diff --git a/MetaAugment/UCB1_JC.ipynb b/MetaAugment/UCB1_JC.ipynb
index 5710a128c11db4392adb0d1372caa890c01e344e..2ebf29b8cbb7ed6c7cf1bbaffd9b55881910bfb4 100644
--- a/MetaAugment/UCB1_JC.ipynb
+++ b/MetaAugment/UCB1_JC.ipynb
@@ -1,485 +1,495 @@
{
- "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",
+ "torch.manual_seed(0)\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",
+ "\n",
+ "from matplotlib import pyplot as plt\n",
+ "from numpy import save, load\n",
+ "from tqdm import trange"
+ ]
},
- "cells": [
- {
- "cell_type": "code",
- "source": [
- "import numpy as np\n",
- "import torch\n",
- "torch.manual_seed(0)\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",
- "\n",
- "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",
- "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",
- " super().__init__()\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"
- ],
- "metadata": {
- "id": "4ksS_duLFADW"
- },
- "execution_count": 2,
- "outputs": []
- },
- {
- "cell_type": "code",
- "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": "LckxnUXGfxjW"
- },
- "execution_count": 3,
- "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": "enaD2xbw5hew"
- },
- "execution_count": 4,
- "outputs": []
- },
- {
- "cell_type": "code",
- "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"
- ],
- "metadata": {
- "id": "xujQtvVWBgMH"
- },
- "execution_count": 5,
- "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",
+ " def __init__(self, img_height, img_width, num_labels, img_channels):\n",
+ " super().__init__()\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",
+ "execution_count": 3,
+ "metadata": {
+ "id": "LckxnUXGfxjW"
+ },
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "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",
+ " 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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "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",
+ "execution_count": 6,
+ "metadata": {
+ "id": "Iql-c88jGGWy"
+ },
+ "outputs": [],
+ "source": [
+ "\"\"\"Randomly generate 10 policies\"\"\"\n",
+ "\"\"\"Each policy has 5 sub-policies\"\"\"\n",
+ "\"\"\"For each sub-policy, pick 2 transformations, 2 probabilities and 2 magnitudes\"\"\"\n",
+ "\n",
+ "def generate_policies(num_policies, num_sub_policies):\n",
+ " \n",
+ " policies = np.zeros([num_policies,num_sub_policies,6])\n",
+ "\n",
+ " # Policies array will be 10x5x6\n",
+ " for policy in range(num_policies):\n",
+ " for sub_policy in range(num_sub_policies):\n",
+ " # pick two sub_policy transformations (0=rotate, 1=shear, 2=scale)\n",
+ " policies[policy, sub_policy, 0] = np.random.randint(0,3)\n",
+ " policies[policy, sub_policy, 1] = np.random.randint(0,3)\n",
+ " while policies[policy, sub_policy, 0] == policies[policy, sub_policy, 1]:\n",
+ " policies[policy, sub_policy, 1] = np.random.randint(0,3)\n",
+ "\n",
+ " # pick probabilities\n",
+ " policies[policy, sub_policy, 2] = np.random.randint(0,11) / 10\n",
+ " policies[policy, sub_policy, 3] = np.random.randint(0,11) / 10\n",
+ "\n",
+ " # pick magnitudes\n",
+ " for transformation in range(2):\n",
+ " if policies[policy, sub_policy, transformation] <= 1:\n",
+ " policies[policy, sub_policy, transformation + 4] = np.random.randint(-4,5)*5\n",
+ " elif policies[policy, sub_policy, transformation] == 2:\n",
+ " policies[policy, sub_policy, transformation + 4] = np.random.randint(5,15)/10\n",
+ "\n",
+ " return policies"
+ ]
+ },
+ {
+ "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",
+ "\n",
+ "def sample_sub_policy(policies, policy, num_sub_policies):\n",
+ " sub_policy = np.random.randint(0,num_sub_policies)\n",
+ "\n",
+ " degrees = 0\n",
+ " shear = 0\n",
+ " scale = 1\n",
+ "\n",
+ " # check for rotations\n",
+ " if policies[policy, sub_policy][0] == 0:\n",
+ " if np.random.uniform() < policies[policy, sub_policy][2]:\n",
+ " degrees = policies[policy, sub_policy][4]\n",
+ " elif policies[policy, sub_policy][1] == 0:\n",
+ " if np.random.uniform() < policies[policy, sub_policy][3]:\n",
+ " degrees = policies[policy, sub_policy][5]\n",
+ "\n",
+ " # check for shears\n",
+ " if policies[policy, sub_policy][0] == 1:\n",
+ " if np.random.uniform() < policies[policy, sub_policy][2]:\n",
+ " shear = policies[policy, sub_policy][4]\n",
+ " elif policies[policy, sub_policy][1] == 1:\n",
+ " if np.random.uniform() < policies[policy, sub_policy][3]:\n",
+ " shear = policies[policy, sub_policy][5]\n",
+ "\n",
+ " # check for scales\n",
+ " if policies[policy, sub_policy][0] == 2:\n",
+ " if np.random.uniform() < policies[policy, sub_policy][2]:\n",
+ " scale = policies[policy, sub_policy][4]\n",
+ " elif policies[policy, sub_policy][1] == 2:\n",
+ " if np.random.uniform() < policies[policy, sub_policy][3]:\n",
+ " scale = policies[policy, sub_policy][5]\n",
+ "\n",
+ " return degrees, shear, scale"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "id": "vu_4I4qkbx73"
+ },
+ "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, early_stop_flag, average_validation, iterations, IsLeNet):\n",
+ "\n",
+ " # get number of policies and sub-policies\n",
+ " num_policies = len(policies)\n",
+ " num_sub_policies = len(policies[0])\n",
+ "\n",
+ " #Initialize vector weights, counts and regret\n",
+ " q_values = [0]*num_policies\n",
+ " cnts = [0]*num_policies\n",
+ " q_plus_cnt = [0]*num_policies\n",
+ " total_count = 0\n",
+ "\n",
+ " best_q_values = []\n",
+ "\n",
+ " for policy in trange(iterations):\n",
+ "\n",
+ " # get the action to try (either initially in order or using best q_plus_cnt value)\n",
+ " if policy >= num_policies:\n",
+ " this_policy = np.argmax(q_plus_cnt)\n",
+ " else:\n",
+ " this_policy = policy\n",
+ "\n",
+ " # get info of transformation for this sub-policy\n",
+ " degrees, shear, scale = sample_sub_policy(policies, this_policy, num_sub_policies)\n",
+ "\n",
+ " # create transformations using above info\n",
+ " transform = torchvision.transforms.Compose(\n",
+ " [torchvision.transforms.RandomAffine(degrees=(degrees,degrees), shear=(shear,shear), scale=(scale,scale)),\n",
+ " torchvision.transforms.ToTensor()])\n",
+ "\n",
+ " # open data and apply these transformations\n",
+ " 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",
+ " device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+ " if IsLeNet == \"LeNet\":\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).to(device) # added .to(device)\n",
+ " else:\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",
+ "\n",
+ " # 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",
+ " 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",
+ " 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",
+ " 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(_.cpu().numpy(), axis=-1) # added .cpu()\n",
+ " _sum += _.shape[0]\n",
+ " \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 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",
+ " if policy < num_policies:\n",
+ " q_values[this_policy] += best_acc\n",
+ " else:\n",
+ " q_values[this_policy] = (q_values[this_policy]*cnts[this_policy] + best_acc) / (cnts[this_policy] + 1)\n",
+ "\n",
+ " best_q_value = max(q_values)\n",
+ " best_q_values.append(best_q_value)\n",
+ "\n",
+ " if (policy+1) % 10 == 0:\n",
+ " print(\"Iteration: {},\\tQ-Values: {}, Best Policy: {}\".format(policy+1, list(np.around(np.array(q_values),2)), max(list(np.around(np.array(q_values),2)))))\n",
+ "\n",
+ " # update counts\n",
+ " cnts[this_policy] += 1\n",
+ " total_count += 1\n",
+ "\n",
+ " # update q_plus_cnt values every turn after the initial sweep through\n",
+ " if policy >= num_policies - 1:\n",
+ " for i in range(num_policies):\n",
+ " q_plus_cnt[i] = q_values[i] + np.sqrt(2*np.log(total_count)/cnts[i])\n",
+ "\n",
+ " return q_values, best_q_values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 342
},
+ "id": "doHUtJ_tEiA6",
+ "outputId": "8195ba17-c95f-4b75-d8dc-19c5d76d5e43"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "source": [
- "\"\"\"Randomly generate 10 policies\"\"\"\n",
- "\"\"\"Each policy has 5 sub-policies\"\"\"\n",
- "\"\"\"For each sub-policy, pick 2 transformations, 2 probabilities and 2 magnitudes\"\"\"\n",
- "\n",
- "def generate_policies(num_policies, num_sub_policies):\n",
- " \n",
- " policies = np.zeros([num_policies,num_sub_policies,6])\n",
- "\n",
- " # Policies array will be 10x5x6\n",
- " for policy in range(num_policies):\n",
- " for sub_policy in range(num_sub_policies):\n",
- " # pick two sub_policy transformations (0=rotate, 1=shear, 2=scale)\n",
- " policies[policy, sub_policy, 0] = np.random.randint(0,3)\n",
- " policies[policy, sub_policy, 1] = np.random.randint(0,3)\n",
- " while policies[policy, sub_policy, 0] == policies[policy, sub_policy, 1]:\n",
- " policies[policy, sub_policy, 1] = np.random.randint(0,3)\n",
- "\n",
- " # pick probabilities\n",
- " policies[policy, sub_policy, 2] = np.random.randint(0,11) / 10\n",
- " policies[policy, sub_policy, 3] = np.random.randint(0,11) / 10\n",
- "\n",
- " # pick magnitudes\n",
- " for transformation in range(2):\n",
- " if policies[policy, sub_policy, transformation] <= 1:\n",
- " policies[policy, sub_policy, transformation + 4] = np.random.randint(-4,5)*5\n",
- " elif policies[policy, sub_policy, transformation] == 2:\n",
- " policies[policy, sub_policy, transformation + 4] = np.random.randint(5,15)/10\n",
- "\n",
- " return policies"
- ],
- "metadata": {
- "id": "Iql-c88jGGWy"
- },
- "execution_count": 6,
- "outputs": []
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 10/10 [01:28<00:00, 8.84s/it]"
+ ]
},
{
- "cell_type": "code",
- "source": [
- "\"\"\"Pick policy and sub-policy\"\"\"\n",
- "\"\"\"Each row of data should have a different sub-policy but for now, this will do\"\"\"\n",
- "\n",
- "def sample_sub_policy(policies, policy, num_sub_policies):\n",
- " sub_policy = np.random.randint(0,num_sub_policies)\n",
- "\n",
- " degrees = 0\n",
- " shear = 0\n",
- " scale = 1\n",
- "\n",
- " # check for rotations\n",
- " if policies[policy, sub_policy][0] == 0:\n",
- " if np.random.uniform() < policies[policy, sub_policy][2]:\n",
- " degrees = policies[policy, sub_policy][4]\n",
- " elif policies[policy, sub_policy][1] == 0:\n",
- " if np.random.uniform() < policies[policy, sub_policy][3]:\n",
- " degrees = policies[policy, sub_policy][5]\n",
- "\n",
- " # check for shears\n",
- " if policies[policy, sub_policy][0] == 1:\n",
- " if np.random.uniform() < policies[policy, sub_policy][2]:\n",
- " shear = policies[policy, sub_policy][4]\n",
- " elif policies[policy, sub_policy][1] == 1:\n",
- " if np.random.uniform() < policies[policy, sub_policy][3]:\n",
- " shear = policies[policy, sub_policy][5]\n",
- "\n",
- " # check for scales\n",
- " if policies[policy, sub_policy][0] == 2:\n",
- " if np.random.uniform() < policies[policy, sub_policy][2]:\n",
- " scale = policies[policy, sub_policy][4]\n",
- " elif policies[policy, sub_policy][1] == 2:\n",
- " if np.random.uniform() < policies[policy, sub_policy][3]:\n",
- " scale = policies[policy, sub_policy][5]\n",
- "\n",
- " return degrees, shear, scale"
- ],
- "metadata": {
- "id": "QE2VWI8o731X"
- },
- "execution_count": 7,
- "outputs": []
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "id": "vu_4I4qkbx73"
- },
- "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, early_stop_flag, average_validation, iterations, IsLeNet):\n",
- "\n",
- " # get number of policies and sub-policies\n",
- " num_policies = len(policies)\n",
- " num_sub_policies = len(policies[0])\n",
- "\n",
- " #Initialize vector weights, counts and regret\n",
- " q_values = [0]*num_policies\n",
- " cnts = [0]*num_policies\n",
- " q_plus_cnt = [0]*num_policies\n",
- " total_count = 0\n",
- "\n",
- " best_q_values = []\n",
- "\n",
- " for policy in trange(iterations):\n",
- "\n",
- " # get the action to try (either initially in order or using best q_plus_cnt value)\n",
- " if policy >= num_policies:\n",
- " this_policy = np.argmax(q_plus_cnt)\n",
- " else:\n",
- " this_policy = policy\n",
- "\n",
- " # get info of transformation for this sub-policy\n",
- " degrees, shear, scale = sample_sub_policy(policies, this_policy, num_sub_policies)\n",
- "\n",
- " # create transformations using above info\n",
- " transform = torchvision.transforms.Compose(\n",
- " [torchvision.transforms.RandomAffine(degrees=(degrees,degrees), shear=(shear,shear), scale=(scale,scale)),\n",
- " torchvision.transforms.ToTensor()])\n",
- "\n",
- " # open data and apply these transformations\n",
- " 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",
- " device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
- " if IsLeNet == \"LeNet\":\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).to(device) # added .to(device)\n",
- " else:\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",
- "\n",
- " # 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",
- " 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",
- " 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",
- " 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(_.cpu().numpy(), axis=-1) # added .cpu()\n",
- " _sum += _.shape[0]\n",
- " \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 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",
- " if policy < num_policies:\n",
- " q_values[this_policy] += best_acc\n",
- " else:\n",
- " q_values[this_policy] = (q_values[this_policy]*cnts[this_policy] + best_acc) / (cnts[this_policy] + 1)\n",
- "\n",
- " best_q_value = max(q_values)\n",
- " best_q_values.append(best_q_value)\n",
- "\n",
- " if (policy+1) % 10 == 0:\n",
- " print(\"Iteration: {},\\tQ-Values: {}, Best Policy: {}\".format(policy+1, list(np.around(np.array(q_values),2)), max(list(np.around(np.array(q_values),2)))))\n",
- "\n",
- " # update counts\n",
- " cnts[this_policy] += 1\n",
- " total_count += 1\n",
- "\n",
- " # update q_plus_cnt values every turn after the initial sweep through\n",
- " if policy >= num_policies - 1:\n",
- " for i in range(num_policies):\n",
- " q_plus_cnt[i] = q_values[i] + np.sqrt(2*np.log(total_count)/cnts[i])\n",
- "\n",
- " return q_values, best_q_values"
- ]
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
},
{
- "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, 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": 342
- },
- "id": "doHUtJ_tEiA6",
- "outputId": "8195ba17-c95f-4b75-d8dc-19c5d76d5e43"
- },
- "execution_count": 9,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "100%|██████████| 10/10 [01:28<00:00, 8.84s/it]"
- ]
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "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"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "\n"
- ]
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ],
- "image/png": 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\n"
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+ "data": {
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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
- ]
-}
\ No newline at end of file
+ ],
+ "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 = 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_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": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "UCB1.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": "3.7.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}