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"metadata": {
"id": "U_ZJ2LqDiu_v"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import torch.optim as optim\n",
"import torch.utils.data as data_utils\n",
"import torchvision\n",
"import torchvision.datasets as datasets\n",
"from tqdm import trange"
"metadata": {
"id": "4ksS_duLFADW"
},
"outputs": [],
"source": [
"\"\"\"Define internal NN module that trains on the dataset\"\"\"\n",
"class LeNet(nn.Module):\n",
" def __init__(self, img_height, img_width, num_labels, img_channels):\n",
" self.conv1 = nn.Conv2d(img_channels, 6, 5)\n",
" self.relu1 = nn.ReLU()\n",
" self.pool1 = nn.MaxPool2d(2)\n",
" self.conv2 = nn.Conv2d(6, 16, 5)\n",
" self.relu2 = nn.ReLU()\n",
" self.pool2 = nn.MaxPool2d(2)\n",
" self.fc1 = nn.Linear(int((((img_height-4)/2-4)/2)*(((img_width-4)/2-4)/2)*16), 120)\n",
" self.relu3 = nn.ReLU()\n",
" self.fc2 = nn.Linear(120, 84)\n",
" self.relu4 = nn.ReLU()\n",
" self.fc3 = nn.Linear(84, num_labels)\n",
" self.relu5 = nn.ReLU()\n",
"\n",
" def forward(self, x):\n",
" y = self.conv1(x)\n",
" y = self.relu1(y)\n",
" y = self.pool1(y)\n",
" y = self.conv2(y)\n",
" y = self.relu2(y)\n",
" y = self.pool2(y)\n",
" y = y.view(y.shape[0], -1)\n",
" y = self.fc1(y)\n",
" y = self.relu3(y)\n",
" y = self.fc2(y)\n",
" y = self.relu4(y)\n",
" y = self.fc3(y)\n",
" y = self.relu5(y)\n",
" return y"
]
},
{
"cell_type": "code",
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"source": [
"\"\"\"Define internal NN module that trains on the dataset\"\"\"\n",
"class EasyNet(nn.Module):\n",
" def __init__(self, img_height, img_width, num_labels, img_channels):\n",
" super().__init__()\n",
" self.fc1 = nn.Linear(img_height*img_width*img_channels, 2048)\n",
" self.relu1 = nn.ReLU()\n",
" self.fc2 = nn.Linear(2048, num_labels)\n",
" self.relu2 = nn.ReLU()\n",
"\n",
" def forward(self, x):\n",
" y = x.view(x.shape[0], -1)\n",
" y = self.fc1(y)\n",
" y = self.relu1(y)\n",
" y = self.fc2(y)\n",
" y = self.relu2(y)\n",
" return y"
],
"metadata": {
"id": "ukf2-C94UWzs"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"\"\"\"Define internal NN module that trains on the dataset\"\"\"\n",
"class SimpleNet(nn.Module):\n",
" def __init__(self, img_height, img_width, num_labels, img_channels):\n",
" super().__init__()\n",
" self.fc1 = nn.Linear(img_height*img_width*img_channels, num_labels)\n",
" self.relu1 = nn.ReLU()\n",
"\n",
" def forward(self, x):\n",
" y = x.view(x.shape[0], -1)\n",
" 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": null,
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"metadata": {
"id": "xujQtvVWBgMH"
},
"outputs": [],
"source": [
"\"\"\"Make toy dataset\"\"\"\n",
"\n",
"def create_toy(train_dataset, test_dataset, batch_size, n_samples):\n",
" \n",
" # shuffle and take first n_samples %age of training dataset\n",
" shuffle_order_train = np.random.RandomState(seed=100).permutation(len(train_dataset))\n",
" shuffled_train_dataset = torch.utils.data.Subset(train_dataset, shuffle_order_train)\n",
" indices_train = torch.arange(int(n_samples*len(train_dataset)))\n",
" reduced_train_dataset = data_utils.Subset(shuffled_train_dataset, indices_train)\n",
"\n",
" # shuffle and take first n_samples %age of test dataset\n",
" shuffle_order_test = np.random.RandomState(seed=1000).permutation(len(test_dataset))\n",
" shuffled_test_dataset = torch.utils.data.Subset(test_dataset, shuffle_order_test)\n",
" indices_test = torch.arange(int(n_samples*len(test_dataset)))\n",
" reduced_test_dataset = data_utils.Subset(shuffled_test_dataset, indices_test)\n",
"\n",
" # push into DataLoader\n",
" train_loader = torch.utils.data.DataLoader(reduced_train_dataset, batch_size=batch_size)\n",
" test_loader = torch.utils.data.DataLoader(reduced_test_dataset, batch_size=batch_size)\n",
"\n",
" return train_loader, test_loader"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vu_4I4qkbx73"
},
"outputs": [],
"source": [
"def run_baseline(batch_size=32, learning_rate=1e-1, ds=\"MNIST\", toy_size=0.02, max_epochs=100, early_stop_num=10, early_stop_flag=True, average_validation=[15,25], IsLeNet=\"LeNet\"):\n",
"\n",
" # create transformations using above info\n",
" transform = torchvision.transforms.Compose([\n",
" torchvision.transforms.ToTensor()])\n",
"\n",
" # open data and apply these transformations\n",
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" if ds == \"MNIST\":\n",
" train_dataset = datasets.MNIST(root='./MetaAugment/train', train=True, download=True, transform=transform)\n",
" test_dataset = datasets.MNIST(root='./MetaAugment/test', train=False, download=True, transform=transform)\n",
" elif ds == \"KMNIST\":\n",
" train_dataset = datasets.KMNIST(root='./MetaAugment/train', train=True, download=True, transform=transform)\n",
" test_dataset = datasets.KMNIST(root='./MetaAugment/test', train=False, download=True, transform=transform)\n",
" elif ds == \"FashionMNIST\":\n",
" train_dataset = datasets.FashionMNIST(root='./MetaAugment/train', train=True, download=True, transform=transform)\n",
" test_dataset = datasets.FashionMNIST(root='./MetaAugment/test', train=False, download=True, transform=transform)\n",
" elif ds == \"CIFAR10\":\n",
" train_dataset = datasets.CIFAR10(root='./MetaAugment/train', train=True, download=True, transform=transform)\n",
" test_dataset = datasets.CIFAR10(root='./MetaAugment/test', train=False, download=True, transform=transform)\n",
" elif ds == \"CIFAR100\":\n",
" train_dataset = datasets.CIFAR100(root='./MetaAugment/train', train=True, download=True, transform=transform)\n",
" test_dataset = datasets.CIFAR100(root='./MetaAugment/test', train=False, download=True, transform=transform)\n",
"\n",
" # check sizes of images\n",
" img_height = len(train_dataset[0][0][0])\n",
" img_width = len(train_dataset[0][0][0][0])\n",
" img_channels = len(train_dataset[0][0])\n",
"\n",
" # check output labels\n",
" if ds == \"CIFAR10\" or ds == \"CIFAR100\":\n",
" num_labels = (max(train_dataset.targets) - min(train_dataset.targets) + 1)\n",
" else:\n",
" num_labels = (max(train_dataset.targets) - min(train_dataset.targets) + 1).item()\n",
"\n",
" # create toy dataset from above uploaded data\n",
" train_loader, test_loader = create_toy(train_dataset, test_dataset, batch_size, toy_size)\n",
"\n",
" # create model\n",
" if IsLeNet == \"LeNet\":\n",
" model = LeNet(img_height, img_width, num_labels, img_channels)\n",
" elif IsLeNet == \"EasyNet\":\n",
" model = EasyNet(img_height, img_width, num_labels, img_channels)\n",
" else:\n",
" model = SimpleNet(img_height, img_width, num_labels, img_channels)\n",
" sgd = optim.SGD(model.parameters(), lr=learning_rate)\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",
"\n",
" # train model\n",
" model.train()\n",
" for idx, (train_x, train_label) in enumerate(train_loader):\n",
" label_np = np.zeros((train_label.shape[0], num_labels))\n",
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" sgd.zero_grad()\n",
" predict_y = model(train_x.float())\n",
" loss = cost(predict_y, train_label.long())\n",
" loss.backward()\n",
" sgd.step()\n",
"\n",
" # check validation accuracy on validation set\n",
" correct = 0\n",
" _sum = 0\n",
" model.eval()\n",
" for idx, (test_x, test_label) in enumerate(test_loader):\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 == test_label\n",
" correct += np.sum(_.numpy(), axis=-1)\n",
" _sum += _.shape[0]\n",
"\n",
" acc = correct / _sum\n",
"\n",
" # update the total validation\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 and using early stopping\n",
" if early_stop_cnt >= early_stop_num and early_stop_flag:\n",
" return best_acc\n",
"\n",
" # exit if using fixed epoch length\n",
" if _epoch >= average_validation[1] and not early_stop_flag:\n",
" return total_val / (average_validation[1] - average_validation[0] + 1)"
]
},
{
"cell_type": "code",
"source": [
"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",
"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",
"\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))"
],
"metadata": {
"colab": {
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"base_uri": "https://localhost:8080/",
"height": 628,
"referenced_widgets": [
"3444759606834a61b3e1600265be1209",
"5bc0168880224bcf80e24f063092dd5c",
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"2e6d4b79fe44499ea169b2bc4b19dbf9",
"74698cccbe58479099794d1225c20845",
"4d2518608b744df4a01c1054c946f7b7",
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"2751bf11c3f642caa95e4fd3d984f587",
"f22c99aefa3a481a9684c7e3499a2cf7",
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"f31f480d91044a1a9e9ca29d28aa602e",
"9e520f656fa142f49eca4f18ba7e6925",
"d93cea42368d471a88b415c81547902f",
"d4b1a281cb004fd58322007e93624e8d",
"580c2f044ee44d5bb713723164924742",
"d2f8a05ad2994e1ebfc2c26f8271b689",
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"a919f3d64c654ee69661c6e1c65eae88",
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"4aaa1d729df34f9ab8ae8f07df8ba0e2"
]
"outputId": "a81aee8f-4c8d-4f8c-9304-4a93ce77535e"
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{
"output_type": "stream",
"name": "stderr",
"text": [
"\r 0%| | 0/5 [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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/169001437 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
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"model_id": "3444759606834a61b3e1600265be1209"
}
},
"metadata": {}
},
{
"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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/169001437 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "9e520f656fa142f49eca4f18ba7e6925"
}
},
"metadata": {}
},
{
"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"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\r 40%|████ | 2/5 [00:35<00:51, 17.21s/it]"
]
},
{
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"text": [
"Files already downloaded and verified\n",
"Files already downloaded and verified\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\r 60%|██████ | 3/5 [00:43<00:26, 13.33s/it]"
]
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"text": [
"Files already downloaded and verified\n",
"Files already downloaded and verified\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\r 80%|████████ | 4/5 [01:12<00:19, 19.50s/it]"
]
},
{
"output_type": "stream",
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"text": [
"Files already downloaded and verified\n",
"Files already downloaded and verified\n"
]
},
{
"output_type": "stream",
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"text": [
"100%|██████████| 5/5 [01:18<00:00, 15.67s/it]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Average best accuracy: 4.00%\n",
"\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\r 0%| | 0/5 [00:00<?, ?it/s]"
]
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{
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"Files already downloaded and verified\n",
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"\r 20%|██ | 1/5 [00:11<00:44, 11.15s/it]"
]
},
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"output_type": "stream",
"name": "stdout",
"text": [
"0\tAverage accuracy: 1.86%\n",
"Files already downloaded and verified\n",
"Files already downloaded and verified\n"
]
},
{
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"text": [
"\r 40%|████ | 2/5 [00:22<00:33, 11.04s/it]"
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"Files already downloaded and verified\n",
"Files already downloaded and verified\n"
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"text": [
"\r 60%|██████ | 3/5 [00:33<00:21, 10.98s/it]"
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"Files already downloaded and verified\n",
"Files already downloaded and verified\n"
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"text": [
"\r 80%|████████ | 4/5 [00:44<00:11, 11.05s/it]"
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"Files already downloaded and verified\n",
"Files already downloaded and verified\n"
]
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"name": "stderr",
"text": [
"100%|██████████| 5/5 [00:55<00:00, 11.06s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Average average accuracy: 1.97%\n",
"\n"
]
},
{
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"name": "stderr",
"text": [
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"\n"
]
}
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "Baseline.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
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