Code for first printing

Author: elistevens

p1ch4/1_image_dog.ipynb

@@ -65,7 +65,8 @@
     "import os\n",
     "\n",
     "data_dir = '../data/p1ch4/image-cats/'\n",
-    "filenames = [name for name in os.listdir(data_dir) if os.path.splitext(name)[-1] == '.png']\n",
+    "filenames = [name for name in os.listdir(data_dir)\n",
+    "             if os.path.splitext(name)[-1] == '.png']\n",
     "for i, filename in enumerate(filenames):\n",
     "    img_arr = imageio.imread(os.path.join(data_dir, filename))\n",
     "    img_t = torch.from_numpy(img_arr)\n",

p1ch4/2_volumetric_ct.ipynb

@@ -22,7 +22,7 @@
      "text": [
       "Reading DICOM (examining files): 1/99 files (1.0%99/99 files (100.0%)\n",
       "  Found 1 correct series.\n",
-      "Reading DICOM (loading data): 87/99  (87.999/99  (100.0%)\n"
+      "Reading DICOM (loading data): 31/99  (31.392/99  (92.999/99  (100.0%)\n"
      ]
     },
     {
@@ -52,7 +52,7 @@
     {
      "data": {
       "text/plain": [
-       "torch.Size([1, 512, 512, 99])"
+       "torch.Size([1, 99, 512, 512])"
       ]
      },
      "execution_count": 3,
@@ -62,7 +62,6 @@
    ],
    "source": [
     "vol = torch.from_numpy(vol_arr).float()\n",
-    "vol = torch.transpose(vol, 0, 2)\n",
     "vol = torch.unsqueeze(vol, 0)\n",
     "\n",
     "vol.shape"
@@ -120,7 +119,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,

p1ch4/3_tabular_wine.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "import numpy as np\n",
     "import torch\n",
-    "torch.set_printoptions(edgeitems=2, precision=2)"
+    "torch.set_printoptions(edgeitems=2, precision=2, linewidth=75)"
    ]
   },
   {
@@ -36,7 +36,8 @@
    "source": [
     "import csv\n",
     "wine_path = \"../data/p1ch4/tabular-wine/winequality-white.csv\"\n",
-    "wineq_numpy = np.loadtxt(wine_path, dtype=np.float32, delimiter=\";\", skiprows=1)\n",
+    "wineq_numpy = np.loadtxt(wine_path, dtype=np.float32, delimiter=\";\",\n",
+    "                         skiprows=1)\n",
     "wineq_numpy"
    ]
   },
@@ -222,8 +223,8 @@
     {
      "data": {
       "text/plain": [
-       "tensor([6.85e+00, 2.78e-01, 3.34e-01, 6.39e+00, 4.58e-02, 3.53e+01, 1.38e+02,\n",
-       "        9.94e-01, 3.19e+00, 4.90e-01, 1.05e+01])"
+       "tensor([6.85e+00, 2.78e-01, 3.34e-01, 6.39e+00, 4.58e-02, 3.53e+01,\n",
+       "        1.38e+02, 9.94e-01, 3.19e+00, 4.90e-01, 1.05e+01])"
       ]
      },
      "execution_count": 10,
@@ -244,8 +245,8 @@
     {
      "data": {
       "text/plain": [
-       "tensor([7.12e-01, 1.02e-02, 1.46e-02, 2.57e+01, 4.77e-04, 2.89e+02, 1.81e+03,\n",
-       "        8.95e-06, 2.28e-02, 1.30e-02, 1.51e+00])"
+       "tensor([7.12e-01, 1.02e-02, 1.46e-02, 2.57e+01, 4.77e-04, 2.89e+02,\n",
+       "        1.81e+03, 8.95e-06, 2.28e-02, 1.30e-02, 1.51e+00])"
       ]
      },
      "execution_count": 11,
@@ -448,7 +449,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,

p1ch4/4_time_series_bikes.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "import numpy as np\n",
     "import torch\n",
-    "torch.set_printoptions(edgeitems=2, threshold=50)"
+    "torch.set_printoptions(edgeitems=2, threshold=50, linewidth=75)"
    ]
   },
   {
@@ -32,11 +32,12 @@
     }
    ],
    "source": [
-    "bikes_numpy = np.loadtxt(\"../data/p1ch4/bike-sharing-dataset/hour-fixed.csv\", \n",
-    "                         dtype=np.float32, \n",
-    "                         delimiter=\",\", \n",
-    "                         skiprows=1, \n",
-    "                         converters={1: lambda x: float(x[8:10])}) # <1>\n",
+    "bikes_numpy = np.loadtxt(\n",
+    "    \"../data/p1ch4/bike-sharing-dataset/hour-fixed.csv\", \n",
+    "    dtype=np.float32, \n",
+    "    delimiter=\",\", \n",
+    "    skiprows=1, \n",
+    "    converters={1: lambda x: float(x[8:10])}) # <1>\n",
     "bikes = torch.from_numpy(bikes_numpy)\n",
     "bikes"
    ]
@@ -113,7 +114,8 @@
     {
      "data": {
       "text/plain": [
-       "tensor([1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2])"
+       "tensor([1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 2, 2,\n",
+       "        2, 2])"
       ]
      },
      "execution_count": 6,
@@ -162,9 +164,9 @@
     {
      "data": {
       "text/plain": [
-       "tensor([[ 1.0000,  1.0000,  1.0000,  0.0000,  1.0000,  0.0000,  0.0000,  6.0000,\n",
-       "          0.0000,  1.0000,  0.2400,  0.2879,  0.8100,  0.0000,  3.0000, 13.0000,\n",
-       "         16.0000,  1.0000,  0.0000,  0.0000,  0.0000]])"
+       "tensor([[ 1.0000,  1.0000,  1.0000,  0.0000,  1.0000,  0.0000,  0.0000,\n",
+       "          6.0000,  0.0000,  1.0000,  0.2400,  0.2879,  0.8100,  0.0000,\n",
+       "          3.0000, 13.0000, 16.0000,  1.0000,  0.0000,  0.0000,  0.0000]])"
       ]
      },
      "execution_count": 8,
@@ -193,7 +195,8 @@
     }
    ],
    "source": [
-    "daily_weather_onehot = torch.zeros(daily_bikes.shape[0], 4, daily_bikes.shape[2])\n",
+    "daily_weather_onehot = torch.zeros(daily_bikes.shape[0], 4,\n",
+    "                                   daily_bikes.shape[2])\n",
     "daily_weather_onehot.shape"
    ]
   },
@@ -214,7 +217,8 @@
     }
    ],
    "source": [
-    "daily_weather_onehot.scatter_(1, daily_bikes[:,9,:].long().unsqueeze(1) - 1, 1.0)\n",
+    "daily_weather_onehot.scatter_(\n",
+    "    1, daily_bikes[:,9,:].long().unsqueeze(1) - 1, 1.0)\n",
     "daily_weather_onehot.shape"
    ]
   },
@@ -245,7 +249,8 @@
     "temp = daily_bikes[:, 10, :]\n",
     "temp_min = torch.min(temp)\n",
     "temp_max = torch.max(temp)\n",
-    "daily_bikes[:, 10, :] = (daily_bikes[:, 10, :] - temp_min) / (temp_max - temp_min)"
+    "daily_bikes[:, 10, :] = ((daily_bikes[:, 10, :] - temp_min)\n",
+    "                         / (temp_max - temp_min))"
    ]
   },
   {
@@ -255,7 +260,8 @@
    "outputs": [],
    "source": [
     "temp = daily_bikes[:, 10, :]\n",
-    "daily_bikes[:, 10, :] = (daily_bikes[:, 10, :] - torch.mean(temp)) / torch.std(temp)"
+    "daily_bikes[:, 10, :] = ((daily_bikes[:, 10, :] - torch.mean(temp))\n",
+    "                         / torch.std(temp))"
    ]
   }
  ],
@@ -275,7 +281,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,

p1ch5/1_parameter_estimation.ipynb

@@ -13,7 +13,7 @@
     "%matplotlib inline\n",
     "import numpy as np\n",
     "import torch\n",
-    "torch.set_printoptions(edgeitems=2)"
+    "torch.set_printoptions(edgeitems=2, linewidth=75)"
    ]
   },
   {
@@ -73,8 +73,8 @@
     {
      "data": {
       "text/plain": [
-       "tensor([35.7000, 55.9000, 58.2000, 81.9000, 56.3000, 48.9000, 33.9000, 21.8000,\n",
-       "        48.4000, 60.4000, 68.4000])"
+       "tensor([35.7000, 55.9000, 58.2000, 81.9000, 56.3000, 48.9000, 33.9000,\n",
+       "        21.8000, 48.4000, 60.4000, 68.4000])"
       ]
      },
      "execution_count": 5,
@@ -128,7 +128,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "shapes: x: torch.Size([]), y: torch.Size([3, 1]), z: torch.Size([1, 3]), a: torch.Size([2, 1, 1])\n",
+      "shapes: x: torch.Size([]), y: torch.Size([3, 1])\n",
+      "        z: torch.Size([1, 3]), a: torch.Size([2, 1, 1])\n",
       "x * y: torch.Size([3, 1])\n",
       "y * z: torch.Size([3, 3])\n",
       "y * z * a: torch.Size([2, 3, 3])\n"
@@ -140,7 +141,8 @@
     "y = torch.ones(3,1)\n",
     "z = torch.ones(1,3)\n",
     "a = torch.ones(2, 1, 1)\n",
-    "print(f\"shapes: x: {x.shape}, y: {y.shape}, z: {z.shape}, a: {a.shape}\")\n",
+    "print(f\"shapes: x: {x.shape}, y: {y.shape}\")\n",
+    "print(f\"        z: {z.shape}, a: {a.shape}\")\n",
     "print(\"x * y:\", (x * y).shape)\n",
     "print(\"y * z:\", (y * z).shape)\n",
     "print(\"y * z * a:\", (y * z * a).shape)"
@@ -290,7 +292,8 @@
    },
    "outputs": [],
    "source": [
-    "def training_loop(n_epochs, learning_rate, params, t_u, t_c, print_params=True):\n",
+    "def training_loop(n_epochs, learning_rate, params, t_u, t_c,\n",
+    "                  print_params=True):\n",
     "    for epoch in range(1, n_epochs + 1):\n",
     "        w, b = params\n",
     "\n",
@@ -637,7 +640,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.7.6"
   },
   "pycharm": {
    "stem_cell": {

p1ch5/2_autograd.ipynb

@@ -18,8 +18,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "t_c = torch.tensor([0.5, 14.0, 15.0, 28.0, 11.0, 8.0, 3.0, -4.0, 6.0, 13.0, 21.0])\n",
-    "t_u = torch.tensor([35.7, 55.9, 58.2, 81.9, 56.3, 48.9, 33.9, 21.8, 48.4, 60.4, 68.4])\n",
+    "t_c = torch.tensor([0.5, 14.0, 15.0, 28.0, 11.0, 8.0,\n",
+    "                    3.0, -4.0, 6.0, 13.0, 21.0])\n",
+    "t_u = torch.tensor([35.7, 55.9, 58.2, 81.9, 56.3, 48.9,\n",
+    "                    33.9, 21.8, 48.4, 60.4, 68.4])\n",
     "t_un = 0.1 * t_u"
    ]
   },
@@ -188,7 +190,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,