diff --git a/Test.ipynb b/Test.ipynb
deleted file mode 100644
index 4ca738159d67171ce1f430fa77f28ca621b77e5e..0000000000000000000000000000000000000000
--- a/Test.ipynb
+++ /dev/null
@@ -1,237 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Test Pytorch"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "tensor([[ 0.6650, 2.0888, -0.3361, -0.6212, 0.6276, -0.3091, 1.6601, -0.1404,\n",
- " 0.0224, -1.1360],\n",
- " [-0.8311, -1.6385, 0.3011, 1.2989, 0.2328, -0.0061, 0.4039, 0.3956,\n",
- " -0.2067, -0.8877],\n",
- " [-0.2899, -1.7310, -0.4318, 1.3837, 3.1771, 1.7354, 0.3680, -0.0743,\n",
- " -0.1610, 0.8281],\n",
- " [-0.2591, -2.0913, 0.8630, -0.3533, 0.9323, -1.4520, 0.4476, -0.2588,\n",
- " -0.0963, 1.8449],\n",
- " [ 0.4599, 0.3258, 0.7780, 0.6943, -0.4343, -0.0536, -0.1049, 0.2867,\n",
- " 0.5493, -1.2934],\n",
- " [-0.6990, 0.0783, -0.8745, 1.2521, 1.5363, 0.8770, -0.5319, -0.2629,\n",
- " 0.7732, -0.5001],\n",
- " [-0.2902, -0.8901, 0.1904, 0.7456, 0.5802, 0.0443, -1.2447, 2.1954,\n",
- " 0.5382, 0.2219],\n",
- " [ 1.0372, -0.7516, 0.7940, 0.8207, 0.6601, 0.0317, 0.1410, -1.7062,\n",
- " -0.6549, 0.6287],\n",
- " [ 0.6680, 1.0136, -0.0813, -1.4382, 0.4640, 1.2923, -1.0299, 0.5684,\n",
- " 1.6626, -1.1921],\n",
- " [-1.1864, -0.6625, -1.0846, 0.7550, -0.8748, 0.2835, -1.4264, 1.0279,\n",
- " -0.6046, 0.6298],\n",
- " [-1.9253, -0.4960, -0.8379, 1.8726, -2.0282, 0.0869, 1.2508, 0.0390,\n",
- " -1.7213, 0.3269],\n",
- " [ 1.7196, -1.6188, -0.4604, -1.0196, -0.8883, 1.2987, 0.4795, 0.5581,\n",
- " -1.0138, -0.2184],\n",
- " [-0.6600, 0.5816, -0.6574, 0.4684, 1.2546, -0.4140, -0.2636, 0.4267,\n",
- " 0.1736, -1.5019],\n",
- " [-0.8852, 0.6677, -1.3074, -1.2241, -1.4054, 0.0919, 1.5832, 1.4357,\n",
- " -1.9016, 0.7274]])\n",
- "AlexNet(\n",
- " (features): Sequential(\n",
- " (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
- " (1): ReLU(inplace=True)\n",
- " (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
- " (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
- " (4): ReLU(inplace=True)\n",
- " (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
- " (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
- " (7): ReLU(inplace=True)\n",
- " (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
- " (9): ReLU(inplace=True)\n",
- " (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
- " (11): ReLU(inplace=True)\n",
- " (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
- " )\n",
- " (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
- " (classifier): Sequential(\n",
- " (0): Dropout(p=0.5, inplace=False)\n",
- " (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
- " (2): ReLU(inplace=True)\n",
- " (3): Dropout(p=0.5, inplace=False)\n",
- " (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
- " (5): ReLU(inplace=True)\n",
- " (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
- " )\n",
- ")\n"
- ]
- }
- ],
- "source": [
- "import torch\n",
- "\n",
- "N, D = 14, 10\n",
- "x = torch.randn(N, D).type(torch.FloatTensor)\n",
- "print(x)\n",
- "\n",
- "from torchvision import models\n",
- "\n",
- "alexnet = models.alexnet()\n",
- "print(alexnet)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise 1: CNN on CIFAR10"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "See if cuda is available ==> it is not"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CUDA is not available. Training on CPU ...\n"
- ]
- }
- ],
- "source": [
- "import torch\n",
- "\n",
- "# check if CUDA is available\n",
- "train_on_gpu = torch.cuda.is_available()\n",
- "\n",
- "if not train_on_gpu:\n",
- " print(\"CUDA is not available. Training on CPU ...\")\n",
- "else:\n",
- " print(\"CUDA is available! Training on GPU ...\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load the CIFAR10 dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Files already downloaded and verified\n",
- "Files already downloaded and verified\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "from torchvision import datasets, transforms\n",
- "from torch.utils.data.sampler import SubsetRandomSampler\n",
- "\n",
- "# number of subprocesses to use for data loading\n",
- "num_workers = 0\n",
- "# how many samples per batch to load\n",
- "batch_size = 20\n",
- "# percentage of training set to use as validation\n",
- "valid_size = 0.2\n",
- "\n",
- "# convert data to a normalized torch.FloatTensor\n",
- "transform = transforms.Compose(\n",
- " [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]\n",
- ")\n",
- "\n",
- "# choose the training and test datasets\n",
- "train_data = datasets.CIFAR10(\"data\", train=True, download=True, transform=transform)\n",
- "test_data = datasets.CIFAR10(\"data\", train=False, download=True, transform=transform)\n",
- "\n",
- "# obtain training indices that will be used for validation\n",
- "num_train = len(train_data)\n",
- "indices = list(range(num_train))\n",
- "np.random.shuffle(indices)\n",
- "split = int(np.floor(valid_size * num_train))\n",
- "train_idx, valid_idx = indices[split:], indices[:split]\n",
- "\n",
- "# define samplers for obtaining training and validation batches\n",
- "train_sampler = SubsetRandomSampler(train_idx)\n",
- "valid_sampler = SubsetRandomSampler(valid_idx)\n",
- "\n",
- "# prepare data loaders (combine dataset and sampler)\n",
- "train_loader = torch.utils.data.DataLoader(\n",
- " train_data, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers\n",
- ")\n",
- "valid_loader = torch.utils.data.DataLoader(\n",
- " train_data, batch_size=batch_size, sampler=valid_sampler, num_workers=num_workers\n",
- ")\n",
- "test_loader = torch.utils.data.DataLoader(\n",
- " test_data, batch_size=batch_size, num_workers=num_workers\n",
- ")\n",
- "\n",
- "# specify the image classes\n",
- "classes = [\n",
- " \"airplane\",\n",
- " \"automobile\",\n",
- " \"bird\",\n",
- " \"cat\",\n",
- " \"deer\",\n",
- " \"dog\",\n",
- " \"frog\",\n",
- " \"horse\",\n",
- " \"ship\",\n",
- " \"truck\",\n",
- "]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "env",
- "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": "3.10.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}