Ensure tensors are on proper device

Remove execution output in regularization notebook
bolimath · Feb 14, 2019 · 9f3a9a9 · 9f3a9a9
1 parent 3e75af6
commit 9f3a9a9
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Showing 11 changed files with 129 additions and 82 deletions.
diff --git a/01-tensor_tutorial.ipynb b/01-tensor_tutorial.ipynb
@@ -910,9 +910,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pytorch_latest",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "pytorch_latest"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -924,7 +924,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.6"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,

diff --git a/02-space_stretching.ipynb b/02-space_stretching.ipynb
@@ -28,14 +28,25 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
    "source": [
     "# generate some points in 2-D space\n",
     "n_points = 1000\n",
-    "X = torch.randn(n_points, 2) \n",
+    "X = torch.randn(n_points, 2).to(device)\n",
     "colors = X[:, 0]\n",
     "\n",
     "show_scatterplot(X, colors, title='X')\n",
-    "OI = torch.cat((torch.zeros(2, 2), torch.eye(2)))\n",
+    "OI = torch.cat((torch.zeros(2, 2), torch.eye(2))).to(device)\n",
     "plot_bases(OI)"
    ]
   },
@@ -76,7 +87,7 @@
     "\n",
     "for i in range(10):\n",
     "    # create a random matrix\n",
-    "    W = torch.randn(2, 2)\n",
+    "    W = torch.randn(2, 2).to(device)\n",
     "    # transform points\n",
     "    Y = X @ W\n",
     "    # compute singular values\n",
@@ -100,16 +111,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [],
    "source": [
     "model = nn.Sequential(\n",
@@ -211,6 +215,7 @@
     "            NL, \n",
     "            nn.Linear(n_hidden, 2)\n",
     "        )\n",
+    "    model.to(device)\n",
     "    with torch.no_grad():\n",
     "        Y = model(X)\n",
     "    show_scatterplot(Y, colors, title='f(x)')\n",
@@ -244,17 +249,25 @@
     "        NL, \n",
     "        nn.Linear(n_hidden, 2)\n",
     "    )\n",
+    "    model.to(device)\n",
     "    with torch.no_grad():\n",
     "        Y = model(X).detach()\n",
     "    show_scatterplot(Y, colors, title='f(x)')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pytorch_latest",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "pytorch_latest"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -266,7 +279,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.6"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,

diff --git a/03-autograd_tutorial.ipynb b/03-autograd_tutorial.ipynb
@@ -278,9 +278,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pytorch_latest",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "pytorch_latest"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -292,7 +292,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.6"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,

diff --git a/04-spiral_classification.ipynb b/04-spiral_classification.ipynb
@@ -38,6 +38,15 @@
     "set_default()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -59,8 +68,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X = torch.zeros(N * C, D)\n",
-    "y = torch.zeros(N * C, dtype=torch.long)\n",
+    "X = torch.zeros(N * C, D).to(device)\n",
+    "y = torch.zeros(N * C, dtype=torch.long).to(device)\n",
     "for c in range(C):\n",
     "    index = 0\n",
     "    t = torch.linspace(0, 1, N)\n",
@@ -114,15 +123,6 @@
     "lambda_l2 = 1e-5"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -261,13 +261,20 @@
     "print(model)\n",
     "plot_model(X, y, model)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pytorch_latest",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "pytorch_latest"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -279,7 +286,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.6"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,

diff --git a/05-regression.ipynb b/05-regression.ipynb
@@ -39,6 +39,15 @@
     "set_default()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -60,8 +69,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)\n",
-    "y = X.pow(3) + 0.3 * torch.rand(X.size())"
+    "X = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1).to(device)\n",
+    "y = X.pow(3) + 0.3 * torch.rand(X.size()).to(device)"
    ]
   },
   {
@@ -81,7 +90,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.scatter(X.numpy(), y.numpy())\n",
+    "plt.scatter(X.cpu().numpy(), y.cpu().numpy())\n",
     "plt.axis('equal')"
    ]
   },
@@ -102,15 +111,6 @@
     "lambda_l2 = 1e-5"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -172,8 +172,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.scatter(X.data.numpy(), y.data.numpy())\n",
-    "plt.plot(X.data.numpy(), y_pred.data.numpy(), 'r-', lw=5)\n",
+    "plt.scatter(X.data.cpu().numpy(), y.data.cpu().numpy())\n",
+    "plt.plot(X.data.cpu().numpy(), y_pred.data.cpu().numpy(), 'r-', lw=5)\n",
     "plt.axis('equal');"
    ]
   },
@@ -283,13 +283,13 @@
     "    # New X that ranges from -5 to 5 instead of -1 to 1\n",
     "    X_new = torch.unsqueeze(torch.linspace(-2, 2, 100), dim=1)\n",
     "        \n",
-    "    plt.plot(X_new.numpy(), y_pretrain_idx.numpy(), 'r-', lw=1)\n",
+    "    plt.plot(X_new.numpy(), y_pretrain_idx.cpu().numpy(), 'r-', lw=1)\n",
     "\n",
-    "plt.scatter(X.numpy(), y.numpy(), label='data')\n",
+    "plt.scatter(X.cpu().numpy(), y.cpu().numpy(), label='data')\n",
     "plt.axis('square')\n",
     "plt.axis((-1.1, 1.1, -1.1, 1.1));\n",
     "y_combo = torch.stack(y_pretrain)\n",
-    "plt.plot(X_new.numpy(), y_combo.var(dim=0).numpy(), 'g', label='variance');\n",
+    "plt.plot(X_new.numpy(), y_combo.var(dim=0).cpu().numpy(), 'g', label='variance');\n",
     "plt.legend()"
    ]
   },
@@ -309,21 +309,28 @@
     "y_pred = list()\n",
     "for model in models:\n",
     "    # New X that ranges from -5 to 5 instead of -1 to 1\n",
-    "    X_new = torch.unsqueeze(torch.linspace(-4, 4, 1001), dim=1)\n",
+    "    X_new = torch.unsqueeze(torch.linspace(-4, 4, 1001), dim=1).to(device)\n",
     "\n",
     "    # Getting predictions from input\n",
     "    with torch.no_grad():\n",
     "        y_pred.append(model(X_new))\n",
     "        \n",
-    "    plt.plot(X_new.numpy(), y_pred[-1].numpy(), 'r-', lw=1)\n",
+    "    plt.plot(X_new.cpu().numpy(), y_pred[-1].cpu().numpy(), 'r-', lw=1)\n",
     "\n",
-    "plt.scatter(X.numpy(), y.numpy(), label='data')\n",
+    "plt.scatter(X.cpu().numpy(), y.cpu().numpy(), label='data')\n",
     "plt.axis('square')\n",
     "plt.axis((-1.1, 1.1, -1.1, 1.1));\n",
     "# plt.axis((-4.1, 4.1, -4.1, 4.1));  # toggle me :)\n",
     "y_combo = torch.stack(y_pred)\n",
-    "plt.plot(X_new.numpy(), y_combo.var(dim=0).numpy(), 'g', label='variance');"
+    "plt.plot(X_new.cpu().numpy(), y_combo.var(dim=0).cpu().numpy(), 'g', label='variance');"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -342,7 +349,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,