dataset\"\"\"\n",
"class SimpleNet(nn.Module):\n",
@@ -111,15 +123,15 @@
" y = self.fc1(y)\n",
" y = self.relu1(y)\n",
" return y"
- ]
+ ],
+ "metadata": {
+ "id": "enaD2xbw5hew"
+ },
+ "execution_count": 4,
+ "outputs": []
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "id": "xujQtvVWBgMH"
- },
- "outputs": [],
"source": [
"\"\"\"Make toy dataset\"\"\"\n",
"\n",
@@ -142,15 +154,15 @@
" test_loader = torch.utils.data.DataLoader(reduced_test_dataset, batch_size=batch_size)\n",
"\n",
" return train_loader, test_loader"
- ]
+ ],
+ "metadata": {
+ "id": "xujQtvVWBgMH"
+ },
+ "execution_count": 5,
+ "outputs": []
},
{
"cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "id": "Iql-c88jGGWy"
- },
- "outputs": [],
"source": [
"\"\"\"Randomly generate 10 policies\"\"\"\n",
"\"\"\"Each policy has 5 sub-policies\"\"\"\n",
@@ -181,15 +193,15 @@
" policies[policy, sub_policy, transformation + 4] = np.random.randint(5,15)/10\n",
"\n",
" return policies"
- ]
+ ],
+ "metadata": {
+ "id": "Iql-c88jGGWy"
+ },
+ "execution_count": 6,
+ "outputs": []
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "id": "QE2VWI8o731X"
- },
- "outputs": [],
"source": [
"\"\"\"Pick policy and sub-policy\"\"\"\n",
"\"\"\"Each row of data should have a different sub-policy but for now, this will do\"\"\"\n",
@@ -226,7 +238,12 @@
" scale = policies[policy, sub_policy][5]\n",
"\n",
" return degrees, shear, scale"
- ]
+ ],
+ "metadata": {
+ "id": "QE2VWI8o731X"
+ },
+ "execution_count": 7,
+ "outputs": []
},
{
"cell_type": "code",
@@ -237,7 +254,7 @@
"outputs": [],
"source": [
"\"\"\"Sample policy, open and apply above transformations\"\"\"\n",
- "def run_UCB1(policies, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, iterations, IsLeNet):\n",
+ "def run_UCB1(policies, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, early_stop_flag, average_validation, iterations, IsLeNet):\n",
"\n",
" # get number of policies and sub-policies\n",
" num_policies = len(policies)\n",
@@ -299,18 +316,20 @@
" train_loader, test_loader = create_toy(train_dataset, test_dataset, batch_size, toy_size)\n",
"\n",
" # create model\n",
+ " device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
" if IsLeNet == \"LeNet\":\n",
- " model = LeNet(img_height, img_width, num_labels, img_channels)\n",
+ " model = LeNet(img_height, img_width, num_labels, img_channels).to(device) # added .to(device)\n",
" elif IsLeNet == \"EasyNet\":\n",
- " model = EasyNet(img_height, img_width, num_labels, img_channels)\n",
+ " model = EasyNet(img_height, img_width, num_labels, img_channels).to(device) # added .to(device)\n",
" else:\n",
- " model = SimpleNet(img_height, img_width, num_labels, img_channels)\n",
+ " model = SimpleNet(img_height, img_width, num_labels, img_channels).to(device) # added .to(device)\n",
" sgd = optim.SGD(model.parameters(), lr=1e-1)\n",
" cost = nn.CrossEntropyLoss()\n",
"\n",
" # set variables for best validation accuracy and early stop count\n",
" best_acc = 0\n",
" early_stop_cnt = 0\n",
+ " total_val = 0\n",
"\n",
" # train model and check validation accuracy each epoch\n",
" for _epoch in range(max_epochs):\n",
@@ -318,6 +337,7 @@
" # train model\n",
" model.train()\n",
" for idx, (train_x, train_label) in enumerate(train_loader):\n",
+ " train_x, train_label = train_x.to(device), train_label.to(device) # new code\n",
" label_np = np.zeros((train_label.shape[0], num_labels))\n",
" sgd.zero_grad()\n",
" predict_y = model(train_x.float())\n",
@@ -330,23 +350,35 @@
" _sum = 0\n",
" model.eval()\n",
" for idx, (test_x, test_label) in enumerate(test_loader):\n",
+ " test_x, test_label = test_x.to(device), test_label.to(device) # new code\n",
" predict_y = model(test_x.float()).detach()\n",
- " predict_ys = np.argmax(predict_y, axis=-1)\n",
- " label_np = test_label.numpy()\n",
+ " #predict_ys = np.argmax(predict_y, axis=-1)\n",
+ " predict_ys = torch.argmax(predict_y, axis=-1) # changed np to torch\n",
+ " #label_np = test_label.numpy()\n",
" _ = predict_ys == test_label\n",
- " correct += np.sum(_.numpy(), axis=-1)\n",
+ " #correct += np.sum(_.numpy(), axis=-1)\n",
+ " correct += np.sum(_.cpu().numpy(), axis=-1) # added .cpu()\n",
" _sum += _.shape[0]\n",
" \n",
- " # update best validation accuracy if it was higher, otherwise increase early stop count\n",
" acc = correct / _sum\n",
+ "\n",
+ " if average_validation[0] <= _epoch <= average_validation[1]:\n",
+ " total_val += acc\n",
+ "\n",
+ " # update best validation accuracy if it was higher, otherwise increase early stop count\n",
" if acc > best_acc :\n",
" best_acc = acc\n",
" early_stop_cnt = 0\n",
" else:\n",
" early_stop_cnt += 1\n",
"\n",
- " # exit if validation gets worse over 10 runs\n",
- " if early_stop_cnt >= early_stop_num:\n",
+ " # exit if validation gets worse over 10 runs and using early stopping\n",
+ " if early_stop_cnt >= early_stop_num and early_stop_flag:\n",
+ " break\n",
+ "\n",
+ " # exit if using fixed epoch length\n",
+ " if _epoch >= average_validation[1] and not early_stop_flag:\n",
+ " best_acc = total_val / (average_validation[1] - average_validation[0] + 1)\n",
" break\n",
"\n",
" # update q_values\n",
@@ -375,216 +407,79 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "source": [
+ "%%time\n",
+ "\n",
+ "batch_size = 32 # size of batch the inner NN is trained with\n",
+ "learning_rate = 1e-1 # fix learning rate\n",
+ "ds = \"MNIST\" # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)\n",
+ "toy_size = 0.02 # total propeortion of training and test set we use\n",
+ "max_epochs = 100 # max number of epochs that is run if early stopping is not hit\n",
+ "early_stop_num = 10 # max number of worse validation scores before early stopping is triggered\n",
+ "early_stop_flag = True # implement early stopping or not\n",
+ "average_validation = [15,25] # if not implementing early stopping, what epochs are we averaging over\n",
+ "num_policies = 5 # fix number of policies\n",
+ "num_sub_policies = 5 # fix number of sub-policies in a policy\n",
+ "iterations = 100 # total iterations, should be more than the number of policies\n",
+ "IsLeNet = \"SimpleNet\" # using LeNet or EasyNet or SimpleNet\n",
+ "\n",
+ "# generate random policies at start\n",
+ "policies = generate_policies(num_policies, num_sub_policies)\n",
+ "\n",
+ "q_values, best_q_values = run_UCB1(policies, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, early_stop_flag, average_validation, iterations, IsLeNet)\n",
+ "\n",
+ "plt.plot(best_q_values)\n",
+ "\n",
+ "best_q_values = np.array(best_q_values)\n",
+ "save('best_q_values_{}_{}percent_{}.npy'.format(IsLeNet, int(toy_size*100), ds), best_q_values)\n",
+ "#best_q_values = load('best_q_values_{}_{}percent_{}.npy'.format(IsLeNet, int(toy_size*100), ds), allow_pickle=True)"
+ ],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 466
+ "height": 342
},
"id": "doHUtJ_tEiA6",
- "outputId": "3a7becf3-7b5d-4403-84d3-96e51bac8bf5"
+ "outputId": "8195ba17-c95f-4b75-d8dc-19c5d76d5e43"
},
+ "execution_count": 9,
"outputs": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 10%|â–ˆ | 10/100 [01:09<09:26, 6.29s/it]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Iteration: 10,\tQ-Values: [0.8, 0.71, 0.79, 0.86, 0.76], Best Policy: 0.86\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 20%|██ | 20/100 [02:18<09:03, 6.80s/it]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Iteration: 20,\tQ-Values: [0.77, 0.75, 0.81, 0.86, 0.78], Best Policy: 0.86\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 30%|███ | 30/100 [03:24<06:50, 5.87s/it]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Iteration: 30,\tQ-Values: [0.81, 0.71, 0.79, 0.8, 0.78], Best Policy: 0.81\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 40%|████ | 40/100 [04:34<06:14, 6.23s/it]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Iteration: 40,\tQ-Values: [0.8, 0.7, 0.76, 0.8, 0.78], Best Policy: 0.8\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 50%|█████ | 50/100 [05:49<06:04, 7.28s/it]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Iteration: 50,\tQ-Values: [0.79, 0.72, 0.76, 0.81, 0.74], Best Policy: 0.81\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 60%|██████ | 60/100 [06:55<04:32, 6.82s/it]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Iteration: 60,\tQ-Values: [0.79, 0.72, 0.77, 0.81, 0.76], Best Policy: 0.81\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 70%|███████ | 70/100 [08:29<04:16, 8.53s/it]"
- ]
- },
- {
- "name": "stdout",
"output_type": "stream",
- "text": [
- "Iteration: 70,\tQ-Values: [0.78, 0.7, 0.78, 0.8, 0.76], Best Policy: 0.8\n"
- ]
- },
- {
"name": "stderr",
- "output_type": "stream",
- "text": [
- " 80%|████████ | 80/100 [09:38<02:05, 6.27s/it]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
"text": [
- "Iteration: 80,\tQ-Values: [0.79, 0.72, 0.78, 0.79, 0.77], Best Policy: 0.79\n"
+ "100%|██████████| 10/10 [01:28<00:00, 8.84s/it]"
]
},
{
- "name": "stderr",
"output_type": "stream",
- "text": [
- " 90%|█████████ | 90/100 [10:41<01:04, 6.47s/it]"
- ]
- },
- {
"name": "stdout",
- "output_type": "stream",
- "text": [
- "Iteration: 90,\tQ-Values: [0.79, 0.71, 0.78, 0.79, 0.77], Best Policy: 0.79\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
"text": [
- "100%|██████████| 100/100 [11:51<00:00, 7.11s/it]"
+ "Iteration: 10,\tQ-Values: [0.77, 0.74, 0.8, 0.72, 0.77], Best Policy: 0.8\n",
+ "CPU times: user 1min 21s, sys: 694 ms, total: 1min 22s\n",
+ "Wall time: 1min 28s\n"
]
},
{
- "name": "stdout",
"output_type": "stream",
- "text": [
- "Iteration: 100,\tQ-Values: [0.79, 0.72, 0.79, 0.79, 0.78], Best Policy: 0.79\n"
- ]
- },
- {
"name": "stderr",
- "output_type": "stream",
"text": [
"\n"
]
},
{
+ "output_type": "display_data",
"data": {
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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
- ]
+ ],
+ "image/png": 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\n"
},
"metadata": {
"needs_background": "light"
- },
- "output_type": "display_data"
+ }
}
- ],
- "source": [
- "batch_size = 32 # size of batch the inner NN is trained with\n",
- "learning_rate = 1e-1 # fix learning rate\n",
- "ds = \"MNIST\" # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)\n",
- "toy_size = 0.02 # total propeortion of training and test set we use\n",
- "max_epochs = 100 # max number of epochs that is run if early stopping is not hit\n",
- "early_stop_num = 10 # max number of worse validation scores before early stopping is triggered\n",
- "num_policies = 5 # fix number of policies\n",
- "num_sub_policies = 5 # fix number of sub-policies in a policy\n",
- "iterations = 100 # total iterations, should be more than the number of policies\n",
- "IsLeNet = \"SimpleNet\" # using LeNet or EasyNet or SimpleNet\n",
- "\n",
- "# generate random policies at start\n",
- "policies = generate_policies(num_policies, num_sub_policies)\n",
- "\n",
- "q_values, best_q_values = run_UCB1(policies, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, iterations, IsLeNet)\n",
- "\n",
- "plt.plot(best_q_values)\n",
- "\n",
- "best_q_values = np.array(best_q_values)\n",
- "save('best_q_values_{}_{}percent_{}.npy'.format(IsLeNet, int(toy_size*100), ds), best_q_values)\n",
- "#best_q_values = load('best_q_values_{}_{}percent_{}.npy'.format(IsLeNet, int(toy_size*100), ds), allow_pickle=True)"
]
}
- ],
- "metadata": {
- "accelerator": "GPU",
- "colab": {
- "collapsed_sections": [],
- "name": "UCB1.ipynb",
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3"
- },
- "language_info": {
- "name": "python"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+ ]
+}
\ No newline at end of file
--
GitLab