Ch10-deeplearning-lab.ipynb

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   "source": [
    "# Deep Learning\n",
    "\n",
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch10-deeplearning-lab.ipynb\">\n",
    "<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>\n",
    "\n",
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch10-deeplearning-lab.ipynb)"
   ]
  },
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   "source": [
    "In this section we  demonstrate how to fit the examples discussed\n",
    "in the text. We use the `Python`{} `torch` package, along with the\n",
    "`pytorch_lightning` package which provides utilities to simplify\n",
    "fitting and evaluating models.  This code can be impressively fast\n",
    "with certain  special processors,  such as Apple’s new M1 chip. The package is well-structured, flexible, and will feel comfortable\n",
    "to `Python`{} users. A good companion is the site\n",
    "[pytorch.org/tutorials](https://pytorch.org/tutorials/beginner/basics/intro.html).\n",
    "Much of our code is adapted from there, as well as the `pytorch_lightning` documentation. {The precise URLs at the time of writing are <https://pytorch.org/tutorials/beginner/basics/intro.html> and <https://pytorch-lightning.readthedocs.io/en/latest/>.}\n",
    "\n",
    "We start with several standard imports that we have  seen before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "71bd6b71",
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   "source": [
    "import numpy as np, pandas as pd\n",
    "from matplotlib.pyplot import subplots\n",
    "from sklearn.linear_model import \\\n",
    "     (LinearRegression,\n",
    "      LogisticRegression,\n",
    "      Lasso)\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.pipeline import Pipeline\n",
    "from ISLP import load_data\n",
    "from ISLP.models import ModelSpec as MS\n",
    "from sklearn.model_selection import \\\n",
    "     (train_test_split,\n",
    "      GridSearchCV)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b799d88",
   "metadata": {},
   "source": [
    "### Torch-Specific Imports\n",
    "There are a number of imports for `torch`. (These are not\n",
    "included with `ISLP`, so must be installed separately.)\n",
    "First we import the main library\n",
    "and essential tools used to specify sequentially-structured networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3298bdf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.optim import RMSprop\n",
    "from torch.utils.data import TensorDataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51c32fa5",
   "metadata": {},
   "source": [
    "There are several other helper packages for `torch`. For instance,\n",
    "the `torchmetrics` package has utilities to compute\n",
    "various metrics to evaluate performance when fitting\n",
    "a model. The `torchinfo` package provides a useful\n",
    "summary of the layers of a model. We use the `read_image()`\n",
    "function when loading test images in Section 10.9.4.\n",
    "\n",
    "If you have not already installed the packages `torchvision`\n",
    "and `torchinfo` you can install them by running\n",
    "`pip install torchinfo torchvision`.\n",
    "We can now import from `torchinfo`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aeae9bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchmetrics import (MeanAbsoluteError,\n",
    "                          R2Score)\n",
    "from torchinfo import summary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1049a398",
   "metadata": {},
   "source": [
    "The package `pytorch_lightning` is a somewhat higher-level\n",
    "interface to `torch` that simplifies the specification and\n",
    "fitting of\n",
    "models by reducing the amount of boilerplate code needed\n",
    "(compared to using `torch` alone)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a5a39702",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytorch_lightning import Trainer\n",
    "from pytorch_lightning.loggers import CSVLogger\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1b27f4d",
   "metadata": {},
   "source": [
    "In order to reproduce results we use `seed_everything()`. We will also instruct `torch` to use deterministic algorithms\n",
    "where possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "11bf06a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed set to 0\n"
     ]
    }
   ],
   "source": [
    "from pytorch_lightning import seed_everything\n",
    "seed_everything(0, workers=True)\n",
    "torch.use_deterministic_algorithms(True, warn_only=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bd719f8",
   "metadata": {},
   "source": [
    "We will use several datasets shipped with `torchvision` for our\n",
    "examples: a pretrained network for image classification,\n",
    "as well as some transforms used for preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "062cd869",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "from torchvision.io import read_image\n",
    "from torchvision.datasets import MNIST, CIFAR100\n",
    "from torchvision.models import (resnet50,\n",
    "                                ResNet50_Weights)\n",
    "from torchvision.transforms import (Resize,\n",
    "                                    Normalize,\n",
    "                                    CenterCrop,\n",
    "                                    ToTensor)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c2a507a",
   "metadata": {},
   "source": [
    "We have provided a few utilities in `ISLP` specifically for this lab.\n",
    "The `SimpleDataModule` and `SimpleModule` are simple\n",
    "versions of objects used in `pytorch_lightning`, the\n",
    "high-level module for fitting `torch` models. Although more advanced\n",
    "uses such as computing on graphical processing units (GPUs) and parallel data processing\n",
    "are possible in this module, we will not be focusing much on these\n",
    "in this lab. The `ErrorTracker` handles\n",
    "collections of targets and predictions over each mini-batch\n",
    "in the validation or test stage, allowing computation\n",
    "of the metric over the entire validation or test data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4a05884a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ISLP.torch import (SimpleDataModule,\n",
    "                        SimpleModule,\n",
    "                        ErrorTracker,\n",
    "                        rec_num_workers)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6f41487",
   "metadata": {},
   "source": [
    "In addition we have included some helper\n",
    "functions to load the\n",
    "`IMDb` database, as well as a lookup that maps integers\n",
    "to particular keys in the database. We’ve included\n",
    "a slightly modified copy of the preprocessed\n",
    "`IMDb` data from `keras`, a separate package\n",
    "for fitting deep learning models. This saves us significant\n",
    "preprocessing and allows us to focus on specifying and fitting\n",
    "the models themselves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1474e88b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ISLP.torch.imdb import (load_lookup,\n",
    "                             load_tensor,\n",
    "                             load_sparse,\n",
    "                             load_sequential)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86123617",
   "metadata": {},
   "source": [
    "Finally, we introduce some utility imports  not directly related to\n",
    "`torch`.\n",
    "The `glob()` function from the `glob` module is used\n",
    "to find all files matching wildcard characters, which we will use\n",
    "in our example applying the `ResNet50` model\n",
    "to some of our own images.\n",
    "The `json` module will be used to load\n",
    "a JSON file for looking up classes to identify the labels of the\n",
    "pictures in the `ResNet50` example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0100ff28",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "import json\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f81cea",
   "metadata": {},
   "source": [
    "## Single Layer Network on Hitters Data\n",
    "We start by fitting the models in Section 10.6 on the `Hitters` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef59d53a",
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    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "Hitters = load_data('Hitters').dropna()\n",
    "n = Hitters.shape[0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e302823e",
   "metadata": {},
   "source": [
    " We will fit two linear models (least squares  and lasso) and  compare their performance\n",
    "to that of a neural network. For this comparison we will use mean absolute error on a validation dataset.\n",
    "\\begin{equation*}\n",
    "\\begin{split}\n",
    "\\mbox{MAE}(y,\\hat{y}) = \\frac{1}{n} \\sum_{i=1}^n |y_i-\\hat{y}_i|.\n",
    "\\end{split}\n",
    "\\end{equation*}\n",
    "We set up the model matrix and the response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4deacf91",
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   },
   "outputs": [],
   "source": [
    "model = MS(Hitters.columns.drop('Salary'), intercept=False)\n",
    "X = model.fit_transform(Hitters).to_numpy()\n",
    "Y = Hitters['Salary'].to_numpy()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d030ccab",
   "metadata": {},
   "source": [
    "The `to_numpy()`  method above converts `pandas`\n",
    "data frames or series to `numpy` arrays.\n",
    "We do this because we will need to  use `sklearn` to fit the lasso model,\n",
    "and it requires this conversion. \n",
    "We also use  a linear regression method from `sklearn`, rather than the method\n",
    "in Chapter~3 from `statsmodels`, to facilitate the comparisons."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13ffb53a",
   "metadata": {},
   "source": [
    "We now split the data into test and training, fixing the random\n",
    "state used by `sklearn` to do the split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8593f62b",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, \n",
    " X_test,\n",
    " Y_train,\n",
    " Y_test) = train_test_split(X,\n",
    "                            Y,\n",
    "                            test_size=1/3,\n",
    "                            random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1e980ad",
   "metadata": {},
   "source": [
    "### Linear Models\n",
    "We fit the linear model and evaluate the test error directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "54b14993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "259.71528833146243"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hit_lm = LinearRegression().fit(X_train, Y_train)\n",
    "Yhat_test = hit_lm.predict(X_test)\n",
    "np.abs(Yhat_test - Y_test).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c18abe13",
   "metadata": {},
   "source": [
    "Next we fit the lasso using `sklearn`. We are using\n",
    "mean absolute error to select and evaluate a model, rather than mean squared error.\n",
    "The specialized solver we used in Section 6.5.2 uses only mean squared error. So here, with a bit more work,  we create a cross-validation grid and perform the cross-validation directly.  \n",
    "\n",
    "We encode a pipeline with two steps: we first normalize the features using a `StandardScaler()` transform,\n",
    "and then fit the lasso without further normalization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "84c668ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler(with_mean=True, with_std=True)\n",
    "lasso = Lasso(warm_start=True, max_iter=30000)\n",
    "standard_lasso = Pipeline(steps=[('scaler', scaler),\n",
    "                                 ('lasso', lasso)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab2c215",
   "metadata": {},
   "source": [
    "We need to create a grid of values for $\\lambda$. As is common practice, \n",
    "we choose a grid of 100 values of $\\lambda$, uniform on the log scale from `lam_max` down to  `0.01*lam_max`. Here  `lam_max` is the smallest value of\n",
    "$\\lambda$ with an  all-zero solution. This value equals the largest absolute inner-product between any predictor and the (centered) response. {The derivation of this result is beyond the scope of this book.}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "093f8ce9",
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    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "X_s = scaler.fit_transform(X_train)\n",
    "n = X_s.shape[0]\n",
    "lam_max = np.fabs(X_s.T.dot(Y_train - Y_train.mean())).max() / n\n",
    "param_grid = {'lasso__alpha': np.exp(np.linspace(0, np.log(0.01), 100))\n",
    "             * lam_max}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def35611",
   "metadata": {},
   "source": [
    "Note that we had to transform the data first, since the scale of the variables impacts the choice of $\\lambda$.\n",
    "We now perform cross-validation using this sequence of $\\lambda$ values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "38773381",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = KFold(10,\n",
    "           shuffle=True,\n",
    "           random_state=1)\n",
    "grid = GridSearchCV(standard_lasso,\n",
    "                    param_grid,\n",
    "                    cv=cv,\n",
    "                    scoring='neg_mean_absolute_error')\n",
    "grid.fit(X_train, Y_train);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53f2dc95",
   "metadata": {},
   "source": [
    "We extract the lasso model with best cross-validated mean absolute error, and evaluate its\n",
    "performance on `X_test` and `Y_test`, which were not used in\n",
    "cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "adc24a51",
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    "lines_to_next_cell": 0
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    {
     "data": {
      "text/plain": [
       "235.67548374780287"
      ]
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     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trained_lasso = grid.best_estimator_\n",
    "Yhat_test = trained_lasso.predict(X_test)\n",
    "np.fabs(Yhat_test - Y_test).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "252e9dc1",
   "metadata": {},
   "source": [
    "This is similar to the results we got for the linear model fit by least squares. However, these results can vary a lot for different train/test splits; we encourage the reader to try a different seed in code block 12 and rerun the subsequent code up to this point.\n",
    "\n",
    "### Specifying a Network: Classes and Inheritance\n",
    "To fit the neural network, we first set up a model structure\n",
    "that describes the network.\n",
    "Doing so requires us to define new classes specific to the model we wish to fit.\n",
    "Typically this is done in  `pytorch` by sub-classing a generic\n",
    "representation of a network, which is the approach we take here.\n",
    "Although this example is simple, we will go through the steps in some detail, since it will serve us well\n",
    "for the more complex examples to follow.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "415cf74b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class HittersModel(nn.Module):\n",
    "\n",
    "    def __init__(self, input_size):\n",
    "        super(HittersModel, self).__init__()\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.sequential = nn.Sequential(\n",
    "            nn.Linear(input_size, 50),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.4),\n",
    "            nn.Linear(50, 1))\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.flatten(x)\n",
    "        return torch.flatten(self.sequential(x))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca326fd5",
   "metadata": {},
   "source": [
    "The `class` statement identifies the code chunk as a\n",
    "declaration for a class `HittersModel`\n",
    "that inherits from the  base class `nn.Module`. This base\n",
    "class is ubiquitous in `torch` and represents the\n",
    "mappings in the neural networks.\n",
    "\n",
    "Indented beneath the `class` statement are the methods of this class:\n",
    "in this case `__init__` and `forward`.  The `__init__` method is\n",
    "called when an instance of the class is created as in the cell\n",
    "below. In the methods, `self` always refers to an instance of the\n",
    "class. In the `__init__` method, we have attached two objects to\n",
    "`self` as attributes: `flatten` and `sequential`. These are used in\n",
    "the `forward` method to describe the map that this module implements.\n",
    "\n",
    "There is one additional line in the `__init__` method, which\n",
    "is a call to\n",
    "`super()`. This function allows subclasses (i.e. `HittersModel`)\n",
    "to access methods of the class they inherit from. For example,\n",
    "the class `nn.Module` has its own `__init__` method, which is different from\n",
    "the `HittersModel.__init__()` method we’ve written above.\n",
    "Using `super()` allows us to call the method of the base class. For\n",
    "`torch` models, we will always be making this `super()` call as it is necessary\n",
    "for the model to be properly interpreted by `torch`.\n",
    "\n",
    "The object `nn.Module` has more methods than simply `__init__` and `forward`. These\n",
    "methods are directly accessible to `HittersModel` instances because of this inheritance.\n",
    "One such method we will see shortly is the `eval()` method, used\n",
    "to disable dropout for when we want to evaluate the model on test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "60469c96",
   "metadata": {},
   "outputs": [],
   "source": [
    "hit_model = HittersModel(X.shape[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "114db3d4",
   "metadata": {},
   "source": [
    "The object `self.sequential` is a composition of four maps. The\n",
    "first maps the 19 features of `Hitters` to 50 dimensions, introducing $50\\times 19+50$ parameters\n",
    "for the weights and *intercept*  of the map (often called the *bias*). This layer\n",
    "is then mapped to a ReLU layer followed by a 40% dropout layer, and finally a\n",
    "linear map down to 1 dimension, again with a bias. The total number of\n",
    "trainable parameters is therefore $50\\times 19+50+50+1=1051$.\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f5be00d",
   "metadata": {},
   "source": [
    "The package `torchinfo` provides a `summary()` function that neatly summarizes\n",
    "this information. We specify the size of the input and see the size\n",
    "of each tensor as it passes through layers of the network. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "84720ca7",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "===================================================================================================================\n",
       "Layer (type:depth-idx)                   Input Shape               Output Shape              Param #\n",
       "===================================================================================================================\n",
       "HittersModel                             [175, 19]                 [175]                     --\n",
       "├─Flatten: 1-1                           [175, 19]                 [175, 19]                 --\n",
       "├─Sequential: 1-2                        [175, 19]                 [175, 1]                  --\n",
       "│    └─Linear: 2-1                       [175, 19]                 [175, 50]                 1,000\n",
       "│    └─ReLU: 2-2                         [175, 50]                 [175, 50]                 --\n",
       "│    └─Dropout: 2-3                      [175, 50]                 [175, 50]                 --\n",
       "│    └─Linear: 2-4                       [175, 50]                 [175, 1]                  51\n",
       "===================================================================================================================\n",
       "Total params: 1,051\n",
       "Trainable params: 1,051\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (Units.MEGABYTES): 0.18\n",
       "===================================================================================================================\n",
       "Input size (MB): 0.01\n",
       "Forward/backward pass size (MB): 0.07\n",
       "Params size (MB): 0.00\n",
       "Estimated Total Size (MB): 0.09\n",
       "==================================================================================================================="
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(hit_model, \n",
    "        input_size=X_train.shape,\n",
    "        col_names=['input_size',\n",
    "                   'output_size',\n",
    "                   'num_params'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48833aad",
   "metadata": {},
   "source": [
    "We have truncated the end of the output slightly, here and in subsequent uses.\n",
    "\n",
    "We now need to transform our training data into a form accessible to `torch`.\n",
    "The basic\n",
    "datatype in `torch` is a `tensor`, which is very similar\n",
    "to an `ndarray` from early chapters.\n",
    "We also note here that `torch` typically\n",
    "works with 32-bit (*single precision*)\n",
    "rather than 64-bit (*double precision*) floating point numbers.\n",
    "We therefore convert our data to `np.float32` before\n",
    "forming the tensor.\n",
    "The $X$ and $Y$ tensors are then arranged into a `Dataset`\n",
    "recognized by `torch`\n",
    "using `TensorDataset()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6f8038ce",
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   "source": [
    "X_train_t = torch.tensor(X_train.astype(np.float32))\n",
    "Y_train_t = torch.tensor(Y_train.astype(np.float32))\n",
    "hit_train = TensorDataset(X_train_t, Y_train_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b0c854a",
   "metadata": {},
   "source": [
    "We do the same for the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ffe8314e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_t = torch.tensor(X_test.astype(np.float32))\n",
    "Y_test_t = torch.tensor(Y_test.astype(np.float32))\n",
    "hit_test = TensorDataset(X_test_t, Y_test_t)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8806a619",
   "metadata": {},
   "source": [
    "Finally, this dataset is passed to a `DataLoader()` which ultimately\n",
    "passes data into our network. While this may seem\n",
    "like a lot of overhead, this structure is helpful for more\n",
    "complex tasks where data may live on different machines,\n",
    "or where data must be passed to a GPU.\n",
    "We provide a helper function `SimpleDataModule()` in `ISLP` to make this task easier for\n",
    "standard usage.\n",
    "One of its arguments is `num_workers`, which indicates\n",
    "how many processes we will use\n",
    "for loading the data. For small\n",
    "data like `Hitters` this will have little effect, but\n",
    "it does provide an advantage for the `MNIST`  and `CIFAR100` examples below.\n",
    "The `torch` package will inspect the process running and determine a\n",
    "maximum number of workers. {This depends on the computing hardware and the number of cores available.} We’ve included a function\n",
    "`rec_num_workers()` to compute this so we know how many\n",
    "workers might be reasonable (here the max was 16)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "337f4f07",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_num_workers = rec_num_workers()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18928238",
   "metadata": {},
   "source": [
    "The general training setup in `pytorch_lightning` involves\n",
    "training, validation and test data. These are each\n",
    "represented by different data loaders. During each epoch,\n",
    "we run a training step to learn the model and a validation\n",
    "step to track the error. The test data is typically\n",
    "used at the end of training to evaluate the model.\n",
    "\n",
    "In this case, as we had split only into test and training,\n",
    "we’ll use the test data as validation data with the\n",
    "argument `validation=hit_test`. The\n",
    "`validation` argument can be a float between 0 and 1, an\n",
    "integer, or a\n",
    "`Dataset`. If a float (respectively, integer), it is interpreted\n",
    "as a percentage (respectively number) of the *training* observations to be used for validation.\n",
    "If it is a `Dataset`, it is passed directly to a data loader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c60b1e3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "hit_dm = SimpleDataModule(hit_train,\n",
    "                          hit_test,\n",
    "                          batch_size=32,\n",
    "                          num_workers=min(4, max_num_workers),\n",
    "                          validation=hit_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e5f527d",
   "metadata": {},
   "source": [
    "Next we must provide a `pytorch_lightning` module that controls\n",
    "the steps performed during the training process. We provide methods for our\n",
    "`SimpleModule()` that simply record the value\n",
    "of the loss function and any additional\n",
    "metrics at the end of each epoch. These operations\n",
    "are controlled by the methods `SimpleModule.[training/test/validation]_step()`, though\n",
    "we will not be modifying these in our examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "916cfd66",
   "metadata": {},
   "outputs": [],
   "source": [
    "hit_module = SimpleModule.regression(hit_model,\n",
    "                           metrics={'mae':MeanAbsoluteError()})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf7f2d1b",
   "metadata": {},
   "source": [
    " By using the `SimpleModule.regression()` method,  we indicate that we will use squared-error loss as in\n",
    "(10.23).\n",
    "We have also asked for mean absolute error to be tracked as well\n",
    "in the metrics that are logged.\n",
    "\n",
    "We log our results via `CSVLogger()`, which in this case stores the results in a CSV file within a directory `logs/hitters`. After the fitting is complete, this allows us to load the\n",
    "results as a `pd.DataFrame()` and visualize them below. There are\n",
    "several ways to log the results within `pytorch_lightning`, though\n",
    "we will not cover those here in detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "44a44fb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "hit_logger = CSVLogger('logs', name='hitters')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f37f19f0",
   "metadata": {},
   "source": [
    "Finally we are ready to train our model and log the results. We\n",
    "use the `Trainer()` object from `pytorch_lightning`\n",
    "to do this work. The argument `datamodule=hit_dm` tells the trainer\n",
    "how training/validation/test logs are produced,\n",
    "while the first argument `hit_module`\n",
    "specifies the network architecture\n",
    "as well as the training/validation/test steps.\n",
    "The `callbacks` argument allows for\n",
    "several tasks to be carried out at various\n",
    "points while training a model. Here\n",
    "our `ErrorTracker()` callback will enable\n",
    "us to compute validation error while training\n",
    "and, finally, the test error.\n",
    "We now fit the model for 50 epochs."
   ]
  },
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      "GPU available: True (mps), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name  | Type         | Params\n",
      "---------------------------------------\n",
      "0 | model | HittersModel | 1.1 K \n",
      "1 | loss  | MSELoss      | 0     \n",
      "---------------------------------------\n",
      "1.1 K     Trainable params\n",
      "0         Non-trainable params\n",
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       "version_minor": 0
      },
      "text/plain": [
       "Validation: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
      },
      "text/plain": [
       "Validation: |                                                                                                 …"
      ]
     },
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     "output_type": "display_data"
    },
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       "version_minor": 0
      },
      "text/plain": [
       "Validation: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "version_minor": 0
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      "text/plain": [
       "Validation: |                                                                                                 …"
      ]
     },
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     "output_type": "display_data"
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       "version_minor": 0
      },
      "text/plain": [
       "Validation: |                                                                                                 …"
      ]
     },
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     "output_type": "display_data"
    },
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       "version_minor": 0
      },
      "text/plain": [
       "Validation: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "version_minor": 0
      },
      "text/plain": [
       "Validation: |                                                                                                 …"
      ]
     },
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     "output_type": "display_data"
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      "text/plain": [
       "Validation: |                                                                                                 …"
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     },
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       "version_minor": 0
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      "text/plain": [
       "Validation: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
      },
      "text/plain": [
       "Validation: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a5f547dca0384fe7a83ca618efc44de4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=50` reached.\n"
     ]
    }
   ],
   "source": [
    "hit_trainer = Trainer(deterministic=True,\n",
    "                      max_epochs=50,\n",
    "                      log_every_n_steps=5,\n",
    "                      logger=hit_logger,\n",
    "                      callbacks=[ErrorTracker()])\n",
    "hit_trainer.fit(hit_module, datamodule=hit_dm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67e67f2d",
   "metadata": {},
   "source": [
    "At each step of SGD, the algorithm randomly selects 32 training observations for\n",
    "the computation of the gradient. Recall from Section 10.7\n",
    "that an epoch amounts to the number of SGD steps required to process $n$\n",
    "observations. Since the training set has\n",
    "$n=175$, and we specified a `batch_size` of 32 in the construction of  `hit_dm`, an epoch is $175/32=5.5$ SGD steps.\n",
    "\n",
    "After having fit the model, we can evaluate performance on our test\n",
    "data using the `test()` method of our trainer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e6012d1f",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0631b0ac5d9c44a0a1ef1e385124e6ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: |                                                                                                    …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "       Test metric             DataLoader 0\n",
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "        test_loss              107904.640625\n",
      "        test_mae             221.8314971923828\n",
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_loss': 107904.640625, 'test_mae': 221.8314971923828}]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hit_trainer.test(hit_module, datamodule=hit_dm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88fcc245",
   "metadata": {},
   "source": [
    "The results of the fit have been logged into a CSV file. We can find the\n",
    "results specific to this run in the `experiment.metrics_file_path`\n",
    "attribute of our logger. Note that each time the model is fit, the logger will output\n",
    "results into a new subdirectory of our directory `logs/hitters`.\n",
    "\n",
    "We now create a plot of the MAE (mean absolute error) as a function of\n",
    "the number of epochs.\n",
    "First we retrieve the logged summaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "48313d02",
   "metadata": {},
   "outputs": [],
   "source": [
    "hit_results = pd.read_csv(hit_logger.experiment.metrics_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3be9d88",
   "metadata": {},
   "source": [
    "Since we will produce similar plots in later examples, we write a\n",
    "simple generic function to produce this plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4f9ccf9b",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "def summary_plot(results,\n",
    "                 ax,\n",
    "                 col='loss',\n",
    "                 valid_legend='Validation',\n",
    "                 training_legend='Training',\n",
    "                 ylabel='Loss',\n",
    "                 fontsize=20):\n",
    "    for (column,\n",
    "         color,\n",
    "         label) in zip([f'train_{col}_epoch',\n",
    "                        f'valid_{col}'],\n",
    "                       ['black',\n",
    "                        'red'],\n",
    "                       [training_legend,\n",
    "                        valid_legend]):\n",
    "        results.plot(x='epoch',\n",
    "                     y=column,\n",
    "                     label=label,\n",
    "                     marker='o',\n",
    "                     color=color,\n",
    "                     ax=ax)\n",
    "    ax.set_xlabel('Epoch')\n",
    "    ax.set_ylabel(ylabel)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b080cf1",
   "metadata": {},
   "source": [
    "We now set up our axes, and use our function to produce the MAE plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3d77370a",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2b99u2hdoSYL68MMPQ9b54IMPTHWBhoz81Rj5aRsQLW2J917aFkPbcjilHLr9WhWiJ+mC0iqPnJwcEzrylyw850GVymWXXWbCnlYjaZVRJPq70JIavT388MPmWGlj0NGjR8c8Ltrmxqkms4USDgBIN716aXcELacPXa4lH7pcn7dEr8a1EebYsWNNMNCeHI7GjRubK2wNBVpl8etf/9o0anRLe1Ro75P+/fub6gOtrskfLJz32LZtm2mzodUIWrUS3rtCT8h6Va9X/9qDQ6t1wmkpiVZ96HvpyVZLZLTNiTZydapTCho4tJRDw0t4lUqjRo2i3pw2F7FoDxktidCGodrQVfdf27tot1YNEtrQVqu0tEGpHqP58+ebXjNOA1Y9LjpWilYZ6XFxettoVcqaNWtMFYxNBA4ASEcaKr75RrtRaAvI0/dadG4xbOSvVtEreK0uyd/eQhtz6lW4LterZW2cGD7QVCxauqDhQUsCtJHpr371K/njH/8Yss5//dd/yahRo0xvEh3cS8ONdovNTxuxam+M9u3bm2qCSF1ztV2Jnqz1BH755Zebtg7a/kEbZhaGVms441p4rU6dOibIaLjQcKBtNXTUVu02q8dO27GsXLnSVDdpcNPfx//8z/8EG91qQ1ctvdExU/S4OKFIu8rWr19f2rVrJzZlBPL3byqmDh06ZFpXHzx4MOGGRwCQKO0doVfg559/fsQGhkhvWqWibTI+/fRTVyUXqXbllVfKPffcI7fpiLUF+My6PYdSwgEAgIe0ZOf555831Rp+991335lutFpNYxuNRgEA8FgiVUmppO1NdPySZKCEAwAAWEfgAAAA1hE4ACBFaLOP4vRZJXAAQJI5Q2XbGh0T8Joz7LmbAcqiodEoACSZjhKp40DooEz6BR4+0Rfgp5INDRt79+41433kn1cmUQQOAEgyHX5ah+7WcQ3Ch+UG/EjDRqzZd30fOKZOnWpu3+hoeWdm5dOx351R0XSkOp0GOT8dKnfatGnBx9rPWcfyd2bQ02FqJ0yYEHWceQDwA53XQofpploFfqelcIUp2XCk9Kxcr149mThxovmj02KbF1980Uz8o7P/OTPn6VCsztTHKv80xzq8a7du3Uzq0uFtdVz/O+64wxwcHU8eAPxMq1IYaRTFhe+GNq9SpYqZ4ljH6tcSDh0rf7LOfBiBTsF70003ya5du4KT7Wjph06NrHWjegURiU7kk38yHx2WNSsri6HNAQBIUNoNba6lFTr7n04V3LZt2+BynQBHR0K75JJLzOyETktZtWrVKjN5Tf6Z/XTSIN35DRs2RH0vrXLRg+PcNGwAAAB7Ut7QYf369SZg6MQw2gZDZwps1qyZeU4nktGJb3SGPJ1SV0sudFpdnXLXmSAnfBph57E+F40Gl9GjR59VwgEAAIpo4NCpcteuXWuKYubNm2cafWpDUQ0dgwcPDq6nJRnaqlunD/7666/lggsuKPB7lilTxtwAAEBypLxKRdtZNGrUSFq1amWqOlq2bClTpkyJuG6bNm3M/ZYtW8y9Nhbds2dPyDrO48J23wEAAEUocITLy8sLadCZn5aEKC3pUFoVo1UyOiCJY+nSpabRilMtAwAAinmViral0DE36tevL4cPH5aZM2dKdna2vPXWW6baRB937dpVqlatatpwjBo1Sq655hpp0aKF+flOnTqZYHH77bfL448/btptPPjggzJ06FCqTAAA8JGUBg4tmdBxM3T8DO0tokFCw8YNN9wg27dvl7ffftt0idWeK9qos3fv3iZQOHQgkkWLFpmBv7S0o3z58qYNSP5xOwAAQOr5bhwOP/chBgAAaT4OBwAAKLoIHAAAwDoCBwAAsI7AAQAArCNwAAAA6wgcAADAOgIHAACwjsABAACsI3AAAADrCBwAAMA6AgcAALCOwAEAAKwjcAAAAOsIHAAAwDoCBwAAsI7AAQAArCNwAAAA6wgcAADAOgIHAACwjsABAACsI3AAAADrCBwAAMA6AgcAALCOwAEAAKwjcAAAAOsIHAAAwDoCBwAAsI7AAQAArCNwAAAA6wgcAADAOgIHAACwjsABAACsI3AAAADrCBwAAMA6AgcAALCOwAEAAKwjcAAAAOsIHAAAwDoCBwAAsI7AAQAArCNwAAAA6wgcAADAOgIHAACwjsABAACsI3AAAADrCBwAAKBoB46pU6dKixYtpGLFiubWtm1bWbx4cfD5Y8eOydChQ6Vq1apSoUIF6d27t+zZsyfkNbZt2ybdunWTcuXKSY0aNeS+++6TU6dOpWBvAACALwNHvXr1ZOLEibJmzRr55JNP5Prrr5cePXrIhg0bzPOjRo2S119/XebOnSsrVqyQXbt2Sa9evYI/n5uba8LGiRMn5IMPPpAXX3xRZsyYIQ8//HAK9woAAITLCAQCAfGRKlWqyJ///Gfp06ePVK9eXWbOnGn+rzZu3ChNmzaVVatWyZVXXmlKQ2666SYTRGrWrGnWmTZtmjzwwAOyb98+KV26tKv3PHTokFSqVEkOHjxoSloAAIB4eg71TRsOLa2YPXu2HD161FStaKnHyZMnpWPHjsF1mjRpIvXr1zeBQ+l98+bNg2FDde7c2ey8U0oSyfHjx806+W8AAMCelAeO9evXm/YZZcqUkbvuuksWLFggzZo1k5ycHFNCUbly5ZD1NVzoc0rv84cN53nnuWgmTJhg0phzy8rKsrJvAADAJ4HjoosukrVr18pHH30kQ4YMkf79+8uXX35p9T3Hjh1rin6c2/bt262+HwAAxV3JVG+AlmI0atTI/L9Vq1by8ccfy5QpU+SWW24xjUEPHDgQUsqhvVRq1apl/q/3q1evDnk9pxeLs04kWpqiNwAAUExKOMLl5eWZNhYaPkqVKiXLli0LPrdp0ybTDVbbeCi91yqZvXv3BtdZunSpabSi1TIAAMAfUlrCoVUbXbp0MQ1BDx8+bHqkZGdny1tvvWXaVgwcOFBGjx5teq5oiBg+fLgJGdpDRXXq1MkEi9tvv10ef/xx027jwQcfNGN3UIIBAIB/pDRwaMnEHXfcIbt37zYBQwcB07Bxww03mOcnTZokmZmZZsAvLfXQHijPPvts8OdLlCghixYtMm0/NIiUL1/etAF59NFHU7hXAADA9+NwpALjcAAAUEzG4QAAAEUXgQMAAFhH4AAAANYROAAAgHUEDgAAYB2BAwAAWEfgAAAARX8uFQAplpsr8u67Irt3i9SuLdKunY6ql+qtAlDEEDiA4mz+fJERI0R27Pj/y+rVE5kyRaRXr1RuGYAihioVoDiHjT59QsOG2rnz9HJ9HgA8QuAAims1ipZsRJrZwFk2cuTp9QDAAwQOoDjSNhvhJRvhoWP79tPrAYAHCBxAcaQNRL1cDwDiIHAAxZH2RvFyPQCIg8ABFEfa9VV7o2RkRH5el2dlnV4PADxA4ACKIx1nQ7u+qvDQ4TyePJnxOAB4hsABFEBubq5kZ2fLrFmzzL0+Tjs6zsa8eSJ164Yu15IPXc44HAA8xMBfQILmz58vI0aMkB35ennUq1dPpkyZIr3S7SSt29ujByONIr0wOm5ayggEInXEL14OHToklSpVkoMHD0rFihVTvTnwedjo06ePhP/ZZJyphpg3b176hQ4gnTA6ru9CmNtzKIGDwAGXtNqkQYMGISUb4aFDSzq2bt0qJbjaAuyNjht+2nLaHRXXqsD5qQ1hbs+htOEAXHr33Xejhg2l2X379u1mPc+uWLKzRWbNOn2fju1EAK8wOm7aT1FA4ABc2u1yECy368WkXxINGoi0by9y222n7/Wxj748gKRidNy0D2EEDhSPHhkeqO1yECy36xWFKxYgaRgdN+1DGIEDcRtJaruF9u3by2233Wbu9bEuL27atWtn2mg4DUTD6fKsrCyzXnG5YgGShtFx0z6EETgQt0dGeLuFnTt3muXFLXRoQ1Dt+qrCQ4fzePLkyYVrMJpmVyxA0jA6btqHMAIHItJqEx1rIlInJmfZyJEji131inZ51a6vdcMGy9KSD0+6xKbZFQuQNIyOm/YhjMABf/TISCMaKr755htZvny5zJw509xrV1hPxt9IsysWIKkYHTetQxgjjSL1PTLSkFabXHfddfauWLSBaKR2HPolos/75IoFSDpGx40cwiKNw6Fhw0chjMCB1PbISDa/D4nsXLFobxQNF/lDRyqvWNwcN78fWxQd+rmyEfjTVa80CWE60mhxd/DgQf1WN/c47dSpU4F69eoFMjIyzLEJv+nyrKwss17aeOWVQKBePT2F//+bPtbl6bCtWVmp2VY3xy2dji2AlJxDGdqcoc3j9lJR+T8maTlvSDoOieyHEgM3x02l2bHVxs7a/kirBLWUTrsyMxw9UDDMpZIAAkdiM6PqWBPa/TNtwoaeuHWUzmiNYJ12EVu3+q8I0u/HzWm8l0bHtkjN9gv4AIEjAQSOIn41qPOQ6NDg8SxfTr1wQY6bGz45tsz2m+YlbkjrcyiNRpG6HhnJkoKxLZIW0myeBLzsgeSD3kzxxpbR0KFjy/To0SO9AnUyMCU8PMA4HCj6kjy2RdKGg7c9wZuXPZCS2Jsp2tw/jC1TQMztA49QpUKVSvFpixBvbAsP2hkkrcg+gUawBS5tyc2VH2rWlHO+/z7ilUmeiByrUkXKlSuXlGNb2PYZx48fNwEwHh3MrW/fvpa3NE3Q/gkenkMp4UgBZl8tmqPxJW04+AQmeCtMaYtu5Yh84SI/5/HIjAzJnTTJFyMdxpv7Z/PmzZ6OLVMs/o6Z2wde8rY3bnpK5jgcr7zyihnfIv+YFvpYl+en41ssX748MHPmTHOfVuNdROCL/bE8toXuV6QxS8Jvul4h3yh0H6LcVowfH3EcFV2mt/DPXLT9uVkksC3stb89szy4PykeN8QZNybaMdf91ee9GlvG7d9x2ps509VnzayHYuugy3MogSOJgUO/jNycAIral5mv9kdPJnqC1C9Ivfcw+GiYchM4dL1knASGVqkS8wQc7+Saf38yRQLXigRuPXOfGWl/PDi2BQ2mbsPe+DMhLPzv0G0IS+TvuDD74xsuw61ZD8XWQQKHvwKHmyswPQHMnTu3UFelfpPIl3O681sJx7WF3Jak7Y8HwTSRsBfpffRvL+R9ooQnt3/Hup6vgnZB6X5ryVVGRuTPmS7Xkqx0C1LwFIHDZ4HD7Zd39erVC3VV6ieJfDkXBUkbDt7FSeBI1aohpRAFKW1J5vD2hQ2miYajU8ePBz6bNCnw/rBh5l4fuxmmPdGSlCIRtHVb9bMW/nlzlqXTvsAKAofPAofbK7BkXlHaluwrZD9wTpyFKbL34iSwfvx4T459MvbHi2CaUDiKNe+Lc1wjXclnZARWjhzp6rhWKWR1lu/4aW4f+A6BI01LOJLSBiBJktamIQHJqFN3VWRv+STgZemE7f3xKpi6CkexAoXeV60avZoqIyPwY/XqrkqOimTQttj+CemNwOHTNhyxTgCxqlPS8YvKbyUcyaxTT1pjwRgnAS9LJ2zuj5fBVPepft26IQ1cz3N+x05VlJtGkDFufapVi/l3HKt0Ix0vHIB4CBw+7qUS7QQwZ86ctJwSPtrJyE9T3BenxqspKW1JQg8TV8H0lVcCeWGhwjzW/XXb4yLO7aORI2P+HWv7DT8F7aIo7Xv/FLH9JXCk0Tgc+U8ASWsDkKRSAz/sT3FrvOqXaiS3pUeeBdM47S8CI0d6Ejg0uMT6O05kf7z43fj9ZOS1ItH7p4jtL4HDp4HDzReEV1eltr+ICjOuiK2r7HSo2vGbwn5O3H4OYr1PoYNpvOoSDRzVqxcuaIR1AS3s/rg+kcSpNvP7ychLxa2k8pU02d+0CByPPfZYoHXr1oEKFSqY9gs9evQIbNy4MWSda6+99qyD/etf/zpknW+//TbQtWvXQNmyZc3rjBkzJnDy5EnfBo5knQRsfhElWmqQyqswPzZe9YvCfk4SGV8m3vsUKpi6rS7R0BFrTAltNOpRF9BY++P6RBKjR026nIy8UtxKKk+l0f6mReDo3LlzYPr06YEvvvgisHbtWhMa6tevHzhy5EhI4Bg0aFBg9+7dwVv+ndKDfckllwQ6duwY+OyzzwJvvvlmoFq1aoGxY8emdeAojGR8EXldamAzkPi2hCPFrf69+JwUpveVpyNzuh2CW6tV4gUKD7uARtof1yeSuXOjVhHlZWQEflW1atRRYP0U+Iv837Ely9Nof9MicITbu3ev2egVK1aEBI4RI0ZE/RkNGJmZmYGcnJzgsqlTpwYqVqwYOJ5/MJ90CxwFPBklKxUnXGqQwmLhlDRejff7izUWhNcibItXn5PCji/j2bFPZAhuN4HCYhh0cyLR0KBdcKPtR96Z+Wx6R5jnZlvYPDdFpdqluJVUzkyj/U3LwLF582az0evXrw8JHFpiUbVq1cDFF18c+M1vfhM4evRo8PmHHnoo0LJly5DX+fe//21e59NPP434PseOHTMHxrlt377dX4GjECejZKXihN4nmcXCUU4UCbURcHOyibVOvN9fvMaNLto8uBZlW7waGMyr8WUKfZWW6BDcKSxdcnMi0ZIKNwFKg0du2LLcMzcNHSPP9Kjx7O8rhdLpir+47e/BdAscubm5gW7dugWuuuqqkOV//etfA0uWLAmsW7cu8I9//CNQt27dwM033xx8XqtbOnXqFPIzGkh057X0I5Jx48ZF/MX5InC4PBl5lYpdndRiXCHHLTVwWSwc8zXcngzinOhdtRGI1a3SzfvE+/3NmRO/caNu05ku0oW6Ko2xLXn5roILc/UU73Pg9ubJBHBpMgS3mxPJrQkEjkjLc8+UgNSsVs27v68U81M3+2Q4lUb7m3aB46677gqcd955prQhlmXLlpkd27JlS4EDh29LONy0tI8zUVIiqdhVUauL0okSYXXI+thcPcU5uTrFwplxZiMNJvh4pQqFLTXQsBHlitF8sXswUqXbnhLXRfmCcX1VGuezlP/YF/bqKVbpUf7HcX/HLkr24gbkNBiC282J5L8L26PGown8CrJvyegZly7DBhSX/T2YToFj6NCh5g9Qq0Li0QalumNa6lHQKhXftuFIcCroWA3S9MMYqzGZq1lpXZzEV913X2BniRIhz+tjXe52fx6MUw9trn5jnYw8CGr63NGqVc8KG/lDx9EqVTwZqdLNTX9nhbqqcXnsIwWbglw9RSs9cgaz6xXld6zL45WEOZ+1SKOI6uNEupH6RdxBAGfPNn9HsT6PhfkcJVKKlcg+JaOtSKq72SfbK2mwv2kROPLy8kzYqFOnTuBf//qXq5957733zI59/vnnIY1G9+zZE1INo41GtSQjrQKH25b2UabYdv649RbrC945CUS76tQvPB0OOrxqIVoXwvBi3bwEB1qKVw+t7Q1inoz0eTfHLcaV3Km3305KkPDqyjQ462m0K0qXn6W+EUoiCnr1FG1bNHzmxvgdr7r33riBUcNg7xifaT99+XpxItHjp5/9WMfNTyUcye6iWxR63RSl/U2LwDFkyJBApUqVAtnZ2SHdXn/44QfzvFabPProo4FPPvkksHXr1sCrr74aaNiwYeCaa645q1usVqto11ot+dCxOHzdLTbaFZjLq9IVZ6a+zoxSlaFf8LGqBpwGgzfHKFlw22gtZiBxWSwcqx56R2ZmIK9u3djvoyUPbrYpxpXchgcfTF6gqF496j67rerQL56YV5QJfJasXj2dKX2Ktb+JfE6inXwHVa2a/C/hwjYujnEicdpiRfobzd87JdpxNRcC9eqZEiDbbQDSabwIP34GioK0CBzRPqA6Nofatm2bCRc6GVKZMmUCjRo1Ctx3331n7dQ333wT6NKlixn4S3u03Hvvvf4d+MtN1UCMlvbOl0i0sNDnTJVGrC+iI2euFmNdPf2PhyfXqPuT5JspxYji7WQEjjNVO3pFH+vYu2nMqfN1xLyidNrPuOi1YfXqyaP5S9w0kFwe4/ebkp5kHvU2i9b25WbnmMRoJOt576w422q7JMVXLH8G0klaBA6/SFrgcNOwMU5Ley2d8KKodU+MdXV5jlcn2VgDLXl4Mv8uzv7EOyHpc9tcvMaP1aoVaqRKbaugV4PRrlx1eYkSJWJelerPJzRwVCp7bbitJvTgZkJjMiTydxxrnRgS6gUWp5Gs295ZBT0xptN4Een0GUgnBA6/BY5EGjbGaGk/6x//iHti9OoLPC/WydXtLdZAS27bXrhseBqv1CDWF55+wWsX3dx4xfZuTuIxfn9urlzzn1QKPRtpqntteDDkeNTSurCbqRazzc3fsT7v9m89zoB4rkonXJRMxOudVZgTo9vPdJEp4fD6M1AEEDj8FjgS7IES7Uvks0mTPDtJF7p0wrmad/MHFWl/3AzW5PzhxlhHR2TUL7ZYpQZuvvCiNbb9NrxhYiFGqnR7NagDNkW7KvVylFfr3A7IFSPI5XlQZebHKiITuOOUKljvoeBBDy+nNCZuT6QicnL19DOwvGiEMAKH3wJHAj1QYsn9xz9cvU6eF7NmxrtC9mKgJTevcWYd0/sl/z6GVVPE6wrs5gsvUtdL7bHjVdfLROq7o12Vpl2dudvPSbTP2pw58bssa/hNxgnNdhVRhL8dX7SxifNZitsTSbvJFxVefgZm+uCiwAMEjnQv4Sjs6zhfXpG+zBJoUFigYbwTLbJ38RqRxvvY4Yz34fEAOTa/4L0YPTCdRiBM+HMS7bPmZlC2dLu69UNxuxcXQm56Ivmw+qDAf+del3C8kv4NSwkcqQwcBa0+cPNH6faPO15jMi+HgfYinbuoy47WDdgJE+kwQI5X4ShdRiD09HPiZth527yoBkzkZGSbFxdCiQ5YePy4qRp+f9gwc6+Pk61Qg5R5VBUcyF+VmOYNSwkcqQocbubZKOxJPk4VQ/B1CjJjqU+HgY7bI+PMvvl9gByHF+EoXQKWp/xQ9JxANWChemd52asj2nHz4kIogVKSmCMTx9tWj3gySFlhPwMZ7udV8lvJUCQEjlQEDrddpbw4yXv1On74Ao8h7dorJMCLcJQuASvdeDJnS2F7Z3n1mY5XZF/YCyGXJRxf3HJL/HYelqsXEh6krLDVybHWWe5RNbsPEDiSHTgSae3t1Une52HBC8Wyjz9SynVxe0FHmfSqetXdzrgrsi/MBYybAQvr1g3szMyM2fD3Ox1V2HL1QkIXMG7CT2FGGp3pTUcCPyBwJDtwFKG06idFuYQD/pO0OUG8bEPlVZfXwlzAxNmffw8YUPjedR6EMLcXMCudIQEshp9AETpnuD2HZgq8sXu3t+vBaNeundSrV08yMjIiPq/Ls7KyzHpAYeTm5sqIESP0Iuys55xlI0eONOsVWq9eIvPmidStG7q8Xr3Ty/X5wnr3XZEdO6I/r/u0ffvp9VSJEiLXXSfSt+/pe33s0f7srlDB1ctkuN3WAqpdu3bw/3ryu1ZEbj1z75wM9f7yl146/Z6RtkONHKkfmEJti+h3lh6fKN9tZnlW1un1iggCh1fyfZA9WQ9GiRIlZMqUKeb/4aHDeTx58mSzHlAY7777ruyIcYLW0LF9+3aznif0JP3NNyLLl4vMnHn6futWb8JGKi6CYuxPuQsu8OY93G6rhoHsbJFZs07fnwkHzgWMHuFvRCRbRGadudfHurx39epyzr590V/bo/Aj+p115rstEPbdFnw8eXJiwc/nCBxeKYZpNVl69eol8+bNk7phV0/6xaHL9XmgsHa7PJm5Xc+VwpQq+PEiKMr+NL/7btlVooTkRfmxaMsLtK3z54s0aCDSvr3IbbedvtfH8+ebC5O5ffvKXBEJK4sxj3X5H9u0cbctXnwOevWSD8eMkd2ZoafiXZmZZnkwfEYJUGknaZU8xamXis162WKMHhmwqci1F0pm41QX4o1GeqxChcJvqzNkQHjbkLCuqDHHMUpkJOZCesXNGENpMDAYjUb9NA6Hz8a2AFBERnBNs4ugmCMGF3Zb3QyK6DZMxJhM0KugdspFF12dMNLLnju2LtoIHH4baRSA76XlCK4eXQQlqwQx5kijhblg83LI8VgTVyapi27mmcnvvOq5U6jRVeMgcPhtLhUAaaFIjuAa5yLI5snI620t7MSWrqtLLJdWz4zTRVerVryq2rHd3dvtOTRD/5Fi7tChQ1KpUiU5ePCgVKxYMdWbAyDFtOur9kbRBqLalVJ7NxTVnlDz58+XPn36nNUd2OkF5jTM9vsxWTt5slw6alTc9U5UqiSlDx06fboOp/usjf+1d43umzbO1N4o2kBUG6xqo3+P9jk7O1vaa4PWKG4904MmLu0RpI10o9DfW4MGDaL2wNLfszbA37p1a4F/n27PoSUL9OoAUITpF+912suimI89oicjHXskLy9PRo0aFXLS0pOUdln3Sy+xjdWrS9UzvU0yo/SE0a3fNmCAXP3UU6fDRf79jtQV1el1Y0G7M110d+7cGfH45yTYcydaIEyku7ftzzzdYgGgmHJ7Mvrv//7vs9bTE6WWjGgJiR/UqltXRkTpZus8Hikip3r2tD/omgdjDL0rIj9UrepqqAX9HWgphpaY3HbbbeZeH+vylHT3joLAAQDFVGFOMp6PvlpIekX/cb168t8ahsKe06ikyz9xRiW2PeiaB2MMzXnlFSn3v/97ekF46MhXGjP/1VdN8IsWCDdv3pzwKKy20IaDNhwAiql47QjcWr58uS+qoJz2KJmBgFytJ1ENVSLynpZyZGT4dqDA3FjtY7QEacSI0GHqtWRj8mTJ7dEjbvsMJ8xEq7pJZhsOSjgAoJiKN1eRW8kojk+kxKB2vXqyQkRmi5j7OllZvg0b+dsM9e3b19yHnPhjlMa4qRLT5wcNGnT6fcLmj3HeJVnTQ9BoFACKKacdgZYKaOjIfwUc/jjVxfFuaajo0aOHr3vUJCxK41W3Qa9x48bywZgxUv/JJ6VOvuovHW5+2+jRcmWSghglHABQjMVsRzBnTtJna9bqBa3qmTVrlrkvSPuQmCUGRUhtl0Gv+ebNcuUTT0jtsGNZOy/PLDfVNklAGw7acABA1HYETrsIFV4CorysqtD30m66fu5+6ye5Z8bYiNU+o37durJV/x+t6iV87BGL51ACB4EDABIOAlqyoXX/XoYNNwOQ+YmbwdBsD5g2P04gzH7kEblm3Lj4L6RtQwrY8Nf1ObRQ45kWEQxtDgCx2Zxrxc1EZn6bOM/NcPDJGjL+lVjD8esQ8W6GSNf1CoihzRNACQcA+L97rt+638YqjVHJLLHJjVaSkp0t4qbrcxJKOOilAgBIKT+NhunFcPD6vPM42jojR440vWm8ql6JOhy/NujVNho7d8aeP8bDhr/R0EsFAJAWvS380P3W7dgXbucvsU4DzZkh1GONWOrVpHSxEDgAAL4egMxG99uC8rKUZXeySmy06sYH88cQOAAAKRVvIrNkjoaZzFKW2skssfHB/DE0GqXRKAAUm+63yRj7Ihnzl/gJ43AkgMABAP5ge9yKZA2GppI1YFqq0UsFAJB2ova2SLJ4o55qYIj0fP7SGDfrFCeUcFDCAQAowKinfhhp1A+oUkkAgQMAkL+NRrRurUWt/UUyz6H0UgEAIIFxNpI2hkYRQ+AAACANRz1NNwQOAADScNTTIh04Vq9ebeq3ojl+/LjMmTPHi+0CACDp0mnU0yIdONq2bSvff/998LE2Dvn3v/8dfHzgwAHp27evt1sIAECSpNOop0U6cIR3aIk2Ex4AAOnKGWfDGTHUoSUfRWnArmTzfOCvaMVQAACkCw0VOn18UR9DI5kYaRQAAB+PelpsA8eXX34pOTk5weqTjRs3ypEjR8zj7777zvstBAAAaS+hkUYzMzNNlUm02e90ud7H6sniR4w0CgCAj0Ya1aFctVeK3offnOX5e63EM2HCBLn88svl3HPPlRo1akjPnj1l06ZNIescO3ZMhg4dKlWrVpUKFSpI7969Zc+ePSHrbNu2Tbp16yblypUzr3PffffJqVOnEtk1AADglyqV8847L+46X3zxhevXW7FihQkTGjo0IPz2t7+VTp06mWqb8uXLm3VGjRolb7zxhsydO9ckqGHDhpnGPO+//755XktTNGzUqlVLPvjgA9O454477pBSpUrJY489lsjuAQAASzyZvO3w4cMya9Ysee6552TNmjUFrlLZt2+fKaHQIHLNNdeY4pnq1avLzJkzzcx9StuMNG3aVFatWiVXXnmlLF68WG666SbZtWuX1KxZ06wzbdo0eeCBB8zrlS5dOu77UqUCAICPJ29buXKl9O/f33QXeuKJJ+T666+XDz/8sMCvpxurqlSpYu41vJw8eVI6duwYXKdJkyZSv359EziU3jdv3jwYNlTnzp3NAdiwYUPUEVH1+fw3AADgo14q2kNlxowZ8vzzz5sT9c9//nNzAl+4cKE0a9aswBuSl5cnI0eOlKuuukouueSS4HtpCUXlypVD1tVw4fSU0fv8YcN53nkuWtuR8ePHF3hbAQBAYhIq4ejevbtcdNFFsm7dOjO0q1ZjPP300+IFbcuh7T9mz54tto0dO9aUpjg3nWoYAAD4pIRD20vcc889MmTIEGncuLFnG6ENQRctWmSqaHToWIc2BD1x4oSZoyV/KYf2UtHnnHV0Urn8nF4szjrhypQpY24AAMCHJRzvvfeeaSDaqlUradOmjfzlL38p1GBf2l5Vw8aCBQvknXfekfPPPz/keX0f7W2ybNmy4DLtNqvdYHUiOaX369evl7179wbXWbp0qWm4UpgqHgAAkOJeKkePHpWXX35ZXnjhheCU9U8++aTceeedZkwNt+6++27TA+XVV181VTUObe1atmxZ838tTXnzzTdNuxENEcOHDzfLtQus0ve+9NJLpU6dOvL444+bdhu33367/OpXv3LdLZZeKgAAFIzbc2ihu8VqiYM2IP373/9uqj5uuOEGee211wo10dv06dNlwIABwYG/7r33XtPtVhunag+UZ599NqS65NtvvzXBJDs724zfoT1nJk6cKCVLuqsxInAAAODzwOHQkgZth6GlHlpikU4IHAAA2D2HJtRoVKtM4tEhyAEAAAocOLQdhQ5v/tOf/jTiBG6xqkkAAEDxlVDg0HYS2pZCJ2n75S9/Kb/4xS+Co4ICAAB40i32mWeeMZOj3X///fL6669LVlaWGWn0rbfeilriAQAAUKhGo9o7RKtZ/va3v5nZXnXuEp1CPt3QaBQAAB9P3paZmWnabGhmKegMsQAAoOhLOHDoWBjajkPH27jwwgvNKJ864qiO/pmOpRsAAMBnjUZ1ZFCdXE3bbmgXWQ0e1apVs7d1AACg+LXh0CqU+vXrm26xsbq/zp8/X9IJbTgAAPDRwF933HEH42wAAAD7A38BAAAkqlC9VAAAANwgcAAAAOsIHAAAwDoCBwAAsI7AAQAArCNwAAAA6wgcAADAOgIHAACwjsABAACsI3AAAADrCBwAAMA6AgcAALCOwAEAAKwjcAAAAOsIHAAAwDoCBwAAsI7AAQAArCNwAAAA6wgcAADAOgIHAACwjsABAACsI3AAAADrCBwAAMA6AgcAALCOwAEAAKwjcAAAAOsIHAAAwDoCBwAAsI7AAQAArCNwAAAA6wgcAADAOgIHAACwjsABAACsI3AAAADrCBwAAMA6AgcAACjagWPlypXSvXt3qVOnjmRkZMjChQtDnh8wYIBZnv924403hqyzf/9+6devn1SsWFEqV64sAwcOlCNHjiR5TwAAgG8Dx9GjR6Vly5byzDPPRF1HA8bu3buDt1mzZoU8r2Fjw4YNsnTpUlm0aJEJMYMHD07C1gMAALdKSgp16dLF3GIpU6aM1KpVK+JzX331lSxZskQ+/vhjad26tVn29NNPS9euXeWJJ54wJSeRHD9+3Nwchw4dKtR+AACANG/DkZ2dLTVq1JCLLrpIhgwZIt9//33wuVWrVplqFCdsqI4dO0pmZqZ89NFHUV9zwoQJUqlSpeAtKyvL+n4AAFCc+TpwaHXK3/72N1m2bJn86U9/khUrVpgSkdzcXPN8Tk6OCSP5lSxZUqpUqWKei2bs2LFy8ODB4G379u3W9wUAgOIspVUq8dx6663B/zdv3lxatGghF1xwgSn16NChQ4FfV6tp9AYAAJLD1yUc4Ro2bCjVqlWTLVu2mMfatmPv3r0h65w6dcr0XInW7gMAACRfWgWOHTt2mDYctWvXNo/btm0rBw4ckDVr1gTXeeeddyQvL0/atGmTwi0FAAC+qVLR8TKc0gq1detWWbt2rWmDobfx48dL7969TWnF119/Lffff780atRIOnfubNZv2rSpaecxaNAgmTZtmpw8eVKGDRtmqmKi9VABAADJlxEIBAKSItoWo3379mct79+/v0ydOlV69uwpn332mSnF0ADRqVMn+f3vfy81a9YMrqvVJxoyXn/9ddM7RQPKU089JRUqVHC9HdotVnuraANSHUAMAAB4ew5NaeDwCwIHAAB2z6Fp1YYDAACkJwIHAACwjsABAACsI3AAAADrCBwAAMA6AgcAALCOwAEAAKwjcAAAAOsIHAAAwDoCBwAAsI7AAQAArCNwAAAA6wgcAADAOgIHAACwjsABAACsI3AAAADrCBwAAMA6AgcAALCOwAEAAKwjcAAAAOsIHAAAwDoCBwAAsI7AAQAArCNwAAAA6wgcAADAOgIHAACwjsABAACsI3AAAADrCBwAAMA6AgcAALCOwAEAAKwjcAAAAOsIHAAAwDoCBwAAsI7AAQAArCNwAAAA6wgcAADAOgIHAACwjsABAACsI3AAAADrCBwAAMA6AgcAALCOwAEAAKwjcAAAAOsIHAAAwDoCBwAAsI7AAQAAinbgWLlypXTv3l3q1KkjGRkZsnDhwpDnA4GAPPzww1K7dm0pW7asdOzYUTZv3hyyzv79+6Vfv35SsWJFqVy5sgwcOFCOHDmS5D0BAAC+DRxHjx6Vli1byjPPPBPx+ccff1yeeuopmTZtmnz00UdSvnx56dy5sxw7diy4joaNDRs2yNKlS2XRokUmxAwePDiJewEAAOLJCGgxgg9oCceCBQukZ8+e5rFulpZ83HvvvTJmzBiz7ODBg1KzZk2ZMWOG3HrrrfLVV19Js2bN5OOPP5bWrVubdZYsWSJdu3aVHTt2mJ+P5Pjx4+bmOHTokGRlZZnX15ISAADgjp5DK1WqFPcc6ts2HFu3bpWcnBxTjeLQHWrTpo2sWrXKPNZ7rUZxwobS9TMzM02JSDQTJkwwr+XcNGwAAAB7fBs4NGwoLdHITx87z+l9jRo1Qp4vWbKkVKlSJbhOJGPHjjVJzLlt377dyj4AAIDTSkoxVKZMGXMDAADFvISjVq1a5n7Pnj0hy/Wx85ze7927N+T5U6dOmZ4rzjoAACD1fBs4zj//fBMali1bFtIwRdtmtG3b1jzW+wMHDsiaNWuC67zzzjuSl5dn2noAAAB/SGmVio6XsWXLlpCGomvXrjVtMOrXry8jR46UP/zhD9K4cWMTQB566CHT88TpydK0aVO58cYbZdCgQabr7MmTJ2XYsGGmB0u0HioAAKCYBY5PPvlE2rdvH3w8evRoc9+/f3/T9fX+++83Y3XouBpaknH11Vebbq/nnHNO8GdeeuklEzI6dOhgeqf07t3bjN0BAAD8wzfjcKRDH2IAAFDExuEAAABFB4EDAABYR+AAAADWETgAAIB1BA4AAGAdgQMAAFhH4AAAANYROAAAgHUEDgAAYB2BAwAAWEfgAAAA1hE4AACAdQQOAABgHYEDAABYR+AAAADWETgAAIB1BA4AAGAdgQMAAFhH4AAAANYROAAAgHUEDgAAYB2BAwAAWEfgAAAA1hE4AACAdQQOAABgHYEDAABYR+AAAADWETgAAIB1BA4AAGAdgQMAAFhH4AAAANYROAAAgHUEDgAAYB2BAwAAWEfgAAAA1hE4AACAdQQOAABgHYEDAABYR+AAAADWETgAAIB1BA4AAGAdgQMAAFhH4AAAANYROAAAgHUEDgAAYB2BAwAAFO/A8cgjj0hGRkbIrUmTJsHnjx07JkOHDpWqVatKhQoVpHfv3rJnz56UbjMAAEizwKEuvvhi2b17d/D23nvvBZ8bNWqUvP766zJ37lxZsWKF7Nq1S3r16pXS7QUAAGcrKT5XsmRJqVWr1lnLDx48KM8//7zMnDlTrr/+erNs+vTp0rRpU/nwww/lyiuvTMHWAgCAtCzh2Lx5s9SpU0caNmwo/fr1k23btpnla9askZMnT0rHjh2D62p1S/369WXVqlUxX/P48eNy6NChkBsAACimgaNNmzYyY8YMWbJkiUydOlW2bt0q7dq1k8OHD0tOTo6ULl1aKleuHPIzNWvWNM/FMmHCBKlUqVLwlpWVZXlPAAAo3nxdpdKlS5fg/1u0aGECyHnnnSdz5syRsmXLFvh1x44dK6NHjw4+1hIOQgcAAMW0hCOclmZceOGFsmXLFtOu48SJE3LgwIGQdbSXSqQ2H/mVKVNGKlasGHIDAAD2pFXgOHLkiHz99ddSu3ZtadWqlZQqVUqWLVsWfH7Tpk2mjUfbtm1Tup0AACCNqlTGjBkj3bt3N9Uo2uV13LhxUqJECenbt69pezFw4EBTNVKlShVTSjF8+HATNuihAgCAv/g6cOzYscOEi++//16qV68uV199tenyqv9XkyZNkszMTDPgl/Y86dy5szz77LOp3mwAABAmIxAIBKSY00ajWmKiY3vQngMAAO/PoWnVhgMAAKQnAgcAALCOwAEAAKwjcAAAAOsIHAAAwDoCBwAAsI7AAQAArCNwAAAA6wgcAADAOgIHAACwjsABAACsI3AAAADrCBwAAMA6AgcAALCOwAEAAKwjcAAAAOsIHAAAwDoCBwAAsI7AAQAArCNwAAAA6wgcAADAOgIHAACwjsABAACsI3AAAADrCBwAAMA6AgcAALCOwAEAAKwjcAAAAOsIHAAAwDoCBwAAsI7AAQAArCNwAAAA6wgcAADAOgIHAACwjsABAACsI3AAAADrCBwAAMA6AgcAALCOwAEAAKwjcAAAAOsIHAAAwDoCBwAAsI7AAQAArCNwAAAA6wgcAADAOgIHAACwjsABAACsKzKB45lnnpEGDRrIOeecI23atJHVq1enepMAAEBRChwvv/yyjB49WsaNGyeffvqptGzZUjp37ix79+5N9aYBAICiEjiefPJJGTRokPzyl7+UZs2aybRp06RcuXLywgsvpHrTAACAiJSUNHfixAlZs2aNjB07NrgsMzNTOnbsKKtWrYr4M8ePHzc3x8GDB839oUOHkrDFAAAUHc65MxAIFO3A8d1330lubq7UrFkzZLk+3rhxY8SfmTBhgowfP/6s5VlZWda2EwCAouzw4cNSqVKlohs4CkJLQ7TNhyMvL0/2798vVatWlYyMDM8SnwaY7du3S8WKFcXP2FY72FY70mlb02172VY7ivq2BgIBEzbq1KkTc720DxzVqlWTEiVKyJ49e0KW6+NatWpF/JkyZcqYW36VK1e2sn36C/P7B8zBttrBttqRTtuabtvLttpRsQhva6ySjSLTaLR06dLSqlUrWbZsWUiJhT5u27ZtSrcNAAAUkRIOpdUj/fv3l9atW8sVV1whkydPlqNHj5peKwAAIPWKROC45ZZbZN++ffLwww9LTk6OXHrppbJkyZKzGpImk1bZ6Lgg4VU3fsS22sG22pFO25pu28u22sG2npYRiNePBQAAoJDSvg0HAADwPwIHAACwjsABAACsI3AAAADrCBwWPPPMM9KgQQM555xzpE2bNrJ69Wrxo0ceecSMrJr/1qRJE/GDlStXSvfu3c3IdbpdCxcuDHle2zprr6TatWtL2bJlzdw5mzdv9uW2Dhgw4KzjfOONN6ZkW3VY/8svv1zOPfdcqVGjhvTs2VM2bdoUss6xY8dk6NChZuTdChUqSO/evc8aWM8v23rdddeddWzvuuuupG/r1KlTpUWLFsHBknQMoMWLF/vumLrZVr8c00gmTpxotmfkyJG+PLbxtvU6nxzbeN/9to4pgcNjL7/8shkXRLsVffrpp9KyZUvp3Lmz7N27V/zo4osvlt27dwdv7733nviBjqOix07DWySPP/64PPXUU2Zm4I8++kjKly9vjrP+ofhtW5UGjPzHedasWZIKK1asMF8kH374oSxdulROnjwpnTp1MvvgGDVqlLz++usyd+5cs/6uXbukV69evtxWpTNF5z+2+tlItnr16pkTjE4k+cknn8j1118vPXr0kA0bNvjqmLrZVr8c03Aff/yx/PWvfzVhKT8/Hdt42+qnYxvru9/aMdVusfDOFVdcERg6dGjwcW5ubqBOnTqBCRMmBPxm3LhxgZYtWwb8Tj+mCxYsCD7Oy8sL1KpVK/DnP/85uOzAgQOBMmXKBGbNmhXw07aq/v37B3r06BHwo71795ptXrFiRfA4lipVKjB37tzgOl999ZVZZ9WqVb7aVnXttdcGRowYEfCjn/zkJ4HnnnvO18c0fFv9ekwPHz4caNy4cWDp0qUh2+fHYxttW/10bGN999s8ppRweOjEiRPmqkGL9x2ZmZnm8apVq8SPtBpCqwIaNmwo/fr1k23btonfbd261Qzwlv846zj+Wn3l1+OcnZ1tqgUuuugiGTJkiHz//ffiBwcPHjT3VapUMff6+dWShPzHVota69evn/JjG76tjpdeesnMqXTJJZeYiRl/+OEHSSWdvXr27NmmJEarK/x8TMO31a/HVEu6unXrFnIMlR+PbbRt9duxjfbdb/OYFomRRv3iu+++M3/A4SOc6uONGzeK3+gJesaMGeYkqEVq48ePl3bt2skXX3xh6s39SsOGinScnef8RKtTtDjy/PPPl6+//lp++9vfSpcuXcwfr048mCo655DWL1911VXmy0/p8dP5icInM0z1sY20req2226T8847z3xxrlu3Th544AHTzmP+/PlJ38b169ebk7ZW62m994IFC6RZs2aydu1a3x3TaNvqt2OqNBBp9bRWU4Tz2+c11rb66djG+u63eUwJHMWYnvQcWteoH0L9Y5gzZ44MHDgwpdtWlNx6663B/zdv3twc6wsuuMCUenTo0CFl26VXYvoF45d2OwXZ1sGDB4ccW21ErMdUg50e42TSL28NF1oSM2/ePDO/k9Z/+1G0bdXQ4adjqlOkjxgxwrTh0Ub4fuZmWwf75NjG+u7XRvi2UKXiIS0m0yvW8Na8+rhWrVrid5poL7zwQtmyZYv4mXMs0/U4axGmflZSeZyHDRsmixYtkuXLl5tGhA49flo1eODAAd8c22jbGol+capUHFu9KmzUqJGZvVp72GhD4ilTpvjymEbbVr8dUy3e1wb3l112mZQsWdLcNBhpg3H9v151++XYxtvW3NxcXx3baN/9Nj+vBA6P/4j1D3jZsmUhRcH6OH/9qF8dOXLEJG1N3X6mVRP6wc9/nA8dOmR6q6TDcd6xY4dpw5GK46ztWvUErkXo77zzjjmW+ennt1SpUiHHVot8tX432cc23rZGolftyg+fYf3bP378uK+Oabxt9dsx1at/rf7RbXBuOiu4tjlw/u+XYxtvW0tEqD71y+c1/3e/1c9roZqc4iyzZ882vSVmzJgR+PLLLwODBw8OVK5cOZCTkxPwm3vvvTeQnZ0d2Lp1a+D9998PdOzYMVCtWjXTGyDVtKX3Z599Zm76MX3yySfN/7/99lvz/MSJE81xffXVVwPr1q0zvUDOP//8wI8//uirbdXnxowZY1p363F+++23A5dddplpxX7s2LGkb+uQIUMClSpVMr/33bt3B28//PBDcJ277rorUL9+/cA777wT+OSTTwJt27Y1N79t65YtWwKPPvqo2UY9tvpZaNiwYeCaa65J+rb+5je/Mb1ndDv086iPMzIyAv/85z99dUzjbaufjmk04T09/HRsY23rFh8d23jf/baOKYHDgqefftr8skqXLm26yX744YcBP7rlllsCtWvXNttZt25d81j/KPxg+fLl5uQdftMupk7X2IceeihQs2ZNE/A6dOgQ2LRpk++2VU+OnTp1ClSvXt10NTvvvPMCgwYNSlkAjbSdeps+fXpwHQ1td999t+kqWa5cucDNN99sTvR+29Zt27aZL+sqVaqYz0CjRo0C9913X+DgwYNJ39Y777zT/G71b0l/1/p5dMKGn45pvG310zF1Gzj8dGxjbes2Hx3beN/9to4p09MDAADraMMBAACsI3AAAADrCBwAAMA6AgcAALCOwAEAAKwjcAAAAOsIHAAAwDoCBwAAsI7AAaBIysjIkIULF6Z6MwCcQeAA4LkBAwaYE3747cYbb0z1pgFIkZKpemMARZuGi+nTp4csK1OmTMq2B0BqUcIBwAoNF7Vq1Qq5/eQnPzHPaWnH1KlTpUuXLlK2bFlp2LChzJs3L+Tndarv66+/3jxftWpVGTx4sJlGO78XXnhBLr74YvNeOrW2Tmef33fffSc333yzlCtXTho3biyvvfZaEvYcQCQEDgAp8dBDD0nv3r3l888/l379+smtt94qX331lXnu6NGj0rlzZxNQPv74Y5k7d668/fbbIYFCA8vQoUNNENFwomGiUaNGIe8xfvx4+fnPfy7r1q2Trl27mvfZv39/0vcVgM71DAAe69+/f6BEiRKB8uXLh9z++Mc/muf1q+euu+4K+Zk2bdoEhgwZYv7/v//7v2Zq7CNHjgSff+ONNwKZmZmBnJwc87hOnTqB3/3ud1G3Qd/jwQcfDD7W19Jlixcv9nx/AcRHGw4AVrRv396UQuRXpUqV4P/btm0b8pw+Xrt2rfm/lnS0bNlSypcvH3z+qquukry8PNm0aZOpktm1a5d06NAh5ja0aNEi+H99rYoVK8revXsLvW8AEkfgAGCFnuDDqzi8ou063ChVqlTIYw0qGloAJB9tOACkxIcffnjW46ZNm5r/67227dC2HI73339fMjMz5aKLLpJzzz1XGjRoIMuWLUv6dgMoGEo4AFhx/PhxycnJCVlWsmRJqVatmvm/NgRt3bq1XH311fLSSy/J6tWr5fnnnzfPaePOcePGSf/+/eWRRx6Rffv2yfDhw+X222+XmjVrmnV0+V133SU1atQwvV0OHz5sQomuB8B/CBwArFiyZInpqpqflk5s3Lgx2INk9uzZcvfdd5v1Zs2aJc2aNTPPaTfWt956S0aMGCGXX365eaw9Wp588snga2kYOXbsmEyaNEnGjBljgkyfPn2SvJcA3MrQlqOu1wYAD2hbigULFkjPnj1TvSkAkoQ2HAAAwDoCBwAAsI42HACSjppcoPihhAMAAFhH4AAAANYROAAAgHUEDgAAYB2BAwAAWEfgAAAA1hE4AACAdQQOAAAgtv0/gHKTqpuldRMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1, 1, figsize=(6, 6))\n",
    "ax = summary_plot(hit_results,\n",
    "                  ax,\n",
    "                  col='mae',\n",
    "                  ylabel='MAE',\n",
    "                  valid_legend='Validation (=Test)')\n",
    "ax.set_ylim([0, 400])\n",
    "ax.set_xticks(np.linspace(0, 50, 11).astype(int));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac0b2b37",
   "metadata": {},
   "source": [
    "We can predict directly from the final model, and\n",
    "evaluate its performance on the test data.\n",
    "Before fitting, we call the `eval()` method\n",
    "of `hit_model`.\n",
    "This tells\n",
    "`torch` to effectively consider this model to be fitted, so that\n",
    "we can use it to predict on new data. For our model here,\n",
    "the biggest change is that the dropout layers will\n",
    "be turned off, i.e. no weights will be randomly\n",
    "dropped in predicting on new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e6a48733",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(221.8315, grad_fn=<MeanBackward0>)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hit_model.eval() \n",
    "preds = hit_module(X_test_t)\n",
    "torch.abs(Y_test_t - preds).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a24663bc",
   "metadata": {},
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b51594bb",
   "metadata": {},
   "source": [
    "### Cleanup\n",
    "In setting up our data module, we had initiated\n",
    "several worker processes that will remain running.\n",
    "We delete all references to the torch objects to ensure these processes\n",
    "will be killed.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "78ca542d",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "del(Hitters,\n",
    "    hit_model, hit_dm,\n",
    "    hit_logger,\n",
    "    hit_test, hit_train,\n",
    "    X, Y,\n",
    "    X_test, X_train,\n",
    "    Y_test, Y_train,\n",
    "    X_test_t, Y_test_t,\n",
    "    hit_trainer, hit_module)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42547131",
   "metadata": {},
   "source": [
    "## Multilayer Network on the MNIST Digit Data\n",
    "The `torchvision` package comes with a number of example datasets,\n",
    "including the `MNIST`  digit data. Our first step is to retrieve\n",
    "the training and test data sets; the `MNIST()` function within\n",
    "`torchvision.datasets` is provided for this purpose. The\n",
    "data will be downloaded the first time this function is executed, and stored in the directory `data/MNIST`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7a6d7416",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset MNIST\n",
       "    Number of datapoints: 60000\n",
       "    Root location: data\n",
       "    Split: Train\n",
       "    StandardTransform\n",
       "Transform: ToTensor()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(mnist_train, \n",
    " mnist_test) = [MNIST(root='data',\n",
    "                      train=train,\n",
    "                      download=True,\n",
    "                      transform=ToTensor())\n",
    "                for train in [True, False]]\n",
    "mnist_train\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23bf4a88",
   "metadata": {},
   "source": [
    "There are 60,000 images in the training data and 10,000 in the test\n",
    "data. The images are $28\\times 28$, and stored as a matrix of pixels. We\n",
    "need to transform each one into a vector.\n",
    "\n",
    "Neural networks are somewhat sensitive to the scale of the inputs, much as ridge and\n",
    "lasso regularization are affected by scaling.  Here the inputs are eight-bit\n",
    "grayscale values between 0 and 255, so we rescale to the unit\n",
    "interval. {Note: eight bits means $2^8$, which equals 256. Since the convention\n",
    "is to start at $0$, the possible values  range from $0$ to $255$.}\n",
    "This transformation, along with some reordering\n",
    "of the axes, is performed by the `ToTensor()` transform\n",
    "from the `torchvision.transforms` package.\n",
    "\n",
    "As in our `Hitters` example, we form a data module\n",
    "from the training and test datasets, setting aside 20%\n",
    "of the training images for validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e6c5e82c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_dm = SimpleDataModule(mnist_train,\n",
    "                            mnist_test,\n",
    "                            validation=0.2,\n",
    "                            num_workers=max_num_workers,\n",
    "                            batch_size=256)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73614d27",
   "metadata": {},
   "source": [
    "Let’s take a look at the data that will get fed into our network. We loop through the first few\n",
    "chunks of the test dataset, breaking after 2 batches:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6ecf940c",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X:  torch.Size([256, 1, 28, 28])\n",
      "Y:  torch.Size([256])\n",
      "X:  torch.Size([256, 1, 28, 28])\n",
      "Y:  torch.Size([256])\n"
     ]
    }
   ],
   "source": [
    "for idx, (X_ ,Y_) in enumerate(mnist_dm.train_dataloader()):\n",
    "    print('X: ', X_.shape)\n",
    "    print('Y: ', Y_.shape)\n",
    "    if idx >= 1:\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e0b2d96",
   "metadata": {},
   "source": [
    "We see that the $X$ for each batch consists of 256 images of size `1x28x28`.\n",
    "Here the `1` indicates a single channel (greyscale). For RGB images such as `CIFAR100` below,\n",
    "we will see that the `1` in the size will be replaced by `3` for the three RGB channels.\n",
    "\n",
    "Now we are ready to specify our neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f4c49bab",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MNISTModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MNISTModel, self).__init__()\n",
    "        self.layer1 = nn.Sequential(\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(28*28, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.4))\n",
    "        self.layer2 = nn.Sequential(\n",
    "            nn.Linear(256, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.3))\n",
    "        self._forward = nn.Sequential(\n",
    "            self.layer1,\n",
    "            self.layer2,\n",
    "            nn.Linear(128, 10))\n",
    "    def forward(self, x):\n",
    "        return self._forward(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e25c568",
   "metadata": {},
   "source": [
    "We see that in the first layer, each `1x28x28` image is flattened, then mapped to\n",
    "256 dimensions where we apply a ReLU activation with 40% dropout.\n",
    "A second layer maps the first layer’s output down to\n",
    "128 dimensions, applying a ReLU activation with 30% dropout. Finally,\n",
    "the 128 dimensions are mapped down to 10, the number of classes in the\n",
    "`MNIST`  data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8dd2770f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_model = MNISTModel()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d0e8c1b",
   "metadata": {},
   "source": [
    "We can check that the model produces output of expected size based\n",
    "on our existing batch `X_` above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c916312b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([256, 10])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_model(X_).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3158809",
   "metadata": {},
   "source": [
    "Let’s take a look at the summary of the model. Instead of an `input_size` we can pass\n",
    "a tensor of correct shape. In this case, we pass through the final\n",
    "batched `X_` from above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "54a6b6b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "===================================================================================================================\n",
       "Layer (type:depth-idx)                   Input Shape               Output Shape              Param #\n",
       "===================================================================================================================\n",
       "MNISTModel                               [256, 1, 28, 28]          [256, 10]                 --\n",
       "├─Sequential: 1-1                        [256, 1, 28, 28]          [256, 10]                 --\n",
       "│    └─Sequential: 2-1                   [256, 1, 28, 28]          [256, 256]                --\n",
       "│    │    └─Flatten: 3-1                 [256, 1, 28, 28]          [256, 784]                --\n",
       "│    │    └─Linear: 3-2                  [256, 784]                [256, 256]                200,960\n",
       "│    │    └─ReLU: 3-3                    [256, 256]                [256, 256]                --\n",
       "│    │    └─Dropout: 3-4                 [256, 256]                [256, 256]                --\n",
       "│    └─Sequential: 2-2                   [256, 256]                [256, 128]                --\n",
       "│    │    └─Linear: 3-5                  [256, 256]                [256, 128]                32,896\n",
       "│    │    └─ReLU: 3-6                    [256, 128]                [256, 128]                --\n",
       "│    │    └─Dropout: 3-7                 [256, 128]                [256, 128]                --\n",
       "│    └─Linear: 2-3                       [256, 128]                [256, 10]                 1,290\n",
       "===================================================================================================================\n",
       "Total params: 235,146\n",
       "Trainable params: 235,146\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (Units.MEGABYTES): 60.20\n",
       "===================================================================================================================\n",
       "Input size (MB): 0.80\n",
       "Forward/backward pass size (MB): 0.81\n",
       "Params size (MB): 0.94\n",
       "Estimated Total Size (MB): 2.55\n",
       "==================================================================================================================="
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(mnist_model,\n",
    "        input_data=X_,\n",
    "        col_names=['input_size',\n",
    "                   'output_size',\n",
    "                   'num_params'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e76a07df",
   "metadata": {},
   "source": [
    "Having set up both  the model and the data module, fitting this model is\n",
    "now almost identical to the `Hitters` example. In contrast to our regression model, here we will use the\n",
    "`SimpleModule.classification()` method which\n",
    "uses the  cross-entropy loss function instead of mean squared error. It must be supplied with the number of\n",
    "classes in the problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c1fa96fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_module = SimpleModule.classification(mnist_model,\n",
    "                                           num_classes=10)\n",
    "mnist_logger = CSVLogger('logs', name='MNIST')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "106c0f38",
   "metadata": {},
   "source": [
    "Now we are ready to go. The final step is to supply training data, and fit the model. We disable the progress bar below to avoid lengthy output in the browser when running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "25515af7",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name  | Type             | Params\n",
      "-------------------------------------------\n",
      "0 | model | MNISTModel       | 235 K \n",
      "1 | loss  | CrossEntropyLoss | 0     \n",
      "-------------------------------------------\n",
      "235 K     Trainable params\n",
      "0         Non-trainable params\n",
      "235 K     Total params\n",
      "0.941     Total estimated model params size (MB)\n",
      "`Trainer.fit` stopped: `max_epochs=30` reached.\n"
     ]
    }
   ],
   "source": [
    "mnist_trainer = Trainer(deterministic=True,\n",
    "                        max_epochs=30,\n",
    "                        logger=mnist_logger,\n",
    "                        enable_progress_bar=False,\n",
    "                        callbacks=[ErrorTracker()])\n",
    "mnist_trainer.fit(mnist_module,\n",
    "                  datamodule=mnist_dm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e12d8c7",
   "metadata": {},
   "source": [
    "We have suppressed the output here, which is a progress report on the\n",
    "fitting of the model, grouped by epoch. This is very useful, since on\n",
    "large datasets fitting can take time. Fitting this model took 245\n",
    "seconds on a MacBook Pro with an Apple M1 Pro chip with 10 cores and 16 GB of RAM.\n",
    "Here we specified a\n",
    "validation split of 20%, so training is actually performed on\n",
    "80% of the 60,000 observations in the training set. This is an\n",
    "alternative to actually supplying validation data, like we did for the `Hitters` data.\n",
    "SGD  uses batches\n",
    "of 256 observations in computing the gradient, and doing the\n",
    "arithmetic, we see that an epoch corresponds to 188 gradient steps."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb25107e",
   "metadata": {},
   "source": [
    "`SimpleModule.classification()` includes\n",
    "an accuracy metric by default. Other\n",
    "classification metrics can be added from `torchmetrics`.\n",
    "We will use  our `summary_plot()` function to display \n",
    "accuracy across epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "872fed35",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnist_results = pd.read_csv(mnist_logger.experiment.metrics_file_path)\n",
    "fig, ax = subplots(1, 1, figsize=(6, 6))\n",
    "summary_plot(mnist_results,\n",
    "             ax,\n",
    "             col='accuracy',\n",
    "             ylabel='Accuracy')\n",
    "ax.set_ylim([0.5, 1])\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_xticks(np.linspace(0, 30, 7).astype(int));\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e1c8d5c",
   "metadata": {},
   "source": [
    "Once again we evaluate the accuracy using the `test()` method of our trainer. This model achieves\n",
    "97% accuracy on the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4551c956",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "       Test metric             DataLoader 0\n",
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "      test_accuracy         0.9620000123977661\n",
      "        test_loss           0.15120187401771545\n",
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_loss': 0.15120187401771545, 'test_accuracy': 0.9620000123977661}]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_trainer.test(mnist_module,\n",
    "                   datamodule=mnist_dm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b0c0bd0",
   "metadata": {},
   "source": [
    "Table 10.1 also reports the error rates resulting from LDA (Chapter 4) and multiclass logistic\n",
    "regression. For LDA we refer the reader to Section 4.7.3.\n",
    "Although we could use the `sklearn` function `LogisticRegression()` to fit  \n",
    "multiclass logistic regression, we are set up here to fit such a model\n",
    "with `torch`.\n",
    "We just have an input layer and an output layer, and omit the hidden layers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cbf4d0d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MNIST_MLR(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MNIST_MLR, self).__init__()\n",
    "        self.linear = nn.Sequential(nn.Flatten(),\n",
    "                                    nn.Linear(784, 10))\n",
    "    def forward(self, x):\n",
    "        return self.linear(x)\n",
    "\n",
    "mlr_model = MNIST_MLR()\n",
    "mlr_module = SimpleModule.classification(mlr_model,\n",
    "                                         num_classes=10)\n",
    "mlr_logger = CSVLogger('logs', name='MNIST_MLR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6ff23014",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "/Users/jonathantaylor/anaconda3/envs/ISLP_312/lib/python3.12/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
      "\n",
      "  | Name  | Type             | Params\n",
      "-------------------------------------------\n",
      "0 | model | MNIST_MLR        | 7.9 K \n",
      "1 | loss  | CrossEntropyLoss | 0     \n",
      "-------------------------------------------\n",
      "7.9 K     Trainable params\n",
      "0         Non-trainable params\n",
      "7.9 K     Total params\n",
      "0.031     Total estimated model params size (MB)\n",
      "`Trainer.fit` stopped: `max_epochs=30` reached.\n"
     ]
    }
   ],
   "source": [
    "mlr_trainer = Trainer(deterministic=True,\n",
    "                      max_epochs=30,\n",
    "                      enable_progress_bar=False,\n",
    "                      callbacks=[ErrorTracker()])\n",
    "mlr_trainer.fit(mlr_module, datamodule=mnist_dm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8368ac1",
   "metadata": {},
   "source": [
    "We fit the model just as before and compute the test results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ca159ef2",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "       Test metric             DataLoader 0\n",
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "      test_accuracy          0.916100025177002\n",
      "        test_loss           0.3469300866127014\n",
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_loss': 0.3469300866127014, 'test_accuracy': 0.916100025177002}]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr_trainer.test(mlr_module,\n",
    "                 datamodule=mnist_dm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be94ca6",
   "metadata": {},
   "source": [
    "The accuracy is above 90% even for this pretty simple model.\n",
    "\n",
    "As in the `Hitters` example, we delete some of\n",
    "the objects we created above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5842c930",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "del(mnist_test,\n",
    "    mnist_train,\n",
    "    mnist_model,\n",
    "    mnist_dm,\n",
    "    mnist_trainer,\n",
    "    mnist_module,\n",
    "    mnist_results,\n",
    "    mlr_model,\n",
    "    mlr_module,\n",
    "    mlr_trainer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd9e98b5",
   "metadata": {},
   "source": [
    "## Convolutional Neural Networks\n",
    "In this section we fit a CNN to the `CIFAR100` data, which is available in the `torchvision`\n",
    "package. It is arranged in a similar fashion as the `MNIST` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cb406355",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "(cifar_train, \n",
    " cifar_test) = [CIFAR100(root=\"data\",\n",
    "                         train=train,\n",
    "                         download=True)\n",
    "             for train in [True, False]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8dcd79cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = ToTensor()\n",
    "cifar_train_X = torch.stack([transform(x) for x in\n",
    "                            cifar_train.data])\n",
    "cifar_test_X = torch.stack([transform(x) for x in\n",
    "                            cifar_test.data])\n",
    "cifar_train = TensorDataset(cifar_train_X,\n",
    "                            torch.tensor(cifar_train.targets))\n",
    "cifar_test = TensorDataset(cifar_test_X,\n",
    "                            torch.tensor(cifar_test.targets))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81f4ea20",
   "metadata": {},
   "source": [
    "The `CIFAR100` dataset consists of 50,000 training images, each represented by a three-dimensional tensor:\n",
    "each three-color image is represented as a set of three channels, each of which consists of\n",
    "$32\\times 32$ eight-bit pixels. We standardize as we did for the\n",
    "digits, but keep the array structure. This is accomplished with the `ToTensor()` transform.\n",
    "\n",
    "Creating the data module is similar to the `MNIST`  example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6980303a",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "cifar_dm = SimpleDataModule(cifar_train,\n",
    "                            cifar_test,\n",
    "                            validation=0.2,\n",
    "                            num_workers=max_num_workers,\n",
    "                            batch_size=128)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b9e007c",
   "metadata": {},
   "source": [
    "We again look at the shape of typical batches in our data loaders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "401888c7",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X:  torch.Size([128, 3, 32, 32])\n",
      "Y:  torch.Size([128])\n",
      "X:  torch.Size([128, 3, 32, 32])\n",
      "Y:  torch.Size([128])\n"
     ]
    }
   ],
   "source": [
    "for idx, (X_ ,Y_) in enumerate(cifar_dm.train_dataloader()):\n",
    "    print('X: ', X_.shape)\n",
    "    print('Y: ', Y_.shape)\n",
    "    if idx >= 1:\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82438c30",
   "metadata": {},
   "source": [
    "Before we start, we look at some of the training images; similar code produced\n",
    "Figure 10.5 on page 406. The example below also illustrates\n",
    "that `TensorDataset` objects can be indexed with integers --- we are choosing\n",
    "random images from the training data by indexing `cifar_train`. In order to display correctly,\n",
    "we must reorder the dimensions by a call to `np.transpose()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "56090846",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = subplots(5, 5, figsize=(10,10))\n",
    "rng = np.random.default_rng(4)\n",
    "indices = rng.choice(np.arange(len(cifar_train)), 25,\n",
    "                     replace=False).reshape((5,5))\n",
    "for i in range(5):\n",
    "    for j in range(5):\n",
    "        idx = indices[i,j]\n",
    "        axes[i,j].imshow(np.transpose(cifar_train[idx][0],\n",
    "                                      [1,2,0]),\n",
    "                                      interpolation=None)\n",
    "        axes[i,j].set_xticks([])\n",
    "        axes[i,j].set_yticks([])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "333dca34",
   "metadata": {},
   "source": [
    "Here the `imshow()` method recognizes from the shape of its argument that it is a 3-dimensional array, with the last dimension indexing the three RGB color channels.\n",
    "\n",
    "We specify a moderately-sized  CNN for\n",
    "demonstration purposes, similar in structure to Figure 10.8.\n",
    "We use several layers, each consisting of  convolution, ReLU, and max-pooling steps.\n",
    "We first define a module that defines one of these layers. As in our\n",
    "previous examples, we overwrite the `__init__()` and `forward()` methods\n",
    "of `nn.Module`. This user-defined  module can now be used in ways just like\n",
    "`nn.Linear()` or `nn.Dropout()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8c0567d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BuildingBlock(nn.Module):\n",
    "\n",
    "    def __init__(self,\n",
    "                 in_channels,\n",
    "                 out_channels):\n",
    "\n",
    "        super(BuildingBlock, self).__init__()\n",
    "        self.conv = nn.Conv2d(in_channels=in_channels,\n",
    "                              out_channels=out_channels,\n",
    "                              kernel_size=(3,3),\n",
    "                              padding='same')\n",
    "        self.activation = nn.ReLU()\n",
    "        self.pool = nn.MaxPool2d(kernel_size=(2,2))\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.pool(self.activation(self.conv(x)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25956312",
   "metadata": {},
   "source": [
    "Notice that we used the `padding = \"same\"` argument to\n",
    "`nn.Conv2d()`, which ensures that the output channels have the\n",
    "same dimension as the input channels. There are 32 channels in the first\n",
    "hidden layer, in contrast to the three channels in the input layer. We\n",
    "use a $3\\times 3$ convolution filter for each channel in all the layers. Each\n",
    "convolution is followed by a max-pooling layer over $2\\times2$\n",
    "blocks.\n",
    "\n",
    "In forming our deep learning model for the `CIFAR100` data, we use several of our `BuildingBlock()`\n",
    "modules sequentially. This simple example\n",
    "illustrates some of the power of `torch`. Users can\n",
    "define modules of their own, which can be combined in other\n",
    "modules. Ultimately, everything is fit by a generic trainer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e4d3eb9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CIFARModel(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(CIFARModel, self).__init__()\n",
    "        sizes = [(3,32),\n",
    "                 (32,64),\n",
    "                 (64,128),\n",
    "                 (128,256)]\n",
    "        self.conv = nn.Sequential(*[BuildingBlock(in_, out_)\n",
    "                                    for in_, out_ in sizes])\n",
    "\n",
    "        self.output = nn.Sequential(nn.Dropout(0.5),\n",
    "                                    nn.Linear(2*2*256, 512),\n",
    "                                    nn.ReLU(),\n",
    "                                    nn.Linear(512, 100))\n",
    "    def forward(self, x):\n",
    "        val = self.conv(x)\n",
    "        val = torch.flatten(val, start_dim=1)\n",
    "        return self.output(val)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cf81883",
   "metadata": {},
   "source": [
    "We  build the model and look at the summary. (We had created examples of `X_` earlier.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5ff4e0a5",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "===================================================================================================================\n",
       "Layer (type:depth-idx)                   Input Shape               Output Shape              Param #\n",
       "===================================================================================================================\n",
       "CIFARModel                               [128, 3, 32, 32]          [128, 100]                --\n",
       "├─Sequential: 1-1                        [128, 3, 32, 32]          [128, 256, 2, 2]          --\n",
       "│    └─BuildingBlock: 2-1                [128, 3, 32, 32]          [128, 32, 16, 16]         --\n",
       "│    │    └─Conv2d: 3-1                  [128, 3, 32, 32]          [128, 32, 32, 32]         896\n",
       "│    │    └─ReLU: 3-2                    [128, 32, 32, 32]         [128, 32, 32, 32]         --\n",
       "│    │    └─MaxPool2d: 3-3               [128, 32, 32, 32]         [128, 32, 16, 16]         --\n",
       "│    └─BuildingBlock: 2-2                [128, 32, 16, 16]         [128, 64, 8, 8]           --\n",
       "│    │    └─Conv2d: 3-4                  [128, 32, 16, 16]         [128, 64, 16, 16]         18,496\n",
       "│    │    └─ReLU: 3-5                    [128, 64, 16, 16]         [128, 64, 16, 16]         --\n",
       "│    │    └─MaxPool2d: 3-6               [128, 64, 16, 16]         [128, 64, 8, 8]           --\n",
       "│    └─BuildingBlock: 2-3                [128, 64, 8, 8]           [128, 128, 4, 4]          --\n",
       "│    │    └─Conv2d: 3-7                  [128, 64, 8, 8]           [128, 128, 8, 8]          73,856\n",
       "│    │    └─ReLU: 3-8                    [128, 128, 8, 8]          [128, 128, 8, 8]          --\n",
       "│    │    └─MaxPool2d: 3-9               [128, 128, 8, 8]          [128, 128, 4, 4]          --\n",
       "│    └─BuildingBlock: 2-4                [128, 128, 4, 4]          [128, 256, 2, 2]          --\n",
       "│    │    └─Conv2d: 3-10                 [128, 128, 4, 4]          [128, 256, 4, 4]          295,168\n",
       "│    │    └─ReLU: 3-11                   [128, 256, 4, 4]          [128, 256, 4, 4]          --\n",
       "│    │    └─MaxPool2d: 3-12              [128, 256, 4, 4]          [128, 256, 2, 2]          --\n",
       "├─Sequential: 1-2                        [128, 1024]               [128, 100]                --\n",
       "│    └─Dropout: 2-5                      [128, 1024]               [128, 1024]               --\n",
       "│    └─Linear: 2-6                       [128, 1024]               [128, 512]                524,800\n",
       "│    └─ReLU: 2-7                         [128, 512]                [128, 512]                --\n",
       "│    └─Linear: 2-8                       [128, 512]                [128, 100]                51,300\n",
       "===================================================================================================================\n",
       "Total params: 964,516\n",
       "Trainable params: 964,516\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (Units.GIGABYTES): 2.01\n",
       "===================================================================================================================\n",
       "Input size (MB): 1.57\n",
       "Forward/backward pass size (MB): 63.54\n",
       "Params size (MB): 3.86\n",
       "Estimated Total Size (MB): 68.97\n",
       "==================================================================================================================="
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cifar_model = CIFARModel()\n",
    "summary(cifar_model,\n",
    "        input_data=X_,\n",
    "        col_names=['input_size',\n",
    "                   'output_size',\n",
    "                   'num_params'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2125ae4a",
   "metadata": {},
   "source": [
    "The total number of trainable parameters is 964,516.\n",
    "By studying the size of the parameters, we can see that the channels halve in both\n",
    "dimensions\n",
    "after each of these max-pooling operations. After the last of these we\n",
    "have a layer with  256 channels of dimension $2\\times 2$. These are then\n",
    "flattened to a dense layer of size 1,024;\n",
    "in other words, each of the $2\\times 2$ matrices is turned into a\n",
    "$4$-vector, and put side-by-side in one layer. This is followed by a\n",
    "dropout regularization layer,  then\n",
    "another dense layer of size 512, and finally, the\n",
    "output layer.\n",
    "\n",
    "Up to now, we have been using a default\n",
    "optimizer in `SimpleModule()`. For these data,\n",
    "experiments show that a smaller learning rate performs\n",
    "better than the default 0.01. We use a\n",
    "custom optimizer here with a learning rate of 0.001.\n",
    "Besides this, the logging and training\n",
    "follow a similar pattern to our previous examples. The optimizer\n",
    "takes an argument `params` that informs\n",
    "the optimizer which parameters are involved in SGD (stochastic gradient descent).\n",
    "\n",
    "We saw earlier that entries of a module’s parameters are tensors. In passing\n",
    "the parameters to the optimizer we are doing more than\n",
    "simply passing arrays; part of the structure of the graph\n",
    "is encoded in the tensors themselves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ab9d9080",
   "metadata": {},
   "outputs": [],
   "source": [
    "cifar_optimizer = RMSprop(cifar_model.parameters(), lr=0.001)\n",
    "cifar_module = SimpleModule.classification(cifar_model,\n",
    "                                    num_classes=100,\n",
    "                                    optimizer=cifar_optimizer)\n",
    "cifar_logger = CSVLogger('logs', name='CIFAR100')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5a86b37d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name  | Type             | Params\n",
      "-------------------------------------------\n",
      "0 | model | CIFARModel       | 964 K \n",
      "1 | loss  | CrossEntropyLoss | 0     \n",
      "-------------------------------------------\n",
      "964 K     Trainable params\n",
      "0         Non-trainable params\n",
      "964 K     Total params\n",
      "3.858     Total estimated model params size (MB)\n",
      "`Trainer.fit` stopped: `max_epochs=30` reached.\n"
     ]
    }
   ],
   "source": [
    "cifar_trainer = Trainer(deterministic=True,\n",
    "                        max_epochs=30,\n",
    "                        logger=cifar_logger,\n",
    "                        enable_progress_bar=False,\n",
    "                        callbacks=[ErrorTracker()])\n",
    "cifar_trainer.fit(cifar_module,\n",
    "                  datamodule=cifar_dm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00c6668f",
   "metadata": {},
   "source": [
    "This model can take 10 minutes or more to run and achieves about 42% accuracy on the test\n",
    "data. Although this is not terrible for 100-class data (a random\n",
    "classifier gets 1% accuracy), searching the web we see results around\n",
    "75%. Typically it takes a lot of architecture carpentry,\n",
    "fiddling with regularization, and time, to achieve such results.\n",
    "\n",
    "Let’s take a look at the validation and training accuracy\n",
    "across epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9cf596b4",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_path = cifar_logger.experiment.metrics_file_path\n",
    "cifar_results = pd.read_csv(log_path)\n",
    "fig, ax = subplots(1, 1, figsize=(6, 6))\n",
    "summary_plot(cifar_results,\n",
    "             ax,\n",
    "             col='accuracy',\n",
    "             ylabel='Accuracy')\n",
    "ax.set_xticks(np.linspace(0, 10, 6).astype(int))\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_ylim([0, 1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3e4a8bb",
   "metadata": {},
   "source": [
    "Finally, we evaluate our model on our test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0f5ad9bb",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "       Test metric             DataLoader 0\n",
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "      test_accuracy         0.4269999861717224\n",
      "        test_loss            2.45865797996521\n",
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_loss': 2.45865797996521, 'test_accuracy': 0.4269999861717224}]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cifar_trainer.test(cifar_module,\n",
    "                   datamodule=cifar_dm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed52c614",
   "metadata": {},
   "source": [
    "### Hardware Acceleration\n",
    "As deep learning has become ubiquitous in machine learning, hardware\n",
    "manufacturers have produced special libraries that can\n",
    "often speed up the gradient-descent steps.\n",
    "\n",
    "For instance, Mac OS devices with the M1 chip may have the *Metal* programming framework\n",
    "enabled, which can speed up the  `torch` \n",
    "computations. We present an example of how to use this acceleration.\n",
    "\n",
    "The main changes are to the `Trainer()` call as well as to the metrics\n",
    "that will be evaluated on the data. These metrics must be told  where\n",
    "the data will be located at evaluation time. This is\n",
    "accomplished with a call to the `to()` method of the metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e763b4c4",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name  | Type             | Params\n",
      "-------------------------------------------\n",
      "0 | model | CIFARModel       | 964 K \n",
      "1 | loss  | CrossEntropyLoss | 0     \n",
      "-------------------------------------------\n",
      "964 K     Trainable params\n",
      "0         Non-trainable params\n",
      "964 K     Total params\n",
      "3.858     Total estimated model params size (MB)\n",
      "`Trainer.fit` stopped: `max_epochs=30` reached.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    for name, metric in cifar_module.metrics.items():\n",
    "        cifar_module.metrics[name] = metric.to('mps')\n",
    "    cifar_trainer_mps = Trainer(accelerator='mps',\n",
    "                                deterministic=True,\n",
    "                                enable_progress_bar=False,\n",
    "                                max_epochs=30)\n",
    "    cifar_trainer_mps.fit(cifar_module,\n",
    "                          datamodule=cifar_dm)\n",
    "    cifar_trainer_mps.test(cifar_module,\n",
    "                          datamodule=cifar_dm)\n",
    "except:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14791a8f",
   "metadata": {},
   "source": [
    "This yields approximately two- or three-fold  acceleration for each epoch.\n",
    "We have protected this code block using `try:` and `except:`\n",
    "clauses; if it works, we get the speedup, if it fails, nothing happens."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51532403",
   "metadata": {},
   "source": [
    "## Using Pretrained CNN Models\n",
    "We now show how to use a CNN pretrained on the  `imagenet` database to classify natural\n",
    "images, and demonstrate how we produced Figure 10.10.\n",
    "We copied six JPEG images from a digital photo album into the\n",
    "directory `book_images`. These images are available\n",
    "from the data section of  <www.statlearning.com>, the ISLP book website. Download `book_images.zip`; when\n",
    "clicked it creates the `book_images` directory. \n",
    "\n",
    "The pretrained network we use is called `resnet50`; specification details can be found on the web.\n",
    "We will read in the images, and\n",
    "convert them into the array format expected by the `torch`\n",
    "software to match the specifications in `resnet50`. \n",
    "The conversion involves a resize, a crop and then a predefined standardization for each of the three channels.\n",
    "We now read in the images and preprocess them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8ddd3831",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([6, 3, 224, 224])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resize = Resize((232,232), antialias=True)\n",
    "crop = CenterCrop(224)\n",
    "normalize = Normalize([0.485,0.456,0.406],\n",
    "                      [0.229,0.224,0.225])\n",
    "imgfiles = sorted([f for f in glob('book_images/*')])\n",
    "imgs = torch.stack([torch.div(crop(resize(read_image(f))), 255)\n",
    "                    for f in imgfiles])\n",
    "imgs = normalize(imgs)\n",
    "imgs.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a735ef73",
   "metadata": {},
   "source": [
    "We now set up the trained network with the weights we read in code block~6. The model has 50 layers, with a fair bit of complexity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1126ee25",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "===================================================================================================================\n",
       "Layer (type:depth-idx)                   Input Shape               Output Shape              Param #\n",
       "===================================================================================================================\n",
       "ResNet                                   [6, 3, 224, 224]          [6, 1000]                 --\n",
       "├─Conv2d: 1-1                            [6, 3, 224, 224]          [6, 64, 112, 112]         9,408\n",
       "├─BatchNorm2d: 1-2                       [6, 64, 112, 112]         [6, 64, 112, 112]         128\n",
       "├─ReLU: 1-3                              [6, 64, 112, 112]         [6, 64, 112, 112]         --\n",
       "├─MaxPool2d: 1-4                         [6, 64, 112, 112]         [6, 64, 56, 56]           --\n",
       "├─Sequential: 1-5                        [6, 64, 56, 56]           [6, 256, 56, 56]          --\n",
       "│    └─Bottleneck: 2-1                   [6, 64, 56, 56]           [6, 256, 56, 56]          --\n",
       "│    │    └─Conv2d: 3-1                  [6, 64, 56, 56]           [6, 64, 56, 56]           4,096\n",
       "│    │    └─BatchNorm2d: 3-2             [6, 64, 56, 56]           [6, 64, 56, 56]           128\n",
       "│    │    └─ReLU: 3-3                    [6, 64, 56, 56]           [6, 64, 56, 56]           --\n",
       "│    │    └─Conv2d: 3-4                  [6, 64, 56, 56]           [6, 64, 56, 56]           36,864\n",
       "│    │    └─BatchNorm2d: 3-5             [6, 64, 56, 56]           [6, 64, 56, 56]           128\n",
       "│    │    └─ReLU: 3-6                    [6, 64, 56, 56]           [6, 64, 56, 56]           --\n",
       "│    │    └─Conv2d: 3-7                  [6, 64, 56, 56]           [6, 256, 56, 56]          16,384\n",
       "│    │    └─BatchNorm2d: 3-8             [6, 256, 56, 56]          [6, 256, 56, 56]          512\n",
       "│    │    └─Sequential: 3-9              [6, 64, 56, 56]           [6, 256, 56, 56]          16,896\n",
       "│    │    └─ReLU: 3-10                   [6, 256, 56, 56]          [6, 256, 56, 56]          --\n",
       "│    └─Bottleneck: 2-2                   [6, 256, 56, 56]          [6, 256, 56, 56]          --\n",
       "│    │    └─Conv2d: 3-11                 [6, 256, 56, 56]          [6, 64, 56, 56]           16,384\n",
       "│    │    └─BatchNorm2d: 3-12            [6, 64, 56, 56]           [6, 64, 56, 56]           128\n",
       "│    │    └─ReLU: 3-13                   [6, 64, 56, 56]           [6, 64, 56, 56]           --\n",
       "│    │    └─Conv2d: 3-14                 [6, 64, 56, 56]           [6, 64, 56, 56]           36,864\n",
       "│    │    └─BatchNorm2d: 3-15            [6, 64, 56, 56]           [6, 64, 56, 56]           128\n",
       "│    │    └─ReLU: 3-16                   [6, 64, 56, 56]           [6, 64, 56, 56]           --\n",
       "│    │    └─Conv2d: 3-17                 [6, 64, 56, 56]           [6, 256, 56, 56]          16,384\n",
       "│    │    └─BatchNorm2d: 3-18            [6, 256, 56, 56]          [6, 256, 56, 56]          512\n",
       "│    │    └─ReLU: 3-19                   [6, 256, 56, 56]          [6, 256, 56, 56]          --\n",
       "│    └─Bottleneck: 2-3                   [6, 256, 56, 56]          [6, 256, 56, 56]          --\n",
       "│    │    └─Conv2d: 3-20                 [6, 256, 56, 56]          [6, 64, 56, 56]           16,384\n",
       "│    │    └─BatchNorm2d: 3-21            [6, 64, 56, 56]           [6, 64, 56, 56]           128\n",
       "│    │    └─ReLU: 3-22                   [6, 64, 56, 56]           [6, 64, 56, 56]           --\n",
       "│    │    └─Conv2d: 3-23                 [6, 64, 56, 56]           [6, 64, 56, 56]           36,864\n",
       "│    │    └─BatchNorm2d: 3-24            [6, 64, 56, 56]           [6, 64, 56, 56]           128\n",
       "│    │    └─ReLU: 3-25                   [6, 64, 56, 56]           [6, 64, 56, 56]           --\n",
       "│    │    └─Conv2d: 3-26                 [6, 64, 56, 56]           [6, 256, 56, 56]          16,384\n",
       "│    │    └─BatchNorm2d: 3-27            [6, 256, 56, 56]          [6, 256, 56, 56]          512\n",
       "│    │    └─ReLU: 3-28                   [6, 256, 56, 56]          [6, 256, 56, 56]          --\n",
       "├─Sequential: 1-6                        [6, 256, 56, 56]          [6, 512, 28, 28]          --\n",
       "│    └─Bottleneck: 2-4                   [6, 256, 56, 56]          [6, 512, 28, 28]          --\n",
       "│    │    └─Conv2d: 3-29                 [6, 256, 56, 56]          [6, 128, 56, 56]          32,768\n",
       "│    │    └─BatchNorm2d: 3-30            [6, 128, 56, 56]          [6, 128, 56, 56]          256\n",
       "│    │    └─ReLU: 3-31                   [6, 128, 56, 56]          [6, 128, 56, 56]          --\n",
       "│    │    └─Conv2d: 3-32                 [6, 128, 56, 56]          [6, 128, 28, 28]          147,456\n",
       "│    │    └─BatchNorm2d: 3-33            [6, 128, 28, 28]          [6, 128, 28, 28]          256\n",
       "│    │    └─ReLU: 3-34                   [6, 128, 28, 28]          [6, 128, 28, 28]          --\n",
       "│    │    └─Conv2d: 3-35                 [6, 128, 28, 28]          [6, 512, 28, 28]          65,536\n",
       "│    │    └─BatchNorm2d: 3-36            [6, 512, 28, 28]          [6, 512, 28, 28]          1,024\n",
       "│    │    └─Sequential: 3-37             [6, 256, 56, 56]          [6, 512, 28, 28]          132,096\n",
       "│    │    └─ReLU: 3-38                   [6, 512, 28, 28]          [6, 512, 28, 28]          --\n",
       "│    └─Bottleneck: 2-5                   [6, 512, 28, 28]          [6, 512, 28, 28]          --\n",
       "│    │    └─Conv2d: 3-39                 [6, 512, 28, 28]          [6, 128, 28, 28]          65,536\n",
       "│    │    └─BatchNorm2d: 3-40            [6, 128, 28, 28]          [6, 128, 28, 28]          256\n",
       "│    │    └─ReLU: 3-41                   [6, 128, 28, 28]          [6, 128, 28, 28]          --\n",
       "│    │    └─Conv2d: 3-42                 [6, 128, 28, 28]          [6, 128, 28, 28]          147,456\n",
       "│    │    └─BatchNorm2d: 3-43            [6, 128, 28, 28]          [6, 128, 28, 28]          256\n",
       "│    │    └─ReLU: 3-44                   [6, 128, 28, 28]          [6, 128, 28, 28]          --\n",
       "│    │    └─Conv2d: 3-45                 [6, 128, 28, 28]          [6, 512, 28, 28]          65,536\n",
       "│    │    └─BatchNorm2d: 3-46            [6, 512, 28, 28]          [6, 512, 28, 28]          1,024\n",
       "│    │    └─ReLU: 3-47                   [6, 512, 28, 28]          [6, 512, 28, 28]          --\n",
       "│    └─Bottleneck: 2-6                   [6, 512, 28, 28]          [6, 512, 28, 28]          --\n",
       "│    │    └─Conv2d: 3-48                 [6, 512, 28, 28]          [6, 128, 28, 28]          65,536\n",
       "│    │    └─BatchNorm2d: 3-49            [6, 128, 28, 28]          [6, 128, 28, 28]          256\n",
       "│    │    └─ReLU: 3-50                   [6, 128, 28, 28]          [6, 128, 28, 28]          --\n",
       "│    │    └─Conv2d: 3-51                 [6, 128, 28, 28]          [6, 128, 28, 28]          147,456\n",
       "│    │    └─BatchNorm2d: 3-52            [6, 128, 28, 28]          [6, 128, 28, 28]          256\n",
       "│    │    └─ReLU: 3-53                   [6, 128, 28, 28]          [6, 128, 28, 28]          --\n",
       "│    │    └─Conv2d: 3-54                 [6, 128, 28, 28]          [6, 512, 28, 28]          65,536\n",
       "│    │    └─BatchNorm2d: 3-55            [6, 512, 28, 28]          [6, 512, 28, 28]          1,024\n",
       "│    │    └─ReLU: 3-56                   [6, 512, 28, 28]          [6, 512, 28, 28]          --\n",
       "│    └─Bottleneck: 2-7                   [6, 512, 28, 28]          [6, 512, 28, 28]          --\n",
       "│    │    └─Conv2d: 3-57                 [6, 512, 28, 28]          [6, 128, 28, 28]          65,536\n",
       "│    │    └─BatchNorm2d: 3-58            [6, 128, 28, 28]          [6, 128, 28, 28]          256\n",
       "│    │    └─ReLU: 3-59                   [6, 128, 28, 28]          [6, 128, 28, 28]          --\n",
       "│    │    └─Conv2d: 3-60                 [6, 128, 28, 28]          [6, 128, 28, 28]          147,456\n",
       "│    │    └─BatchNorm2d: 3-61            [6, 128, 28, 28]          [6, 128, 28, 28]          256\n",
       "│    │    └─ReLU: 3-62                   [6, 128, 28, 28]          [6, 128, 28, 28]          --\n",
       "│    │    └─Conv2d: 3-63                 [6, 128, 28, 28]          [6, 512, 28, 28]          65,536\n",
       "│    │    └─BatchNorm2d: 3-64            [6, 512, 28, 28]          [6, 512, 28, 28]          1,024\n",
       "│    │    └─ReLU: 3-65                   [6, 512, 28, 28]          [6, 512, 28, 28]          --\n",
       "├─Sequential: 1-7                        [6, 512, 28, 28]          [6, 1024, 14, 14]         --\n",
       "│    └─Bottleneck: 2-8                   [6, 512, 28, 28]          [6, 1024, 14, 14]         --\n",
       "│    │    └─Conv2d: 3-66                 [6, 512, 28, 28]          [6, 256, 28, 28]          131,072\n",
       "│    │    └─BatchNorm2d: 3-67            [6, 256, 28, 28]          [6, 256, 28, 28]          512\n",
       "│    │    └─ReLU: 3-68                   [6, 256, 28, 28]          [6, 256, 28, 28]          --\n",
       "│    │    └─Conv2d: 3-69                 [6, 256, 28, 28]          [6, 256, 14, 14]          589,824\n",
       "│    │    └─BatchNorm2d: 3-70            [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
       "│    │    └─ReLU: 3-71                   [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
       "│    │    └─Conv2d: 3-72                 [6, 256, 14, 14]          [6, 1024, 14, 14]         262,144\n",
       "│    │    └─BatchNorm2d: 3-73            [6, 1024, 14, 14]         [6, 1024, 14, 14]         2,048\n",
       "│    │    └─Sequential: 3-74             [6, 512, 28, 28]          [6, 1024, 14, 14]         526,336\n",
       "│    │    └─ReLU: 3-75                   [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
       "│    └─Bottleneck: 2-9                   [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
       "│    │    └─Conv2d: 3-76                 [6, 1024, 14, 14]         [6, 256, 14, 14]          262,144\n",
       "│    │    └─BatchNorm2d: 3-77            [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
       "│    │    └─ReLU: 3-78                   [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
       "│    │    └─Conv2d: 3-79                 [6, 256, 14, 14]          [6, 256, 14, 14]          589,824\n",
       "│    │    └─BatchNorm2d: 3-80            [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
       "│    │    └─ReLU: 3-81                   [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
       "│    │    └─Conv2d: 3-82                 [6, 256, 14, 14]          [6, 1024, 14, 14]         262,144\n",
       "│    │    └─BatchNorm2d: 3-83            [6, 1024, 14, 14]         [6, 1024, 14, 14]         2,048\n",
       "│    │    └─ReLU: 3-84                   [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
       "│    └─Bottleneck: 2-10                  [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
       "│    │    └─Conv2d: 3-85                 [6, 1024, 14, 14]         [6, 256, 14, 14]          262,144\n",
       "│    │    └─BatchNorm2d: 3-86            [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
       "│    │    └─ReLU: 3-87                   [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
       "│    │    └─Conv2d: 3-88                 [6, 256, 14, 14]          [6, 256, 14, 14]          589,824\n",
       "│    │    └─BatchNorm2d: 3-89            [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
       "│    │    └─ReLU: 3-90                   [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
       "│    │    └─Conv2d: 3-91                 [6, 256, 14, 14]          [6, 1024, 14, 14]         262,144\n",
       "│    │    └─BatchNorm2d: 3-92            [6, 1024, 14, 14]         [6, 1024, 14, 14]         2,048\n",
       "│    │    └─ReLU: 3-93                   [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
       "│    └─Bottleneck: 2-11                  [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
       "│    │    └─Conv2d: 3-94                 [6, 1024, 14, 14]         [6, 256, 14, 14]          262,144\n",
       "│    │    └─BatchNorm2d: 3-95            [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
       "│    │    └─ReLU: 3-96                   [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
       "│    │    └─Conv2d: 3-97                 [6, 256, 14, 14]          [6, 256, 14, 14]          589,824\n",
       "│    │    └─BatchNorm2d: 3-98            [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
       "│    │    └─ReLU: 3-99                   [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
       "│    │    └─Conv2d: 3-100                [6, 256, 14, 14]          [6, 1024, 14, 14]         262,144\n",
       "│    │    └─BatchNorm2d: 3-101           [6, 1024, 14, 14]         [6, 1024, 14, 14]         2,048\n",
       "│    │    └─ReLU: 3-102                  [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
       "│    └─Bottleneck: 2-12                  [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
       "│    │    └─Conv2d: 3-103                [6, 1024, 14, 14]         [6, 256, 14, 14]          262,144\n",
       "│    │    └─BatchNorm2d: 3-104           [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
       "│    │    └─ReLU: 3-105                  [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
       "│    │    └─Conv2d: 3-106                [6, 256, 14, 14]          [6, 256, 14, 14]          589,824\n",
       "│    │    └─BatchNorm2d: 3-107           [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
       "│    │    └─ReLU: 3-108                  [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
       "│    │    └─Conv2d: 3-109                [6, 256, 14, 14]          [6, 1024, 14, 14]         262,144\n",
       "│    │    └─BatchNorm2d: 3-110           [6, 1024, 14, 14]         [6, 1024, 14, 14]         2,048\n",
       "│    │    └─ReLU: 3-111                  [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
       "│    └─Bottleneck: 2-13                  [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
       "│    │    └─Conv2d: 3-112                [6, 1024, 14, 14]         [6, 256, 14, 14]          262,144\n",
       "│    │    └─BatchNorm2d: 3-113           [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
       "│    │    └─ReLU: 3-114                  [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
       "│    │    └─Conv2d: 3-115                [6, 256, 14, 14]          [6, 256, 14, 14]          589,824\n",
       "│    │    └─BatchNorm2d: 3-116           [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
       "│    │    └─ReLU: 3-117                  [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
       "│    │    └─Conv2d: 3-118                [6, 256, 14, 14]          [6, 1024, 14, 14]         262,144\n",
       "│    │    └─BatchNorm2d: 3-119           [6, 1024, 14, 14]         [6, 1024, 14, 14]         2,048\n",
       "│    │    └─ReLU: 3-120                  [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
       "├─Sequential: 1-8                        [6, 1024, 14, 14]         [6, 2048, 7, 7]           --\n",
       "│    └─Bottleneck: 2-14                  [6, 1024, 14, 14]         [6, 2048, 7, 7]           --\n",
       "│    │    └─Conv2d: 3-121                [6, 1024, 14, 14]         [6, 512, 14, 14]          524,288\n",
       "│    │    └─BatchNorm2d: 3-122           [6, 512, 14, 14]          [6, 512, 14, 14]          1,024\n",
       "│    │    └─ReLU: 3-123                  [6, 512, 14, 14]          [6, 512, 14, 14]          --\n",
       "│    │    └─Conv2d: 3-124                [6, 512, 14, 14]          [6, 512, 7, 7]            2,359,296\n",
       "│    │    └─BatchNorm2d: 3-125           [6, 512, 7, 7]            [6, 512, 7, 7]            1,024\n",
       "│    │    └─ReLU: 3-126                  [6, 512, 7, 7]            [6, 512, 7, 7]            --\n",
       "│    │    └─Conv2d: 3-127                [6, 512, 7, 7]            [6, 2048, 7, 7]           1,048,576\n",
       "│    │    └─BatchNorm2d: 3-128           [6, 2048, 7, 7]           [6, 2048, 7, 7]           4,096\n",
       "│    │    └─Sequential: 3-129            [6, 1024, 14, 14]         [6, 2048, 7, 7]           2,101,248\n",
       "│    │    └─ReLU: 3-130                  [6, 2048, 7, 7]           [6, 2048, 7, 7]           --\n",
       "│    └─Bottleneck: 2-15                  [6, 2048, 7, 7]           [6, 2048, 7, 7]           --\n",
       "│    │    └─Conv2d: 3-131                [6, 2048, 7, 7]           [6, 512, 7, 7]            1,048,576\n",
       "│    │    └─BatchNorm2d: 3-132           [6, 512, 7, 7]            [6, 512, 7, 7]            1,024\n",
       "│    │    └─ReLU: 3-133                  [6, 512, 7, 7]            [6, 512, 7, 7]            --\n",
       "│    │    └─Conv2d: 3-134                [6, 512, 7, 7]            [6, 512, 7, 7]            2,359,296\n",
       "│    │    └─BatchNorm2d: 3-135           [6, 512, 7, 7]            [6, 512, 7, 7]            1,024\n",
       "│    │    └─ReLU: 3-136                  [6, 512, 7, 7]            [6, 512, 7, 7]            --\n",
       "│    │    └─Conv2d: 3-137                [6, 512, 7, 7]            [6, 2048, 7, 7]           1,048,576\n",
       "│    │    └─BatchNorm2d: 3-138           [6, 2048, 7, 7]           [6, 2048, 7, 7]           4,096\n",
       "│    │    └─ReLU: 3-139                  [6, 2048, 7, 7]           [6, 2048, 7, 7]           --\n",
       "│    └─Bottleneck: 2-16                  [6, 2048, 7, 7]           [6, 2048, 7, 7]           --\n",
       "│    │    └─Conv2d: 3-140                [6, 2048, 7, 7]           [6, 512, 7, 7]            1,048,576\n",
       "│    │    └─BatchNorm2d: 3-141           [6, 512, 7, 7]            [6, 512, 7, 7]            1,024\n",
       "│    │    └─ReLU: 3-142                  [6, 512, 7, 7]            [6, 512, 7, 7]            --\n",
       "│    │    └─Conv2d: 3-143                [6, 512, 7, 7]            [6, 512, 7, 7]            2,359,296\n",
       "│    │    └─BatchNorm2d: 3-144           [6, 512, 7, 7]            [6, 512, 7, 7]            1,024\n",
       "│    │    └─ReLU: 3-145                  [6, 512, 7, 7]            [6, 512, 7, 7]            --\n",
       "│    │    └─Conv2d: 3-146                [6, 512, 7, 7]            [6, 2048, 7, 7]           1,048,576\n",
       "│    │    └─BatchNorm2d: 3-147           [6, 2048, 7, 7]           [6, 2048, 7, 7]           4,096\n",
       "│    │    └─ReLU: 3-148                  [6, 2048, 7, 7]           [6, 2048, 7, 7]           --\n",
       "├─AdaptiveAvgPool2d: 1-9                 [6, 2048, 7, 7]           [6, 2048, 1, 1]           --\n",
       "├─Linear: 1-10                           [6, 2048]                 [6, 1000]                 2,049,000\n",
       "===================================================================================================================\n",
       "Total params: 25,557,032\n",
       "Trainable params: 25,557,032\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (Units.GIGABYTES): 24.54\n",
       "===================================================================================================================\n",
       "Input size (MB): 3.61\n",
       "Forward/backward pass size (MB): 1066.99\n",
       "Params size (MB): 102.23\n",
       "Estimated Total Size (MB): 1172.83\n",
       "==================================================================================================================="
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resnet_model = resnet50(weights=ResNet50_Weights.DEFAULT)\n",
    "summary(resnet_model,\n",
    "        input_data=imgs,\n",
    "        col_names=['input_size',\n",
    "                   'output_size',\n",
    "                   'num_params'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59077750",
   "metadata": {},
   "source": [
    "We set the mode to `eval()` to ensure that the model is ready to predict on new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9e53e23b",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (4): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (5): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resnet_model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d63ea9",
   "metadata": {},
   "source": [
    "Inspecting the output above, we see that when setting up the\n",
    "`resnet_model`, the authors defined a `Bottleneck`, much like our\n",
    "`BuildingBlock` module.\n",
    "\n",
    "We now feed our six images through the fitted network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0890e08a",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_preds = resnet_model(imgs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d8f653",
   "metadata": {},
   "source": [
    "Let’s look at the predicted probabilities for each of the top 3 choices. First we compute\n",
    "the probabilities by applying the softmax to the logits in `img_preds`. Note that\n",
    "we have had to call the `detach()` method on the tensor `img_preds` in order to convert\n",
    "it to a more familiar `ndarray`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "60ec5152",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_probs = np.exp(np.asarray(img_preds.detach()))\n",
    "img_probs /= img_probs.sum(1)[:,None]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ee6c41c",
   "metadata": {},
   "source": [
    "In order to see the class labels, we must download the index file associated with `imagenet`. {This is avalable from the book website and  [s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json](https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json).}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9408bbd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "labs = json.load(open('imagenet_class_index.json'))\n",
    "class_labels = pd.DataFrame([(int(k), v[1]) for k, v in \n",
    "                           labs.items()],\n",
    "                           columns=['idx', 'label'])\n",
    "class_labels = class_labels.set_index('idx')\n",
    "class_labels = class_labels.sort_index()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d134d92",
   "metadata": {},
   "source": [
    "We’ll now construct a data frame for each image file\n",
    "with the labels with the three highest probabilities as\n",
    "estimated by the model above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5369faef",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image: book_images/Cape_Weaver.jpg\n",
      "      label      prob\n",
      "0   jacamar  0.297499\n",
      "1     macaw  0.068107\n",
      "2  lorikeet  0.051105\n",
      "Image: book_images/Flamingo.jpg\n",
      "            label      prob\n",
      "0        flamingo  0.609515\n",
      "1       spoonbill  0.013586\n",
      "2  American_egret  0.002132\n",
      "Image: book_images/Hawk_Fountain.jpg\n",
      "            label      prob\n",
      "0            kite  0.184682\n",
      "1           robin  0.084021\n",
      "2  great_grey_owl  0.061274\n",
      "Image: book_images/Hawk_cropped.jpg\n",
      "            label      prob\n",
      "0            kite  0.453833\n",
      "1  great_grey_owl  0.015914\n",
      "2             jay  0.012210\n",
      "Image: book_images/Lhasa_Apso.jpg\n",
      "             label      prob\n",
      "0            Lhasa  0.260317\n",
      "1         Shih-Tzu  0.097196\n",
      "2  Tibetan_terrier  0.032820\n",
      "Image: book_images/Sleeping_Cat.jpg\n",
      "         label      prob\n",
      "0  Persian_cat  0.163070\n",
      "1        tabby  0.074143\n",
      "2    tiger_cat  0.042578\n"
     ]
    }
   ],
   "source": [
    "for i, imgfile in enumerate(imgfiles):\n",
    "    img_df = class_labels.copy()\n",
    "    img_df['prob'] = img_probs[i]\n",
    "    img_df = img_df.sort_values(by='prob', ascending=False)[:3]\n",
    "    print(f'Image: {imgfile}')\n",
    "    print(img_df.reset_index().drop(columns=['idx']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8da904e",
   "metadata": {},
   "source": [
    "We see that the model\n",
    "is quite confident about `Flamingo.jpg`, but a little less so for the\n",
    "other images.\n",
    "\n",
    "We end this section with our usual cleanup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f585e5d2",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "del(cifar_test,\n",
    "    cifar_train,\n",
    "    cifar_dm,\n",
    "    cifar_module,\n",
    "    cifar_logger,\n",
    "    cifar_optimizer,\n",
    "    cifar_trainer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e80c2601",
   "metadata": {},
   "source": [
    "## IMDB Document Classification\n",
    "We now implement models for sentiment classification (Section 10.4)  on the `IMDB`\n",
    "dataset. As mentioned above code block~8, we are using\n",
    "a preprocessed version of the `IMDB` dataset found in the\n",
    "`keras` package. As `keras` uses `tensorflow`, a different\n",
    "tensor and deep learning library, we have\n",
    "converted the data to be suitable for `torch`. The\n",
    "code used to convert from `keras` is\n",
    "available in the module `ISLP.torch._make_imdb`. It\n",
    "requires some of the `keras` packages to run. These data use a dictionary of size 10,000.\n",
    "\n",
    "We have stored three different representations of the review data for this lab:\n",
    "\n",
    "* `load_tensor()`, a sparse tensor version usable by `torch`;\n",
    "* `load_sparse()`, a sparse matrix version usable by `sklearn`, since we will compare with a lasso fit;\n",
    "* `load_sequential()`, a padded\n",
    "version of the original sequence representation, limited to the last\n",
    "500 words of each review.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "30285383",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1,   14,   22,   16,   43,  530,  973, 1622, 1385,   65,  458,\n",
       "       4468], dtype=int32)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(imdb_seq_train,\n",
    " imdb_seq_test) = load_sequential(root='data/IMDB')\n",
    "padded_sample = np.asarray(imdb_seq_train.tensors[0][0])\n",
    "sample_review = padded_sample[padded_sample > 0][:12]\n",
    "sample_review[:12]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf823309",
   "metadata": {},
   "source": [
    "The datasets `imdb_seq_train` and `imdb_seq_test` are\n",
    "both instances of the class `TensorDataset`. The\n",
    "tensors used to construct them can be found in the `tensors` attribute, with\n",
    "the first tensor  the features `X` and the second  the outcome `Y`.\n",
    "We have taken the first row of features and stored it as `padded_sample`. In the preprocessing\n",
    "used to form these data, sequences were padded with 0s in the beginning if they were\n",
    "not long enough, hence we remove this padding by restricting to entries where\n",
    "`padded_sample > 0`. We then provide the first 12 words of the sample review.\n",
    "\n",
    "We can find these words in the `lookup` dictionary from the `ISLP.torch.imdb` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0bfe31c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"<START> this film was just brilliant casting location scenery story direction everyone's\""
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lookup = load_lookup(root='data/IMDB')\n",
    "' '.join(lookup[i] for i in sample_review)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be8794d4",
   "metadata": {},
   "source": [
    "For our first model, we have created a binary feature for each\n",
    "of the 10,000 possible words in the dataset, with an entry of one\n",
    "in the $i,j$ entry if word $j$ appears in review $i$. As most reviews\n",
    "are quite short, such a feature matrix has over 98% zeros. These data\n",
    "are accessed using `load_tensor()` from the `ISLP` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "46a4749f",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "max_num_workers=10\n",
    "(imdb_train,\n",
    " imdb_test) = load_tensor(root='data/IMDB')\n",
    "imdb_dm = SimpleDataModule(imdb_train,\n",
    "                           imdb_test,\n",
    "                           validation=2000,\n",
    "                           num_workers=min(6, max_num_workers),\n",
    "                           batch_size=512)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1772177",
   "metadata": {},
   "source": [
    "We’ll use a two-layer model for our first model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7400ab60",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "class IMDBModel(nn.Module):\n",
    "\n",
    "    def __init__(self, input_size):\n",
    "        super(IMDBModel, self).__init__()\n",
    "        self.dense1 = nn.Linear(input_size, 16)\n",
    "        self.activation = nn.ReLU()\n",
    "        self.dense2 = nn.Linear(16, 16)\n",
    "        self.output = nn.Linear(16, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        val = x\n",
    "        for _map in [self.dense1,\n",
    "                     self.activation,\n",
    "                     self.dense2,\n",
    "                     self.activation,\n",
    "                     self.output]:\n",
    "            val = _map(val)\n",
    "        return torch.flatten(val)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4596108",
   "metadata": {},
   "source": [
    "We now instantiate our model and look at a summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "51960f4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "===================================================================================================================\n",
       "Layer (type:depth-idx)                   Input Shape               Output Shape              Param #\n",
       "===================================================================================================================\n",
       "IMDBModel                                [25000, 10003]            [25000]                   --\n",
       "├─Linear: 1-1                            [25000, 10003]            [25000, 16]               160,064\n",
       "├─ReLU: 1-2                              [25000, 16]               [25000, 16]               --\n",
       "├─Linear: 1-3                            [25000, 16]               [25000, 16]               272\n",
       "├─ReLU: 1-4                              [25000, 16]               [25000, 16]               --\n",
       "├─Linear: 1-5                            [25000, 16]               [25000, 1]                17\n",
       "===================================================================================================================\n",
       "Total params: 160,353\n",
       "Trainable params: 160,353\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (Units.GIGABYTES): 4.01\n",
       "===================================================================================================================\n",
       "Input size (MB): 1000.30\n",
       "Forward/backward pass size (MB): 6.60\n",
       "Params size (MB): 0.64\n",
       "Estimated Total Size (MB): 1007.54\n",
       "==================================================================================================================="
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_model = IMDBModel(imdb_test.tensors[0].size()[1])\n",
    "summary(imdb_model,\n",
    "        input_size=imdb_test.tensors[0].size(),\n",
    "        col_names=['input_size',\n",
    "                   'output_size',\n",
    "                   'num_params'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6433c5a4",
   "metadata": {},
   "source": [
    "We’ll again use\n",
    "a smaller learning rate for these data,\n",
    "hence we pass an `optimizer` to the\n",
    "`SimpleModule`. \n",
    "Since the reviews are classified into\n",
    "positive or negative sentiment, we use\n",
    "`SimpleModule.binary_classification()`. {Our use of\n",
    "  `binary_classification()` instead of  `classification()` is\n",
    "  due to some subtlety in how `torchmetrics.Accuracy()` works,\n",
    "as well as the data type of the targets.}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "75faa2be",
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_optimizer = RMSprop(imdb_model.parameters(), lr=0.001)\n",
    "imdb_module = SimpleModule.binary_classification(\n",
    "                         imdb_model,\n",
    "                         optimizer=imdb_optimizer)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71fbb840",
   "metadata": {},
   "source": [
    "Having loaded the datasets into a data module\n",
    "and created a `SimpleModule`, the remaining steps\n",
    "are familiar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2637adf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name  | Type              | Params\n",
      "--------------------------------------------\n",
      "0 | model | IMDBModel         | 160 K \n",
      "1 | loss  | BCEWithLogitsLoss | 0     \n",
      "--------------------------------------------\n",
      "160 K     Trainable params\n",
      "0         Non-trainable params\n",
      "160 K     Total params\n",
      "0.641     Total estimated model params size (MB)\n",
      "/Users/jonathantaylor/anaconda3/envs/ISLP_312/lib/python3.12/site-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (45) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
      "`Trainer.fit` stopped: `max_epochs=30` reached.\n"
     ]
    }
   ],
   "source": [
    "imdb_logger = CSVLogger('logs', name='IMDB')\n",
    "imdb_trainer = Trainer(deterministic=True,\n",
    "                       max_epochs=30,\n",
    "                       logger=imdb_logger,\n",
    "                       enable_progress_bar=False,\n",
    "                       callbacks=[ErrorTracker()])\n",
    "imdb_trainer.fit(imdb_module,\n",
    "                 datamodule=imdb_dm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e077efc8",
   "metadata": {},
   "source": [
    "Evaluating the test error yields roughly 86% accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0500cc62",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "       Test metric             DataLoader 0\n",
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "      test_accuracy         0.8450000286102295\n",
      "        test_loss           1.2167928218841553\n",
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_loss': 1.2167928218841553, 'test_accuracy': 0.8450000286102295}]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_results = imdb_trainer.test(imdb_module, datamodule=imdb_dm)\n",
    "test_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f73c7526",
   "metadata": {},
   "source": [
    "### Comparison to Lasso\n",
    "We now fit a lasso logistic regression model\n",
    " using `LogisticRegression()` from `sklearn`. Since `sklearn` does not recognize\n",
    "the sparse tensors of `torch`, we use a sparse\n",
    "matrix that is recognized by `sklearn.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a6e81d74",
   "metadata": {},
   "outputs": [],
   "source": [
    "((X_train, Y_train),\n",
    " (X_valid, Y_valid),\n",
    " (X_test, Y_test)) = load_sparse(validation=2000,\n",
    "                                 random_state=0,\n",
    "                                 root='data/IMDB')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be8b857",
   "metadata": {},
   "source": [
    "Similar to what we did in\n",
    "Section 10.9.1,\n",
    "we construct a series of 50 values for the lasso reguralization parameter $\\lambda$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b3049398",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "lam_max = np.abs(X_train.T * (Y_train - Y_train.mean())).max()\n",
    "lam_val = lam_max * np.exp(np.linspace(np.log(1),\n",
    "                                       np.log(1e-4), 50))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f08819f7",
   "metadata": {},
   "source": [
    "With `LogisticRegression()` the regularization parameter\n",
    "$C$ is specified as the inverse of $\\lambda$. There are several\n",
    "solvers for logistic regression; here we use `liblinear` which\n",
    "works well with the sparse input format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e075f0b6",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "logit = LogisticRegression(penalty='l1', \n",
    "                           C=1/lam_max,\n",
    "                           solver='liblinear',\n",
    "                           warm_start=True,\n",
    "                           fit_intercept=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94c8b4ff",
   "metadata": {},
   "source": [
    "The path of 50 values takes approximately 40 seconds to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a546c39a",
   "metadata": {},
   "outputs": [],
   "source": [
    "coefs = []\n",
    "intercepts = []\n",
    "\n",
    "for l in lam_val:\n",
    "    logit.C = 1/l\n",
    "    logit.fit(X_train, Y_train)\n",
    "    coefs.append(logit.coef_.copy())\n",
    "    intercepts.append(logit.intercept_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a33b90e",
   "metadata": {},
   "source": [
    "The coefficient and intercepts have an extraneous dimension which can be removed\n",
    "by the `np.squeeze()`  function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2190268a",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "coefs = np.squeeze(coefs)\n",
    "intercepts = np.squeeze(intercepts)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1271d2a2",
   "metadata": {},
   "source": [
    "We’ll now make a plot to compare our neural network results with the\n",
    "lasso."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "39221738",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "fig, axes = subplots(1, 2, figsize=(16, 8), sharey=True)\n",
    "for ((X_, Y_),\n",
    "     data_,\n",
    "     color) in zip([(X_train, Y_train),\n",
    "                    (X_valid, Y_valid),\n",
    "                    (X_test, Y_test)],\n",
    "                    ['Training', 'Validation', 'Test'],\n",
    "                    ['black', 'red', 'blue']):\n",
    "    linpred_ = X_ * coefs.T + intercepts[None,:]\n",
    "    label_ = np.array(linpred_ > 0)\n",
    "    accuracy_ = np.array([np.mean(Y_ == l) for l in label_.T])\n",
    "    axes[0].plot(-np.log(lam_val / X_train.shape[0]),\n",
    "                 accuracy_,\n",
    "                 '.--',\n",
    "                 color=color,\n",
    "                 markersize=13,\n",
    "                 linewidth=2,\n",
    "                 label=data_)\n",
    "axes[0].legend()\n",
    "axes[0].set_xlabel(r'$-\\log(\\lambda)$', fontsize=20)\n",
    "axes[0].set_ylabel('Accuracy', fontsize=20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d40e3d0e",
   "metadata": {},
   "source": [
    "Notice the use of `%%capture`, which suppresses the displaying of the partially completed figure. This is useful\n",
    "when making a complex figure, since the steps can be spread across two or more cells.\n",
    "We now add a plot of the lasso accuracy, and display the composed figure by simply entering its name at the end of the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9ba471b4",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_results = pd.read_csv(imdb_logger.experiment.metrics_file_path)\n",
    "summary_plot(imdb_results,\n",
    "             axes[1],\n",
    "             col='accuracy',\n",
    "             ylabel='Accuracy')\n",
    "axes[1].set_xticks(np.linspace(0, 30, 7).astype(int))\n",
    "axes[1].set_ylabel('Accuracy', fontsize=20)\n",
    "axes[1].set_xlabel('Epoch', fontsize=20)\n",
    "axes[1].set_ylim([0.5, 1]);\n",
    "axes[1].axhline(test_results[0]['test_accuracy'],\n",
    "                color='blue',\n",
    "                linestyle='--',\n",
    "                linewidth=3)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e2c89f",
   "metadata": {},
   "source": [
    "From the graphs we see that the accuracy of the lasso logistic regression peaks at about $0.88$,  as it does for  the neural network.\n",
    "\n",
    "Once again, we end with a cleanup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5f8c7df0",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "del(imdb_model,\n",
    "    imdb_trainer,\n",
    "    imdb_logger,\n",
    "    imdb_dm,\n",
    "    imdb_train,\n",
    "    imdb_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2a9982b",
   "metadata": {},
   "source": [
    "## Recurrent Neural Networks\n",
    "In this lab we fit the models illustrated in\n",
    "Section 10.5."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51642336",
   "metadata": {},
   "source": [
    "### Sequential Models for Document Classification\n",
    "Here we  fit a simple  LSTM RNN for sentiment prediction to\n",
    "the `IMDb` movie-review data, as discussed in Section 10.5.1.\n",
    "For an RNN we use the sequence of words in a document, taking their\n",
    "order into account. We loaded the preprocessed \n",
    "data at the beginning of\n",
    "Section 10.9.5.\n",
    "A script that details the preprocessing can be found in the\n",
    "`ISLP` library. Notably, since more than 90% of the documents\n",
    "had fewer than 500 words, we set the document length to 500. For\n",
    "longer documents, we used the last 500 words, and for shorter\n",
    "documents, we padded the front with blanks.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "af4d3414",
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_seq_dm = SimpleDataModule(imdb_seq_train,\n",
    "                               imdb_seq_test,\n",
    "                               validation=2000,\n",
    "                               batch_size=300,\n",
    "                               num_workers=min(6, max_num_workers)\n",
    "                               )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aa82266",
   "metadata": {},
   "source": [
    "The first layer of the RNN is an embedding layer of size 32, which will be\n",
    "learned during  training. This layer one-hot encodes  each document\n",
    "as a matrix of dimension $500 \\times 10,003$, and then maps these\n",
    "$10,003$ dimensions down to $32$. {The extra 3 dimensions\n",
    "correspond to commonly occurring non-word entries in the reviews.}\n",
    " Since each word is represented by an\n",
    "integer, this is effectively achieved by the creation of an embedding\n",
    "matrix of size $10,003\\times 32$; each of the 500 integers in the\n",
    "document are then mapped to the appropriate 32 real numbers by\n",
    "indexing the appropriate rows of this matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed2d5d9b",
   "metadata": {},
   "source": [
    "The second  layer is an LSTM with 32 units, and the output\n",
    "layer is a single logit for the binary classification task.\n",
    "In the last line of the `forward()` method below,\n",
    "we take the last 32-dimensional output of the LSTM and map it to our response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "053cda21",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "class LSTMModel(nn.Module):\n",
    "    def __init__(self, input_size):\n",
    "        super(LSTMModel, self).__init__()\n",
    "        self.embedding = nn.Embedding(input_size, 32)\n",
    "        self.lstm = nn.LSTM(input_size=32,\n",
    "                            hidden_size=32,\n",
    "                            batch_first=True)\n",
    "        self.dense = nn.Linear(32, 1)\n",
    "    def forward(self, x):\n",
    "        val, (h_n, c_n) = self.lstm(self.embedding(x))\n",
    "        return torch.flatten(self.dense(val[:,-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aad0a983",
   "metadata": {},
   "source": [
    "We instantiate and take a look at the summary of the model, using the\n",
    "first 10 documents in the corpus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1375cc01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "===================================================================================================================\n",
       "Layer (type:depth-idx)                   Input Shape               Output Shape              Param #\n",
       "===================================================================================================================\n",
       "LSTMModel                                [10, 500]                 [10]                      --\n",
       "├─Embedding: 1-1                         [10, 500]                 [10, 500, 32]             320,096\n",
       "├─LSTM: 1-2                              [10, 500, 32]             [10, 500, 32]             8,448\n",
       "├─Linear: 1-3                            [10, 32]                  [10, 1]                   33\n",
       "===================================================================================================================\n",
       "Total params: 328,577\n",
       "Trainable params: 328,577\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (Units.MEGABYTES): 45.44\n",
       "===================================================================================================================\n",
       "Input size (MB): 50.00\n",
       "Forward/backward pass size (MB): 2.56\n",
       "Params size (MB): 1.31\n",
       "Estimated Total Size (MB): 53.87\n",
       "==================================================================================================================="
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lstm_model = LSTMModel(X_test.shape[-1])\n",
    "summary(lstm_model,\n",
    "        input_data=imdb_seq_train.tensors[0][:10],\n",
    "        col_names=['input_size',\n",
    "                   'output_size',\n",
    "                   'num_params'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa9049a4",
   "metadata": {},
   "source": [
    "The 10,003 is suppressed in the summary, but we see it in the\n",
    "parameter count, since $10,003\\times 32=320,096$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f89f58af",
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm_module = SimpleModule.binary_classification(lstm_model)\n",
    "lstm_logger = CSVLogger('logs', name='IMDB_LSTM')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ae8fc1fe",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name  | Type              | Params\n",
      "--------------------------------------------\n",
      "0 | model | LSTMModel         | 328 K \n",
      "1 | loss  | BCEWithLogitsLoss | 0     \n",
      "--------------------------------------------\n",
      "328 K     Trainable params\n",
      "0         Non-trainable params\n",
      "328 K     Total params\n",
      "1.314     Total estimated model params size (MB)\n",
      "`Trainer.fit` stopped: `max_epochs=20` reached.\n"
     ]
    }
   ],
   "source": [
    "lstm_trainer = Trainer(deterministic=True,\n",
    "                       max_epochs=20,\n",
    "                       logger=lstm_logger,\n",
    "                       enable_progress_bar=False,\n",
    "                       callbacks=[ErrorTracker()])\n",
    "lstm_trainer.fit(lstm_module,\n",
    "                 datamodule=imdb_seq_dm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf6061b3",
   "metadata": {},
   "source": [
    "The rest is now similar to other networks we have fit. We\n",
    "track the test performance as the network is fit, and see that it attains 85% accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9c8dd061",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "       Test metric             DataLoader 0\n",
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "      test_accuracy          0.841759979724884\n",
      "        test_loss           0.7341755032539368\n",
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_loss': 0.7341755032539368, 'test_accuracy': 0.841759979724884}]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lstm_trainer.test(lstm_module, datamodule=imdb_seq_dm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40823178",
   "metadata": {},
   "source": [
    "We once again show the learning progress, followed by cleanup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "774197a2",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 1.0)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lstm_results = pd.read_csv(lstm_logger.experiment.metrics_file_path)\n",
    "fig, ax = subplots(1, 1, figsize=(6, 6))\n",
    "summary_plot(lstm_results,\n",
    "             ax,\n",
    "             col='accuracy',\n",
    "             ylabel='Accuracy')\n",
    "ax.set_xticks(np.linspace(0, 20, 5).astype(int))\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_ylim([0.5, 1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "dbae5960",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "del(lstm_model,\n",
    "    lstm_trainer,\n",
    "    lstm_logger,\n",
    "    imdb_seq_dm,\n",
    "    imdb_seq_train,\n",
    "    imdb_seq_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8a3c8f",
   "metadata": {},
   "source": [
    "### Time Series Prediction\n",
    "We now show how to fit the models in Section 10.5.2\n",
    "for  time series prediction.\n",
    "We first load and standardize the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "023c7d14",
   "metadata": {},
   "outputs": [],
   "source": [
    "NYSE = load_data('NYSE')\n",
    "cols = ['DJ_return', 'log_volume', 'log_volatility']\n",
    "X = pd.DataFrame(StandardScaler(\n",
    "                     with_mean=True,\n",
    "                     with_std=True).fit_transform(NYSE[cols]),\n",
    "                 columns=NYSE[cols].columns,\n",
    "                 index=NYSE.index)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03d5171b",
   "metadata": {},
   "source": [
    "Next we set up the lagged versions of the data, dropping\n",
    "any rows with missing values using the `dropna()`  method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "24aa4a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for lag in range(1, 6):\n",
    "    for col in cols:\n",
    "        newcol = np.zeros(X.shape[0]) * np.nan\n",
    "        newcol[lag:] = X[col].values[:-lag]\n",
    "        X.insert(len(X.columns), \"{0}_{1}\".format(col, lag), newcol)\n",
    "X.insert(len(X.columns), 'train', NYSE['train'])\n",
    "X = X.dropna()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2a02e25",
   "metadata": {},
   "source": [
    "Finally, we extract the response, training indicator, and drop the current day’s `DJ_return` and\n",
    "`log_volatility` to predict only from previous day’s data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5f0a4c0e",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DJ_return_1', 'log_volume_1', 'log_volatility_1', 'DJ_return_2',\n",
       "       'log_volume_2', 'log_volatility_2', 'DJ_return_3', 'log_volume_3',\n",
       "       'log_volatility_3', 'DJ_return_4', 'log_volume_4', 'log_volatility_4',\n",
       "       'DJ_return_5', 'log_volume_5', 'log_volatility_5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y, train = X['log_volume'], X['train']\n",
    "X = X.drop(columns=['train'] + cols)\n",
    "X.columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc66b8e",
   "metadata": {},
   "source": [
    "We first fit a simple linear model and compute the $R^2$ on the test data using\n",
    "the `score()`  method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "4ec80811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4128912938562521"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = LinearRegression()\n",
    "M.fit(X[train], Y[train])\n",
    "M.score(X[~train], Y[~train])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3053ae12",
   "metadata": {},
   "source": [
    "We refit this model, including the factor variable `day_of_week`.\n",
    "For a categorical series in `pandas`, we can form the indicators\n",
    "using the `get_dummies()`  method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ec2de6ca",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "X_day = pd.concat([X, \n",
    "                  pd.get_dummies(NYSE['day_of_week'])],\n",
    "                  axis=1).dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "765b70b4",
   "metadata": {},
   "source": [
    " Note that we do not have\n",
    "to reinstantiate the linear regression model\n",
    "as its `fit()` method accepts a design matrix and a response directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8ff00b65",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4595563133053273"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.fit(X_day[train], Y[train])\n",
    "M.score(X_day[~train], Y[~train])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88374112",
   "metadata": {},
   "source": [
    "This model achieves an $R^2$ of about 46%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2160545",
   "metadata": {},
   "source": [
    "To fit the RNN, we must reshape the data, as it will expect 5 lagged\n",
    "versions of each feature as indicated by the  `input_shape` argument\n",
    "to the layer `nn.RNN()`  below. We first\n",
    "ensure the columns of  our data frame are such that a reshaped\n",
    "matrix will have the variables correctly lagged. We use the\n",
    "`reindex()`  method to do this.\n",
    "\n",
    "For an input shape `(5,3)`, each row represents a lagged version of the three variables.\n",
    "The `nn.RNN()` layer also expects the first row of each\n",
    "observation to be earliest in time, so we must reverse the current order.  Hence we loop over\n",
    "`range(5,0,-1)` below, which is\n",
    "an example of using a  `slice()`  to index\n",
    "iterable objects. The general notation is `start:end:step`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "08107439",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DJ_return_5', 'log_volume_5', 'log_volatility_5', 'DJ_return_4',\n",
       "       'log_volume_4', 'log_volatility_4', 'DJ_return_3', 'log_volume_3',\n",
       "       'log_volatility_3', 'DJ_return_2', 'log_volume_2', 'log_volatility_2',\n",
       "       'DJ_return_1', 'log_volume_1', 'log_volatility_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_cols = []\n",
    "for lag in range(5,0,-1):\n",
    "    for col in cols:\n",
    "        ordered_cols.append('{0}_{1}'.format(col, lag))\n",
    "X = X.reindex(columns=ordered_cols)\n",
    "X.columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7f38634",
   "metadata": {},
   "source": [
    "We now reshape the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "1776f3d2",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6046, 5, 3)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_rnn = X.to_numpy().reshape((-1,5,3))\n",
    "X_rnn.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "385f3387",
   "metadata": {},
   "source": [
    "By specifying the first size as -1, `numpy.reshape()` deduces its size based on the remaining arguments.\n",
    "\n",
    "Now we are ready to proceed with the RNN, which uses 12 hidden units, and  10%\n",
    "dropout.\n",
    "After passing through the RNN,  we extract the final time point as `val[:,-1]`\n",
    "in `forward()` below. This gets passed through a 10% dropout and then flattened through\n",
    "a linear layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "355e86eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "class NYSEModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(NYSEModel, self).__init__()\n",
    "        self.rnn = nn.RNN(3,\n",
    "                          12,\n",
    "                          batch_first=True)\n",
    "        self.dense = nn.Linear(12, 1)\n",
    "        self.dropout = nn.Dropout(0.1)\n",
    "    def forward(self, x):\n",
    "        val, h_n = self.rnn(x)\n",
    "        val = self.dense(self.dropout(val[:,-1]))\n",
    "        return torch.flatten(val)\n",
    "nyse_model = NYSEModel()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3abd764",
   "metadata": {},
   "source": [
    "We  fit the model in a similar fashion to previous networks. We\n",
    "supply the `fit` function with test data as validation data, so that when\n",
    "we monitor its progress and plot the history function we can see the\n",
    "progress on the test data. Of course we should not use this as a basis for\n",
    "early stopping, since then the test performance would be biased.\n",
    "\n",
    "We form the training dataset similar to\n",
    "our `Hitters` example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "72ab9a66",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_8578/3890359531.py:4: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  Y_t = torch.tensor(Y[mask].astype(np.float32))\n",
      "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_8578/3890359531.py:4: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  Y_t = torch.tensor(Y[mask].astype(np.float32))\n"
     ]
    }
   ],
   "source": [
    "datasets = []\n",
    "for mask in [train, ~train]:\n",
    "    X_rnn_t = torch.tensor(X_rnn[mask].astype(np.float32))\n",
    "    Y_t = torch.tensor(Y[mask].astype(np.float32))\n",
    "    datasets.append(TensorDataset(X_rnn_t, Y_t))\n",
    "nyse_train, nyse_test = datasets\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b81c1f1e",
   "metadata": {},
   "source": [
    "Following our usual pattern, we inspect the summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "1c10263b",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "===================================================================================================================\n",
       "Layer (type:depth-idx)                   Input Shape               Output Shape              Param #\n",
       "===================================================================================================================\n",
       "NYSEModel                                [1770, 5, 3]              [1770]                    --\n",
       "├─RNN: 1-1                               [1770, 5, 3]              [1770, 5, 12]             204\n",
       "├─Dropout: 1-2                           [1770, 12]                [1770, 12]                --\n",
       "├─Linear: 1-3                            [1770, 12]                [1770, 1]                 13\n",
       "===================================================================================================================\n",
       "Total params: 217\n",
       "Trainable params: 217\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (Units.MEGABYTES): 1.83\n",
       "===================================================================================================================\n",
       "Input size (MB): 0.11\n",
       "Forward/backward pass size (MB): 0.86\n",
       "Params size (MB): 0.00\n",
       "Estimated Total Size (MB): 0.97\n",
       "==================================================================================================================="
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(nyse_model,\n",
    "        input_data=X_rnn_t,\n",
    "        col_names=['input_size',\n",
    "                   'output_size',\n",
    "                   'num_params'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ba24d5c",
   "metadata": {},
   "source": [
    "We again put the two datasets into a data module, with a\n",
    "batch size of 64."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c36032ef",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "nyse_dm = SimpleDataModule(nyse_train,\n",
    "                           nyse_test,\n",
    "                           num_workers=min(4, max_num_workers),\n",
    "                           validation=nyse_test,\n",
    "                           batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "719a7cf8",
   "metadata": {},
   "source": [
    "We run some data through our model to be sure the sizes match up correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "90df0c75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64]) torch.Size([64])\n",
      "torch.Size([64]) torch.Size([64])\n",
      "torch.Size([64]) torch.Size([64])\n"
     ]
    }
   ],
   "source": [
    "for idx, (x, y) in enumerate(nyse_dm.train_dataloader()):\n",
    "    out = nyse_model(x)\n",
    "    print(y.size(), out.size())\n",
    "    if idx >= 2:\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a85106c8",
   "metadata": {},
   "source": [
    "We follow our previous example for setting up a trainer for a\n",
    "regression problem, requesting the $R^2$ metric\n",
    "to be be computed at each epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "064fcd0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "nyse_optimizer = RMSprop(nyse_model.parameters(),\n",
    "                         lr=0.001)\n",
    "nyse_module = SimpleModule.regression(nyse_model,\n",
    "                                      optimizer=nyse_optimizer,\n",
    "                                      metrics={'r2':R2Score()})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9a430a",
   "metadata": {},
   "source": [
    "Fitting the model should by now be familiar.\n",
    "The results on the test data are very similar to the linear AR model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95ff00af",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name  | Type      | Params\n",
      "------------------------------------\n",
      "0 | model | NYSEModel | 217   \n",
      "1 | loss  | MSELoss   | 0     \n",
      "------------------------------------\n",
      "217       Trainable params\n",
      "0         Non-trainable params\n",
      "217       Total params\n",
      "0.001     Total estimated model params size (MB)\n"
     ]
    }
   ],
   "source": [
    "nyse_trainer = Trainer(deterministic=True,\n",
    "                       max_epochs=200,\n",
    "                       enable_progress_bar=False,\n",
    "                       callbacks=[ErrorTracker()])\n",
    "nyse_trainer.fit(nyse_module,\n",
    "                 datamodule=nyse_dm)\n",
    "nyse_trainer.test(nyse_module,\n",
    "                  datamodule=nyse_dm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a0fb76e",
   "metadata": {},
   "source": [
    "We could also fit a model without the `nn.RNN()` layer by just\n",
    "using a `nn.Flatten()` layer instead. This would be a nonlinear AR model. If in addition we excluded the \n",
    "hidden layer, this would be equivalent to our earlier linear AR model.  \n",
    "\n",
    "Instead we will fit a nonlinear AR model using the feature set `X_day` that includes the `day_of_week` indicators.\n",
    "To do so, we\n",
    "must first create our test and training datasets and a corresponding\n",
    "data module. This may seem a little burdensome, but is part of the\n",
    "general pipeline for `torch`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b1b56fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = []\n",
    "for mask in [train, ~train]:\n",
    "    X_day_t = torch.tensor(\n",
    "                   np.asarray(X_day[mask]).astype(np.float32))\n",
    "    Y_t = torch.tensor(np.asarray(Y[mask]).astype(np.float32))\n",
    "    datasets.append(TensorDataset(X_day_t, Y_t))\n",
    "day_train, day_test = datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75ea59a7",
   "metadata": {},
   "source": [
    "Creating a data module follows a familiar pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10c70d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "day_dm = SimpleDataModule(day_train,\n",
    "                          day_test,\n",
    "                          num_workers=min(4, max_num_workers),\n",
    "                          validation=day_test,\n",
    "                          batch_size=64)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c2a76bb",
   "metadata": {},
   "source": [
    "We build a `NonLinearARModel()` that takes as input the 20 features and a hidden layer  with 32 units. The remaining steps are familiar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42fca594",
   "metadata": {},
   "outputs": [],
   "source": [
    "class NonLinearARModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(NonLinearARModel, self).__init__()\n",
    "        self._forward = nn.Sequential(nn.Flatten(),\n",
    "                                      nn.Linear(20, 32),\n",
    "                                      nn.ReLU(),\n",
    "                                      nn.Dropout(0.5),\n",
    "                                      nn.Linear(32, 1))\n",
    "    def forward(self, x):\n",
    "        return torch.flatten(self._forward(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a134064",
   "metadata": {},
   "outputs": [],
   "source": [
    "nl_model = NonLinearARModel()\n",
    "nl_optimizer = RMSprop(nl_model.parameters(),\n",
    "                           lr=0.001)\n",
    "nl_module = SimpleModule.regression(nl_model,\n",
    "                                        optimizer=nl_optimizer,\n",
    "                                        metrics={'r2':R2Score()})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ac1a2df",
   "metadata": {},
   "source": [
    "We continue with the usual training steps, fit the model,\n",
    "and evaluate the test error. We see the test $R^2$ is a slight improvement over the linear AR model that also includes `day_of_week`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0591bf1d",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "nl_trainer = Trainer(deterministic=True,\n",
    "                     max_epochs=20,\n",
    "                     enable_progress_bar=False,\n",
    "                     callbacks=[ErrorTracker()])\n",
    "nl_trainer.fit(nl_module, datamodule=day_dm)\n",
    "nl_trainer.test(nl_module, datamodule=day_dm) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bf8c38e",
   "metadata": {},
   "source": [
    "              \n",
    "\n",
    " \n"
   ]
  }
 ],
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