diff --git a/MetaAugment/Baseline_JC.ipynb b/MetaAugment/Baseline_JC.ipynb
index fbb62a3fbc9f3fa20077a071a49db636079cd398..5edf2a523cf3c23888578f81c20b89d620c3978c 100644
--- a/MetaAugment/Baseline_JC.ipynb
+++ b/MetaAugment/Baseline_JC.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {
"id": "U_ZJ2LqDiu_v"
},
@@ -21,7 +21,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {
"id": "4ksS_duLFADW"
},
@@ -84,7 +84,7 @@
"metadata": {
"id": "ukf2-C94UWzs"
},
- "execution_count": null,
+ "execution_count": 3,
"outputs": []
},
{
@@ -106,12 +106,12 @@
"metadata": {
"id": "Fd9_36R3zx5B"
},
- "execution_count": null,
+ "execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {
"id": "xujQtvVWBgMH"
},
@@ -142,7 +142,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {
"id": "vu_4I4qkbx73"
},
@@ -186,12 +186,13 @@
" 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=learning_rate)\n",
" cost = nn.CrossEntropyLoss()\n",
"\n",
@@ -206,6 +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",
" label_np = np.zeros((train_label.shape[0], num_labels))\n",
" sgd.zero_grad()\n",
" predict_y = model(train_x.float())\n",
@@ -218,11 +220,14 @@
" _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",
" acc = correct / _sum\n",
@@ -250,16 +255,18 @@
{
"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 = \"CIFAR100\" # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10,...)\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 = 5 # how many iterations are we averaging over\n",
- "IsLeNet = \"LeNet\" # using LeNet or EasyNet or SimpleNet\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",
@@ -283,61 +290,128 @@
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 628,
+ "height": 1000,
"referenced_widgets": [
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},
"id": "KVhYheLfBP33",
- "outputId": "a81aee8f-4c8d-4f8c-9304-4a93ce77535e"
+ "outputId": "acd383d5-52f4-43bd-a36e-103bc70f54cc"
},
- "execution_count": null,
+ "execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
- "\r 0%| | 0/5 [00:00<?, ?it/s]"
+ "\r 0%| | 0/10 [00:00<?, ?it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
- "Downloading https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz to ./MetaAugment/train/cifar-100-python.tar.gz\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/train/MNIST/raw/train-images-idx3-ubyte.gz\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
- " 0%| | 0/169001437 [00:00<?, ?it/s]"
+ " 0%| | 0/9912422 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "3444759606834a61b3e1600265be1209"
+ "model_id": "bcbc913a6c3642dbb96dff550f5d66e6"
}
},
"metadata": {}
@@ -346,20 +420,22 @@
"output_type": "stream",
"name": "stdout",
"text": [
- "Extracting ./MetaAugment/train/cifar-100-python.tar.gz to ./MetaAugment/train\n",
- "Downloading https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz to ./MetaAugment/test/cifar-100-python.tar.gz\n"
+ "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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
- " 0%| | 0/169001437 [00:00<?, ?it/s]"
+ " 0%| | 0/28881 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "9e520f656fa142f49eca4f18ba7e6925"
+ "model_id": "ec2cbf2353b14d688e177602f0165abd"
}
},
"metadata": {}
@@ -368,174 +444,212 @@
"output_type": "stream",
"name": "stdout",
"text": [
- "Extracting ./MetaAugment/test/cifar-100-python.tar.gz to ./MetaAugment/test\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "\r 20%|██ | 1/5 [00:19<01:16, 19.24s/it]"
- ]
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "0\tBest accuracy: 1.00%\n",
- "Files already downloaded and verified\n",
- "Files already downloaded and verified\n"
+ "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"
]
},
{
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "\r 40%|████ | 2/5 [00:35<00:51, 17.21s/it]"
- ]
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " 0%| | 0/1648877 [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "2e161aa0ca2349bf8dbe65649fb4ca56"
+ }
+ },
+ "metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
- "Files already downloaded and verified\n",
- "Files already downloaded and verified\n"
+ "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"
]
},
{
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "\r 60%|██████ | 3/5 [00:43<00:26, 13.33s/it]"
- ]
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " 0%| | 0/4542 [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "f0bd92d5abbf4dbb9a90d131eb5bf4b1"
+ }
+ },
+ "metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
- "Files already downloaded and verified\n",
- "Files already downloaded and verified\n"
+ "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"
]
},
{
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "\r 80%|████████ | 4/5 [01:12<00:19, 19.50s/it]"
- ]
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " 0%| | 0/9912422 [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "0396266d973248bdad617b8781e5d275"
+ }
+ },
+ "metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
- "Files already downloaded and verified\n",
- "Files already downloaded and verified\n"
+ "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"
]
},
{
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "100%|██████████| 5/5 [01:18<00:00, 15.67s/it]\n"
- ]
+ "output_type": "display_data",
+ "data": {
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "8286d534cbe14527a46aa7a0be632661"
+ }
+ },
+ "metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
- "Average best accuracy: 4.00%\n",
- "\n"
+ "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"
]
},
{
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "\r 0%| | 0/5 [00:00<?, ?it/s]"
- ]
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " 0%| | 0/1648877 [00:00<?, ?it/s]"
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+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
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+ "model_id": "10213db38b8246a3822f24c33415df5e"
+ }
+ },
+ "metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
- "Files already downloaded and verified\n",
- "Files already downloaded and verified\n"
+ "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"
]
},
{
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- "name": "stderr",
- "text": [
- "\r 20%|██ | 1/5 [00:11<00:44, 11.15s/it]"
- ]
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