{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "CfDd0DKVtknP" }, "source": [ "# TP2: Approximate Inference in Classification\n", "\n", "In classification taks, even for a mere Logistic Regression, we don't have access to a closed form of the posterior $p(\\pmb{w} \\vert \\mathcal{D})$. Unlike in Linear regression, the likelihood isn't conjugated to the Gaussian prior anymore. We ill need to approximate this posterior.\n", "\n", "During this session, we will explore and compare approximate inference approaches on 2D binary classification datasets. Studied approaches include Laplacian approximation, variational inference with mean-field approximation and Monte Carlo dropout.\n", "\n", "**Goal**: Take hand on approximate inference methods and understand how they works on linear and non-linear 2D datasets." ] }, { "cell_type": "markdown", "metadata": { "id": "clhZGFZ2wOxg" }, "source": [ "### All Imports and Useful Functions" ] }, { "cell_type": "markdown", "metadata": { "id": "i7Cv7C3O7qaw" }, "source": [ "Here we are going to install and import everything we are going to need for this tutorial. \n", "\n", "**Note**: *You can double-click the title of the collapsed cells (as the ones below) to expand them and read their content.*" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "id": "p-kN2Wxbv2Ui" }, "outputs": [], "source": [ "#@title Import libs\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.datasets import make_blobs, make_moons\n", "from IPython import display\n", "%matplotlib inline\n", "\n", "import torch\n", "import torch.nn as nn\n", "from torch.utils import data\n", "import torch.nn.functional as F\n", "from torch.autograd import grad\n", "import torch.distributions as dist" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "cellView": "form", "id": "CMDnycZQPMpL" }, "outputs": [], "source": [ "#@title Useful plot function \n", "def plot_decision_boundary(model, X, Y, epoch, accuracy, model_type='classic', \n", " nsamples=100, posterior=None, tloc=(-4,-7), \n", " nbh=2, cmap='RdBu'): \n", " \"\"\" Plot and show learning process in classification \"\"\"\n", " h = 0.02*nbh\n", " x_min, x_max = X[:,0].min() - 10*h, X[:,0].max() + 10*h\n", " y_min, y_max = X[:,1].min() - 10*h, X[:,1].max() + 10*h\n", " xx, yy = np.meshgrid(np.arange(x_min*2, x_max*2, h),\n", " np.arange(y_min*2, y_max*2, h))\n", " \n", " test_tensor = torch.from_numpy(np.c_[xx.ravel(), yy.ravel()]).type(torch.FloatTensor)\n", " model.eval()\n", " with torch.no_grad():\n", " if model_type=='classic':\n", " pred = torch.sigmoid(model(test_tensor))\n", " elif model_type=='laplace':\n", " #Save original mean weight\n", " original_weight = model.state_dict()['fc.weight'].detach().clone()\n", " outputs = torch.zeros(nsamples, test_tensor.shape[0], 1)\n", " for i in range(nsamples):\n", " state_dict = model.state_dict()\n", " state_dict['fc.weight'] = torch.from_numpy(posterior[i].reshape(1,2))\n", " model.load_state_dict(state_dict)\n", " outputs[i] = torch.sigmoid(model(test_tensor))\n", " \n", " pred = outputs.mean(0).squeeze()\n", " state_dict['fc.weight'] = original_weight\n", " model.load_state_dict(state_dict)\n", " \n", " elif model_type=='vi':\n", " outputs = torch.zeros(nsamples, test_tensor.shape[0], 1)\n", " for i in range(nsamples):\n", " outputs[i] = model(test_tensor)\n", " \n", " pred = outputs.mean(0).squeeze()\n", " \n", " elif model_type=='mcdropout':\n", " model.eval()\n", " model.training = True\n", " outputs = torch.zeros(nsamples, test_tensor.shape[0], 1)\n", " for i in range(nsamples):\n", " outputs[i] = model(test_tensor)\n", " \n", " pred = outputs.mean(0).squeeze()\n", " \n", " Z = pred.reshape(xx.shape).detach().numpy()\n", "\n", " plt.cla()\n", " ax.set_title('Classification Analysis')\n", " ax.contourf(xx, yy, Z, cmap=cmap, alpha=0.25)\n", " ax.contour(xx, yy, Z, colors='k', linestyles=':', linewidths=0.7)\n", " ax.scatter(X[:,0], X[:,1], c=Y, cmap='Paired_r', edgecolors='k');\n", " ax.text(tloc[0], tloc[1], f'Epoch = {epoch+1}, Accuracy = {accuracy:.2%}', fontdict={'size': 12, 'fontweight': 'bold'})\n", " display.display(plt.gcf())\n", " display.clear_output(wait=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "aAk_gzoRtknr" }, "source": [ "## Part I: Bayesian Logistic Regression" ] }, { "cell_type": "markdown", "metadata": { "id": "Q4aNuqE77jsS" }, "source": [ "In linear regression, model prediction is of the continuous form $f(\\pmb{x})=\\pmb{w}^T\\pmb{x}+b$.\n", "\n", "For classification, we wish to predict discrete class labels $\\mathcal{C}_k$ to a sample $\\pmb{x}$. \n", "For simplicity, let's consider here binary classification:\n", "$$f(\\pmb{x}) = \\sigma(\\pmb{w}^T\\pmb{x} + b)$$\n", "where $\\sigma(t)= \\frac{1}{1+e^t}$ is the sigmoid function.\n", "\n", "As in linear regression, we define a Gaussian prior: \n", "$$ p(\\pmb{w}) = \\mathcal{N}(\\pmb{w}; \\pmb{\\mu}_0, \\pmb{\\Sigma}_0^2) $$\n", "Unfortunately, the posterior distribution isn't tractable as the likelihood isn't conjugate to the prior anymore.\n", "\n", "We will explore in the following different methods to obtain an estimate of the posterior distribution and hence the predictive distribution." ] }, { "cell_type": "markdown", "metadata": { "id": "ZCp4ytXhV6IU" }, "source": [ "### I.0 Dataset" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "cellView": "form", "id": "m-QrdljAUG5E" }, "outputs": [], "source": [ "#@title Hyperparameters for model and approximate inference { form-width: \"30%\" }\n", "\n", "WEIGHT_DECAY = 5e-2 #@param\n", "NB_SAMPLES = 400 #@param\n", "TEXT_LOCATION = (-5,-7)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "GPI60gDOtkns" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load linear dataset\n", "X, y = make_blobs(n_samples=NB_SAMPLES, centers=[(-2,-2),(2,2)], cluster_std=0.80, n_features=2)\n", "X, y = torch.from_numpy(X), torch.from_numpy(y)\n", "X, y = X.type(torch.float), y.type(torch.float)\n", "torch_train_dataset = data.TensorDataset(X,y) # create your datset\n", "train_dataloader = data.DataLoader(torch_train_dataset, batch_size=len(torch_train_dataset))\n", "\n", "# Visualize dataset\n", "plt.scatter(X[:,0], X[:,1], c=y, cmap='Paired_r', edgecolors='k')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "sNGfQhFqtknu" }, "source": [ "### I.1 Maximum-A-Posteriori Estimate" ] }, { "cell_type": "markdown", "metadata": { "id": "t1CE5ewMVdt9" }, "source": [ "\n", "In this \"baseline\", we reduce our posterior distribution $p(\\pmb{w} | \\mathcal{D})$ to a point estimate $\\pmb{w}_{MAP}$. For a new sample $\\pmb{x^*}$, the predictive distribution can then be approximated by\n", "$$ p(\\mathbf{y} = 1|\\pmb{x^*},\\mathcal{D}) = \\int p(\\mathbf{y} =1 |\\pmb{x},\\pmb{w})p(\\pmb{w} | \\mathcal{D})d\\pmb{w} \\approx p(y =1 |\\pmb{x},\\pmb{w}_{\\textrm{MAP}}).$$\n", "This approximation is called the **plug-in approximation**.\n", "\n", "The point estimate corresponds to the Maximum-A-Posteriori minimum given by:\n", "$$ \\pmb{w}_{\\textrm{MAP}} = arg \\max_{\\pmb{w}} p(\\pmb{w} \\vert \\mathcal{D}) = arg \\max_{\\pmb{w}} p(\\mathcal{D} \\vert \\pmb{w})p(\\pmb{w}) = arg \\max_{\\pmb{w}} \\prod_{n=1}^N p(y_n \\vert \\pmb{x}_n, \\pmb{w})p(\\pmb{w}) $$\n", "Looking for the maximum solution of previous equation is equivalent to the minimum solution of $- \\log p(\\pmb{w} \\vert \\mathcal{D})$. In case of a Gaussian prior, it can further be derived as:\n", "$$ \\pmb{w}_{\\textrm{MAP}} = arg \\min_{\\pmb{w}} \\sum_{n=1}^N \\big ( -y_n \\log \\sigma(\\pmb{w}^T \\pmb{x}_n + b) - (1-y_n) \\log (1 - \\sigma(\\pmb{w}^T \\pmb{x}_n + b)) + \\frac{1}{2 \\sigma_0^2} \\vert \\vert \\pmb{w} \\vert \\vert_2^2 \\big ) $$\n", "\n", "Note that:\n", "- This actually correspond to the minimum given by the standard **cross-entropy** loss in classification with a weight decay regularization\n", "- Unlike in linear regression, $\\pmb{w}_{MAP}$ **cannot be computed analytically**\n", "- But we can use optimization methods to compute it, e.g. **stochastic gradient descent**\n", "- Nevertheless, we only obtain a **point-wise estimate**, and not a full distribution over parameters $\\pmb{w}$\n", "\n", "\n", "Consequently, **the objective is simply to implement and train a Logistic Regression model** with Pytorch and then compute $p(\\mathbf{y} = 1|\\pmb{x}^*,\\mathcal{D})$ on a new sample $\\pmb{x}^*$ as in a deterministic model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0_I-DiH3tknv" }, "outputs": [], "source": [ "class LogisticRegression(nn.Module):\n", " \"\"\" A Logistic Regression Model with sigmoid output in Pytorch\"\"\"\n", " def __init__(self, input_size):\n", " super().__init__()\n", " self.fc = nn.Linear(input_size, 1)\n", "\n", " def forward(self, x):\n", " return self.fc(x)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hx-vyfGdtknw" }, "outputs": [], "source": [ "#@title **[CODING TASK]** Train a Logistic Regression model with stochastic gradient descent for 20 epochs.\n", "\n", "net = LogisticRegression(input_size=X.shape[1])\n", "net.train()\n", "criterion = nn.BCEWithLogitsLoss()\n", "\n", "\n", "optimizer = torch.optim.SGD(net.parameters(), lr=0.2)\n", "\n", "fig, ax = plt.subplots(figsize=(7,7))\n", "\n", "# ============ YOUR CODE HERE ============\n", "# Train previously defined network for 20 epochs with SGD \n", "# and plot result for each epoch by uncommenting function below\n", "\n", "for epoch in range(20): # loop over the dataset multiple times\n", " # ============ YOUR CODE HERE ============\n", "\n", " # Loss function : binary cross entropy + add a L2 regularization \"manually\" (WEIGHT_DECAY ||w||^2)\n", "\n", " # For plotting and showing learning process at each epoch\n", " # plot_decision_boundary(net, X, y, epoch, ((output>=0.0) == y).float().mean(), \n", " # model_type='classic', tloc=TEXT_LOCATION)" ] }, { "cell_type": "markdown", "metadata": { "id": "FkFuifc5tknx" }, "source": [ "**[Question 1.1]: Analyze the results provided by previous plot. Looking at $p(\\mathbf{y}=1 | \\pmb{x}, \\pmb{w}_{\\textrm{MAP}})$, what can you say about points far from train distribution?**" ] }, { "cell_type": "markdown", "metadata": { "id": "DsTozn8kQccU" }, "source": [ "### I.2 Laplace Approximation\n" ] }, { "cell_type": "markdown", "metadata": { "id": "BadWX2XfWEZe" }, "source": [ "We will use Laplace approximation to estimate the intractable posterior $p(\\pmb{w} | \\mathcal{D})$.\n", "\n", "Here, $p(\\pmb{w} | \\mathcal{D})$ is approximated with a normal distribution $\\mathcal{N}(\\pmb{w} ; \\pmb{\\mu}_{lap}, \\pmb{\\Sigma}_{lap}^2)$ where: \n", "\n", "- the mean of the normal distribution $\\pmb{\\mu}_{lap}$ corresponds to the mode of $p(\\pmb{w} | \\mathcal{D})$. In other words, it simply consists in taking the optimum weights of Maximum-A-Posteriori estimation : \n", "$$\\pmb{\\mu}_{lap} = \\pmb{w}_{\\textrm{MAP}} = \\arg \\min_{\\pmb{w}} -\\log p(\\pmb{w} | \\mathcal{D})$$. \n", "- the covariance matrix is obtained by computing the Hessian of the loss function $-\\log p(\\pmb{w} \\vert \\mathcal{D})$ at $\\pmb{w}=\\pmb{w}_{\\textrm{MAP}}$: \n", "$$(\\pmb{\\Sigma}^2_{lap})^{-1} = \\nabla\\nabla_{\\pmb{w}} [p(\\pmb{w} \\vert \\mathcal{D}) ]_{\\pmb{w}=\\pmb{w}_{\\textrm{MAP}}}$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0LrZKf_dUs5q" }, "outputs": [], "source": [ "#@title **[CODING TASK]** Extract μ_lap from previously trained model. \n", "# NB: Select only weights parameters (without bias)\n", "\n", "# ============ YOUR CODE HERE ============\n", "w_map = None" ] }, { "cell_type": "markdown", "metadata": { "id": "brSZS4nZx4_U" }, "source": [ "To compute the Hessian, we first compute the gradient at $\\pmb{w}_{\\textrm{MAP}}$:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UEOmQfiHS6L-" }, "outputs": [], "source": [ "# Computing first derivative w.r.t to model's weights\n", "optimizer.zero_grad()\n", "output = net(X).squeeze()\n", "loss = criterion(output, y) + WEIGHT_DECAY*net.fc.weight.norm()**2\n", "gradf_weight = grad(loss, net.fc.weight, create_graph=True)[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ADH0i667exQY" }, "outputs": [], "source": [ "#@title **[CODING TASK]** Compute the Hessian from the previous derivative\n", "\n", "# ============ YOUR CODE HERE ============\n", "# Apply the same grad function on each scalar element of the gradient to get \n", "# each raw of the Hessian. Concatenate both and compute the covariance \n", "# by inverting the Hessian\n", "# NB: to avoid accumulated gradient when debugging and running the cell \n", "# multiple times, you should convert your grad results to numpy straight away\n", "hess_weights = None\n", "Sigma_laplace = None" ] }, { "cell_type": "markdown", "metadata": { "id": "_g7CKY0Myq1o" }, "source": [ "We now compute the posterior approximate $\\mathcal{N}(\\pmb{w} ; \\pmb{\\mu}_{lap}, \\pmb{\\Sigma}_{lap}^2)$ with the parameters found. \n", "\n", "Given this distribution, we can compute the posterior thanks to Monte-Carlo sampling and plot results for the last epoch corresponding to $\\pmb{w}_{\\textrm{MAP}}$:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7xf9fF5OuB_K" }, "outputs": [], "source": [ "# Defining posterior distribution\n", "laplace_posterior = np.random.multivariate_normal(w_map.detach().numpy().reshape(2,), Sigma_laplace.detach().numpy(), NB_SAMPLES)\n", "\n", "# Plotting results\n", "fig, ax = plt.subplots(figsize=(7,7))\n", "plot_decision_boundary(net, X, y, epoch, ((output.squeeze()>=0.0) == y).float().mean(), model_type='laplace', \n", " tloc=TEXT_LOCATION, nsamples=NB_SAMPLES, posterior=laplace_posterior)" ] }, { "cell_type": "markdown", "metadata": { "id": "qUPjVjot4IKE" }, "source": [ "**[Question 1.2]: Analyze the results provided by previous plot. Compared to previous MAP estimate, how does the predictive distribution behave?**\n", "\n", "**[Question 1.3]: Comment the effect of the regularisation hyper-parameter WEIGHT_DECAY.**" ] }, { "cell_type": "markdown", "metadata": { "id": "qgNFac420Nh_" }, "source": [ "### I.3 Variational Inference" ] }, { "cell_type": "markdown", "metadata": { "id": "wuJcY6rMa7Ff" }, "source": [ "In this part, we will reimplement variational inference by hand with Pytorch tools.

\n", "\n", "**Optimization problem** \n", "We define an approximating variational distribution $q_{\\pmb{\\theta}}(\\pmb{w})$ parametrized by $\\pmb{\\theta}$ and minimize its Kullback-Leibler (KL) divergence with the unknown true posterior $p(\\pmb{w} \\vert \\mathcal{D})$. This is equivalent to maximizing the **evidence lower bound (ELBO)** w.r.t to $q_{\\pmb{\\theta}}(\\pmb{w})$:\n", "\n", "$$ arg \\max_{\\pmb{\\theta}}~ \\mathbb{E}_{q_{\\pmb{\\theta}}(\\pmb{w})} \\big [\\underbrace{\\log p(\\mathcal{D} \\vert \\pmb{w})}_{likelihood} \\big ] - \\underbrace{\\textrm{KL}(q_{\\pmb{\\theta}}(\\pmb{w})\\vert\\vert p(\\pmb{w}))}_{regularization} $$\n", "where we have a likelihood term and the KL divergence between the prior and the variational distribution. By assuming that samples are *i.i.d*, maximizing the ELBO is equivalent to minimizing the following loss:\n", "$$ \\mathcal{L}_{\\textrm{VI}}(\\pmb{\\theta}; \\mathcal{D}) = - \\sum_{n=1}^N \\mathbb{E}_{q_{\\pmb{\\theta}}(\\pmb{w})} \\Big [ \\log p(y_n \\vert \\pmb{x}_n, \\pmb{w}) \\Big ]+ \\textrm{KL}(q_{\\pmb{\\theta}}(\\pmb{w})\\vert\\vert p(\\pmb{w})) = NLL(\\pmb{\\theta}; \\mathcal{D}) + \\textrm{KL}(q_{\\pmb{\\theta}}(\\pmb{w})\\vert\\vert p(\\pmb{w}))$$\n", "\n", "\n", "- **Likelihood term:** computind the expectations for the negative log likelihhod $NLL(\\pmb{\\theta}; \\mathcal{D})$ can be tedious mathematics, or maybe not even possible. \n", " - **Monte Carlo estimator:** luckily, we can get estimates of the expectations by taking samples from $q_{\\pmb{\\theta}}(\\pmb{w})$ and average over those results. Even more simple, we can show that using only one sample is stil an unbiased gradient estimator. Hence, $NLL(\\pmb{\\theta}; \\mathcal{D})$ rewrites:\n", "$$ NLL(\\pmb{\\theta}; \\mathcal{D}) = \\sum_{n=1}^N - \\log p(y_n \\vert \\pmb{x}_n, \\pmb{w}_s),$$ \n", "where $\\pmb{w}_s \\sim q_{\\pmb{\\theta}}$ is a sample from the variational distribution.

\n", " \n", "- **Mean-field approximation:** assumes a factorisation over weights: $ q_{\\pmb{\\theta}}(\\pmb{w}) = \\prod\\limits_{i=1}^{N_w} q_{\\pmb{\\theta}}(w_{i}) =\\prod\\limits_{i=1}^{N_w} \\mathcal{N}(w_{i}; \\mu_{i}, \\sigma^2_{i}) $. We use this Mean-field approximation for the variational posterior $q_{\\pmb{\\theta}}(\\pmb{w})$ and the prior $p(\\pmb{w})$. \n", " - **Reparametrization trick:** if we start taking samples from $q_{\\pmb{\\theta}}$, we leave the deterministic world, and the gradient can not flow through the model anymore. We avoid this problem by reparameterizing the samples $\\pmb{w}_{i} \\sim \\mathcal{N}(\\mu_{i}, \\sigma_{i}^2)$ from the distribution.
\n", "Instead of sampling directly from the variational distribution, we sample from a centered isotropic multivariate Gaussian and recreate samples from the variational distribution. Now the stochasticity of $\\pmb{\\varepsilon}$ is external and will not prevent the flow of gradients.\n", "$$ \\pmb{w}_{i} = \\mu_{i}+ \\sigma_{i}\\odot\\pmb{\\varepsilon}_s$$\n", "where $\\pmb{\\varepsilon}_s \\sim \\mathcal{N}(0,1)$.\n", "

\n", " - **Closed-for solution for the regularization term $ \\textrm{KL}(q_{\\pmb{\\theta}}(\\pmb{w})\\vert\\vert p(\\pmb{w})$**. For univariate Gaussian distribution, the KL term between the approximate variational distribution $q_{\\theta}(w)\\sim \\mathcal{N}(\\mu_{i},\\sigma_{i}^2)$ and the prior $p(w)\\sim \\mathcal{N}(0,\\sigma_{p}^2)$ rewrites: \n", "\n", "$$\n", "\\textrm{KL}\\left[q_{\\theta}(w))\\vert\\vert p(w) \\right]= \\sum\\limits_{i=1}^d \\left[ log\\left(\\frac{\\sigma_{p}}{\\sigma_{i}}\\right) + \\frac{\\sigma_i^2+\\mu_i^2}{2\\sigma_{p}^2} - \\frac{1}{2} \\right]\n", "$$\n", "\n", "**Predictive distribution** \n", "For a new sample $\\pmb{x^*}$, the predictive distribution can be approximated using **Monte Carlo sampling**:\n", "\\begin{equation}\n", "p(\\mathbf{y} =1|\\pmb{x}^*,\\mathcal{D}) \\approx \\int p(\\mathbf{y} = 1|x^*,w)q_\\theta^*(w) \\approx \\frac{1}{S} \\sum_{s=1}^S p(\\mathbf{y}=1|\\pmb{x}^*,\\pmb{w}_s)\n", "\\end{equation}\n", "where $\\pmb{w}_s \\sim q^*_{\\pmb{\\theta}}$ are samples from the optimum variational distribution." ] }, { "cell_type": "markdown", "metadata": { "id": "H2eNammRvajL" }, "source": [ "#### Step 1: Implement a variational layer\n", "\n", "Let's first implement variational inference for a single layer. Remind that we defined our Logistic regression model as $f(x) = \\sigma(w^T x + b)$ where $\\sigma(t)= \\frac{1}{1+\\exp(t)}$ is the sigmoid function. As such, we need to place Gaussian distributions on parameters $w$ and $b$. \n", "\n", "**Implementation constraint** Variance can not be negative. To avoid numerical issues, we will use $\\rho$. Std can be retrieve with the following formula: $ \\sigma = \\log(1 + e^{\\rho}) $" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Vb6ofPETDZOb" }, "outputs": [], "source": [ "#@title **[CODING TASK]** Implement a variational layer from scratch\n", "\n", "class LinearVariational(nn.Module):\n", " \"\"\"\n", " Mean field approximation of nn.Linear\n", " \"\"\"\n", " def __init__(self, input_size, output_size, prior_std):\n", " super().__init__()\n", " self.prior_std = prior_std # \\sigma_p\n", " \n", " # ============ YOUR CODE HERE ============\n", " # Initialize the variational parameters for weight and bias\n", " # with nn.Parameter.\n", " # Mean and rho can be initialised to zeros . We use point-base estimate for biaises (no rho)\n", " self.w_mu = None\n", " self.w_rho = None\n", " self.b_mu = None\n", " \n", " def sampling(self, mu, rho):\n", " \"Sample weights using the reparametrization trick\"\n", " # ============ YOUR CODE HERE ============\n", " # Given parameter mu and rho, return sampling using \n", " # the reparametrization trick.\n", " # NB: you may look for torch.randn_like...\n", " return None\n", " \n", " def kl_divergence(self): \n", " \"Compute KL divergence between all univariates posterior q(w)~N(\\mu_i,\\sigma_i) and the prior p(w)~N(0,\\sigma_p) \"\n", " \n", " return None\n", " \n", " def forward(self, x):\n", " \"Usual forward function for pytorch layer\"\n", " # ============ YOUR CODE HERE ============\n", " # Sample parameters w and b using self.sampling\n", " # Then, perform a forward pass using those sampled parameters\n", " out = None\n", " \n", " return out" ] }, { "cell_type": "markdown", "metadata": { "id": "mLazbzY0bIjw" }, "source": [ "#### Step 2: Variational Logistic Regression\n", "\n", "Now, let's use this `LinearVariational` layer in a Logistic regression model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pYGaD-CaD-Zt" }, "outputs": [], "source": [ "class VariationalLogisticRegression(nn.Module):\n", " def __init__(self, input_size, prior_std=4.0):\n", " super().__init__()\n", " self.prior_std = prior_std\n", " self.fc_var = LinearVariational(input_size, 1,self.prior_std)\n", " \n", " def forward(self, x):\n", " out = self.fc_var(x)\n", " return torch.sigmoid(out)\n", "\n", " def kl_divergence(self):\n", " return self.fc_var.kl_divergence()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**[Question 1.4]: Comment the code of the VariationalLogisticRegression and LinearVariational classes.**" ] }, { "cell_type": "markdown", "metadata": { "id": "tlRYhjTXxbvy" }, "source": [ "**We can now train our variational model as any other network in Pytorch**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "var_net = VariationalLogisticRegression(input_size=X.shape[1],prior_std=4)\n", "var_net.train()\n", "optimizer = torch.optim.SGD(var_net.parameters(), lr=0.1)\n", "criterion = nn.BCELoss()\n", "\n", "nbEpochs=40\n", "loss_plt = np.zeros((nbEpochs,3))\n", "\n", "fig, ax = plt.subplots(figsize=(7,7))\n", "\n", "for epoch in range(nbEpochs): \n", " optimizer.zero_grad()\n", "\n", " # forward + backward + optimize\n", " output = var_net(X).squeeze()\n", " elbo = var_net.kl_divergence() + criterion(output, y)\n", "\n", " elbo.backward()\n", " optimizer.step()\n", "\n", " # Computing prediction for visualization purpose\n", " preds = torch.zeros(NB_SAMPLES, X.shape[0], 1)\n", " for i in range(NB_SAMPLES):\n", " preds[i] = var_net(X)\n", " pred = preds.mean(0).squeeze()\n", " accuracy = ((pred>=0.5) == y).float().mean()\n", " \n", "\n", " # For plotting and showing learning process at each epoch\n", " plot_decision_boundary(var_net, X, y, epoch, accuracy, model_type='vi', tloc=TEXT_LOCATION)" ] }, { "cell_type": "markdown", "metadata": { "id": "fRbXTWZU48w6" }, "source": [ "**[Question 1.5]: Comment the code of the training loop, especially the loss computation. Analyze the results provided by previous plot. Compared to previous MAP estimate, how does the predictive distribution behave? What is the main difference between the Variational approximation and the Laplace approximation?**" ] }, { "cell_type": "markdown", "metadata": { "id": "uZV6pC6v7Bqh" }, "source": [ "## Part II: Bayesian Neural Networks\n" ] }, { "cell_type": "markdown", "metadata": { "id": "-pqscdtHyT97" }, "source": [ "Moving on to a non-linear dataset, we will leverage our variational implementation to a Multi-Layer Perceptron (MLP). Finally, we will also review one last approximate inference method which has the particularity to be very easy to implement: Monte-Carlo Dropout" ] }, { "cell_type": "markdown", "metadata": { "id": "_g0QmYJ7WJ8p" }, "source": [ "### II.0 Dataset" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "cellView": "form", "id": "NVUva--mk2jk" }, "outputs": [], "source": [ "#@title Hyperparameters for model and approximate inference { form-width: \"30%\" }\n", "\n", "NOISE_MOON = 0.05 #@param\n", "WEIGHT_DECAY = 5e-2 #@param\n", "NB_SAMPLES = 100 #@param\n", "TEXT_LOCATION = (-1.5, -1.5)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "FjIhWLPrkVdp" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load two moons dataset\n", "X, y = make_moons (n_samples=1000, noise=NOISE_MOON)\n", "X, y = torch.from_numpy(X), torch.from_numpy(y)\n", "X, y = X.type(torch.float), y.type(torch.float)\n", "torch_train_dataset = data.TensorDataset(X,y) # create your datset\n", "train_dataloader = data.DataLoader(torch_train_dataset, batch_size=len(torch_train_dataset))\n", "N_DIM = X.shape[1]\n", "\n", "# Visualize dataset\n", "plt.scatter(X[:,0], X[:,1], c=y, cmap='Paired_r', edgecolors='k')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "s0rMZBLxbHY9" }, "source": [ "### II.1 Variational Inference with Bayesian Neural Networks" ] }, { "cell_type": "markdown", "metadata": { "id": "XV8BP1t1yuu1" }, "source": [ "Such as for Logistic Regression, we will use `LinearVariational` layer to define a MLP with 1 hidden layer.\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PCOP9aDQo7zh" }, "outputs": [], "source": [ "#@title **[CODING TASK]** Implement a Variational MLP\n", "\n", "class VariationalMLP(nn.Module):\n", " def __init__(self, input_size, hidden_size, prior_std):\n", " super().__init__()\n", " self.prior_std = prior_std\n", "\n", "\n", " # ============ YOUR CODE HERE ============\n", " # Define a variational MLP with 1 hidden layer and ReLU activation\n", " \n", " def kl_divergence(self):\n", " # ============ YOUR CODE HERE ============\n", " return None\n", " \n", " \n", " def forward(self, x):\n", " # ============ YOUR CODE HERE ============\n", " # Don't forget to apply the sigmoid function when returning the output\n", " return None" ] }, { "cell_type": "markdown", "metadata": { "id": "be7NXnFs0AmB" }, "source": [ "**We can now train our variational model as any other network in Pytorch**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ar5ZX1SfpdgV" }, "outputs": [], "source": [ "var_net = VariationalMLP(input_size=X.shape[1], hidden_size=50, prior_std=4)\n", "var_net.train()\n", "optimizer = torch.optim.Adam(var_net.parameters(), lr=0.1)\n", "\n", "criterion = nn.BCELoss(reduction='mean')\n", "\n", "nbEpochs=8000\n", "beta = 3e-2\n", "\n", "loss_plt = np.zeros((nbEpochs,3))\n", "fig, ax = plt.subplots(figsize=(7,7))\n", "\n", "for epoch in range(nbEpochs): # loop over the dataset multiple times\n", " # zero the parameter gradients\n", " optimizer.zero_grad()\n", "\n", " # forward + backward + optimize\n", " output = var_net(X).squeeze()\n", " elbo = beta * var_net.kl_divergence() + criterion(output, y)\n", "\n", " loss_plt[epoch,0] =elbo\n", " loss_plt[epoch,1] =criterion(output, y)\n", " loss_plt[epoch,2] =beta * var_net.kl_divergence()\n", " \n", " elbo.backward()\n", " optimizer.step()\n", "\n", " # For plotting and showing learning process at each epoch\n", " if (epoch+1)%100==0:\n", " # Computing prediction for visualization purpose\n", " preds = torch.zeros(NB_SAMPLES, X.shape[0], 1)\n", " for i in range(NB_SAMPLES):\n", " preds[i] = var_net(X)\n", " pred = preds.mean(0).squeeze()\n", " accuracy = ((pred>=0.5) == y).float().mean()\n", "\n", " plot_decision_boundary(var_net, X, y, epoch, accuracy, model_type='vi', tloc=TEXT_LOCATION)\n", " \n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(np.arange(nbEpochs),loss_plt[:,0], label='total loss')\n", "ax.plot(np.arange(nbEpochs),loss_plt[:,1], label='CE loss')\n", "ax.plot(np.arange(nbEpochs),loss_plt[:,2], label='KL')\n", "legend = ax.legend(loc='center right', shadow=True, fontsize='x-large')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**[Question 2.1]: Analyze the results showed on plot.**" ] }, { "cell_type": "markdown", "metadata": { "id": "3i_scDA7bC1c" }, "source": [ "### II.2 Monte Carlo Dropout" ] }, { "cell_type": "markdown", "metadata": { "id": "O8J9UNT41TJ2" }, "source": [ "Training a neural network with randomly dropping some activations, such as with dropout layers, can actually be seen as an **approximate variational inference method**!\n", "\n", "[Gal and Ghahramani, 2016] showed this can be fullfilled for:\n", "- $p(\\pmb{w}) = \\prod_l p(\\pmb{W}_l) = \\prod_l \\mathcal{MN}(\\pmb{W}_l; 0, I/ l_i^2, I)$ $\\Rightarrow$ Multivariate Gaussian distribution factorized over layers\n", "- $q(\\pmb{w}) = \\prod_l q(\\pmb{W}_l) = \\prod_l \\textrm{diag}(\\varepsilon_l)\\odot\\pmb{M}_l $ with $\\varepsilon_l \\sim \\textrm{Ber}(1-p_l)$.\n", "\n", "We will now implement a MLP with dropout layers and perform Monte-Carlo sampling to obtain the predictive distribution $p(\\mathbf{y} \\vert \\pmb{x}^*, \\pmb{w})$ for a new sample $\\pmb{x}^*$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xxGmkjd0kVgZ" }, "outputs": [], "source": [ "#@title **[CODING TASK]** Implement a MLP with dropout (p=0.9)\n", "# Code MLP with 1 hidden layer and a dropout layer. Be careful, the dropout \n", "# layer should be also activated during test time.\n", "# (Hint: we may want to look out at F.dropout())\n", "\n", "class MLP(nn.Module):\n", " \"\"\" Pytorch MLP for binary classification model with an added dropout layer\"\"\"\n", " def __init__(self, input_size, hidden_size):\n", " super().__init__()\n", " # ============ YOUR CODE HERE ============\n", "\n", " def forward(self, x):\n", " # ============ YOUR CODE HERE ============\n", " # Don't forget to apply the sigmoid function when returning the output\n", " return None" ] }, { "cell_type": "markdown", "metadata": { "id": "qMlasuRN08Yk" }, "source": [ "We train our model as usual:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gqSZJDWIlf5e" }, "outputs": [], "source": [ "net = MLP(input_size=X.shape[1], hidden_size=50)\n", "net.train()\n", "criterion = nn.BCELoss()\n", "optimizer = torch.optim.Adam(net.parameters(), lr=0.01)\n", "fig, ax = plt.subplots(figsize=(7,7))\n", "\n", "for epoch in range(500): # loop over the dataset multiple times\n", "\n", " # zero the parameter gradients\n", " optimizer.zero_grad()\n", "\n", " # forward + backward + optimize\n", " output = net(X).squeeze()\n", " loss = criterion(output, y)\n", " loss.backward()\n", " optimizer.step()\n", "\n", " # For plotting and showing learning process at each epoch, uncomment and indent line below\n", " if (epoch+1)%50==0:\n", " plot_decision_boundary(net, X, y, epoch, ((output.squeeze()>=0.5) == y).float().mean(), tloc=TEXT_LOCATION, model_type='classic')\n", " " ] }, { "cell_type": "markdown", "metadata": { "id": "U_RyglMRa4d5" }, "source": [ "Now let's look at the results given by MC Dropout:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JRh6BQDcazFx" }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(7,7))\n", "plot_decision_boundary(net, X, y, epoch, ((output.squeeze()>=0.5) == y).float().mean(), tloc=TEXT_LOCATION, model_type='mcdropout', nsamples=500)" ] }, { "cell_type": "markdown", "metadata": { "id": "05OBuwko5EvS" }, "source": [ "**[Question 2.2]: Again, analyze the results showed on plot. What is the benefit of MC Dropout variational inference over Bayesian Logistic Regression with variational inference?**" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "clhZGFZ2wOxg", "ZCp4ytXhV6IU", "sNGfQhFqtknu", "DsTozn8kQccU", "qgNFac420Nh_", "_g0QmYJ7WJ8p", "s0rMZBLxbHY9", "3i_scDA7bC1c" ], "name": "TP2_RDFIA_Approximate_Inference.ipynb", "provenance": [ { "file_id": "1ObknbWrb32m4DzjuNkDLbYsPFrBQFxbY", "timestamp": 1611617981429 }, { "file_id": "1PLhcxazEKZ_VQRqt0p3RasaLedb5WNgP", "timestamp": 1611229201493 } ] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 4 }