FINAL_dl-assignment-7.ipynb

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   "source": [
    "# Deep Learning &mdash; Assignment 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seventh assignment for the 2023 Deep Learning course (NWI-IMC070) of the Radboud University."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----\n",
    "\n",
    "**Names:** Vojko Pjanovic (s1084416); Carmen Gascó Gálvez (s1098566)\n",
    "\n",
    "**Group:** 10\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "For this assignment we are doing things a bit different.\n",
    "* Your task is to reproduce the paper [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks;      Jonathan Frankle, Michael Carbin](https://arxiv.org/pdf/1803.03635.pdf).\n",
    "* Try to follow the experimental settings in that paper, unless there is a reason to deviate.\n",
    "* If the paper is not clear on some details, make a reasonable choice yourself, and motivate that choice.\n",
    "* You will have 3 weeks to work on this assignment.\n",
    "* Be aware that this assignment will take more time than previous ones. It is ok if you do not completely finish it.\n",
    "* We will *not* be providing you with much code. You will have to implement many things yourself.\n",
    "* You may freely use code from earlier weeks, and from the d2l books. Please add a comment to reference the original source.\n",
    "* You may *not* use implementations of the paper you find online.\n",
    "\n",
    "**Tips and hints**\n",
    "* It is allowed and recommended to use more than just this notebook. Make separate python files for a library of functions, and for training and analyses.\n",
    "* If you like working with jupyter notebooks: make a separate notebook for trying things out, and keep this one clean.\n",
    "* Use checkpoint files before and during training.\n",
    "* In the notebook only display and discuss these results.\n",
    "* You may add new cells to this notebook as needed.\n",
    "* While the task is to reproduce parts of a paper, the big picture is more important than the exact details.\n",
    "* It is allowed to discuss the assignment with other groups, but try not to spoil too much.\n",
    "* If you get stuck, contact the teachers via discord."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Required software\n",
    "\n",
    "If you need to import any additional libraries, add them below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "\n",
    "%config InlineBackend.figure_formats = ['png']\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "import torchvision\n",
    "import matplotlib.pyplot as plt\n",
    "from d2l import torch as d2l\n",
    "from dl_assignment_7_common import *  # Your functions should go here if you want to use them from scripts\n",
    "import os\n",
    "import numpy as np\n",
    "import torch.nn.utils.prune as prune\n",
    "import copy\n",
    "import glob\n",
    "\n",
    "device = d2l.try_gpu()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1 The paper (2 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(a) Read sections 1, 2, and 5 of the paper [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks; by Jonathan Frankle, Michael Carbin](https://arxiv.org/pdf/1803.03635.pdf)**\n",
    "\n",
    "We will refer to this as the \"LTH paper\" from now on, or just \"the paper\".\n",
    "To answer some later questions you will also need to look at other sections, and search through the appendices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(b) In your own words, briefly explain the key message of the paper.<span style=\"float:right\"> (2 points)</span>**\n",
    "\n",
    "Note: \"briefly\" means: a few sentences at most."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The key message of the paper is that a dense feed-forward neural network possibly contains one or multiple sub-networks which are as accurate as the whole network, but with less nodes - the winning lottery ticket. Taking only the sub-network and re-training it with it's initial conditions will lead to similar performance as with the full network, only with less computational needs. The phenomenon is not attributed to the structure of the sub-network, as re-initializing with different parameters does not lead to good performance. Therefore, the specific initialization and structure are required for the sub-network to be efficient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.2 Models and datasets (9 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(a) What neural network architectures are used in the paper?<span style=\"float:right\"> (1 point)</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lenet, Conv-2, Conv-4, Conv-6, VGG-19, Resnet-18"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To keep things simple, we will start with a simple architecture, corresponding to what the paper calls `Lenet`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(b) Define a function that constructs a `Lenet` network using PyTorch.<span style=\"float:right\"> (2 points)</span>**\n",
    "\n",
    "Copy these definitions to `dl_assignment_7_common.py`.\n",
    "\n",
    "Hint: see Figure 2.\n",
    "\n",
    "Note: the LTH paper is not entirely clear about this, but the convolution layers use `padding='same'`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lenet has:\n",
    "\n",
    "0 convolutions\n",
    "\n",
    "FC Layers: 300,100,10\n",
    "\n",
    "All weights: 266K\n",
    "\n",
    "epochs/Batch: 50K/60\n",
    "\n",
    "Optimizer: Adam 1.2e-3\n",
    "\n",
    "Pruning rate: fc20%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(c) Define a function that can construct a network given the architecture name.**\n",
    "\n",
    "To keep the code as generic as possible, we can make function\n",
    "\n",
    "Move the function below to `dl_assignment_7_common.py`, and don't forget to remove it here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will do all our experiments with two datasets: MNIST and FashionMNIST\n",
    "\n",
    "**(d) Are these datasets also used in the papers?<span style=\"float:right\"> (1 point)</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MNIST is used with Lenet, but FashionMNIST is not used; for the convolutional networks, they used CIFAR10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(e) Define a function that loads a dataset given the dataset name.<span style=\"float:right\"> (3 points)</span>**\n",
    "\n",
    "Hint: Standard datasets such as MNIST and CIFAR10 are available in the [torchvision](https://pytorch.org/vision/stable/datasets.html#image-classification) library."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: Function implemented as: `TODO` in `dl_assignment_7_common.py`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(f) Most of these datasets come with a predefined train/test split. Is this used in the LTH paper? If so, update the dataset loader to return a pair `(trainset, testset)`.<span style=\"float:right\"> (1 point)</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a predefined train/test split: \n",
    "\n",
    "For MNIST:\n",
    "\n",
    "\"The MNIST dataset consists of 60,000 training examples and 10,000 test examples. We randomly sampled a 5,000-example validation set from the training set and used the remaining 55,000 training examples as our training set for the rest of the paper\"\n",
    "\n",
    "FashionMNIST has the same sizes for the train and test examples, so we will use the same split for this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(g) Does the LTH paper use a validation set? If so, update the dataset loader to return `(train_dataset, validation_dataset, test_dataset)`.<span style=\"float:right\"> (1 point)</span>**\n",
    "\n",
    "Hint: [`random_split`](https://pytorch.org/docs/stable/data.html#torch.utils.data.random_split), and/or see assignment 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, they use a validation set as seen above; it will consist of 5000 samples out of 60000 for both MNIST and FashionMNIST, and the 55000 remaining will be used for training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.3 Training (12 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(a) What optimization algorithm is used in the paper? What values are used for the hyperparameters?<span style=\"float:right\"> (1.5 points)</span>**\n",
    "\n",
    "If you are unable to find the values used for some of the hyperparameters, use reasonable default values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the Conv-nets, only Adam was used as an optimizer; for VGG-19 and Resnet-18 SGD and Momentum were additionally used.\n",
    "\n",
    "We set up a table to denote the used hyperparameters\n",
    "\n",
    "| Model | Optimizer | Learning Rate | Momentum | Batch Size | epoch | Pruning rate \n",
    "|-------|-----------|---------------|----------|------------|--------|---------|\n",
    "| LeNet | Adam      | 0.0012         | -      |  60      | 50K    | 0.2    \n",
    "| Conv-2 | Adam      | 0.0002        | -      |  60       | 20K   | 0.2    \n",
    "| Conv-4 | Adam      | 0.0003        | -      |  60       | 25K    |  0.2 \n",
    "| Conv-6 | Adam      | 0.0003        | -      |  60       | 30K    |  0.2 \n",
    "| VGG-19 | Adam, SGD, Momentum       | 0.001  | 0.9  |128     | 30K    | 0.2  \n",
    "| Resnet-18 | Adam, SGD, Momentum    | 0.001   | 0.9  |  128   | 112K | 0.2  \n",
    "\n",
    "Note: \n",
    "\n",
    "1. Many more hyperparameters are tried out in the appendices, but the mentioned values come from the main body of the paper. \n",
    "2. The pruning rate pertains to the layers excepting the output layer, which is pruned by half of the mentioned pruning rate.\n",
    "3. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(b) Implement an evaluation function, that evaluates a model on a validation or test set (passed as an argument).<span style=\"float:right\"> (2 points)</span>**\n",
    "\n",
    "The function should return loss and accuracy.\n",
    "\n",
    "Hints: the book defines a function for this that you may use (see assignment 3)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the train/val loss and accuracy we will respectively use the CrossEntropyLoss measure predefined in PyTorch, and the d2l.accuracy function.\n",
    "\n",
    "Later on, for the test accuracy, we will rely on the d2l.evaluate_accuracy_gpu function to evaluate the model accuracy.\n",
    "\n",
    "Below functions are from the book, and they will be adapted in the training loop to be more befitting for the task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "def evaluate_loss(net, data_iter, loss = nn.CrossEntropyLoss):  #@save\n",
    "    \"\"\"Evaluate the loss of a model on the given dataset.\"\"\"\n",
    "    metric = d2l.Accumulator(2)  # Sum of losses, no. of examples\n",
    "    for X, y in data_iter:\n",
    "        out = net(X)\n",
    "        y = y.reshape(out.shape)\n",
    "        l = loss(out, y)\n",
    "        metric.add(l.sum(), l.numel())\n",
    "    return metric[0] / metric[1]\n",
    "\n",
    "def evaluate_accuracy_gpu(net, data_iter, device=d2l.try_gpu): #@save\n",
    "    \"\"\"Compute the accuracy for a model on a dataset using a GPU.\"\"\"\n",
    "    if isinstance(net, nn.Module):\n",
    "        net.eval()  # Set the model to evaluation mode\n",
    "        if not device:\n",
    "            device = next(iter(net.parameters())).device\n",
    "    # No. of correct predictions, no. of predictions\n",
    "    metric = d2l.Accumulator(2)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(c) Implement a training loop.<span style=\"float:right\"> (4 points)</span>**\n",
    "\n",
    "Make sure that the network parameters are saved to a file before and during training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "implemented in `dl_assignment_7_common.py` as function `train`; all the following steps are also integrated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because you will be doing many experiments, it would be a shame to have to re-run them when you reload the notebook. A better solution is to save model checkpoints.\n",
    "See [the tutorial on saving and loading model parameters](https://pytorch.org/tutorials/beginner/basics/saveloadrun_tutorial.html) for how to implement this in PyTorch.\n",
    "\n",
    "**(d) Change the training function so that it saves the model at the start and at the end of training.<span style=\"float:right\"> (1 point)</span>**\n",
    "\n",
    "Hint: Saving a model requires a filename. Because you will be running many experiments, come up with a descriptive naming convention and/or directory structure. Example: `path = f\"checkpoints/model-{arch}-{dataset}-{run}-{phase_of_the_moon}-{iteration}.pth\"`.\n",
    "\n",
    "Hint 2: it is easier to save the whole model, see the bottom of the tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(e) Train a simple network on a simple dataset.<span style=\"float:right\"> (1 point)</span>**\n",
    "\n",
    "You may want to create a new python script (`simple_training.py`), and just load the trained network here instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train': <torch.utils.data.dataloader.DataLoader at 0x7fec97fe79a0>,\n",
       " 'val': <torch.utils.data.dataloader.DataLoader at 0x7fec97fe7b20>,\n",
       " 'test': <torch.utils.data.dataloader.DataLoader at 0x7fec97fe7c40>}"
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     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "download_datasets = False\n",
    "get_dataset('mnist','./data', download=download_datasets)\n",
    "get_dataset('fashionmnist','./data', download=download_datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "model performance {'train loss': 0.015600315507518297, 'train acc': 0.9946, 'val loss': 0.08281274160854518, 'val acc': 0.9816, 'test acc': 0.9802}\n",
      "early stop model performance {'iteration': 6419, 'train_loss': 0.026775068931560963, 'train_acc': 0.9908181818181818, 'val_loss': 0.08055280934050679, 'val_acc': 0.979, 'test_acc': 0.9779}\n"
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   "source": [
    "dataset_used = get_dataset('mnist','./data')\n",
    "net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)\n",
    "model_performance, early_stop_values = train(net, optimizer, dataset_used, epochs = 10, val_interval=2, plot = True)"
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  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'train loss': 0.015600315507518297,\n",
       "  'train acc': 0.9946,\n",
       "  'val loss': 0.08281274160854518,\n",
       "  'val acc': 0.9816,\n",
       "  'test acc': 0.9802},\n",
       " {'iteration': 6419,\n",
       "  'train_loss': 0.026775068931560963,\n",
       "  'train_acc': 0.9908181818181818,\n",
       "  'val_loss': 0.08055280934050679,\n",
       "  'val_acc': 0.979,\n",
       "  'test_acc': 0.9779})"
      ]
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     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "model_performance, early_stop_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(f) Does the training converge? How well does your network perform?<span style=\"float:right\"> (1 point)</span>**"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is safe to say that training has converged; it performs well on both training and validation, and the model generalizes to unseen data as shown by the validation accuracy, and doesn't seem to overfit. \n",
    "\n",
    "The test accuracy is also not bad - all-in-all, good performance for the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(g) Re-train the same network, with the same initial weights. Are the results *exactly* the same?<span style=\"float:right\"> (2 points)</span>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
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    {
     "name": "stdout",
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     "text": [
      "model performance {'train loss': 0.013287278039816936, 'train acc': 0.9955272727272727, 'val loss': 0.0935360619071871, 'val acc': 0.9786, 'test acc': 0.9779}\n",
      "early stop model performance {'epoch': 2, 'train_loss': 0.06920890383151444, 'train_acc': 0.9784181818181819, 'val_loss': 0.07753174423575401, 'val_acc': 0.9736, 'test_acc': 0.9762}\n"
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       "<Figure size 800x500 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_used = get_dataset('mnist','./data')\n",
    "\n",
    "net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)\n",
    "retrain_net = torch.load(f'./checkpoints/model_LeNet-run_1-before.pth')\n",
    "net.load_state_dict(retrain_net.state_dict())\n",
    "\n",
    "retrained_model_performance, retrain_early = train(net, optimizer, dataset_used, epochs = 10, val_interval=2, plot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are not *exactly* the same, but they are close to each other; also, the early stopping for the retrained model is at the 2nd epoch instead of the 4th for the initial model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(h) The LTH paper uses a variant of 'early-stopping'. How is this done? Implement it in your training loop.<span style=\"float:right\"> (1 point)</span>**\n",
    "\n",
    "Hint: A simple way to keep track of the best model is to create a model checkpoint in a file `\"checkpoints/model-...-best.pth\"`.\n",
    "\n",
    "Hint 2: It is okay to compute the validation scores less often, this can speed up training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "implemented in the above code block. It is a proxy to early-stopping as it does not explicitly stop the training, but merely notes the network at which early-stopping is reached. The test accuracy is also presented at the iteration of minimum validation loss, which has been implemented above as well."
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.4 Pruning (11 points)\n",
    "\n",
    "Next up, you should implement pruning. Starting with the one-shot pruning method.\n",
    "\n",
    "Hint: \n",
    "Pruning is implemented already in PyTorch, in the module [torch.utils.prune](https://pytorch.org/docs/stable/nn.html#module-torch.nn.utils).\n",
    "The pruning method used in the LTH paper corresponds is called [L1Unstructured](https://pytorch.org/docs/stable/generated/torch.nn.utils.prune.L1Unstructured.html#torch.nn.utils.prune.L1Unstructured) in PyTorch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(a) The PyTorch pruning function accepts an amount to prune. Is that the amount of weights to set to 0 or the amount to keep nonzero? Is the paper using the same?<span style=\"float:right\"> (1 point)</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the documentation we find that *\"**amount** (int or float) – quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune.\"*\n",
    "\n",
    "Therfore, it corresponds to the fraction/amount to be set to 0. \n",
    "\n",
    "In the paper it is stated in the pseudo-code for pruning *\"prune p% of the parameters in $\\theta_j$, creating a mask m\"*\n",
    "\n",
    "Which is esssentially the same, if accounting for the fraction/percentage discrepancy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(b) Should all parameters be pruned or only weights? Is the pruning rate the same for all types of layers?<span style=\"float:right\"> (1 point)</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only the weights are pruned. The pruning rate is not the same for all layers; the connections to the output are pruned at half the rate of the rest of the network.\n",
    "\n",
    "*\"We use a simple layer-wise pruning heuristic: remove a percentage of the weights with the lowest magnitudes within each layer (as in Han et al. (2015)). Connections to outputs are pruned at half of the rate of the rest of the network.\"*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(c) Define a function to prune a network as used in the LTH paper. It should take an amount to prune as an argument.<span style=\"float:right\"> (3 points)</span>**\n",
    "\n",
    "Hint: for a `Sequential` layer, you can access the layers as `net.children()`. For a layer, you can use `isinstance(layer, torch.nn.Linear)` to check if it is a linear layer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "defined in `dl_assignment_7_common.py`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(d) Check your pruning function on a very simple neural network. Print the network parameters before and after pruning to make sure you understand what is going on.<span style=\"float:right\"> (1 point)</span>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      "tensor([[-7.0267e-03, -2.9162e-02,  1.8467e-02,  ..., -5.8051e-05,\n",
      "         -7.4527e-03, -8.9706e-03],\n",
      "        [-2.6670e-02, -2.7273e-02, -2.5651e-02,  ...,  2.3359e-02,\n",
      "          2.5158e-02,  1.4038e-02],\n",
      "        [ 2.1423e-02, -3.4883e-02, -2.3201e-02,  ...,  2.7664e-02,\n",
      "         -1.4171e-02,  1.3049e-02],\n",
      "        ...,\n",
      "        [ 3.1263e-02,  1.5436e-02, -2.4171e-02,  ..., -3.8572e-04,\n",
      "         -5.9465e-03,  2.2793e-02],\n",
      "        [-1.8434e-02,  2.6492e-02, -1.0312e-02,  ..., -1.7978e-02,\n",
      "         -3.0895e-02, -2.8790e-02],\n",
      "        [ 3.1027e-02, -1.2256e-02, -3.0608e-02,  ..., -1.6220e-02,\n",
      "          3.5066e-02,  3.5254e-02]], requires_grad=True)\n",
      "\n",
      "tensor([[-0.0000, -0.0292,  0.0185,  ..., -0.0000, -0.0075, -0.0090],\n",
      "        [-0.0267, -0.0273, -0.0257,  ...,  0.0234,  0.0252,  0.0140],\n",
      "        [ 0.0214, -0.0349, -0.0232,  ...,  0.0277, -0.0142,  0.0130],\n",
      "        ...,\n",
      "        [ 0.0313,  0.0154, -0.0242,  ..., -0.0000, -0.0000,  0.0228],\n",
      "        [-0.0184,  0.0265, -0.0103,  ..., -0.0180, -0.0309, -0.0288],\n",
      "        [ 0.0310, -0.0123, -0.0306,  ..., -0.0162,  0.0351,  0.0353]],\n",
      "       grad_fn=<MulBackward0>)\n"
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       "        ...,\n",
       "        [1., 1., 1.,  ..., 0., 0., 1.],\n",
       "        [1., 1., 1.,  ..., 1., 1., 1.],\n",
       "        [1., 1., 1.,  ..., 1., 1., 1.]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)\n",
    "retrain_net = torch.load(f'./checkpoints/model_LeNet-before-LTH_1.pth')\n",
    "net.load_state_dict(retrain_net.state_dict())\n",
    "net.state_dict().keys()\n",
    "\n",
    "print(net.net[0].weight)\n",
    "mask = L1_prune(net, 0.2)\n",
    "print('')\n",
    "print(net.net[0].weight)\n",
    "\n",
    "mask[0].weight_mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example we have printed the weights of the first layer before and after pruning; we see that the values with lowest L1 norm are indeed set to 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(e) Define a function that applies the pruning mask from a pruned network to another network of the same architecture.**\n",
    "\n",
    "This function should only do pruning (so some weights become 0), other weights should remain the same.\n",
    "\n",
    "Hint: the pruning functions already generate and store pruning masks. You should be able to extract these from a pruned network.\n",
    "\n",
    "Hint 2: `custom_from_mask`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "defined in `dl_assignment_7_common.py`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(f) Check your mask copy function on a very simple neural network. Check that only the pruning mask is copied.<span style=\"float:right\"> (1 point)</span>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      "tensor([[-7.0267e-03, -2.9162e-02,  1.8467e-02,  ..., -5.8051e-05,\n",
      "         -7.4527e-03, -8.9706e-03],\n",
      "        [-2.6670e-02, -2.7273e-02, -2.5651e-02,  ...,  2.3359e-02,\n",
      "          2.5158e-02,  1.4038e-02],\n",
      "        [ 2.1423e-02, -3.4883e-02, -2.3201e-02,  ...,  2.7664e-02,\n",
      "         -1.4171e-02,  1.3049e-02],\n",
      "        ...,\n",
      "        [ 3.1263e-02,  1.5436e-02, -2.4171e-02,  ..., -3.8572e-04,\n",
      "         -5.9465e-03,  2.2793e-02],\n",
      "        [-1.8434e-02,  2.6492e-02, -1.0312e-02,  ..., -1.7978e-02,\n",
      "         -3.0895e-02, -2.8790e-02],\n",
      "        [ 3.1027e-02, -1.2256e-02, -3.0608e-02,  ..., -1.6220e-02,\n",
      "          3.5066e-02,  3.5254e-02]], requires_grad=True)\n",
      "\n",
      "tensor([[-0.0000, -0.0292,  0.0185,  ..., -0.0000, -0.0075, -0.0090],\n",
      "        [-0.0267, -0.0273, -0.0257,  ...,  0.0234,  0.0252,  0.0140],\n",
      "        [ 0.0214, -0.0349, -0.0232,  ...,  0.0277, -0.0142,  0.0130],\n",
      "        ...,\n",
      "        [ 0.0313,  0.0154, -0.0242,  ..., -0.0000, -0.0000,  0.0228],\n",
      "        [-0.0184,  0.0265, -0.0103,  ..., -0.0180, -0.0309, -0.0288],\n",
      "        [ 0.0310, -0.0123, -0.0306,  ..., -0.0162,  0.0351,  0.0353]],\n",
      "       grad_fn=<MulBackward0>)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[0., 1., 1.,  ..., 0., 1., 1.],\n",
       "        [1., 1., 1.,  ..., 1., 1., 1.],\n",
       "        [1., 1., 1.,  ..., 1., 1., 1.],\n",
       "        ...,\n",
       "        [1., 1., 1.,  ..., 0., 0., 1.],\n",
       "        [1., 1., 1.,  ..., 1., 1., 1.],\n",
       "        [1., 1., 1.,  ..., 1., 1., 1.]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)\n",
    "retrain_net = torch.load(f'./checkpoints/model_LeNet-before-LTH_1.pth')\n",
    "net.load_state_dict(retrain_net.state_dict())\n",
    "\n",
    "print(net.net[0].weight)\n",
    "new_net = prune_using_mask(net, mask)\n",
    "print('')\n",
    "print(new_net.net[0].weight)\n",
    "\n",
    "mask[0].weight_mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(g) Define a function that randomly prunes a network.<span style=\"float:right\"> (1 point)</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "defined in `dl_assignment_7_common.py`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(h) Check the above function.<span style=\"float:right\"> (1 point)</span>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      "tensor([[-7.0267e-03, -2.9162e-02,  1.8467e-02,  ..., -5.8051e-05,\n",
      "         -7.4527e-03, -8.9706e-03],\n",
      "        [-2.6670e-02, -2.7273e-02, -2.5651e-02,  ...,  2.3359e-02,\n",
      "          2.5158e-02,  1.4038e-02],\n",
      "        [ 2.1423e-02, -3.4883e-02, -2.3201e-02,  ...,  2.7664e-02,\n",
      "         -1.4171e-02,  1.3049e-02],\n",
      "        ...,\n",
      "        [ 3.1263e-02,  1.5436e-02, -2.4171e-02,  ..., -3.8572e-04,\n",
      "         -5.9465e-03,  2.2793e-02],\n",
      "        [-1.8434e-02,  2.6492e-02, -1.0312e-02,  ..., -1.7978e-02,\n",
      "         -3.0895e-02, -2.8790e-02],\n",
      "        [ 3.1027e-02, -1.2256e-02, -3.0608e-02,  ..., -1.6220e-02,\n",
      "          3.5066e-02,  3.5254e-02]], requires_grad=True)\n",
      "\n",
      "tensor([[-7.0267e-03, -2.9162e-02,  1.8467e-02,  ..., -5.8051e-05,\n",
      "         -7.4527e-03, -8.9706e-03],\n",
      "        [-0.0000e+00, -2.7273e-02, -2.5651e-02,  ...,  0.0000e+00,\n",
      "          0.0000e+00,  0.0000e+00],\n",
      "        [ 2.1423e-02, -3.4883e-02, -2.3201e-02,  ...,  2.7664e-02,\n",
      "         -0.0000e+00,  1.3049e-02],\n",
      "        ...,\n",
      "        [ 3.1263e-02,  1.5436e-02, -0.0000e+00,  ..., -3.8572e-04,\n",
      "         -5.9465e-03,  2.2793e-02],\n",
      "        [-1.8434e-02,  2.6492e-02, -1.0312e-02,  ..., -1.7978e-02,\n",
      "         -3.0895e-02, -2.8790e-02],\n",
      "        [ 0.0000e+00, -1.2256e-02, -0.0000e+00,  ..., -0.0000e+00,\n",
      "          3.5066e-02,  0.0000e+00]], grad_fn=<MulBackward0>)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[1., 1., 1.,  ..., 1., 1., 1.],\n",
       "        [0., 1., 1.,  ..., 0., 0., 0.],\n",
       "        [1., 1., 1.,  ..., 1., 0., 1.],\n",
       "        ...,\n",
       "        [1., 1., 0.,  ..., 1., 1., 1.],\n",
       "        [1., 1., 1.,  ..., 1., 1., 1.],\n",
       "        [0., 1., 0.,  ..., 0., 1., 0.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)\n",
    "\n",
    "retrain_net = torch.load(f'./checkpoints/model_LeNet-before-LTH_1.pth')\n",
    "net.load_state_dict(retrain_net.state_dict())\n",
    "net.state_dict().keys()\n",
    "\n",
    "print(net.net[0].weight)\n",
    "mask = random_prune(net, 0.2)\n",
    "print('')\n",
    "print(net.net[0].weight)\n",
    "\n",
    "mask[0].weight_mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(i) Define a function that performs the experiment described in Section 1 of the LTH paper on a given dataset and with a given architecture.<span style=\"float:right\"> (2 points)</span>**\n",
    "\n",
    "Save all needed results to a file, such as test loss and accuracy. This will make your job easier later on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "defined in `dl_assignment_7_common.py` as `winning_tickets` function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.5 Confirming the Lottery Ticket Hypothesis (10 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(a) Perform the experiments needed to reproduce the red lines in Figure 1 from the LTH paper.<span style=\"float:right\"> (6 points)</span>**\n",
    "\n",
    "* It is ok to ignore the error bars for now, and focus on doing one series of experiments.\n",
    "* You may also reduce the number of points in the plot to keep the computation time managable.\n",
    "* You do not have to match the visual style of the figure.\n",
    "\n",
    "Hint: create a python script (`experiment1-{dataset}.py`) that does all the training as needed.\n",
    "Then load the checkpoint files and do your analysis here.\n",
    "You may also want to define more helper functions.\n",
    "\n",
    "Hint 2: look at previous assignments for how to plot. If you do want to include error bars, see also [documentation for `plt.errorbar`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.errorbar.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: Training implemented in `TODO.py`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x_tick_values = np.array([100, 41.1, 16.9, 7.0, 2.9, 1, 0.5, 0.2])\n",
    "\n",
    "files = [\"large\",\"small\",\"mini\"]\n",
    "observables = [\"early_stop_iterations_lenet_L1\",\"early_stop_testacc_lenet_L1\",\"early_stop_iterations_lenet_random\",\"early_stop_testacc_lenet_random\"]\n",
    "\n",
    "def extract_observation(files,observable):\n",
    "    data = np.concatenate([np.loadtxt(f'results/{file}/{observable}_{file}.txt')[:,1:] for file in files ],axis=0)\n",
    "    fracs = np.concatenate([np.loadtxt(f'results/{file}/{observable}_{file}.txt')[:,0] for file in files ],axis=0)\n",
    "    data_avg = np.mean(data,axis=1)\n",
    "    return data, data_avg, fracs \n",
    "\n",
    "early_stop_iterations_lenet_L1, early_stop_iterations_lenet_L1_avg, fracs = extract_observation(files,observables[0])\n",
    "early_stop_testacc_lenet_L1, early_stop_testacc_lenet_L1_avg, fracs = extract_observation(files,observables[1])\n",
    "early_stop_iterations_lenet_random, early_stop_iterations_lenet_random_avg, fracs = extract_observation(files,observables[2])\n",
    "early_stop_testacc_lenet_random, early_stop_testacc_lenet_random_avg, fracs =  extract_observation(files,observables[3])\n",
    "\n",
    "\n",
    "plt.figure(figsize=(7, 5))\n",
    "x_positions = np.linspace(start=0, stop=len(x_tick_values) - 1, num=len(fracs))\n",
    "plt.xticks(ticks=np.linspace(0, len(x_tick_values) - 1, num=len(x_tick_values)), labels=[str(val) for val in x_tick_values])\n",
    "\n",
    "plt.errorbar(x_positions,early_stop_iterations_lenet_random_avg,yerr=np.std(early_stop_iterations_lenet_random,axis=1),label='random')\n",
    "plt.errorbar(x_positions,early_stop_iterations_lenet_L1_avg,yerr=np.std(early_stop_iterations_lenet_L1,axis=1),label='L1')\n",
    "\n",
    "plt.xlabel('Percent of Weights Remaining')\n",
    "plt.ylabel('EarlyStop Iteration (Val)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(7, 5))\n",
    "x_tick_values = np.array([100, 41.1, 16.9, 7.0, 2.9, 1, 0.5, 0.2])\n",
    "x_positions = np.linspace(start=0, stop=len(x_tick_values) - 1, num=len(fracs))\n",
    "plt.xticks(ticks=np.linspace(0, len(x_tick_values) - 1, num=len(x_tick_values)), labels=[str(val) for val in x_tick_values])\n",
    "\n",
    "plt.errorbar(x_positions,early_stop_testacc_lenet_random_avg,yerr=np.std(early_stop_testacc_lenet_random,axis=1),label='random')\n",
    "plt.errorbar(x_positions,early_stop_testacc_lenet_L1_avg,yerr=np.std(early_stop_testacc_lenet_L1,axis=1),label='L1')\n",
    "\n",
    "plt.xlabel('Percent of Weights Remaining')\n",
    "plt.ylabel('Accuracy at Early-Stop (Test)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.ylim([0.9,1.0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(b) Do your results match the paper? Discuss similarities and differences.<span style=\"float:right\"> (2 points)</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The lottery ticket hypothesis states that there is a sub-network of the original network which can be trained to the same accuracy as the original network, but with less weights and almost the same number of iterations.\n",
    "\n",
    "According to the test accuracy, we can see that there is a smaller percentage of weights remaining for which the L1-pruned network outperforms the initial network. However, this behavior is not observed in the random network. Therefore, this confirms the Lottery ticket hypothesis.\n",
    "\n",
    "These results are pretty similar to the ones in the Fig. 1 of the paper. Nonetheless, the results from the paper show a more evident increase in the test accuracy for the L1-pruned network. \n",
    "Other than this, the behavior reproduced in this assignment is the barely the same as the one found in the paper."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(c) What can you conclude from this experiment?<span style=\"float:right\"> (2 points)</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As stated before, according to the test accuracy, we can see that there is a smaller percentage of weights remaining for which the L1-pruned network outperforms the initial network. However, this behavior is not observed in the random network.Therefore, this confirms the Lottery ticket hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.6 Experiments from Section 2 (12 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(a) What is the difference between the experiment in Figure 1 and Figure 3 of the paper.<span style=\"float:right\"> (1 point)</span>**\n",
    "\n",
    "Hint: are there differences in the method, the architecture, or the dataset?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Fig. 1 we can see the iteration at which early-stopping would occur and the test accuracy at that iteration with respect to the percentage of weights remaining. They are implementing a L1_prune for Lenet  and a random prune for randomly sampled sparse networks using MNIST dataset.\n",
    "In Fig. 3 we can see the test accuracy of Lenet trained again on MNIST dataset with respect to the training iterations using iterative pruning, for different weights remaining. \n",
    "\n",
    "Therefore, the main difference is that they used iterative prunning in Fig. 3. Plotting the results in this way, makes it easier to notice that the networks learn faster and reach a higher test accuracy the more they are pruned, and\n",
    "the winner ticket reaches the higest accuracy for $P_{m} = 21.1 \\%$ (78.9% pruned). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(b) Implement the iterative pruning method from the paper.<span style=\"float:right\"> (3 points)</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing iterative pruning functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model performance {'train loss': 0.12495704983689568, 'train acc': 0.9615454545454546, 'val loss': 0.13468465378284455, 'val acc': 0.9594, 'test acc': 0.9597}\n",
      "early stop model performance {'iteration': 4585, 'train_loss': 0.12495704983689568, 'train_acc': 0.9615454545454546, 'val_loss': 0.13468465378284455, 'val_acc': 0.9594, 'test_acc': 0.9597}\n"
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      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_used = get_dataset('mnist','./data', download = False)\n",
    "cp_path = './checkpoints/test'\n",
    "\n",
    "base_net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)\n",
    "_, early_stop_values = train(base_net, optimizer, dataset_used, epochs = 10, file_specifier = f'LTH_fig4a_base', val_interval = 2, cp_path=cp_path , plot = True)\n",
    "\n",
    "n_rounds = 5\n",
    "prune_fraction = 0.99\n",
    "previous_mask = None\n",
    "all_masks = []\n",
    "\n",
    "# Calculate the pruning fraction for each round\n",
    "round_prune_fraction = 1 - (1 - prune_fraction)**(1/n_rounds)\n",
    "    \n",
    "for k in range(1,n_rounds+1):\n",
    "\n",
    "    if k == 1:\n",
    "        trained_net = torch.load(f\"{cp_path}/model_LeNet-after-LTH_fig4a_base.pth\")\n",
    "    else:\n",
    "        trained_net = torch.load(f\"{cp_path}/model_LeNet-after-LTH_4a_L1_pruned{prune_fraction}_{k}.pth\")\n",
    "    \n",
    "    net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)\n",
    "    net.load_state_dict(trained_net.state_dict())\n",
    "    \n",
    "    current_mask = iterative_L1_prune(net, round_prune_fraction, k)\n",
    "    all_masks.append(current_mask)\n",
    "    \n",
    "    init_net = torch.load(f\"{cp_path}/model_LeNet-before-LTH_fig4a_base.pth\") # off for random reinit\n",
    "    net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)\n",
    "    net.load_state_dict(init_net.state_dict()) # off for random reinit\n",
    "    pruned_net = prune_using_mask(net, current_mask)\n",
    "    previous_mask = current_mask\n",
    "\n",
    "    optimizer = torch.optim.Adam(pruned_net.parameters(), lr=0.0012)\n",
    "    _, early_stop_values = train(pruned_net, optimizer, dataset_used, epochs = 4, file_specifier = f'LTH_4a_L1_pruned{prune_fraction}_{k+1}', val_interval = 2, cp_path=cp_path , plot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3981079931972789\n",
      "0.1584906462585034\n",
      "0.0630952380952381\n",
      "0.02511904761904762\n",
      "0.01\n"
     ]
    }
   ],
   "source": [
    "total_net = torch.load(f\"{cp_path}/model_LeNet-after-LTH_fig4a_base.pth\")\n",
    "base_net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)\n",
    "base_net.load_state_dict(total_net.state_dict())\n",
    "\n",
    "total = np.count_nonzero(base_net.net[0].weight.cpu().detach().numpy()),np.count_nonzero(base_net.net[2].weight.cpu().detach().numpy()),np.count_nonzero(base_net.net[4].weight.cpu().detach().numpy())\n",
    "\n",
    "for i in range(len(all_masks)):\n",
    "    print(np.count_nonzero(all_masks[i][0].weight.cpu().detach().numpy())/total[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that iterative pruning works indeed, such that in the end only 0.01 of the weights is left in the first layer, as expected when pruning 0.99 of the weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(c) Perform the experiments needed to reproduce Figure 4a from the paper.<span style=\"float:right\"> (4 points)</span>**\n",
    "\n",
    "Hint: see previous section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "observables = ['early_stop_iterations', 'early_stop_testacc','early_stop_trainacc']\n",
    "strategies = ['iterative_random','oneshot_random','oneshot_winning','iterative_winning']\n",
    "\n",
    "x_tick_values = np.array([100, 51.3, 26.3, 13.5, 7.0, 3.6,1.9, 1, 0.5, 0.3])\n",
    "\n",
    "for observable in observables:\n",
    "    plt.xticks(ticks=np.linspace(0, len(x_tick_values) - 1, num=len(x_tick_values)), labels=[str(val) for val in x_tick_values])\n",
    "    for strategy in strategies:\n",
    "        \n",
    "        if strategy == \n",
    "                \n",
    "        data = np.loadtxt(f'./results/{strategy}/{observable}_{strategy}.txt')\n",
    "        frac_remaining = data[:,0]\n",
    "        data = data[:,1:]\n",
    "        data_avg = np.mean(data, axis=1)\n",
    "        data_std = np.std(data, axis=1)\n",
    "        x_positions = np.linspace(start=0, stop=len(x_tick_values) - 1, num=len(frac_remaining))    \n",
    "        plt.errorbar(x_positions, data_avg, yerr=data_std, label=strategy)\n",
    "\n",
    "    plt.xlabel('Fraction of weights remaining')\n",
    "    plt.ylabel(observable)\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(d) Do your results match the paper? Discuss similarities and differences.<span style=\"float:right\"> (2 points)</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Iterative random is not correct. While debugging the code, we found that the epoch number was set to 2. We do not have enough time to run it again before the deadline, but we are confident that it will be correct if we run it again (we have requested an extension to submit the correct results).\n",
    "\n",
    "- Without considering the results for the iterative random, our findings are similar to those in the paper. We observe that the oneshot reinitialized network (orange) learns more slowly than the winning ticket ones, and the test accuracy decreases more rapidly when increasing the percentage of pruning. However, in our case, for a fraction of remaining weights, we can't observe this difference anymore, but it is still noticeable in the paper.\n",
    "\n",
    "- The iterative winning ticket method is the most optimal one, as it learns faster and reachs a higher accuracy at smaller network sizes. This behavior is both observed in the paper and our results.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(e) What can you conclude from this experiment?<span style=\"float:right\"> (2 points)</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The iterative winning ticket method is the best one, as it learns faster and reaches a higher accuracy at smaller network sizes.  This aligns with our goal of finding the smallest network (winning ticket), and this procedure is therefore designed for that purpose.\n",
    "\n",
    "This experiment provides evidence in support of the lottery ticket hypothesis, highlighting the significance of initialization. The original initialization exhibits resilience and gains advantages through pruning, while the performance of random reinitialization rapidly deteriorates and consistently diminishes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.7 Experiments from Section 4 (7 points)\n",
    "\n",
    "Section 3 and 4 deal with convolutional neural networks. We are going to skip the networks in section 3, and move on to Figure 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(a) Section 4 of the paper describes a slightly different pruning method. Implement that method.<span style=\"float:right\"> (2 points)</span>**\n",
    "\n",
    "Hint: look at [`torch.nn.utils.prune.global_unstructured`](https://pytorch.org/docs/stable/generated/torch.nn.utils.prune.global_unstructured.html) and at the examples on that page.\n",
    "\n",
    "If you get stuck on this step, you can continue with the same pruning methods as before."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: Function implemented as: `TODO` in `dl_assignment_7_common.py`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[ 0.0000,  0.0250, -0.0000, -0.0323, -0.0028,  0.0228, -0.0212],\n",
       "         [ 0.0036, -0.0000, -0.0106, -0.0102, -0.0042, -0.0366, -0.0333],\n",
       "         [ 0.0177, -0.0114, -0.0157, -0.0103, -0.0208, -0.0282,  0.0279],\n",
       "         [ 0.0310, -0.0145, -0.0000,  0.0135, -0.0000, -0.0008,  0.0005],\n",
       "         [-0.0057, -0.0046, -0.0159,  0.0126, -0.0300, -0.0030,  0.0080],\n",
       "         [-0.0254,  0.0026,  0.0288, -0.0264,  0.0072, -0.0000, -0.0000],\n",
       "         [-0.0024, -0.0136, -0.0416, -0.0258,  0.0000,  0.0000,  0.0276]],\n",
       "        grad_fn=<SelectBackward0>),\n",
       " tensor([[0., 1., 0., 1., 1., 1., 1.],\n",
       "         [1., 0., 1., 1., 1., 1., 1.],\n",
       "         [1., 1., 1., 1., 1., 1., 1.],\n",
       "         [1., 1., 0., 1., 0., 1., 1.],\n",
       "         [1., 1., 1., 1., 1., 1., 1.],\n",
       "         [1., 1., 1., 1., 1., 0., 0.],\n",
       "         [1., 1., 1., 1., 0., 0., 1.]]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net,optimizer = create_network(\"ResNet18\")\n",
    "\n",
    "masks = global_pruning_resnet(net,0.2)\n",
    "\n",
    "net2,optimizer2 = create_network(\"ResNet18\")\n",
    "\n",
    "pruned_net = global_prune_using_mask(net2,masks)\n",
    "\n",
    "pruned_net.net.conv1.weight[0][0], masks['net.conv1'][0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(b) Implement a function that constructs the network architecture used in Figure 8.<span style=\"float:right\"> (1 point)</span>**\n",
    "\n",
    "Extend the `create_network` function defined earlier.\n",
    "\n",
    "Hint: VGG16 and Resnet18 are [predefined in torchvision](https://pytorch.org/vision/0.12/models.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "resnet = create_network(\"ResNet18\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(c) Perform the experiments needed to reproduce Figure 8 from the LTH paper.<span style=\"float:right\"> (2 points)</span>**\n",
    "\n",
    "* Again: you do not need to include error bars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: Training implemented in `TODO.py`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_used = get_dataset('cifar10','./data', download = False)\n",
    "resnet, optimizer = create_network(arch = 'ResNet18')\n",
    "test_accs = train_resnet(resnet, optimizer, dataset_used, epochs = 4, val_interval=2, plot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_rounds = 3\n",
    "prune_fraction = 0.9\n",
    "round_prune_fraction = 1 - (1 - prune_fraction)**(1/n_rounds)\n",
    "\n",
    "for k in range(1,n_rounds+1):\n",
    "    round_prune_fraction = 1 - (1 - prune_fraction)**(1/n_rounds)\n",
    "    remaining_fraction_after_pruning = (1 - round_prune_fraction)**(i)\n",
    "    total_pruned_amount = 1 - remaining_fraction_after_pruning\n",
    "    \n",
    "    trained_net = torch.load(f\"{cp_path}/model_LeNet-after-LTH_4a_L1_pruned{prune_fraction}.pth\")\n",
    "    \n",
    "    net, optimizer = create_network(arch = 'ResNet18')\n",
    "    net.load_state_dict(trained_net.state_dict())\n",
    "    mask = global_pruning_resnet(net, total_pruned_amount)\n",
    "    \n",
    "    init_net = torch.load(f\"{cp_path}/model_LeNet-before-LTH_fig4a_base.pth\")\n",
    "    net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)\n",
    "    net.load_state_dict(init_net.state_dict())\n",
    "    init_net_pruned = global_prune_using_mask(net, mask)\n",
    "    optimizer = torch.optim.Adam(init_net_pruned.parameters(), lr=0.0012)\n",
    "    test_accs = train_resnet(init_net_pruned, optimizer, dataset_used, epochs = 30, file_specifier = f'LTH_4a_L1_pruned{prune_fraction}', val_interval = 2, cp_path=cp_path , plot = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(d) Do your results match the paper? Discuss similarities and differences.<span style=\"float:right\"> (2 points)</span>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We finished the code for this part, but we didn't have enough time to get the results. Our problem was that we were unable to train the network, but we have checked that our functions are right as you can see above.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The end\n",
    "\n",
    "Well done! Please double check the instructions at the top before you submit your results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*This assignment has 63 points.*\n",
    "<span style=\"float:right;color:#aaa;font-size:10px;\"> Version 024c4f2 / 2023-10-16</span>"
   ]
  }
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