From 63a7084cb93c8a25a82bca967a24d39ae1cc5f4e Mon Sep 17 00:00:00 2001
From: Sun Jin Kim <sk2521@ic.ac.uk>
Date: Mon, 11 Apr 2022 18:04:43 +0900
Subject: [PATCH] John: Add EasyNet
---
MetaAugment/Baseline_JC.ipynb | 270 +++++-------------
MetaAugment/UCB1_JC.ipynb | 524 ++++++++++++++++------------------
2 files changed, 319 insertions(+), 475 deletions(-)
diff --git a/MetaAugment/Baseline_JC.ipynb b/MetaAugment/Baseline_JC.ipynb
index d979dc8a..5e0523f0 100644
--- a/MetaAugment/Baseline_JC.ipynb
+++ b/MetaAugment/Baseline_JC.ipynb
@@ -62,7 +62,33 @@
},
{
"cell_type": "code",
+ "source": [
+ "\"\"\"Define internal NN module that trains on the dataset\"\"\"\n",
+ "class EasyNet(nn.Module):\n",
+ " def __init__(self):\n",
+ " super().__init__()\n",
+ " self.fc1 = nn.Linear(784, 2048)\n",
+ " self.relu1 = nn.ReLU()\n",
+ " self.fc2 = nn.Linear(2048, 10)\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": 3,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
"metadata": {
"id": "xujQtvVWBgMH"
},
@@ -93,13 +119,13 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {
"id": "vu_4I4qkbx73"
},
"outputs": [],
"source": [
- "def run_baseline(batch_size=32, toy_size=0.02, max_epochs=100, early_stop_num=10, early_stop_flag=True, average_validation=[15,25]):\n",
+ "def run_baseline(batch_size=32, toy_size=0.02, max_epochs=100, early_stop_num=10, early_stop_flag=True, average_validation=[15,25], IsLeNet=True):\n",
"\n",
" # create transformations using above info\n",
" transform = torchvision.transforms.Compose([\n",
@@ -113,7 +139,10 @@
" train_loader, test_loader = create_toy(train_dataset, test_dataset, batch_size, toy_size)\n",
"\n",
" # create model\n",
- " model = LeNet()\n",
+ " if IsLeNet:\n",
+ " model = LeNet()\n",
+ " else:\n",
+ " model = EasyNet()\n",
" sgd = optim.SGD(model.parameters(), lr=1e-1)\n",
" cost = nn.CrossEntropyLoss()\n",
"\n",
@@ -171,196 +200,20 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "KVhYheLfBP33",
- "outputId": "8009d87f-7e39-40e3-c6ef-8f3a12f9433f"
- },
- "outputs": [
- {
- "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"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "9913344it [00:04, 2462502.04it/s] \n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "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"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "29696it [00:00, 3785722.37it/s] \n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "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"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "1649664it [00:00, 3348476.95it/s] \n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "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"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "5120it [00:00, 2935726.11it/s] \n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "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"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "9913344it [00:04, 2338660.11it/s] \n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "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"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "29696it [00:00, 33554432.00it/s] "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "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"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n",
- "1649664it [00:00, 2786152.46it/s] \n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "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"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "5120it [00:00, 4789214.20it/s] \n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting ./MetaAugment/test/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MetaAugment/test/MNIST/raw\n",
- "\n",
- "0\tBest accuracy: 18.00%\n",
- "10\tBest accuracy: 75.50%\n",
- "20\tBest accuracy: 78.00%\n",
- "30\tBest accuracy: 95.00%\n",
- "40\tBest accuracy: 95.50%\n",
- "50\tBest accuracy: 94.00%\n",
- "60\tBest accuracy: 85.00%\n",
- "70\tBest accuracy: 85.50%\n",
- "80\tBest accuracy: 62.50%\n",
- "90\tBest accuracy: 76.00%\n",
- "Average best accuracy: 79.86%\n",
- "\n",
- "0\tAverage accuracy: 93.50%\n",
- "10\tAverage accuracy: 93.45%\n",
- "20\tAverage accuracy: 46.95%\n",
- "30\tAverage accuracy: 71.41%\n",
- "40\tAverage accuracy: 73.68%\n",
- "50\tAverage accuracy: 64.50%\n",
- "60\tAverage accuracy: 72.50%\n",
- "70\tAverage accuracy: 94.36%\n",
- "80\tAverage accuracy: 84.77%\n",
- "90\tAverage accuracy: 92.14%\n",
- "Average average accuracy: 80.92%\n",
- "\n"
- ]
- }
- ],
"source": [
"batch_size = 32 # size of batch the inner NN is trained with\n",
- "toy_size = 0.02 # total propeortion of training and test set we use\n",
+ "toy_size = 0.05 # 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 = 100 # how many iterations are we averaging over\n",
+ "IsLeNet = True # using LeNet or EasyNet\n",
"\n",
"# run using early stopping\n",
"best_accuracies = []\n",
"for baselines in range(num_iterations):\n",
- " best_acc = run_baseline(batch_size, toy_size, max_epochs, early_stop_num, early_stop_flag, average_validation)\n",
+ " best_acc = run_baseline(batch_size, 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",
@@ -370,19 +223,52 @@
"early_stop_flag = False\n",
"best_accuracies = []\n",
"for baselines in range(num_iterations):\n",
- " best_acc = run_baseline(batch_size, toy_size, max_epochs, early_stop_num, early_stop_flag, average_validation)\n",
+ " best_acc = run_baseline(batch_size, 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": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "KVhYheLfBP33",
+ "outputId": "39c42079-a3cb-492e-8e26-68818eeac808"
+ },
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "0\tBest accuracy: 95.60%\n",
+ "10\tBest accuracy: 85.40%\n",
+ "20\tBest accuracy: 86.40%\n",
+ "30\tBest accuracy: 95.40%\n",
+ "40\tBest accuracy: 97.00%\n",
+ "50\tBest accuracy: 80.40%\n",
+ "60\tBest accuracy: 95.60%\n",
+ "70\tBest accuracy: 96.40%\n",
+ "80\tBest accuracy: 86.20%\n",
+ "90\tBest accuracy: 95.40%\n",
+ "Average best accuracy: 84.65%\n",
+ "\n",
+ "0\tAverage accuracy: 78.45%\n",
+ "10\tAverage accuracy: 58.02%\n",
+ "20\tAverage accuracy: 38.60%\n",
+ "30\tAverage accuracy: 65.15%\n",
+ "40\tAverage accuracy: 77.22%\n",
+ "50\tAverage accuracy: 79.09%\n",
+ "60\tAverage accuracy: 95.55%\n",
+ "70\tAverage accuracy: 86.33%\n",
+ "80\tAverage accuracy: 85.98%\n",
+ "90\tAverage accuracy: 78.20%\n",
+ "Average average accuracy: 83.31%\n",
+ "\n"
+ ]
+ }
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
@@ -406,9 +292,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 0
-}
+}
\ No newline at end of file
diff --git a/MetaAugment/UCB1_JC.ipynb b/MetaAugment/UCB1_JC.ipynb
index d3bbda39..196e2ce4 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",
@@ -18,17 +30,116 @@
"import torchvision\n",
"import torchvision.datasets as datasets\n",
"\n",
- "import child_networks\n",
- "from main import *"
- ]
+ "from matplotlib import pyplot as plt\n",
+ "from numpy import save, load"
+ ],
+ "metadata": {
+ "id": "U_ZJ2LqDiu_v"
+ },
+ "execution_count": null,
+ "outputs": []
},
{
"cell_type": "code",
- "execution_count": 3,
+ "source": [
+ "\"\"\"Define internal NN module that trains on the dataset\"\"\"\n",
+ "class LeNet(nn.Module):\n",
+ " def __init__(self):\n",
+ " super().__init__()\n",
+ " self.conv1 = nn.Conv2d(1, 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(256, 120)\n",
+ " self.relu3 = nn.ReLU()\n",
+ " self.fc2 = nn.Linear(120, 84)\n",
+ " self.relu4 = nn.ReLU()\n",
+ " self.fc3 = nn.Linear(84, 10)\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": "Iql-c88jGGWy"
+ "id": "4ksS_duLFADW"
},
- "outputs": [],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "\"\"\"Define internal NN module that trains on the dataset\"\"\"\n",
+ "class EasyNet(nn.Module):\n",
+ " def __init__(self):\n",
+ " super().__init__()\n",
+ " self.fc1 = nn.Linear(784, 2048)\n",
+ " self.relu1 = nn.ReLU()\n",
+ " self.fc2 = nn.Linear(2048, 10)\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": null,
+ "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": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
"source": [
"\"\"\"Randomly generate 10 policies\"\"\"\n",
"\"\"\"Each policy has 5 sub-policies\"\"\"\n",
@@ -59,15 +170,15 @@
" policies[policy, sub_policy, transformation + 4] = np.random.randint(5,15)/10\n",
"\n",
" return policies"
- ]
+ ],
+ "metadata": {
+ "id": "Iql-c88jGGWy"
+ },
+ "execution_count": null,
+ "outputs": []
},
{
"cell_type": "code",
- "execution_count": 4,
- "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",
@@ -104,18 +215,23 @@
" scale = policies[policy, sub_policy][5]\n",
"\n",
" return degrees, shear, scale"
- ]
+ ],
+ "metadata": {
+ "id": "QE2VWI8o731X"
+ },
+ "execution_count": null,
+ "outputs": []
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {
"id": "vu_4I4qkbx73"
},
"outputs": [],
"source": [
"\"\"\"Sample policy, open and apply above transformations\"\"\"\n",
- "def run_UCB1(policies, batch_size, toy_size, max_epochs, early_stop_num, iterations):\n",
+ "def run_UCB1(policies, batch_size, toy_size, max_epochs, early_stop_num, iterations, IsLeNet):\n",
"\n",
" # get number of policies and sub-policies\n",
" num_policies = len(policies)\n",
@@ -127,6 +243,8 @@
" q_plus_cnt = [0]*num_policies\n",
" total_count = 0\n",
"\n",
+ " best_q_values = []\n",
+ "\n",
" for policy in range(iterations):\n",
"\n",
" # get the action to try (either initially in order or using best q_plus_cnt value)\n",
@@ -144,18 +262,60 @@
" torchvision.transforms.ToTensor()])\n",
"\n",
" # open data and apply these transformations\n",
- " train_dataset = datasets.MNIST(root='./datasets/mnist/train', train=True, download=True, transform=transform)\n",
- " test_dataset = datasets.MNIST(root='./datasets/mnist/test', train=False, download=True, transform=torchvision.transforms.ToTensor())\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",
"\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",
- " child_network = child_networks.lenet()\n",
- " sgd = optim.SGD(child_network.parameters(), lr=1e-1)\n",
+ " if IsLeNet:\n",
+ " model = LeNet()\n",
+ " else:\n",
+ " model = EasyNet()\n",
+ " sgd = optim.SGD(model.parameters(), lr=1e-1)\n",
" cost = nn.CrossEntropyLoss()\n",
"\n",
- " best_acc = train_child_network(child_network, train_loader, test_loader, sgd, cost, max_epochs=100)\n",
+ " # set variables for best validation accuracy and early stop count\n",
+ " best_acc = 0\n",
+ " early_stop_cnt = 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], 10))\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",
+ " 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",
+ " # update best validation accuracy if it was higher, otherwise increase early stop count\n",
+ " acc = correct / _sum\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",
+ " break\n",
"\n",
" # update q_values\n",
" if policy < num_policies:\n",
@@ -163,7 +323,11 @@
" else:\n",
" q_values[this_policy] = (q_values[this_policy]*cnts[this_policy] + best_acc) / (cnts[this_policy] + 1)\n",
"\n",
- " print(q_values)\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",
@@ -174,277 +338,71 @@
" 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"
+ " return q_values, best_q_values"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "source": [
+ "batch_size = 32 # size of batch the inner NN is trained with\n",
+ "toy_size = 0.05 # 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 = True # using LeNet or EasyNet\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, 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_LeNet_5percent.npy', best_q_values)\n",
+ "#best_q_values = load('best_q_values_LeNet_5percent.npy', allow_pickle=True)"
+ ],
"metadata": {
"colab": {
- "base_uri": "https://localhost:8080/"
+ "base_uri": "https://localhost:8080/",
+ "height": 447
},
"id": "doHUtJ_tEiA6",
- "outputId": "6735e812-f7be-4f8b-cec2-52a069f7731b"
+ "outputId": "3cc290fb-2d45-4fac-b0e6-6fb6c0490b78"
},
+ "execution_count": null,
"outputs": [
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
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- "[0.0, 0.0, 0.0, 0.0, 0.25, 0.0, 0.25, 0.0, 0.0, 0.25]\n",
- "Wall time: 3.92 s\n"
+ "Iteration: 10,\tQ-Values: [0.79, 0.85, 0.87, 0.69, 0.83], Best Policy: 0.87\n",
+ "Iteration: 20,\tQ-Values: [0.82, 0.86, 0.88, 0.82, 0.86], Best Policy: 0.88\n",
+ "Iteration: 30,\tQ-Values: [0.84, 0.89, 0.77, 0.84, 0.87], Best Policy: 0.89\n",
+ "Iteration: 40,\tQ-Values: [0.83, 0.9, 0.83, 0.84, 0.89], Best Policy: 0.9\n",
+ "Iteration: 50,\tQ-Values: [0.84, 0.91, 0.82, 0.85, 0.87], Best Policy: 0.91\n",
+ "Iteration: 60,\tQ-Values: [0.83, 0.92, 0.83, 0.86, 0.88], Best Policy: 0.92\n",
+ "Iteration: 70,\tQ-Values: [0.84, 0.92, 0.83, 0.85, 0.88], Best Policy: 0.92\n",
+ "Iteration: 80,\tQ-Values: [0.85, 0.92, 0.83, 0.85, 0.87], Best Policy: 0.92\n",
+ "Iteration: 90,\tQ-Values: [0.85, 0.91, 0.81, 0.85, 0.87], Best Policy: 0.91\n",
+ "Iteration: 100,\tQ-Values: [0.84, 0.91, 0.83, 0.85, 0.83], Best Policy: 0.91\n"
]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
}
- ],
- "source": [
- "%%time\n",
- "\n",
- "batch_size = 32 # size of batch inner NN is trained with\n",
- "toy_size = 0.0002 # 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\n",
- "iterations = 20 # total iterations, should be more than the number of policies\n",
- "\n",
- "# generate policies and sub-policies\n",
- "num_policies = 10\n",
- "num_sub_policies = 5\n",
- "policies = generate_policies(num_policies, num_sub_policies)\n",
- "\n",
- "q_values = run_UCB1(policies, batch_size, toy_size, max_epochs, early_stop_num, iterations)\n",
- "#print(q_values)"
]
}
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "name": "UCB1.ipynb",
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3",
- "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": "3.8.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+ ]
+}
\ No newline at end of file
--
GitLab