Create a custom dataset

RNN_building_blocks.ipynb

@@ -14,10 +14,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "outputs": [],
    "source": [
     "import torch\n",
+    "from torch.utils.data import Dataset\n",
     "from torchvision import datasets, transforms\n",
     "import torch.nn.functional as F\n",
     "from torch import nn\n",
@@ -34,6 +35,45 @@
      "name": "#%%\n"
     }
    }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [],
+   "source": [
+    "from torchvision import datasets, transforms\n",
+    "import torch.nn.functional as F\n",
+    "from torch import nn\n",
+    "\n",
+    "class mySeries(Dataset):\n",
+    "    \"\"\"Series for RNN dataset.\"\"\"\n",
+    "\n",
+    "    def __init__(self,  length=1000, transform=None):\n",
+    "        self.transform = transform\n",
+    "        self.length = length\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return self.length\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        if torch.is_tensor(idx):\n",
+    "            idx = idx.tolist()\n",
+    "\n",
+    "        X = idx+2\n",
+    "        y = X+2\n",
+    "\n",
+    "        return X,y\n",
+    "\n",
+    "# mySeries dataset\n",
+    "trainset = mySeries()\n",
+    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64)\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   }
  ],
  "metadata": {