diff --git a/.gitignore b/.gitignore
index 436c59a7f2c32b71a4ff11afc6f90d4804e95038..ecc21d1f0075366856eb049ebcc775747a78fd85 100644
--- a/.gitignore
+++ b/.gitignore
@@ -161,4 +161,5 @@ MetaAugment/__pycache__/main.cpython-38.pyc
**/mnist/
-**/.DS_Store
\ No newline at end of file
+**/.DS_Store
+node_modules
diff --git a/MetaAugment/Baseline_JC.ipynb b/MetaAugment/Baseline_JC.ipynb
index 5edf2a523cf3c23888578f81c20b89d620c3978c..cc183c9d9be0cbcba6a796622270d9150cc50eea 100644
--- a/MetaAugment/Baseline_JC.ipynb
+++ b/MetaAugment/Baseline_JC.ipynb
@@ -63,6 +63,11 @@
},
{
"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",
@@ -80,15 +85,15 @@
" y = self.fc2(y)\n",
" y = self.relu2(y)\n",
" return y"
- ],
- "metadata": {
- "id": "ukf2-C94UWzs"
- },
- "execution_count": 3,
- "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",
@@ -102,12 +107,7 @@
" y = self.fc1(y)\n",
" y = self.relu1(y)\n",
" return y"
- ],
- "metadata": {
- "id": "Fd9_36R3zx5B"
- },
- "execution_count": 4,
- "outputs": []
+ ]
},
{
"cell_type": "code",
@@ -254,39 +254,7 @@
},
{
"cell_type": "code",
- "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,...)\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_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 = []\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.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)*100))\n",
- "\n",
- "# run using average validation losses\n",
- "early_stop_flag = False\n",
- "best_accuracies = []\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.append(best_acc)\n",
- " if baselines % 10 == 0:\n",
- " print(\"{}\\tAverage accuracy: {:.2f}%\".format(baselines, best_acc*100))\n",
- "print(\"Average average accuracy: {:.2f}%\\n\".format(np.mean(best_accuracies)*100))"
- ],
+ "execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -385,40 +353,39 @@
"id": "KVhYheLfBP33",
"outputId": "acd383d5-52f4-43bd-a36e-103bc70f54cc"
},
- "execution_count": 7,
"outputs": [
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"\r 0%| | 0/10 [00:00<?, ?it/s]"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"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"
]
},
{
- "output_type": "display_data",
"data": {
- "text/plain": [
- " 0%| | 0/9912422 [00:00<?, ?it/s]"
- ],
"application/vnd.jupyter.widget-view+json": {
+ "model_id": "bcbc913a6c3642dbb96dff550f5d66e6",
"version_major": 2,
- "version_minor": 0,
- "model_id": "bcbc913a6c3642dbb96dff550f5d66e6"
- }
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/9912422 [00:00<?, ?it/s]"
+ ]
},
- "metadata": {}
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Extracting ./MetaAugment/train/MNIST/raw/train-images-idx3-ubyte.gz to ./MetaAugment/train/MNIST/raw\n",
"\n",
@@ -427,22 +394,22 @@
]
},
{
- "output_type": "display_data",
"data": {
- "text/plain": [
- " 0%| | 0/28881 [00:00<?, ?it/s]"
- ],
"application/vnd.jupyter.widget-view+json": {
+ "model_id": "ec2cbf2353b14d688e177602f0165abd",
"version_major": 2,
- "version_minor": 0,
- "model_id": "ec2cbf2353b14d688e177602f0165abd"
- }
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/28881 [00:00<?, ?it/s]"
+ ]
},
- "metadata": {}
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Extracting ./MetaAugment/train/MNIST/raw/train-labels-idx1-ubyte.gz to ./MetaAugment/train/MNIST/raw\n",
"\n",
@@ -451,22 +418,22 @@
]
},
{
- "output_type": "display_data",
"data": {
- "text/plain": [
- " 0%| | 0/1648877 [00:00<?, ?it/s]"
- ],
"application/vnd.jupyter.widget-view+json": {
+ "model_id": "2e161aa0ca2349bf8dbe65649fb4ca56",
"version_major": 2,
- "version_minor": 0,
- "model_id": "2e161aa0ca2349bf8dbe65649fb4ca56"
- }
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/1648877 [00:00<?, ?it/s]"
+ ]
},
- "metadata": {}
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Extracting ./MetaAugment/train/MNIST/raw/t10k-images-idx3-ubyte.gz to ./MetaAugment/train/MNIST/raw\n",
"\n",
@@ -475,22 +442,22 @@
]
},
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"data": {
- "text/plain": [
- " 0%| | 0/4542 [00:00<?, ?it/s]"
- ],
"application/vnd.jupyter.widget-view+json": {
+ "model_id": "f0bd92d5abbf4dbb9a90d131eb5bf4b1",
"version_major": 2,
- "version_minor": 0,
- "model_id": "f0bd92d5abbf4dbb9a90d131eb5bf4b1"
- }
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/4542 [00:00<?, ?it/s]"
+ ]
},
- "metadata": {}
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Extracting ./MetaAugment/train/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MetaAugment/train/MNIST/raw\n",
"\n",
@@ -499,22 +466,22 @@
]
},
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"data": {
- "text/plain": [
- " 0%| | 0/9912422 [00:00<?, ?it/s]"
- ],
"application/vnd.jupyter.widget-view+json": {
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"version_major": 2,
- "version_minor": 0,
- "model_id": "0396266d973248bdad617b8781e5d275"
- }
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/9912422 [00:00<?, ?it/s]"
+ ]
},
- "metadata": {}
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Extracting ./MetaAugment/test/MNIST/raw/train-images-idx3-ubyte.gz to ./MetaAugment/test/MNIST/raw\n",
"\n",
@@ -523,22 +490,22 @@
]
},
{
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"data": {
- "text/plain": [
- " 0%| | 0/28881 [00:00<?, ?it/s]"
- ],
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"version_major": 2,
- "version_minor": 0,
- "model_id": "8286d534cbe14527a46aa7a0be632661"
- }
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/28881 [00:00<?, ?it/s]"
+ ]
},
- "metadata": {}
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Extracting ./MetaAugment/test/MNIST/raw/train-labels-idx1-ubyte.gz to ./MetaAugment/test/MNIST/raw\n",
"\n",
@@ -547,22 +514,22 @@
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"version_major": 2,
- "version_minor": 0,
- "model_id": "10213db38b8246a3822f24c33415df5e"
- }
+ "version_minor": 0
+ },
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+ " 0%| | 0/1648877 [00:00<?, ?it/s]"
+ ]
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- "metadata": {}
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Extracting ./MetaAugment/test/MNIST/raw/t10k-images-idx3-ubyte.gz to ./MetaAugment/test/MNIST/raw\n",
"\n",
@@ -571,80 +538,80 @@
]
},
{
- "output_type": "display_data",
"data": {
- "text/plain": [
- " 0%| | 0/4542 [00:00<?, ?it/s]"
- ],
"application/vnd.jupyter.widget-view+json": {
+ "model_id": "8f2cc891f97b4458b550a053fe817f9c",
"version_major": 2,
- "version_minor": 0,
- "model_id": "8f2cc891f97b4458b550a053fe817f9c"
- }
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/4542 [00:00<?, ?it/s]"
+ ]
},
- "metadata": {}
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Extracting ./MetaAugment/test/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MetaAugment/test/MNIST/raw\n",
"\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"\r 10%|â–ˆ | 1/10 [00:17<02:34, 17.17s/it]"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"0\tBest accuracy: 62.00%\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"100%|██████████| 10/10 [01:13<00:00, 7.40s/it]\n"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Average best accuracy: 78.95%\n",
"\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
" 10%|â–ˆ | 1/10 [00:07<01:03, 7.02s/it]"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"0\tAverage accuracy: 84.05%\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"100%|██████████| 10/10 [01:09<00:00, 6.95s/it]"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Average average accuracy: 81.36%\n",
"\n",
@@ -653,16 +620,60 @@
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"\n"
]
}
+ ],
+ "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(\"{}\\tAverage 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"
]
}
],
"metadata": {
+ "accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "Baseline.ipynb",
@@ -685,13 +696,64 @@
"pygments_lexer": "ipython3",
"version": "3.7.7"
},
- "accelerator": "GPU",
"widgets": {
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+ "model_module_version": "1.2.0",
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+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
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+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
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+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
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- "model_name": "HBoxModel",
"model_module_version": "1.5.0",
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"_model_module": "@jupyter-widgets/controls",
@@ -703,17 +765,17 @@
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- "IPY_MODEL_7f6c45b22f3845378b7b847ce21f1962"
+ "IPY_MODEL_ebe7ef1c0cd44014a792fe42c89bcc0f",
+ "IPY_MODEL_d623753cff4e4c9390d4b1544e97ac7e",
+ "IPY_MODEL_23a8175537344bf69c1939f0ded70034"
],
- "layout": "IPY_MODEL_812dc9c854ad4daea90212cd0d7a3579"
+ "layout": "IPY_MODEL_cee302836f8140438e4d2ac1b416280f"
}
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"0873bab106ce479da9fb7206f4801a47": {
"model_module": "@jupyter-widgets/controls",
- "model_name": "HTMLModel",
"model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
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@@ -731,107 +793,101 @@
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