diff --git a/extras/exercises/02_pytorch_classification_exercises.ipynb b/extras/exercises/02_pytorch_classification_exercises.ipynb
index 8592b6a8..1f99a5ca 100644
--- a/extras/exercises/02_pytorch_classification_exercises.ipynb
+++ b/extras/exercises/02_pytorch_classification_exercises.ipynb
@@ -1,640 +1,986 @@
{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
- "colab": {
- "name": "02_pytorch_classification_exercises.ipynb",
- "provenance": [],
- "collapsed_sections": [],
- "authorship_tag": "ABX9TyNloicnciRwCXd2bJo6F2iS",
- "include_colab_link": true
- },
- "kernelspec": {
- "name": "python3",
- "display_name": "Python 3"
- },
- "language_info": {
- "name": "python"
- },
- "accelerator": "GPU"
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "view-in-github"
+ },
+ "source": [
+ "
"
+ ]
},
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "view-in-github",
- "colab_type": "text"
- },
- "source": [
- "
"
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZKJFt7YxH8yl"
+ },
+ "source": [
+ "# 02. PyTorch Classification Exercises\n",
+ "\n",
+ "The following is a template for 02. PyTorch Classification exercises.\n",
+ "\n",
+ "It's only starter code and it's your job to fill in the blanks.\n",
+ "\n",
+ "Because of the flexibility of PyTorch, there may be more than one way to answer the question.\n",
+ "\n",
+ "Don't worry about trying to be *right* just try writing code that suffices the question.\n",
+ "\n",
+ "## Resources\n",
+ "* These exercises are based on [notebook 02 of the learn PyTorch course](https://www.learnpytorch.io/02_pytorch_classification/).\n",
+ "* You can see one form of [solutions on GitHub](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/extras/solutions) (but try the exercises below yourself first!)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 216,
+ "metadata": {
+ "id": "CSrUPgapO0tf"
+ },
+ "outputs": [],
+ "source": [
+ "# Import torch\n",
+ "import torch\n",
+ "\n",
+ "# Setup device agnostic code\n",
+ "\n",
+ "\n",
+ "# Setup random seed\n",
+ "RANDOM_SEED = 42"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pH7jIZ2SPFee"
+ },
+ "source": [
+ "## 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. \n",
+ " * Split the data into training and test sets using `train_test_split` with 80% training and 20% testing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 217,
+ "metadata": {
+ "id": "5t4VhPV1PX1X"
+ },
+ "outputs": [],
+ "source": [
+ "# Create a dataset with Scikit-Learn's make_moons()\n",
+ "from sklearn.datasets import make_moons"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 218,
+ "metadata": {
+ "id": "SUeHZ3-3P9C7"
+ },
+ "outputs": [],
+ "source": [
+ "# Turn data into a DataFrame\n",
+ "import pandas as pd\n",
+ "X, y = make_moons(n_samples=1000, noise=0.1, random_state=42)\n",
+ "moons = pd.DataFrame({\"X1\": X[:,0], \"X2\": X[:,1], \"label\": y})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 219,
+ "metadata": {
+ "id": "owrkPSFvQPFI"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
]
+ },
+ "execution_count": 219,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "markdown",
- "source": [
- "# 02. PyTorch Classification Exercises\n",
- "\n",
- "The following is a template for 02. PyTorch Classification exercises.\n",
- "\n",
- "It's only starter code and it's your job to fill in the blanks.\n",
- "\n",
- "Because of the flexibility of PyTorch, there may be more than one way to answer the question.\n",
- "\n",
- "Don't worry about trying to be *right* just try writing code that suffices the question.\n",
- "\n",
- "## Resources\n",
- "* These exercises are based on [notebook 02 of the learn PyTorch course](https://www.learnpytorch.io/02_pytorch_classification/).\n",
- "* You can see one form of [solutions on GitHub](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/extras/solutions) (but try the exercises below yourself first!)."
- ],
- "metadata": {
- "id": "ZKJFt7YxH8yl"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "# Import torch\n",
- "import torch\n",
- "\n",
- "# Setup device agnostic code\n",
- "\n",
- "\n",
- "# Setup random seed\n",
- "RANDOM_SEED = 42"
- ],
- "metadata": {
- "id": "CSrUPgapO0tf"
- },
- "execution_count": 1,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## 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. \n",
- " * Split the data into training and test sets using `train_test_split` with 80% training and 20% testing."
- ],
- "metadata": {
- "id": "pH7jIZ2SPFee"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "# Create a dataset with Scikit-Learn's make_moons()\n",
- "from sklearn.datasets import make_moons"
- ],
- "metadata": {
- "id": "5t4VhPV1PX1X"
- },
- "execution_count": 2,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Turn data into a DataFrame\n",
- "import pandas as pd\n"
- ],
- "metadata": {
- "id": "SUeHZ3-3P9C7"
- },
- "execution_count": 3,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Visualize the data on a scatter plot\n",
- "import matplotlib.pyplot as plt\n"
- ],
- "metadata": {
- "id": "owrkPSFvQPFI"
- },
- "execution_count": 4,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Turn data into tensors of dtype float\n",
- "\n",
- "\n",
- "# Split the data into train and test sets (80% train, 20% test)\n",
- "from sklearn.model_selection import train_test_split\n"
- ],
- "metadata": {
- "id": "bDhyHn9fR4dq"
- },
- "execution_count": 5,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## 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."
- ],
- "metadata": {
- "id": "cMIjxZdzQfPz"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "import torch\n",
- "from torch import nn\n",
- "\n",
- "# Inherit from nn.Module to make a model capable of fitting the mooon data\n",
- "class MoonModelV0(nn.Module):\n",
- " ## Your code here ##\n",
- "\n",
- " def forward(self, x):\n",
- " ## Your code here ##\n",
- " return \n",
- "\n",
- "# Instantiate the model\n",
- "## Your code here ##"
- ],
- "metadata": {
- "id": "hwtyvm34Ri6Q"
- },
- "execution_count": 6,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## 3. Setup a binary classification compatible loss function and optimizer to use when training the model built in 2."
- ],
- "metadata": {
- "id": "DSj97RwyVeFE"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "# Setup loss function\n",
- "\n",
- "# Setup optimizer to optimize model's parameters"
- ],
- "metadata": {
- "id": "whSGw5qgVvxU"
- },
- "execution_count": 7,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## 4. Create a training and testing loop to fit the model you created in 2 to the data you created in 1.\n",
- " * Do a forward pass of the model to see what's coming out in the form of logits, prediction probabilities and labels.\n",
- " * To measure model accuray, 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."
- ],
- "metadata": {
- "id": "nvk4PfNTWUAt"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "# What's coming out of our model?\n",
- "\n",
- "# logits (raw outputs of model)\n",
- "print(\"Logits:\")\n",
- "## Your code here ##\n",
- "\n",
- "# Prediction probabilities\n",
- "print(\"Pred probs:\")\n",
- "## Your code here ##\n",
- "\n",
- "# Prediction labels\n",
- "print(\"Pred labels:\")\n",
- "## Your code here ##"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "AgnFdlamd2-D",
- "outputId": "627d8c33-071e-4925-f18b-5d5ba6126729"
- },
- "execution_count": 8,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Logits:\n",
- "Pred probs:\n",
- "Pred labels:\n"
- ]
- }
+ "data": {
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",
+ "text/plain": [
+ ""
]
- },
- {
- "cell_type": "code",
- "source": [
- "# Let's calculuate the accuracy using accuracy from TorchMetrics\n",
- "!pip -q install torchmetrics # Colab doesn't come with torchmetrics\n",
- "from torchmetrics import Accuracy\n",
- "\n",
- "## TODO: Uncomment this code to use the Accuracy function\n",
- "# acc_fn = Accuracy(task=\"multiclass\", num_classes=2).to(device) # send accuracy function to device\n",
- "# acc_fn"
- ],
- "metadata": {
- "id": "rUSDNHB4euoJ"
- },
- "execution_count": 9,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "## TODO: Uncomment this to set the seed\n",
- "# torch.manual_seed(RANDOM_SEED)\n",
- "\n",
- "# Setup epochs\n",
- "\n",
- "\n",
- "# Send data to the device\n",
- "\n",
- "\n",
- "# Loop through the data\n",
- "# for epoch in range(epochs):\n",
- " ### Training\n",
- " \n",
- "\n",
- " # 1. Forward pass (logits output)\n",
- " \n",
- " # Turn logits into prediction probabilities\n",
- " \n",
- "\n",
- " # Turn prediction probabilities into prediction labels\n",
- " \n",
- "\n",
- " # 2. Calculaute the loss\n",
- " # loss = loss_fn(y_logits, y_train) # loss = compare model raw outputs to desired model outputs\n",
- "\n",
- " # Calculate the accuracy\n",
- " # acc = acc_fn(y_pred, y_train.int()) # the accuracy function needs to compare pred labels (not logits) with actual labels\n",
- "\n",
- " # 3. Zero the gradients\n",
- " \n",
- "\n",
- " # 4. Loss backward (perform backpropagation) - https://brilliant.org/wiki/backpropagation/#:~:text=Backpropagation%2C%20short%20for%20%22backward%20propagation,to%20the%20neural%20network's%20weights.\n",
- " \n",
- " # 5. Step the optimizer (gradient descent) - https://towardsdatascience.com/gradient-descent-algorithm-a-deep-dive-cf04e8115f21#:~:text=Gradient%20descent%20(GD)%20is%20an,e.g.%20in%20a%20linear%20regression) \n",
- " \n",
- "\n",
- " ### Testing\n",
- " # model_0.eval() \n",
- " # with torch.inference_mode():\n",
- " # 1. Forward pass (to get the logits)\n",
- " \n",
- " # Turn the test logits into prediction labels\n",
- " \n",
- "\n",
- " # 2. Caculate the test loss/acc\n",
- " \n",
- "\n",
- " # Print out what's happening every 100 epochs\n",
- " # if epoch % 100 == 0:\n",
- " "
- ],
- "metadata": {
- "id": "SHBY3h7XXnxt"
- },
- "execution_count": 10,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## 5. Make predictions with your trained model and plot them using the `plot_decision_boundary()` function created in this notebook."
- ],
- "metadata": {
- "id": "8Nwihtomj9JO"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "# Plot the model predictions\n",
- "import numpy as np\n",
- "\n",
- "def plot_decision_boundary(model, X, y):\n",
- " \n",
- " # Put everything to CPU (works better with NumPy + Matplotlib)\n",
- " model.to(\"cpu\")\n",
- " X, y = X.to(\"cpu\"), y.to(\"cpu\")\n",
- "\n",
- " # Source - https://madewithml.com/courses/foundations/neural-networks/ \n",
- " # (with modifications)\n",
- " x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n",
- " y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n",
- " xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101), \n",
- " np.linspace(y_min, y_max, 101))\n",
- "\n",
- " # Make features\n",
- " X_to_pred_on = torch.from_numpy(np.column_stack((xx.ravel(), yy.ravel()))).float()\n",
- "\n",
- " # Make predictions\n",
- " model.eval()\n",
- " with torch.inference_mode():\n",
- " y_logits = model(X_to_pred_on)\n",
- "\n",
- " # Test for multi-class or binary and adjust logits to prediction labels\n",
- " if len(torch.unique(y)) > 2:\n",
- " y_pred = torch.softmax(y_logits, dim=1).argmax(dim=1) # mutli-class\n",
- " else: \n",
- " y_pred = torch.round(torch.sigmoid(y_logits)) # binary\n",
- " \n",
- " # Reshape preds and plot\n",
- " y_pred = y_pred.reshape(xx.shape).detach().numpy()\n",
- " plt.contourf(xx, yy, y_pred, cmap=plt.cm.RdYlBu, alpha=0.7)\n",
- " plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n",
- " plt.xlim(xx.min(), xx.max())\n",
- " plt.ylim(yy.min(), yy.max())"
- ],
- "metadata": {
- "id": "0YRzatb8a1P2"
- },
- "execution_count": 11,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Plot decision boundaries for training and test sets\n"
- ],
- "metadata": {
- "id": "PMrcpyirig1d"
- },
- "execution_count": 12,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## 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."
- ],
- "metadata": {
- "id": "EtMYBvtciiAU"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "# Create a straight line tensor\n"
- ],
- "metadata": {
- "id": "BlXaWC5TkEUE"
- },
- "execution_count": 13,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Test torch.tanh() on the tensor and plot it\n"
- ],
- "metadata": {
- "id": "vZPCcQmIkZjO"
- },
- "execution_count": 14,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Replicate torch.tanh() and plot it\n"
- ],
- "metadata": {
- "id": "J-ne__Kjkdc1"
- },
- "execution_count": 15,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## 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",
- " * Split the data into training and test sets (80% train, 20% test) as well as turn it into PyTorch tensors.\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) - 1000 epochs should be plenty.\n",
- " * Plot the decision boundaries on the spirals dataset from your model predictions, the `plot_decision_boundary()` function should work for this dataset too."
- ],
- "metadata": {
- "id": "Lbt1bNcWk5G9"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "# Code for creating a spiral dataset from CS231n\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "RANDOM_SEED = 42\n",
- "np.random.seed(RANDOM_SEED)\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.RdYlBu)\n",
- "plt.show()"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 265
- },
- "id": "tU-UNZsKlJls",
- "outputId": "8b7b745a-070d-4ecb-c639-c4ee4d8eae06"
- },
- "execution_count": 16,
- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- }
- }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Visualize the data on a scatter plot\n",
+ "import matplotlib.pyplot as plt\n",
+ "plt.scatter(x=X[:,0], y=X[:, 1], c=y, cmap=plt.cm.RdYlBu)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 220,
+ "metadata": {
+ "id": "bDhyHn9fR4dq"
+ },
+ "outputs": [],
+ "source": [
+ "# Turn data into tensors of dtype float\n",
+ "X = torch.from_numpy(X).type(torch.float)\n",
+ "y = torch.from_numpy(y).type(torch.float)\n",
+ "\n",
+ "# Split the data into train and test sets (80% train, 20% test)\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cMIjxZdzQfPz"
+ },
+ "source": [
+ "## 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."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 221,
+ "metadata": {
+ "id": "hwtyvm34Ri6Q"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MoonModelV0(\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": [
+ "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\n",
+ "\n",
+ "# Inherit from nn.Module to make a model capable of fitting the mooon data\n",
+ "class MoonModelV0(nn.Module):\n",
+ " ## Your code here ##\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() \n",
+ " def forward(self, x):\n",
+ " ## Your code here ##\n",
+ " y = self.layer_1(x)\n",
+ " y = self.relu(y)\n",
+ " y = self.layer_2(y)\n",
+ " y = self.relu(y)\n",
+ " y = self.layer_3(y) \n",
+ " return y\n",
+ " \n",
+ "# Instantiate the model\n",
+ "## Your code here ##\n",
+ "model_0 = MoonModelV0().to(device)\n",
+ "print(model_0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DSj97RwyVeFE"
+ },
+ "source": [
+ "## 3. Setup a binary classification compatible loss function and optimizer to use when training the model built in 2."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 222,
+ "metadata": {
+ "id": "whSGw5qgVvxU"
+ },
+ "outputs": [],
+ "source": [
+ "# Setup loss function\n",
+ "loss_fn = torch.nn.BCEWithLogitsLoss()\n",
+ "\n",
+ "# Setup optimizer to optimize model's parameters\n",
+ "optimizer = torch.optim.Adam(params=model_0.parameters(), lr=0.01)\n",
+ "#optimizer = torch.optim.SGD(params=model_0.parameters(), lr=0.1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nvk4PfNTWUAt"
+ },
+ "source": [
+ "## 4. Create a training and testing loop to fit the model you created in 2 to the data you created in 1.\n",
+ " * Do a forward pass of the model to see what's coming out in the form of logits, prediction probabilities and labels.\n",
+ " * To measure model accuray, 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."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 223,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "AgnFdlamd2-D",
+ "outputId": "627d8c33-071e-4925-f18b-5d5ba6126729"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Logits:\n",
+ "tensor([[ 3.3151e-02],\n",
+ " [ 3.5847e-02],\n",
+ " [-3.0447e-03],\n",
+ " [ 3.7900e-02],\n",
+ " [ 9.7156e-05],\n",
+ " [ 1.4751e-03],\n",
+ " [ 3.6681e-02],\n",
+ " [ 2.9405e-02],\n",
+ " [-1.0985e-02],\n",
+ " [ 2.5357e-02]], device='cuda:0')\n",
+ "Pred probs:\n",
+ "tensor([[0.5083],\n",
+ " [0.5090],\n",
+ " [0.4992],\n",
+ " [0.5095],\n",
+ " [0.5000],\n",
+ " [0.5004],\n",
+ " [0.5092],\n",
+ " [0.5074],\n",
+ " [0.4973],\n",
+ " [0.5063]], device='cuda:0')\n",
+ "Pred labels:\n",
+ "tensor([[1.],\n",
+ " [1.],\n",
+ " [0.],\n",
+ " [1.],\n",
+ " [1.],\n",
+ " [1.],\n",
+ " [1.],\n",
+ " [1.],\n",
+ " [0.],\n",
+ " [1.]], device='cuda:0')\n"
+ ]
+ }
+ ],
+ "source": [
+ "# What's coming out of our model?\n",
+ " \n",
+ "# logits (raw outputs of model)\n",
+ "print(\"Logits:\")\n",
+ "## Your code here ##\n",
+ "model_0.eval()\n",
+ "with torch.inference_mode():\n",
+ " y_logits_0 = model_0(X_test.to(device))[:10]\n",
+ "print(y_logits_0)\n",
+ " \n",
+ "# Prediction probabilities\n",
+ "print(\"Pred probs:\")\n",
+ "## Your code here ##\n",
+ "y_preds_0 = torch.sigmoid(y_logits_0)\n",
+ "print(y_preds_0)\n",
+ "\n",
+ "# Prediction labels\n",
+ "print(\"Pred labels:\")\n",
+ "## Your code here ##\n",
+ "y_labels_0 = torch.round(y_preds_0)\n",
+ "print(y_labels_0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 224,
+ "metadata": {
+ "id": "rUSDNHB4euoJ"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Accuracy()"
]
- },
- {
- "cell_type": "code",
- "source": [
- "# Turn data into tensors\n",
- "import torch\n",
- "X = torch.from_numpy(X).type(torch.float) # features as float32\n",
- "y = torch.from_numpy(y).type(torch.LongTensor) # labels need to be of type long\n",
- "\n",
- "# Create train and test splits\n",
- "from sklearn.model_selection import train_test_split\n"
- ],
- "metadata": {
- "id": "OWVrmkEyl0VP"
- },
- "execution_count": 17,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Let's calculuate the accuracy for when we fit our model\n",
- "!pip -q install torchmetrics # colab doesn't come with torchmetrics\n",
- "from torchmetrics import Accuracy\n",
- "\n",
- "## TODO: uncomment the two lines below to send the accuracy function to the device\n",
- "# acc_fn = Accuracy(task=\"multiclass\", num_classes=4).to(device)\n",
- "# acc_fn"
- ],
- "metadata": {
- "id": "a-v-7f0op0tG"
- },
- "execution_count": 18,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Prepare device agnostic code\n",
- "# device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
- "\n",
- "# Create model by subclassing nn.Module\n",
- "\n",
- "\n",
- "\n",
- "# Instantiate model and send it to device\n"
- ],
- "metadata": {
- "id": "DB3u3ldumapf"
- },
- "execution_count": 19,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Setup data to be device agnostic\n",
- "\n",
- "\n",
- "# Print out first 10 untrained model outputs (forward pass)\n",
- "print(\"Logits:\")\n",
- "## Your code here ##\n",
- "\n",
- "print(\"Pred probs:\")\n",
- "## Your code here ##\n",
- "\n",
- "print(\"Pred labels:\")\n",
- "## Your code here ##"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "QE7XWSSunMTS",
- "outputId": "00b31909-87c9-41e3-9dbb-fb4c4bd3aabd"
- },
- "execution_count": 20,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Logits:\n",
- "Pred probs:\n",
- "Pred labels:\n"
- ]
- }
+ },
+ "execution_count": 224,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Let's calculuate the accuracy using accuracy from TorchMetrics\n",
+ "!pip -q install torchmetrics \n",
+ "# Colab doesn't come with torchmetrics\n",
+ "from torchmetrics import Accuracy\n",
+ "\n",
+ "## TODO: Uncomment this code to use the Accuracy function\n",
+ "#acc_fn = Accuracy(task=\"multiclass\", num_classes=2, is_multilabel=True).to(device)# # send accuracy function to device\n",
+ "acc_fn = Accuracy(task=\"binary\").to(device)\n",
+ "acc_fn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 225,
+ "metadata": {
+ "id": "SHBY3h7XXnxt"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 0 | Loss: 0.69525, Accuracy: 0.38% | Test loss: 0.68871, Test acc: 0.71%\n",
+ "Epoch: 100 | Loss: 0.15394, Accuracy: 0.94% | Test loss: 0.13726, Test acc: 0.95%\n",
+ "Epoch: 200 | Loss: 0.02263, Accuracy: 0.99% | Test loss: 0.01442, Test acc: 1.00%\n",
+ "Epoch: 300 | Loss: 0.01141, Accuracy: 1.00% | Test loss: 0.00572, Test acc: 1.00%\n",
+ "Epoch: 400 | Loss: 0.00843, Accuracy: 1.00% | Test loss: 0.00351, Test acc: 1.00%\n",
+ "Epoch: 500 | Loss: 0.00706, Accuracy: 1.00% | Test loss: 0.00246, Test acc: 1.00%\n",
+ "Epoch: 600 | Loss: 0.00627, Accuracy: 1.00% | Test loss: 0.00185, Test acc: 1.00%\n",
+ "Epoch: 700 | Loss: 0.00576, Accuracy: 1.00% | Test loss: 0.00144, Test acc: 1.00%\n",
+ "Epoch: 800 | Loss: 0.00540, Accuracy: 1.00% | Test loss: 0.00115, Test acc: 1.00%\n",
+ "Epoch: 900 | Loss: 0.00513, Accuracy: 1.00% | Test loss: 0.00094, Test acc: 1.00%\n"
+ ]
+ }
+ ],
+ "source": [
+ "## TODO: Uncomment this to set the seed\n",
+ "torch.manual_seed(RANDOM_SEED)\n",
+ "\n",
+ "# Setup epochs\n",
+ "epochs = 1000\n",
+ "\n",
+ "# Send data to the 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",
+ "# Loop through the data\n",
+ "for epoch in range(epochs):\n",
+ " ### Training \n",
+ " \n",
+ " # 1. Forward pass (logits output)\n",
+ " y_logits = model_0(X_train).squeeze()\n",
+ " \n",
+ " # Turn logits into prediction probabilities\n",
+ " y_prob = torch.sigmoid(y_logits)\n",
+ " \n",
+ " # Turn prediction probabilities into prediction labels\n",
+ " y_pred = torch.round(y_prob)\n",
+ " \n",
+ " # 2. Calculaute the loss\n",
+ " loss = loss_fn(y_logits, y_train) # loss = compare model raw outputs to desired model outputs\n",
+ " \n",
+ " # Calculate the accuracy\n",
+ " acc = acc_fn(y_pred, y_train.int()) # the accuracy function needs to compare pred labels (not logits) with actual labels\n",
+ " \n",
+ " # 3. Zero the gradients\n",
+ " optimizer.zero_grad()\n",
+ " \n",
+ " # 4. Loss backward (perform backpropagation) - https://brilliant.org/wiki/backpropagation/#:~:text=Backpropagation%2C%20short%20for%20%22backward%20propagation,to%20the%20neural%20network's%20weights.\n",
+ " loss.backward()\n",
+ " \n",
+ " # 5. Step the optimizer (gradient descent) - https://towardsdatascience.com/gradient-descent-algorithm-a-deep-dive-cf04e8115f21#:~:text=Gradient%20descent%20(GD)%20is%20an,e.g.%20in%20a%20linear%20regression) \n",
+ " optimizer.step()\n",
+ " \n",
+ " ### Testing\n",
+ " model_0.eval() \n",
+ " with torch.inference_mode():\n",
+ " # 1. Forward pass (to get the logits)\n",
+ " test_logits = model_0(X_test).squeeze() \n",
+ " \n",
+ " # Turn the test logits into prediction labels\n",
+ " test_pred = torch.round(torch.sigmoid(test_logits))\n",
+ " \n",
+ " # 2. Caculate the test loss/acc\n",
+ " test_loss = loss_fn(test_logits, y_test.float())\n",
+ " test_acc = acc_fn(test_pred, y_test.int()) \n",
+ " # Print out what's happening every 100 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}%\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8Nwihtomj9JO"
+ },
+ "source": [
+ "## 5. Make predictions with your trained model and plot them using the `plot_decision_boundary()` function created in this notebook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 226,
+ "metadata": {
+ "id": "0YRzatb8a1P2"
+ },
+ "outputs": [],
+ "source": [
+ "# Plot the model predictions\n",
+ "import numpy as np\n",
+ "\n",
+ "def plot_decision_boundary(model, X, y):\n",
+ " \n",
+ " # Put everything to CPU (works better with NumPy + Matplotlib)\n",
+ " model.to(\"cpu\")\n",
+ " X, y = X.to(\"cpu\"), y.to(\"cpu\")\n",
+ "\n",
+ " # Source - https://madewithml.com/courses/foundations/neural-networks/ \n",
+ " # (with modifications)\n",
+ " x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n",
+ " y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n",
+ " xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101), \n",
+ " np.linspace(y_min, y_max, 101))\n",
+ "\n",
+ " # Make features\n",
+ " X_to_pred_on = torch.from_numpy(np.column_stack((xx.ravel(), yy.ravel()))).float()\n",
+ "\n",
+ " # Make predictions\n",
+ " model.eval()\n",
+ " with torch.inference_mode():\n",
+ " y_logits = model(X_to_pred_on)\n",
+ "\n",
+ " # Test for multi-class or binary and adjust logits to prediction labels\n",
+ " if len(torch.unique(y)) > 2:\n",
+ " y_pred = torch.softmax(y_logits, dim=1).argmax(dim=1) # mutli-class\n",
+ " else: \n",
+ " y_pred = torch.round(torch.sigmoid(y_logits)) # binary\n",
+ " \n",
+ " # Reshape preds and plot\n",
+ " y_pred = y_pred.reshape(xx.shape).detach().numpy()\n",
+ " plt.contourf(xx, yy, y_pred, cmap=plt.cm.RdYlBu, alpha=0.7)\n",
+ " plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n",
+ " plt.xlim(xx.min(), xx.max())\n",
+ " plt.ylim(yy.min(), yy.max())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 227,
+ "metadata": {
+ "id": "PMrcpyirig1d"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
]
+ },
+ "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[:,[0,1]], y_train)\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.title(\"Test\")\n",
+ "plot_decision_boundary(model_0, X_test[:,[0,1]], y_test)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EtMYBvtciiAU"
+ },
+ "source": [
+ "## 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."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 238,
+ "metadata": {
+ "id": "BlXaWC5TkEUE"
+ },
+ "outputs": [],
+ "source": [
+ "# Create a straight line tensor\n",
+ "A = torch.arange(-10, 10, 0.1, dtype=torch.float32)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 239,
+ "metadata": {
+ "id": "vZPCcQmIkZjO"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 239,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "code",
- "source": [
- "# Setup loss function and optimizer\n",
- "# loss_fn =\n",
- "# optimizer = "
- ],
- "metadata": {
- "id": "54EqLRKLo0AW"
- },
- "execution_count": 21,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Build a training loop for the model\n",
- "\n",
- "# Loop over data\n",
- "\n",
- "\n",
- " ## Training\n",
- " \n",
- " # 1. Forward pass\n",
- " \n",
- "\n",
- " # 2. Calculate the loss\n",
- " \n",
- " \n",
- " # 3. Optimizer zero grad\n",
- " \n",
- "\n",
- " # 4. Loss backward\n",
- " \n",
- "\n",
- " # 5. Optimizer step\n",
- " \n",
- "\n",
- " ## Testing\n",
- " \n",
- "\n",
- " # 1. Forward pass\n",
- " \n",
- " # 2. Caculate loss and acc\n",
- " \n",
- " # Print out what's happening every 100 epochs\n",
- " "
- ],
- "metadata": {
- "id": "vIlExkUHnmxi"
- },
- "execution_count": 22,
- "outputs": []
+ "data": {
+ "image/png": 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yvAnGnKICAhsBBwDO+vfhugnGSTYmGAOBjoADAGfVn55i9AYIfAQcADhrx9kL/DH/Bgh8BBwAOIsJxoB5EHAAQNLp6hrtLT8liVNUgBkQcABA0ldHTsowpLjOViYYAybQIQHnhRdeUGpqqiIjI5Wenq5PP/20ybZr166VxWJp8Pjyyy892i1fvlxDhgyR1WrVkCFDtGLFCm93A4CJfVnikCQNTorxcSUA2oPXA86yZcs0a9YsPfrooyooKNC4ceM0adIkFRUVNfu+PXv2qKSkxP3o37+/+7X8/HxlZ2crJydH27ZtU05OjqZOnaqNGzd6uzsATOrL0hOSpEGJBBzADCyGYRje/AJjxozRyJEj9eKLL7q3DR48WLfeeqsWLFjQoP3atWt13XXX6fjx4+rSpUuj+8zOzpbD4dAHH3zg3jZx4kR17dpVS5YsabEmh8Mhm80mu92u2NjYtncKgOlkv5SvjfuO6U93DNft6b18XQ6ARrTl89urIzjV1dXavHmzsrKyPLZnZWVp/fr1zb53xIgRSkpK0g033KA1a9Z4vJafn99gnxMmTGhyn1VVVXI4HB4PAKhnGMa5ERxOUQGm4NWAU15ertraWiUkJHhsT0hIUGlpaaPvSUpK0ssvv6zly5frrbfe0sCBA3XDDTfok08+cbcpLS1t0z4XLFggm83mfqSkpFxizwCYSamjUvYzToWGWHR5fGdflwOgHYR1xBexWCwezw3DaLCt3sCBAzVw4ED384yMDBUXF+u///u/dc0111zUPufOnavZs2e7nzscDkIOALfdZycY9+sRLWtYqI+rAdAevDqCExcXp9DQ0AYjK2VlZQ1GYJozduxYFRYWup8nJia2aZ9Wq1WxsbEeDwCot7ukfoIxvxsAs/BqwImIiFB6erry8vI8tufl5SkzM7PV+ykoKFBSUpL7eUZGRoN9fvjhh23aJwDUY/4NYD5eP0U1e/Zs5eTkaNSoUcrIyNDLL7+soqIizZgxQ1Ld6aNDhw7ptddekyQ9/fTT6tu3r4YOHarq6mq98cYbWr58uZYvX+7e50MPPaRrrrlGTzzxhG655Ra98847Wr16tT777DNvdweACbmvgcMIDmAaXg842dnZOnr0qObPn6+SkhKlpaVp5cqV6tOnjySppKTE45o41dXVeuSRR3To0CF16tRJQ4cO1fvvv6/vfve77jaZmZlaunSpfvWrX+mxxx5Tv379tGzZMo0ZM8bb3QFgMpXOWvctGhjBAczD69fB8UdcBwdAvZ2H7Jr8l8/UJSpcBY/d1ORiBQC+5zfXwQEAf1dYVjf/ZkB8DOEGMBECDoCgVnjkpCSpfwLXvwHMhIADIKgVlp0NOFzgDzAVAg6AoPZNfcBJYIIxYCYEHABBq6qmVvuP1q2gYgQHMBcCDoCgta/8lFyGFBsZph4xVl+XA6AdEXAABK1zE4xZQQWYDQEHQNBigjFgXgQcAEHr67PXwLmcgAOYDgEHQNA6/xQVAHMh4AAISs5al/aVs4IKMCsCDoCgdODoKdW4DEVHhCrJFunrcgC0MwIOgKBUf3rqclZQAaZEwAEQlOpXUF3eg9NTgBkRcAAEpW++rQs4/eKjfVwJAG8g4AAISnu/rZtgfFkcIziAGRFwAAQdwzC0t34EpwcjOIAZEXAABJ2yE1U6VV2rEIvUu3uUr8sB4AUEHABBp37+TUq3KFnDQn1cDQBvIOAACDrn5t9wegowKwIOgKDjDjgsEQdMi4ADIOjsK687RXUZE4wB0yLgAAg6e8tZIg6YHQEHQFCpqqlV8bHTklgiDpgZAQdAUCk6elouQ+psDVOPGKuvywHgJQQcAEHlG/cE42husgmYGAEHQFDZWz/BmCXigKkRcAAEFZaIA8GBgAMgqNTfgyqVERzA1Ag4AILK/qN1K6gIOIC5EXAABA37GaeOnaqWJPUl4ACm1iEB54UXXlBqaqoiIyOVnp6uTz/9tMm2b731lm666Sb16NFDsbGxysjI0L/+9S+PNosWLZLFYmnwqKys9HZXAASwA0fr5t/EdbaqszXMx9UA8CavB5xly5Zp1qxZevTRR1VQUKBx48Zp0qRJKioqarT9J598optuukkrV67U5s2bdd111+nmm29WQUGBR7vY2FiVlJR4PCIjI73dHQABbN/ZKxinxkX5uBIA3ub1/8I89dRTmj59uu677z5J0tNPP61//etfevHFF7VgwYIG7Z9++mmP57///e/1zjvv6L333tOIESPc2y0WixITE71aOwBz2V9eN/+mb3dOTwFm59URnOrqam3evFlZWVke27OysrR+/fpW7cPlcunEiRPq1q2bx/aTJ0+qT58+6tWrlyZPntxghOd8VVVVcjgcHg8Awaf+FBXzbwDz82rAKS8vV21trRISEjy2JyQkqLS0tFX7+NOf/qRTp05p6tSp7m2DBg3SokWL9O6772rJkiWKjIzU1VdfrcLCwkb3sWDBAtlsNvcjJSXl4jsFIGDtqw84jOAAptchk4wvvBy6YRitukT6kiVLNG/ePC1btkzx8fHu7WPHjtUPf/hDDR8+XOPGjdM//vEPDRgwQH/5y18a3c/cuXNlt9vdj+Li4kvrEICAtL+8fgSHOTiA2Xl1Dk5cXJxCQ0MbjNaUlZU1GNW50LJlyzR9+nT985//1I033ths25CQEF111VVNjuBYrVZZrdxUDwhm9tNOHT/tlMQIDhAMvDqCExERofT0dOXl5Xlsz8vLU2ZmZpPvW7Jkie699179z//8j773ve+1+HUMw9DWrVuVlJR0yTUDMKf9Z09PxcdYFc0SccD0vP5TPnv2bOXk5GjUqFHKyMjQyy+/rKKiIs2YMUNS3emjQ4cO6bXXXpNUF27uvvtuPfPMMxo7dqx79KdTp06y2WySpMcff1xjx45V//795XA49Oyzz2rr1q16/vnnvd0dAAFqPxOMgaDi9YCTnZ2to0ePav78+SopKVFaWppWrlypPn36SJJKSko8ronz0ksvqaamRvfff7/uv/9+9/Z77rlHixYtkiRVVFToxz/+sUpLS2Wz2TRixAh98sknGj16tLe7AyBA1V8Dp2935t8AwcBiGIbh6yI6msPhkM1mk91uV2xsrK/LAdABZi0t0NtbD+vnEwfqp+Mv93U5AC5CWz6/uRcVgKDgvskmE4yBoEDAARAUmIMDBBcCDgDTs592quLsEvE+zMEBggIBB4DpHThWN3rTI8aqqAiWiAPBgIADwPQOnJ1/06cbozdAsCDgADC9omN1Aac3p6eAoEHAAWB69XcR79ONCcZAsCDgADA99ykqRnCAoEHAAWB6xZyiAoIOAQeAqVXV1KrEUSlJ6s0kYyBoEHAAmFrxsTMyDCk6IlTdoyN8XQ6ADkLAAWBqRWevgdO7e7QsFouPqwHQUQg4AEyNa+AAwYmAA8DUWEEFBCcCDgBT4yJ/QHAi4AAwNS7yBwQnAg4A03K5DBUfPyOJJeJAsCHgADCtIycqVV3jUliIRcldIn1dDoAORMABYFr1E4x7du2ksFB+3QHBhJ94AKZVdDbgcHoKCD4EHACmdeDsRf5YIg4EHwIOANM6d5E/VlABwYaAA8C0uAYOELwIOABMqz7gcIoKCD4EHACmZD/jVMVppyQppSsBBwg2BBwAplS/giqus1XR1jAfVwOgoxFwAJgSK6iA4EbAAWBK51ZQEXCAYETAAWBK7ov8MYIDBCUCDgBT4hQVENwIOABM6dxtGrjIHxCMOiTgvPDCC0pNTVVkZKTS09P16aefNtt+3bp1Sk9PV2RkpC677DL99a9/bdBm+fLlGjJkiKxWq4YMGaIVK1Z4q3wAAaaqplYljkpJjOAAwcrrAWfZsmWaNWuWHn30URUUFGjcuHGaNGmSioqKGm2/b98+ffe739W4ceNUUFCgX/7yl3rwwQe1fPlyd5v8/HxlZ2crJydH27ZtU05OjqZOnaqNGzd6uzsAAsDB42dkGFJURKi6R0f4uhwAPmAxDMPw5hcYM2aMRo4cqRdffNG9bfDgwbr11lu1YMGCBu3nzJmjd999V7t373ZvmzFjhrZt26b8/HxJUnZ2thwOhz744AN3m4kTJ6pr165asmRJizU5HA7ZbDbZ7XbFxsZeSvcA+KE1X5bpR4u+0KDEGK2adY2vywHQTtry+e3VEZzq6mpt3rxZWVlZHtuzsrK0fv36Rt+Tn5/foP2ECRO0adMmOZ3OZts0tc+qqio5HA6PBwDzOnCUCcZAsPNqwCkvL1dtba0SEhI8tickJKi0tLTR95SWljbavqamRuXl5c22aWqfCxYskM1mcz9SUlIutksAAsAB9z2omGAMBKsOmWRssVg8nhuG0WBbS+0v3N6Wfc6dO1d2u939KC4ublP9AALLuRVUjOAAwcqrN2iJi4tTaGhog5GVsrKyBiMw9RITExttHxYWpu7duzfbpql9Wq1WWa3Wi+0GgABzgLuIA0HPqyM4ERERSk9PV15ensf2vLw8ZWZmNvqejIyMBu0//PBDjRo1SuHh4c22aWqfAIKHy2WouD7gcA0cIGh5/Ra7s2fPVk5OjkaNGqWMjAy9/PLLKioq0owZMyTVnT46dOiQXnvtNUl1K6aee+45zZ49W7m5ucrPz9fChQs9Vkc99NBDuuaaa/TEE0/olltu0TvvvKPVq1frs88+83Z3APi5shNVqqpxKTTEouQukb4uB4CPeD3gZGdn6+jRo5o/f75KSkqUlpamlStXqk+fPpKkkpISj2vipKamauXKlfrZz36m559/XsnJyXr22Wd1++23u9tkZmZq6dKl+tWvfqXHHntM/fr107JlyzRmzBhvdweAn6tfQdWzSyeFhXKxdiBYef06OP6I6+AA5vWPTcX6+ZvbNa5/nF6fzn96ADPxm+vgAEBHYwUVAImAA8BkWEEFQCLgADCZorNzcLiLOBDcCDgATIURHAASAQeAidjPOFVxuu6edczBAYIbAQeAadRf4C+uc4SirV6/CgYAP0bAAWAaB1hBBeAsAg4A0zhwrG6CMXcRB0DAAWAaXAMHQD0CDgDTqD9FxQoqAAQcAKZRxBJxAGcRcACYQqWzVoftZyQxBwcAAQeASRw8flqGIXW2hql7dISvywHgYwQcAKawv/zc6SmLxeLjagD4GgEHgCnsP3sPqr6cngIgAg4Ak6hfQdU3jgnGAAg4AEyifgSHCcYAJAIOAJNwj+AQcACIgAPABKprXDp4vD7gcIoKAAEHgAkcqjgjlyF1Cg9Vjxirr8sB4AcIOAAC3rn5NywRB1CHgAMg4B0oZ4k4AE8EHAABb3/9TTZZIg7gLAIOgIB3gIv8AbgAAQdAwKtfIs5dxAHUI+AACGg1tS4VH+caOAA8EXAABLQSe6WctYYiwkKUGBvp63IA+AkCDoCA5l4i3i1KISEsEQdQh4ADIKC5V1BxegrAeQg4AALauWvgMMEYwDkEHAAB7dw1cBjBAXCOVwPO8ePHlZOTI5vNJpvNppycHFVUVDTZ3ul0as6cORo2bJiio6OVnJysu+++W4cPH/ZoN378eFksFo/HtGnTvNkVAH5q/1FGcAA05NWAc+edd2rr1q1atWqVVq1apa1btyonJ6fJ9qdPn9aWLVv02GOPacuWLXrrrbf01VdfacqUKQ3a5ubmqqSkxP146aWXvNkVAH6o1mWo6ChLxAE0FOatHe/evVurVq3Shg0bNGbMGEnS3/72N2VkZGjPnj0aOHBgg/fYbDbl5eV5bPvLX/6i0aNHq6ioSL1793Zvj4qKUmJiorfKBxAASh2Vqq51KTzUoiQbS8QBnOO1EZz8/HzZbDZ3uJGksWPHymazaf369a3ej91ul8ViUZcuXTy2L168WHFxcRo6dKgeeeQRnThxosl9VFVVyeFweDwABL76CcYpXaMUFsqUQgDneG0Ep7S0VPHx8Q22x8fHq7S0tFX7qKys1C9+8Qvdeeedio2NdW+/6667lJqaqsTERO3cuVNz587Vtm3bGoz+1FuwYIEef/zxi+sIAL+1n1s0AGhCm//LM2/evAYTfC98bNq0SZJksTS86JZhGI1uv5DT6dS0adPkcrn0wgsveLyWm5urG2+8UWlpaZo2bZrefPNNrV69Wlu2bGl0X3PnzpXdbnc/iouL29ptAH6o/iabXAMHwIXaPIIzc+bMFlcs9e3bV9u3b9eRI0cavPbtt98qISGh2fc7nU5NnTpV+/bt08cff+wxetOYkSNHKjw8XIWFhRo5cmSD161Wq6xWa7P7ABB4WEEFoCltDjhxcXGKi4trsV1GRobsdrs+//xzjR49WpK0ceNG2e12ZWZmNvm++nBTWFioNWvWqHv37i1+rV27dsnpdCopKan1HQEQ8A5wDRwATfDarLzBgwdr4sSJys3N1YYNG7Rhwwbl5uZq8uTJHiuoBg0apBUrVkiSampq9B//8R/atGmTFi9erNraWpWWlqq0tFTV1dWSpG+++Ubz58/Xpk2btH//fq1cuVJ33HGHRowYoauvvtpb3QHgZwzDOG8Eh4ADwJNXlx0sXrxYw4YNU1ZWlrKysnTFFVfo9ddf92izZ88e2e12SdLBgwf17rvv6uDBg7ryyiuVlJTkftSvvIqIiNBHH32kCRMmaODAgXrwwQeVlZWl1atXKzQ01JvdAeBHyk5UqdLpUmiIRT27dPJ1OQD8jNdWUUlSt27d9MYbbzTbxjAM99/79u3r8bwxKSkpWrduXbvUByBw7T+7RLxnl06KCGOJOABP/FYAEJD2u1dQMcEYQEMEHAABae+3dQHnMiYYA2gEAQdAQPqmPuD06OzjSgD4IwIOgIC0t/ykJOmyHozgAGiIgAMg4DhrXe67iDOCA6AxBBwAAaf42GnVuAxFhocoKZa7iANoiIADIODUTzBOjeuskJCW720HIPgQcAAEHObfAGgJAQdAwKkfwenHEnEATSDgAAg4e1kiDqAFBBwAAYdTVABaQsABEFDsZ5wqP1ktSUrlFBWAJhBwAASUvd/Wjd7Ex1gVExnu42oA+CsCDoCAcm7+DaM3AJpGwAEQUM7Nv2GCMYCmEXAABBTuIg6gNQg4AAJKYVndCM6AhBgfVwLAnxFwAASM6hqX9pfXjeD0T+AUFYCmEXAABIwDR0+pxmWoszVMidxkE0AzCDgAAkb96anL4zvLYuEmmwCaRsABEDAKj9QFnP7xnJ4C0DwCDoCAUVh2QhLzbwC0jIADIGB8XVY/gsMKKgDNI+AACAg1tS73NXAu5xQVgBYQcAAEhKJjp1Vd61Kn8FD17NLJ1+UA8HMEHAAB4fwVVCEhrKAC0DwCDoCAcG7+DaenALSMgAMgIBQeqVtBdTkrqAC0AgEHQEAoZAUVgDYg4ADwezW1Lk5RAWgTAg4Av7f/6ClV1bgUFRGq3t2ifF0OgADg1YBz/Phx5eTkyGazyWazKScnRxUVFc2+595775XFYvF4jB071qNNVVWVHnjgAcXFxSk6OlpTpkzRwYMHvdgTAL60u6Ru/s3AxBhWUAFoFa8GnDvvvFNbt27VqlWrtGrVKm3dulU5OTktvm/ixIkqKSlxP1auXOnx+qxZs7RixQotXbpUn332mU6ePKnJkyertrbWW10B4ENfljokSYMSY31cCYBAEeatHe/evVurVq3Shg0bNGbMGEnS3/72N2VkZGjPnj0aOHBgk++1Wq1KTExs9DW73a6FCxfq9ddf14033ihJeuONN5SSkqLVq1drwoQJ7d8ZAD715dkRnMFJTDAG0DpeG8HJz8+XzWZzhxtJGjt2rGw2m9avX9/se9euXav4+HgNGDBAubm5Kisrc7+2efNmOZ1OZWVlubclJycrLS2tyf1WVVXJ4XB4PAAEji9L6wIOIzgAWstrAae0tFTx8fENtsfHx6u0tLTJ902aNEmLFy/Wxx9/rD/96U/64osvdP3116uqqsq934iICHXt2tXjfQkJCU3ud8GCBe55QDabTSkpKZfQMwAdyX7GqUMVZyTVzcEBgNZoc8CZN29eg0nAFz42bdokSbJYGk4GNAyj0e31srOz9b3vfU9paWm6+eab9cEHH+irr77S+++/32xdze137ty5stvt7kdxcXEbegzAl/acHb3p2aWTbJ3CfVwNgEDR5jk4M2fO1LRp05pt07dvX23fvl1Hjhxp8Nq3336rhISEVn+9pKQk9enTR4WFhZKkxMREVVdX6/jx4x6jOGVlZcrMzGx0H1arVVartdVfE4D/ODfBmNEbAK3X5oATFxenuLi4FttlZGTIbrfr888/1+jRoyVJGzdulN1ubzKINObo0aMqLi5WUlKSJCk9PV3h4eHKy8vT1KlTJUklJSXauXOnnnzyybZ2B4Cfq18iPogJxgDawGtzcAYPHqyJEycqNzdXGzZs0IYNG5Sbm6vJkyd7rKAaNGiQVqxYIUk6efKkHnnkEeXn52v//v1au3atbr75ZsXFxen73/++JMlms2n69Ol6+OGH9dFHH6mgoEA//OEPNWzYMPeqKgDmwRJxABfDa8vEJWnx4sV68MEH3SuepkyZoueee86jzZ49e2S32yVJoaGh2rFjh1577TVVVFQoKSlJ1113nZYtW6aYmHP/e/vzn/+ssLAwTZ06VWfOnNENN9ygRYsWKTQ01JvdAdDBXC7DPQeHJeIA2sJiGIbh6yI6msPhkM1mk91uV2ws/ysE/NX+8lMa/99rFREWon8/PkFhodxdBghmbfn85rcFAL+141Dd6O7gxBjCDYA24TcGAL+182zASetp83ElAAINAQeA36ofwbmiFwEHQNsQcAD4JcMw3AGHERwAbUXAAeCXDhw9rROVNYoIC9GABFZQAWgbAg4Av+SeYJwUq3AmGANoI35rAPBL9QFnWE8u5QCg7Qg4APzSjoNnJxj37OLbQgAEJAIOAL/jchnaeZgJxgAuHgEHgN85cOzcBOP+CZ19XQ6AAETAAeB36uffDGGCMYCLxG8OAH5na1GFJGk4F/gDcJEIOAD8zuai45KkkX26+rgSAIGKgAPAr1Q6a7Xr7CmqdAIOgItEwAHgV7YftKvGZSg+xqqeXTr5uhwAAYqAA8CvbD5Qd3oqvU9XWSwWH1cDIFARcAD4lfMDDgBcLAIOAL9hGIa2MMEYQDsg4ADwG/uPntaxU9WKCAvR0GTuQQXg4hFwAPiN+tNTw3raZA0L9XE1AAIZAQeA32D+DYD2QsAB4Dc27j0qSRpFwAFwiQg4APxCif2M9pafUohFGnNZd1+XAyDAEXAA+IX8b+pGb9J62mTrFO7jagAEOgIOAL+w/mzAyejH6A2AS0fAAeBzhmG4R3Cu7hfn42oAmAEBB4DPFR07rUMVZxQeatGovkwwBnDpCDgAfK7+9NSIlK6KigjzcTUAzICAA8DnmH8DoL0RcAD4lMtlKP+bcklSJgEHQDsh4ADwqR2H7Co/Wa3O1jCN6M38GwDtw6sB5/jx48rJyZHNZpPNZlNOTo4qKiqafY/FYmn08cc//tHdZvz48Q1enzZtmje7AsBLPvqyTJJ0zYA4RYTxfy4A7cOrs/nuvPNOHTx4UKtWrZIk/fjHP1ZOTo7ee++9Jt9TUlLi8fyDDz7Q9OnTdfvtt3tsz83N1fz5893PO3Xq1I6VA+goH+0+Ikm6flCCjysBYCZeCzi7d+/WqlWrtGHDBo0ZM0aS9Le//U0ZGRnas2ePBg4c2Oj7EhMTPZ6/8847uu6663TZZZd5bI+KimrQFkBgKbVXatdhhywWafzAHr4uB4CJeG08OD8/XzabzR1uJGns2LGy2Wxav359q/Zx5MgRvf/++5o+fXqD1xYvXqy4uDgNHTpUjzzyiE6cONHkfqqqquRwODweAHzv47Onp65M6aK4zlYfVwPATLw2glNaWqr4+PgG2+Pj41VaWtqqfbz66quKiYnRbbfd5rH9rrvuUmpqqhITE7Vz507NnTtX27ZtU15eXqP7WbBggR5//PG2dwKAV338Zd3pqRsGNfxdAQCXos0jOPPmzWtyInD9Y9OmTZLqJgxfyDCMRrc35pVXXtFdd92lyMhIj+25ubm68cYblZaWpmnTpunNN9/U6tWrtWXLlkb3M3fuXNntdvejuLi4jb0G0N4qnbX67Ou65eHMvwHQ3to8gjNz5swWVyz17dtX27dv15EjRxq89u233yohoeVfZp9++qn27NmjZcuWtdh25MiRCg8PV2FhoUaOHNngdavVKquV4W/An6z76ltVOl1KtkVqcFKMr8sBYDJtDjhxcXGKi2v5ZngZGRmy2+36/PPPNXr0aEnSxo0bZbfblZmZ2eL7Fy5cqPT0dA0fPrzFtrt27ZLT6VRSUlLLHQDgF97bdliS9N1hSa0e1QWA1vLaJOPBgwdr4sSJys3N1YYNG7Rhwwbl5uZq8uTJHiuoBg0apBUrVni81+Fw6J///Kfuu+++Bvv95ptvNH/+fG3atEn79+/XypUrdccdd2jEiBG6+uqrvdUdAO3oVFWNVp9dHj7lymQfVwPAjLx6Va3Fixdr2LBhysrKUlZWlq644gq9/vrrHm327Nkju93usW3p0qUyDEM/+MEPGuwzIiJCH330kSZMmKCBAwfqwQcfVFZWllavXq3Q0FBvdgdAO1m9+4gqnS716R6lYT1tvi4HgAlZDMMwfF1ER3M4HLLZbLLb7YqNjfV1OUDQue/VL7R6d5keuP5yPZzV+DWxAOBCbfn85rroADpUxelqrfvqW0nSlOGcngLgHQQcAB3qg52lctYaGpQYo/4JrJ4C4B0EHAAdaunnRZKkW0f09HElAMyMgAOgw+w4aNe2g3ZFhIbojvRevi4HgIkRcAB0mMUbD0iSJg1LVHfuPQXAiwg4ADqEo9Kpd7bWXdzvrjF9fFwNALMj4ADoEG8XHNIZZ636x3fWVX27+rocACZHwAHgdbUuQ4v+b78k6c4xvbk1AwCvI+AA8LoPdpZob/kp2TqF645RKb4uB0AQIOAA8CrDMPT8mm8kSfdm9lVna5vv8QsAbUbAAeBVH39Zpt0lDkVHhOpHV/f1dTkAggQBB4DXGIah59Z8LUn6YUYfdYmK8HFFAIIFAQeA1/xrV6kKiipkDQvRfd+5zNflAAgiBBwAXlFVU6vfrdwtSfrJtf3UI4YL+wHoOAQcAF7xymf7VXzsjBJirZpxLaM3ADoWAQdAuytzVOr5s3Nv5kwcpKgIVk4B6FgEHADtyjAMzX1rh05W1Wh4ShfdeiV3DQfQ8Qg4ANrVPzcf1EdflikiNERP3n6FQkK4ajGAjkfAAdBuDh4/rfnv/VuSNDtrgAYmxvi4IgDBioADoF1UOmv108VbdLKqRiN7d1HuOCYWA/AdAg6AS2YYhn6xfLu2H7SrS1S4ns4eoVBOTQHwIQIOgEv24rpv9PbWwwoNseiFO0eqd/coX5cEIMgRcABcklfX79eTq/ZIkn49eYgyL4/zcUUAQMABcAkWbzyg37y7S5L00/H9dHdGHx9XBAB1uPoWgDZzuQw9vforPftx3cX8fnzNZfqvCQNlsTDvBoB/IOAAaJNTVTWas3y7/nd7iaS6kRvCDQB/Q8AB0GoFRcf1s2Vbtf/oaYWHWvS77w/T1FEpvi4LABog4ABokaPSqb98VKhX/m+/al2GkmyRejr7So25rLuvSwOARhFwADSp0lmrf24q1tOrC3X0VLUk6ZYrkzV/SppsUeE+rg4AmkbAAdDA0ZNV+semg3rl//bp2xNVkqTLekTrsclDdN3AeB9XBwAtI+AAkFQ3eXjdV9/qf7cfVt6/j8hZa0iSkm2RmjG+n34wurfCQ7myBIDA4NXfVr/73e+UmZmpqKgodenSpVXvMQxD8+bNU3Jysjp16qTx48dr165dHm2qqqr0wAMPKC4uTtHR0ZoyZYoOHjzohR4A5lXprFVB0XG9/Mk3uvuVzzXy/8vTTxdv0codpXLWGhqe0kV//I8rtPa/rtPdGX0JNwACildHcKqrq3XHHXcoIyNDCxcubNV7nnzyST311FNatGiRBgwYoN/+9re66aabtGfPHsXE1N2ZeNasWXrvvfe0dOlSde/eXQ8//LAmT56szZs3KzQ01JtdAgJOdY1LhyrOqOjYae0vP6Wdh+zacciuwrKTqnUZHm17d4vSpLRE3TqipwYnxfqoYgC4dBbDMIyWm12aRYsWadasWaqoqGi2nWEYSk5O1qxZszRnzhxJdaM1CQkJeuKJJ/STn/xEdrtdPXr00Ouvv67s7GxJ0uHDh5WSkqKVK1dqwoQJLdbjcDhks9lkt9sVG8svcQSGWpeh09U1OuOs1ZnqWp1x1up0da1OVtbo2KlqHT1VrWOnqur+frJax05Vq8ReqRL7Gbma+CnvFh2hK1O66OrL4/Sdy+M0IKEz17MB4Lfa8vntV3Nw9u3bp9LSUmVlZbm3Wa1WXXvttVq/fr1+8pOfaPPmzXI6nR5tkpOTlZaWpvXr1zcacKqqqlRVVeV+7nA4vFJ/+ckqPXf2yq6t0Vi2bCptNhZDjSZaNxVZG9vcdLxtpLam9tvk12vDPpqqog39bmxz0/tt4t+ulTU03bbp/bpchmpchmrdf7rkrPV8XnP2ef02Z61Lp6vrAk11rauJ3rQsMjxEvbtFqXe3KA1OilVaT5uG9bQpyRZJoAFgSn4VcEpLSyVJCQkJHtsTEhJ04MABd5uIiAh17dq1QZv6919owYIFevzxx71QsSfHGacWrd/v9a+D4GaxSJ3CQxUVEapOEaGKjghTt+gIdYuOUPfoCHWLtqpb57q/x8dY1bt7lHp0thJkAASVNgecefPmtRgWvvjiC40aNeqii7rwF7FhGC3+cm6uzdy5czV79mz3c4fDoZSU9r/6apeoCM287vJGX2uq/EY3N9G4sa1N77eJfTSyual/2UbbtvFDsvGv1/ra6tq3pW3DF9r6ud5YH9v0b9RE29DQEIWFWBQaYjnvz5Bzz0Mbbg8PtZwNMmHuUGMNCyGsAEAL2hxwZs6cqWnTpjXbpm/fvhdVTGJioqS6UZqkpCT39rKyMveoTmJioqqrq3X8+HGPUZyysjJlZmY2ul+r1Sqr1XpRNbVFt+gIPTJhoNe/DgAAaF6bA05cXJzi4uK8UYtSU1OVmJiovLw8jRgxQlLdSqx169bpiSeekCSlp6crPDxceXl5mjp1qiSppKREO3fu1JNPPumVugAAQGDx6hycoqIiHTt2TEVFRaqtrdXWrVslSZdffrk6d+4sSRo0aJAWLFig73//+7JYLJo1a5Z+//vfq3///urfv79+//vfKyoqSnfeeackyWazafr06Xr44YfVvXt3devWTY888oiGDRumG2+80ZvdAQAAAcKrAefXv/61Xn31Vffz+lGZNWvWaPz48ZKkPXv2yG63u9v8/Oc/15kzZ/TTn/5Ux48f15gxY/Thhx+6r4EjSX/+858VFhamqVOn6syZM7rhhhu0aNEiroEDAAAkddB1cPwN18EBACDwtOXzm2uvAwAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0/HqrRr8Vf3Fmx0Oh48rAQAArVX/ud2amzAEZcA5ceKEJCklJcXHlQAAgLY6ceKEbDZbs22C8l5ULpdLhw8fVkxMjCwWS7vu2+FwKCUlRcXFxaa9z5XZ+2j2/knm76PZ+yfRRzMwe/+k9u+jYRg6ceKEkpOTFRLS/CyboBzBCQkJUa9evbz6NWJjY037DVvP7H00e/8k8/fR7P2T6KMZmL1/Uvv2saWRm3pMMgYAAKZDwAEAAKZDwGlnVqtVv/nNb2S1Wn1diteYvY9m759k/j6avX8SfTQDs/dP8m0fg3KSMQAAMDdGcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcNrRCy+8oNTUVEVGRio9PV2ffvqpr0u6aAsWLNBVV12lmJgYxcfH69Zbb9WePXs82tx7772yWCwej7Fjx/qo4raZN29eg9oTExPdrxuGoXnz5ik5OVmdOnXS+PHjtWvXLh9W3HZ9+/Zt0EeLxaL7779fUmAev08++UQ333yzkpOTZbFY9Pbbb3u83prjVlVVpQceeEBxcXGKjo7WlClTdPDgwQ7sRdOa65/T6dScOXM0bNgwRUdHKzk5WXfffbcOHz7ssY/x48c3OK7Tpk3r4J40raVj2JrvS38+hlLLfWzs59JiseiPf/yju40/H8fWfD74w88iAaedLFu2TLNmzdKjjz6qgoICjRs3TpMmTVJRUZGvS7so69at0/33368NGzYoLy9PNTU1ysrK0qlTpzzaTZw4USUlJe7HypUrfVRx2w0dOtSj9h07drhfe/LJJ/XUU0/pueee0xdffKHExETddNNN7vuYBYIvvvjCo395eXmSpDvuuMPdJtCO36lTpzR8+HA999xzjb7emuM2a9YsrVixQkuXLtVnn32mkydPavLkyaqtre2objSpuf6dPn1aW7Zs0WOPPaYtW7borbfe0ldffaUpU6Y0aJubm+txXF966aWOKL9VWjqGUsvfl/58DKWW+3h+30pKSvTKK6/IYrHo9ttv92jnr8exNZ8PfvGzaKBdjB492pgxY4bHtkGDBhm/+MUvfFRR+yorKzMkGevWrXNvu+eee4xbbrnFd0Vdgt/85jfG8OHDG33N5XIZiYmJxh/+8Af3tsrKSsNmsxl//etfO6jC9vfQQw8Z/fr1M1wul2EYgX38DMMwJBkrVqxwP2/NcauoqDDCw8ONpUuXutscOnTICAkJMVatWtVhtbfGhf1rzOeff25IMg4cOODedu211xoPPfSQd4trJ431saXvy0A6hobRuuN4yy23GNdff73HtkA6jhd+PvjLzyIjOO2gurpamzdvVlZWlsf2rKwsrV+/3kdVtS+73S5J6tatm8f2tWvXKj4+XgMGDFBubq7Kysp8Ud5FKSwsVHJyslJTUzVt2jTt3btXkrRv3z6VlpZ6HE+r1aprr702YI9ndXW13njjDf3nf/6nxw1mA/n4Xag1x23z5s1yOp0ebZKTk5WWlhaQx9Zut8tisahLly4e2xcvXqy4uDgNHTpUjzzySECNPErNf1+a7RgeOXJE77//vqZPn97gtUA5jhd+PvjLz2JQ3myzvZWXl6u2tlYJCQke2xMSElRaWuqjqtqPYRiaPXu2vvOd7ygtLc29fdKkSbrjjjvUp08f7du3T4899piuv/56bd682e+vzDlmzBi99tprGjBggI4cOaLf/va3yszM1K5du9zHrLHjeeDAAV+Ue8nefvttVVRU6N5773VvC+Tj15jWHLfS0lJFRESoa9euDdoE2s9qZWWlfvGLX+jOO+/0uInhXXfdpdTUVCUmJmrnzp2aO3eutm3b5j5F6e9a+r400zGUpFdffVUxMTG67bbbPLYHynFs7PPBX34WCTjt6Pz/GUt1B/7CbYFo5syZ2r59uz777DOP7dnZ2e6/p6WladSoUerTp4/ef//9Bj+s/mbSpEnuvw8bNkwZGRnq16+fXn31VfeERjMdz4ULF2rSpElKTk52bwvk49ecizlugXZsnU6npk2bJpfLpRdeeMHjtdzcXPff09LS1L9/f40aNUpbtmzRyJEjO7rUNrvY78tAO4b1XnnlFd11112KjIz02B4ox7GpzwfJ9z+LnKJqB3FxcQoNDW2QOsvKyhok2EDzwAMP6N1339WaNWvUq1evZtsmJSWpT58+Kiws7KDq2k90dLSGDRumwsJC92oqsxzPAwcOaPXq1brvvvuabRfIx09Sq45bYmKiqqurdfz48Sbb+Dun06mpU6dq3759ysvL8xi9aczIkSMVHh4esMf1wu9LMxzDep9++qn27NnT4s+m5J/HsanPB3/5WSTgtIOIiAilp6c3GDrMy8tTZmamj6q6NIZhaObMmXrrrbf08ccfKzU1tcX3HD16VMXFxUpKSuqACttXVVWVdu/eraSkJPew8PnHs7q6WuvWrQvI4/n3v/9d8fHx+t73vtdsu0A+fpJaddzS09MVHh7u0aakpEQ7d+4MiGNbH24KCwu1evVqde/evcX37Nq1S06nM2CP64Xfl4F+DM+3cOFCpaena/jw4S229afj2NLng9/8LLbLVGUYS5cuNcLDw42FCxca//73v41Zs2YZ0dHRxv79+31d2kX5f//v/xk2m81Yu3atUVJS4n6cPn3aMAzDOHHihPHwww8b69evN/bt22esWbPGyMjIMHr27Gk4HA4fV9+yhx9+2Fi7dq2xd+9eY8OGDcbkyZONmJgY9/H6wx/+YNhsNuOtt94yduzYYfzgBz8wkpKSAqJv56utrTV69+5tzJkzx2N7oB6/EydOGAUFBUZBQYEhyXjqqaeMgoIC9yqi1hy3GTNmGL169TJWr15tbNmyxbj++uuN4cOHGzU1Nb7qlltz/XM6ncaUKVOMXr16GVu3bvX4uayqqjIMwzC+/vpr4/HHHze++OILY9++fcb7779vDBo0yBgxYoRf9M8wmu9ja78v/fkYGkbL36eGYRh2u92IiooyXnzxxQbv9/fj2NLng2H4x88iAacdPf/880afPn2MiIgIY+TIkR5LqgONpEYff//73w3DMIzTp08bWVlZRo8ePYzw8HCjd+/exj333GMUFRX5tvBWys7ONpKSkozw8HAjOTnZuO2224xdu3a5X3e5XMZvfvMbIzEx0bBarcY111xj7Nixw4cVX5x//etfhiRjz549HtsD9fitWbOm0e/Le+65xzCM1h23M2fOGDNnzjS6detmdOrUyZg8ebLf9Lu5/u3bt6/Jn8s1a9YYhmEYRUVFxjXXXGN069bNiIiIMPr162c8+OCDxtGjR33bsfM018fWfl/68zE0jJa/Tw3DMF566SWjU6dORkVFRYP3+/txbOnzwTD842fRcrZYAAAA02AODgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMJ3/HzLR1cTldnx4AAAAAElFTkSuQmCC",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Test torch.tanh() on the tensor and plot it\n",
+ "plt.plot(torch.tanh(A))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 240,
+ "metadata": {
+ "id": "J-ne__Kjkdc1"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 240,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "code",
- "source": [
- "# Plot decision boundaries for training and test sets\n"
- ],
- "metadata": {
- "id": "JrwVRbaE0keT"
- },
- "execution_count": 23,
- "outputs": []
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
- ]
+ ],
+ "source": [
+ "# Replicate torch.tanh() and plot it\n",
+ "def tanh(z):\n",
+ "\treturn (np.exp(z) - np.exp(-z)) / (np.exp(z) + np.exp(-z))\n",
+ "plt.plot(tanh(A))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Lbt1bNcWk5G9"
+ },
+ "source": [
+ "## 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",
+ " * Split the data into training and test sets (80% train, 20% test) as well as turn it into PyTorch tensors.\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) - 1000 epochs should be plenty.\n",
+ " * Plot the decision boundaries on the spirals dataset from your model predictions, the `plot_decision_boundary()` function should work for this dataset too."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 348,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 265
+ },
+ "id": "tU-UNZsKlJls",
+ "outputId": "8b7b745a-070d-4ecb-c639-c4ee4d8eae06"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Code for creating a spiral dataset from CS231n\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "RANDOM_SEED = 42\n",
+ "np.random.seed(RANDOM_SEED)\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.RdYlBu)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 349,
+ "metadata": {
+ "id": "OWVrmkEyl0VP"
+ },
+ "outputs": [],
+ "source": [
+ "# Turn data into tensors\n",
+ "import torch\n",
+ "X = torch.from_numpy(X).type(torch.float) # features as float32\n",
+ "y = torch.from_numpy(y).type(torch.LongTensor) # labels need to be of type long\n",
+ "\n",
+ "# Create train and test splits\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 350,
+ "metadata": {
+ "id": "a-v-7f0op0tG"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Accuracy()"
+ ]
+ },
+ "execution_count": 350,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Let's calculuate the accuracy for when we fit our model\n",
+ "!pip -q install torchmetrics \n",
+ "# colab doesn't come with torchmetrics\n",
+ "from torchmetrics import Accuracy\n",
+ "\n",
+ "## TODO: uncomment the two lines below to send the accuracy function to the device\n",
+ "acc_fn = Accuracy(task=\"multiclass\", num_classes=3).to(device)\n",
+ "acc_fn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 351,
+ "metadata": {
+ "id": "DB3u3ldumapf"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "SpiralModelV0(\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=3, bias=True)\n",
+ " (relu): ReLU()\n",
+ " (tanh): Tanh()\n",
+ ")\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Prepare device agnostic code\n",
+ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+ "\n",
+ "# Create model by subclassing nn.Module\n",
+ "class SpiralModelV0(nn.Module):\n",
+ " ## Your code here ##\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=3)\n",
+ " self.relu = nn.ReLU() \n",
+ " self.tanh = nn.Tanh()\n",
+ " def forward(self, x):\n",
+ " ## Your code here ##\n",
+ " y = self.layer_1(x)\n",
+ " y = self.tanh(y)\n",
+ " y = self.layer_2(y)\n",
+ " y = self.tanh(y)\n",
+ " y = self.layer_3(y) \n",
+ " return y\n",
+ " \n",
+ "# Instantiate the model\n",
+ "## Your code here ##\n",
+ "model_0 = SpiralModelV0().to(device)\n",
+ "print(model_0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 352,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "QE7XWSSunMTS",
+ "outputId": "00b31909-87c9-41e3-9dbb-fb4c4bd3aabd"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Logits:\n",
+ "tensor([[-0.2370, -0.1379, 0.0691],\n",
+ " [-0.1750, -0.0614, 0.0822],\n",
+ " [-0.2233, -0.0100, 0.1017],\n",
+ " [-0.2386, -0.1188, 0.0741],\n",
+ " [-0.2423, -0.2107, 0.0532],\n",
+ " [-0.2442, -0.1965, 0.0564],\n",
+ " [-0.3232, -0.2132, 0.0751],\n",
+ " [-0.2256, -0.1285, 0.0700],\n",
+ " [-0.2352, -0.1217, 0.0729],\n",
+ " [-0.2877, -0.0975, 0.0925]], device='cuda:0')\n",
+ "Pred probs:\n",
+ "tensor([[0.2888, 0.3189, 0.3923],\n",
+ " [0.2929, 0.3282, 0.3789],\n",
+ " [0.2761, 0.3417, 0.3821],\n",
+ " [0.2862, 0.3226, 0.3912],\n",
+ " [0.2962, 0.3057, 0.3981],\n",
+ " [0.2942, 0.3085, 0.3973],\n",
+ " [0.2774, 0.3096, 0.4131],\n",
+ " [0.2902, 0.3198, 0.3900],\n",
+ " [0.2873, 0.3218, 0.3909],\n",
+ " [0.2723, 0.3294, 0.3983]], device='cuda:0')\n",
+ "Pred labels:\n",
+ "tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2], device='cuda:0')\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Setup data to be device agnostic\n",
+ "\n",
+ "# logits (raw outputs of model)\n",
+ "print(\"Logits:\")\n",
+ "## Your code here ##\n",
+ "model_0.eval()\n",
+ "with torch.inference_mode():\n",
+ " y_logits_0 = model_0(X_test.to(device))[:10]\n",
+ "print(y_logits_0)\n",
+ " \n",
+ "# Prediction probabilities\n",
+ "print(\"Pred probs:\")\n",
+ "## Your code here ##\n",
+ "y_preds_0 = torch.softmax(y_logits_0, dim=1)\n",
+ "print(y_preds_0)\n",
+ "\n",
+ "# Prediction labels\n",
+ "print(\"Pred labels:\")\n",
+ "## Your code here ##\n",
+ "y_labels_0 = torch.argmax(y_preds_0, dim=1)\n",
+ "print(y_labels_0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 353,
+ "metadata": {
+ "id": "54EqLRKLo0AW"
+ },
+ "outputs": [],
+ "source": [
+ "# Setup loss function\n",
+ "loss_fn = torch.nn.CrossEntropyLoss() # For multi-class classification\n",
+ "# Setup optimizer to optimize model's parameters\n",
+ "optimizer = torch.optim.Adam(params=model_0.parameters(), lr=0.001)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 354,
+ "metadata": {
+ "id": "vIlExkUHnmxi"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 0 | Loss: 1.09763, Accuracy: 0.32% | Test loss: 1.08968, Test acc: 0.37%\n",
+ "Epoch: 100 | Loss: 0.94186, Accuracy: 0.52% | Test loss: 0.94544, Test acc: 0.50%\n",
+ "Epoch: 200 | Loss: 0.78690, Accuracy: 0.53% | Test loss: 0.80320, Test acc: 0.48%\n",
+ "Epoch: 300 | Loss: 0.73909, Accuracy: 0.53% | Test loss: 0.75394, Test acc: 0.50%\n",
+ "Epoch: 400 | Loss: 0.70654, Accuracy: 0.55% | Test loss: 0.71468, Test acc: 0.55%\n",
+ "Epoch: 500 | Loss: 0.64610, Accuracy: 0.59% | Test loss: 0.64733, Test acc: 0.58%\n",
+ "Epoch: 600 | Loss: 0.55631, Accuracy: 0.68% | Test loss: 0.54282, Test acc: 0.70%\n",
+ "Epoch: 700 | Loss: 0.45644, Accuracy: 0.80% | Test loss: 0.42859, Test acc: 0.78%\n",
+ "Epoch: 800 | Loss: 0.36548, Accuracy: 0.86% | Test loss: 0.33279, Test acc: 0.90%\n",
+ "Epoch: 900 | Loss: 0.29137, Accuracy: 0.91% | Test loss: 0.25774, Test acc: 0.93%\n",
+ "Epoch: 1000 | Loss: 0.23497, Accuracy: 0.95% | Test loss: 0.20121, Test acc: 0.98%\n",
+ "Epoch: 1100 | Loss: 0.19287, Accuracy: 0.95% | Test loss: 0.15968, Test acc: 0.98%\n",
+ "Epoch: 1200 | Loss: 0.16139, Accuracy: 0.95% | Test loss: 0.12896, Test acc: 1.00%\n",
+ "Epoch: 1300 | Loss: 0.13755, Accuracy: 0.97% | Test loss: 0.10587, Test acc: 1.00%\n",
+ "Epoch: 1400 | Loss: 0.11922, Accuracy: 0.98% | Test loss: 0.08821, Test acc: 1.00%\n",
+ "Epoch: 1500 | Loss: 0.10498, Accuracy: 0.98% | Test loss: 0.07440, Test acc: 1.00%\n",
+ "Epoch: 1600 | Loss: 0.09375, Accuracy: 0.98% | Test loss: 0.06332, Test acc: 1.00%\n",
+ "Epoch: 1700 | Loss: 0.08468, Accuracy: 0.98% | Test loss: 0.05427, Test acc: 1.00%\n",
+ "Epoch: 1800 | Loss: 0.07717, Accuracy: 0.98% | Test loss: 0.04679, Test acc: 1.00%\n",
+ "Epoch: 1900 | Loss: 0.07083, Accuracy: 0.99% | Test loss: 0.04059, Test acc: 1.00%\n"
+ ]
+ }
+ ],
+ "source": [
+ "## TODO: Uncomment this to set the seed\n",
+ "torch.manual_seed(RANDOM_SEED)\n",
+ "\n",
+ "# Setup epochs\n",
+ "epochs = 2000\n",
+ "\n",
+ "# Send data to the 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",
+ "# Loop through the data\n",
+ "for epoch in range(epochs):\n",
+ " ### Training \n",
+ " \n",
+ " # 1. Forward pass (logits output)\n",
+ " y_logits = model_0(X_train)\n",
+ " \n",
+ " # Turn logits into prediction probabilities\n",
+ " y_prob = torch.softmax(y_logits, dim=1)\n",
+ " \n",
+ " # Turn prediction probabilities into prediction labels\n",
+ " y_pred = torch.argmax(y_prob, dim=1)\n",
+ " \n",
+ " # 2. Calculaute the loss\n",
+ " loss = loss_fn(y_logits, y_train) # loss = compare model raw outputs to desired model outputs\n",
+ " \n",
+ " # Calculate the accuracy\n",
+ " acc = acc_fn(y_pred, y_train) # the accuracy function needs to compare pred labels (not logits) with actual labels\n",
+ " \n",
+ " # 3. Zero the gradients\n",
+ " optimizer.zero_grad()\n",
+ " \n",
+ " # 4. Loss backward (perform backpropagation) - https://brilliant.org/wiki/backpropagation/#:~:text=Backpropagation%2C%20short%20for%20%22backward%20propagation,to%20the%20neural%20network's%20weights.\n",
+ " loss.backward()\n",
+ " \n",
+ " # 5. Step the optimizer (gradient descent) - https://towardsdatascience.com/gradient-descent-algorithm-a-deep-dive-cf04e8115f21#:~:text=Gradient%20descent%20(GD)%20is%20an,e.g.%20in%20a%20linear%20regression) \n",
+ " optimizer.step()\n",
+ " \n",
+ " ### Testing\n",
+ " model_0.eval() \n",
+ " with torch.inference_mode():\n",
+ " # 1. Forward pass (to get the logits)\n",
+ " test_logits = model_0(X_test)\n",
+ " \n",
+ " # Turn the test logits into prediction labels\n",
+ " test_pred = torch.softmax(test_logits, dim=1).argmax(dim=1)\n",
+ " \n",
+ " # 2. Caculate the test loss/acc\n",
+ " test_loss = loss_fn(test_logits, y_test)\n",
+ " test_acc = acc_fn(test_pred, y_test) \n",
+ " \n",
+ " # Print out what's happening every 100 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}%\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 355,
+ "metadata": {
+ "id": "JrwVRbaE0keT"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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[:,[0,1]], y_train)\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.title(\"Test\")\n",
+ "plot_decision_boundary(model_0, X_test[:,[0,1]], y_test)"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "authorship_tag": "ABX9TyNloicnciRwCXd2bJo6F2iS",
+ "collapsed_sections": [],
+ "include_colab_link": true,
+ "name": "02_pytorch_classification_exercises.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": ".venv_pytorch_gpu (py311)",
+ "language": "python",
+ "name": ".venv_pytorch_gpu"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
}