change category

patel-zeel · Aug 31, 2023 · ae14e33 · ae14e33
1 parent 7327fe2
commit ae14e33
Showing 1 changed file with 16 additions and 16 deletions.
diff --git a/posts/2023-08-31-bayesian-gaussian-basis-regression.ipynb b/posts/2023-08-31-bayesian-gaussian-basis-regression.ipynb
@@ -8,7 +8,7 @@
     "---\n",
     "author: Zeel B Patel\n",
     "badges: true\n",
-    "categories: NumPy, Mathematics\n",
+    "categories: ML\n",
     "description: Bayesian Gaussian Basis Regression\n",
     "title: Bayesian Gaussian Basis Regression\n",
     "date: '2023-08-31'\n",
@@ -39,7 +39,7 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "device = \"cuda\"\n",
-    "rd.set_backend(\"torch\")\n"
+    "rd.set_backend(\"torch\")"
    ]
   },
   {
@@ -99,7 +99,7 @@
     "y = y.to(device)\n",
     "print(x.dtype, y.dtype)\n",
     "\n",
-    "plt.scatter(x.cpu().numpy()[:, 1], y.cpu().numpy())\n"
+    "plt.scatter(x.cpu().numpy()[:, 1], y.cpu().numpy())"
    ]
   },
   {
@@ -149,7 +149,7 @@
     "            ],\n",
     "            dim=1,\n",
     "        )\n",
-    "        return features\n"
+    "        return features"
    ]
   },
   {
@@ -169,7 +169,7 @@
     }
    ],
    "source": [
-    "GaussianBasis(0.0, 10).to(device)(x).shape"
+    "GaussianBasis(0.0, 10).to(device)(x).shape\n"
    ]
   },
   {
@@ -228,7 +228,7 @@
     "            alpha=0.2,\n",
     "            label=\"95% CI\",\n",
     "        )\n",
-    "        return ax"
+    "        return ax\n"
    ]
   },
   {
@@ -247,7 +247,7 @@
     "    log_var_noise = nn.Parameter(torch.tensor(0.1)).to(device)\n",
     "    log_var_noise.requires_grad = True\n",
     "    m0.requires_grad = False\n",
-    "    s0.requires_grad = True\n"
+    "    s0.requires_grad = True"
    ]
   },
   {
@@ -295,7 +295,7 @@
     "    losses.append(loss.item())\n",
     "    pbar.set_description(f\"loss: {loss.item():.4f}\")\n",
     "\n",
-    "plt.plot(losses)"
+    "plt.plot(losses)\n"
    ]
   },
   {
@@ -323,7 +323,7 @@
     "with torch.no_grad():\n",
     "    ax.scatter(x.cpu().numpy()[:, 1], y.cpu().numpy())\n",
     "    # ax.vlines(mlp.transform.grid.cpu().numpy(), -1, 1, color=\"black\", alpha=0.2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -344,7 +344,7 @@
     }
    ],
    "source": [
-    "torch.exp(log_var_noise), s0\n"
+    "torch.exp(log_var_noise), s0"
    ]
   },
   {
@@ -370,7 +370,7 @@
     "    log_var_noise = nn.Parameter(torch.tensor(0.1)).to(device)\n",
     "    log_var_noise.requires_grad = True\n",
     "    m0.requires_grad = False\n",
-    "    s0.requires_grad = True\n"
+    "    s0.requires_grad = True"
    ]
   },
   {
@@ -418,7 +418,7 @@
     "    losses.append(loss.item())\n",
     "    pbar.set_description(f\"loss: {loss.item():.4f}\")\n",
     "\n",
-    "plt.plot(losses)"
+    "plt.plot(losses)\n"
    ]
   },
   {
@@ -446,7 +446,7 @@
     "with torch.no_grad():\n",
     "    ax.scatter(x.cpu().numpy()[:, 1], y.cpu().numpy())\n",
     "    ax.vlines(mlp.transform.grid.cpu().numpy(), -1, 1, color=\"black\", alpha=0.2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -472,7 +472,7 @@
     "    log_var_noise = nn.Parameter(torch.tensor(0.1)).to(device)\n",
     "    log_var_noise.requires_grad = True\n",
     "    m0.requires_grad = False\n",
-    "    s0.requires_grad = True\n"
+    "    s0.requires_grad = True"
    ]
   },
   {
@@ -520,7 +520,7 @@
     "    losses.append(loss.item())\n",
     "    pbar.set_description(f\"loss: {loss.item():.4f}\")\n",
     "\n",
-    "plt.plot(losses)"
+    "plt.plot(losses)\n"
    ]
   },
   {
@@ -548,7 +548,7 @@
     "with torch.no_grad():\n",
     "    ax.scatter(x.cpu().numpy()[:, 1], y.cpu().numpy())\n",
     "    ax.vlines(mlp.grid.cpu().numpy(), -1, 1, color=\"black\", alpha=0.2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   }
  ],