02_pytorch_classification.ipynb

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    "<a href=\"https://colab.research.google.com/github/mrdbourke/pytorch-deep-learning/blob/main/02_pytorch_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "\n",
    "[View Source Code](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/02_pytorch_classification.ipynb) | [View Slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/02_pytorch_classification.pdf) | [Watch Video Walkthrough](https://youtu.be/Z_ikDlimN6A?t=30691) "
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   "source": [
    "# 02. PyTorch Neural Network Classification\n",
    "\n",
    "## What is a classification problem?\n",
    "\n",
    "A [classification problem](https://en.wikipedia.org/wiki/Statistical_classification) involves predicting whether something is one thing or another.\n",
    "\n",
    "For example, you might want to:\n",
    "\n",
    "| Problem type | What is it? | Example |\n",
    "| ----- | ----- | ----- |\n",
    "| **Binary classification** | Target can be one of two options, e.g. yes or no | Predict whether or not someone has heart disease based on their health parameters. |\n",
    "| **Multi-class classification** | Target can be one of more than two options | Decide whether a photo is of food, a person or a dog. |\n",
    "| **Multi-label classification** | Target can be assigned more than one option | Predict what categories should be assigned to a Wikipedia article (e.g. mathematics, science & philosophy). |\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/02-different-classification-problems.png\" alt=\"various different classification in machine learning such as binary classification, multiclass classification and multilabel classification\" width=900/>\n",
    "</div>\n",
    "    \n",
    "Classification, along with regression (predicting a number, covered in [notebook 01](https://www.learnpytorch.io/01_pytorch_workflow/)) is one of the most common types of machine learning problems.\n",
    "\n",
    "In this notebook, we're going to work through a couple of different classification problems with PyTorch. \n",
    "\n",
    "In other words, taking a set of inputs and predicting what class those set of inputs belong to.\n",
    "\n",
    "\n"
   ]
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    "## What we're going to cover\n",
    "\n",
    "In this notebook we're going to reiterate over the PyTorch workflow we covered in [01. PyTorch Workflow](https://www.learnpytorch.io/02_pytorch_classification/).\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/01_a_pytorch_workflow.png\" alt=\"a pytorch workflow flowchart\" width=900/>\n",
    "\n",
    "Except instead of trying to predict a straight line (predicting a number, also called a regression problem), we'll be working on a **classification problem**.\n",
    "\n",
    "Specifically, we're going to cover:\n",
    "\n",
    "| **Topic** | **Contents** |\n",
    "| ----- | ----- |\n",
    "| **0. Architecture of a classification neural network** | Neural networks can come in almost any shape or size, but they typically follow a similar floor plan. |\n",
    "| **1. Getting binary classification data ready** | Data can be almost anything but to get started we're going to create a simple binary classification dataset. |\n",
    "| **2. Building a PyTorch classification model** | Here we'll create a model to learn patterns in the data, we'll also choose a **loss function**, **optimizer** and build a **training loop** specific to classification. | \n",
    "| **3. Fitting the model to data (training)** | We've got data and a model, now let's let the model (try to) find patterns in the (**training**) data. |\n",
    "| **4. Making predictions and evaluating a model (inference)** | Our model's found patterns in the data, let's compare its findings to the actual (**testing**) data. |\n",
    "| **5. Improving a model (from a model perspective)** | We've trained and evaluated a model but it's not working, let's try a few things to improve it. |\n",
    "| **6. Non-linearity** | So far our model has only had the ability to model straight lines, what about non-linear (non-straight) lines? |\n",
    "| **7. Replicating non-linear functions** | We used **non-linear functions** to help model non-linear data, but what do these look like? |\n",
    "| **8. Putting it all together with multi-class classification** | Let's put everything we've done so far for binary classification together with a multi-class classification problem. |\n"
   ]
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   "source": [
    "## Where can you get help?\n",
    "\n",
    "All of the materials for this course [live on GitHub](https://github.com/mrdbourke/pytorch-deep-learning).\n",
    "\n",
    "And if you run into trouble, you can ask a question on the [Discussions page](https://github.com/mrdbourke/pytorch-deep-learning/discussions) there too.\n",
    "\n",
    "There's also the [PyTorch developer forums](https://discuss.pytorch.org/), a very helpful place for all things PyTorch. \n",
    "\n"
   ]
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    "## 0. Architecture of a classification neural network\n",
    "\n",
    "Before we get into writing code, let's look at the general architecture of a classification neural network.\n",
    "\n",
    "| **Hyperparameter** | **Binary Classification** | **Multiclass classification** |\n",
    "| --- | --- | --- |\n",
    "| **Input layer shape** (`in_features`) | Same as number of features (e.g. 5 for age, sex, height, weight, smoking status in heart disease prediction) | Same as binary classification |\n",
    "| **Hidden layer(s)** | Problem specific, minimum = 1, maximum = unlimited | Same as binary classification |\n",
    "| **Neurons per hidden layer** | Problem specific, generally 10 to 512 | Same as binary classification |\n",
    "| **Output layer shape** (`out_features`) | 1 (one class or the other) | 1 per class (e.g. 3 for food, person or dog photo) |\n",
    "| **Hidden layer activation** | Usually [ReLU](https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html#torch.nn.ReLU) (rectified linear unit) but [can be many others](https://en.wikipedia.org/wiki/Activation_function#Table_of_activation_functions) | Same as binary classification |\n",
    "| **Output activation** | [Sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function) ([`torch.sigmoid`](https://pytorch.org/docs/stable/generated/torch.sigmoid.html) in PyTorch)| [Softmax](https://en.wikipedia.org/wiki/Softmax_function) ([`torch.softmax`](https://pytorch.org/docs/stable/generated/torch.nn.Softmax.html) in PyTorch) |\n",
    "| **Loss function** | [Binary crossentropy](https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_loss_function_and_logistic_regression) ([`torch.nn.BCELoss`](https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html) in PyTorch) | Cross entropy ([`torch.nn.CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html) in PyTorch) |\n",
    "| **Optimizer** | [SGD](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html) (stochastic gradient descent), [Adam](https://pytorch.org/docs/stable/generated/torch.optim.Adam.html) (see [`torch.optim`](https://pytorch.org/docs/stable/optim.html) for more options) | Same as binary classification |\n",
    "\n",
    "Of course, this ingredient list of classification neural network components will vary depending on the problem you're working on.\n",
    "\n",
    "But it's more than enough to get started.\n",
    "\n",
    "We're going to get hands-on with this setup throughout this notebook."
   ]
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    "## 1. Make classification data and get it ready\n",
    "\n",
    "Let's begin by making some data.\n",
    "\n",
    "We'll use the [`make_circles()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html) method from Scikit-Learn to generate two circles with different coloured dots. "
   ]
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   "source": [
    "from sklearn.datasets import make_circles\n",
    "\n",
    "\n",
    "# Make 1000 samples \n",
    "n_samples = 1000\n",
    "\n",
    "# Create circles\n",
    "X, y = make_circles(n_samples,\n",
    "                    noise=0.03, # a little bit of noise to the dots\n",
    "                    random_state=42) # keep random state so we get the same values"
   ]
  },
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   "source": [
    "Alright, now let's view the first 5 `X` and `y` values."
   ]
  },
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   "execution_count": 2,
   "metadata": {
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    "outputId": "b7316d88-7733-4981-9b4a-0a98c7cdd829"
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     "text": [
      "First 5 X features:\n",
      "[[ 0.75424625  0.23148074]\n",
      " [-0.75615888  0.15325888]\n",
      " [-0.81539193  0.17328203]\n",
      " [-0.39373073  0.69288277]\n",
      " [ 0.44220765 -0.89672343]]\n",
      "\n",
      "First 5 y labels:\n",
      "[1 1 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(f\"First 5 X features:\\n{X[:5]}\")\n",
    "print(f\"\\nFirst 5 y labels:\\n{y[:5]}\")"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Looks like there's two `X` values per one `y` value. \n",
    "\n",
    "Let's keep following the data explorer's motto of *visualize, visualize, visualize* and put them into a pandas DataFrame."
   ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.754246</td>\n",
       "      <td>0.231481</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>-0.815392</td>\n",
       "      <td>0.173282</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.393731</td>\n",
       "      <td>0.692883</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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       "      <td>-0.896723</td>\n",
       "      <td>0</td>\n",
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       "      <th>5</th>\n",
       "      <td>-0.479646</td>\n",
       "      <td>0.676435</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.771513</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.121486</td>\n",
       "      <td>1.021509</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "         X1        X2  label\n",
       "0  0.754246  0.231481      1\n",
       "1 -0.756159  0.153259      1\n",
       "2 -0.815392  0.173282      1\n",
       "3 -0.393731  0.692883      1\n",
       "4  0.442208 -0.896723      0\n",
       "5 -0.479646  0.676435      1\n",
       "6 -0.013648  0.803349      1\n",
       "7  0.771513  0.147760      1\n",
       "8 -0.169322 -0.793456      1\n",
       "9 -0.121486  1.021509      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make DataFrame of circle data\n",
    "import pandas as pd\n",
    "circles = pd.DataFrame({\"X1\": X[:, 0],\n",
    "    \"X2\": X[:, 1],\n",
    "    \"label\": y\n",
    "})\n",
    "circles.head(10)"
   ]
  },
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   "cell_type": "markdown",
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    "id": "FK2T7GpYW2BE"
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   "source": [
    "It looks like each pair of `X` features (`X1` and `X2`) has a label (`y`) value of either 0 or 1.\n",
    "\n",
    "This tells us that our problem is **binary classification** since there's only two options (0 or 1).\n",
    "\n",
    "How many values of each class are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "1    500\n",
       "0    500\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check different labels\n",
    "circles.label.value_counts()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "id": "ytQ5rm9eXa65"
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   "source": [
    "500 each, nice and balanced.\n",
    "\n",
    "Let's plot them."
   ]
  },
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize with a plot\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x=X[:, 0], \n",
    "            y=X[:, 1], \n",
    "            c=y, \n",
    "            cmap=plt.cm.RdYlBu);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h0e1oR27HC76"
   },
   "source": [
    "Alrighty, looks like we've got a problem to solve.\n",
    "\n",
    "Let's find out how we could build a PyTorch neural network to classify dots into red (0) or blue (1).\n",
    "\n",
    "> **Note:** This dataset is often what's considered a **toy problem** (a problem that's used to try and test things out on) in machine learning. \n",
    "> \n",
    "> But it represents the major key of classification, you have some kind of data represented as numerical values and you'd like to build a model that's able to classify it, in our case, separate it into red or blue dots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ny6_J7F4HC76"
   },
   "source": [
    "### 1.1 Input and output shapes\n",
    "\n",
    "One of the most common errors in deep learning is shape errors.\n",
    "\n",
    "Mismatching the shapes of tensors and tensor operations will result in errors in your models.\n",
    "\n",
    "We're going to see plenty of these throughout the course.\n",
    "\n",
    "And there's no surefire way to make sure they won't happen, they will.\n",
    "\n",
    "What you can do instead is continually familiarize yourself with the shape of the data you're working with.\n",
    "\n",
    "I like referring to it as input and output shapes.\n",
    "\n",
    "Ask yourself:\n",
    "\n",
    "\"What shapes are my inputs and what shapes are my outputs?\"\n",
    "\n",
    "Let's find out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r0h--vKdHC77",
    "outputId": "16e65bb5-83e1-49c7-af5e-90ecede4eeae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1000, 2), (1000,))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the shapes of our features and labels\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PW7zolShbfDg"
   },
   "source": [
    "Looks like we've got a match on the first dimension of each.\n",
    "\n",
    "There's 1000 `X` and 1000 `y`. \n",
    "\n",
    "But what's the second dimension on `X`?\n",
    "\n",
    "It often helps to view the values and shapes of a single sample (features and labels).\n",
    "\n",
    "Doing so will help you understand what input and output shapes you'd be expecting from your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k-L5zobVHC78",
    "outputId": "b3a96ca8-45f1-47d1-a98b-c2a7be79c0d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values for one sample of X: [0.75424625 0.23148074] and the same for y: 1\n",
      "Shapes for one sample of X: (2,) and the same for y: ()\n"
     ]
    }
   ],
   "source": [
    "# View the first example of features and labels\n",
    "X_sample = X[0]\n",
    "y_sample = y[0]\n",
    "print(f\"Values for one sample of X: {X_sample} and the same for y: {y_sample}\")\n",
    "print(f\"Shapes for one sample of X: {X_sample.shape} and the same for y: {y_sample.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vn692XaRaifl"
   },
   "source": [
    "This tells us the second dimension for `X` means it has two features (vector) where as `y` has a single feature (scalar).\n",
    "\n",
    "We have two inputs for one output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eLyDPN6ZR_ho"
   },
   "source": [
    "### 1.2 Turn data into tensors and create train and test splits\n",
    "\n",
    "We've investigated the input and output shapes of our data, now let's prepare it for being used with PyTorch and for modelling.\n",
    "\n",
    "Specifically, we'll need to:\n",
    "1. Turn our data into tensors (right now our data is in NumPy arrays and PyTorch prefers to work with PyTorch tensors).\n",
    "2. Split our data into training and test sets (we'll train a model on the training set to learn the patterns between `X` and `y` and then evaluate those learned patterns on the test dataset)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z2gcR31aHC78",
    "outputId": "cd4e10c1-c358-4b74-81e0-bab7610197cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[ 0.7542,  0.2315],\n",
       "         [-0.7562,  0.1533],\n",
       "         [-0.8154,  0.1733],\n",
       "         [-0.3937,  0.6929],\n",
       "         [ 0.4422, -0.8967]]),\n",
       " tensor([1., 1., 1., 1., 0.]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn data into tensors\n",
    "# Otherwise this causes issues with computations later on\n",
    "import torch\n",
    "X = torch.from_numpy(X).type(torch.float)\n",
    "y = torch.from_numpy(y).type(torch.float)\n",
    "\n",
    "# View the first five samples\n",
    "X[:5], y[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r9XNJv8lfmRG"
   },
   "source": [
    "Now our data is in tensor format, let's split it into training and test sets.\n",
    "\n",
    "To do so, let's use the helpful function [`train_test_split()`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) from Scikit-Learn.\n",
    "\n",
    "We'll use `test_size=0.2` (80% training, 20% testing) and because the split happens randomly across the data, let's use `random_state=42` so the split is reproducible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DW6PU82BHC79",
    "outputId": "a2d3f3df-701e-44ef-a506-fd31d5443e90"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 200, 800, 200)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split data into train and test sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                    y, \n",
    "                                                    test_size=0.2, # 20% test, 80% train\n",
    "                                                    random_state=42) # make the random split reproducible\n",
    "\n",
    "len(X_train), len(X_test), len(y_train), len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t-wYDWy9gSRc"
   },
   "source": [
    "Nice! Looks like we've now got 800 training samples and 200 testing samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iCyQ93VTHC79"
   },
   "source": [
    "## 2. Building a model\n",
    "\n",
    "We've got some data ready, now it's time to build a model.\n",
    "\n",
    "We'll break it down into a few parts.\n",
    "\n",
    "1. Setting up device agnostic code (so our model can run on CPU or GPU if it's available).\n",
    "2. Constructing a model by subclassing `nn.Module`.\n",
    "3. Defining a loss function and optimizer.\n",
    "4. Creating a training loop (this'll be in the next section).\n",
    "\n",
    "The good news is we've been through all of the above steps before in notebook 01.\n",
    "\n",
    "Except now we'll be adjusting them so they work with a classification dataset.\n",
    "\n",
    "Let's start by importing PyTorch and `torch.nn` as well as setting up device agnostic code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "2itMTezPRnVR",
    "outputId": "507bf4a3-b5ae-4943-aa21-4eb181b0d741"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard PyTorch imports\n",
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "# Make device agnostic code\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p1Fil1v04nXr"
   },
   "source": [
    "Excellent, now `device` is setup, we can use it for any data or models we create and PyTorch will handle it on the CPU (default) or GPU if it's available.\n",
    "\n",
    "How about we create a model?\n",
    "\n",
    "We'll want a model capable of handling our `X` data as inputs and producing something in the shape of our `y` data as outputs.\n",
    "\n",
    "In other words, given `X` (features) we want our model to predict `y` (label).\n",
    "\n",
    "This setup where you have features and labels is referred to as **supervised learning**. Because your data is telling your model what the outputs should be given a certain input.\n",
    "\n",
    "To create such a model it'll need to handle the input and output shapes of `X` and `y`.\n",
    "\n",
    "Remember how I said input and output shapes are important? Here we'll see why.\n",
    "\n",
    "Let's create a model class that:\n",
    "1. Subclasses `nn.Module` (almost all PyTorch models are subclasses of `nn.Module`).\n",
    "2. Creates 2 `nn.Linear` layers in the constructor capable of handling the input and output shapes of `X` and `y`.\n",
    "3. Defines a `forward()` method containing the forward pass computation of the model.\n",
    "4. Instantiates the model class and sends it to the target `device`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C_EsAE3VHC7-",
    "outputId": "d9c976ea-e23f-4993-c847-a15b0bf7b0b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CircleModelV0(\n",
       "  (layer_1): Linear(in_features=2, out_features=5, bias=True)\n",
       "  (layer_2): Linear(in_features=5, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Construct a model class that subclasses nn.Module\n",
    "class CircleModelV0(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        # 2. Create 2 nn.Linear layers capable of handling X and y input and output shapes\n",
    "        self.layer_1 = nn.Linear(in_features=2, out_features=5) # takes in 2 features (X), produces 5 features\n",
    "        self.layer_2 = nn.Linear(in_features=5, out_features=1) # takes in 5 features, produces 1 feature (y)\n",
    "    \n",
    "    # 3. Define a forward method containing the forward pass computation\n",
    "    def forward(self, x):\n",
    "        # Return the output of layer_2, a single feature, the same shape as y\n",
    "        return self.layer_2(self.layer_1(x)) # computation goes through layer_1 first then the output of layer_1 goes through layer_2\n",
    "\n",
    "# 4. Create an instance of the model and send it to target device\n",
    "model_0 = CircleModelV0().to(device)\n",
    "model_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TtdkMniZ7KyK"
   },
   "source": [
    "What's going on here?\n",
    "\n",
    "We've seen a few of these steps before.\n",
    "\n",
    "The only major change is what's happening between `self.layer_1` and `self.layer_2`.\n",
    "\n",
    "`self.layer_1` takes 2 input features `in_features=2` and produces 5 output features `out_features=5`.\n",
    "\n",
    "This is known as having 5 **hidden units** or **neurons**.\n",
    "\n",
    "This layer turns the input data from having 2 features to 5 features.\n",
    "\n",
    "Why do this?\n",
    "\n",
    "This allows the model to learn patterns from 5 numbers rather than just 2 numbers, *potentially* leading to better outputs.\n",
    "\n",
    "I say potentially because sometimes it doesn't work.\n",
    "\n",
    "The number of hidden units you can use in neural network layers is a **hyperparameter** (a value you can set yourself) and there's no set in stone value you have to use.\n",
    "\n",
    "Generally more is better but there's also such a thing as too much. The amount you choose will depend on your model type and dataset you're working with. \n",
    "\n",
    "Since our dataset is small and simple, we'll keep it small.\n",
    "\n",
    "The only rule with hidden units is that the next layer, in our case, `self.layer_2` has to take the same `in_features` as the previous layer `out_features`.\n",
    "\n",
    "That's why `self.layer_2` has `in_features=5`, it takes the `out_features=5` from `self.layer_1` and performs a linear computation on them, turning them into `out_features=1` (the same shape as `y`).\n",
    "\n",
    "![A visual example of what a classification neural network with linear activation looks like on the tensorflow playground](https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/02-tensorflow-playground-linear-activation.png)\n",
    "*A visual example of what a similar classification neural network to the one we've just built looks like. Try creating one of your own on the [TensorFlow Playground website](https://playground.tensorflow.org/).*\n",
    "\n",
    "You can also do the same as above using [`nn.Sequential`](https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html).\n",
    "\n",
    "`nn.Sequential` performs a forward pass computation of the input data through the layers in the order they appear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H7I8GyLjHC7_",
    "outputId": "def65504-863a-4361-816e-909dbfa4d624"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=2, out_features=5, bias=True)\n",
       "  (1): Linear(in_features=5, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replicate CircleModelV0 with nn.Sequential\n",
    "model_0 = nn.Sequential(\n",
    "    nn.Linear(in_features=2, out_features=5),\n",
    "    nn.Linear(in_features=5, out_features=1)\n",
    ").to(device)\n",
    "\n",
    "model_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MiRHq9mxFIf-"
   },
   "source": [
    "Woah, that looks much simpler than subclassing `nn.Module`, why not just always use `nn.Sequential`?\n",
    "\n",
    "`nn.Sequential` is fantastic for straight-forward computations, however, as the namespace says, it *always* runs in sequential order.\n",
    "\n",
    "So if you'd like something else to happen (rather than just straight-forward sequential computation) you'll want to define your own custom `nn.Module` subclass.\n",
    "\n",
    "Now we've got a model, let's see what happens when we pass some data through it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Tt339-_CC8sz",
    "outputId": "a4014167-4181-434e-ab75-905094229b3a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of predictions: 200, Shape: torch.Size([200, 1])\n",
      "Length of test samples: 200, Shape: torch.Size([200])\n",
      "\n",
      "First 10 predictions:\n",
      "tensor([[0.0555],\n",
      "        [0.0169],\n",
      "        [0.2254],\n",
      "        [0.0071],\n",
      "        [0.3345],\n",
      "        [0.3101],\n",
      "        [0.1151],\n",
      "        [0.1840],\n",
      "        [0.2205],\n",
      "        [0.0156]], device='cuda:0', grad_fn=<SliceBackward0>)\n",
      "\n",
      "First 10 test labels:\n",
      "tensor([1., 0., 1., 0., 1., 1., 0., 0., 1., 0.])\n"
     ]
    }
   ],
   "source": [
    "# Make predictions with the model\n",
    "untrained_preds = model_0(X_test.to(device))\n",
    "print(f\"Length of predictions: {len(untrained_preds)}, Shape: {untrained_preds.shape}\")\n",
    "print(f\"Length of test samples: {len(y_test)}, Shape: {y_test.shape}\")\n",
    "print(f\"\\nFirst 10 predictions:\\n{untrained_preds[:10]}\")\n",
    "print(f\"\\nFirst 10 test labels:\\n{y_test[:10]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q7v8TVnqGMZh"
   },
   "source": [
    "Hmm, it seems there are the same amount of predictions as there are test labels but the predictions don't look like they're in the same form or shape as the test labels.\n",
    "\n",
    "We've got a couple steps we can do to fix this, we'll see these later on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8aoQn39pHC7_"
   },
   "source": [
    "### 2.1 Setup loss function and optimizer\n",
    "\n",
    "We've setup a loss (also called a criterion or cost function) and optimizer before in [notebook 01](https://www.learnpytorch.io/01_pytorch_workflow/#creating-a-loss-function-and-optimizer-in-pytorch).\n",
    "\n",
    "But different problem types require different loss functions. \n",
    "\n",
    "For example, for a regression problem (predicting a number) you might use mean absolute error (MAE) loss.\n",
    "\n",
    "And for a binary classification problem (like ours), you'll often use [binary cross entropy](https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a) as the loss function.\n",
    "\n",
    "However, the same optimizer function can often be used across different problem spaces.\n",
    "\n",
    "For example, the stochastic gradient descent optimizer (SGD, `torch.optim.SGD()`) can be used for a range of problems, and the same applies to the Adam optimizer (`torch.optim.Adam()`). \n",
    "\n",
    "| Loss function/Optimizer | Problem type | PyTorch Code |\n",
    "| ----- | ----- | ----- |\n",
    "| Stochastic Gradient Descent (SGD) optimizer | Classification, regression, many others. | [`torch.optim.SGD()`](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html) |\n",
    "| Adam Optimizer | Classification, regression, many others. | [`torch.optim.Adam()`](https://pytorch.org/docs/stable/generated/torch.optim.Adam.html) |\n",
    "| Binary cross entropy loss | Binary classification | [`torch.nn.BCELossWithLogits`](https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html) or [`torch.nn.BCELoss`](https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html) |\n",
    "| Cross entropy loss | Multi-class classification | [`torch.nn.CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html) |\n",
    "| Mean absolute error (MAE) or L1 Loss | Regression | [`torch.nn.L1Loss`](https://pytorch.org/docs/stable/generated/torch.nn.L1Loss.html) | \n",
    "| Mean squared error (MSE) or L2 Loss | Regression | [`torch.nn.MSELoss`](https://pytorch.org/docs/stable/generated/torch.nn.MSELoss.html#torch.nn.MSELoss) |  \n",
    "\n",
    "*Table of various loss functions and optimizers, there are more but these are some common ones you'll see.*\n",
    "\n",
    "Since we're working with a binary classification problem, let's use a binary cross entropy loss function.\n",
    "\n",
    "> **Note:** Recall a **loss function** is what measures how *wrong* your model predictions are, the higher the loss, the worse your model.\n",
    ">\n",
    "> Also, PyTorch documentation often refers to loss functions as \"loss criterion\" or \"criterion\", these are all different ways of describing the same thing.\n",
    "\n",
    "PyTorch has two binary cross entropy implementations:\n",
    "1. [`torch.nn.BCELoss()`](https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html) - Creates a loss function that measures the binary cross entropy between the target (label) and input (features).\n",
    "2. [`torch.nn.BCEWithLogitsLoss()`](https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html) - This is the same as above except it has a sigmoid layer ([`nn.Sigmoid`](https://pytorch.org/docs/stable/generated/torch.nn.Sigmoid.html)) built-in (we'll see what this means soon).\n",
    "\n",
    "Which one should you use? \n",
    "\n",
    "The [documentation for `torch.nn.BCEWithLogitsLoss()`](https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html) states that it's more numerically stable than using `torch.nn.BCELoss()` after a `nn.Sigmoid` layer. \n",
    "\n",
    "So generally, implementation 2 is a better option. However for advanced usage, you may want to separate the combination of `nn.Sigmoid` and `torch.nn.BCELoss()` but that is beyond the scope of this notebook.\n",
    "\n",
    "Knowing this, let's create a loss function and an optimizer. \n",
    "\n",
    "For the optimizer we'll use `torch.optim.SGD()` to optimize the model parameters with learning rate 0.1.\n",
    "\n",
    "> **Note:** There's a [discussion on the PyTorch forums about the use of `nn.BCELoss` vs. `nn.BCEWithLogitsLoss`](https://discuss.pytorch.org/t/bceloss-vs-bcewithlogitsloss/33586/4). It can be confusing at first but as with many things, it becomes easier with practice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "DjpQsdOZHC7_"
   },
   "outputs": [],
   "source": [
    "# Create a loss function\n",
    "# loss_fn = nn.BCELoss() # BCELoss = no sigmoid built-in\n",
    "loss_fn = nn.BCEWithLogitsLoss() # BCEWithLogitsLoss = sigmoid built-in\n",
    "\n",
    "# Create an optimizer\n",
    "optimizer = torch.optim.SGD(params=model_0.parameters(), \n",
    "                            lr=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RKmi3fp9wYnV"
   },
   "source": [
    "Now let's also create an **evaluation metric**.\n",
    "\n",
    "An evaluation metric can be used to offer another perspective on how your model is going.\n",
    "\n",
    "If a loss function measures how *wrong* your model is, I like to think of evaluation metrics as measuring how *right* it is.\n",
    "\n",
    "Of course, you could argue both of these are doing the same thing but evaluation metrics offer a different perspective.\n",
    "\n",
    "After all, when evaluating your models it's good to look at things from multiple points of view.\n",
    "\n",
    "There are several evaluation metrics that can be used for classification problems but let's start out with **accuracy**.\n",
    "\n",
    "Accuracy can be measured by dividing the total number of correct predictions over the total number of predictions.\n",
    "\n",
    "For example, a model that makes 99 correct predictions out of 100 will have an accuracy of 99%.\n",
    "\n",
    "Let's write a function to do so.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "RcaZQR0mHC7_"
   },
   "outputs": [],
   "source": [
    "# Calculate accuracy (a classification metric)\n",
    "def accuracy_fn(y_true, y_pred):\n",
    "    correct = torch.eq(y_true, y_pred).sum().item() # torch.eq() calculates where two tensors are equal\n",
    "    acc = (correct / len(y_pred)) * 100 \n",
    "    return acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OuplloDPxviL"
   },
   "source": [
    "Excellent! We can now use this function whilst training our model to measure it's performance alongside the loss."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4UpJKZ8PHC8A"
   },
   "source": [
    "## 3. Train model\n",
    "\n",
    "Okay, now we've got a loss function and optimizer ready to go, let's train a model.\n",
    "\n",
    "Do you remember the steps in a PyTorch training loop?\n",
    "\n",
    "If not, here's a reminder.\n",
    "\n",
    "Steps in training:\n",
    "\n",
    "<details>\n",
    "    <summary>PyTorch training loop steps</summary>\n",
    "    <ol>\n",
    "        <li><b>Forward pass</b> - The model goes through all of the training data once, performing its\n",
    "            <code>forward()</code> function\n",
    "            calculations (<code>model(x_train)</code>).\n",
    "        </li>\n",
    "        <li><b>Calculate the loss</b> - The model's outputs (predictions) are compared to the ground truth and evaluated\n",
    "            to see how\n",
    "            wrong they are (<code>loss = loss_fn(y_pred, y_train</code>).</li>\n",
    "        <li><b>Zero gradients</b> - The optimizers gradients are set to zero (they are accumulated by default) so they\n",
    "            can be\n",
    "            recalculated for the specific training step (<code>optimizer.zero_grad()</code>).</li>\n",
    "        <li><b>Perform backpropagation on the loss</b> - Computes the gradient of the loss with respect for every model\n",
    "            parameter to\n",
    "            be updated (each parameter\n",
    "            with <code>requires_grad=True</code>). This is known as <b>backpropagation</b>, hence \"backwards\"\n",
    "            (<code>loss.backward()</code>).</li>\n",
    "        <li><b>Step the optimizer (gradient descent)</b> - Update the parameters with <code>requires_grad=True</code>\n",
    "            with respect to the loss\n",
    "            gradients in order to improve them (<code>optimizer.step()</code>).</li>\n",
    "    </ol>\n",
    "</details>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NMeDqPpsnst6"
   },
   "source": [
    "### 3.1 Going from raw model outputs to predicted labels (logits -> prediction probabilities -> prediction labels)\n",
    "\n",
    "Before the training loop steps, let's see what comes out of our model during the forward pass (the forward pass is defined by the `forward()` method).\n",
    "\n",
    "To do so, let's pass the model some data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e5stsbDl0zKl",
    "outputId": "829a0842-0a37-455a-b058-3e547200f836"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.0555],\n",
       "        [0.0169],\n",
       "        [0.2254],\n",
       "        [0.0071],\n",
       "        [0.3345]], device='cuda:0', grad_fn=<SliceBackward0>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the frist 5 outputs of the forward pass on the test data\n",
    "y_logits = model_0(X_test.to(device))[:5]\n",
    "y_logits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b1sNl8D8fWXm"
   },
   "source": [
    "Since our model hasn't been trained, these outputs are basically random.\n",
    "\n",
    "But *what* are they?\n",
    "\n",
    "They're the output of our `forward()` method.\n",
    "\n",
    "Which implements two layers of `nn.Linear()` which internally calls the following equation:\n",
    "\n",
    "$$\n",
    "\\mathbf{y} = x \\cdot \\mathbf{Weights}^T  + \\mathbf{bias}\n",
    "$$\n",
    "\n",
    "The *raw outputs* (unmodified) of this equation ($y$) and in turn, the raw outputs of our model are often referred to as [**logits**](https://datascience.stackexchange.com/a/31045).\n",
    "\n",
    "That's what our model is outputing above when it takes in the input data ($x$ in the equation or `X_test` in the code), logits.\n",
    "\n",
    "However, these numbers are hard to interpret.\n",
    "\n",
    "We'd like some numbers that are comparable to our truth labels.\n",
    "\n",
    "To get our model's raw outputs (logits) into such a form, we can use the [sigmoid activation function](https://pytorch.org/docs/stable/generated/torch.sigmoid.html).\n",
    "\n",
    "Let's try it out.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QGC7UmBi0s6E",
    "outputId": "3564ee3b-e34f-40a3-a40a-b9c9b948da04"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.5139],\n",
       "        [0.5042],\n",
       "        [0.5561],\n",
       "        [0.5018],\n",
       "        [0.5829]], device='cuda:0', grad_fn=<SigmoidBackward0>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use sigmoid on model logits\n",
    "y_pred_probs = torch.sigmoid(y_logits)\n",
    "y_pred_probs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HxmWz_GVjZoB"
   },
   "source": [
    "Okay, it seems like the outputs now have some kind of consistency (even though they're still random).\n",
    "\n",
    "They're now in the form of **prediction probabilities** (I usually refer to these as `y_pred_probs`), in other words, the values are now how much the model thinks the data point belongs to one class or another.\n",
    "\n",
    "In our case, since we're dealing with binary classification, our ideal outputs are 0 or 1.\n",
    "\n",
    "So these values can be viewed as a decision boundary.\n",
    "\n",
    "The closer to 0, the more the model thinks the sample belongs to class 0, the closer to 1, the more the model thinks the sample belongs to class 1.\n",
    "\n",
    "More specificially:\n",
    "* If `y_pred_probs` >= 0.5, `y=1` (class 1)\n",
    "* If `y_pred_probs` < 0.5, `y=0` (class 0)\n",
    "\n",
    "To turn our prediction probabilities into prediction labels, we can round the outputs of the sigmoid activation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "naZxlTTwlMwX",
    "outputId": "6134e47d-bbb2-46c3-e2ec-b4f42d517e39"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([True, True, True, True, True], device='cuda:0')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([1., 1., 1., 1., 1.], device='cuda:0', grad_fn=<SqueezeBackward0>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the predicted labels (round the prediction probabilities)\n",
    "y_preds = torch.round(y_pred_probs)\n",
    "\n",
    "# In full\n",
    "y_pred_labels = torch.round(torch.sigmoid(model_0(X_test.to(device))[:5]))\n",
    "\n",
    "# Check for equality\n",
    "print(torch.eq(y_preds.squeeze(), y_pred_labels.squeeze()))\n",
    "\n",
    "# Get rid of extra dimension\n",
    "y_preds.squeeze()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5cMsgFWWmPLU"
   },
   "source": [
    "Excellent! Now it looks like our model's predictions are in the same form as our truth labels (`y_test`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MaQ0CN4ZmU1W",
    "outputId": "6b1cd7b4-f1d0-49b5-a8c3-338cf75c6cb0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 0., 1., 0., 1.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NXqUulG3maPH"
   },
   "source": [
    "This means we'll be able to compare our model's predictions to the test labels to see how well it's performing. \n",
    "\n",
    "To recap, we converted our model's raw outputs (logits) to prediction probabilities using a sigmoid activation function.\n",
    "\n",
    "And then converted the prediction probabilities to prediction labels by rounding them.\n",
    "\n",
    "> **Note:** The use of the sigmoid activation function is often only for binary classification logits. For multi-class classification, we'll be looking at using the [softmax activation function](https://pytorch.org/docs/stable/generated/torch.nn.Softmax.html) (this will come later on).\n",
    ">\n",
    "> And the use of the sigmoid activation function is not required when passing our model's raw outputs to the `nn.BCEWithLogitsLoss` (the \"logits\" in logits loss is because it works on the model's raw logits output), this is because it has a sigmoid function built-in."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Va7gg8yxn6Sg"
   },
   "source": [
    "### 3.2 Building a training and testing loop\n",
    "\n",
    "Alright, we've discussed how to take our raw model outputs and convert them to prediction labels, now let's build a training loop.\n",
    "\n",
    "Let's start by training for 100 epochs and outputing the model's progress every 10 epochs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DFABVGo2HC8A",
    "outputId": "e0341074-b603-41d5-a389-c401b4934d73"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Loss: 0.70034, Accuracy: 50.00% | Test loss: 0.69484, Test acc: 52.50%\n",
      "Epoch: 10 | Loss: 0.69718, Accuracy: 53.75% | Test loss: 0.69242, Test acc: 54.50%\n",
      "Epoch: 20 | Loss: 0.69590, Accuracy: 51.12% | Test loss: 0.69161, Test acc: 53.50%\n",
      "Epoch: 30 | Loss: 0.69530, Accuracy: 50.62% | Test loss: 0.69136, Test acc: 53.00%\n",
      "Epoch: 40 | Loss: 0.69497, Accuracy: 49.75% | Test loss: 0.69131, Test acc: 53.50%\n",
      "Epoch: 50 | Loss: 0.69474, Accuracy: 50.12% | Test loss: 0.69134, Test acc: 53.50%\n",
      "Epoch: 60 | Loss: 0.69457, Accuracy: 49.88% | Test loss: 0.69139, Test acc: 53.50%\n",
      "Epoch: 70 | Loss: 0.69442, Accuracy: 49.62% | Test loss: 0.69146, Test acc: 54.00%\n",
      "Epoch: 80 | Loss: 0.69430, Accuracy: 49.62% | Test loss: 0.69153, Test acc: 54.50%\n",
      "Epoch: 90 | Loss: 0.69418, Accuracy: 49.62% | Test loss: 0.69161, Test acc: 54.50%\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "# Set the number of epochs\n",
    "epochs = 100\n",
    "\n",
    "# Put data to target device\n",
    "X_train, y_train = X_train.to(device), y_train.to(device)\n",
    "X_test, y_test = X_test.to(device), y_test.to(device)\n",
    "\n",
    "# Build training and evaluation loop\n",
    "for epoch in range(epochs):\n",
    "    ### Training\n",
    "    model_0.train()\n",
    "\n",
    "    # 1. Forward pass (model outputs raw logits)\n",
    "    y_logits = model_0(X_train).squeeze() # squeeze to remove extra `1` dimensions, this won't work unless model and data are on same device \n",
    "    y_pred = torch.round(torch.sigmoid(y_logits)) # turn logits -> pred probs -> pred labls\n",
    "  \n",
    "    # 2. Calculate loss/accuracy\n",
    "    # loss = loss_fn(torch.sigmoid(y_logits), # Using nn.BCELoss you need torch.sigmoid()\n",
    "    #                y_train) \n",
    "    loss = loss_fn(y_logits, # Using nn.BCEWithLogitsLoss works with raw logits\n",
    "                   y_train) \n",
    "    acc = accuracy_fn(y_true=y_train, \n",
    "                      y_pred=y_pred) \n",
    "\n",
    "    # 3. Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backwards\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Optimizer step\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "    model_0.eval()\n",
    "    with torch.inference_mode():\n",
    "        # 1. Forward pass\n",
    "        test_logits = model_0(X_test).squeeze() \n",
    "        test_pred = torch.round(torch.sigmoid(test_logits))\n",
    "        # 2. Caculate loss/accuracy\n",
    "        test_loss = loss_fn(test_logits,\n",
    "                            y_test)\n",
    "        test_acc = accuracy_fn(y_true=y_test,\n",
    "                               y_pred=test_pred)\n",
    "\n",
    "    # Print out what's happening every 10 epochs\n",
    "    if epoch % 10 == 0:\n",
    "        print(f\"Epoch: {epoch} | Loss: {loss:.5f}, Accuracy: {acc:.2f}% | Test loss: {test_loss:.5f}, Test acc: {test_acc:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "It1Xy_f5perA"
   },
   "source": [
    "Hmm, what do you notice about the performance of our model?\n",
    "\n",
    "It looks like it went through the training and testing steps fine but the results don't seem to have moved too much.\n",
    "\n",
    "The accuracy barely moves above 50% on each data split.\n",
    "\n",
    "And because we're working with a balanced binary classification problem, it means our model is performing as good as random guessing (with 500 samples of class 0 and class 1 a model predicting class 1 every single time would achieve 50% accuracy)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WCeyddo-HC8A"
   },
   "source": [
    "## 4. Make predictions and evaluate the model\n",
    "\n",
    "From the metrics it looks like our model is random guessing.\n",
    "\n",
    "How could we investigate this further?\n",
    "\n",
    "I've got an idea.\n",
    "\n",
    "The data explorer's motto!\n",
    "\n",
    "\"Visualize, visualize, visualize!\"\n",
    "\n",
    "Let's make a plot of our model's predictions, the data it's trying to predict on and the decision boundary it's creating for whether something is class 0 or class 1.\n",
    "\n",
    "To do so, we'll write some code to download and import the [`helper_functions.py` script](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/helper_functions.py) from the [Learn PyTorch for Deep Learning repo](https://github.com/mrdbourke/pytorch-deep-learning).\n",
    "\n",
    "It contains a helpful function called `plot_decision_boundary()` which creates a NumPy meshgrid to visually plot the different points where our model is predicting certain classes.\n",
    "\n",
    "We'll also import `plot_predictions()` which we wrote in notebook 01 to use later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QakJVGI8t2gB",
    "outputId": "4f63a8a3-5eae-49fb-e17e-df6c5721ab5a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "helper_functions.py already exists, skipping download\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from pathlib import Path \n",
    "\n",
    "# Download helper functions from Learn PyTorch repo (if not already downloaded)\n",
    "if Path(\"helper_functions.py\").is_file():\n",
    "  print(\"helper_functions.py already exists, skipping download\")\n",
    "else:\n",
    "  print(\"Downloading helper_functions.py\")\n",
    "  request = requests.get(\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/helper_functions.py\")\n",
    "  with open(\"helper_functions.py\", \"wb\") as f:\n",
    "    f.write(request.content)\n",
    "\n",
    "from helper_functions import plot_predictions, plot_decision_boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "bEbDUTKjHC8B",
    "outputId": "aa344fe8-0207-43df-f0e9-61b9302459a5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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.com/XOPCYKA22+/HS+//HLJ58XjcbS2tkKSJFx++eX4whe+gJ07d67mUDcMzjnCQz5MHR9BMhAH+Pxdqa2zDvV7W6G1GTD+6gCmT4zMV05nDIFzk9DaDOi+a8+qrgLlJYZXo2b0JfVgALYZErjcHIWCAaGcEsdiJmQkAQ2aDBg4HvG5cDxuAiAXXWvRpnHAEsHN9hDMSnHVxknIxeQmewhKxvHDyXqE8vN7pPSKPO6t8+INTj8AIM8ZVlqg7Z9Hm7HTkIBDnVvRearlOzO+vO3sM8/JxtIYfOY0tt97RU3HRQgprl2XQrM2hbG0tuSWMwkMtzmKFGAkhKyKQwE7/nWiAVnOoJgp5vqfXjd2GWP47y0jMG6Ae25/74QcgC/xmp9LZhGbDC3adruZrWoQ7vf7IYoi3G53weNutxs9PT1Fn7N161Z873vfw549exCJRPCP//iPuPbaa3H69Gk0NTUtOj6TySCTmZ9pjUajtX0Ta4hzjqHnzpbsoxvq9yI85Iejyw1/70y1dD73ZABAOpzEuUePYce9V86lqNdST0KPfxpuRTSvggJyGsmjfhesihza9Ukcj5khYXZuQL4wswWrchmuQH9Kj7ikwM32UM3HR8jF7DpbGNdYwzgTNyKQU8GkzGO3MQ7Vgp67ekHEyiqly7+9zwTteLunup7eKyGJUnVVUsvhHKlQAtHxICxNy2uLSMr7xje+gS996UuYmprC3r178f/+3//DlVdeWfTYhx56aC77bZZGo0E6Pb8awznHpz71KXz7299GOBzGddddh29961vo7l6f+iUrlYmm4D0zhmD/NMSsCI1JC9e2Rji21kOhqiZ1e3NhDHh/wwQ+P9ABFOncwMBxmSmK3cb4+gyQkEvMCyErvjM+HyctrCNzJm7EFwfb8UDXeSjXeSE52D+97BqyK75X2GA23Gada665Bu9973uxb98+3HTTTfj5z38Ol8uFf/7nfy56/Be/+EVYLJa5/zU3N6/xiGvHe3qsZAA+i4vSfABe9ACOTCSF8JCvZuPiHAjmlDgZM+ILAx2I5eW5GxECxJkfobCoxNGYeWbfNyu4IF94cZbAMJ3R4AfjDTUbI/n/2bvv+Lau+/D7n3OxAQIEwb239rIlW5JHPOIVZ9hZddO0SdM2edIkTVM3TZM8Gc3oz9m7vzqjWU+TJs1OnMRL3rEsD9mSrU2Key8QxB73PH+ApEiJxCABihLP+/VSZAIHF4cRcO/9nvH9KmuFJmCL0881ngkudU3NC8ABLnUtP7u5jkiWB1wBQhO5qfstBN6O3J33lDNmcrt8/OMf5+DBg2zfvp2bb76Z4eHFy8S5XC4GBgZm/3R1dc17/nOf+xxf+9rXuPvuuzlw4AAOh4Obb755XqB+oZjqn+DIL55m+Egv8VAsmcfAG6TnqVMc/+1zswkFLzabCgJ8uOn0OUvTjULnpuIx3lffraqaKMoK0CX8eKCSxaJbHcHpkJ3nfOe/Ykgimt0y9LmM1osrU3peg/CSkhIMBgNDQ/MDy6GhoYz3fJtMJi655BLa2toWfP5DH/oQk5OTs396enqW3e/zQUrJ4KHu3BxMkDaYz4Qu4YExD/90Yj3vPraJ/9PRREyKRZaeZbcEVkfw1KQb7womgVKUteDqogkchpnZ8IXkrw74UgghcNctPZHcGZJE7PwvtbsYzc3tsmnTJu6++27sdjvf/e53F33NTG6XmT9zV8RJKfnKV77CRz7yEW677Ta2bdvGD3/4Q/r7+/n1r3+9Ar9R7sQjMdoeeBGZ0Bf8aoW9ATofW3jl38Vgc0GAL60/wceb23hHTQ/vqe3mPzce46+r+88ZIFQUJT+OBhx44yZS3YdrSB4dP/9LuS0u25IG3g0WI86qi6vkYV6DcLPZzM6dO9m3b9/sY7qus2/fPvbu3ZvRMRKJBC+++CKVlZULPm+xWHC5XPP+XGiklHQ8fJR4KEfLLCTEQssbeZcS7u6p4bt91QxFzXOeyd2wto6gLZi6/rmiKNmxG3T+taETm6YzPypI/rc1gxJmAkmLPcDxgJ3jAXveMxyXb83NCiaLy5aT4yhnzOR2ueGGG2Yfyya3S21tLbfddhtHjhyZfa6jo4PBwcF5xywsLGT37t0pj7kajZ8aQk81+CNhsnuUyNTFW/lDCNjgCHKdZ4Iri7yrYt+poqwl3lj6GWIdwXgG7fKtdEP1kuYCqi5tzMs22/Mp79OQd955J29961vZtWsXl19+OV/5ylcIBAKz+8Xe8pa3UF1dzV133QXAJz/5Sfbs2UNLSwter5fPf/7zdHV18Xd/93f57up5IaWk6/HjTJxefFlf1oTA4lxeYrb9k4U87s3/iJk/fvHtlVOU863VEeSL60+wb6yYP3ndhHSNclOUG4rH2Oue5LOdDRxdtGxg8ur4h5FSfjuSnL00CZ1rPeP8ecUgdsNy6pAvrKDCja24gNDYMvaPSihZv/BgrbJ0+cjtMjg4OHuMs48589xCVmMOmMm+sYzaTfVPYFlvQ48nGD89zNiJAaLBCCabmeJ1lXiayy/KveOKouSfy5h+ibdA4jae/60xRU2lDB9zJUstLxaMi5n/STaourSR0k3VK9TDlZP3IPyOO+5gZGSEj33sYwwODrJjxw7uvffe2Ytvd3c32pw61hMTE7z97W9ncHCQoqIidu7cyZNPPsmmTZvy3dUVp8cTtD/4Er7eHGcPlZKSdcu7Gb13tASxQLKV3JKcDNq5tlglaFOUXCsyxXlDxRBvqBjiZMDOvaMl/GCgiv/qq8ZpjCNnr35nvuMakpkQOz5noVRMauwbK+ZUwM7HW9qx5mGZqbXQTmjcv+TV8uXb6rA41Uz4arB37955q92uuOIKNm7cyDe/+U0+9alPLfm4d911F5/4xCdy0cWckYnMPrBSl8SCUU7+4XnC3uDs49GpMIFhH0OHuln3yh2rpryooigXjk0FfqxagrC++ECeRPCyVZAQWWgarbdsp/uJk4yfnp+kzeyyUr6phrAvhB5PYHHZKG6txOywnL8O59GKbMh9z3vew3ve854Fn3vkkUfm/fzlL3+ZL3/5yyvQq/Ov49FjSwrANaMBPb7Ici8BzsoinNXL2zdxOmjPcwAOINg/6eZvavrOe7ZGRblY3TNSwo8GquYNqkViC12oJS5jDG/ctOB3X0fQFbbxx5FSXluew5U709z1JUtaEaSZDFTuqKd8W13O+6TkJ7fLzOuGhobmbTUbGhpix44dix7nQx/6EHfeeefszz6f77wnY7WXOJkamEg7eGQvLqB934uEJxdelh7xhzj5hxfYcNtOjJbzv2RUUZQLx1jUTFhPtVRbYhY6l7vO/+ohAIPJSON1m6i+vBlf3zgyoWMtclBQXogQaycguLgW119Awt7A0jL5aoKWm7fScM1GDObpMZQ5H9iipjKab9y67A+xECuTUCWsG/DHVXI2RcmHI34HPxpIViHIZFAtXWIXCdw/VjxTETGn3A2lmByWeeezdGwlTra/+UoqttevqQv3SspHbpfGxkYqKirmHdPn83HgwIGUx1yNOWBKN1alDsAF2IocSCAw5GPRL49Mljk79N9P0PnYMWLByMLtFEVRzrJv3JMmoBNEpYHBeTmezj+zw0LJukpKN1bjrHCvueu4in7Ok4mOkbnbHTJS1FxG5Y4GbEXJ0kFFjaV4u0aJ+EIYTEYK60uWvRd8xkZHgCOL7hmdce5y1qWwaLnfY6ooSnJfd+bbSjJr442bCOoajhzvDdcMGutesYOTf3g+41qg1Tsb0YxqH22+5Tq3ixCC973vfXz605+mtbWVxsZGPvrRj1JVVcXtt99+vn7NJbE4bdTsbqb3QPu5TwqBZhA0XLsxWTZUiMWD8BkSxk4N4esdZ8Ntuy7aZZiKouTOqYA9zf16UnvQTo1VDfCtFioIP0/ikRhCCGSGU0pGu5mm6zbPe0wzGvA0ly/yiuW5pWSUF/3OFC0kzbYg7SE7yWA8+0BcQ7K5wI8tD4meFGWtkxIO+wvysq3EmKeVMla3neYbt3L8t8+lHaA0OcyEJgI4Sl3zaoeGvQGGj/Qy0TEyvafMTummaopbKy66zKorJR+5XT7wgQ8QCAR4xzvegdfr5aqrruLee+/Far3w9kSXb63D7LDS/3wn4YlA8kEB7rpiqnY1YStyMN42lFEMDoCUxEJRep9qo+nlm9O3VxRlTdMEZHIvrq3QKlclMyoIP0/MDitSz/zLEM9wZihXLnVN8ZrSYX47Usb8L/aZPtdaw1zvGefbfTVLSOKWTAD1mrLc7y1VFCUpIZcSgKe+kFeaw1jyWP939MQAmSwTigWi9D3dTv9zHTS/fDOFdSV4u0Y5ve+lZKAzHe2Exv10P3GCsZMDtL5iOwaTuuwtRa5zuwgh+OQnP8knP/nJXHXxvCpqKsPdWEp0KkwiGsfksGCynVn6aXU7srrmI2Gic5hYsBWTfXUtIVUUZXXZXODnWMCR5qop2eAIrFCPlEyoaYHzxNNcntXeR8248v9Ub6oc5FLn5FmPitk/j054OBW0c5Xbm1UALpBowDtqetlSoE4IipIPQiQHyrJLN55+JH0qYSSbWCJbE6eHMpwuTJIJnfYHX2KydzwZgOtywdcHRnz07G/LZVcVZR4hBBaXDXuJc14ADskgPevruISQV10jFUVJ7TrPOAYhEYtc7zUkO50+Ss3nv0SZcoYKws8Tk91M5Y76zBoLgbu+NL8dWsB4zMjzUy4WuymXCB6ZKOYJb2aZ2M0iQY0lxG1lw3xlw3GuWwWlEhTlYnZzcWY1jM9IPwPtTxg5GnAsuU/pJGKLVH5IQUrofbot9fYeCeNtg8TDK7uqSFEADCYDdVeuz/p1QltbiYoURclekSnOP9R1I0gG3GckA/Myc5S31/aer+4pi1Dr8s6jyksbEJqg/2BnmpkfSdnWlS3DEkpofL+vOsM5tFSzZ5JiU4x/quuiyR7KZvJfUZRleplnnKcnXRzyO8ksb0Nm+R28sfyVUDIXWIlOhbN7kZSEx9PPGEpd4h+cxN2w8oOailLcWoFmMtD3dDsR38KlyubSTAYcJalysyiKoiRdXujj062n+MNIKU9NFhKXGkXGODcWj3FTyWjOk6kqy6eC8BUgdR1v1yjB0SkQAldVEQWVyVT8lZc0ULa5ho5HjjLZfdas1fS9cOM1m1b0QtwftvDp001MZFw6LNVNu2AsZqbUElUBuKKsMKOA9zd08dPBcv4wWppB9tSZmfDU7VzGeK66eI7SjdX0Pb1ApukcyWpfrqLkWFFDKe76EiZOD9Px8NGUbUs3Vuc9+39cwotTTsZjJgqMcXY4p/Ka80FRlPxptIV5d10P75I96IBB3XevaioIzzNf/wQdDx0hHo4l0xdKGHyhazYLsLXQjsFspOWmbQRGpxg52ktg2IcQAleNh9KN1VhcthXrb0wX/J+ORibjRpZbemyuxybc3Fg8ri7uirLCjJrkzVWDvLZsmN+NlvLkhJvhWKqyR6m/93YtzqYCf247OUfphirGTgwQ9oWy2hueKVtxQc6PqSjZEELgaS5H6pLOx44B56ZNd1YXUbWzMa/9eGzCzY/6K/ElzqxssWoJbisb5rbSETVwrigXKCFAFe9c/VQQnkeBER9t9x46M/MyZwYmPBnkxD0H2fS6yzHZzEgpSYRjxCNx9HgCoeV3u/5U3MCjE0UcnnISl4JmW4iXF4/RHrQzFst1JlbJjwaq+PVwOXfWd7FJJWNTlBVnN+rcUTFEmTnKt3qXvr2lyBTHmMebc4PZyLpXXULXY8eZ7Ml2T3sKApwVbqyF9twdU1GWobi1AluRg6EjvUx2jSJ1HavbQdmmajwt5Xm9D3horIhv99Vydg6IsG7gp4OVBBMG/qJyMG/vryiKstapIDyP+p/rWDxRkIR4OMbI0T4qL22g6/HjjJ0cZG4h0aGXehg+0kvj9ZspyuEexhenCvhiZz1ROZO+QXAi4OCe0RLqreEllBtLJ3msYMLAZzoauav1FNXWSA6PryjKSumLWPHHDRQYs0+glimTzUzLzduI+EJMDXiJhaIMHupCjyeyS/Y+QySD+7qrsk+MpSj5ZC9x0njNxhV9z4mYgf/qq5n+aeFr/e9GSrmheIwylU1ZURQlL1R29DyJhaL4esdT3zBKGD3Rz9CLPckAHOYvSZPJ/Yun9x0hPBnMSb8GImY+39kwHYDPlBtjeq+ooCtsW9I9biYkgoQU3DOikiIpyvnSYl/+uSSor8ylw+KyUbK+ksod9Wy8bScWZ4Zbc+bGFUJQ1FjGxtt2qVlwRQE+09FEuhRNGvDIuGcluqMoirImqSA8T+KhzMrgxEIxhg53p203crRvuV0C4N7REhJSpJnpzt9aUx3BE153XusMK4qyuFprhA12/1llTDJnFHpeE7Mtxup2ULO7JbPGEkwOC803bmX7m6+k6frNK5pbQ1FWq/agje6wjXTXeQkMR3O9NU1RFEWZoYLwHIpMhel5qo1D//0ER3/1bEavMZgNyaRtqUjJROdIDnoI+73uDDIk51dcakSlyviiKOfLO2t7cRrjWQfiGpIr3F6s5ynBor3EmfEYYSwQof2BFxk7pfa1KmvXSNTEoakCjvkdRHXBk143IsPvvVVTJY0URVHyRe0Jz5HAsI+Tf3whuz2LAlw1Hibah9M2lYncXAzDy1pGOvOLLS+AtmoJLEJNhSvK+VJuifJ/Wk/xm+EyHhgrzjAHhEQHTgbs/G64hFtLRolJjf6IBYOQVFvDeU3YBmB2WHDXleDtGs34Nb0H2jBaTRS3VuSxZ8paE5kKEZkMoRk1HGWuvCdTzVZ/2MIP+qs47C9g5pptN8QpMcVmCxGmIhHsKZzMdzcVRVHWLBWE54AeT9B2/+GskwZpBgOxYAbL1gVYixxL7+AcpeYoAxHLkhKvOQ1xthZM8eTk0veJaUiu94yr0ieKcp54Y0ZOh5JLs19bNswlTh+f7ZwphXT2F3PuwFvyucGohR8PVvHTwQo0IDa9oMppiHNLySivKRvOazBee8U6/MO+jLf8APQf7Ehmm1YnHmWZgmN+ep86xdSAd/Yxo9VE+bY6yrfWrorPWH/YwkfaWojoGnO/08GEke5EJoWLJB5jjM15LEWoKIqy1qkgPAcmOkbSLylfgJ5I4B/0pm8ooWxTdfYdW8CNxWP8sL8qq9dUmMO8u66HZlsICRw66iKQSPfRkZx9Q68hsRsS3FqS+SyWoii54Ysb+H5fFQcmz2xJ0ZDsKfTyjupefjBQRUTXMExXR1h820ry8QSCxJw2UwkjPx8qpyNk45/qu9DyFIuYHRZab9nOsV89k/FrolNhgmN+HCXO/HRKWROCY1Oc+N1B9LNWpsXDMfqebicyFaL+yvOfgf/7/cnv8sLf4cy+mB9uOq0GyxVFUfJoda2fukD5+idY0tVKktHMeWFdMe763GQUv9YzTo01nMVeUInHFKfFHkII0AS83DOe8vUCiV1Lli/SkLNtKy0R/q25nWJV8kRRVlQgofFvbc3zAnBIJkp8atLN70ZL+dK6E/xddR9XF03gNMwkXjszA36ucx+XCJ71FfInrzvXv8I8ejyOMGR3zo1MV5iIh2MEx/xEpsL56JpyEet+4mQyAF/k8jd6rB//0Pldwj0SNfGi35km90vqm4/Xlw1Rbc18pYmiKIqSPTUTngt6htF0loRBUL6llqqdjYgcTStZNcnHmk7zrd4anvW50i5LF8AlLt+8x15VOsJ+byFjMfM5F3oNSak5yieb2+gK2zgVTJYE2ugIsMERUCPrinIe/H6klKGoZcEbcx3BUMTCvvFi3lgxxDO+BqbSrnRZnEBy32gJVxd5l9HjxfkHvZz8wwvILEssdDx8lN4DbfO2ANmKC6i6pAF3gyqbqKQWGvcTGPGlbiQEI8f7KCgvXJlOLaAvbM2glcAqEtOlSpPXbR2BQUheWzbMa8vS56lRFEVRlkcF4TlgL3Ey3j6U24MKKFlXRfVlzbk9LlBgTHBnQxcjURP/t7uWE0HHgsG4QGLRdK4pmpj3uNOY4JMt7fxXXzXPzQnkBZJLXT7+troPlynBVpOfrU61p0xRziddwgNjxSlnxnQED4wVc3mhl0NTrmW9n0TQlVEgsIRjS0nn48ezDsBnnJ2DIzTmp/3Bl6jd20rZ5ppcdFG5SIUmAukbSUlo7Pxe88wZZjT3mGN8tOk0T00WMhU34jHF2F04SYExkeceKoqiKKCC8Jwobq2g75n2Jd8YLkgIhCG3uwXiEp6bLOTQlJO4FNTZQryrrpuvd9dzKuhg7j5ugcQsdD7Q0IlzgYuy2xTnnxu6GIuaOBW0I4AWe1AtNVeUVSaYMODPYGZ7KmHkwGTh7KzYchjyVP0gMDRJZDKU8+P27D9FYV0JFmd+Bg+UC5+W4fVYM2aS+Cx/WuxBbFqCkL54PzQkl7l8uE1xbikZW8HeKYpyoYnpggOThZwI2kEK1jkC7C6cxHyeSpVmIzTuZ7x9iHg4hsluwdNSjrXQfr67NUsF4TlgtJqo2tlI3zOnc3dQXeKsdOfscD1hC5/taGQsZp7do/2E182PByqmPwRzv0zJxEzVlggNttQ3vMXmGMVmVcZEUVYrUxa1fl+cWn7iMg3J9oKpZR9nISFvMC/HRcDoiX6qdzXl5/jKBa+g0o0waGnLhbrrS1aoRwsza5JXlIzyq+GyRVe4GYTkhmIVfCuKktrxgJ0vdSa3qBlInvseHC/mh/1x/qm+i00FGawQWgYpJROnhxk+2ktgKLkdSDNqGMxGHGWFlG2qTp6bz9rrqscTdDxyDG/nyJycXZKB5zspWV9J3ZXrVkVZyfPfgwtcLBih/2AHoycHc3dQkcwAXFhbnJPD+eIGPtXezETMBCSXnuoI5PSfZImhuQmYkn+fDtv4f45u5Ad9VQxEzDnpi6IoK8uiSTY7pjJIxihpD9mWPQuuI3hFaX4qIGQ6G5k1CcF0+32VNc1oMVGyvjJlG81ooHhd6jYr4XXlQ+yervE993uvITEKyZ0NnZSoVWuKoqTQG7bwf0434Z8ua5hAIzEdNgYSBj7T0UhP2JK395dS0vXYcToePjobgAPocZ1YMIq3c4STf3iBrsdPIOX8+5uOR47i7RqZOdD0n+SPoycG6HmqLW/9zoYKwpdhasDLS//7FAPPd85m3s0Fg8lI801bc5aM7cGxYvwJwxLKlQhi0sB9Y8V84OQ6DvpUeR9FuRC9umwkg+BaIDO+JMiz/j7z31sLfGxw5GfG2lXjWVolikysglFxZXWr2d2S/AzC/EunSM7OtNy8DZPt/A9YGwS8t66bDzR0sN05RYkpSqUlzKtLR/jS+hPsULlaFEVJ47fDpSSkWHBFjUSQkILfDJfl7f1HTwwwdir9BOfYyQGGXuyZ/Tk45sfbOZoyX/bIsT6igUguurks6q5jiWKhKG33HUaPL16uZKlabtmGvTh3Ae/jE0XL6qJEEJeCL3fVMxY15axfiqKsjO1OP2+t6oM5JQOXqtAQ4+xVM3P/+0W/iwPe/GSHNtnMFLdWpK6apolMSyHP46ouWk7XlDVAM2i03LSNppdvxllZhMluwVJop3JHPZvfuCenW8iWSwi4xDXFBxo7+frG43xp/Un+vHJQzYAripJWXML+s0qank1H8JTXTUzP/cC4lJKhF7szbj90uBupJ5fLj7cNph+slzBx+vxXgVB7wpdo9EQ/ejwPWUQF+AcnKSjL3U3sVMLAku5K5xHoEvaNe/izihxnglcUJe9uKRljc4GfB8aKOeovYCxmIqzPbEXJXEA3MDeJ49kEkntGStjtzk+uiLorWon6Q0z1e5NdmDOmYDAZqb1iHZ2PHs3qmJrRkAzuFSUNoQmKGssoaszfDJCiKMr5FE4YiMv087QJBCFdw6TlNh6Kh2JZJWGNh2MER6dwlBUSD6cfaBSaIB6Opm2XbyoIXyJvZ372PAohMvoAZcNjjBHIQSCuI3jO51JBuKJcoGqtEf6muh+Aj7U1T1dFyIZMe2GWCNpCDqbiGk5j5knhMqUZDbTesh1v1ygjx/qJ+EIYzAY8zeUUr6ucXg4s6Xz0GCCSe8EWI0BoGs03bcVoUat8FEVRFMVmSGASOrE013uj0LFnkfw1U2fv8c6EHk/2w2RPvyVI6hKTPX/72TOlgvAlysssODMfjNzuKbu+eJwf9lflZNV8XOZpP6aiKCuq0BhfQjmyzNt+6nQzH2k6jSsPdYeFpqWcjSxuqcBR4mLkWB+TPWNIXcde7MRkNzPZO04sEEEzJQP3ss01q6pkiaIoiqKcTwYBLyua4OFxz6L3CBqSq9xejHkoVWaymTHazMRDmc9WW6av48WtFQweSrOUXQiKms7/aiYVhC+RzVNAeDKUepZlKYTA01ye00NeUzTBfaMlDEfNy8p8rCFpTFOyTFGUC8NVRRM868vP3m2AvrCVL3Q28Inm9rzlUkvF6rZTu7eV2r2tK//miqIoinIBe3XpCH/yuonq2jmxg4bErOm8piw/+6qFJijbVE3/cx0ZNIbCmmLMjuTMttXtwNNawXiKpG4V22pXRRJNlZhtiUo3VuU+ACc/HwybQedjze202pdXz09HcJOqLaooF4VdLh/11tCyE7UtRkdwKujgVFDNMiuKoijKhaTcEuVjTafxmJJbZA3os7XCi0wxPtJ0mkpL/vZVl2+txVHuSt1IJHPB1Oxpmfdw/VXrKdlQOdtGzCRsFYKK7XVU7WrKT6ezpGbCl6igwk1xa8XC6fNFcm+31DO4uZ1JLCQE5Vtr8/LBiOoCTUg+0tTBl7vqeH7KtWDJgcUlkzDdXDzKujyVHlIUZWUZBHy46TRf6GzgVNCBAYnOTJ6z3ExdG5AcmCxU5w1FURRFucA02kN8dcNxDk05ORFwIIH1jgA7nFPkqIryojSjgXWv2MHgoS6GjvSiR8/d2lZQ4ab+qvXnbCnTDBr1V22gckcD4+1DxMMxTA4LnqbynG/5XQ4VhC+REIL6qzdgcdkYerGHRDQ++1xBuZvKSxvpePilZJK1RWLxouZyzA4LJrs5Lx+M00Ebvxku5RlfIRKBSeg4DfEMAvCZDifbFRnjvKJkBLOQfLevCpOQXOrysckROC/LTBVFyQ2XMcEnmts5GbTz9GQhgYSBo34HIzHz9Pjgcr/gcjoD++oTC0YYPTlIeCKAZtQorCumsLYYoeqFK4qiKAqQrPp5iWuKS1xTK//eRgNVO5so31pH5+PH8XaMJJ+YnsAMjfkJDE0umtfFXGClYnv9ynU4SyoIXwahCSovaaB8Wx2BoUn0eAJLoX32w9D6ih203fsCseB0tvM5s961e1so21STt74d9Dn5UmcDkjM30jGpMR43kaq80ExH/6aql3pbGJOm0xm08b3+auJSTC9dFfxhtJQ6a4j3N3RSquqOKsoFSwhY7wiyfnq2OqYL9o17uG+0hMGohfTni8XpCMrN578MyNmGXuyh9+m2OY8IRk8MYHZaab1lu0rUpiiKoiirgJSSjkePMdk1pyrV9FxhIhqn87HjoAmKWy68MqMqCM8BzaDhrCqa/VlKyfCRXoZe7JkXgFucNoqayyhZV8lU3wRdjx8HwFlZhLuxFM2QmxmYYELja911JGbeeJ5MbqYlR/wOfj5cTkzXCM2pC5yY8/resJVPtTfz2XUnsRlyX6JAUZSVZ9Ikt5SMcUvJGDFd8IuhMn4zsrRkkYJkhtXVZOR4P70H2s56NHlFj/rDnPz982x+w24MZnV5VFKLBSNMdI6SiCSXOhY1lKrPjaIoSg4FhibnB+AL6H2qDU9T2QW3kk1dLXJMSknnY8fPzconITIVYvRYP8Mv9aLHEsys5R49MYDxKRPNN26loHz52YqfmCgiomssfV+n4Bmfe042xIVnwnQEIzETj08UcVOJStimKBcbkya5o2IIlzHBr4bL8Ceyu2S8vnwItymevmEWwt4goyeS9cE1kwF3fSnu+nOXkesJncCIDxnXsbrtmOxm+p49zdDhnsUPLiEWjDJ2cpCyLflbqaRc2KSu0/NUGyPH+pMJWkWyHn33n05StbOR8q21iBXcq9UetNEZsmEQks0FfrU6TVGUi8boyYHZc+xi4uEYR3/5DBXb6ylqKsvZpGa+qSA8xya7RxdPiy+TH5QzP5/5QMUjMU798QU2vvayZS+FPBm0owHLmZueX44g9c3EIyoIV5SLlhBwa+koNxWP8ch4Ef/Vnz44tWtx3lAxxC1pqimEEhr9EQsaUGMNY0pRb1RKSd/T7Qy92DNnaw+Mtw1hcdlofcV2LE4bUtcZeKGL4Zd65+XqMNpMxEOZBSdjbSoIVxbX9fiJ+UlZp6/lMqHT93Q7ABXb6vLfj5CV/+yppStsY+YLIZDscvl4R00vBcZzExkpiqLkmpQQlwKjkDnPFRWZCmdUjSrsDdL56DGGXuph3a07MFpMue1IHqggPMeGj/SduUHMhgQ9IRl6sYf6q9bno2t5IvDG1MdIUS52Rk1yQ8k4/VELfxwtTdFSUmMJUWqK0hW2UmcNn5NF1R838NOhch4d9xCTyRFrhyHOzcVjvLZsGOMCwfjQ4e5kAJ58i3l/R6ZCnPzDC2x63WWcfvAIvr7xc16faQCebLv69rErq0NoIrBwVZQ5+p/roHRDVV6XpveFLfxbezPR2cSHyS+ZRPCcz8UnTzfxyZY2rCkGthRFUZZjPGbkDyOlPDzuIagbMAudq4smeGXpSM7KlxmtpqziqtC4n85HjtFy87acvH8+XRjz9ReQ4OhU9gH4DCkZbxtELrP++HpHYFmz4NkQSIpyvNxUUZTV668qB/iLigHs2mLfe8HJkJMvdjXyoVPreO/xDTw45pkdyA4kND7e3sxDY8WzAXjycSO/Gi7j850NxM86BerxBAMvdC3eKQnRqTCn7j20YACeLVOBddnHUC5OYzNLI1OQCZ2JzpG89uOng+VEde2sVWtJOoLesJVHxj157YOiKGtXf9jCB0+u44+jJQR1AwBRqfHwuIcPnlzH8UBuEpx6msqyi6skTPaMEZ5c/aVRVRCeI5GpMCPH+pD68sJfPa5nVl88havcXswimRd9cbkZHZfAtZ7l3/QqinJhEAJeXTbCTcVjZHIeGYuZ+K++Gn46mMxc+suhcgYjlgWDB4ngsL+Ax84KHnz9E8k8GmkEhnyZ/RJplK6vzMlxlItPNBAh7edeCKL+cN764IsbeNZXuOB3aK4Hx4rz1gdFUdamvrCFw74CPtvZQCBhOOc8pCOIScEXOxuI6stfm+6uL8Hqtqcd/JxHJLcHr3ZqHfEyJaJxuh4/zkRHbka9DSYD4uy1mxmK64KnfS6enHCTPo4XmIVOdHYmSmIWkqgUZJrQTUNSao5y9SrLfqwoSn5JCfvGPGR2rki2+c1IGbtckzw87kkZPAjgvrFiri8+M7iXiKzQahsBtiIHRU1lK/N+ygUnuc8wzdpIKZNLKPNkLGqeLT26aBcQjETNeeuDoihryxG/gx8NVNIRSj/DLRH4E0aemizkZUXeZb2v0LTpks+HCE0EMn0VemL1V21SQfgySF3n1L2HCIzkZvYFAcXrq5aUVXUsauLfOxoZiFgRyLQXaA3JlUXj7Cn0EZeCGmuYgz4XP+yvSnFrkTyqBiQQNNhC/HNDp9pzpihrTEjXmNKzCzI0JH8cLZkuebg4iaAnbJ1NOg1gXqHl4c7KIpqu34RmTN1HZe3yNJcxcqwvdSMhKGpIlTdheeyGzBKuWTWVmE1RlOU76HPyxc6GrNbQakiO+QuWHYQDmB0WNr72Mnx94/Q9c5rQuD/1C6TE5nYs+33zTQXhyzDRMUJgOHcBuMFspHxr9hl5dQl3dTQyFLEApA3AIblcZCBsZVtN/+xjL/eMc8BbyImg45xjaEgKjTF2uXw4jAkucU7Rag/mPAuioiirn2l2u0vmJ4CZfaqZMJyVYbWgohBzgTWvS3xLN1VTd8W6vB1fuTg4ygspqCjEPzS56GR46cYqTHZL3vpQZo5Saw3RG7Yuer3XkFyRg5tfRVHWtriEb/bUkrzqZ3fTn8spOqEJCmuLsXkcvPiT/SkPbrSaKKxb/dtx1J7wZRg53r/0UtxnsbhsrH/VpZgd2c/4PD/lpC9iTbs/bD6JUcz/BJs0yYeaOnhl6ci8EXST0LneM87n15/kb2r6uaNiiHUOFYArylowFjXx6+FSv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.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.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.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.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.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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot decision boundaries for training and test sets\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Train\")\n",
    "plot_decision_boundary(model_0, X_train, y_train)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Test\")\n",
    "plot_decision_boundary(model_0, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aeHx9MbTvrJH"
   },
   "source": [
    "Oh wow, it seems like we've found the cause of model's performance issue.\n",
    "\n",
    "It's currently trying to split the red and blue dots using a straight line...\n",
    "\n",
    "That explains the 50% accuracy. Since our data is circular, drawing a straight line can at best cut it down the middle.\n",
    "\n",
    "In machine learning terms, our model is **underfitting**, meaning it's not learning predictive patterns from the data.\n",
    "\n",
    "How could we improve this?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6VivsLTmHC8B"
   },
   "source": [
    "## 5. Improving a model (from a model perspective) \n",
    "\n",
    "Let's try to fix our model's underfitting problem.\n",
    "\n",
    "Focusing specifically on the model (not the data), there are a few ways we could do this.\n",
    "\n",
    "| Model improvement technique* | What does it do? |\n",
    "| ----- | ----- |\n",
    "| **Add more layers** | Each layer *potentially* increases the learning capabilities of the model with each layer being able to learn some kind of new pattern in the data. More layers are often referred to as making your neural network *deeper*. |\n",
    "| **Add more hidden units** | Similar to the above, more hidden units per layer means a *potential* increase in learning capabilities of the model. More hidden units are often referred to as making your neural network *wider*. |\n",
    "| **Fitting for longer (more epochs)** | Your model might learn more if it had more opportunities to look at the data. |\n",
    "| **Changing the activation functions** | Some data just can't be fit with only straight lines (like what we've seen), using non-linear activation functions can help with this (hint, hint). |\n",
    "| **Change the learning rate** | Less model specific, but still related, the learning rate of the optimizer decides how much a model should change its parameters each step, too much and the model overcorrects, too little and it doesn't learn enough. |\n",
    "| **Change the loss function** | Again, less model specific but still important, different problems require different loss functions. For example, a binary cross entropy loss function won't work with a multi-class classification problem. |\n",
    "| **Use transfer learning** | Take a pretrained model from a problem domain similar to yours and adjust it to your own problem. We cover transfer learning in [notebook 06](https://www.learnpytorch.io/06_pytorch_transfer_learning/). |\n",
    "\n",
    "> **Note:** *because you can adjust all of these by hand, they're referred to as **hyperparameters**. \n",
    ">\n",
    "> And this is also where machine learning's half art half science comes in, there's no real way to know here what the best combination of values is for your project, best to follow the data scientist's motto of \"experiment, experiment, experiment\".\n",
    "\n",
    "Let's see what happens if we add an extra layer to our model, fit for longer (`epochs=1000` instead of `epochs=100`) and increase the number of hidden units from `5` to `10`.\n",
    "\n",
    "We'll follow the same steps we did above but with a few changed hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "j-GRUN9QHC8B",
    "outputId": "9174e74b-862a-4bca-9f17-c7176d22f81e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CircleModelV1(\n",
       "  (layer_1): Linear(in_features=2, out_features=10, bias=True)\n",
       "  (layer_2): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (layer_3): Linear(in_features=10, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class CircleModelV1(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.layer_1 = nn.Linear(in_features=2, out_features=10)\n",
    "        self.layer_2 = nn.Linear(in_features=10, out_features=10) # extra layer\n",
    "        self.layer_3 = nn.Linear(in_features=10, out_features=1)\n",
    "        \n",
    "    def forward(self, x): # note: always make sure forward is spelt correctly!\n",
    "        # Creating a model like this is the same as below, though below\n",
    "        # generally benefits from speedups where possible.\n",
    "        # z = self.layer_1(x)\n",
    "        # z = self.layer_2(z)\n",
    "        # z = self.layer_3(z)\n",
    "        # return z\n",
    "        return self.layer_3(self.layer_2(self.layer_1(x)))\n",
    "\n",
    "model_1 = CircleModelV1().to(device)\n",
    "model_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ACkcim2k2G5R"
   },
   "source": [
    "Now we've got a model, we'll recreate a loss function and optimizer instance, using the same settings as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "AXYwYPpSHC8B"
   },
   "outputs": [],
   "source": [
    "# loss_fn = nn.BCELoss() # Requires sigmoid on input\n",
    "loss_fn = nn.BCEWithLogitsLoss() # Does not require sigmoid on input\n",
    "optimizer = torch.optim.SGD(model_1.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "drHt2W7x1JEW"
   },
   "source": [
    "Beautiful, model, optimizer and loss function ready, let's make a training loop.\n",
    "\n",
    "This time we'll train for longer (`epochs=1000` vs `epochs=100`) and see if it improves our model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aX0QGBozHC8C",
    "outputId": "b00e48e9-1075-4f6e-c0c3-e511451d3fe9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Loss: 0.69396, Accuracy: 50.88% | Test loss: 0.69261, Test acc: 51.00%\n",
      "Epoch: 100 | Loss: 0.69305, Accuracy: 50.38% | Test loss: 0.69379, Test acc: 48.00%\n",
      "Epoch: 200 | Loss: 0.69299, Accuracy: 51.12% | Test loss: 0.69437, Test acc: 46.00%\n",
      "Epoch: 300 | Loss: 0.69298, Accuracy: 51.62% | Test loss: 0.69458, Test acc: 45.00%\n",
      "Epoch: 400 | Loss: 0.69298, Accuracy: 51.12% | Test loss: 0.69465, Test acc: 46.00%\n",
      "Epoch: 500 | Loss: 0.69298, Accuracy: 51.00% | Test loss: 0.69467, Test acc: 46.00%\n",
      "Epoch: 600 | Loss: 0.69298, Accuracy: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n",
      "Epoch: 700 | Loss: 0.69298, Accuracy: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n",
      "Epoch: 800 | Loss: 0.69298, Accuracy: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n",
      "Epoch: 900 | Loss: 0.69298, Accuracy: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "epochs = 1000 # Train for longer\n",
    "\n",
    "# Put data to target device\n",
    "X_train, y_train = X_train.to(device), y_train.to(device)\n",
    "X_test, y_test = X_test.to(device), y_test.to(device)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    ### Training\n",
    "    # 1. Forward pass\n",
    "    y_logits = model_1(X_train).squeeze()\n",
    "    y_pred = torch.round(torch.sigmoid(y_logits)) # logits -> prediction probabilities -> prediction labels\n",
    "\n",
    "    # 2. Calculate loss/accuracy\n",
    "    loss = loss_fn(y_logits, y_train)\n",
    "    acc = accuracy_fn(y_true=y_train, \n",
    "                      y_pred=y_pred)\n",
    "\n",
    "    # 3. Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backwards\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Optimizer step\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "    model_1.eval()\n",
    "    with torch.inference_mode():\n",
    "        # 1. Forward pass\n",
    "        test_logits = model_1(X_test).squeeze() \n",
    "        test_pred = torch.round(torch.sigmoid(test_logits))\n",
    "        # 2. Caculate loss/accuracy\n",
    "        test_loss = loss_fn(test_logits,\n",
    "                            y_test)\n",
    "        test_acc = accuracy_fn(y_true=y_test,\n",
    "                               y_pred=test_pred)\n",
    "\n",
    "    # Print out what's happening every 10 epochs\n",
    "    if epoch % 100 == 0:\n",
    "        print(f\"Epoch: {epoch} | Loss: {loss:.5f}, Accuracy: {acc:.2f}% | Test loss: {test_loss:.5f}, Test acc: {test_acc:.2f}%\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o0ca3sIV1WrZ"
   },
   "source": [
    "What? Our model trained for longer and with an extra layer but it still looks like it didn't learn any patterns better than random guessing.\n",
    "\n",
    "Let's visualize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "GUjJqRw7HC8C",
    "outputId": "12d163f9-b602-459e-f34b-54c58abf8d7d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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74jcIXQ+fEA6IrzpL8S9+SNazT8bYO0VRFEWZfqauThJOHo24HUtqGqm7d0Y8j242gxblCiwl0mzGsXINvsSk8NdbIfAlJuFQA9eKMinTGoT39/ezevVqfvzjH8fUvrq6mptvvpnt27dz+PBhPvOZz/CRj3yEF154YTq7qShTKv7MSYp+9gOW/fu/sOzzn6D44QexnzwaGoW2NtZhbWmKujw7rqaSkp88SMlD3yHjhb+w5Mv3s+Sb/4+EU8cnHIDD4AU0KWXEMVNXJwW//TlIfcTFXhAcbbe0tZD7xG+JP3uK3D/9IfjY8HaD/8564S9kvPCXsBfvcfUTSN39Kug6gXg7NR/5JFIzjEoqIwHdbKHllttHHLfV1cT0fR56PHPH30g4fnjS/VaUC810Daj/+Mc/pri4GKvVyiWXXMLevXunvvOKMo/Z6qqjX8N0nfiqsxHbOFasjZ5XRUocy1eDwUjth+8LDnyftyVNahq6yUzth++DMbZ/KYoSu2kNwm+88Ua+8Y1vcNttt8XU/uGHH6akpITvfve7LF26lPvuu4877riD733ve9PZTUWZMlnPPUXpQ98h4dQxDO4BDB439tMnKPnp98l98rcgJZamxpjONRTGxpefJuv5pzH29ow4PmFS0r358hGHUve8FgzAw/VF10k6sp+cJ34zKggecWohsLS1Rr1piIUA4murWPA/X8HY240nK4eAxTJmO83jpuSnP0AbcIWOW9paxvV6UtNI3/nSJHutKBee6RhQf/zxx7n//vv5yle+wsGDB1m9ejXXX389bW1t0/U2FGX+iXlAO3I7x8o1eFPTRwXVQ6TQcC5bhTQYQdcZKC6j/F+/QvfGLeiDeVN0o5HujVuo+NcvM1BcNp53oSjKGObUnvA9e/ZwzTXXjDh2/fXX85nPfGZ2OqQo45B49CCZL/4VOG+WeDAgTXtjJ8burtBS71hNNOgea/+11DQ8GVn0rLtkxPH4MydjmjG2tbdGbSMC/kntVz+ftbWZsge/gS8xGeOAa+wl/FJi7mgj7fVXaL/uFmBw+d04CF0nvvJsKJGcoihBN954IzfeeGPM7YcPqAMsXbqU3bt3873vfY/rr78egAcffJCPfvSj3HPPPaHnPPfcc/zyl7/kC1/4wtS/CUWZh1wlC5BCG3N71xCpafQtXhb5RAYj1R//HKU/+u8RA/pD55YGA4knj5L4lc/htyfQedlVtF99A413fojG934Ag9tNwGpVdcEVZQpN60z4eLW0tJCVlTXiWFZWFg6Hg4GBgTGf4/F4cDgcI/4oykzQ3AMkHDtE0oG3sDbUkv7qC1GXYSeePIr97Mlp75tuNCINwYziusEQGv32ZGTRevPto7Kex1L+a7YIXcfc0x0sUxZpoEDK4PL1QX2LlqIbTeN8tcnP4CvKxS7cgPqePXsA8Hq9HDhwYEQbTdO45pprQm3Goq73ysXGn5hE79oN4WewAaSka+u2qOfyZmZx9kvfpOmO9+MqLsOTkYUvMREA4feF2hn7nGS+8CylP/o2wusBgzFYk1wF4Ioypeb9b9S3vvUtHnjggdnuhnIxCfjJ/utTpL2+A83ni95+mOmeX/Xb4qj70L/gKl0AAZ3kQ3uxnzxKwukTGDxurK3NFP3yx+gmE+3bb6DtxneCpuEqXYCtsW5KgvGA1YZuMGIc6I/pfLHOmkdrIwBzbzcEAmAwoNvi6LpsO2mvvRTT8ngJDOQVqllwRZmkaAPq3d3dBAKBMducPh2+9KK63isXo6Y77sLa1BDKcD50hRoqbdpw54fwpmfGdC7daqPr8qvpuvxqEo8coOiXwS0mY9UOt9VVk/nCX2h9xx2Y21qJq60EBP2lC/ClZUzZ+5suwush8fhhTL09+OPtOFaujVj6VVFm2pwKwrOzs2ltHbnctbW1lcTERGw225jP+eIXv8j9998f+trhcFBQUDCt/VQuYlJS+OuHSTx6aEr2PU8lqWn0bLyU/kVLgweMwRnxpCMHRrXVfD4yX/wL5vZWGj74Mbq2bifttZcn3weh0bV1G90bt1Dyk+9i6u0Jrg4YKsV2fnsIBr1T9L2UgGHARcCeAEDLO+7A1NVB0tHoJd0E4EtLn5J+KIoy9dT1XrkYBeLtVH72/5G2awdpu18JXlc1DceKNXRcdUOwNOgEpO16OeJSdyElaa+/gq2mioSKc4NjEnAuW0XjP96DPzFpQq89raQkbdfLZD33NAaPO3QPIo1G2q++ibYb/mHUakBFmQ1zKgi/9NJLef7550cce+mll7j00kvDPsdisWAZI1mTokwH++kTJB05GPbxqdwLPR5DIW7nZVedOxjwk//7Xw4+MpoAUg7tpeuKq3GVLqT51veR+8xjSCEmPMDgTUun7Zqb0OPiOfPl/ybp8H4Sjh9G83rxJSah+XwkHj+MwT2ALyGJri1X4EnLoPAPv5zQ642l6Oc/oOoz/w+EQBqN1H3oX0g4ejA04h9JXFX5lPVDUS5W0QbUDQYDBoNhzDbZ2dlhz6uu98pMEx4PyQf2kLJvD4Y+J76UNLo3X07v6nUzujxbt9pov+4W2q+7BeH3ITXDpAPJuNqqiHvNgWBy2cozI44JIOH0cUq//00qP/fl4FL1OST91RfI+fMToa+H7meE30/mC8+iedy03Pa+2eqeooRM61BQX18fhw8f5vDhw0AwY+rhw4epq6sDgqPaH/jAB0LtP/axj1FVVcXnP/95Tp8+zUMPPcQTTzzBZz/72enspqKMTUpstdUkHj1IXOVZ0HVS33g17N4smKUAXAikwUDdPR/Hm5UTOp6+82U0PRC1TxkvPAtA5/brqPnIJ3EVlU6oH7rBSOVnvoQeFx/sl9FEz4ZLab79Ttx5BSScPEbSkf0E4uJouelWyr/wNdpuug3H+s344+1TshtbAPE1VeT86ffn6qsLQcCeGNP/jdHRO2Wz8opysbr00kvZsWPHiGPDB9TNZjPr168f0UbXdXbs2BFx0F1RZpKps4NF/99/kvf4b4irqsDa1oL97EkKf/0wZd//FprLFf0k00AaTVM0kxvbHctYg/JC1zF3dZH22tyqKKK5XGQ993TYxwWQvvNFTJ0dM9cpRQljWofx9u/fz/bt20NfDy0ju/vuu3n00Udpbm4OBeQAJSUlPPfcc3z2s5/lBz/4Afn5+fziF78IZVNVlJmScPwwOc88jmVYNnBfUjLock4kMRuacffZE+i55DI6t24btUfLfvpYTOeyNdaH/u1cuRbnyrUYnA4M/U4WfPcbGLyeGPoj6Lj6BgIJiSOOW5obKf3hf2NwuUIj7qauTrL+9mfS3thJ1ae+gDc9k+bb3kfB734xJSsJJJD2+itoXi+N/3gPCEHKm6/F9FzdYlV7whXlPH19fVRUVIS+HhpQT01NpbCwkC9+8Ys0Njbym9/8BggOqP/oRz/i85//PB/60Id45ZVXeOKJJ3juuedC57j//vu5++672bBhA5s2beL73/8+/f39oWzpijKrdJ3ihx/E1NM1eE0anE0dDEhtDbUU/PZn1H70UyScPErygbcxOh34klPo3rSV/oVL5vy1pG/BYhLOnJjwPY2QOmm7X6XtxlvnzHtNOrwXEa0CjaaRsnd3sN+KMoumNQjftm0bMsKs0qOPPjrmcw4dOjSNvVKUyJIO7aXg0YdHHTf29gRLehA9UBxvMDne9gOFJbRfcxOO1esjtIp+RgnBZW3nCSQkEkhIpHPbtWS8+NeIZ5JC4E3PpH37eYNluk7xT7+PYcA1YsmbAJASo9NB4S9+SMW/f42ejVtASnKefgyjqx8ptIh1yyMZek7q27txrF5P/4LFJB/ZF/V5EujZMIlZOCmx1VaRdOQAmnsAX2o63Zu24k9Knvg5FWUOmI4B9fe+9720t7fz5S9/mZaWFtasWcPf//73UcnaFGU2JJw6hrWtJezjQtdJPHmUhf/zFazNjUhNQ+g6UtNI2fcmzsXLqf3wfcg5vH2i88prSTwV22B9OMb+PoTXO6Xv09TRRvprL5O8/00MAwP4EpPp3nIlnZdfFXXpu7mzE2nQEIFA5Nfo6pyy/irKRM2pPeGKMtuEz0fe478O/vv8x877O5yA1YZz4VKSj0VPBgagG00jyoOEIwFvcgqV//qVUTPOY3EuW4X97MmoychCidzG0L1hM3GVZ7FXng3dZIzsk6B39Qaa3v3+0DL0IQknjmDuDn+hE7qOrbmR+Mqz9C9YTM+mrfSu20TC8SOYuzqwnzqG/eypiddJ1zTSdu3Am5oecxb7jm3XTui1DP19FP7yx9grziA1DYlASJ2s556m7fpbaLvhnXNmpkBRxmu6BtTvu+8+7rvvvsl2T1HOGRwMja8qBylxlSwIJi4b5+dv4rFDY17zRrwUYGlpAs6V+Rz62372JPl/eIT6ez4xsfcxA/oXLkE3mtBiuP8IR2oa0jh1oURcdQXFD30Xze8LfS/Nvd1k/v3PpOzZRdWnv4gvNQ10HUtLE5rPhzc9IxScB+LiYprZV1nSlblABeGKMkzi0QMYwtSkj4UkGMi13fBOOmqrSH/1RZIO7ycYro7VXhCwWjH2+Ylao1oIuq64JqYAHKD70svJefZJZJh94UOv1vwP7x71mLWxjpyn/g97xZkR7QNx8fgTEnHnFdK3eBnOpSvwJ6WM+fr2MyeRmgGhhx+RlpqG/cwJ+hcsDn5tNOFYswGAjiuvJe+xX5G6981QX8dzGyV0HVt9zbhuEJIO76f96htGJ9wJBEg+8BZpu3ZgbWlCGo3BzLRXXos7r4Din34fW31N6HXP9VOS9fdn0S1WOq66YRy9VxRFUcbD3NZC4aMPY2usC2bEJrh8fCAnj/q7P4YnJy/i8+Mqz5L2+g7iq8oxuFzn8oqEMbSqa8zHpCT58H5a21vxZszN1R32U8eiBuCRJhKkpuFYuRYMo1fTTYTweij62Q/QfN5R+9CFlJgcPRQ8+hN6120i45W/Y+rtGeyHgZ51m2h5x7voXb2e7GefjPw6uk7Puk1T0mdFmQwVhCsXJ10PjoyfNzpuaW1BGgxRlzKdbyijePemLbRd/w8gBAPFZdTf83F6Thyh8JEfga6HLixDI+y9q9aRdPRATMGlNzWdrq3bYu6TbrVR/08foeDXPx3d38G/m991J4HzSoxYG+oo+8F/IXwj91Vpuo5w9eNYuTa01zqSWL6HUohgXe+xGAw0vv8jdG67jvSXnyfxxBEMHs+4lu5LTcObnoknPQNzR3vUVQFZzz9NXE0ltR++L3RjIfw+in72vyScOXEuc7zPS/K+N0keXHYYV1sVsR+Zf3+WzsuuQprNEAiQeOwQqW/sxNLeSsBmo3ftJrq2XEHAHtsAi6IoinKOsaebsh98C4OrHxiZTMza2kzpD75Fxb99NWwZyqy//onMl54bMfs92RSdUggSjxyg45qbJnUeQ58zOLMfCOAuKIq5Jng0pt6emEp3jtVGAkhJ+xQOLicf3IvB1R+2P0LXia+tIr62asT/jdADJB98G/uZk1R+7j/pWX8JyQf3jplQTmoa/aULGZhgAlpFmUoqCFcuHrpOyt43SHvtZaxN9SAE/QuW0LH9OpzLVwMgTSbQx3fplYA/IZG6D34cV+nCUcGpwdVPwBaHqc857DmCrkuvpOmO95P0bx+LOuIOUPvhf0G32sbVt951l+BLSCL3T7/H1twY6q83I4umd91J39KVo56T++RvED7/mKVLBMG91t2btuIanL0OZ6CgEN6MHIhrgQDu/KKIbdx5hTTc/TGQkriqcmz1tcRVnSXpSOTBC6lpOJeuBCHouOpG8p74TcTXgeCNW8KJI6Ts2UX3ZcE9sFnPP4P97MnQ46G2BL+XiWdORL2RMXjcJJw8inPZSop/9r/Yy0+F6rPKbrA2N5L+yt+p+cTnGCgsidpPRVEU5ZyMV/4eDODGuJYKXcfgcZPx8vM0vfcDox5POvg2mS89F2obel6E14tldZbUtEmtrNPcA+Q89X+k7N8zYlDbuWgZje/9AL5JBuOB+Pjo+W2GamwPqycuhQChUX/XhxkoLptUH4aLP3sKBvPBRDNqu6CuY+x3kv30/9Fw10cxeDwkHj88Yq++0HVcRSXBQXa1PUyZA1QQrsxLxp5uUt7ejaWtBd1swbFyDX1LVoQv2xEIUPirh0g8dgiECC0ji684g738FG3X3ULrzbfjWLGG7L/+aVx9EQT3krvKFo16LOXNneQ//ptRI+qaHiBtz2v4UtNwLl4eMUPpUNDsyS0YV7+GuBYuoeILX8fo6MXQ58SfmBh2xtXS3Eh8TeRZXalppL2xM2oQ7iooDfU/3HL4QLwdx6p1MbwLQAhcZYtwlS2ic9u1eP7yRzJffj78TYSu03nFNQB0bbkSS3Mj6a/viGnkP/fp/8O5ci0Bm43U3a+ErZsea54ACRj7nOT+6Q/EV5wOPmfwRmPoZ9HgHqD4Jw9y5iv/M+7BFkVRlIuWrpPy1usR9wILXSdl3xs0v+vOUVuUMl7+27lVTmM4/5oRajUYoIZ9zUAAb5iZ92iEz0fJj7+Drb521IC4veI0Cx78BhX/+mV8qRM7PwTzxgTMlogVUISUNLz3bsyd7cRXngUh6Fu4hO5Lr8SXkjrh1x77tXSirT+IdP0Wuk7S0UM0uweo/cgniaupJHnvG5h6uvHbE+jZsJn+hUunqLybokyeCsKV+UVKMl56jqznh+pAChCQ9uZO3Nm51Nz72WDSjvOk73yRxGOHRu3hGrq4Zb74V/pLF9K3dCV9ZYuIrzw7voRgY3yoa+4Bcp96bKiXY8p6/hnq7v7niBlKBQSXfE1y5NafmIT/vKXn57O0Nkc9j9B1rM0NUdvl/OXJ4IBHhAC2/aobJ5zUpe2mW7E1N5J44siIGyipaaDrNL3nA7gLBmfZhaD5XXfiKi6j8Lc/i3pu4fdT9LMf0HzrezF4opdoi3o+QDeZSNn7Rvjvh5QYXP0k73+Lrsu2j9lGURRFGUnzejB43NHb+XwYXP3B66CUxJ89Sdrrr2BrrIv4vPOvvN6MLNquuYn8xx6N+DxpMtE7wb3HKW+9jq2uesx7B6HrGAaC9bAb/umjEzo/BEtytl97E9lh6mpLTcOdk0/3JZdN2b7vSAYKikk6FLmSSbS7ICF1LM2N+BOTziXlGyfN4ybpwFsknDyG5vPhzs2n69Ir8WbOzb39yvylgnBlXkl9YyfZzz017IgMDZxa2loo+dH/UP6FryHNFiytzcSfPYXw+8jY8beI55WaRvrOl+hbuhJ3fmFwxDdGUtNwLlk+6njSoX0InzfaszF3dtD8jjvI+csfR+5HG/x355ZtdF96Rcz9mQxpMkVvA+hmc8Q2ps52Es6ciHweIULJzCbEYKT2w/eRfHAvaa/vwNpUj9QMOJevpuPKa0YvkxOC/gWjVyuMRQBxDbXY6msn3r9hgkmCYqsxn3jskArCFUVRYqSbzFGTgELwczhgtSL8Pgp/+VBwAHccs6K1d9+LLzU9uJ9YCCztbWS+/FzY9i233DHhVU1pu1+J+LjQdZIO7aXpXe9Hj5t4pu/2a2/B4HaTvuNvIyYThK4zkF9EzT9/ekYCcIDuSy4j669PQcAfdvVcLFMRchL9tdVVU/zw9zD094UmEexnT5Lxyt9pvenWYM4fRZkiKghX5o+An6y/PRP2YaHrmDvbSX3jNRJOHiXh7MmYs2oLXQ8tE046tH/cWbiHlj0PZ25vjZrkTQqBpb2Vxn+8B1fJAtJ2vYz9zEmElLiKSum84urgfvUZ2r/UX7YI3WRGizR4IASOlZGXkFsH959HIqTE1jDJINdgoGfjpfRsjK2+tz8pJaYkbRAcBLG2No3YCzdRQkqMvb3R20EMAzeKoihKiMGAc+lyEk4eC7+kXNNwLF+NNFvIffzXJJw8ChDTwKgEvGkZONZdMuJ46y23o1utZLz4Vwzec0lD/bY4Wm9516QGU80dbVGvUVoggLm7E/ckgnCEoOUf3k3n1m2kvL0bc2cHutVK75qNwaolM7h3OhBvp/HOe8j/3S+C38th/5dDgyVSyrD/xwABi5WBwuIJvb7R0UvJQ99Bc7tHrJoc+hnJev4ZfEkpdG++fELnV5TzqSBcmTfiK8sxDktuNiYhyP7rH0OB7/iC6eAH7lB21WiGlkA33f6PYyYn0S3WiBeLof7pFgtAaL/zbNKtNjovv4r0V18YO7OoEOhmM11RLkLSGH1GfTztpowQdGy/gbwnfxtbeynpXbOepMP7o/5fRhNLaTmpaXhy8yf1OoqiKBcLbcBFwW9/TuKJo0giZPIG2q+9GYPTQepbr4/v81wIOi+/eszj7dfeTMcV15B48shgzpVknMtXTfraJk1m8Pujthu6f5gsX1oGbTfdNiXnmoyeDZfiS0gi84VnsQ+uSJQiWAqta+NWih/5ETJcyVch6Lz8KqR5Yt+T1DdfCwbgEXIDZL7wLN2btoKmYejvC66aDARw5xWq5erKuKkgXJk3DP19UdsIKRExXLjOJ4XAVVQMBPdOm7o6omZGdS5dSfs1N4UNnB2r1pH9/Nh7rUL91XV6V28Yd3+nU+vNt2NubyXp2KGRy+OFQDeZqbn3s1EDSldJWdSEL8EL65qp7HpMurZcSdL+PdirKyI3lBJvRhbdm7YQV12Bqad7fHkCzuNcsgJ3Zg6W9pbw+8J1na5Lr5zEqyiKolwkAgGKH36QuLoaYHTytKEKFtJkpu7uexkoKiX57d0xVSMJnUcIXEUlEcuDSouF3rVTW3e6d/X6YA6RCAlbPVk5eNMypvR154L+xcuoXrwsmEzW1Y8vMQk9Lh4gWHb1t79ACkbcmwgpcS5dQduNt074dZMO7Ik4OCMAc1cntuoKUvbtIWXvbrRhKx37Fi6h8T0fwJuZPeE+KBcXlSJQmTf8SclR20x0rlJIeS6T9qVXRFyCJYVG39IV1N77mYgz156cPBxLV4bdcyY1DVdhSbCs2RwijUbqPvQv1Hz0UziXrMCbmo47J4+2G97J2f/4Vkyz9brFStdl2wf3Qo/xGoA0aHRt2Ta1nY+FplH7sc+iG41Rfl4E3Zdchj8phZZb7gAm/vMVMJvRLRZcpQsiZtNt33497ryJZcFXFEW5mCQeO0R8TdXYZckYrMARF8+pB76Dc+VagGCizRiXWOsmM52XX031J/4NGSUPylTruPJaQIS95giCM/sXcqktf2ISnuzcUAAO0Lt+M+Wf/2rw2mxPIGC14Soqpe6f/pnaj3xqwolegZjLyeU98WtS39o1IgAHiK88S9n3vom5o23CfVAuLmomXJk3XMVleNIyMHeG38873svR0AWuZ8Ol9K7ZCEDX1m2kvrETk6Nn1MVdCoHUNFpjXLZV/4F7Kf7p94ivqTxXr3Jw1NadlUPtRz81Ny+imoZzxRqcK9ZM+BStN9+OpaWRxJPHRtYY1TSk0Ki75xNjZrKfCbrVRtMdd5H/2KNhk7203nxbaFVE/mO/gjDtYiENBhZ852uYuzrGfDxgtdF64zvpvPJajL3Dyu+ZYii/pyiKchFKefv1iDk7BGB09WNtbQ4NdnsyMqMuRZdC4Fi5lob3f3jWykV6cvOp++DHKPj1w6DLEddPoeu0Xv8P9GzcMit9m22e3Hwa3/dBGt/3wSk9rzctA2OfM+rPh7WlOXzWevcAWX/9E/Uf/PiU9k25MKkgXJk/NI2WW99L0SM/Crvva7xBkjctg47t19F9yeUknDyK0dFLwJ5A9Sc+R8Fvf0Zcfe3gTLZA6AH8CYnUf+BeBgpLYjq/HhdH1ae/SMKJI6S8vRtTTxf+hCR6Nm7BsWrdpEZt5zppNFL70U+TdGQ/qbtfxdrShG4y4Vi9gc7LtuPNmN39U92XXoFuMpPz7BOYentCx/3xdlpvvDWUVCd196uIgD7xABwAgbm7c+x99gTryfZs3EL6jr+R/dengkcHB2fOld/7zKRqwiqKolxIzF2dMSXNNPV0h/7dt3g5vqRkjL094QfzpaT1xltnLQAf4li9nrP/+d+kvrkT+6ljiECAgaJSOi+7Cnd+4az27ULUtXUb8TWVYR8f2pKn+bxhV7QJXSfpyAGa+vsIxNunq6vKBeLCjQCUC4rw+7GfOoaxt5uOrdtI2b8Hg8eD1Awg9eBe8BjOIwnWoqy75xNIg4Y/KYWUt15nyVf/FeOwPecBq422699B07vuwl5+Mnjxyy/CuWzV+Mt1aBrOlWtDy+EuKppG79pNI/bLOXsGaK3rxV/XiC3eTE5JCmbL7HwU9W7YTO+6TcRXnMHU24PfnkDfoiVgONefpKMHJ5UdXQDGAVfEx/H7yH381yQfOXDugWEX+WD5vW+Hyu8piqJc7Pz2hNDKsojthgdDmkbjez5A0S9+GMy0fV5bCXRdtn3OJMj0paTSevPttN58+2x35YLXu25TsG58fe2oa74UIlj6TNejJ9wdrNQzoIJwJQoVhCtzXurrO8j585NoPu+I2W4pBK78QvqWLCdt1w6M7uj7eQTQdsM/4EtLD50774+/H9XO4B4g589P0HrDO2m78Z1T92YuYn5fgCNv1NLR6AxO8gqQOpw51MzCVdkUL8tAzMbSfE2jf9HSsA/HWjJszNUZQsOXkIDJ0Rt5kEhKEo8fCbuaY+iinnxwryqPoiiKAvRs3EJ8xZmIbfz2BPoXjMxj4lyxhtqPfoqcP/0BS2d76HjAYqXj6htou/aWaemvMrdJo4nqT3yOvMceJenIgcGBcIFA4k3PpOHOD1H0yI+gzxf1XLoaLFdioIJwZU7LevZJMnf8bcx630JK4uqqMXd34lyxmuSDeyNmEgVoes8HQvuctcFAO5LMF/5C15Yr8CelTOp9XOyklBzcWU13e//g14T+U6QuOXu4Gc0oKFo89zK9unMLgkF0hIy6EvCmpmPp6jiXlVcI+hcsinqTyGB7EYiS1V8Ikve9qYJwRVEUoGfdJjJe/Gtwq0+Yz+fWG28dsbJpiHP5apzLVhFXVY65q4OA1Ubf4mVqpdFFTrfFUX/PJ2jp7sJ++jjC78Odkx9MSCsEvWs2kPrmaxHvNb3pGXiycma248q8pIJwZXboOqbuLoTU8SWnjtobbXA6yHrmcVL37wHC7/UWgLHPibWpYXBpuhxzqZAA6t/3QXouvSJ0LOng2whftBFNScreN4NZSJUJ62zpo7stcv31iiMt5JelYTDOrQRkXZdtJ/Hk0bCPS02jb/Eyau79LHFV5VibG5FGI87Fyyn90X8HfyajvEYsSyqFlBidjgm8A0VRlAuPNFuovu/zFP/0e1hbmoL3AMjBAV5J6023RiwthhC4yhbFVPFDubj4UlLpHna/OKTzimtI2bMLCLNqDWi/5sLOWq9MHRWEKzNL10l7fQfpr76IubsTAH9cHD3rLqF33SV40zNBCMq+982otbqHCCmxNTXQ8J4PkPPM42iDtamDJUoECGi+/c4RATiApaMdaTAgziszMZwUQpWbmAJNVd0IEbE6F36fTkeTg6zC5BnrVyycy1bRs3YjSYf2jV5urmnoFgtNt/3jqBu6+IozWDraR5/w/HPE2A9JsGRO/u8fQRtw4UvLoGvz5Xhy8sb1fhRFUS4UvtQ0yv/9a9hPHyfx2CE0jwdvVjZdl1yGPzl1trunXGA8WTnUfehfKPzVQxAIhAbPh7LWt11zs1qtpsRMBeHKuAmvl6TD+4ivKgegv2QBvWs3Ra+jqevk//bnJB98e8Rho8tF+u5XSd/9KgC+xCSMTse4slFLITB3daBbrRi8Hs6tdpYgBWOFOgGrFaFHqxQdrHmtTI5nwBcxAD/XLsqS7NkgBPX/9M94snJIf+2lUC1RCfQtWkbT7f+Id4ylZ5bW5pgy9gug4T0fIPOFZzH1dEdc9RHXUIutqR50HTSN9J0v0rllG03vvkuVMFMU5eKkafQtW0XfslWz3RPlIuBcsYYzX/5vUt98jYQTRxEBPwOFJcGs9QVFs909ZR5RQbgyLvHlpyn85Y8xuvoHS3dByp5d5D79GLX3fIL+xcvCPjfp0F5SzgvAx2KMlsQqjLRdL6P5g0HcyOdLcv/0B/z2RHrXncvS7Vi1nuznno54TqHr9K7ZMIHeKMNZbMaoM+EAZtsc/UgyGGi78Vbar7mZuNoqhM+HJys7Yskw3WyOvgwdcBUUk3jiCOae7tDg0VgZexk8HtqLNvh36ps7kZpgoKgUoeu4ikpjmx3XdeLLT2NtbkAaTfQtWR5ciaIoijKDhNeDwdVPwBaPtExsT7bmHiDp0D4s7a3oFgu9K9fNmQznyoXHn5RC24230nbjrbPdFWUem6N3vMpcZGlupPjh74USSA1PTKG5Byj+2fepvP8/cecVjPn8tF07Ytv7OoG+CSnRvN6wz5VA1vNP07t2Y2ivjic7F8eKNcGRzDFKUElNw1VYgqtkwQR6pAyXU5JCc01PxDYGo0ZGbuLMdGiCpMlE/4LFMbV1LlkRWqIWieb3E3fiCDB28C3GOD6cANJ2v4oYXEkC0F+6kIY7PxS2Fntc5Vnyf/8Ils525LDREeeKNTTc+SFV31RRlGlnaWkk84W/knR4H0LXkULDsWotbdfdgjs/9hnFlDdfI/ep/0P4vMEtZlKS9fwzOJesoO7ue9Hj4qfxXSiKokyMWr+oxCzj5ecRemDsxGdSInSdjJefD/t8W31N1AB8IoYl2g5LAJb2VqxNDSOO1//TR+kvDQbZQzP7UgT/dufkU/vRT6oEG1MgPSeB5PS4iNFk2cqsOZeUbTICCYl0XXJZMMgdgxQagbh4bM0NYX8vhjZSxPLzPVxcTeVgXoXOUW1ttdWUPPQdzF0dwecOJo4TQMLJo5T8+NsIb2xl2RRFUSYirrqCBd/5eigABxBSJ/HYIcoe/AbxZ07EdJ7kfW+S//iv0XzBQXgtEAidz372JCU/eRAi5H1RFEWZLRfOHa8yvQJ+kg6FLwEGwZnxpMP7EP4wGcfF1P+4DZ8pjCVUNrhGZujWrTaq7/s81R/7LL2rN9BfsgDHyrXUfvg+Kj73nwTsc3tmdojUJW0NvRx9o5YDr1Rxcm8DvZ2u2e5WiBCCddtKSM2yD359bmxDiGAAXrx07pUnm6zm2++kb1Fwi0ZokIdgDoNAXBzOpStCxyMZ7zCQ0HUMAy4yX/zLqMeyn30CEdDHHkzTdayN9SQPViVQFEWZcoEAhb96COH3jbqnELqO0HUKf/WT6NVLdJ3sZ58MO0gpdJ24umoSjx+ekm4riqJMJbUcXYmJ5vGgxTCaLHSdnD/+ARHw40tNo3vTZfjSgvtm+xYuIeHMiajLc2OlawbQtPBB/xh8yWPU+9Y0+paupG/pyinp10zzDPjY/0oVfT3u0NSpEFBf3kl2UTIrLy1AM4x/AMTvCxAI6JjMRjRt8qsBTBYjG68uo7fTRWtdL35fAJvdTG5JChabCSklfb1ufJ4A1jgTNnuURH/zgDSbqfnYZ0k4eZTUN1/D3N6KboujZ90mujdtJe+Pv4+6UX6i33mh6yTv20PT7f8Yqn1r6uzAHq1uuRCkvrmT7i1XTvCVFUVRwks8cQRTb0/Yx4WUGAdcJB3aS8+mrWHbxZefxuTojfhaUmgk792NY/X6iXZXUeYcS0sjxt4eAvEJwS2gasXmvKSCcCUmusWKbjKhRa2rDalv7Qp9IGT+/Vk6tl9Pyz+8m85t15J46ti4Xnd4kqqhvattV99I5/brsNXVUvKz78d2HiFwFZeG3SM7X0ldsv+VKvp73YMHBv8a/LultgeTxcCyjbEnqOlodlJ9oo2u1j4AjCaN/AVplCzLxGyd/EdGUlocSWlxI4611vVQfrSF/l5P6FhKZjyL1uaQnD7P9/NpGs4Va3CuWDPyuJSgB6Jnq5vMS/t9GB29+AYTrpm6Ry9PP5+QEvMYy9gVRVGmgq2mEqkZEHr4gX1dMxBXUxUxCI8WgENwibu5u3tC/YxE1yU+jx+DScNoNEz5+RVlLPFnTpDz7JPYGupCx3xJyfSs3UjPhkuDuRRUQD5vqCBciY3BQPfGraS+tSvsTHZoabiUIwKL9FdfQLdYaLvxVlpveCdZf/9z1NJNUtPwpqQRsCdgbWpAGgw4lq+m88prGSgqCXbJHdtyawkgBC3veHdM7ecyvy9Ac00Pfb1uDAaByWwMzoBH0FDeSdnKLCxWU9Tz153t4NS+xhH/OX6fTs3pdurLO9EMAqlLElJsFC5KJ6swCTHJD/z6sx2c3Nc46nh3ez97X6pk/fYS0rITJvUac43mclH0yA+jz0pPAd16rsSebouL0PKcQIztFEVRxk2IqPcAgigNAL89+nVBCoEvMWkcnYvMM+Cj+mQbDRVdBPzBe6G0HDuly7NC260UZTokHj1I4S9/POq4qbeHjJ0vkbHzJQZy8mm64/24Ykwgq8wuFYQrMWu/5kaSD76N5vGMmU08Un3jjB1/o2P79cgYl0X74+1U3/d5fKlpYdv4ksZYWj4GaTRS+9FP4ypbFFP7uaqxqouTexvQAxKhBVcFxDKJKiW0NTgoWBD+ewnQ7/AEA3AYnQlMQsCvM5gYn+72frrb+sksSGL1ZUUTXq7uGfBxav/oAHzoNaWUHHuzjitvXRZ8zxcCKSn65Y+Jryqf3pcRAldRKbr1XEDtzs3Hk5qOuasjfCUBIejZsHla+6YoysWrv2wRmRGSuAIIPRC1EkXfoiX44+0Y+/vCn0dKejZtmVA/zzfQ5+XtF8vxuv0jrr2dLX10Nvex4tIC8kpTp+S1FGU44feR93+/gsFEquFYmxso/fF3qP6Xf425kosye1RiNiVmvrQMqj7173jTgsGc1AxILbgMK1osqPl8ZD7/51Bd7oiz4ELQX7ooYgAO4CpdiDclNepr19/1UfqWLI/SamY4ulwcf6ueN587w56/naXiaAtuV/Ql/q11PRzfU48eCL5bqccWgENwZZLfG30/f315R+yrmAZfu62+l6oTrTE+abTGqq6o78Mz4Ke9yTnh15hrbLVV2MtPTVluhHCElMTVVrHic//M4gc+T/orf8fY04XB64lYyk+3WOjasm1a+6YoysWrb8kKPGnpYZNSSiHwJSTiWLku8okMRlpvui3sw1LTcGfn4Vg1NfvBj79VNyoAD77Q0OP1DPSryhLK1Es8ehCjqz9qjhgBIHVyH//NtG51U6aGmglXxsWdV8jZ//ct7GdPEVd1FgD7qePE11VHfJ4UgsRjB6MuQYPB4KGuGvupY9ga6pAGA32Ll4+uP65pNL/zvRQ9+pMxzztU59uxKsqFfAZIKak40kLViTaGlWXG0T1A9Yk21lxRTEbe2JnYpZScOdQ8idcmpiRnXa19E/rMrjvdQemyzFHJ33xeP7Wn2+nr9WC2GMkqTCI1yz5i+bqz+1wyuXCEgN7OfkDi9+nEJVhISrNNehn8bEk++HZM9cMnSiIQSKQQoQzopq4Osp99kvRX/o4hwqwRQNeWbfiTkqelb4qiKGgadR++j5If/g8Gj3vEZ6HUNHSTidqPfBJpjH6L2rV1G5p7gOznngquDhMagmDJ1IH8Imo/+qmYzhNNv8NNV2t/1HYNFV0sXJ096ddTlOGszY3oBkNsCZKlxNrWTFxNJa6SBTPQO2WiVBCujJ+m0bdkeWh22eToJa6hNnL5MimxDNYljoWpt5uSh78XHCmXEiGfoK9sEfV3f2xEgOBYu5F6v4/cJ3+HweNGNxiCJU6kxLl0JfV3fRQMs580pbGqi6oTbcB5g5MSdCk5tKuGrTcvIj7ROuJ5UkrOHm5moG/io+tGk4HM8wJ8KSXdbf10NjvRpSQpNY5AYGJBoc8bwNHtDtYBJ5iw5vibdTTX9oxoV1/eic1uZv32UuITg9m6NU1Ei8GREqpPtiP1c63iEy0s3ZhPWvb824N3fpm8qSYGv5vDS5AFR8clRqcj6iBYwokjtLzzPdPWP0VRLhyGPicpb+8m6dA+NI8bT1YOXVu3B+8PIgyUuvMKqfj8V0l/9UVS3n4dg8eDbjLTvWkLHduvjz2JqhB0XHMTPZu2krzvTSztrehmC72r1+MqXThlSap62mPIQSOhuy3yIOdscXS5qC/vpLfThaZpZOQlkr8gFYsteq4YZfbpRtOYZUUjMbe1qCB8jlNBuDJp3ZdcRuqeXRHbBMwWDF5PxDZDJMBgQD88sI+vrqD0f/8/Kv7tK+hWW+h4z8Yt9K5eT9KRA1jaWtDNFhyr1uHJyhn3e5kOUkqqjrdFa0TtmY5RWcwrj7VSc7J9Uq+/ZH3uiFnqfqeHQ69Vj8hEPlly8P9JSsn+HZV0t40daA7tqdt6y2IsVhPpuQk0VUfPXDs8AIfg/vUDr1Sy/qrSeZe0zZccfc/g+Ss7YllBMtSOCG1jWcpmaWsJjnzM05UGiqLMDFttFSU/+S6a2x3aq2rpaCPp2CF6V6+j7u6PgSH8baYvNZ3md91J8+3/iPD5kCbThD93/IlJdFx94wTfydSZax+bUkrKj7RQfd4qvN4uF1UnWoOr8HLHXoWnzB3OZavIfv7pcT1HHyxNqsxdak+4Mmmu4jIcy1cHS4iF0XrL7fgSkqLu3x4y1pmErmPubB8z4JdmCz0bt9B68+20X3vznAnAIRgwRpvJlhJaz5s5djk9VB6b+H5rk9nAis0F5JWdC/q8bj97X6zA5Zi6ABwgMLhXvbW+N2wAPsTnCVB/NlgCK6sgCYvNOKFi2FLCyb2NyHm276l702XRl6ILDX3YzatutqDH8E0STLyu+BBpMMy9O0lFUeYUQ38fJT95EM3tRgxLFjX02ZZ49BDZf30qtpMJgTSb5/TnTnJmDKUyBaRkzq3VWY2VXVSHW4UXkBx+rYZ+59TeDyhTz11QRH/pwpjvoXVNo2/x3MiFpISnZsKVyROCug9+nLzHfkXygbeDF1JNg0AAaTTRevNtdF5xDQb3AJl/+3PYZBGhEmeRXktKUt58jY7t10/DGxkfKSU+bwBNExhNo5e867qkt9OFo2sgpvMNBbJDGio6o+6XHosQsHxzATlFySNmwHVdcnJfA163f3wnjMGBV6pITLXFvKe8vqKTBauy0Qwa67aX8vbfy9EnEEy7nB562l0kpFhpqOiivrwTt8uL0WQgtySFwkXpMe2Hn0nezCw6L9tO6u5Xw/6st7zjXXRt3Ya5ow2pGfBk5ZC8fw8Ff/hlxHPHOmMe9vmahmP56kmcQVGUi0HK3jfQ3ANhl8gKKUl7/RXarn/HiJVr81V8goW0bHvE3CkCyI9ShWQmxbIKT0pJ3ZkOlm7Im6FeRSalJODXEUJgMKp5wuHq7vkES778OaSMPiTfu2YDepwqNTrXqSBcmRLSbKbhA/fSetNtJB05gGHAhTc1jd61m0K1ifuLFwQTUo2RWEISXLJujLJkXQCmnujLl6dTwK9Te7qdurMdeAaCAW1imo30nATsSVbMNiOOzgFqTrbj9cQe8MYljFw61OfwjDsAh6GVxAKfL4DH4cFsMeD36Rx4pSqmTOwT5eweiDkIHz4Q4HX70fWJz2Y7OoMZ513DRvO9gWBSuPryTtZfVUpKRgyzGDOo6V3vRzdbSH/tJQgEgoNWuo40mWm96dbgIJMQuPOLQs9xrlgTU2KW4QnZxk3X6dx23cSeqyjKRSPxyIGo2Zc1n5f48tM4V64d17k19wDJ+94k8dghNK8Xd24+XVu24c4vnEyXJ23FpYW8/UI57gHfiGvz0DLvlVsKscbNnT3Wfb3uqNnapYSW2p5pC8L1wVwz5yduHdVOlzRUdFJ7uiN0LU9IsVG8JJ2ckpR5m4h1KvkTk/AlJcV0D9yz/tIZ6JEyWSoIV6acO68Q3WLBVVgSSopmbayn+Oc/CBuA62YLtf/8acp+9D9Rzx+wzd7ont8fYN/LlTg6R85uOzoHRh0br8JFI0fQDQZtQjPhAFUnWjn2Zl3o6+F7wabLeM4vCCawScm0U3u6fcLvE6CuopOBvtGDN3KwtvnBndVcedtSjMbZT9AXomm0vPM9tF99I4lHD2Ds68OXnIJj1bqws0aBeDvdl15J6huvjhlkSyGCA1ked9iXHT5TPuLfgwkQG9/zT8FkRoqiKBFonvClDke0840vqaittprihx8MJbAUQFxtFWlv7KRj23U03/reWVu2bo0zcemNi6gZHOD1ewMgICMvkZJlmSTPscHegD+2ZKv6BJOyhiOlpLmmh9rT7aGVgPYkK4VL0skvTUVoI//github.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.com/kCfKL0oY6OUvutr6CEgjNMBQUKAnN/L5X/GfvvaalTWGZl91tWsf2NjazdUcX6G2q4+e1rWL2tatYCoUQiyQyOAc+0NLWoKKRUrShT+ItL5zTfh7Ky8a5Yk4ERXT0y+oQ0Go1s2bKFAwcOTB7TNI0DBw6wa1dyDnfhcJjXX3+d8vLyqOdNJhO5ubnTvgBcX/ofmoKH2bOvlrDQaHd4E35dNTSNqh/+G4YRd1pqAStCoPOMzKsPIQSnDndy9uVuRj2BNIwq2j3ANbQEFkgkkiWE3qBjyxvq0RmiP951+iSeMgrYirIZHvAwPOAhFAyneZTTse+9fVrpxTmhKASKolfckMydCW+Xffv2TR5Lxdulurqat73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.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R/52jl0iirnaclV4+LJXi6fHkhowjoTg1HHzW9fM2fj14Uklbl+QbLV77//fu6//github.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.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.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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot decision boundaries for training and test sets\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Train\")\n",
    "plot_decision_boundary(model_1, X_train, y_train)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Test\")\n",
    "plot_decision_boundary(model_1, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8UqtuZYwHC8C"
   },
   "source": [
    "Hmmm.\n",
    "\n",
    "Our model is still drawing a straight line between the red and blue dots.\n",
    "\n",
    "If our model is drawing a straight line, could it model linear data? Like we did in [notebook 01](https://www.learnpytorch.io/01_pytorch_workflow/)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nam5esXj2Mj_"
   },
   "source": [
    "### 5.1 Preparing data to see if our model can model a straight line\n",
    "Let's create some linear data to see if our model's able to model it and we're not just using a model that can't learn anything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xLoEBQ6fHC8C",
    "outputId": "176a4674-3142-420c-bace-97cdbfbf473e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(tensor([[0.0000],\n",
       "         [0.0100],\n",
       "         [0.0200],\n",
       "         [0.0300],\n",
       "         [0.0400]]),\n",
       " tensor([[0.3000],\n",
       "         [0.3070],\n",
       "         [0.3140],\n",
       "         [0.3210],\n",
       "         [0.3280]]))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create some data (same as notebook 01)\n",
    "weight = 0.7\n",
    "bias = 0.3\n",
    "start = 0\n",
    "end = 1\n",
    "step = 0.01\n",
    "\n",
    "# Create data\n",
    "X_regression = torch.arange(start, end, step).unsqueeze(dim=1)\n",
    "y_regression = weight * X_regression + bias # linear regression formula\n",
    "\n",
    "# Check the data\n",
    "print(len(X_regression))\n",
    "X_regression[:5], y_regression[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wquTX_wX275-"
   },
   "source": [
    "Wonderful, now let's split our data into training and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UIoO2k8yHC8D",
    "outputId": "2d9144d9-18d9-427a-a898-7a5c920cd9aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80 80 20 20\n"
     ]
    }
   ],
   "source": [
    "# Create train and test splits\n",
    "train_split = int(0.8 * len(X_regression)) # 80% of data used for training set\n",
    "X_train_regression, y_train_regression = X_regression[:train_split], y_regression[:train_split]\n",
    "X_test_regression, y_test_regression = X_regression[train_split:], y_regression[train_split:]\n",
    "\n",
    "# Check the lengths of each split\n",
    "print(len(X_train_regression), \n",
    "    len(y_train_regression), \n",
    "    len(X_test_regression), \n",
    "    len(y_test_regression))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sQtonoMn3s90"
   },
   "source": [
    "Beautiful, let's see how the data looks.\n",
    "\n",
    "To do so, we'll use the `plot_predictions()` function we created in notebook 01. \n",
    "\n",
    "It's contained within the [`helper_functions.py` script](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/helper_functions.py) on the Learn PyTorch for Deep Learning repo which we downloaded above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "id": "pcg5OvncHC8D",
    "outputId": "77b50411-c589-4d5f-e7ca-d03b1b9a68cc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(train_data=X_train_regression,\n",
    "    train_labels=y_train_regression,\n",
    "    test_data=X_test_regression,\n",
    "    test_labels=y_test_regression\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kZRflODP66kG"
   },
   "source": [
    "### 5.2 Adjusting `model_1` to fit a straight line\n",
    "\n",
    "Now we've got some data, let's recreate `model_1` but with a loss function suited to our regression data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rc13zblAHC8D",
    "outputId": "7bd16b1f-bd2e-486b-b963-9733aaa757be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=1, out_features=10, bias=True)\n",
       "  (1): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (2): Linear(in_features=10, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same architecture as model_1 (but using nn.Sequential)\n",
    "model_2 = nn.Sequential(\n",
    "    nn.Linear(in_features=1, out_features=10),\n",
    "    nn.Linear(in_features=10, out_features=10),\n",
    "    nn.Linear(in_features=10, out_features=1)\n",
    ").to(device)\n",
    "\n",
    "model_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FOtBAv1E7OqX"
   },
   "source": [
    "We'll setup the loss function to be `nn.L1Loss()` (the same as mean absolute error) and the optimizer to be `torch.optim.SGD()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "f06fraapHC8E"
   },
   "outputs": [],
   "source": [
    "# Loss and optimizer\n",
    "loss_fn = nn.L1Loss()\n",
    "optimizer = torch.optim.SGD(model_2.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lU7GfFLm7a21"
   },
   "source": [
    "Now let's train the model using the regular training loop steps for `epochs=1000` (just like `model_1`).\n",
    "\n",
    "> **Note:** We've been writing similar training loop code over and over again. I've made it that way on purpose though, to keep practicing. However, do you have ideas how we could functionize this? That would save a fair bit of coding in the future. Potentially there could be a function for training and a function for testing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YTitEWgSHC8E",
    "outputId": "16da5efa-3c3b-494b-ef4e-e5244f4cf097"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Train loss: 0.75986, Test loss: 0.54143\n",
      "Epoch: 100 | Train loss: 0.09309, Test loss: 0.02901\n",
      "Epoch: 200 | Train loss: 0.07376, Test loss: 0.02850\n",
      "Epoch: 300 | Train loss: 0.06745, Test loss: 0.00615\n",
      "Epoch: 400 | Train loss: 0.06107, Test loss: 0.02004\n",
      "Epoch: 500 | Train loss: 0.05698, Test loss: 0.01061\n",
      "Epoch: 600 | Train loss: 0.04857, Test loss: 0.01326\n",
      "Epoch: 700 | Train loss: 0.06109, Test loss: 0.02127\n",
      "Epoch: 800 | Train loss: 0.05599, Test loss: 0.01426\n",
      "Epoch: 900 | Train loss: 0.05571, Test loss: 0.00603\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "torch.manual_seed(42)\n",
    "\n",
    "# Set the number of epochs\n",
    "epochs = 1000\n",
    "\n",
    "# Put data to target device\n",
    "X_train_regression, y_train_regression = X_train_regression.to(device), y_train_regression.to(device)\n",
    "X_test_regression, y_test_regression = X_test_regression.to(device), y_test_regression.to(device)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    ### Training \n",
    "    # 1. Forward pass\n",
    "    y_pred = model_2(X_train_regression)\n",
    "    \n",
    "    # 2. Calculate loss (no accuracy since it's a regression problem, not classification)\n",
    "    loss = loss_fn(y_pred, y_train_regression)\n",
    "\n",
    "    # 3. Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backwards\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Optimizer step\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "    model_2.eval()\n",
    "    with torch.inference_mode():\n",
    "      # 1. Forward pass\n",
    "      test_pred = model_2(X_test_regression)\n",
    "      # 2. Calculate the loss \n",
    "      test_loss = loss_fn(test_pred, y_test_regression)\n",
    "\n",
    "    # Print out what's happening\n",
    "    if epoch % 100 == 0: \n",
    "        print(f\"Epoch: {epoch} | Train loss: {loss:.5f}, Test loss: {test_loss:.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IoyLsZW78m_6"
   },
   "source": [
    "Okay, unlike `model_1` on the classification data, it looks like `model_2`'s loss is actually going down.\n",
    "\n",
    "Let's plot its predictions to see if that's so.\n",
    "\n",
    "And remember, since our model and data are using the target `device`, and this device may be a GPU, however, our plotting function uses matplotlib and matplotlib can't handle data on the GPU.\n",
    "\n",
    "To handle that, we'll send all of our data to the CPU using [`.cpu()`](https://pytorch.org/docs/stable/generated/torch.Tensor.cpu.html) when we pass it to `plot_predictions()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "id": "AvltMCW_HC8E",
    "outputId": "5887db7d-128b-46f3-c978-c12af112d470"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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.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.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.com/T+vjhP+xd3z5qdOnDhhunTpYp577jlvDRF+ypNz58SJE2bMmDHmv//7v012drbJyspqh5HCn7h73jz99NPmzDPPNLW1te01RPghd8+bGTNmmIsvvthhW05Ojhk7dqxXxwn/Jcm8/vrrLe4ze/Zs8/Of/9xh2+TJk01GRoYXR+YcV3ROUltbqw0bNig9Pd2+LSwsTOnp6SovL3d6THl5ucP+kpSRkdHs/gg+npw3P/XDDz/o+PHj6tq1q7eGCT/k6bnzwAMPKD4+Xtdff317DBN+xpPzpqioSCkpKZoxY4YSEhJ07rnn6qGHHlJdXV17DRs+5sl5M2bMGG3YsME+vW379u166623dMUVV7TLmBGY/Ol34w7t/o5+7NChQ6qrq1NCQoLD9oSEBG3dutXpMZWVlU73r6ys9No44V88OW9+6j//8z/Vq1evJv9hQHDz5Nx5//33tWzZMm3atKkdRgh/5Ml5s337dr377rv63e9+p7feekvbtm3TLbfcouPHjys3N7c9hg0f8+S8ueaaa3To0CFdcMEFMsboxIkTuummm5i6hhY197uxzWbT0aNH1bFjx3YbC1d0AB97+OGH9corr+j1119XdHS0r4cDP3bkyBFNmTJFS5cuVffu3X09HASQ+vp6xcfH67/+6780cuRITZ48Wffee6+WLFni66HBj5WVlemhhx7SU089pY0bN2rVqlVavXq1HnzwQV8PDXAJV3RO0r17d4WHh6uqqsphe1VVlXr06OH0mB49eri1P4KPJ+dNo0cffVQPP/yw1qxZo/POO8+bw4Qfcvfc+eqrr7Rz505NmDDBvq2+vl6S1KFDB1VUVKh//github.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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Turn on evaluation mode\n",
    "model_2.eval()\n",
    "\n",
    "# Make predictions (inference)\n",
    "with torch.inference_mode():\n",
    "    y_preds = model_2(X_test_regression)\n",
    "\n",
    "# Plot data and predictions with data on the CPU (matplotlib can't handle data on the GPU)\n",
    "# (try removing .cpu() from one of the below and see what happens)\n",
    "plot_predictions(train_data=X_train_regression.cpu(),\n",
    "                 train_labels=y_train_regression.cpu(),\n",
    "                 test_data=X_test_regression.cpu(),\n",
    "                 test_labels=y_test_regression.cpu(),\n",
    "                 predictions=y_preds.cpu());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cZFiXR8B9wYx"
   },
   "source": [
    "Alright, it looks like our model is able to do far better than random guessing on straight lines.\n",
    "\n",
    "This is a good thing.\n",
    "\n",
    "It means our model at least has *some* capacity to learn.\n",
    "\n",
    "> **Note:** A helpful troubleshooting step when building deep learning models is to start as small as possible to see if the model works before scaling it up. \n",
    ">\n",
    "> This could mean starting with a simple neural network (not many layers, not many hidden neurons) and a small dataset (like the one we've made) and then **overfitting** (making the model perform too well) on that small example before increasing the amount of data or the model size/design to *reduce* overfitting.\n",
    "\n",
    "So what could it be?\n",
    "\n",
    "Let's find out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j82n3OyWHC8E"
   },
   "source": [
    "## 6. The missing piece: non-linearity \n",
    "\n",
    "We've seen our model can draw straight (linear) lines, thanks to its linear layers.\n",
    "\n",
    "But how about we give it the capacity to draw non-straight (non-linear) lines?\n",
    "\n",
    "How?\n",
    "\n",
    "Let's find out.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cmfOV8v6__17"
   },
   "source": [
    "### 6.1 Recreating non-linear data (red and blue circles)\n",
    "\n",
    "First, let's recreate the data to start off fresh. We'll use the same setup as before. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "owqilKGBHC8F",
    "outputId": "b8c1d692-6f3e-43ca-9323-98bd40e39a89"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HLO4fP/epTTGLrNOhGvQWeTDsnZ0YdWOv0Squdw6fYmr1b1ANhmg3P0mWkSTovm4my7sPJ+DV69gXkCSGndpE5hKFYj29uPMQTi3daLZx4sc4ubsRGhAY63VFW9Tl/NodsVwVne92LyVvjQomxxj0erb+PJn9fy0kLDDYKhttSb2RA2gwakC0YyEBgYzKUx1/Lx/jPz8JMhbNz5OL140KJlmnULBBdXpt+LdK89NL15la/RsCfN/3k9I0ZEVB0zS+mTOOcl1b2eJtCf4jWHP//qyyjQSCT0nQWz92jPmb4RnL0sc+O9+nKcGmHyfg/8KyeI3dE2Yzs1F3Hpy8kMCWmke2Mx/OphoM2DnGvsUQbS6djr47FhoVLpqmsaLPTzGEC0TEkWiqxtoBoxl2dgse2TLGuN7B1YXv/lliVLgEvHrNqWWbrBYuKTKnZ9jZLeSvWzlaG4IUmdPTafEkirduaHYOxU5HxqL5zY/T6Wg89nvGe5+l385F1B7eB2zQ9dlcj6SP2f7LVHb8Ni3asTMrNvP2mZfpn58Gj89dNenpUfUG7h49E/U6NDCIP2u2J+iN3/sU9AgVHPk5WNp9WLTxAoEt+ayyjQSCT4W/zysmlGvOq/uPo27A/t4v2f3HLI4vWMPQY+tJmfXfG6+maXjfvEewnz8eWTIQ9Mbv32Z6nyCGJXKLQdbpSJktIz53Hpq9xs7RgVLfNOb4wrUmM3g6L5tCzkqljZ5/fP4qXh+k+n6Mpmm8fvSM1w+f8uudQ9w/eYGzq7YQFhRCvtqVKNK0tskeR4/OXo5TA8gmv/+AZ/bM9Nkyn7fPX/Dy7kMckriQvlBeZFlmao1vzM5RrHVDozE4seHo6kK+2pXJV7sy/t6vOLl0fbyyoyRZImW2LPjceWBxYb0tIyaSpVThqMylC+t3IiGh2aBO8oe1Z06v2My7l75GPXeyIrNn4hyyly8R73UFgo8R4kUgAFb2HoHvwycxhIdqMBDw6jUL2w/g+2MbADi/fidbRkzE+2ZEMKUky3hkSR8VaJnYNJ80AiRwcktCrmrl+DlHJfMCSpLotHgSmYoX5OzqbYQFBsXosiwrChmL5adY87omp/J9+NQiO18/eoYkSWQrU5RsZYpadE2krRYNex/f4ujmSsupP1O8VYOoc0nTepI07b/Vu18/ec7NvUdjmyYauavEvUxDtUFdObVsI5KkxjkWRtUbqD+qP/bOTqzq+zNvn70we42kyOybOj9KvIQGBtksFscj679xLNd3HUKSJKNzq3oD13Ydssm6AsHHCPEi+M/z5pk3FzftNnrDV/UG7h8/z6Nzlzk8cznH5q+Odl5TVV7ee2zVmrKikMTTA3/vl3H21EiyjGfOLFQb2DXKc/H68TPzT/qSROEmtaI6Tg/cv5I5zXvj+/BpRNdlTUMzqOSuVpauK/82W+nVJUVSi+x1scKD8SFZShVG52CPPjTM5LiaQ3uQvlAeCjWuZTTjJpLXj5+bXVdWFN75xL3OTrr8ueizZS5zmvchNCg4IsBZklD1ehzdkpC+UB7e+bzC5/aDWAWAJMu4pEhK0eZ10dnbE+j7lqXdfjC7rmZQuXv43+2a9IXy8uDURZvUx3l87ipPL10nfaG8GPR6s4HTqt6ApmkmPWsCQVwQ4kXwn+fpxesWCYjJldsQGhBokzXT5M2BZ66sXNz4T5yc+ZIsI8kybWf9Fu3G4Jw8aVRqqzFkWY4Wx5GpWAF+vXeYG7sP8+jsFRR7O/LXrUK6/LkssiV7+RK4p0mFn5eP0THOydzJXb2c0fOmcE7qTtkuLTkye0Ws/06yopC/flWa/D7M4jldPcwLKVVV4yy4IslXuzK/Pz/FqWWbeHTmEoqdHflqV6Jgg2oodnaEBQUzpXo7Hp66EE0IyDoFRaej57qZ6Owj+jyVaNOQ9UPHRsSYmOMDrVChZ1sOzVgar/cRiT4sjCnV2vHLrf1kLFaAK1v3GfW8SLJMxmL5hXARJAgiYFfwn0extzM/CGwmXGoN682wM5vJWbk0ahy9LplKFGTQgZUxYlEcXV0o2ryuyX5CmqpSun2TaMdkWSZf7crUHdGPWt/3sli4QESwapM/TAuHRr8Nwc7BweI5P6b5pBHkrBLxXmUl4msrMpg1XaHcdFwwwar5PHNmJX2hPCZL/it2umjNIuOKk1sSKvdpT8eFE/lmzjiKNK0d1UXb3tmJgftW0GzCjxFbMpKEvYszpTs048cL26MVKrR3dqL35rkoZoKxZZ1Cnhr/dqtOXzAPlft2iPf7AECDoDdv2fLTJA5NX2JyO0pTVar27wJAWFAwFzbu4sjclVzfc+STbK8Kvi5EqrTgP09IQCDfexYnLChxUlwr9mpHjkqlyFauOL/krUGYhTEJOns7eqybSaqcWUidK5vRcd637jGuREPCg0JivUnUGNKDZhP+F6/3EBtH561i3eAxhPgHRAUQ27s403jsUKp+1zne8xv0ei5v2cvReat4/egZbqlTUrZzC4q2qBsnYXRt10Gm1e0c4fmK5edf96fvaDh6ULzttgZLtlgenr3M+DJNTQYxDzm6juzlike9nlSpJXcOn7aZnUhSRLyLCfFdvnsb2s76jQN/LWTryCmE+AdEnUuaPjXtZo2lQL2qtrNJ8MUj6rwI8SKwgLfPX3Bw+hK8b9zF68ZdXty0vJqpLbBzdKBI8zqcW7MdTVXNxiQ0+m0odf73rUVzP718gyVdvufxuStRxxySuFDrh97U+d+3CebKDwsO4fKWPbx99gK31Ckp1KhGVHn9z5ELG3axrMdwAn3fRAkuxd4OpVJFbmTMiYuzAw0rF6VVnVK4OMXdc2Rrbu47xvQGXdGHhUWlN8s6BdWg0nraaCr3aR819sWdB4zMGbPSeULSaOxQag/rw74p82MtThjR9FLiu3+WRAUWCwRCvAjxIjDDmoGj2T91gckxkdlDOgcH9KGhZueUZBnZTochLNyqbshlu7bEzsGB8+t2EhoQiCFcH63kvL2zE/V+/o6a3/eyWnQ8vXQdrxt3cXB1IXfVstFKwQsi0IeFcWX7fnwfPOXYzadMPHkPvb0DBoMalU2TLlUy/pkzlJyZUpufMJF488ybo3NWcGX7AQxh4WSvUIKKvb+JseV3ZccBptcz7/lySOJK6LsAs+PMIet0VO7bgYajBzHUszjhwSGxjpNkiXQF8zDigvlCgYL/BkK8CPHyn0M1RGQ1KDrj8QBhwSGcWraRbb9Mxc+ClNNUOTLTevqv7P9rYUSTOjOeEUmRaTHlZzYMHYuqN1i8ry8pMuMen4hK5TXo9VzffSSikWOKpOSvVxVHVxeL5hLEnZ1HLtPou6mxnlMUmXSpknFj8zjsLCgA+Dlx99hZJpZvbnZcjcHd2TtlfrzrFMmKQslvGpOnenkWth9odvzI63tJkyd7vNYUfB1Yc//+sn4LBYKPuPbPIfZMnMOt/cfRVI30hfJQoVc70uTJjiRJZCiSD8ckrgT4vmFKlTY8u3LT4rl97jwkWfo0VO7Tnqvb9psc6+jmSuelUyjUsAbZyhZj848TuP7PYcsW0uDsqq1UH9QNiAiALVA3cd38Api4aAeKLGOI5eZtMKg89vJl88ELNK/xZRVdy1KqsNlsMDtHB+r+1I88NSuwbshYnlvxexITjZRZM/LOx9ei/kxvnnrx9NJ1ru06hCFcT+aShSjdoRkuydzjYYPga0eIF0GiE+z/jpNLNnB21VaC/fxJmy8nFXq2JWflMlZti+z/ayFr+v8S0UvlfZrp00s3WNl7RNQYOydHKvRow8v7j/G6fscqO2Wdwskl62k89nuTfXAylyrM4EOro4JGPXNmwT1NKrMpy1HrxLOeiCD+hIaFc+S88SrBADpFZvexK1+ceFF0OhqMHsSy7sYzwmoM7YmTuxt5a1bkp0sV8LpxlxOL1nJx426LuoF/iKZplO7UnOML1ljUWHJxp8H4PfeJ+D1G4+zKLWz633i6r55OwfrVrFpb8N9BiBdBovLy3iMmVWrF2+fvt23el9k/u3obFXq0pc3MMTG6FceG9827rBkwGsDk9kx4cAj7/1pkVQxKJKregO/DpwS9ecsNY9VYpYg6MW+eeJEqe2bCQ0Mjuh+fu2JxR2BVrydZhrRW2yewHQYL/q00INwGhd4+BeW7tSbEP4CNw/9ADde/b8oZ8V6qD+pG/Q+aOUqSRNq8OWg2/n80G/8/Qt4FcHLJBlb1/dmitRqMHsyusTM4Mnu56YGyhKLT8e6FLxD99zg8OJRZTXrSft7vXN99hMfnrmDv7ESRZnUo3701bqk8LLLl7tEzHJy2mHvHz6PY6SjYoBqV+3YkVfbMFl0v+HwRMS+CRENVVUbnq4HP3YdGPRKtp42m8rfma1KsGTiag9MW26RqqCmSpvMkNDCY4Lf+RsfIOoVKfdrT6s9RHFuwhqVdv7dqDcXejvFeZ3BJnjSe1griiqZp5G/yP+4+fmFU50oSTPm+HX1af7negMDXbzmzcgtvnjwnSSoPirduEK1tgjHuHDnNpIotzY5rNHYojklcWd1vpOmBkoQkSyYFviRLaKoW3YMpSTi5J2HA3uVkKlbA5BI7xvzNlp8mRbte1ilIskyvjXPE1uxniAjYFeLls+T67sP8Vcu0MEmeKR1j7h8x632ZWLEFd498Ph1r3dN68sezU/xRpgkPT1+yKuix2cQfqTG4u03tCQ4J441/IMncXHBytLfp3KZ4+PwVCzYe5trdZzg72tOoalEaVS7yRQS5zl5zgO/GLY214rEkSTg52PFo92Tck3y+qd8JhaZpjMxVhZf3Hsf62ZYUmaxlizH44Gp+zFKeN+baL8gSmGktYAqdowPt5/5Omrw5yFAkX4zt5mv/HOLv2h1jv1iS0DnYM/bhUdw8U8bZBoHtEQG7gs+SWwdORLirTRTXev3oGWdXbeHipt08u3wTR7ckFG9Vn7JdWkYL4LNzdLC4y25iEJkO+ubJc4uFi3uaVDQYPYjy3VrbzI47j14wZs5m1u4+g15vwE6n0LJWSUb0bES2DKlstk5sTF+5l0ETViJLEgZVRZFlVu86RY5MnvwzeyjpPZPHeW5N0zh//SEXbz3G3k5HjTL5Se1h24DObs0qcejcTdbtPoMsS6jvb646RUaWJVZN6IOrsyPbD1/k7LWH6BSZWuUKUDxfFpva8TkiSRIdF01iSrW2qOH6aFs8sqJg7+JEu5m/8frRM/PCBeIlXAD0IaFRmUypc2ej5V+jyFujQtT5fVPmG2+UqmkYwsI5Om81dX/sGy87BJ8O4XkRJBobfhjH3inzUcONi5dIPnT1SrKEq0dyBh1cHZVSeXD6Elb1G/lZiBdZUcheoQSDDqzit2L1eXLhmnG7JIm0+XLSduYYspQuYjK121ou335C1S7jCAoJQ/+BO16nyLg4O3Bo4f/Imy1dvNcJD9dz+/ELVFUjZyZPHOzt2H74Ik36/xXreJ0ikzNzas6vGW1RPNPH3HzgRYf/zebizX+bXyqyTIeG5RjbvwW7jl3hqfdrUiZLQpPqxUjmFve0clVVWbHjJDNX7+fqnSc4OtjRpFox+rWrSWhoOC0GT+OJ92t07xtYGgwqZQvnYPXEPnim+PqzYx5fuMrWn6dwdft+NE1DVhSKNq9Dg9GDePPUmy0/TeL+8XOJalNkwbtvt80nf52IraDvXPKYrZidp2YF+v9jm55PAtsgto2EePksubRlDzMbxW17RFYUkmVMy693DiIrCsH+7xiRtQKBvm8tniNp+tS8feodtZduS3qsm0nRZnU4MG0xq78bZVJUdVw0kTIdzdfdsJZSbX/h8q0nsab6KopMiXxZOLz4xzjPbzCoTF6yiz+X/YPP63cAJE3iTK9WVTlw6jpnrj2I8lbExtZpA6lVznScwsc88X5NidYj8QsIjhFUK0m89/Jo6JSIFGd7nY7/9WjAsK71bVpF+NHzVxRr+TOBwWExfr6KIpMnS1pOrfj5i9geswWBr98S4PsGt1QpcHJ348TidSzuPBRZlj9J3yJJkkiRJQO/3j2EJEl855qHsEDT4iVvrYp8t2tJIlkosARr7t+iMaMg0ShQryrJM6ZFVow3DTSGajDg++AJV7b/W2/FztHRomslWSJVziyMuLiTDgvGk7VsMTyyZiBHpVJkLJo/TvZ8SJlOzaMa+JXp1BzPnFlinVNWFNIXykvxVvXjtV5sXLj5iAs3HsUqXCBCeJy8fI+CzX6k5eBp7DhyyaqmkJqm0W3kfEb8tS5KuAC8fRfE+PnbOXXlvknhYqdT2HnksuVviAgvyIA/lvPaLzDWbCBNA8P7NfUGFU2D0HA9I6dvZOKinVatZY6/V+whMCSmcIGIn+3Vu0/ZfPCCTdf8nHFJnhTPHFlwcnfD3+dVRBq2pn2yhouapvHq/mPuHT+Hqqo4m6kRI8kSuaqWTSTrBAmBEC+CRENWFPpsnY+TexIk5d+PnmSheFDsdNw6cAKA4wvX8vaZt0XX6RzsGbBnObcPnuTqjgPo7O3JW7MiLSb/xMD9K8hSukiUfYDJjswf4ujmyjdzf6f9/PFRT/mOri4MPrwmWldfACSJ/PWrMnD/CotFlzXcuG9BnAFw874XWw9dpPF3f9K4/5+EhoWbvUbTNP5esYfl20/EGsyqWuC81YDQcPNrRRIerqfN0BlsjaMgGDt3KwFBsZelhwiPTscf55C9zlAy1hhIvT6T2HnkslFBt2LHSZPp1Iossfaf0wQGh7Jgw2G6j1pAr9GLWL3rFGEWbJN+yZxYuBbVwrIACc3lLXs4uWQ9b5+a/m7QOTpQrov57CnB58t/w8cp+GxIXzAPP1/bzeFZyzmzYgsh7wJInSc7xVs1YEUvCzodv79RHp+/xuI1FTs7ZjTsxtNL16Mqft45fIrDs5ZT5bvODDq4iuv/HOb08k28e/malFkzUKZLS7b/MpXru4/ESOeUFBmdvT0/nNwUa1lzt1Qe9Nu5mBd3HnDv6BmQJHJWKo1HlgwW22wt1jQNjLwJ7z52lR//WsfEIW2Mjn3tF0DzQdM4aqaAmzn0egNFcmeK9Zz3Kz/+Wr6bxVuO4vs2gORuLiR3d+X2I8vEaWwEBoey8+hlWtQsGePcLzM38tucrTFs2HPiGnUrFGTNpL7Yf7T98y7A9BaEQdV4+OwVWWoN5u27IHTvxfmCjYdJlyoZW6cPJH/29HF+P58zz67citjD+wzY/9ciUmXPZHZruFT7prh6xD2AXPDpETEvgs8CVVUZkbUCrx89MzkuMrZkYLKCJmuvRCJJEnbOTuhDQo26tFv9/QtV+sZMqwwNDGJ+2/5c3rIncjLQNJJlSEP31dPIWqaY+TeWSLwLDCZD9YEEhYRZdZ2jgx1P907FzTVmw0ZN06ja5XdOXrlnURE3Y0iShLOjPY/3TCaJS/R17j/1oXKncbx8887olldc6dG8Mj/1ahQtkHb+hkP0/nWxyesGdazN7wP+fSr3DwimVNtfuP/EJ1bPE0QEEEtShBcqtu0zezuF5b/3olHVz+czYyuW9RjO8YVrTWYRJhqWZCBKEkWb16XHmumJY5PAYkTMi+CLQ5bliN4+Rp7gZEUhafo0FGpU4/1ryz66mqYRFhhkci9+9/hZsZ6/uGk313YeiLIpyjJJwjVlCovWtxVh4XrOX3/Imav3Y90OSeLixMAOta2eNyQ0nFNX7sV67tiFOxy7eCdewkWnyCiyzPI/ekUTLgFBIcxbf5By7cfw4rWfzYULwJx1B8lcazBdfpqH//uA35+nbzB73azV+wkICuHG/ee0GToDz0r9uGdCuAAYVBW9QTUa9xMWbqDF4OmMmrERiPhcbjlwgdo9J+BZqR8Zqw+k39il3HzgFZe3+kkp1KiGSeEi6yKC7SP/PyGxJJtNkiWUBLZDkPCIbSPBZ0Plvh15eOYSp5dtilajQZJlHN1c6bttflRqsWMSV4syjZKmT4O/l49J8fLmiRcvbj+ItgV07/g5FrUfyIeOycj/93v+gqnV2/HLrf1R/YwSCoNB5Y8F2/lr+W5e+wUC4OxoT82y+Unu5kJIuJ5cmVNTp3xBjl+I29aOMXGycd9ZdIqCPo5BmI72dtSpUIDsGVNz6vI9Hnv50qJmSe49eUGDvlN54x8Yp3mtwWBQWbnjJLceevHHwFa8/CDY2BhBIWHM33CIUTM2ERIWblNhNXbuVkoXzMa2QxeZs+5gtEaQ8zccYuGmw6yf8p1FWVl3H7/g7mMfbtx/xolLd3ni/Zq0qZLSvkE56lcsjC6RbtD5alciXYHceN24E7PitRSxfdN1+Z/ow8I5PGsZXtfu4OjuSonWDUmSKgWHZy7jzuHTNrPH0S0Joe8CMLapoBlU8tWuZLP1BJ8GsW0kSFSC/d+hDw3DJUWyWJ+SfO49YsPQsdzcf5zwoBAc3V0p07E5NYf2iKqGaUmlXogoAlemcwt2j59t1qU94tJO0hfME/V6VpMeXN62z2T7ga4r/qREm0Zm7YgrmqbR6ce5rNp10qgnXJYkNDQ0LeL/LQme/RCdIvNw92RSJY/5u9Jr9CKWbDkarWaMpaRK7kaf1tX4bc4WDAYVRZHRG1QUWUJRZML1BpPZSQnB4I61mbR4V6KuGRuZ0qbg0XPfWM9JkoSjgx0Pdk0kubtrrGOu3HnKgN+XxdpIMlIMlS+aky1/D8DV2fbB4bHh5+XDX7U78uzyDWSdDtDQDCqKvR2dFk+ieKsGJq9f0XsER+etTPB2H7KikCRVCsbcP5wggfOC+CEq7Ao+Oy5v28eucdO5f/w8AG6pU1K5b0dqDOke5b04s2pLRNVM7d8mbUFv/Dg4fQm5q5WNKkC17ZepFq3ZZuYYFDs7VL3pvW0HV+dojdo0TePK9v0mv0glReby1n3xEi9nrt5n/obD3HnkTXJ3V1rWLknjKkWjaoUcPHOTlTtPmpzjQ7FirXBRZJmWtUpFEy5BwaHIsoyjgx15sqaNs8AwqGrUFgkQ9bPUG7Q4iaH4IssSh87eSvR1Y8OYcIGIz15wSBhLtx6n/zc1o46rqsoT79fcfuRNq8HTCQ6NPXMr0otz7MJtWg+dwdZpA21a78YY7mlS8eOF7dzYc4RLm/cQHhxC+kJ5KN2hmUU9u2oM6c6pZRsICw6JESAvKwrIUsQDSHz0rgSuHsnov2eZEC5fAUK8CBKcA9MWs7rfSKQPPC3+3i/Z+vMkbu47Rr+di/C5/YCF3wyIkXKpGVT0ahizmvRk9O0D6BzsowSQKbJXKEHhRjVRDQaSZ0rH26desaZzSopM+e5tsHf+Nx5DU1UMZtJbNVVDH2pdcGwkqqrS//flzF57AN17j4QsS2w+cJ4COdKza9YQUiZ3Y/6GQ1HnbUnkvSxf9rRMHdYOVVVZuOkIkxfv4s7jiG7fZQplo0fzKuh0itWpvrIs8S7QeJryp0BVNV69eUeNMvnZc+LqpzbHLMu3R4gXTdOYtWY/k5f8w6Pnryy+XtNg9/Gr1Oo1gU1T++NsRTZaXJFlmXy1KpGvlvVbMimzZWLAvhXMbtqLt8+8I7w37+vGZK9Qgjo/9mV2s16EBQbHqZaMS4pkNBozmJLtGuOYJHaPluDLQogXQYLy+vEz1vT/BSBGzx9N1bhz8CSHZy7D6/pd4+mWmoZBr+fw7BUoFlQwlRWFzCULR/1/rw2zmVy1TUTg7gfdaSUgc4lCNPx1cIzr0+TLgff1u0b3zSVJIkORfGZtiY1pK/cye+0BgChhEunhuH7/Oa2/n8G+ecO498THZsIlvWcynB0d8HntT4bUyenWrBIdG5bHydGe2j0ncuDMjWjjT16+x4lL92hWozjr95y1eB2JiJicgKBQm9htKyRJIk3KpPw5rB1lv/mVt++CPrVJJrl48zHLtx3n+KW7zF13MM7zHDx9k/p9p5AnS1rC9HoypfUgc1oP3FycqFAsZ7xaKdiaLCUL89vDo1zdcYBHZ6+gs7cjf90qZCyaH4CfLu/iwN+LObNiM0F+73B0dcElRVJUg4GXdx+aTI0O9H3DrQMncE6RjNQ5s5CuYB6LPVKapnHv2FmOL1zLm6deuKdOSclvmpC7Wrk4tbsQ2AYR8yJIULaOnMzO36Ybf1qSJDyyZsAQFs6bJ6YzLdIVyoPPrfuEh5i5MUrQbvY4KnT/t36J76On7Ju6gNPLNxHiH0CKLBmo2KsdFXu2jdWFfGTOCpb3NF53RtYpjHt8Avc01jU71OsNZK0zBO9XfibHnVo5kp/+Xs/ek9fiHRuiKDKNqhRl1YQ+Mc71Gr2IBRsPm7zezcUJfzOl1iNJ4+FOr1bVGGlBVo81ONjpCNPr49XKas7IznRqXIEHz14y4q91rN97NtHjbqwhVQo3fHzNlwOwBEWWUVU12q6Lg52Ors0q8cfAljjY29lknU/B6u9GcXjWMrPe0g/xzJWVZhN/pGD9aibHGcLDWdhhEGdXbY3qtxb5d95aFem1cQ72TmILylaIVGnBZ8Pzq7dNd1nWNF7de4zBgkC9wFdvCLdgq8bO0ZESraMHCKbIlJ6WU35mos95poXc5pcb+6jWv4vRve9yXVtRpFkdgGhPaLJOQZIkOiyYYLVwgYjmieaEiyLL7Dl+lTZ1y9jk5mowqHRqVD7G8Wc+b8wKF8Bi4QLwx8BWVC+d1yr7TJHc3YXJQ9vgf2o293dNivM8Lk4OZEyTAlVVyZIuJcv/6M2rI9NJlsTZZrbaGh9ffxTZNvEqho+EC0S0Upi1ej9tvp9p1MP4JeCRLaPVW0k+tx8wo2E3zq833UZi84iJnF29Dfg3bivy7xt7jrKq789xsFhgC4R4ESQYqsGAztEhWiuA2FDs7MhRsaTJGhCyTsE9nadF9Rnq/dQv3vvasqLQffU02s0eS+q82aNsKFC3KoMPr6F0+6ZWzzlz9T6qd/vD7DhJithOal6jOHmzpUOxsKaNsblqlStAzbL5Y5z7fd62OM9rjE0HzlE8XxbyZE2LbOTGG6kFlQ9c7pFjB7avxemVI1kxvjfbZwzi8Z4p9G1bA0mS8EjqipODfZzsCgwOpXavieSs/wNbDkS0HHB1dqRb88rR7PjcMCSwZ0jVNLYdusihszcTdJ2EpFS7xlb3J9M0DTSNlX1GYDDStiLkXQAHpy0xWvROU1VOLlmPn7eP1TYL4s/n+1sr+GK5f+IcMxt1p69DTs6s2Gw6/VGSyFGpJFX6dTI5TlNV8tYob9ETVqk4CIvYkBWFCj3aMvLqHmbo7zE97C69N88le/kSVs81bcUe+v++nIBg87EgeoNKifxZcLC3Y/ecoZQvkjMu5kegwaQhbaLtzZ+99oASrUdFxd3YEv+AECRJYvbPnbDX6WIIA0WWcbC34/eBLSlTKFuUkCmWNwvL/+jF7wNbUjh3JprXKEGNMvmjlel3sLejXqVC8bLvibcvLQb/HSVgBnxTk9Qp3aPK+X+IJEmkS5UsXut9CegUmcWbj35qM+KEITycbb/8GeeGkO98fLn2T+zex3vHzxEWZNrrqOoN3Nx3PE5rC+KHEC8CmxASEMihmUsZmbsq48s249KWPZZ9oWgaN/ceY9e46TQa+z0QvQpn5DZN+3l/UKVfp2gZSx8jyTI5KpYkWfo08X4/HyMrikUBfqqqcubqff45diWqWmpgcCg/TbMsBkSRZbKkT0m1UhFbL6mSu7F7zlBSe5jukmvUbllm2fZ/v1wv3npMta6/c/n24zjNZwpFkcmTNeJnX7pQdg4uHE6Vkv/WzpEkqFY6L4cX/8igDrXZv2A4QWfmEXx2HseWjqBFzZImf8bPfd6w98S1eNkY+RA9aMIKVFUlZXI3Di/6kSolo291OTnYM6hjbb5rWyPOa8Vnx0dRZHJmSp0oLYP0BpVnL94k/EIJwLIewzk0fYnJYF2TSJLRliQGC5qWRoyLW9ahIH6IbCNBvHn9+BmTKrfG98GTOM9xbechnNzdGHJkLfv/WsjtgyeRJIm8tSpRtX9nMhWLqDhac2hPdo2bEeN6SZaQZDlKAH0Klm07xpCJq6Iq4QJkTJOCDg3KEWiBxyUCjYW/dovmKRk5Y6PZOBljGFSVK7f//XcZ8dc6QsLC4xX4anQtg0rnxhWiXhfNm5kdMwfj/cqPF75+eKZwjyHCrNkSm7lmv01SsDUNHnv5cvziXcoXzUmG1MnZPmMQ95/6cOnWExzsdVQompMkLk5cu/uUH6Za0QRUkTEYVCRA1d4LGElCVTXk90pE1bT3wib2ooKKIuPi5MDisd1pPvBvnr/0S9CYFJ0ik9bTuIdJVVU27D3H7/O38djLF0cHO7KkS8kbvwC8fP1JmdSVjo0q0KNFZZK5ufDytT+LNh/l4JmbgEaFYrno3LhCtB5TtsD75l1OLFoXv0k0DVePf9+776On+D1/QRLPlGQokg9Jksz+7DMVLxg/GwRxQogXQZwJDwkhwPctMxp1582T5/GaS1NVzqzcQqPfhtBjTUxxEknDMUPQOdjzz+8zI7KOJEAD97Sp6bBgPNnLFY+XHXFl5ur99P99WYzjj718GTNni0VfghBxY12y5ShlC+cAIhoX/jE/7rEpshxRsRXgha8fe45fjVedL3P8PH0jqyf0iSq0B5DaI6ZoiQsrt580WapfkkCnKIRbWKX1+cu30V5nTZ+KrOmjB2HbWZCaH4lnCncurP2F0u1G8+zFGwyqhqoR5e6RJAlXZwcGdazN9XvPkWWJjKmTc+zCHY5euANE/Hs1rFyEX/s1Q0Ii4nLL/8Ui6wJZUx9Ib1Dp0KBcrOfCwvVU6/YHpy5/0P/qHdHEtN+7IEbO2MDc9QcZ068ZvUYvIiQ0PEqY7Tt5nbFzt7J2Ul+L2h5YyukVW6Iyf+KKvYszBepX49HZy6wb/Bt3Dp+KOpe5VGGylS/O/ePnY/UiyzqFLKUKk65A7jivL4g7QrwIrOb142ds++VPTi/fFOdCbbEhAVe3H6Dyt8ZL/8uyTP2RA6g2oAtXth8g2M+flNkyRdRcsDJoz1YEh4QxeMIKk2MsvQGpmsaybccZN6AlydxcWLLlGLIsx7k5oqpqNKhcBIi44dhCuHimcOOFkRTe7YcvMXr2Zn7t28wGK0XHz0zWk6ZBwZwZGNyxNm1/mGV2vjQWCKqMqVPg6uxgUd2apeN6sPXgRR57vY71vEFVCQgKJTA4jKXjekY799znDb5+gaRNmZQUSV0JDQsnX+P/WZUqLUkSO2YOpmiezDg72jNrzX4GTViJLEtGs9ZkSSJfjvScu/6Qpy/e0KhKEdw/yMDqN3ZpdOFiBFXVePbiNZ1+nBtDcKmaRmiYnuYD/+bShjExBGJcCXj1Ot7VgxuOHsSzKzeZXLk1ho9aiDw6cxkkcPP0wN/7VbSsSVlRcPVITuelU+O1viDuiJgXgVW8vPeIscUacHLJepsKF4iIWQkLtmxbwMndjZJtG1Gpd3vy1qz4yYQLEOf+P8YICzdw+f1Wz6Pnr4jr17NOkcmU1oNm1SO8UbH1L7KW3q2qGhUuEHHTmrl6P8Ehto8DyJExldEMJoh4v7mzpKFJteKkSZnU5FzpPZNTrkgOs2s6OtjRpUnFqC0fY3RvXpnKJfKwZvdpkzdUg6qycseJGMfTpkpGgRzpSZE0Iktuw75zPPbyNfm5ivA0yciShL2djgW/dqNyiTy4uTqh0yn0bVuDTX/1p2yh7LFf//4/V24/4adp6+k2cj4Zagxk0uKdaJpGQFAIS7ceM/m+o783DVXTYhXqmqahV1Vmr7FdkHiKTOlirZptkvf/NvbOTjSb+CPVBnZlRe8RGML1MdoSaKqKpmpIskz9UQNIliENsk7BLXVKav7QixEXd+CRJYOt3o7ASoTnRRCFajBwdccBLmzYReAbf8ICg3jz+DlBfv54ZE5PhZ5tOb1iM0Fv/OIc3W9u/fQFPx8XbEhoOCt2nGDD3rO88PUjYxoPmlQrRvMaJaK2YgBOX71v87Ujs18ib2bWXqs3qGRMk4IdMwdHFSBLkzIphXJl5NIt64N1a5TJx4D2taK2O0zVn/EPCObKnSeULJDN6nVM0bNFFbqPWmj0vN6g0q1ZJRRFZtLQNrT9fqbRsROHtLa4OupPPRux/9QNrt97FmuMSufGFZj2v/YAvPUPMutlsyRuZ8fhS9E6TseORJu6pcmfPT0dGpaP9bNSt0Ih7HQKzQb8RUhYdM+CBlGBrpH/niGh4QyfuhZ7Ox0FcqS3qSg3GFS2Hb7IH4Na2WS+Uu2bsvnHCUa9iZIs45kzC+kL5yVltowA3D54kmC/d3jmykbKbBl5fP4qTy9eN76IpvH2qTdpC+Ri3OOYolPw6RDiRQCAn7cPf9WK6AorKXKMp5CAl748OHUxwdaXZJlkGdKQu3rMYmqfgp1HLtP2h5nRAm0v3XrC1oMX+GHyarbNGESR3JkATHoi4kISF0eK5skMQOs6pflr+R6Lr5VliXb1y1KnfEEaVi6C7qO6ONN/7ED5DmMsmkuRZXSKzO4531OmcMTT+7W7sWdmfExCxJe2rVuGlTtOcvDMzVhFRNemlaJihZrXKIH2h8aQiavw+iC2JW2qpEwa0pam1S2PjXJP4syhRcOZtHgXc9Ye4NXbAGRJolyRHIzt34JSBf8VabmzpOHSrcdGb/qyLJEzc2qza4aG6c022tQ0jTkju5gMej577QENvp1iddPOX2Zuom+b6lZdYwnh4bZ76Ema1pP6vwxiy4iJMc7JioJLiqR8t3spydKnYfV3ozg4bXFUjIzXjbtcWL8zStSYY93AXynSuJbNbBfEH9EeQICmaYwr0ZCnl64neEt6Yyj2dvTdvoA81SuYH5zAnLx0l0qdx5q8ASdzc+HaprE89XlDydajbLa2JEkM7librOlTsmn/eQKCQnju85bHXr5mb0CyLFEkdyZOLDdd9XPGqn0M+GO5WVsql8jNuP4tKJYvS9Sxs9ceUPabX01e5+LkwNN9U3FJgGaAoWHhjJmzhdlrDkT1J0qbKimDOtSmb5vqMbwpBoPKobM38XrlR9qUSalYLFe8iv5pmsa7wBCcHOxiDeY9fvEOlTuPMznHvF+60qFh7AGykYydu5XRszYZ9XBJkkSerGm4uM60EK3fZxK745lebit0ikzzmiVYMvbfeJ97T3xYsPEwtx95k8TZkabVi1OnfEGL/400TePo3JVs++VP/J5HNBWVJIn89arS6u9ReGTOwL6p81k7MPbPrCTLpiuAf0D28iUo26UlSVKlIEORfCRN62nRdQLLseb+LcSLgJv7jzO1WlubzinJEpqqYe/iRJgV5eWzlS9B3R+/JV/tyja1x1JUVSVrnSE893lrcpwkSYzp14wzV+6z9dAFm1VCrV2uAFfuPIlY/32GkiJLFs8/f3RX2hvJHPmQc9ceMGjCCk5cigjGtNMptK1bmq7NKqNTZFJ7uJPeM3ms15b95lcu3nwUq3dBliW+a1uD8YNbW2RvXAkNC+fuEx90ikz2DJ7xEiS2RNM0+v++nFlr9sc4J0sSNcrmZ+PU72J4xD7G6+VbstUdit7Ew8SMER3o1qyy0fP+AcF4VPjWYtvjiyzFnvr9IYcX/Y/S72Nwxi/Yzk/T1kcFpEemmRfKlZHtMwZZFaOlGgw8OneF0IAgPHNmiar1ZNDrGZ6hDP7eL+P+xmJBkmWKNKtNm+m/kiRliqjjYcEhvLh1D1lRSJ07G4rdl9sz6lMgxIsQL1axZuBoDk5bgqq3vLGZKZJ4epA6V1Yq9mpHzsqlGZa+jMVPN5FbVu3mRG+smFjMXL2P/r+b90oAONjrCA2zzc8MwM3VieRuzjx98cbotkPJAlk5feV+tLgT6X26eLOaJVg2rqdVnW7fvgvijX8gqZK7WewpefT8FVW7/s6zF2+iblaR9lQpmYfNfw2IFhP0X0PTNGat2c/ERTt54h2ReZTC3ZU+barxQ5d60aoGm2L59hN0+WkeiixFfR4iU+6bVivG8j96mxRtT7xfk63OkPi/IQtJldyN7s0q8dvcrVFCBP6tffNr32b80LUeAKt3naL98NmxzqMoMiXyZeHQov/FO5vo6eUbjClUx/Sg978/1iLJEs5J3akzoi/FWzfkwJ8LODRzGSH+AQAkSZWC6oO7U2NID9F92kKEeBHixSpW9BnBsXmrrOrKGiuSRLUBXWgx+adoh1d/N4oDfy+yaipZpzDuyQncU9smrdJS8jYaxt3Hn2evElmWKJQrI7/0acLERTs5fO4WADkyefJd25pRwaqJwdt3QSzceJhl247z6m0A2dKnpGuzyrSsWcKq2ihfM6qq8uDZKwyqSpa0HnH6uRy/eIfJi3ex48gl9AaVvNnS0rdNdTo3rmj23zokNByPCt8SFt/fawuRJYmqpfIyqGNtpq/cy4HTN9A0qFg8F9+1q0GNMhH9tTRNo0iLn7hx38tkcPOHXpq48ujsZcaVaGjabkWJfwJCpMj6+P1IEqU7NKXjwonxFmL/BYR4EeLFKo7MWcHyXv+L09PHh5Tu2Iz28/5A0UX/kjaEh7Os5/84sXBtVOl/c7E1kizTaMxgag9PPLd3cEgY7mV6Jdp6sWE+wwReHp6GexJnQsPCMRhUnBztxRfjV46maaiqZrU4/XbMEuauP5gwRhlh/ZR+UbWFYuOZzxuy1Bpscg6dIjO4U5141wsKeRfAUM/ihJspwZCzShluH0i4bKKB+1eQq0rZBJv/a8Ga+7fwZQko1rIe9k7O5geaQJJl2s/9PYZwgYiu0R0XTGDUjb3UHv4tZTpa8IUkSXhdvxsvm6zFVA2RhEaSIrKMLCnqErmF4GBvh7OTgxAu/wEkSYqTV21Ez4akTJYkASyKHUWWWbgxZqPDc9ce8Pv8bfw2Zwv7TpoPIJYkiTAbbMk6JnGlXNdWRjvby4pCymyZ6L15Ls7JbNu+IGoNncLRuasSZO7/MsK/+x8mLDiEPRNmc3D6EsKCguI8j6xTKNykltngtNS5s9Nw9CAM4eEcX7jOZByMJEnYOzvG2aa44GBvR96sabh+3ytR14UIb3OjKkVZts14h1pJgoxpPEju7pKIlgm+ZNKkTMqplSP5dswSdh69nODrGVSVR16+Ua9f+PrRasgMjl+8gyLLSFKE+DYX3BuuN1A4Tyab2NR47FDunzjHk/PXIpzLkXFaioJDEhd6rp+JUxJXem+ey+TKrS2Oz7MUVW/gxS3b14L6ryPEy3+U8JAQ/qz5DfePn4/+y/pB8Jpib2e2s6okSUiSRO1hfUyO8751jwN/LeLChl3oQ0NxTuZO0Bs/o18Uql5P4aa1rXlLNsEvIP6N/6whsqDcgG9qMrJ3Y7Yduoh/YHDsKbIafNeuhvC0CKwivWdyNv89gJev/bn1wAtNkiiZLzMrd52ih4mif3FBliVSeyQFIjLCavaYwO1H3gDRtkNN7VDLskTSJM40rVbMJjY5JnFlyOG1HJ61nEMzl/H60TMc3Vwp1b4J1QZ0IUWm9ADkqFCSPlvmsbD9QILexK0RamxIsoxLiqQ2m08QgRAv/1H2/7mQ+8fPxWwl/8FLUwG8ip0OQ7ge52TudF35FxmL5jc69vruw8xo2A3VYPg31kWWwYhwkXUK6QrkJk+NxK35cunWY575vIn3PA72OsLC9UbrxMiShEeyJGTLkIo8WdPQrVllir+vpbJucj8a9J1CmF4fla0RmcnTpFox+rSqFm/7BP9NUiZ3I+UH6cedGlVg7JytPHz+ymZrqKoWVcNm/d6z3Lgfe8NWY6GWOkVGkWVWTegTVRnaFtg7O1F9UDeqD+pmclyBelX5w+s0+/9cyNafJqEPD493LKCmqji4OvPs6i3S5sspHj5shBAvXzlvnnpxcdNuQt8FkCpnVgo2qIZiZ8eBaYtjCpePMeHWzV29PKXbN6Fw09rYORhPsQ3282d2014YwsOjr/eRcJHtdKBFeFwyFM7Ht9sXJHp64bz1h2wyT5cmFWOt8xGJqmnM/KljrEGNFYvn4uzqX5i+ci/r9pwhODSMvNnS0btVVVrVKvXZ1DMRfB3YUiAoskzh3BmRgCb9/+TIudsWXZfC3RVfvwAc7e1oWaskAzrUIn/29Dazy1rsHByo9X0vCjepxa6x0zm1fBNqPDO2Lm7czcWNu0mRJQONxw6lRGvTGVAC84hso68UQ3g4q74bxdE5K4GI5mKq3oBLimR8M2css5v1jtf8bqlTMt7rjNlxB/5exOr+vxgXQhK4p/GkYINq2Dk5UqhRDXJWKm3y6eSNfyArd5zk5oPnuDg70rx68WhVYONKpU5jOXEpfkHC/drW4Ne+TanY6Teu3Xseoxu0LEtUKZmHbdMGCSEi+OR8P2kVf6/cG+eu5ZFIkkSDSoV57OXLRSt7Z0mSxMnlP3P7kReLNx/jmc8b0qVKRsdG5WlardgnT73Xh4UR8i6QW/tPMLel6e1xS2k+eQTVB5r2Av0XEanSQrywpOv3nFi4Nnb3rCyBDSrCjnlwBI/Mpruqzm/Xn7Ort8bolfQxf4fcMunBiWTp1mN8O2YJoeF6dIoc0a3WoFKjTD5Wju+Dm6uTVe/hQ+p/O5ndx6/G6VoXJ3smDWlDl6aVgAiB1f/3Zaz950zUXr+DnY7OTSryx8CWODnax9lOgcBW3HviQ8GmP6I3GKzuR+Xm4kj5YrloUKkw1Uvno+WQ6Vy48ShOdkSG2kVukUb+XaZQNrZNH0QSl7j/XtuKF7fvMzJXVZvMJSsKYx8fFy0GPkKkSv/HeXnvEccXrDFeAErVkHU6pHhuywS+Mh8fIisKkgX5v5ZsEe05cZVuP88nJCwcTdMI1xui0ob3n7phsotwbBgMarSfUdNqljfrkyWJxtWKMvWHdqyd3Bfvg39HCReI6H20ZGxPHvwziY1/fsfmvwbweO8U/hr+jRAugs+GbBlSsXrit9jpdChWlAoY+10LvA78xeIx3fELCKZ8h9/iLFzg37AS9aMu16ev3Oe7ccviPK8t8cyZleQZ09pkLk3TOLFonU3m+q8ixMtXyNnV25DNbEmoen28UwKTpjffHTdPjfImq1dKikKOiqUs6gHy25wtSEa+YA2qyu4TV81+gYaGhTPi73VkqD4Ap+LdcCvVk/bDZ3Px1mNa1SlFes/kFm3naMD4Qa3p07oajaoUNRo7kNrDnXoVC1OnQkGSuYkUZ8HnR/1Khbm59Xd+6Fqf0gWzoTPz+Vdkmbb1SuPz5h0l2ozif1PX8sLXdtk5H2JQNVbtOoX3q4SZ31qylrVNBpQky7y8+9Cqa/RhYRjCTWd//pcQ4uUrJOiNn8VeFcUhbl6AfHUrW1S6v1iLurilTomsxN6ITjMYqDG0h9l5Xr15x/GLd4122QVQZInZ6w4YbWZ38cZD0lcbwPgFO3jh6w9AaLietf+cptw3v3Lk3C32zP2eLOlSArHXi4ssZDd+UCsyp/Uwa7dA8CWQ3jM5o/o04fDiH/ntu+ZGx8myRPsGZUmbKhldRszjqfdrs80Y44vBoHLswp0EXcNS0hfKY/QByloc3c0XD9Q0jVPLN/Fb0Xr0dcjJt/Y5GF+uGRc27rKJDV8yItvoK8QjawaL+hTJOoVS7RpzfMEaq+a3d3Wm2YQfLRpr5+hI/91LmVKtHQGvIprUoWnIOgVVb6DJH8MoWN98+m9gcKjZMQZVY8GGw2zce46cmTzJkj4lmdJ44B8YzModJ3n7LvZCfKqmoeoNtBs2i8d7pnBlw2/sOnaZXUevcO7aQ2499OJdUET9lyJ5MjGsa30aVSlq0fsXCL40BrSvhfcrP6Ys/ScqrkySIppD1qtYmL+Gt+fG/eccOHMj0WzS4puvbCOKtajHpuHj4z2PqtdTvFV9k2M0TWNN/1848PeiaA+jD05eYHbTXtQfNYD6IwfE25YvFRGw+xUS9NaPoalLYAgNMzlOsdORp0YFru44YPHcafLmoOuqv0lfILdVNgX7+XNi8XoubtpNeFAwGYsVoGLvb0iXP5dF14eGhZOmSn8CghK2iNzsnzvRuUnFaMcMBpUXvn442NuRIqlrgq4vEHwu3H7kzaJNR3js5UvKZEloW68MJfJnBWDhxsP0HL0oUeyQJIm7OyaQIXXyRFnPHIu7DOXk4nXmS00YQVJk8lSvQL+di0xmVV7bdZC/63QyOdew05vJXKJQnOz4HLHm/i08L18hzkndaT7pR1b3HWlynEGvJ3lmy+spSLKM1/U7nFqygfQT/meVTU7ublT9rjNVv+ts1XWRONjb0aVJBaav3Ge2cWFckWWJCzcf8bGFr96+Y+aa/SzadBRfvwDSeLjTrVklereqRtIk8esJJRB8ruTMlJqx/VvEei6x0vwVRaZhpcIxhEvkA4W9nQ6PROzdBNBu1m8oOoVj81ab7IodjfeVyDVVpWCD6nReOsVssbqD05dEeahjQ9YpHJqxlMwLvx7xYg0i5uUrpcq3HSnYsLrxAZKEnaMjfl4+Fs8ZGeC7Z+Icbuw9Gl8TrWZEz0bkyOSJkkDF6zQNHD4KHH7w7CUlWo9i4sKdvPD1Q6838MT7Nb/M3ESZdqMTLFBRIPicqVQ8NwlZKFaSIv7kzpKGGT91ijoeFq7nj/nbyVxrMJlrDSZt1f6UaD2KtbtPJ5wxH6Gzt+ebOb/z28OjpMmbA3M/COfkSak7oh+NfhvCyOt76b1xDo6u5oP3H5+/alS4QETPpEdnE75f1eeKEC9fMV2WTX0fYBb9n1lWFCQJwoNDuLJ1r9XzyjqFg9MW28pMi0maxJnDi3/ku3Y1Ijow2xhN06hXKfpTTMcf5/DqzbsY3h5V1Xj4/BXfjV1qczsEgs+dTGk9aFy1mMUemA87tn94r1dkKSq7SafIZEyTAs8UbhTOlYm/hrXn2JIRUVu14eF6GvWbys/TN0R7aLh86zHtfpjF7/O32eCdWU7yjOmo82Nfk5XIZUWhUu9vaDh6ELWH9SFJyuTsmTSXhe0HsrTbD1zcvBuDPvb4RDtH899xdk6fvv7Np0JsG33FOCZxZciRteweP5tDM5cR6PsGJImsZYty90hEdVxTyt4Yn1rxZ8uQiqbVirFq1ylCw+JXtvtD8mZNS6Xi/8byXLnzlJOX7hkdbzCobD54IaoiqEDwX2L2yM489X7NmWsPUGQJg6qhKDIGg0rW9ClpWaskFYvnJjxcz+Qluzh09hYAuTKnoV/bGuTLlo5N+88REBxK7ixp+KZ+WZK7x4wpCwkNZ/qqvYxfsJ03/jGD7iOlw8/TNtCoSlHyZLVNLRZLKNKsNmnH5cL75t0Y36WyouCU1I3K33YA4OzqrSzsMAhVrwdJRgKOzV+NW5pUfLt1PpmKFYh4P5rGrQMn0JmpByXJMkWa1kqQ9/UlIMTLV0jAq9ccnbuS06u2EvjyNUk8PSjfow3le7TB3TMl6wb9yv0T5+MkXCLRWVAN19bMXnOAIRNXEhrPPiOx4ehgx85ZQ6LtQ5+79sDsdaqqcfHmIyFeBP85kiZx5uDC4Ww6cJ4lW47h/cqPTGlS0LlJBWqXKxjNK1OnQiHCwvWoqoajw79bs2WL5DC5RkhoOPX6TOLohTsWxZc0+m4qebKkJb1nMorkyUS2DJ5UKJoTnS72Ug3xxc7BgQH7ljO35bfcOXQKSZGRkFANBlJmz0TPDbNxT5OKe8fPMb9t/w9qaxmiRJe/lw/jijegfPfWNJv0I4s7DuHixn+MlpeAiKBfJzdXyndrHe145M/ov9D8UYiXr4xnV28xpUobAnzfRLkz/bx8eHrxOnvGz6buT/24dfBkvISLrCgUaVrbViZbxKqdJ+k3LuG2aHbOHEyalEmjHbO0p4r9J+69IhB8KuzsdLSoWZIWNUuaHRuX35PJS3ZxzELhAvDw2SsePoveJTtpEmf6tK7GTz0bJUigsVsqDwYfXM3jC1e5secoqt5AltJFyFWlTJSI2P3HrIiAXRPzHJ23mtuHTkUVrzNV3NMleVL67VyMq0dyVIOBYwvWcODvRTy/ehudgz2FGlanxpAeX1Um0seIVOmvCINez0/ZKvLmqbfJ6rkuHsksKu0vyXKMeSRZQudgz8hre/HIYrqvka3QNI3cDYfx4OnLBFtjydgetK5TOtqx5z5vyFZnqMnsJmdHe57um4qrs+1jcASC/xqv/QJ44euPR1JXUiR1JWPNQfi8LygZX9xdnfh9YEu6ftDGIzFQVZW+9jlMihGLkSSaTxpBxZ5tsXd2wqDXM6dFHy5t3hPRH+r97VzWKWiaRrdV0yjWvG78100kRKr0f5Qr2/bx+vFzs+MCfd9aNJ+9sxOhgYFIUsTTiqZp2Ds703vz3EQTLgCXbz9JUOECxPpElDZVMtrULc2KHSdirewrSRJ9WlcTwkUgiCc3H3jx87T1bDl4AVXVkCSoWDy3zYQLgF9AML1/Xczbd0EM7ljHZvOaQ9XrbSNcADQNN08P7J0jAnWPzFnJpc17QItexk/VG0CSWPjNAHJVKYNriq9vW1uIl6+IO4dPm6wLEIkkSWbdsLJOoeQ3jclQOC+39h9H0yBHhRKU7tAUJ/fE9Wj5BwTH63pZkkyWMJckKFsoe6znpv2vPd6v/Nh78ho6RUZvUKP+bl6jOL/0aRIv2wSC/zpX7z6lUsexBIWGRT0kaBocPXcrQdb7adoGOjQoR8rkifM9prO3J2X2TLy899hkZpKlHJq5jKxli+KROQP7/1xgfKCmYQjXc2LROmoM7h7vdT83hHj5DxLbdlAso9AMKhV7tqNiz3aJYpcxsmZIZZHgMkbx/Fk4d+1hrNs/iiJTt0JBMhnpU+Ts5MC26QPZf/oGy7Yd58UrPzKkTk6nxhUoUyj7fyIwTiBISPr9tpSgkLAYv5+GOFawNYdqUFm16xT92tZIkPljo0q/TqwZMNomc90/cY5xxRow8NBqfG6bTyp4fO6KTdb93BB1Xr4iclQqZVEgrqaqOLiargyrGgykK2hdC4CEIl2qZNQpXyDO1/drU4NCuSK2ueT3YkOSJCQi0jbnjOxi8npZlqleOh+LxnRn56whzBnVhbKFcwjhIhDEk9uPvDl28U6CVc2ODVmWeOLlm2jrAVTq/Q15a1U0P9ACNINKsN871vQbZbZAniRLKEY63n/pCPHyhaAaDFzZcYC1g35ldf9RnF29FX1Y9N5FBepVJXmmdGbnkmSJir2+Md55WpKwc3SgdPvPZ0tk0tC2cbrO2dGextWKcnDh/5j9cydKFMhKulTJKJY3M9N+7MDxpSNEvyKB4BNx55F3oq9pULVEbymg2Nnx7ZZ51B81wGQKtKWoBgO3D54ge/niJudT9QYKWND49kskUcTL9OnTyZw5M46OjpQqVYrTp42Xcl60KKJZ1Yd/HC2oNPg143P3IaPyVmd6vc4cnLaYwzOXMa91P/6XsSwPTl+MGqfodPTdvhDnpO4m56s7oh8NRg8iS+kiRqrvSnRaMjnRY1tMkS1DKqqWzGP1dWP6NcfB3g5HBzs6N6nIkcU/8uCfSRxf9hPdm1fG2Snx69UIBIII3M14gCPJnDaFzdbUNI0KRS1rCGtLFDs76o8cwK/3D5O/bhWbzKkPDTcaDCwrCimyZKBwo8TbHktMEly8rF69mkGDBjFy5EjOnz9PoUKFqFWrFj4+xnvquLm54eXlFfXn0aNHCW3mZ0tIQCBTqrbh1b3HABjC9RjeF2l79+o1U6u15fbhkzw4dYE7R07jlNSN0XcOUKV/Z+w/yoKxc3IkTb4cPL18kzMrt9Bnyzwa/TaEpOk8gQiPTP66lRlyZO1nmV43rJvpFvIf07NFZfq2NdHfSSAQfFJKF8xGquSmvSBODvYcX/Yz80d3pXyRHDibqTxrCbV6TeDA6RvxnicupMiYjr7bFzLuyQm+mft7vOZ6fP5K1NZR5DZ25ANp0vSpGbBnGYrd17ltlOB1XkqVKkWJEiWYNm0aEJHzniFDBvr168ewYcNijF+0aBEDBgzg7du3cVrva6vzcnjWMlb0GRF7Lm9sSJC/ThWajh+O78On3D95gScXrnJt5yE0NFC1qIBdN08P+u9dTtp8OQkNDEJnb4fOPv5fDObwfRvA/A2HWLf7DAFBIRTIkZ6eLatSpWQeJEnizNX7LNx0hAdPX5IyuRtt6pTG3k5hxN/rOXf9Ycy3LP0bxC8BhXNnYsLgVlQs/nnE7AgEAuPMXXeQb39bYvT8iJ4N+blX46jXpdv+wvkb8XuglSUJJ0d77u6Y8Em3jYP93zE4ReF4FQ39kIzFC5Aya0YKNapJkWa1sfsEldDjw2dT5yUsLIxz584xfPjwqGOyLFO9enVOnDhh9LqAgAAyZcqEqqoULVqUsWPHki9fvljHhoaGEhoaGvXa3992dQE+B86t2U7ELdlC9aLBtV0HubrzoNG0vMhMo4BXb/izejt+vXfYoi6ntuDq3afU7D6e136BUenLD5+/YuP+8/RoXpmwcD2LNh+NSkdWZJlVO08C/wbbfogENKlWnP91q0/K5G64OjuQxOW/26xMIPjS6N68Mn4BwYycvgGDQY3oj6SqSEj0b1+TET0aRhtvi7pKqqYRHBLG4i1HGdQhcauFf4iTWxLSF8zD4/NX4z+ZJBEWEES3VdP+E8kECSpeXr16hcFgwNPTM9pxT09Pbt68Ges1uXLlYsGCBRQsWBA/Pz8mTpxI2bJluXbtGunTp48xfty4cfzyyy8JYv/nQLB/gNW1ATQLUwxVgwH/F684s3JLjB4ZtiAkNJzl248zf8Mhnni/JlVyNx57v+ZdQHC0uit6Q4SYmrPuYIxjH2YhxFarRZZlnvu8oWCujDa3XyAQJA5DOtWhc+MKrN51iicvXuOZ3I2WtUqSNpaeYQVypOfo+dsmazdZgqppHDpz85OKF4C8tSraRrxoGt437+Fz5wGeObPGf77PnM+uzkuZMmUoU6ZM1OuyZcuSJ08eZs+eza+//hpj/PDhwxk0aFDUa39/fzJkSLzqrwlNugK5eXrpus3cih8jSRJXtu+3uXjxexdE7V4TOXf9YVSNlhc2rJYZiUFVOXn5HrceepErcxqbzy8QCBKHFEld6dPafGZMl6aVmL5qn03W1DSNN/6B/HPsCkHBYeTJmpbShbIlqueiYINq7Bo3w2bzhQbE7Lz9NZKg4sXDwwNFUXjx4kW04y9evCB16tQWzWFnZ0eRIkW4e/durOcdHBxw+ML29ayhQs82nFi0NsHm1zSN8JBQ8wOtZNCEFVy8+ThqjYTm0NlbQrwIBP8BCuRIz489GvLbnC3xmkcCAoNDyVh9YLRO9bmzpGHhr90oli9LPC21jCyli5KhSD6eXLgW77kUOx0pErF1y6ckQbON7O3tKVasGPv2/auSVVVl37590bwrpjAYDFy5coU0af6bNyZFp0vwIkMps2Wy6Xw+r/1ZueNkohaeWrndeAyVQCD4uhjZuzELx3QnSRzjXyRJQpZljl64HU24QETtmWrd/uD6vWe2MNUiW3pumBVvb4+sUyjWqgEuyUyXyvhaSPBU6UGDBjF37lwWL17MjRs36N27N4GBgXTu3BmADh06RAvoHT16NLt37+b+/fucP3+eb775hkePHtGtW7eENvWzw6DXM6NRdwzh4Qm6js7G4ujctYdRMSuJxbGLd/B5/XUFawsEAuO0q1eGfu2sr2EiSWCnUzCoaqzhhAZVIyxcz29zttrASsvwyJyBrOWKma2YawxZp+CeJhXNxg83P/grIcHFS6tWrZg4cSI//github.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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make and plot data\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_circles\n",
    "\n",
    "n_samples = 1000\n",
    "\n",
    "X, y = make_circles(n_samples=1000,\n",
    "    noise=0.03,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdBu);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D63mlmxR_pxt"
   },
   "source": [
    "Nice! Now let's split it into training and test sets using 80% of the data for training and 20% for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0UkH4cKLHC8F",
    "outputId": "5619ad2f-ad5d-476a-dcfd-9f8d48036899"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[ 0.6579, -0.4651],\n",
       "         [ 0.6319, -0.7347],\n",
       "         [-1.0086, -0.1240],\n",
       "         [-0.9666, -0.2256],\n",
       "         [-0.1666,  0.7994]]),\n",
       " tensor([1., 0., 0., 0., 1.]))"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to tensors and split into train and test sets\n",
    "import torch\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Turn data into tensors\n",
    "X = torch.from_numpy(X).type(torch.float)\n",
    "y = torch.from_numpy(y).type(torch.float)\n",
    "\n",
    "# Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                    y, \n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42\n",
    ")\n",
    "\n",
    "X_train[:5], y_train[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wNm_OBgD_4tk"
   },
   "source": [
    "### 6.2 Building a model with non-linearity \n",
    "\n",
    "Now here comes the fun part.\n",
    "\n",
    "What kind of pattern do you think you could draw with unlimited straight (linear) and non-straight (non-linear) lines?\n",
    "\n",
    "I bet you could get pretty creative.\n",
    "\n",
    "So far our neural networks have only been using linear (straight) line functions.\n",
    "\n",
    "But the data we've been working with is non-linear (circles).\n",
    "\n",
    "What do you think will happen when we introduce the capability for our model to use **non-linear activation functions**?\n",
    "\n",
    "Well let's see.\n",
    "\n",
    "PyTorch has a bunch of [ready-made non-linear activation functions](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity) that do similar but different things. \n",
    "\n",
    "One of the most common and best performing is [ReLU](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)) (rectified linear-unit, [`torch.nn.ReLU()`](https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html)).\n",
    "\n",
    "Rather than talk about it, let's put it in our neural network between the hidden layers in the forward pass and see what happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i_yG7AFHHC8F",
    "outputId": "00a69e38-369f-47fb-b729-fd4c7b5b47de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CircleModelV2(\n",
      "  (layer_1): Linear(in_features=2, out_features=10, bias=True)\n",
      "  (layer_2): Linear(in_features=10, out_features=10, bias=True)\n",
      "  (layer_3): Linear(in_features=10, out_features=1, bias=True)\n",
      "  (relu): ReLU()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Build model with non-linear activation function\n",
    "from torch import nn\n",
    "class CircleModelV2(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.layer_1 = nn.Linear(in_features=2, out_features=10)\n",
    "        self.layer_2 = nn.Linear(in_features=10, out_features=10)\n",
    "        self.layer_3 = nn.Linear(in_features=10, out_features=1)\n",
    "        self.relu = nn.ReLU() # <- add in ReLU activation function\n",
    "        # Can also put sigmoid in the model \n",
    "        # This would mean you don't need to use it on the predictions\n",
    "        # self.sigmoid = nn.Sigmoid()\n",
    "\n",
    "    def forward(self, x):\n",
    "      # Intersperse the ReLU activation function between layers\n",
    "       return self.layer_3(self.relu(self.layer_2(self.relu(self.layer_1(x)))))\n",
    "\n",
    "model_3 = CircleModelV2().to(device)\n",
    "print(model_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1UASf5SWEPNJ"
   },
   "source": [
    "![a classification neural network on TensorFlow playground with ReLU activation](https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/02-tensorflow-playground-relu-activation.png)\n",
    "*A visual example of what a similar classification neural network to the one we've just built (using ReLU activation) looks like. Try creating one of your own on the [TensorFlow Playground website](https://playground.tensorflow.org/).*\n",
    "\n",
    "> **Question:** *Where should I put the non-linear activation functions when constructing a neural network?*\n",
    ">\n",
    "> A rule of thumb is to put them in between hidden layers and just after the output layer, however, there is no set in stone option. As you learn more about neural networks and deep learning you'll find a bunch of different ways of putting things together. In the meantime, best to experiment, experiment, experiment.\n",
    "\n",
    "Now we've got a model ready to go, let's create a binary classification loss function as well as an optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "uWNlx4lTHC8F"
   },
   "outputs": [],
   "source": [
    "# Setup loss and optimizer \n",
    "loss_fn = nn.BCEWithLogitsLoss()\n",
    "optimizer = torch.optim.SGD(model_3.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NQL9GF5yFTGD"
   },
   "source": [
    "Wonderful! \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "INnCmr2RMk8L"
   },
   "source": [
    "### 6.3 Training a model with non-linearity\n",
    "\n",
    "You know the drill, model, loss function, optimizer ready to go, let's create a training and testing loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gTS8yTN_HC8F",
    "outputId": "df71bd5d-cd0d-4e39-f3e0-7fc90bfaaeb0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Loss: 0.69295, Accuracy: 50.00% | Test Loss: 0.69319, Test Accuracy: 50.00%\n",
      "Epoch: 100 | Loss: 0.69115, Accuracy: 52.88% | Test Loss: 0.69102, Test Accuracy: 52.50%\n",
      "Epoch: 200 | Loss: 0.68977, Accuracy: 53.37% | Test Loss: 0.68940, Test Accuracy: 55.00%\n",
      "Epoch: 300 | Loss: 0.68795, Accuracy: 53.00% | Test Loss: 0.68723, Test Accuracy: 56.00%\n",
      "Epoch: 400 | Loss: 0.68517, Accuracy: 52.75% | Test Loss: 0.68411, Test Accuracy: 56.50%\n",
      "Epoch: 500 | Loss: 0.68102, Accuracy: 52.75% | Test Loss: 0.67941, Test Accuracy: 56.50%\n",
      "Epoch: 600 | Loss: 0.67515, Accuracy: 54.50% | Test Loss: 0.67285, Test Accuracy: 56.00%\n",
      "Epoch: 700 | Loss: 0.66659, Accuracy: 58.38% | Test Loss: 0.66322, Test Accuracy: 59.00%\n",
      "Epoch: 800 | Loss: 0.65160, Accuracy: 64.00% | Test Loss: 0.64757, Test Accuracy: 67.50%\n",
      "Epoch: 900 | Loss: 0.62362, Accuracy: 74.00% | Test Loss: 0.62145, Test Accuracy: 79.00%\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "torch.manual_seed(42)\n",
    "epochs = 1000\n",
    "\n",
    "# Put all data on target device\n",
    "X_train, y_train = X_train.to(device), y_train.to(device)\n",
    "X_test, y_test = X_test.to(device), y_test.to(device)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    # 1. Forward pass\n",
    "    y_logits = model_3(X_train).squeeze()\n",
    "    y_pred = torch.round(torch.sigmoid(y_logits)) # logits -> prediction probabilities -> prediction labels\n",
    "    \n",
    "    # 2. Calculate loss and accuracy\n",
    "    loss = loss_fn(y_logits, y_train) # BCEWithLogitsLoss calculates loss using logits\n",
    "    acc = accuracy_fn(y_true=y_train, \n",
    "                      y_pred=y_pred)\n",
    "    \n",
    "    # 3. Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backward\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Optimizer step\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "    model_3.eval()\n",
    "    with torch.inference_mode():\n",
    "      # 1. Forward pass\n",
    "      test_logits = model_3(X_test).squeeze()\n",
    "      test_pred = torch.round(torch.sigmoid(test_logits)) # logits -> prediction probabilities -> prediction labels\n",
    "      # 2. Calculate loss and accuracy\n",
    "      test_loss = loss_fn(test_logits, y_test)\n",
    "      test_acc = accuracy_fn(y_true=y_test,\n",
    "                             y_pred=test_pred)\n",
    "\n",
    "    # Print out what's happening\n",
    "    if epoch % 100 == 0:\n",
    "        print(f\"Epoch: {epoch} | Loss: {loss:.5f}, Accuracy: {acc:.2f}% | Test Loss: {test_loss:.5f}, Test Accuracy: {test_acc:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x89XvV-EMqvB"
   },
   "source": [
    "Ho ho! That's looking far better!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tfViHC1aM15t"
   },
   "source": [
    "### 6.4 Evaluating a model trained with non-linear activation functions\n",
    "\n",
    "Remember how our circle data is non-linear? Well, let's see how our models predictions look now the model's been trained with non-linear activation functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wGHQiWT2HC8G",
    "outputId": "e95a412b-bef1-4d3d-b705-1eb75a55449e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([1., 0., 1., 0., 0., 1., 0., 0., 1., 0.], device='cuda:0'),\n",
       " tensor([1., 1., 1., 1., 0., 1., 1., 1., 1., 0.]))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions\n",
    "model_3.eval()\n",
    "with torch.inference_mode():\n",
    "    y_preds = torch.round(torch.sigmoid(model_3(X_test))).squeeze()\n",
    "y_preds[:10], y[:10] # want preds in same format as truth labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "gEeMinjyHC8G",
    "outputId": "3566e274-ef7a-4eb8-ef34-57b97e7781bc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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.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.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.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.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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot decision boundaries for training and test sets\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Train\")\n",
    "plot_decision_boundary(model_1, X_train, y_train) # model_1 = no non-linearity\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Test\")\n",
    "plot_decision_boundary(model_3, X_test, y_test) # model_3 = has non-linearity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cAfU5dXrNGuC"
   },
   "source": [
    "Nice! Not perfect but still far better than before.\n",
    "\n",
    "Potentially you could try a few tricks to improve the test accuracy of the model? (hint: head back to section 5 for tips on improving the model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rarkXnX-Nhj2"
   },
   "source": [
    "## 7. Replicating non-linear activation functions\n",
    "\n",
    "We saw before how adding non-linear activation functions to our model can help it to model non-linear data.\n",
    "\n",
    "> **Note:** Much of the data you'll encounter in the wild is non-linear (or a combination of linear and non-linear). Right now we've been working with dots on a 2D plot. But imagine if you had images of plants you'd like to classify, there's a lot of different plant shapes. Or text from Wikipedia you'd like to summarize, there's lots of different ways words can be put together (linear and non-linear patterns). \n",
    "\n",
    "But what does a non-linear activation *look* like?\n",
    "\n",
    "How about we replicate some and what they do?\n",
    "\n",
    "Let's start by creating a small amount of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uvpqD28OscTm",
    "outputId": "befa3534-f5b1-48f2-88c5-63836759d834"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,   1.,\n",
       "          2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a toy tensor (similar to the data going into our model(s))\n",
    "A = torch.arange(-10, 10, 1, dtype=torch.float32)\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vvSZ4M3-ssZn"
   },
   "source": [
    "Wonderful, now let's plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "FTCID1lRsrte",
    "outputId": "477fdd3d-ae28-4caf-b9ed-b2a9ec369bee"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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.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.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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the toy tensor\n",
    "plt.plot(A);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ovyXy-Qxsx9x"
   },
   "source": [
    "A straight line, nice.\n",
    "\n",
    "Now let's see how the ReLU activation function influences it.\n",
    "\n",
    "And instead of using PyTorch's ReLU (`torch.nn.ReLU`), we'll recreate it ourselves.\n",
    "\n",
    "The ReLU function turns all negatives to 0 and leaves the positive values as they are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XhdCQKbcsxi1",
    "outputId": "47c7fa54-6d03-47b7-a745-0bb3599c82fd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 2., 3., 4., 5., 6., 7.,\n",
       "        8., 9.])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create ReLU function by hand \n",
    "def relu(x):\n",
    "  return torch.maximum(torch.tensor(0), x) # inputs must be tensors\n",
    "\n",
    "# Pass toy tensor through ReLU function\n",
    "relu(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dqw_P9GiuiB9"
   },
   "source": [
    "It looks like our ReLU function worked, all of the negative values are zeros.\n",
    "\n",
    "Let's plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "fICVVmAgsxal",
    "outputId": "4ada6523-81e8-450e-89b5-0be3da23f04c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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.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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ReLU activated toy tensor\n",
    "plt.plot(relu(A));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AlmI4CHzsxEt"
   },
   "source": [
    "Nice! That looks exactly like the shape of the ReLU function on the [Wikipedia page for ReLU](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)).\n",
    "\n",
    "How about we try the [sigmoid function](https://en.wikipedia.org/wiki/Sigmoid_function) we've been using?\n",
    "\n",
    "The sigmoid function formula goes like so:\n",
    "\n",
    "$$ out_i = \\frac{1}{1+e^{-input_i}} $$ \n",
    "\n",
    "Or using $x$ as input:\n",
    "\n",
    "$$ S(x) = \\frac{1}{1+e^{-x_i}} $$\n",
    "\n",
    "Where $S$ stands for sigmoid, $e$ stands for [exponential](https://en.wikipedia.org/wiki/Exponential_function) ([`torch.exp()`](https://pytorch.org/docs/stable/generated/torch.exp.html)) and $i$ stands for a particular element in a tensor.\n",
    "\n",
    "Let's build a function to replicate the sigmoid function with PyTorch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hcDQDy8bvDcR",
    "outputId": "899e7595-5b1c-4182-c1ca-94aadaa097e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([4.5398e-05, 1.2339e-04, 3.3535e-04, 9.1105e-04, 2.4726e-03, 6.6929e-03,\n",
       "        1.7986e-02, 4.7426e-02, 1.1920e-01, 2.6894e-01, 5.0000e-01, 7.3106e-01,\n",
       "        8.8080e-01, 9.5257e-01, 9.8201e-01, 9.9331e-01, 9.9753e-01, 9.9909e-01,\n",
       "        9.9966e-01, 9.9988e-01])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a custom sigmoid function\n",
    "def sigmoid(x):\n",
    "  return 1 / (1 + torch.exp(-x))\n",
    "\n",
    "# Test custom sigmoid on toy tensor\n",
    "sigmoid(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qiwvlDWmxPUt"
   },
   "source": [
    "Woah, those values look a lot like prediction probabilities we've seen earlier, let's see what they look like visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "dxihhxGBxOWf",
    "outputId": "c6a6d3de-e9fb-445d-8d63-3964753a4559"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot sigmoid activated toy tensor\n",
    "plt.plot(sigmoid(A));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IpOqVYpdxgWl"
   },
   "source": [
    "Looking good! We've gone from a straight line to a curved line.\n",
    "\n",
    "Now there's plenty more [non-linear activation functions](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity) that exist in PyTorch that we haven't tried.\n",
    "\n",
    "But these two are two of the most common.\n",
    "\n",
    "And the point remains, what patterns could you draw using an unlimited amount of linear (straight) and non-linear (not straight) lines?\n",
    "\n",
    "Almost anything right?\n",
    "\n",
    "That's exactly what our model is doing when we combine linear and non-linear functions.\n",
    "\n",
    "Instead of telling our model what to do, we give it tools to figure out how to best discover patterns in the data.\n",
    "\n",
    "And those tools are linear and non-linear functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_1OeaW0FHC8G"
   },
   "source": [
    "## 8. Putting things together by building a multi-class PyTorch model\n",
    "\n",
    "We've covered a fair bit.\n",
    "\n",
    "But now let's put it all together using a multi-class classification problem.\n",
    "\n",
    "Recall a **binary classification** problem deals with classifying something as one of two options (e.g. a photo as a cat photo or a dog photo) where as a **multi-class classification** problem deals with classifying something from a list of *more than* two options (e.g. classifying a photo as a cat a dog or a chicken).\n",
    "\n",
    "![binary vs multi-class classification image with the example of dog vs cat for binary classification and dog vs cat vs chicken for multi-class classification](https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/02-binary-vs-multi-class-classification.png)\n",
    "*Example of binary vs. multi-class classification. Binary deals with two classes (one thing or another), where as multi-class classification can deal with any number of classes over two, for example, the popular [ImageNet-1k dataset](https://www.image-net.org/) is used as a computer vision benchmark and has 1000 classes.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f5Ephtx6f1jB"
   },
   "source": [
    "### 8.1 Creating multi-class classification data\n",
    "\n",
    "To begin a multi-class classification problem, let's create some multi-class data.\n",
    "\n",
    "To do so, we can leverage Scikit-Learn's [`make_blobs()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html) method.\n",
    "\n",
    "This method will create however many classes (using the `centers` parameter) we want.\n",
    "\n",
    "Specifically, let's do the following:\n",
    "\n",
    "1. Create some multi-class data with `make_blobs()`.\n",
    "2. Turn the data into tensors (the default of `make_blobs()` is to use NumPy arrays).\n",
    "3. Split the data into training and test sets using `train_test_split()`.\n",
    "4. Visualize the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 515
    },
    "id": "x3_UwcwHHC8G",
    "outputId": "7cd92d66-41e3-4aa0-ab94-f9f9ef40fcce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-8.4134,  6.9352],\n",
      "        [-5.7665, -6.4312],\n",
      "        [-6.0421, -6.7661],\n",
      "        [ 3.9508,  0.6984],\n",
      "        [ 4.2505, -0.2815]]) tensor([3, 2, 2, 1, 1])\n"
     ]
    },
    {
     "data": {
      "image/png": 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.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.com/d8P7fQHUVRCSikPexFEREREREQHhd3hiIiIiIhopDAIIiIiIiKikcIgiIiIiIiIRgqDICIiIiIiGikMgoiIiIiIaKQwCCIiIiIiopHCIIiIiIiIiEYKgyAiIiIiIhopDIKIiIiIiGikMAgiIiIiIqKRwiCIiIiIiIhGyv8PoShvSC8aTj0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import dependencies\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Set the hyperparameters for data creation\n",
    "NUM_CLASSES = 4\n",
    "NUM_FEATURES = 2\n",
    "RANDOM_SEED = 42\n",
    "\n",
    "# 1. Create multi-class data\n",
    "X_blob, y_blob = make_blobs(n_samples=1000,\n",
    "    n_features=NUM_FEATURES, # X features\n",
    "    centers=NUM_CLASSES, # y labels \n",
    "    cluster_std=1.5, # give the clusters a little shake up (try changing this to 1.0, the default)\n",
    "    random_state=RANDOM_SEED\n",
    ")\n",
    "\n",
    "# 2. Turn data into tensors\n",
    "X_blob = torch.from_numpy(X_blob).type(torch.float)\n",
    "y_blob = torch.from_numpy(y_blob).type(torch.LongTensor)\n",
    "print(X_blob[:5], y_blob[:5])\n",
    "\n",
    "# 3. Split into train and test sets\n",
    "X_blob_train, X_blob_test, y_blob_train, y_blob_test = train_test_split(X_blob,\n",
    "    y_blob,\n",
    "    test_size=0.2,\n",
    "    random_state=RANDOM_SEED\n",
    ")\n",
    "\n",
    "# 4. Plot data\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.scatter(X_blob[:, 0], X_blob[:, 1], c=y_blob, cmap=plt.cm.RdYlBu);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xBCnUs0oHC8G"
   },
   "source": [
    "Nice! Looks like we've got some multi-class data ready to go.\n",
    "\n",
    "Let's build a model to separate the coloured blobs. \n",
    "\n",
    "> **Question:** Does this dataset need non-linearity? Or could you draw a succession of straight lines to separate it?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_dINSA0Chiof"
   },
   "source": [
    "### 8.2 Building a multi-class classification model in PyTorch\n",
    "\n",
    "We've created a few models in PyTorch so far.\n",
    "\n",
    "You might also be starting to get an idea of how flexible neural networks are.\n",
    "\n",
    "How about we build one similar to `model_3` but this is still capable of handling multi-class data?\n",
    "\n",
    "To do so, let's create a subclass of `nn.Module` that takes in three hyperparameters:\n",
    "* `input_features` - the number of `X` features coming into the model.\n",
    "* `output_features` - the ideal numbers of output features we'd like (this will be equivalent to `NUM_CLASSES` or the number of classes in your multi-class classification problem).\n",
    "* `hidden_units` - the number of hidden neurons we'd like each hidden layer to use.\n",
    "\n",
    "Since we're putting things together, let's setup some device agnostic code (we don't have to do this again in the same notebook, it's only a reminder).\n",
    "\n",
    "Then we'll create the model class using the hyperparameters above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "g9OPjDfQk1AU",
    "outputId": "64d275af-144c-4b5f-99b7-a2d2ebf235f8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create device agnostic code\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TGoZCzOrHC8H",
    "outputId": "0f8d1d8e-e578-4bf6-eea6-1cf27bda4764"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BlobModel(\n",
       "  (linear_layer_stack): Sequential(\n",
       "    (0): Linear(in_features=2, out_features=8, bias=True)\n",
       "    (1): Linear(in_features=8, out_features=8, bias=True)\n",
       "    (2): Linear(in_features=8, out_features=4, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torch import nn\n",
    "\n",
    "# Build model\n",
    "class BlobModel(nn.Module):\n",
    "    def __init__(self, input_features, output_features, hidden_units=8):\n",
    "        \"\"\"Initializes all required hyperparameters for a multi-class classification model.\n",
    "\n",
    "        Args:\n",
    "            input_features (int): Number of input features to the model.\n",
    "            out_features (int): Number of output features of the model\n",
    "              (how many classes there are).\n",
    "            hidden_units (int): Number of hidden units between layers, default 8.\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        self.linear_layer_stack = nn.Sequential(\n",
    "            nn.Linear(in_features=input_features, out_features=hidden_units),\n",
    "            # nn.ReLU(), # <- does our dataset require non-linear layers? (try uncommenting and see if the results change)\n",
    "            nn.Linear(in_features=hidden_units, out_features=hidden_units),\n",
    "            # nn.ReLU(), # <- does our dataset require non-linear layers? (try uncommenting and see if the results change)\n",
    "            nn.Linear(in_features=hidden_units, out_features=output_features), # how many classes are there?\n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.linear_layer_stack(x)\n",
    "\n",
    "# Create an instance of BlobModel and send it to the target device\n",
    "model_4 = BlobModel(input_features=NUM_FEATURES, \n",
    "                    output_features=NUM_CLASSES, \n",
    "                    hidden_units=8).to(device)\n",
    "model_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_eOASXWAHC8H"
   },
   "source": [
    "Excellent! Our multi-class model is ready to go, let's create a loss function and optimizer for it.\n",
    "\n",
    "### 8.3 Creating a loss function and optimizer for a multi-class PyTorch model\n",
    "\n",
    "Since we're working on a multi-class classification problem, we'll use the `nn.CrossEntropyLoss()` method as our loss function.\n",
    "\n",
    "And we'll stick with using SGD with a learning rate of 0.1 for optimizing our `model_4` parameters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "3ngHLo--HC8H"
   },
   "outputs": [],
   "source": [
    "# Create loss and optimizer\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model_4.parameters(), \n",
    "                            lr=0.1) # exercise: try changing the learning rate here and seeing what happens to the model's performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "orcVVmLzo3gX"
   },
   "source": [
    "### 8.4 Getting prediction probabilities for a multi-class PyTorch model\n",
    "\n",
    "Alright, we've got a loss function and optimizer ready, and we're ready to train our model but before we do let's do a single forward pass with our model to see if it works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "--d6YmldpZh_",
    "outputId": "2b1fb56f-cf42-49a3-f1ec-7978cfd68b56"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1.2711, -0.6494, -1.4740, -0.7044],\n",
       "        [ 0.2210, -1.5439,  0.0420,  1.1531],\n",
       "        [ 2.8698,  0.9143,  3.3169,  1.4027],\n",
       "        [ 1.9576,  0.3125,  2.2244,  1.1324],\n",
       "        [ 0.5458, -1.2381,  0.4441,  1.1804]], device='cuda:0',\n",
       "       grad_fn=<SliceBackward0>)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform a single forward pass on the data (we'll need to put it to the target device for it to work)\n",
    "model_4(X_blob_train.to(device))[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0fk1K9VlpoPI"
   },
   "source": [
    "What's coming out here?\n",
    "\n",
    "It looks like we get one value per feature of each sample.\n",
    "\n",
    "Let's check the shape to confirm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3W4jnvmWp0OH",
    "outputId": "f515c93d-2c57-43fe-f5ce-d6b6aa20cd0f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([4]), 4)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many elements in a single prediction sample?\n",
    "model_4(X_blob_train.to(device))[0].shape, NUM_CLASSES "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NyQSNaSVqBBN"
   },
   "source": [
    "Wonderful, our model is predicting one value for each class that we have.\n",
    "\n",
    "Do you remember what the raw outputs of our model are called?\n",
    "\n",
    "Hint: it rhymes with \"frog splits\" (no animals were harmed in the creation of these materials).\n",
    "\n",
    "If you guessed *logits*, you'd be correct.\n",
    "\n",
    "So right now our model is outputing logits but what if we wanted to figure out exactly which label is was giving the sample?\n",
    "\n",
    "As in, how do we go from `logits -> prediction probabilities -> prediction labels` just like we did with the binary classification problem?\n",
    "\n",
    "That's where the [softmax activation function](https://en.wikipedia.org/wiki/Softmax_function) comes into play.\n",
    "\n",
    "The softmax function calculates the probability of each prediction class being the actual predicted class compared to all other possible classes.\n",
    "\n",
    "If this doesn't make sense, let's see in code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6hU_Wxudrbiq",
    "outputId": "c12e6a9f-c80f-466c-aa5c-27e30cfe9963"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-1.2549, -0.8112, -1.4795, -0.5696],\n",
      "        [ 1.7168, -1.2270,  1.7367,  2.1010],\n",
      "        [ 2.2400,  0.7714,  2.6020,  1.0107],\n",
      "        [-0.7993, -0.3723, -0.9138, -0.5388],\n",
      "        [-0.4332, -1.6117, -0.6891,  0.6852]], device='cuda:0',\n",
      "       grad_fn=<SliceBackward0>)\n",
      "tensor([[0.1872, 0.2918, 0.1495, 0.3715],\n",
      "        [0.2824, 0.0149, 0.2881, 0.4147],\n",
      "        [0.3380, 0.0778, 0.4854, 0.0989],\n",
      "        [0.2118, 0.3246, 0.1889, 0.2748],\n",
      "        [0.1945, 0.0598, 0.1506, 0.5951]], device='cuda:0',\n",
      "       grad_fn=<SliceBackward0>)\n"
     ]
    }
   ],
   "source": [
    "# Make prediction logits with model\n",
    "y_logits = model_4(X_blob_test.to(device))\n",
    "\n",
    "# Perform softmax calculation on logits across dimension 1 to get prediction probabilities\n",
    "y_pred_probs = torch.softmax(y_logits, dim=1) \n",
    "print(y_logits[:5])\n",
    "print(y_pred_probs[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A_pbSytSrzHF"
   },
   "source": [
    "Hmm, what's happened here?\n",
    "\n",
    "It may still look like the outputs of the softmax function are jumbled numbers (and they are, since our model hasn't been trained and is predicting using random patterns) but there's a very specific thing different about each sample.\n",
    "\n",
    "After passing the logits through the softmax function, each individual sample now adds to 1 (or very close to).\n",
    "\n",
    "Let's check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5fC5No7IsSiB",
    "outputId": "db517fa9-04eb-4efe-a75b-970bdf2a3163"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1., device='cuda:0', grad_fn=<SumBackward0>)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum the first sample output of the softmax activation function \n",
    "torch.sum(y_pred_probs[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yhwu9ln1sbl7"
   },
   "source": [
    "These prediction probabilities are essentially saying how much the model *thinks* the target `X` sample (the input) maps to each class.\n",
    "\n",
    "Since there's one value for each class in `y_pred_probs`, the index of the *highest* value is the class the model thinks the specific data sample *most* belongs to.\n",
    "\n",
    "We can check which index has the highest value using `torch.argmax()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6X3Yf5gCsbME",
    "outputId": "a7e4db7e-08fd-426c-8b54-dfd7d3943d79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.1872, 0.2918, 0.1495, 0.3715], device='cuda:0',\n",
      "       grad_fn=<SelectBackward0>)\n",
      "tensor(3, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "# Which class does the model think is *most* likely at the index 0 sample?\n",
    "print(y_pred_probs[0])\n",
    "print(torch.argmax(y_pred_probs[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9veE071JtSZJ"
   },
   "source": [
    "You can see the output of `torch.argmax()` returns 3, so for the features (`X`) of the sample at index 0, the model is predicting that the most likely class value (`y`) is 3.\n",
    "\n",
    "Of course, right now this is just random guessing so it's got a 25% chance of being right (since there's four classes). But we can improve those chances by training the model.\n",
    "\n",
    "> **Note:** To summarize the above, a model's raw output is referred to as **logits**.\n",
    "> \n",
    "> For a multi-class classification problem, to turn the logits into **prediction probabilities**, you use the softmax activation function (`torch.softmax`).\n",
    ">\n",
    "> The index of the value with the highest **prediction probability** is the class number the model thinks is *most* likely given the input features for that sample (although this is a prediction, it doesn't mean it will be correct)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hlqJ3_xTupU3"
   },
   "source": [
    "### 8.5 Creating a training and testing loop for a multi-class PyTorch model\n",
    "\n",
    "Alright, now we've got all of the preparation steps out of the way, let's write a training and testing loop to improve and evaluate our model.\n",
    "\n",
    "We've done many of these steps before so much of this will be practice.\n",
    "\n",
    "The only difference is that we'll be adjusting the steps to turn the model outputs (logits) to prediction probabilities (using the softmax activation function) and then to prediction labels (by taking the argmax of the output of the softmax activation function).\n",
    "\n",
    "Let's train the model for `epochs=100` and evaluate it every 10 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "55s1Pis9HC8H",
    "outputId": "4a7b0fd7-8a08-40a4-8694-435178b70832"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Loss: 1.04324, Acc: 65.50% | Test Loss: 0.57861, Test Acc: 95.50%\n",
      "Epoch: 10 | Loss: 0.14398, Acc: 99.12% | Test Loss: 0.13037, Test Acc: 99.00%\n",
      "Epoch: 20 | Loss: 0.08062, Acc: 99.12% | Test Loss: 0.07216, Test Acc: 99.50%\n",
      "Epoch: 30 | Loss: 0.05924, Acc: 99.12% | Test Loss: 0.05133, Test Acc: 99.50%\n",
      "Epoch: 40 | Loss: 0.04892, Acc: 99.00% | Test Loss: 0.04098, Test Acc: 99.50%\n",
      "Epoch: 50 | Loss: 0.04295, Acc: 99.00% | Test Loss: 0.03486, Test Acc: 99.50%\n",
      "Epoch: 60 | Loss: 0.03910, Acc: 99.00% | Test Loss: 0.03083, Test Acc: 99.50%\n",
      "Epoch: 70 | Loss: 0.03643, Acc: 99.00% | Test Loss: 0.02799, Test Acc: 99.50%\n",
      "Epoch: 80 | Loss: 0.03448, Acc: 99.00% | Test Loss: 0.02587, Test Acc: 99.50%\n",
      "Epoch: 90 | Loss: 0.03300, Acc: 99.12% | Test Loss: 0.02423, Test Acc: 99.50%\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "torch.manual_seed(42)\n",
    "\n",
    "# Set number of epochs\n",
    "epochs = 100\n",
    "\n",
    "# Put data to target device\n",
    "X_blob_train, y_blob_train = X_blob_train.to(device), y_blob_train.to(device)\n",
    "X_blob_test, y_blob_test = X_blob_test.to(device), y_blob_test.to(device)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    ### Training\n",
    "    model_4.train()\n",
    "\n",
    "    # 1. Forward pass\n",
    "    y_logits = model_4(X_blob_train) # model outputs raw logits \n",
    "    y_pred = torch.softmax(y_logits, dim=1).argmax(dim=1) # go from logits -> prediction probabilities -> prediction labels\n",
    "    # print(y_logits)\n",
    "    # 2. Calculate loss and accuracy\n",
    "    loss = loss_fn(y_logits, y_blob_train) \n",
    "    acc = accuracy_fn(y_true=y_blob_train,\n",
    "                      y_pred=y_pred)\n",
    "\n",
    "    # 3. Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backwards\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Optimizer step\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "    model_4.eval()\n",
    "    with torch.inference_mode():\n",
    "      # 1. Forward pass\n",
    "      test_logits = model_4(X_blob_test)\n",
    "      test_pred = torch.softmax(test_logits, dim=1).argmax(dim=1)\n",
    "      # 2. Calculate test loss and accuracy\n",
    "      test_loss = loss_fn(test_logits, y_blob_test)\n",
    "      test_acc = accuracy_fn(y_true=y_blob_test,\n",
    "                             y_pred=test_pred)\n",
    "\n",
    "    # Print out what's happening\n",
    "    if epoch % 10 == 0:\n",
    "        print(f\"Epoch: {epoch} | Loss: {loss:.5f}, Acc: {acc:.2f}% | Test Loss: {test_loss:.5f}, Test Acc: {test_acc:.2f}%\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m_JNlpd4L6dL"
   },
   "source": [
    "### 8.6 Making and evaluating predictions with a PyTorch multi-class model\n",
    "\n",
    "It looks like our trained model is performing pretty well.\n",
    "\n",
    "But to make sure of this, let's make some predictions and visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NjCKKhsGHC8H",
    "outputId": "583e10fb-fa1c-463a-fbd0-4d31d76b82ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[  4.3377,  10.3539, -14.8948,  -9.7642],\n",
       "        [  5.0142, -12.0371,   3.3860,  10.6699],\n",
       "        [ -5.5885, -13.3448,  20.9894,  12.7711],\n",
       "        [  1.8400,   7.5599,  -8.6016,  -6.9942],\n",
       "        [  8.0727,   3.2906, -14.5998,  -3.6186],\n",
       "        [  5.5844, -14.9521,   5.0168,  13.2890],\n",
       "        [ -5.9739, -10.1913,  18.8655,   9.9179],\n",
       "        [  7.0755,  -0.7601,  -9.5531,   0.1736],\n",
       "        [ -5.5918, -18.5990,  25.5309,  17.5799],\n",
       "        [  7.3142,   0.7197, -11.2017,  -1.2011]], device='cuda:0')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions\n",
    "model_4.eval()\n",
    "with torch.inference_mode():\n",
    "    y_logits = model_4(X_blob_test)\n",
    "\n",
    "# View the first 10 predictions\n",
    "y_logits[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lpAdeJRMNHjG"
   },
   "source": [
    "Alright, looks like our model's predictions are still in logit form.\n",
    "\n",
    "Though to evaluate them, they'll have to be in the same form as our labels (`y_blob_test`) which are in integer form.\n",
    "\n",
    "Let's convert our model's prediction logits to prediction probabilities (using `torch.softmax()`) then to prediction labels (by taking the `argmax()` of each sample).\n",
    "\n",
    "> **Note:** It's possible to skip the `torch.softmax()` function and go straight from `predicted logits -> predicted labels` by calling `torch.argmax()` directly on the logits.\n",
    ">\n",
    "> For example, `y_preds = torch.argmax(y_logits, dim=1)`, this saves a computation step (no `torch.softmax()`) but results in no prediction probabilities being available to use. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "faDQ4oLpHC8H",
    "outputId": "ca32986f-8dc0-419d-84df-1cc3ba8577e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: tensor([1, 3, 2, 1, 0, 3, 2, 0, 2, 0], device='cuda:0')\n",
      "Labels: tensor([1, 3, 2, 1, 0, 3, 2, 0, 2, 0], device='cuda:0')\n",
      "Test accuracy: 99.5%\n"
     ]
    }
   ],
   "source": [
    "# Turn predicted logits in prediction probabilities\n",
    "y_pred_probs = torch.softmax(y_logits, dim=1)\n",
    "\n",
    "# Turn prediction probabilities into prediction labels\n",
    "y_preds = y_pred_probs.argmax(dim=1)\n",
    "\n",
    "# Compare first 10 model preds and test labels\n",
    "print(f\"Predictions: {y_preds[:10]}\\nLabels: {y_blob_test[:10]}\")\n",
    "print(f\"Test accuracy: {accuracy_fn(y_true=y_blob_test, y_pred=y_preds)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AMA5SSixOSru"
   },
   "source": [
    "Nice! Our model predictions are now in the same form as our test labels.\n",
    "\n",
    "Let's visualize them with `plot_decision_boundary()`, remember because our data is on the GPU, we'll have to move it to the CPU for use with matplotlib (`plot_decision_boundary()` does this automatically for us)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "kLOzBFdRHC8I",
    "outputId": "71dcaf7f-a30e-457d-8949-916cf9c5cc79"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qtnbV6rYgCIIgCMJ6LM0xvvzMAAPhOtRnjlN3X4JzQWvRwHxJFomXfPZtHOn+oFApsXXXlw+sJXD2NZV9Ho1RlysYUIHGpF3rEIUNEIH53iWC833mToA+sWp1WxAEQRAEYb3anUYkJFAoUZitBOLqsoH5klBGRUbUE9t09cc7UKqVxdPSJQm1XkvtkRZkWSY852Pq7CATr93EdXmMVDQBgLWzDqnMzjoAMth7Gzf/GxDKEoH53iSC832o2Oq2CNAFQRAEQdhMJm2a8hXJcrRSBuUubpUtyzKhGS8Tr91g5LkBJt+4RWQ+sOsrlGtr9PR+8B50NtOqrxlrzfR+8B5kGW5//wJD/3KJhZvTeIfmmH1nlGt/9wZzl8ZQqJU0nuoq+zq1h1vQ1ui36tsQhH1FVODYp9qdRsYXInz5mQF+49c+zpkz0MllRl8Xb66CIAiCIGycWZuh3xjmWsRUcgddgczDVv/2DmwN0okUo89dIeIO5lLEZRkkCc+tGcxtTjoeO4xCpdzpYZaksxrp+6l7iS4Eic7n2m2ZGqzo7SbkbJZb3z1PPBDNXbxisWHunVGUaiV1R1qRJInZiyO5QnMSIIOkVFB3tJWGk53b/F0Jwt4lgvN9rN1pZMgdLgjQHWdfwJNp2emhCYIgCIKwBzxd5+LaqAkJGXlFgC4ho5Jk3u+Y36HRlSfLMqMvDBCZDy59ouC/wYkFJt+4Rfujh3dohNWRJAljrQVjraXg84HxBeL+SNnHzl0aw3GwmdrDLTgONBKYWCAVTaDSabC0OVFqRCghCJtJpLXvcz11JiQkXr0eItLyLlHBXRAEQRCEDXl1wEfIfBKAB2pu89nWcVSSjISMYvEPyBgUGX6zfYRGbXJnB1xC1B0kMhcom5nvG3aRDMe3b1CbyDs8V7FNWiaZJjTjBRaLzHXVU3e0DXtPgwjMBWELiH9VAuoqWm0IgrB7LbVVUdeHRIEYQRB2VL5tK+185unPYuar3Pd6kD/pu85rfhvDMQMK4JAxzBmLH61i957b9o2676SylyKBf8xN3dG27RvYJknHktWUBCAdT239YARBAERwLgiCcFdb2e9UEARhJy3VtFkeoHfyVUZf1/OkwwN4dnqIVcv17q4UvUpkEndnj2+1UQeeUMVvUW3QbM+ABEEQae2CsNdk0xlS0QTZdGanhyJssZWBudg1FwRhN7jTtnX8rm7bWlXvbllGY9Jt/WC2gL23oWJgrtKpqWm0bc+ABEEQwbkg7BXRhRCjL1zlyl+9wrW/fYOBb73K+Ks3SCxVYRX2FBGYC4Kwm61s25r46K/ddQG6vaexYvAqKRVYO+u2Z0CbzNzswFhnLnvuvOm+HiSFCBcEYbuIf20CAOdfHGMg4LhrV7f3u+C0h8EfXCAwMZ+fSMhZGd+wi1v/dJ7oQmhnByhsiZrjxwARmAuCsDstD9CvRhpQnzl+V80xtGY9zkPNZa9pvKfzri2MJikkut57HHPLYjFgKfc5AIVKQcuDfdh7GnZwhMJKSwvzwt4lgnNhT6xu72eZVIaxF68hZ+XVK/yyTDaTYezFq8jlCtoIgiAIwhZYPsc4W/Pxuy5Abz7dS/2x9nzQurTLrFArab6/h9ojrTs3uE2g1KjoeuIYB5++n8Z7uqg92krrQwc58FP3YbCbSATvnr+rvW55xpxYlN+77s6lPmHTLRVwyfU87+fMmeN0cpnR1/U7PTShAv+oi2yqzPlyGZLhOKEZL+Zm0SpPEARB2F7tTiND7vDiHOPjnDkDjrMv4Mm07PTQKpIkicZTuaA1MLFAJp5CbdRiaXOiUCl3enibRmc1orMaiS6EmL0wzOTrN/Nf09tNNJzsxNLm3PZxZVJpfMMuQjM+ZFnG4KjBcaARtaGKegB7iAjM9w+xcy7ktTuNSEgFq9sifWb3i7iDuVYv5UhS7jpBEARB2AE9daaCOYbjc+++q+YYKq0aR28jdf1t2Lrq91RgviQ852fwBxcIzfoKPh/zhhl9fgDPrZltH8/1v3uTqTdvExifJzixwNylUa793ZvbPpadJLqy7C8iOBcK3O03T0EQBEEQdqelOcar14JEWt6F+YxDzDF2CVmWGX/levEjcosm37xNKpbclvEkQjFGfnKZTGpFmzoZkGUm37hFYHJhW8ayk0Tx1/1HBOfCKstvniJA3/1M9RaodJ5clnPXLUrFkgQnPQSnPKTj23OjFQRBEAS1Qspne6nM5h0ejbAkNOMlFUmUv0iWGfz+BQZ/cIHptwaJb2E3mPnrU2Qz2bLV8ucujW3Z6+8movjr/iLOnAtF9dSZuPDSBNAGR36aM58DvnJ3nA/bb6yddUy/NZRbXS52E5NAW6PH1GgjHU8ydW4Q/+j8nYBekrB11dF8uheVVr2tYxcEQRAEYefFPOFcsbsKa/3JcJxkOE5kPsj8tSma7u2mrr9t08fjG3ZVHEtsIUQyHL9r+8wLQjFi51woaSlAf/VaiEjjo2IHfZdSqJR0vudorg/pyrPnkoRSraLj3UfJJNMM/uAi/lF34U67LOMbcTH0zxfJJFekjwmCIAiCsOflq9FXa3EaMXN+ODevWAdZlgnN+pi/PsXCrRmS4Xj+a9mV6ewliHmLsNeInXOhLLVC4sJLEzxLG585/SjmM5fh7JTYQd9lTA02+n7qXtxXJ/ANu5CzMgqVAntvI3VH29CYdMxcGCYRihVfiZYhHogyf32ShhOi4IggCIIg7Cc1TXaQh9f12LnLY1g6apEqFaddJjznZ+K1GyRD8YLPW9praXv4IGqjdtXXivGNuFDp1Puueruwd4mdc6GsdqfxToB+Tkfy6c+KAi67lM5qpO3hQxz7vx6j/5OP0P/JR2k5cwCNSYeclfHcnCmfIibDwo1p0Q/9LqKuD+30EARBENbs/ItjDAQcKI71Yj4jWnzuBnq7CWODNd/HfS3ivkjBrnclkfkAwz+6VDT4DkzMM/TDS9h7G6t6LvfVCa793RvMXxfzUmFvEMG5UNFSgH7+pfGCAF3YnSRFLpV9+Qp2JpmqKvUrHU+RTZfpmS7sCg7llKjcKgjCXandaUSjUPDlZwZ4U/9R1GeO0/lQbKeHJQAdjx1GY9Kv67FrmTvMvD1ceiNAhpgnhFKjQlOjr9wqVs79mT43iG+d6fWCsJuI4FyoytLNNBega0k+/VlxM72LSMrq/6lLCvG2sJs5lFP5xTERmAuCcDdqdxoL2raqzxwXGXlAKppg9p1Rrv/9mwx861Vufuct5q9PrW4ntkXUBi19P3UvTff3oNCs4eSrJFWdVp4Ixoi4AhWLvfmGXfR+4CSmBmvVw5h7Z1Rk/wl3PTELF6p2J0CfYCBcJwL0u4hSrcJYbymfriZBTZMNRRWBfDadIRVNiF32bbY0eVWZzSIwF7bEK6+8woc//GGampqQJInvfOc7BV+XZZnf+Z3fobGxEb1ezxNPPMHg4GDF533mmWfo6OhAp9Nx+vRp3nrrrS36Dva3dCrD5JCHc88P89o/3+b8S6PMTvjJZndfwLLUtnUpQN/vRWejnhA3//EtXJfHSIbjZJJp4r4I0+cGuf29C9vWX1ypUVF3pBWFqsoQQQJrZ23V3V6qTX9PhmKoDVp63n+Cg0/fj7KK508EosT9kaqeXxB2KxGcC2uyPB1tIFwn0tHuInX9bRXPnFdqhxL1hBh94SpX/uoVrv3tGwx861XGX72xpb1OhZylSav9fZ0iMBe2TCQS4fjx4zzzzDNFv/5Hf/RH/PEf/zFf+9rXOHfuHEajkSeffJJ4vPSE+2//9m/5/Oc/z+/+7u9y8eJFjh8/zpNPPonbLVJQN1M0nOCNHw5x4+IsAU+MaDiJ1x1h4OwUb70wQiq5+xZTlwL0V68F93WAns1kGfnJlZItURPBKOMvX9vc10xnCLv8hGZ8pKKr+5vLmWxVz6NQKmk82VX16yqr3JFffp3OaqTiVvuiTEJUbxfubqJau7Bm7U4jQ+4wKJQozNadHo5QJUurk6b7upl5ezh3hmtZn3NkmebTvblqrSUEp72MPncl97DFh8pZGd+wi8DYPD1PncTgrNn6b2Qfqzl+DBBF4ISt89RTT/HUU08V/Zosy3zlK1/ht3/7t/nIRz4CwDe/+U3q6+v5zne+wyc+8Ymij/t//9//l1/5lV/hF3/xFwH42te+xg9+8AP+7M/+jP/4H//j1nwj+0w2K3PhlXES8VTRrwe9MS6/Ocm9j3Vs2RiSiTRTw15mxv2kEhl0BjXNXTaaOmyoyuzCLrVthTb6Tz+K43PAV17YV11hAmPzpMvtjMsQnvUT80XQ24wbei05m2Xu0hjz16cL2pVZ2p0039+b7xmut5sIz/krxsTdTx5Da67+nLreYUJj0pXfQZfA1t1Q8CmNUUcsEa74/GqjqNou3N3Ezrkg7CIxbxjXlXHm3hnFP+ZGzla3cl2tuqNt9H3kPhwHGtGa9WgtBpx9TfR99H5qD5eeCGXTGcZevIqclQt7pAPIMtnM4tfFWS9B2LNGR0eZm5vjiSeeyH/OYrFw+vRp3nzzzaKPSSaTXLhwoeAxCoWCJ554ouRjhLVbmA0RCydXvT0v53WFGXhraktS3MOBOK//cJChq26ioSSpZIaQP87Ni7Oc+8kwiXj53cylAP3ZczoijY/uux304LSncpV0CULTng29jizLjL10Ddfl8VV9xAMTC9z+3vl80Ow82FIxMLd21mGss65pDJIkUX+io8wFuaN4jr6mgk+v/LjY44z1FrQ16ytoJwi7hdg53ycioQRTw1687txZHFutkdZuO0azWGHcDVKxJOMvXcutUksAud1slU5N68MHsbQ6N+219HYTrQ/2rekxvhEX2VSZlEg5d44sNO3F3CIq+QvCXjQ3NwdAfX19wefr6+vzX1tpYWGBTCZT9DE3b94s+VqJRIJE4k6qbTAYXO+w94X5mWBBQlQps2N+5KxM/+mWNfWkLieblbn4ynjJtPloOMGVNye4713lU5/zbVtp4zOnH8V85jKcndoXO+hyVQsmUpXXlRYYnycwvlBiEJBOpJh5e4iOdx3F0u7E2lGLf2y+2FBQ6TQ03de9rnE4ehtJRxPMXhzNzXmWfVtKjZru9x1HrdcUPMbe08DCjWnigUjRRQNJkmi6d33jEYTdROyc7wNTw15e/5dBxgc9hPxxQv44k0MeXv/hIJNDG1uFFTYum84w9C/vEHb5c5+Qyc+w0vEUo88PEJr17dj4ACLuYOV2JpJExB3YngEJgrCn/eEf/iEWiyX/p7W1daeHtKtl0nKVJ3JhbiJAwLt5tWLc00HisVTJXVZZBt98lJC/fCGw/dy2VW83Vb5IltHbqriujIWb0+V36GXwj8+TjieRJIn2xw7TcLITpXbZXp4kYe2o48CHT6Ex6tY9lvrjHRz6+BnqjrZR02LH0l5L60MHOfKvHyh6RE6hUtLz1AlqGm3LxpL7j9qgpeu9xzHWWdY9HkHYLcTO+R7ndYe5fmEm98GyG+fS6vqNi7MYTFocDWt/w391wEf/gyfR8DIO5f5Y3d4K3mEXiXIF1WSYOT9M34fv3b5BrVTtBssm7cQIgrD7NDTkzoC6XC4aGxvzn3e5XJw4caLoY5xOJ0qlEpfLVfB5l8uVf75ivvCFL/D5z38+/3EwGBQBehlryYKTJJge8WJ1GDbltT1zocq79hIszIWosZYP5tqdRsYXIpx/aZyB/hM88PApHGd/vOfnF/bextwucpklFrVBS01z6bow1Yh5i+86F5AhOOPD3lWPpFDQcKKDuv42Yt4wciaLzmpApdNUeJLqaGv0a9rtVuk0dD95grg/QnDai5zJorebqGmyIynE/EPYG8TO+R43enOhYvus0ZtFUpYqyK9uv6HeV6vbW8Fze6biNbGF0I5WRDfVWyvnS8oypnqxai0Ie1VnZycNDQ08//zz+c8Fg0HOnTvHAw88UPQxGo2GU6dOFTwmm83y/PPPl3wMgFarxWw2F/wRSmvutFVbzBpZhkh489pyZTOVd+2lxeuqsdQDfT9R6zW0nOkt/kUJJIVE26OHNhyAKpTVPT4wVthJQaFUYKw1Y2qwblpgvhE6q5G6I63UH2vH3OLYs4H5Ut0Fdb0oBLufiOB8D8tmsnjmwhXbZ3ndEdLptRUeu9PzfJxnz2lFz/MNSEVWtzApfl11vUG3grWjNpfWVur+J4HWrMdQZyE47cU/6iY6HxQF4gThLhMOh7l06RKXLl0CckXgLl26xMTEBJIk8bnPfY4vfelL/NM//RMDAwP83M/9HE1NTXz0ox/NP8d73vMe/uRP/iT/8ec//3m+/vWv8xd/8RfcuHGDT3/600QikXz1dmHjdAY1Pf31lS9cpFYrN+21TRZdxYUBWabirvl+5zzYTMe7jiy2DbvDWG+l56mThenc62Rqqu45gtM7e5ROKGyfCogWqvuISGvfwzJVrlIDZNNZKNPqpJg76WcTDBzpp//pz9LJVxl9XVTKXAuVVl2+hcoiSaEg7o+g0qm3dOU6nUjhHZojMD5PNpVBZzPi6Gui411HGfnJldUV26XcWTBzi4Prf/sGmeSdCrBaq4GWCi3aBEHYPc6fP8+73vWu/MdLqeU//github.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.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.com/M/j8nJSTidThw8eBC/+Zu/iVQqVfJ573//+8EYy7v9wi/8QoNGTRAEQRD7B6XZAyAIgmg4ShDQVrHluxtevAEsxZs9IoJoea5cuQJd1/Gnf/qnOHToEC5fvozPfvaziEaj+P3f//2Sz/3sZz+L3/7t397+3uVy1Xu4BEEQBLHvIHFOEERnogQBrIqv+50k0AnChI985CP4yEc+sv39gQMHcPXqVfzJn/yJqTh3uVwYGBio9xAJgiAIYl9Dae0EQXQuSpAM4giiCkKhEHp6ekyP+4u/+AsEg0HceeedePrppxGLxUoen0wmEQ6H824EQRD1JKwMiy+o5I1oIhQ5JwiCIAiibK5fv47nnnvONGr+r//1v8b4+DiGhobw1ltv4T/9p/+Eq1ev4q//+q+LPueZZ57BF7/4xVoPmSAIojDZcrfAw/CuvUwZdUTToMg5QRAEQXQwv/7rv77HsG337cqVK3nPuX37Nj7ykY/gp3/6p/HZz3625Pn/w3/4D/jwhz+MkydP4ud+7ufw3//7f8ff/M3f4MaNG0Wf8/TTTyMUCm3fZmdna/JaCYIgikI9z4kWgCLnBEEQBNHB/Mqv/Ao+85nPlDzmwIED21/Pz8/j8ccfx0MPPYSvfe1rZV/vzJkzAETk/eDBgwWPsdvtsNvtZZ+bIAiiKiiCTjQZEucEQRAE0cH09vait7fX0rG3b9/G448/jnvvvRd//ud/DkkqPwHvzTffBAAMDg6W/VyCIIi6s1ugE0QDobR2oqXQdCCUFLd0Zu/jnAMbCeDqOsPFFYbLqwy3txhSBY4lCKtQz3OCMOf27dt4//vfj7GxMfz+7/8+VlZWsLi4iMXFxbxjjh07htdeew0AcOPGDfzO7/wOLly4gKmpKfzv//2/8elPfxqPPfYYTp061ayXQhAEURojxZ0gGg1FzomawzkQSgHrcQZNB1QZCDo5PCrAWOHnZHRgbothLQFwZA/i6LIDY14OVQZ0DlzfZNhKMQAcAIMGYDHGsRxjONTN4bU15jUSbUTuDjlepvQ1gijC888/j+vXr+P69esYGRnJe4xzDgBIp9O4evXqthu7zWbDd77zHXzpS19CNBrF6OgoPvGJT+A3fuM3Gj5+giAIgmh1SJwTNUXTgWsbDDFtR0AjzbGekOC1cRzs4pB3CXSdA+9tMMQ0iOO3YdhMcsTSDMcCHAsRhq3UzmO5x+nguL7BcCLIYZPr9/qINiUr0H13w4s3SKATRAE+85nPmNamT0xMbAt1ABgdHcVLL71U55ERBEEQRHtAae1EzeBGZFuIbGBHQIv/t1LAVGhv6HwlhgLCfOccKR24vcWwGi92jLhfB3BpleHdNYa1uBgPQVhGCQKqS3xNKe4EQRAEQRBEgyFxTtSMaBqIphlKCejNJENCy793JV7s+J3nreeluxc/DhCbA1NhCbdCrGyBns4Ai1FgOswwt8UQSZHI7yiUILVQIQiCIAhiB1oPEA2ExDlRMzaS2VT2UnBsJnO+40AyU0rQZ59lJsxzYcZ4gOWYtWdwDixEgLdWGW5HROR9KQZc3ZBwdUPUzhOdA/NPNHsIBEEQBEE0GdqwJxoNiXOiZmQsCFgGIMPZrvvqF5pejlmLni/HgfmohGz0nWNnwyCaFjXxFEEnCIIgCILoEAzHdhLoRCMhcU7UDLtsrl45ALu0cxxjgN+efaT4s5wKh1vhJsfthiGlM6RNNg10DixESkXmGeIaQyhZ4hCCIAiCIAiivSCBTjQYEucNhnNgMyGM0W5uihTqZJv06A5Y+LxiALod+ff1u7KCu5jwZhhwc4z5OCRW6rjCmB0dTu6N5hc6y1qinNR6giAIgiAIYt+zW6ATRB0hcd5Akhrw9hrDjZCEtYSoiV6MApdXGeYj+zdtmnOR+r0YZXCYtDEb8XLIu37rPDZg3FdIoIuvB906ehyASwWO9ZTXy1xhHDaT33Jr9eTmEXiifQgrw80eAkEQBEEQrYIh0Jl/gqLnRF2hPucNImP08k5tC7z8KOxCFFAkjj5Xw4dWFRkduBliCKdyzeCM/uY5X8uMY9jD0Vvk9QWdgEflWI4xhFPiPG4V6HPpcKs7xzkV4Eg3RzLDsRIDlmKllLe4HjMJeKuW+qJzqLSV1Rlke54HHoYXL1PPc4IgCIIgCKIhkDhvEOtxGMK8uFJciDD0OrmpmGwlhDDPfrd74BxuBRhw6/DZYaSkF8ehAGM+a+kDdhkY9gCpDMfGdi147gU4nAow4DY/n9cmIuxaydR2hqCTQucdQ1ag++6GF2+QQCcIgiAIgiDqDsUCG8S6hXpljTNspUwPazqpDHA7wnB5NRsxL97XPKqJdHQzYV7o/O+tM1zbYFiMomBKOWPApJ9jxMuh7jk/Q1wDbm4ypExq+iUGDHtKG9K5VQ5fGen0RBugBAHVSPWgFDaCIAiCIAiizpA4bxCaSdR8+7gWrzvfTIga+cVotj+5ORsJ6+dfiQGXjPNvpUVU/naE4dIKy+uPnoUxIOiAUce++81jCKWAK+vm9eJBFzDq1Y22bnz7fwDw2YDDXfsro4GoEUqQHFoJgiDaDJ0LM9jNBJDQmj0agiCIHSitvUHYZCCRya3FLnJcC2+XxDXgRig7fmtKlQHQ9Nx69OKEksDMloT8mnXxPwfHjU2GO3o4XGr+85bjQCJTbEwMaZ1jMcow6i09hj4XEHBwrCfExoPEOLodIj2e6GyYfwI8NNXsYRAEQRBVwLnw+FmOsbwuLW6VY8y7d31BELsJK8PwYkps2FPJG1EHWlgKthdBp5kw57DLPM/8rNVYjpUnzAEhs1UL/c8B4faeL8xzEfctxfY+tlLgvt3PXY2LnXIzZAnodQlX+SEPyhbmnAPJjEjN36/u+wRBEATRbnAOTIUZFqJsT/vUaFpk2UXTTRocsT+gnudEAyBx3iC67GJntnAEWdw34m3t1GmRnl7eABmAHofpYdB0IJIuVb8uzraRyBe9OgfSutnzAJ0ziy3TKkPnwHwEeGuF4fKqhEurEi6vMixFSaQTBEEQRLOJpLP+P4UDABzAbLiFF2FEa0ACnagzJM4bBGOibrnLDmC7nlmoNpkBB/zZx1qXSkTmoJtDsfBblrEonPmuSTWb9G6FckzpykE32uQtRFme43tKB+YiDDdD+7eHPUEQBEGUS4aLDLJMC819Isuu1IAYopowkyWIkuwW6ARRQ6iatoHIEnCwiyOpAZspIeocMoffQpuxVsCuAHHNPD0/i0uBZYdzVRb71rvF957jpPzsAsZEVsJmstS4RLmAlU2CSliMwkiF23198f1mkmMtIXq5E/uT7RozgiAIoiixtChREy1OhRjutouNemeTy/aE6DZfbCW08kvaiA7EaLnK/BPgmKL6c6JmUOS8CdgVoN8FDLqBbsf+EOYA0Oc02wLPN3KLacCVDQlTFiLHEsumv5duadZbYAz9Lr79eGGYpX7nlcB5dje+9A9x2bQunmhZcnfIKX2NIAiiIFtGd5QdYS7+30gC7643v1WsWGuZrwX2y5qMIIj2hMQ5YZkeJwzDuuJ184J847i1BDAfMZ/tBj0cctHJk8MmC7O23XhswISvkEA3avk9et1KBlI68lLZCyPS5Ci1fR+TFei+u0mgEwRB7ELnwM1NZsy6e7PIOIzHmzgPdjssCHNweFrYmJcgiPaHxDlhGYkBh7s5go7sVJtLqegxw2LMvK7cLgPHejjc2+lkO9fw2YBj3cXr1wNO4M4AR78LcCocToWj1wkcD+jod5u/tkqhDfYOQgkCqrE7RAKdIAhim81kdqO6+DpA4wybyUaOKp+A0yx6ztHrEiWIBEEQzYKqajoQzoFwSjikqxLgtcGyS7zMgHE/x7AXiKS5SOuOZ9PVzJzWOYIFIt+5OBTgWIAjluaIpgHGxC62w8Jvql0RjveNRJUAm8SR0oFSNe8e1fp7TLQwShBbgYfhXXuZepwSBEEYRNPM1DeGgSOSZpYi2PVAlYQx77VNBn07hL/TwrXLDgx7KMWNIIjmQuK8w1iJiRTz3FRsVeIY9nAEyggGKhK2U8WXY4B5DJkjnEJBcc65aHGSyojzem2ASxU3M3QuNhkkVj/Dt1IwBvS7OWa3Sm9M9Lvq2MeNaCwk0AmCIMqmFWSvxyay7FbjwEZSiHSHDPS6dPjKCFQQRJZtw1haDxA1gsR5B7EYBW5H9irYtA5MhSXoXC9Y022GKgH5ZnCFYIajef70vJEAZreY0atcIDOOQTdHn6v4RJnWhSPsalz0MAcAj8ox4Bbu942k1wlEUjBMcHLfB/F1v6vxYyLqDwl0giAIgVflWDatlGTwqs3fqFZlYNAjfG4IoioMx3ZaDxC1hCprOoR0Brhd1JRNpHXNbjHL/cZz8dnMhLlA23XutThwMyQhvev+DGeYi0hYiBY+ZyoDvLvGsBzbEeaAiL5f35SMSL4gnRFtUSp5XVZhDJj0c0z4dLgUINvD3qMCB/06RrycduPbjd09TqkGnSCIDsZvF1l4peq5VYk2qok2hNYDRI2hyHmHsJYwO0JUi60nCjuil8Jt0dk0tz2JzmGkghcX9gtRYeBil/PvnwkzQ9AX7is+uyVq39YSDNH0ThS72w4Mebil+vVyYUyMNeDk2260JMjbHNoxJwiCACDmu4NdHO9tZOu5cydADokBh7poo5poU3avBwiiCihy3iEkM+YzIrN43G4cCmCXS+2YAwA3+pgLNhMiQm4WcV+L5z+ezAAhU/M5YGZLMtLos+z0Wo2liz3LGrE0sBoXkf9UZu/jjJEw7xh275gTBEF0KG4VON4jOqVIxnpAQrZzCrfkI0MQ+xZjPcD8ExQ9J6qCIucdgmRBLHIAEiu/BosxYNDNMRUuttcj/Ft7XTvnTmSyVyw9sMQu8SuEtXlf8cLnFjv6U2GGO3rK38GPa8BUiCGm5UcEuu3AmK94mzeizTF2zAmCIDoduyLmw1Evtr3babOaIAjCOiQnOoQuu5W6cIbuCuvBAk5gyJ0t7OY5/4vJ+VBXfjq5XEbrtsooXl8f19iuqLo5CQ24us4Q0/aebyMJI5WvgmESBEEQRJvBmAgKkDAnCIIoD4qcdwgeVZi15Lqi58PhtQHOKtLOBj1Al0PHaowhqglR7reLFm3qrm0gnw2wslnQZc93chP17WYbDeaPxzTRUsUqtyMMGY4i52WIa6I1S18FbvcEQRAEQRAEQRAkzjuEjSRKCHPBoLv60K9TAUZ95ueZj5Y2gwNE71HfLgFtk0V/9c1ksedaew3lbOZrOrCZNH/WSoyhz0Xhc4IgCGJ/o+nAVkqYtzoUwKVQFJwgCKIRkDjvADiH0ZasdER5Lc7gtdVfXEZSwGbSfJY/kOPsyrlYKCzHGbaS2SP2OsJag8FTxusUpm/mUf5khoR5x0OO7QRB7GOynVTW4tmiNIFT4Rj3ccvdWbLE0sKvhTGx2U7eLES7E1aG4cUUrQeIiqGPyQ4gpQMJzcwZXdRO15toGri2aW37Peuqzjkwt8VwbVNCKAnoyL6WfGHeZQcOd3HD/K54r1WvjcNZxraU1bp3K6Z7RPtCPU4JgtjPcA5c32RY3SXMASGwr67v9WvRORBOAhsJMb9nW4nG0sA7awzvrkuYCku4FZLw1grDdIj8WYg2hnqeEzWAxHkHYHUi5HWeMOMa8N669Yk5bZSbryVExFxQWAHLDBj1cvjswEF/dlmReyFhTmeTgEkLafe52GTAUWarOKLDoAmZIIh9zmYS2EoV28hn4BAb5YBYLyzFgEsrYuP8ZkjClXUJ76wxrMSEkI/vMlDlYFhNANc2WN3XGwTRNGg9QFQJifMOwCZlp9VScNjk+o5jPsIg9La1ELMiGQuA7ZT84mQ4MGMsGnx20VM16My2huNQJWDIw3FHgEMt83UyBgx6StfHM4DqzTsdmpAJgtjHrMTN5lqGSJohoYn5fG5Lgsbz58VERszFxed6cY5GZOoRRNPYvR4giDJoqjifmJgAYyzv9ru/+7vNHFJbIktAtwMwE7iViMtURjiZX1ljeHeNYXZLTNy7sWqqloVBGL9pOpDImKXki2eEktn6cGFgM+7juLuP454+jlO9HIPuyuvdehzAsKd0qzi7LNL6NhIizY8iAx0ICXSCIPYpYu42n6O3UsBirHi70r1lZ7vhWCn6fIJoE3LXA7QWIMqg6YZwv/3bv43Pfvaz2997vd4mjqZ9GfZwhJMMGi8UARY12MEyPzvWE8BUKBuTF+eMaRzLMYYRD0e/e+dYTd85xhyOPqcQtynd/OgdREuz3RkAjIlzpXUhp22SNdfZWBoIp8RzXSrQ7wK67DpW4gzRNIcEwGe0ioulgbfXGJKZnRMrjGPQw9HrJJfbjkIJAtoqmH8CPDTV7NEQBEFYQmZA2vwwI/XdrGVpKRgSZKBKdALGeoAgyqHp4tzr9WJgYKDZw2h7bDJwrIdjOsywlTf7ipRvzkUdWI9D1E7LJhHmaBq4FSpUBy6+nosw2BUdXXZxr1VTNUC0bFlLAEvx7CAqXwRwDqzGgaXYjnCWGUevCxhw8YKvM5UBboYYouncFD8GVeKY9HOMevMXFZsJ4EZo7/g0LjIJ0jrHsIcWIgRBEETr0u3gWIgCpeZbhXHUQleXsyYgCILoJJpec/67v/u7CAQCuPvuu/F7v/d70LQCOdFETbArwJEejhMBHeM+HU5FiN60kToeSYtasbfXCqem5yLqwIFSddiLkZwo8nbde2lTNYAjpgFaBZM/Q36bF86BmTDDzJaEZGbn/gxnWIwCVzcYMrsi85q+25F2Jz0vrYsNjFjO5gbnwHS49HuxGAWS9GtNEARBtDC9Tph0OwEG3NyisC4913e3gYFqQhMp/mbrJYIgiHJoauT8P/7H/4h77rkHPT09eOWVV/D0009jYWEBf/iHf1j0OclkEsnkjpNIOBxuxFDbCocCLEZznVTzxWVa57i2wXAiyAu2B+PcSv04Q1QT51IlYdi2uzVLoeeUd3/eqBB07tSUazowHwFWE8WEs0iBn4+yvEj4ajybSl/MrZbjdoThcLd4TiiJPYY4hViNMwx7KXpOEARBtCaqLNqRXt9kyPCdrLFs9lqvk6PPBWwkODaSZrGd4hv3EgN6nft3PgwnhddOTMvvAz/s4fDbmzgwgiDagppHzn/91399j8nb7tuVK1cAAF/4whfw/ve/H6dOncIv/MIv4A/+4A/w3HPP5Ynv3TzzzDPw+/3bt9HR0Vq/hLYnnRFp46XEcEpnhgAvjLnQFmTbptUmg61Q5F3c51aBYS8H52LSfGuFWXKeXY3nt5pbjZtvIIRT4j0EhDOtmdHeznEEQRAE0bp4bMCdQY4RD4dHBVwKR8ABHOvRMebjYAzocgCqVCoTjhvtR5FzjDheYsJAtd7dYerFRgK4tskQ2xUtj2uiR/x6vDnjIvYBZApHWKTmkfNf+ZVfwWc+85mSxxw4cKDg/WfOnIGmaZiamsLRo0cLHvP000/jC1/4wvb34XCYBHqZhFJWjuLYTIga9N0wJibmtKnJG0dCA+yySJVzyNwQqZWbyOwI9B3hLTFgzCtS7ea2GJZi1q+hc4aExuEy0uFTlsYn6shVGQUzCwqR1MS59+uChCifsDIML6bEhLxEKzaCIPYHigT0u4F+d2HxLTHgSDfHextiLhTsRNg9qhDgyQzHSpwhlhaivMswUK20a0qz0UuWsYnXPx1m8NsL+9kQnctW4GF4116m9QBhiZqL897eXvT29lb03DfffBOSJKGvr6/oMXa7HXY75Q1VgzUzl9y0tr30ujjmI+bK9MYmwx0B4Qbf5+LbvcgrZ6/5nM45bmyKVPNyhPn2WXIOVyQYmw6lyU68fjswu2V6BcQzHJdXGQ74ObraoNaOMMFwaKUJmSCIehHXRCRX5wx2WdRxN0r4OhTgRIBjLQGsJxg0XbQT7XXq8NvFvOqSgHF1/6av72YjITxrisOgg2M9AfS6GjYsotWh9QBRJk3b2/vhD3+IL33pS7h48SJu3ryJv/iLv8Av//Iv49/8m3+D7u7uZg2rI3DIgLmA5XAoonZ7IQK8tcJwYYnhzWWG2TCDX82ep3TaOIeobwdEq7Zue/Y5uc+rdvIWafjzkfKfqTCxoMjSY9oPnsOl7DzHLsNwpDd7DeK9uBnKrfUn2hrqeU60IRMTE3tK1X73d3+35HMSiQSefPJJBAIBeDwefOITn8DS0lKDRtx+ZAxz0nfWJCxEGZZjwsz1rRXxdaOQJaDPJTrB3BnkONwtNp/btXVoXGNgFtYrca1N3wCicnavB4j/f3tnHixpWd3/7/P2vt997uwrs7CZMcJkxvqpBAJJsIyRMooLQghoBBegCEyKBMFCjBqXsjRqhUClkFJJWdGoCREUUjIjCILIMgMzzDDbvTN37X1/z++P0/v2vt23+/btvudTdWtud7/L83T33POc85zzPUIDuuacOxwOfO9738Pb3/52nHPOObjnnntw00034Tvf+U63hrRs8NuN6sUAQCFgJ7wyo3Aqqgop7FlSOBNnpXNz7cEUZhOcDqYUsDFA8FTla5QaslYddcq1PmvunFF3uejdmDuvRFtrHPzcqop5b/BzbV7pMbVhsz4VE8O9bBAHXehD7r77bkxMTBR+PvGJTzQ8/qabbsJ//dd/4eGHH8YTTzyBU6dO4T3vec8ijba/IOKa51ChPE3lNGD43+NhDVOL6KB3AyIglmZB1miaHy8GSplboTQTnEhkuKvMi9P8cyRY2i1G6CtK1wOyFhAa0DW19je/+c349a9/3a3bL2uUAtb5ORW8Xg/xMTcrmddWLufUraMhVfPcGndEOpfylswC0YZR5fLe4oy5Pufme6/ygX47MO4pf8VuAc4aJByaU7l2bsWLKvD7VqnGatG4/m4qRjgeMVawnU0Q1vnNjlXoeXIpbSqwARQ82u3RCMKC8fl8GB8fN3VsMBjEfffdh4ceegh//Md/DAC4//77sWPHDvz617/GH/3RH3VyqH1HKAXDQPSpiMKwq3a3lV4nmGRtmUS2ODm7xkrpQx32d/x2wmTU2MYH7CZq48DdYSpr2JNZwmxCw0oPVW0ECH1Abj0gCI0QyYplyoCDBVscFQJlFkVY7dUx5MjvRNdXdM9WOK/1IQQT/NtswkhBHTXuaS4AwOMxPtaSE5DbMlB78eKxAeeNEjb4dQw5gUEnsNpLOH+U27XVvLtilVszLFaUXxAEoRN8/vOfx/DwMHbu3IkvfvGLyGTq1+o8++yzSKfTuOSSSwrPbd++HevWrcP+/fsXY7h9xbRhFxJu7xk2JfzaW8wlWBG9svtJSgeOhLSOp/R7bdwyrZFKvcNC8JlYC0RS9cTl+PeJqCi/C8Jypat9zoXuEnBwJDiaJqSyvAPss7MS62QUMN6xNh+Wn00qjHkIGXMB5RrXrtxNr3eO0XGELCnYLdQw9UxTwLALGG6iF6u9UIPfWMHeIf/rBEHoUT75yU/izW9+M4aGhrBv3z7s3bsXExMT+PKXv1zz+MnJSdjtdgwMDJQ9v2LFCkxOTta9TzKZLGurGgqF2jL+Xse4S0ruuD5r32mslM476oNOgq1D205KAZsHCK/OKqSqVOoBm8abHmbS2k/H6s0lD2EyxvPp1xp+QRBqIzvny5z8ju+Qi5315tLgzDququCUc617K5hPoTd+3ZzSfClZnVuhZRsEF6xaqeBdfUabcPgFQRA6ze23314l8lb5c+DAAQDAzTffjHe84x04//zz8bGPfQz//M//jK9//etljnQ7uPfeexEIBAo/0jKVYcfT2Ib0aruyeswXlNLr224CMNPh3WaHBTh7mPvAOy2cbeiwcHbd2cMspGsEEafnG202xDPKVPcYQRD6C9nDE2ritgKt9yOvhGDPLRSGnMCpaJsuWxdjBz2W4dquyrT+SmJpTi+bLxhSwqADWOnl9nCVrPYRQql8G7rq3X+PlXfkBUEQlgq33HILrr766obHbNq0qebzu3btQiaTwdGjR7Ft27aq18fHx5FKpTA/P1+2e3769OmGdet79+7FzTffXHgcCoXEQQcw7CTMJxt73hZF8Pd4x9lYGjgT4+4mmspbU2P9mUTGTOncwrAY9IE3A5lcX0kZXB8jLdWEOohzLtTEZwfsFk53r20Mm7EYCiMuDv86rMCIE5hOmBN56yRZHUAD5zyU5Po2nmkxBW0uSZhLKtg0Fs4bcRV3KRwWbitzLKQQLlFcVSAMOYG1vv4U6RGMCVlXw4ejYpCFJcfo6ChGR0dbOvf555+HpmkYGxur+fof/uEfwmaz4bHHHsMVV1wBADh48CCOHTuG3bt3172uw+GAw9HjHmYHCDgAt5UQywD1bOhKb2fsDBGnl2uqc+3SiICTEZVL+86vE8yvN3rBvioFOCyEZN31FaOBYDPYQBB6E+l5LjRCnHOhJkoBmwKEg7MKVBWtbsYxJ7itwICz+Mw6P0HPqZY376C3y6nnurRQilubRVI8Z5+dHW6XFThc5Zjn4cdpnXAywn1ltw4W09mcVmDrECGRIcQzfLTX3n9phkIT5BRaxSALvcz+/fvx1FNP4aKLLoLP58P+/ftx00034UMf+hAGBwcBACdPnsTFF1+Mf//3f8eFF16IQCCAa6+9FjfffDOGhobg9/vxiU98Art37xal9hZQijuKHJ5XiKSBSnu8ykMYa3N2VjIDTMYUZuK845sPNo97zKVxN8NUvFY9tlmbrxBw9EYe+KiLcKJheR1h2NUbwQahSWQ9IBggzrlQF48N2DHM9dnzyaJT7LSgSi21Hgqsjj4VA0ZcnA6W73cOUphNNutst8cx99l4AVAWnSdgNsFtTMwFAYpO+qF5hXOGy4VbnFa0feEi9DBikIUex+Fw4Hvf+x4+85nPIJlMYuPGjbjpppvK0s/T6TQOHjyIWKwonf2Vr3wFmqbhiiuuQDKZxGWXXYZvfvOb3ZhCW0lluT2oRQEua+d2kyux5tp3xjKEuQR3KnFYCMNOtH2nNZoGXp1T0Eu6oRAUZhJ8761DBI+tPfciAiaj9Vu85o6q8xqXqfntPOYzMYVgko92WYExF2HQuXifkRGjbmA2AcQytUvg7BpnQAh9SuV6QBBKUES9XdESCoUQCARwbOoI/H5ft4fTd6SzQCzDqWw2DbBrwIE5ZUIxtjQdrZiWtjHAEXegqL5a3V6tvlHOu821d7TNwGeu9BAmDPuVNofbqmPLQPsXR0KfketxWjDI4qD3JaFIHIMX3YpgMAi/39/t4fQ8S8nWxzOsDB5KAXk7ZNMIKz1c5rRUHMCFQgT8frqRvSdYNeD8kfYoikfTwIFZM3a5tlL6tkFCKAUcC1fuuPM6ZMBB2BRYOurnWZ2/RzOJ0hp0woCD273KWmIZULoekLVAT9IJWy/7ej1OVgfmkiyCoin+o+5uQxQ7lQWOh0uF0Lhu2m8H0nozquml/xKOBBWsObEaLbeDvsJNmEkoxNIoSdOrfQ+97JqtpLgrjLt1zCWNovPNQohlFF6YZtE7l5WDEHYxrkIlsoMuCD1LLA0crNhJBrjF2bGwhmSWsMbX03seBYJJI3vPnVjmk7wrvVB0k2+blstUSGc5ODDs4qyBZDbvmNcuRZtPcpvYld6Fj7UdWDRgfYCw2gdE0zx5t1UC/MuK0vUAxEEXGHHOe5jpOHA8pKAj7/oqTEQVfHaODrda45zOAgdmq6PlBIVgKr9v3YpTW2xj5ncUrbDbBrhthFMRIJJuNOhW696L59g0Tq+fjLU7dF4MGMwm+PHJCDvq630ETerNhVIkpU0QepJjoWrHnOHHp2MKQ05qS5C820TSXF/eWFmcEElxP+6FYtQ9JX8/txXYNlR9v9NhI7uucDrGKusLreUm4p3+uSR/HxwWrhFvpce6VWOhP2GZkl8P+HfCh+fEQRfEOe9VZhPAG6FibXSpmQqnuEZsx5D59C2dgNk4MB3n1iXlO9SlLLRNiUI0A0zHCRbFNdkuK+/UTxjWmtUah9njOSV+c4A63GSlfCyzCWAuwdFwu4UXH8Ou9ovoCD2IRMwFoaeIp4FoxsjeEKbiCutt/bF7bmYW7Zqp3QL47IRwSblANQqj7tqibyHD3uHcKz2RWVjwJJ3lTi6xsrZtHJBf7SWMe1q/trBMsY4A4BR3yaYTxEXoQXSdd8zrO6bsYM8li/XdjUhn2ZkviryZE0JbCBxYYDxWQrZg3TshDscX16FwaB7wLvKOBkEhmiFEM/x4MqZhxEVY52sueDJfUr4QcKBmn3WhxxAHXRB6hljGzFEKsXR/OOYeGwEw2gpW8Nrbp5C+1ks4MKug11zfELw2YLDOLrPZd30hn45OwKvzConCd6F8jCcjChalY9S9gJsIyxPriJS7CQCM/+oKS4hQEnhtTuG5KYUM1aqrKoUwEzfn+R0O5h1zo2uWX79dRDNAItvMvc1SlI7LXztDCvOpNt/GFAql7+90nI24GeYSwAtTCkeCGiaifN7LMxpem+N6P6HHsY4Uf1/R5h5IgiC0DXOp0EtHcGyhDDhY6K6+vSdYc1o37cJl45R1d0XwWYHF9s4arP/+8m5447WJyqm6t0o+SN5ovXIqotDbUstC18g56ABkPbCMEee8R5iIAK/Na2XqsI1RSJlodxZNA9F0q45xqdFuZMCNWGiqvJnrN3rcDbg/upFzPZ8EXg+qiswCHn8oVdriRuhpSg2yIAhLEp+9soisNgOO/vijrBRY3RxAtY3m5zcNLLx+uxJ3ro3rjiEdG/w6NgV0nD9KWO9vfK8xl1GZG2cTtqrHAyC36dH4881QXslfEFpDBTZ0ewhCFxHnvAcIpYBThbZf5ne2zSh+BpN8bHMQBnOtPtxWQFN5g7gQC90ph7l9auwLC0DUviKr4dd5nbjNClOvfEFhLtG2IQmCIAh1sGrIlYrV30nWFDDcRxteXjs7ykPOYmBC5dYAO4YJPnvn7u228Xs5aOBQ6wRMxfLZaPVsNfcOX71AJX3jNrKMZLUJgtAqUrXaA5xpSigtj8Kw09g66NSs88qGbYWH4LEBo27CKzMKsUw725K1i3aMh4XkRlzcHu2NcHtbsDUy4LEMkMwaiw9NxxWGXf2xUyMIgrCUWesnJLOqRttP3kneMkAtKXYvZVxWbnu63g9kdYJFM5vi33kyOmeQxatqwIs2UYGV1Fd5F/7Z2DUgbmIN0G/fAUEQFg9xznuAxsqlteCaKjN9R11WM4IvxesCwAY/O+YA93xlkZwlYqnbChtgr50w7uFMBLuVcCykkDRRMmCMgl2r71SnTd1DIS157YIgCIuCRQFbBwmzCVZlT2S488igk4PVC6lnXupoCtCW2PyOhvKOea3yNQ6unzNMsLdptTvsIgRTjdZMHADoZEaBIAj9jTjnPYB514udSbcV2FxRBxZLc//VYJJTwJxWYNRVdLKNrqsBGHHzOaVtwMyKmvUmxdrug3MK2wcJsXT1brdVcbpczHCXuxxNEQYaBFDM1cURrP38ESxHRKVVEJY0Kpe63gsZSzqxqOhcgrVLHBZgJGf7e124LpnJl+bVb7umgxBMAaNtWu0OOAC3lRpsSiis9uo9/94K3SVkXQ0fjnZ7GEKXEOe8B/DYgEi6cRpVXsl00KnDW2F0ZxPAkWB57XI8QzgWVnCaioIrWDXC2oparXQWTQjU9TIKySzhhWkFqjHXDAGZLLDOp+NMTOWU541Z7eUASiKD3A4Mv5MDTq7v89hYKdeoxq0XFoiCOaSNiiAI7SKZ4ZTvlF4sx4qkCTMJDYMOwsZAbyvLmxVdCyYVRt3tsZNKsWL8kWBe9I1QfHeBtT7qK80BoQvkW6z6d8KH52QtsAwR57wHGHMTIsHGaVRjbmBNDaGTZKbaMS/9PZE1Z7Bq1ZeZFUbpF2o55gyb5smowjnDhDfChNlEvc+L3+/VHsKIkzMPJss0BQjBlMLJMC8AVnmprCd85bXsBYEioecp7Xk+82S3RyMIQg+jU94xzz9Tvg6YSwK2sMJaf+8Gdxejr3ktrBrb51iaMJ9k7R6HhYPqFqk1F9qBdQTANP8uwfplh/wZ6QEGHMCQs74CqcsKrPTUNj9n4rUc81LMtDGrnX69vIyQURCCdyciaWCDH1jl1VGuGsvvscMCnDtMGPcC0wnkHPPS67PqfSa3sBpwAGsaXGvrEC2zz6HPyfU8Dw+/VXqcCoLQMvMJ5HbM69v+qXhvq4q7rICZDYLKnuntwm0DVnl5Y2TUvdzWRELHkZ7nyxbZOe8BlCqKsJ2OFiPhFkUYdQHjnvoOWqhhPVbhDqivQM77xaM1UqcdFsBlIcSzZu6xGBj3OO3sOAmJLOBXwEoPMOIkzCRYcd2igAGHXqjzIwImGqrwK2SJMB0Hxj0cnJlOAImMgpa7lt/e+zWDQg1Kd9DxpETMBUFomtmkcWcRgkIoSRjq0XW/1wY4LJQTaK0/zxGDlPZUlkvLQjlNnnwnGnOaPILQQXIOupS7LS/EOe8RlALGcoJsKZ0AAmwW43YmZtO5vFYgksmfUd0axl5Sm57WgTMxhekYkKHSvqKL5SmWtkipfNSIeq+TiWPMoGBRxWvZLOxY1/oUomkgoxvfazahCkrxK+tcS+hDcg66IAhCKVkdmElwHTUR4LJVC7XmjzO2ZwSTlW1tgwjIEo9soTvNSnGLt4OzClS1BuHHa7yNFfTnE8DrwWIHd4DL/WYSGla4Cau9zdflE7GTr9TSaTkn9DYqsAEUPNrtYQiLhDjnPYZSaKpVi9sKJLPGO8obAoRkllvDxDPslA86WdW11DFPZYEDs6qi3rzUQS99vlMoaOB67LSuYNMIgw5gMsbR71YCBW4rEMssdNwEv8PckeYWRArZijZpREAkzQszVt3nOjdzyu6CIAhCrxJNA6/NqRL7oRBOE87ENKz0EFZ6io6k02osJAtwrfRioBNwOgZMxRTSucC020pY4WH73WoWmMcGbB8inIwohFLF+ToswCqP3jArIJ6pdsxR8vvpmILTQhhxmxtLRudzpmJANrdx4bdzdqO0VhMEwSzinPcZRKxgGk2zYfDaCXPJxmJyATvgsPKP39HYUL8erHTM87TmFLeKDmDUze3I8qzxUs45b3YMHJBY69NxPGxUo18PFnizmXSS7aYCLOUR/1QWODSvEM+U5gsonAizQuyoyQWEIAiC0FuksqxDwvHaakdyIsqB6rwdGHERpuNLox93loDXZhWimfLnYxngSFBDzE01BW3N4raxQFsqC6SyXObntBg7/FOxWo55KYSJmMKwy3j3PK0DB2dVRYo9BwxCKYV1YqMFQTCJOOd9RDQNvD5f3jYln2qdpVrOM8GqAetMqrVGknmnvx7VKWXlj42OaQaFVLY8lY+NZ2vXJCh4rK0vDnw2NKV667Ia9Url50fcLDCQV95lw19+DgE4FlawaLootwuCIPQh0/FajnkphImowkjOkXRbWatkNlHrHLZV63yL00ptIpJ3zGsF9Xm32W83n3lWD7vFbOCbmav53pSikMpymrvLYLV8LFTpmBevAQDHwoDPXl1+IAiCUIkkw/YJ8QxHbcvbprBRyBKg5dpuFV/ldOgdQ2RozLI68EZQ4eB8M1a8eKxFcau3MTfKarIXCovdldxRISfg0so9CPPJVnfNgQ0Barq2rNg3vtZ4+bmpGBv82TgMRG8IJ8Ncgyj0GaLSKgjLntpOdimcLh7L7U7nhWRXuNneM/yvTQM2B2p3YWlEVgdOR4EXpxV+e1rhd1MKJ8KlQeNqdAKm4kZjJ5yILH5xtlmhet3ArqaywLwJ8d2p+OLPUegPQtbV3R6CsIhIDK9PmIg0Ss9S0EEYcACjbh06cT2WDq6RSmdZvKwW+R3bxju8jSCcN8KOazgFpLO8453WURJIaAXC8YiCw0oIlETbV7gJrzfsCV+fSBpoZY4ql4HQLF47sHWQcGReIU1A+a6/Kozp4KwykS7PgZlomuCV2ra+QVRaBUEA8gJvxpQ6kkpxm6+VHiCYImR1Vjf3tdDpozxtGwBUrsaay8nOGqhtexIZ7gPeGIV4BpiIUk74dHFwWoBYxliTx0jnJ2pq7aAQTkn0XGiBfAcX/0748JysBZYBsnPeB2R1YM4waqswk2CRGALXjr8wpfDyjIYXpjW8OqcQSVWfdTqKBTjmfF7ewX9tXsNcEohmlEH/VXPXBYDD86ps0TLgKG371owhzKcMNms8CcOu1hVZfXbkdi/qt1RL60DCZLu6Xu5ZK1RQ2vNcEIRljcMKmLFPtTLhLBow5GSdFn+L4mtHg6Vp2+U17zqxHorZAEI9TkU0JDLGx7WLUYMWawCL1YngqtB1rCOALSdaINl0fY/8yekDMg3r0IpkSSGYYLX1UKr8nHAKODincqlZTDoLnIouLA3LoghvhFRuV9rcOM3D2QKc7pd7RnG6+KaA3kKKe/OOuQIrsbYKETBjmPJnPnBQLwNC6FFKHXQxyIKwbOGgc+MdXp/NeJe3FRIZIJRqFFBnBflSW5zHaS0Xbm0MYXoRU7+HnI1K4QgWBaz2Go/d3FpDFNuFBZLreQ5A1gN9jjjnfYClIITWGAXCkVC9ump+fCTI0e+pKPDCdOs12Ayn0gcbGvWFE06VX1spbgO3fYjw5jGC317a5q0+xVZqZhcSClatudZ2lWR0QDf13hj3q3VYCG4pVOk/xEEXhGXPoBM50dLajqQCsHoBiueN4GC+8bVDKYVYmo/P74BrChh1mTsf4PMXC02xyvuIs7Qun/HZeQ3hMGFT7RbO2jOao0OTtHZhgYiDviyQpXwfYNUAvx1lPT6rIXhtQNhAbV0nwksz+T6kC1FTJ1gVYCkYo84556EUp/ZbSkJNRCySl8wCXhshlGrcUqY6VW9x2sJpmtl7ERwakNRrHcvPrfbqi6K8K3SBXM0ZAKk/F4RlSN6RfCOkMJfM2wH+12lhUVLewW0/ZoVGg0lgvqR1q8dGWOMlrPKy4Goj4bjcnRbdhlkUsD5AWO0DIikCgbupNKuqvs5HiGVYU6eePT8e0aBDx/gi1tUL/UlBj0boS2TnvE9YWUitrh1V18DR3crocC3SZYrvraKw3s9GqrO2ltPpjoWLdwklgZdnFF6Z1fB6UMOpqIbi+1I5//q13sYsfDFkURxYaRxx5wyEbUOl96PcZ8mf7Xq/jkFpo9bfSMRcEJY1Fg3YNMAiq+v9Otb5CNsGdZw93DnHHOA+4mYEzyqtWDTN5XKRNLBlwFwGW8DRnd1lq8b6L4PO5h1zgEvKdgzlywpqzYHfv5MRDfFFzA4Q+pBcNp3Qv4hz3id47dwaRSukuBcNoTUXcbdqZhO22+FOE6JpBYeFWmps1hwKswmukQ8mgdfmVU5ArfyY8n8rn2/tvkXxudYxCqwAXNduswDbBgnbhzjyPuoG1vsI548SRsRXWx6Igy4Iyx67BRhxsQ3wtqC83ixeG6u8G68gatvXo0EFhwUYbJj6zTXewz0cZNbJqOUpABDOSEs1QRAaIM55HzHgBM4fIazzEYadbOQ2+HWcN8otTrj22jj63S4yxIIrC8N8/XcoBbxRt6a+nWPgRcqwsz0CL147sNFPuRFT2Y8CsKkkXTHfy321l7DWRxhxl6fzC8sDcdAFQVgslAI2BkptVCnGDntaZ/u83p/XRal09HljYcsA9bQ9i5lsqVarM44gCEIeqTnvMywaR9NHaxhMn52j3/Uju83Uhxs5+gqzCa5zd1vz7diagTDkIMwmzVvqWBq5WvlOUJyvTeN+6mPu9u1YDLkAn50wnQAiOYE7n53btBn3OBeWFfmep9IDXRCERcJj47TtkxGFYIm+jaYAnYyV5OMZIJArz5qNA1NxhWSWd8u5zRuVtYHL17n3o45KP85JEIT2Ic75MkLlItMHZxUyVcaUa5fNKYebQyfgaEiDq6Aw29y1Z5PNHa8KKf3tt3wr3IRBJ+8cuKydMa42C7DSA2ABrdmEZULOQVeBDaDg0W6PRhCEZYDLBmwZJKR1IJ1zrE9GFeZqtFCrRFPFf0fcwEiux3gyA0wnFE5FFDQFKEUI5sTjFNihX+Hm7L+ljqegH2PQ8q4H5iIsfcLDb4UPT0pwvg8R53yZ4bQCZw9zzdNMnI2sVQNGnMCIi/DSTH7/vL7qu6aAQQdhJmEuRT7e9K55/lyCTaOcQF1jY+exLqylmdFY8m3KFiviTZTvX8+aARJpFwRBEJYCNq2Y0RWwE+YSRuldKldWV4QIOB5WmIrnVefz5AVcWWBuPslK7+t8hFF326bQEWwaZwHMJuo56DzPdmjVCMsc6eDS14hzvgyxWbhmebW3+rV1fsIboaJxrESBe386LYDTquNURIE6sFOdv5tFI8NUdQVuhdI5/5VwPKxwLNz5SD4RMBUHTscUUlmekV0jjHkIYy5x0gVBEISlw6ATOBEhZOoG0QkBe7UC+omIwlTBn2gk1Mq/HwsDXjvBtcRXrWt9hHhGIZ6pLBPkx+v91JIavCBUkROIlfK2/kOqWYUyRlwsIle9CU1wWwlnD1MhrXvcg5xKuHEU2FMwRs1EjAl2jZXK651r1wg7hnhMvEPfCe9VFbIJCArzSW4PMx1r712IgNeDCsfDCqkStfmUDpwIKxyeV6b7zQqCIAhCp9EUcNYAp7iXC73x7y4r92AvJa0DZ2JAs/Z6Krb0o9NWjbuqrPZSiV5Mrh3qoHRWEdqMdHDpSyR+JyCVBabjQDDJTqjHyq3XMkRIZNj4DjhrC5PpppxFhViGMOLSEU6pnCCdOXx2wrgH8NkIZ2IK4TQBxLVvYy7CoJMDBUTswHaq5rxWJP+NMOBpUyRfJ+CNIDCfrLxX8XEwRZiKA2NLPLVPEARBWD64bcA5I4TpODAbZ00bu8Yib0POYr15nnkTNerVKIRSvRGdtmi8eTHuIVAuoi5Zb0InkR30/kKc82VOMAnekQVQrBEnTCc0jHsIqzxU16gkM8BsonheIwjAdFzDGq8On51T4E5FFaJ1W49wmno+yux3AH5HtWFO60A6A0xEFVIdU2qvz0REYdPAwhYMaR14dbZWb/ZqzsS4t7oYegHWEYQA+HBUDLIgCIsGUTEMnrdFNo0FTVeaEDTlFPjW7ttriK0WOo4IxPYd4pwvYxKZasccJb9PRlkIrV4a1umYaiJJna95MqJw/ijBbQNcVsKBWYWUXq0crwBsGiBY6xReRNPAqQj3Tu3MTrkZ8i1lFsbRYN4xNxbYS2aBLBGsYvAFQGrOBEFYNGJpXhfMJ9lKWxSvD1Z4qKmWn7aWxFsJHlsr53UGnYBwCsgSi9EupmCsIAj9jTjny5gzsVqOeSmEyajCsLN6p5YImIk3OrfeFXm3fczNBnrHMOFMjNPq07qCAqeqr3CzA1+LcAp4bc5o7PXubnR8c2nx5tL66xPPAKGUWHRhYYiDLghCJwkmgUPzeVvF/2ZJ4XSMMJtQ2DZEZR1Tau2u5xl0AMfQnAINoDDmbnHLvY0QAadjHKTIUnFiTgthrY/gd3RxcIIg9AXinC9j5gxT0nmnNpmtVhcltN4T/URY4VQE8NuBMTdhlRdY5TVXm5UXTWveMS9coc55hWVEC9csuQoBoRQwm1BIZzkAMezkvqa15hVKNhpT9RidFuSEdwQhRy6lTRx0QRA6QUbnLDumWhMlrROOBBW2D7FOzemYwkycd9c1RRhx8u66Pee8WzS2+Scj5uweoJZMr/MTYYUz8epxJ7LAa/MKWwYIAXHQBUFYAKLWvowxu+ubrXGcAqCp1raNCRxxnksCB+c0TEaLryWzQCSFMrXyUuaTQEZvzYl25HqQ5kdRrizbmsdbmsqX0VnF/dC8htkEEE4rzCaA1+Y1vDankK0R9G/2HRxzS725UAPrCABABTZ0dxyCIPQd7GgD9e2kQjTNDvkrMwrT8XxxGqCTwpk48PKMQjxdPGOFG1jl0VFuh2sz6NCxxtf9gvN4BjUdcyYnFBuSripCdwhZV3d7CEKbkJ3zZYzDilwvzkbeXnmqWh6lgGEnMBVvVh29urb9ZERDVtcxn1JIZIqvOyyEAQfXtOV37mMZTn1vvqs5YYWbMJoTrJlNKKRzzvJ0y5uMfM08rwfzAndAcZ78bzgNHAlxVL0UVno3l2o/5IC0YREEQRAWlbCp0ivCsbACm9Xq3fUsEQ4HFc4Z5gCzUsBKLzDsIhysqT3D13RbgQ2BNkyiDXArt0ZrHl5XhFOS3i4sMrkMOgCSPdcHyM75MmbUZRTeJQw6UFeUbYWntLfpQiBMxjQkMuXPJrOcHvfSjIZXZxXORIF4utW7KRyLaJiKsaO/yktY7y93rpsds0MrOsvRdH4BU99oB5Mq14u9iN8O2DTjnYPVXh0bArJrLgiCICwuZneCdWpsA5NZhXCq/Fm7hduwrXCXZ+Npip/bNkRVrdi6hTnhVjLVeUUQ2o70PO8bxDlfxgy7uKd5bceQHe/V3vpW2WEBtg5yP9P8OeXXMuv41q9lK915Ph7REGzoABuRi+yXDMthAayacZCi8nHAAWwfJlhyc59LqBrHVV9nrqK/q1LABj8VXq9137U+7vUujrkgCIKw2FgtZjLkzNnAcLr6OpoC1vgIbxol7BjSsWNIx5tGCWt8S8cxB2ByM0JBU1w2GExySUAo2Ztt4IQeRBz0vkDS2pcxmgLOGiQcDyvMJMqNr9cGrPcTHAbfELcNOHeEEEoRwikWkIumCekW68LrU53u1vz1+fjjYcJ6f+4ZBYy5CKeite5RPC9g1+GzcxaB146qVP9adfm17s7qruUH+x3AtkHCiUhpWjzfY5VXL6mTF4TGhKyrue+5IAh9TSrLzl+WePd5wIG2OLJpHchkWbQtL+DG9rwVm1uDBrZS5e6fzHJ9d6BB5l43GHAQ5pNGAyKks8ALU+Vq7jaNsNpLGBZ/SVgERCC2txHnfJlj0YANAcIaH9dJEbhfZ6U6eyOUYiNKRDgda6eCesO71jjPjLgbYTqu4LESRtz8zAoPEEkDoVS98wnBFPdUX+urXYPvMLGzQIXjqvHage1DhGSGkNI5Qu+SvqlCM0jNmdBhHn/8cVx00UU1X3v66adxwQUX1HztHe94B5544omy5z760Y/iW9/6VtvH2O/oBBwLKcyUZWFxz/G1vtadv2gaOBVhO5e3ZV4bYZWXcgKt7TBGCh5b7XZocwngWFjlBF/Zniqw5sxa39Io6Rp0AicjlNOrqd31xWUBTkWrHfi0DhwNadBJx6i70yMVljW5tYAKbAAFj3Z7NEILiHMuAODo9OACdmh1Ao6G6qWnd4riLrRV47Qx4x3snKJqGPA7uLWLpoAtA4SpOHAmqpCsWjvwOQRePFi06t3sYSdwMmJ853q74KEk950P53bOPTZWZg/UacEmCDXJpbRJxFzoBHv27MHExETZc//wD/+Axx57DG95y1sannvdddfh7rvvLjx2u8VDaZZ8K9FgEqi0s1li5w/Qm3bQwyngtbnqFqWRNPDqnMoFpI0D0Hz3+u1KbRpqthmbSwCvBzVUBtgJClNxQkZX2LgENFfy2Yavzilk9NKx8pzdViCWqXc2H3cirDDkLJbECYIgVCLOudAWOL1usXbNS1HYOsgp50eDwEzCrMVTmIwS1pWmt7t59/9EBA3GRDgVURh0lC8UbBZgpQeYiNY5DSxCVytF71REYSJangkQThHCKQ2jLloyuwZC7yAOutAJ7HY7xsfHC4/T6TR+9KMf4ROf+ASUwR8pt9tddq7QPJE0EEw2auVVdP7M2gwi4Eiw2jEvvWbShMCZRRE2+lmRnWpktSkAm2o42ETA8XCjtHluuzqW7lyfcyJuhQoFWFXjgLjLCpw7TJhJsNZMRufyvyGHXrJBUQ8FHYS5pHReEQShPhK7E9oCq5C3rny+EMV3S84ervI2N4b5GoucmYSxcU1mq1XXAW7RttqrlyjO8r+aIqz16VhRY6NoPoGcY87XLr0PAEzFK9MXBcGAXM9zEYUROs2Pf/xjzMzM4JprrjE89rvf/S5GRkZw7rnnYu/evYjFYg2PTyaTCIVCZT/LnZm4keiaQobyqenmCCZhoBHDz2sGYmjjHkLAyeVZA47SY7nry47h2s51KGV0f77GdN3+4q1DBJyOAi9OK7wwreGFKQ0vzSiciTUWcLNoHMzfNkQ4Z4SwZYAV2hv3gkfh1dKWsYIgCJXIzrnQFlo3NaWLjWZ30Vkp3pX7FtstwLibMGmy7l2vYXwzdWvJyjk8p7DWTxgoSVNXChj3AKNuQjBJyOgsAhNoINRz2rBvKuF0VGG4iZ0QQcjXnBV20AWhA9x333247LLLsGbNmobHfeADH8D69euxatUqvPDCC7jttttw8OBB/PCHP6x7zr333ou77rqr3UPuaZImW3mlmmjlFc2YE3vz2YBoBjXTucfcVAg+u23A5gFCVgeyxNlijYTqeKxG91dImlFdbYBOnD4fzanFe2yE2UR1ICOZ5Z38cKr2Tn89OHhgfDCBswwEodOIQGzvIs65sGB0wgJbnLHwy5gbmI5Tnbrx2uluKz16mfFc7QNiGR2hlLGiaq0Uc5sGpHXjhUqKgMNBDWuyOlZ4yl+zqPq15WUjIE5TNFqUJLJAhgg2cc6FZigViBOEBtx+++34p3/6p4bHvPLKK9i+fXvh8YkTJ/DII4/gBz/4geH1r7/++sLv5513HlauXImLL74Yhw8fxubNm2ues3fvXtx8882Fx6FQCGvXrjW8Vz/DNsvYkbUYtgctPdpcUNxmAc4bIMwmgPmEgk4EpxUYdelw2aqPt2hADe3UmseZCTgspEY7nAIOz6uybilT8XyNe61UfmA+SZiOw7R4W7q2zl0NFAac4pwLHUYEYnsacc6FBfNGsLwFWCsQFFZ4dKzxcSR9Os6iN4lsrR1uNqgrPbWVadf7gd9PGy84RlzVBnLExb3QjeFjTkQUAg5qSt0+j5kUuMKxYssFQegQt9xyC66++uqGx2zatKns8f3334/h4WG8613vavp+u3btAgAcOnSornPucDjgcNRQD1vGDDmNW3kpsJCoWfz20tKq+lf12XVoimula9nOVgnYecxkEHAYcrL3m9a5HMxsC7lYulTsjq9lljMxhVG3ubkaFRzk8dmokO0nCB1FBGJ7FvkTIbREVgem41wTbS7Vzggq1I7rxMY3nq2fbue2ElZ6aqec2S1s8IOp+qqxmqodER92AWdiQCJrPsV+Kq6w1tf8YkVT3FrN6P2zKla5FQRB6ASjo6MYHR01fTwR4f7778dVV10Fm63GtqkBzz//PABg5cqVTZ+7nBlwAE4L1zfXs23jHjS1y+yxAS4r5XRU6qusD3QoTpKv3z4dq2+vHTmbfiykMFXmX3ALuTU+qiuwNhmtJ3ZX77nia7w5QIb949O6WbUbwqYBibQLi4g46D2JLPmFpkllgVdmFU5EzDrmRoJvLBijKa73OjCrEC/UzNU2qLGMati6bGOA4C6Enkpr2tnQbh0sBgNKyb9mN/0/QyHShPhOJWOGUXnCqFvqzQVBWDr84he/wJEjR/A3f/M3Va+dPHkS27dvx9NPPw0AOHz4MD772c/i2WefxdGjR/HjH/8YV111Fd72trfh/PPPX+yh9zQq18rLVWbbivZ11MXCpM1ec3MgHwCuPJft5JYBYwd1Iaz2EoYLpWDlgqoOC8/5jYJjrkp+eAf9jZCG6Ro+h07AXI22c+0m08QGRa1yOkHoNCIQ21vIzrlQFyI2blpFa5HXg83ulud3wGtFxjmZbTy3oJiIqFzNubHDfzqmMOriNiaVWDRWUp1LUG53n2BVnBY44kbDnWibhdP2TkXNjGNhfchHXMB8klun1Xpv3FZU1bQLQtNIxFxoI/fddx/27NlTVoOeJ51O4+DBgwU1drvdjkcffRRf/epXEY1GsXbtWlxxxRW44447FnvYfYHdAuwYIoRShLkE20uHBRh26S2nSzuswNnDhDMxzojL6KydMuziALLdTPG4AUQcfCcC7FaUBcfTOjDsIvjshEhaIZXloMCgU8eAg3uHz5lsIVcaRMiaFHitM2K4rI1T5vOYdbjFMRe6Qq7+XAU2gIJHuz0awQTinAsA2GCGU2w8dWJF0/kku84auLZ7hYcVWPNqp01cvWCI2fEuRuctihVR3Ta+72wCMGdM+ZhXZnlRUWvxoOUWF8Mt1MfZTC9GCP4F9F7VcrsSk1GFMzEq9IrXFPEuiLf2Dr8gmEJS2oQO8NBDD9V9bcOGDaASkYy1a9fiiSeeWIxhLRuUAgIOIOBoX4q0VeN2pKu87U27JgLOxIHTUZVrmcY15iMuYNBJOBnJa9bwaxZFGPew+ns+8F1sIVffQc8SEEwSBkvEWC2amXr2eiiscJtTebNZAK+NDAReS7MDBEEQ6iPOuYC5BLcOYcOZN4BFQ6hDYSrObUeGnPV2wBuhkCXCzjHCbIIQSfG5XjthyFmMTGd0NG1EswS8OqdwznDrqd86AbNxFqFL6byrPugk0wIvCxXH0RQviMY9QDJDIABOkxF7QTBEHHRBELoAEfBGSGEmUfF8bk0xVaP9WJaAkxEN8QxhY4Bta6st5DQFDDqB2UTjdqWMKnnM7UvNdF3Js8pLeHWuXhCBg+zGZWyCIAhSc77smUtwmnqxDYiq+Lf4fJaA2URrHqMGFJReNwQIGwIcOS91QFvbIVZIZnmXvxXSWeCVGYU3wgrRDJDWFWIZ4GREGTj7HKTYFGhPyh/A74XLxn1ixTEX2o3UnAmCsJgEk8BMorxGvEi99qv83GxCIZSz69aa9fDV1BLCW+mh3EK31vkchHeU2HC3Fdjg17He31zA32fnLMDivYpaADYtp2XTprWCIAj9jeycL2OIeMecMZdKXrsHueGd4DOR+m3RAJ+dEE6ZHU/x+jMJhcE6vUPzKfvxDDu9fkfRGL8eVDWUb3MZA0SwKu67Xt6GheDQWHTOs4CUdkFYNHI1Z7KDLgjCYnE6ZpSO3ghOefc7CINOwmzCqIVcbUV5pxXYOkQ4PJ/fhMhnxSnYNWDzQL6sjq38QgLjg07Ab+de8LEMX8hv57p5EXUVuk3Iuho+HO32MAQTiHO+TEnrwEQEhRow87Dgim5KtC2PwpjJ2q2VHkI41bxBDya5n6m7oqtPOAUcDSqkCin7AMJcrzfqZPGZRuPOELA5oCNLrFJvUVzTZr4mXRCWCOKgC4KwSBDBoAbbCIV4hm12wG7c7m3UVV9wzWMDzhshBFOEaKGsToffXnSa25WtZtHybVolhV1YQuTsPwCx/T2ApLUvM3RiZ/WFKZWr92qy7QrYUDLVbVdqPV7p0eGv0yOViH/y+OzARn++8tyoBVvpqIBD86rsWpEU8Noc15EXj+OfYBJ4PWTGGhNCKYVhF7DSC4x5xDEXehjrCABJcRcEobPUam3WKkoBZw3UbyE37ATW+BqvFZTinfXVPsJqHyEgu9nCciOnPwNAbP8SR3bOlxkHZ7mmutVoNkFhyKVjzM112RwZZ2w5ZdS8M+yzAyvcOgI1HPNgklPewrke4S4ri6UMO4EhF6e3TyeAYEIhmjGzi84pa8EkYSAn4nIionKmu3Zdm24yLiHxb6GvkB10Qehr4hkgmgKgAK+NU7sXmzPxhXq+VBYIt9VoIWe3AMNOHQRgMgYQKbit4ngLQl1EILYnEOd8GXE6WqyDag2CTeOdc6W4j3gqywqpVo3ruJUq7oTXM46nIgoT0fLU9XiG8EaIhd02B9gor/RwmvtsAjgSNJPqzrvcA05CImOm5Zu51Hm7Rkjm5ihtzYS+oNJBFwSh50lmOTOuslzLZyds8C+eIBkRkFjAJgCjsLKiHK6yhVwqCxyeV7l1TT6MrsGqETb6qW7GniAsd8RBX9pIWvsyougQtwKrkG4eKFcwtVsAr50j8/nnlarvmIeS+XEA1SJsKrejXn7OkBMI2JsbdzJrfIzZhcOpqIYXpzX87ozCkaDKLToEocfJpbirwAZJcROEHieVBQ7Mlmez5Qmn+LW0OemXtrCwODbBZSEMN/izlNVLMwHzd+S7ZnTgtXmFSGpBgxCE/qSyvE1YcohzvkxIZIAs1WtdUgmV/avA/T53DBM8tvpnmaGo3lqfM7Hy2nEAGHKZ2+V22whpnVPa2w1BYTYBvDKrEKuxABIEQRCEbjAZVcjoQL0yrrQOnIkuTuqXUsh1aGlk6wk2jWCpWG8AhAEHsH24cSuzqThyJXT127Gd6sA6QBD6gpyDLixNJK19mWDemSw1lAoWRVjj457k7cC4TRovIpJZKquTG3AAVkXI1FWJZxX5AQdwcM7s7ra5WvbKxzoRjgQVzjZYPAiCIAhCpyECZuKAkW2dihNWecsz24iAWIZ3oh0WwNGmVeEKNyGUarT/o7DSo2PEBUTSXIqmKYLfbk50ddqwpl0hnAZSWekvLghCbyHO+TJhLmm+7VkpWQLeCGmwKB2DzvaPqx6V8XZNcV/xQ/MKVOVU89EbA4RgEkiYqqvP9zptNIJ61+He6NE0wSt9zgVBEIQuktEB3URmWZY4uGxRRYf+VFSVtVT12jggv9AsOb8DWO3VcTKiodye8u8jTg7653fZfU3a0nTdXfPq48Q5FwShlxDnfBlAxOrorcEu7PGwwoBj4TvFbhs7tY2MqkURHDWMqd/BInQTEYVgKn8NjrSv9LCj/OqcuR7pllxrllCar1e8GgzPZQjRNMQ5FwRBELoK9+g2kwlGhX7eE1FgIpp3nItE0lzLvXVw4cHncQ/gsek4E1MIpzis7rEBY269ICzbKlYFpExI0URTgNu6sHuZIaNz+7jpOJcXWDVgxMUBiHr91wVBEGohzvkyIEv5feJW4VTzcGrh6qdjbsKRYCNLxcZMqzNcjw3YMkjI6EBaJ1g1buGWJ53l8Rqxxkfw2AGPHRh1sSJ8Kstp/JEUEJKacmGZELKuhg9HRbVVEHoUiwb47UAo1chB51pupViDhh1z1Dies9OOhhTOaUPpFu+Kt78h6bALmIgad3A5HtGQyBLW+jpXhpbMcDldUXBPIZslnIwonIkB2wapbeUCgtBOwv6d8OE5sf1LDInnLQMsis3tQnk9qDAZRZVYWzMMOoAhJ4Gj9ZUXIritwEpv+fPcv5x/MjnjZ9W4N7qt4hvMtWrGAyzdmbdqwJibHfaVXiDgNDNB1XQaniAsOSpVW0W1XRB6knFPpbBaKVR2DNdrN7JzCslsbeX3hZDKAmdiwGQUmEsA+gLWEqMuyrU2bTwPAJiKc5vWTkAEHJpXJWn2pd1o+PlD89Uit4LQdawjgM3Nv4vtX1KIc74MUIrbkRkppxqRJeBkROHwAgyNUsAGP0exS+vALIow7gG2DlGhl3haB16fV3hhSuHQvIZD8xpemFI4GlTI1mkJM+w0jqTbNIK3QT3dsLM0TbD2NdxWgnuBNXmCsCQQB10Qeh6fnW0rU/ov581tChTryOOmepBT7riFoxNwJKjw+2mF42GFkxGF14Nsz2db3LCzWYCtgwRrYRqN1zfcKab9hFNAItuoE45CogOBDkFoC9YRsf1LEHHOlwnjHsp92PWi6mYMV64XeYprq1pFKd6pPneYcN6IjnNHdLxplLDaW3TMM7kepnPJ/H3zI1WYSXAKWS0HfdAJOK21duWLc1jtbZzeZtGAzYF8IUD17r5NAzYNSBhc6COk76kg9DzDLuC8EcJKD+CzEXx2YJWHcN4IlQm61rZtlai65WXNQAQcnuc2pMWdZb5wloAjIQ1zierzklnuMsOlarVx24AdQ/l5NFaqj6YXlvVXj1DKuD0sQAiaFuUVhEVGHPQlR8ec83vuuQd79uyB2+3GwMBAzWOOHTuGyy+/HG63G2NjY7j11luRybQpVCuU4bQCZw1SQZhEgQqp7lYF2LRGDm01Z9oQhVaKVVQdlmqxlsmoQrJu/bhCPMN9TivRFLB1oFRpNj9P/netT8ewib89fgf3dR92ouR94t39HcO1BesEoafJOegqsEEMtCD0KHYLsMpL2DpE2DrIpVqVrckCDjO2nsVWF0o4lXdg6/UjZ8HZvOMcSgIHZhRenNbwyqyGF6Y1vDrHznVNTC9FOuMc62SuIauE84WljjjoS4eOSVSkUim8973vxe7du3HfffdVvZ7NZnH55ZdjfHwc+/btw8TEBK666irYbDZ87nOf69SwljVeO3D+CGE+SYik2Zx4bToGHJxC/toctwgzho/TidoSWa+EKL8z3/jiZ2KqpM6uiM3CAiyxDGE+oZAl3k0fcjanmuqyAhsChPV+AJCe5oIgCELvM+QETkaALNXLmiMMOtrTgqxY314/7TsvOJvWgaOh6uPCKc6kO2uQqrRebBpvLjRurUZwdUix3W0jkEHPdQLgsop7LixhrCNAZhoqsAEUPNrt0Sx7Ouac33XXXQCABx54oObr//u//4uXX34Zjz76KFasWIE/+IM/wGc/+1ncdttt+MxnPgO7XdS2OoFSnPo9WCF6ZrcAZw8TZhOEo6HuVjukde7H2hg26PUCBEqxsrvHtnCDKE65IAiC0C9YNM6ke21OVTjo/LvHCqz317edkRQHx8O53WyvjTux1BJJrZ8BV048C5wMl4qplcIK8keCCueNlAfKlQJG3YRTkcb3GHN3xjkedALHw7wWqRfo0Aq6P4IgCMZ0zQvbv38/zjvvPKxYsaLw3GWXXYZQKISXXnqpW8Na1igFxDOlRroenCbeKfVT87vxC2sQJwiCIAjLEY8NOGeYsNJDcFgIVo3LwTb4dRZmrbM6nIgCB+c0zCWBjK6Q0VkJ/dU5DSdrOMjcUcXYMY6n80c12mFXCNZYd6xwIyfyWlmex48HHSz02gksqpYQH8oeb/B3JstQEIT+pGudFycnJ8sccwCFx5OTk3XPSyaTSCaLf51DoVBnBrgMiaZRomjaWFwFIBwNKgTs9Y14q1g1wGMlRBsqynI9nOxqC4IgCELz2CzAKi/XqJthPgmcitTqj86/T0YV3NZy8bkhJyGYarxI0BSZDLUTYhmFgQonWFOcCXA6BkzFiv3G7RZghZsw6ursWmHQCZyluK95rEQ2yWPl99bv6Ny9BUHoP5pyq26//XYopRr+HDhwoFNjBQDce++9CAQChZ+1a9d29H7LialYM7IlLJM2U0NltR2s8BgpyNeuNxcEYWGErKv5FxGFEQShhNNRozUCYTJabrcHnIDD0lhwdtxjPgtO1bmOpoCVHlarP29E504ww4Qx9+IE8fMisucO69g2qOPcYR3bh8UxF3qLgv0XukpTO+e33HILrr766obHbNq0ydS1xsfH8fTTT5c9d/r06cJr9di7dy9uvvnmwuNQKCQOeptgNdTmrBinwbffSR50Aquyei5KX1kTB6z11a5vEwRhAeREYcLDb4Vv5kl20E8voG+iIAh9gU4oCMnWh3eOs3oxo05T3I+8KDhLZcevcBPG3cBckjCTMNovUvAbKM3nu8B0C4cVEH9c6Ely9h+A2P4u05RzPjo6itHR0bbcePfu3bjnnntw5swZjI2NAQB+/vOfw+/34+yzz657nsPhgMMhf/o6QSvR5U4GpFd6AL9dx1RMIZJmg+y3s/iLq2sFGYLQ54iDLghCBc30CNcBlPrHecHZYKrYQcVhAUZcOpw5Wz7gMFJdJ7itgFtsvyB0jlzPc7H93aVjf+aOHTuG2dlZHDt2DNlsFs8//zwAYMuWLfB6vbj00ktx9tln48Mf/jC+8IUvYHJyEnfccQduuOEGcb67RMABxDNG6eSlKPjseieHxIrrAUlfF4RFpdJBFwRhWaMpMy3LAKsiWOt0UBlwAAN1dr41BWwZILxaU0EesGvA5gFpaSoIHUcc9K7TMbX2f/zHf8TOnTtx5513IhKJYOfOndi5cyeeeeYZAIDFYsFPfvITWCwW7N69Gx/60Idw1VVX4e677+7UkAQDRl35ui8zzjDBphEGJI4iCP2JdQQAEB5+q9SfC8IyRykz7cgIowuo8XbbeId93MOBAAWC3QKs9hJ2DFNX09UFYbkRHn4r/yL2f9FRRM0kKy09QqEQAoEAjk0dgd/v6/Zwep75BPB6UFW0NMl/RYqPLQrYNkhw2RZ7hIIgLCq5GjTfzJMSQW+CUCSOwYtuRTAYhN/v7/Zweh6x9d1HJ+DVWVWnkwqXm20bbH8HF0EQukCp7QfE/tehE7Ze/oQKZQw4WXF01MXpaQoEm8a9PFVut3zcw/1RxTEXhGVAfgfdv1Mi6IKwjNEUcNYQK6BrJRl2CrxmEMdcEPqI0uw5QOz/IiLSGkIVLiuwzk9YJ5s9giAAOSMtKq6CsNyxKO6WssqT16jhNYM45YLQh4hAbFeQP6dC20lngYkocDSocCykEEo2p/QqCMISJCcSA0Ai6IKwzLFogNfOP+KYC0IfIzvoi478SRXaBhEwGQVemFY4FVGYSQBTceC1eQ2vzCqkst0eoSAIC6LUQRcEQRAEof/JOegqsKG741gmiHMutI3pOHAyooGFYkp/gHgGeHVOQZcddEEQBEEQBEEQhCrEORfaAhFwKtKof4pCMqswl1i0IQmCIAiCIAiCIPQM4pwLbSGcAjJk1NyUMJNosQGqIAiCIAiCIAhCHyPOudAWMrqZoxTSUncuCIIgCIIgCIJQhTjnQluwWcwcRbCbOk4QhKVMePitotgqCIIgCMuIkHU1/yL2v6OIcy60Ba8NsGkEoJHim8KIq7uKcBkdOB0FDswovDStcGhOISit3gTBPPm2Kv6dYqAFQRAEYTkgLdUWDWu3ByD0B0oBa3yEI0EN7KBX1pYTPFZgwNGFweWIpYHX5hQyBUdcIZElBFMa/HbCpgAhlAKiaR67104I2HlugiCUYB0BMM2/r3ABp+NdHY4gCL0JETCXBKZiCoksoClg0AGMugkOybQThKWFdQTITCM8/Fb4Zp4U+98hZOdcaBtDTmCDX4emAICgUNxJ99uBLYPUNUc3q5c65sUWb/l/Qyngd1MKrwc1nI4Bp2PA4XkNL04rxNLdGbMgLGlKe55LBF0QhCbRCTg0r3AkqCGSBjK6QiqrcDoGvDStEEp2e4SCIFQhO+gdR5xzoa0Mu4A3jRI2+AkrPMAqL+HsYR1nDRKsXfy2zSZQ4pjXQpUk5Bed95TO/dmTImQnCNWIgy4IQoucjCiEUvlHpbaZ7fGheRGRFYQlSaWDLrQVcc6FtqMpdtJXewkrPYBrCRRPzCfNbNnXOkYhS8DpqOS2C0I9VGBDt4cgCEIPkdWBqRhgFDCfloxZQVia5Bx0of2Icy4sC7INd82NUJhJiGicIAiCILSDcJqL3xqjTAbWBUEQ+gdxzoVlAe/et+5d66QWcLYgCIIgCHnMBrt1MbyCICwzlkDC8cKg3F/4cDjc5ZEISxlHGoiGW4/AKxDCTlFuF4QqMhEgEwWF40BEclDzhKIJAEUbJSwMsfX9RSZjxiYT7E4gFFqUIQmC0CyZyLK3/Z2w9T3vnOcN9Tmbzu/ySARBEAShnHA4jEAg0O1h9Dxi6wVBEISlyszMTNtsvaIeD+vruo5Tp07B5/NBNdjWDIVCWLt2LY4fPw6/37+II2wfMofu0+vjB2QOSwWZw9KgU3MgIoTDYaxatQqaJhVkC8Wsre8l+uH/TyX9Nqd+mw8gc+oVZE69QTAYxLp16zA3N4eBgYG2XLPnd841TcOaNWtMH+/3+3v+CyFz6D69Pn5A5rBUkDksDToxB9kxbx/N2vpeoh/+/1TSb3Pqt/kAMqdeQebUG7QzCC/hfEEQBEEQBEEQBEHoMuKcC4IgCIIgCIIgCEKXWTbOucPhwJ133gmHw9HtobSMzKH79Pr4AZnDUkHmsDTohzkIvUk/fvf6bU79Nh9A5tQryJx6g07MqecF4QRBEARBEARBEASh11k2O+eCIAiCIAiCIAiCsFQR51wQBEEQBEEQBEEQuow454IgCIIgCIIgCILQZcQ5FwRBEARBEARBEIQu0zfO+T333IM9e/bA7XZjYGCg6vXf/e53uPLKK7F27Vq4XC7s2LEDX/va1wyvu2HDBiilyn4+//nPd2AGxnMAgGPHjuHyyy+H2+3G2NgYbr31VmQymYbXnZ2dxQc/+EH4/X4MDAzg2muvRSQS6cAMynn88cer3rv8z29+85u6573jHe+oOv5jH/tYx8dbj1a+A4lEAjfccAOGh4fh9XpxxRVX4PTp04s04nKOHj2Ka6+9Fhs3boTL5cLmzZtx5513IpVKNTyv25/DN77xDWzYsAFOpxO7du3C008/3fD4hx9+GNu3b4fT6cR5552Hn/3sZ4s00mruvfdeXHDBBfD5fBgbG8O73/1uHDx4sOE5DzzwQNX77XQ6F2nE1XzmM5+pGs/27dsbnrOUPgOg9v9dpRRuuOGGmscvtc9A6G36zaZX0i82vpJet/mV9OoaoJJeXhNU0g9rhEr6Yc1QSbfWENaFDnypkEql8N73vhe7d+/GfffdV/X6s88+i7GxMTz44INYu3Yt9u3bh+uvvx4WiwU33nhjw2vffffduO666wqPfT5f28cPGM8hm83i8ssvx/j4OPbt24eJiQlcddVVsNls+NznPlf3uh/84AcxMTGBn//850in07jmmmtw/fXX46GHHurIPPLs2bMHExMTZc/9wz/8Ax577DG85S1vaXjuddddh7vvvrvw2O12d2SMZmn2O3DTTTfhpz/9KR5++GEEAgHceOONeM973oMnn3yy00Ot4sCBA9B1Hd/+9rexZcsWvPjii7juuusQjUbxpS99qeG53focvv/97+Pmm2/Gt771LezatQtf/epXcdlll+HgwYMYGxurOn7fvn248sorce+99+Kd73wnHnroIbz73e/Gb3/7W5x77rmLMuZSnnjiCdxwww244IILkMlk8Pd//github.com/e49NJL8fLLL8Pj8dQ9z+/3lxlopdRiDLcu55xzDh599NHCY6u1vslYap8BAPzmN79BNpstPH7xxRfxJ3/yJ3jve99b95yl9hkIvUu/2fRK+snGV9LLNr+SXlwDVNLra4JK+mWNUEmvrxkq6doagvqM+++/nwKBgKljP/7xj9NFF13U8Jj169fTV77ylYUPrAnqzeFnP/sZaZpGk5OThef+5V/+hfx+PyWTyZrXevnllwkA/eY3vyk899//github.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.com/2UyWRausZifg4XXngh3XjjjYXH2WyWVq9e3VD8pTKiuXv37q4Ji+i6TjfccAOtWrWKXn311ZaukclkaNu2bXTTTTe1eXStEQ6HaXBwkL72ta/VfH2pfQal3HnnnTQ+Pk7pdLqp85baZyD0Jv1m0yvpNxtfSS/a/Ep6bQ1QSa+vCSrpxzVCJb28ZqhksdcQfeOcv/HGG/Tcc8/RXXfdRV6vl5577jl67rnnKBwOExGnso+OjtKHPvQhmpiYKPycOXOmcI2nnnqKtm3bRidOnCAion379tFXvvIVev755+nw4cP04IMP0ujoKF111VVdmUMmk6Fzzz2XLr30Unr++efpf/7nf2h0dJT27t1bdw5ERH/6p39KO3fupKeeeop+9atf0VlnnUVXXnllR+ZQi0cffbRuytiJEydo27Zt9NRTTxER0aFDh+juu++mZ555ho4cOUI/+tGPaNOmTfS2t71t0cZbipnvQOUciDhNad26dfSLX/yCnnnmGdq9ezft3r27G1OgEydO0JYtW+jiiy+mEydOlH3/S49ZSp/D9773PXI4HPTAAw/Qyy+/TNdffz0NDAwUVI0//OEP0+233144/sknnySr1Upf+tKX6JVXXqE777yTbDYb/f73v1+U8Vbyt3/7txQIBOjxxx8ve79jsVjhmMo53HXXXfTII4/Q4cOH6dlnn6X3v//95HQ66aWXXurGFOiWW26hxx9/nI4cOUJPPvkkXXLJJTQyMlL4m7nUP4M82WyW1q1bR7fddlvVa0v9MxB6m3616ZX0so2vpB9sfiW9uAaopNfXBJX0wxqhkn5ZM1TSjTVE3zjnH/nIR2rW2/zyl78kIo561Hp9/fr1hWv88pe/JAB05MgRIiJ69tlnadeuXRQIBMjpdNKOHTvoc5/7nOl683bPgYjo6NGj9Gd/9mfkcrloZGSEbrnllrJITuUciIhmZmboyiuvJK/XS36/n6655prC4mAxuPLKK2nPnj01Xzty5EjZHI8dO0Zve9vbaGhoiBwOB23ZsoVuvfVWCgaDizbeUsx8ByrnQEQUj8fp4x//OA0ODpLb7aa//Mu/LDOEi8n9999ftx4tz1L8HL7+9a/TunXryG6304UXXki//vWvC6+9/e1vp4985CNlx//gBz+grVu3kt1up3POOYd++tOfLtpYK6n3ft9//github.com/2FYyrn8OlPf7ow3xUrVtCf//mf029/+9vFH3yO973vfbRy5Uqy2+20evVqet/73ldWa7jUP4M8jzzyCAGggwcPVr221D8DobfpV5teSS/b+Er6weZX0qtrgEp6eU1QST+sESrplzVDJd1YQygiouYS4QVBEARBEARBEARBaCfLus+5IAiCIAiCIAiCICwFxDkXBEEQBEEQBEEQhC4jzrkgCIIgCIIgCIIgdBlxzgVBEARBEARBEAShy4hzLgiCIAiCIAiCIAhdRpxzQRAEQRAEQRAEQegy4pwLgiAIgiAIgiAIQpcR51wQBEEQBEEQBEEQuow454IgCIIgCIIgCILQZcQ5FwRBEARBEARBEIQuI865IAiCIAiCIAiCIHQZcc4FQRAEQRAEQRAEocv8f6Hlye8e405sAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Train\")\n",
    "plot_decision_boundary(model_4, X_blob_train, y_blob_train)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Test\")\n",
    "plot_decision_boundary(model_4, X_blob_test, y_blob_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p0vFchUQx7mc"
   },
   "source": [
    "## 9. More classification evaluation metrics\n",
    "\n",
    "So far we've only covered a couple of ways of evaluating a classification model (accuracy, loss and visualizing predictions).\n",
    "\n",
    "These are some of the most common methods you'll come across and are a good starting point.\n",
    "\n",
    "However, you may want to evaluate your classification model using more metrics such as the following:\n",
    "\n",
    "| **Metric name/Evaluation method** | **Defintion** | **Code** |\n",
    "| --- | --- | --- |\n",
    "| Accuracy | Out of 100 predictions, how many does your model get correct? E.g. 95% accuracy means it gets 95/100 predictions correct. | [`torchmetrics.Accuracy()`](https://torchmetrics.readthedocs.io/en/stable/classification/accuracy.html#id3) or [`sklearn.metrics.accuracy_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html) |\n",
    "| Precision | Proportion of true positives over total number of samples. Higher precision leads to less false positives (model predicts 1 when it should've been 0). | [`torchmetrics.Precision()`](https://torchmetrics.readthedocs.io/en/stable/classification/precision.html#id4) or [`sklearn.metrics.precision_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) |\n",
    "| Recall | Proportion of true positives over total number of true positives and false negatives (model predicts 0 when it should've been 1). Higher recall leads to less false negatives. | [`torchmetrics.Recall()`](https://torchmetrics.readthedocs.io/en/stable/classification/recall.html#id5) or [`sklearn.metrics.recall_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html) |\n",
    "| F1-score | Combines precision and recall into one metric. 1 is best, 0 is worst. | [`torchmetrics.F1Score()`](https://torchmetrics.readthedocs.io/en/stable/classification/f1_score.html#f1score) or [`sklearn.metrics.f1_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) |\n",
    "| [Confusion matrix](https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/)  | Compares the predicted values with the true values in a tabular way, if 100% correct, all values in the matrix will be top left to bottom right (diagnol line). | [`torchmetrics.ConfusionMatrix`](https://torchmetrics.readthedocs.io/en/stable/classification/confusion_matrix.html#confusionmatrix) or [`sklearn.metrics.plot_confusion_matrix()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html#sklearn.metrics.ConfusionMatrixDisplay.from_predictions) |\n",
    "| Classification report | Collection of some of the main classification metrics such as precision, recall and f1-score. | [`sklearn.metrics.classification_report()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html) |\n",
    "\n",
    "Scikit-Learn (a popular and world-class machine learning library) has many implementations of the above metrics and you're looking for a PyTorch-like version, check out [TorchMetrics](https://torchmetrics.readthedocs.io/en/latest/), especially the [TorchMetrics classification section](https://torchmetrics.readthedocs.io/en/stable/pages/classification.html). \n",
    "\n",
    "Let's try the `torchmetrics.Accuracy` metric out.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6_HrLXfutFFX",
    "outputId": "ca9af26f-3d97-4019-fd7d-105f1dc2e68c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.9950, device='cuda:0')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try:\n",
    "    from torchmetrics import Accuracy\n",
    "except:\n",
    "    !pip install torchmetrics==0.9.3 # this is the version we're using in this notebook (later versions exist here: https://torchmetrics.readthedocs.io/en/stable/generated/CHANGELOG.html#changelog)\n",
    "    from torchmetrics import Accuracy\n",
    "\n",
    "# Setup metric and make sure it's on the target device\n",
    "torchmetrics_accuracy = Accuracy(task='multiclass', num_classes=4).to(device)\n",
    "\n",
    "# Calculate accuracy\n",
    "torchmetrics_accuracy(y_preds, y_blob_test)"
   ]
  },
  {
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    "## Exercises\n",
    "\n",
    "All of the exercises are focused on practicing the code in the sections above.\n",
    "\n",
    "You should be able to complete them by referencing each section or by following the resource(s) linked.\n",
    "\n",
    "All exercises should be completed using [device-agonistic code](https://pytorch.org/docs/stable/notes/cuda.html#device-agnostic-code).\n",
    "\n",
    "Resources:\n",
    "* [Exercise template notebook for 02](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/exercises/02_pytorch_classification_exercises.ipynb)\n",
    "* [Example solutions notebook for 02](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/solutions/02_pytorch_classification_exercise_solutions.ipynb) (try the exercises *before* looking at this)\n",
    "\n",
    "1. Make a binary classification dataset with Scikit-Learn's [`make_moons()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html) function.\n",
    "    * For consistency, the dataset should have 1000 samples and a `random_state=42`.\n",
    "    * Turn the data into PyTorch tensors. Split the data into training and test sets using `train_test_split` with 80% training and 20% testing.\n",
    "2. Build a model by subclassing `nn.Module` that incorporates non-linear activation functions and is capable of fitting the data you created in 1.\n",
    "    * Feel free to use any combination of PyTorch layers (linear and non-linear) you want.\n",
    "3. Setup a binary classification compatible loss function and optimizer to use when training the model.\n",
    "4. Create a training and testing loop to fit the model you created in 2 to the data you created in 1.\n",
    "    * To measure model accuracy, you can create your own accuracy function or use the accuracy function in [TorchMetrics](https://torchmetrics.readthedocs.io/en/latest/).\n",
    "    * Train the model for long enough for it to reach over 96% accuracy.\n",
    "    * The training loop should output progress every 10 epochs of the model's training and test set loss and accuracy.\n",
    "5. Make predictions with your trained model and plot them using the `plot_decision_boundary()` function created in this notebook.\n",
    "6. Replicate the Tanh (hyperbolic tangent) activation function in pure PyTorch.\n",
    "    * Feel free to reference the [ML cheatsheet website](https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html#tanh) for the formula.\n",
    "7. Create a multi-class dataset using the [spirals data creation function from CS231n](https://cs231n.github.io/neural-networks-case-study/) (see below for the code).\n",
    "    * Construct a model capable of fitting the data (you may need a combination of linear and non-linear layers).\n",
    "    * Build a loss function and optimizer capable of handling multi-class data (optional extension: use the Adam optimizer instead of SGD, you may have to experiment with different values of the learning rate to get it working).\n",
    "    * Make a training and testing loop for the multi-class data and train a model on it to reach over 95% testing accuracy (you can use any accuracy measuring function here that you like).\n",
    "    * Plot the decision boundaries on the spirals dataset from your model predictions, the `plot_decision_boundary()` function should work for this dataset too.\n",
    "\n",
    "```python\n",
    "# Code for creating a spiral dataset from CS231n\n",
    "import numpy as np\n",
    "N = 100 # number of points per class\n",
    "D = 2 # dimensionality\n",
    "K = 3 # number of classes\n",
    "X = np.zeros((N*K,D)) # data matrix (each row = single example)\n",
    "y = np.zeros(N*K, dtype='uint8') # class labels\n",
    "for j in range(K):\n",
    "  ix = range(N*j,N*(j+1))\n",
    "  r = np.linspace(0.0,1,N) # radius\n",
    "  t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta\n",
    "  X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]\n",
    "  y[ix] = j\n",
    "# lets visualize the data\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)\n",
    "plt.show()\n",
    "```\n",
    "\n",
    "## Extra-curriculum\n",
    "\n",
    "* Write down 3 problems where you think machine classification could be useful (these can be anything, get creative as you like, for example, classifying credit card transactions as fraud or not fraud based on the purchase amount and purchase location features). \n",
    "* Research the concept of \"momentum\" in gradient-based optimizers (like SGD or Adam), what does it mean?\n",
    "* Spend 10-minutes reading the [Wikipedia page for different activation functions](https://en.wikipedia.org/wiki/Activation_function#Table_of_activation_functions), how many of these can you line up with [PyTorch's activation functions](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity)?\n",
    "* Research when accuracy might be a poor metric to use (hint: read [\"Beyond Accuracy\" by Will Koehrsen](https://willkoehrsen.github.io/statistics/learning/beyond-accuracy-precision-and-recall/) for ideas).\n",
    "* **Watch:** For an idea of what's happening within our neural networks and what they're doing to learn, watch [MIT's Introduction to Deep Learning video](https://youtu.be/7sB052Pz0sQ)."
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