1 files changed, 103 insertions, 48 deletions

diff --git a/src/notebooks/01-look-at-emnist.ipynb b/src/notebooks/01-look-at-emnist.ipynb
index 8648afb..93083a5 100644
--- a/src/notebooks/01-look-at-emnist.ipynb
+++ b/src/notebooks/01-look-at-emnist.ipynb
@@ -31,16 +31,104 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = EmnistDataset(train=False, sample_to_balance=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import random_split, DataLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "dataset = EmnistDataset()"
+    "d1, d2 = random_split(dataset, [len(dataset)-10, 10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "55898"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(d1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dl = DataLoader(d1, batch_size=16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3494"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(dl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "only one element tensors can be converted to Python scalars",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-14-69c3b5027f10>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: only one element tensors can be converted to Python scalars"
+     ]
+    }
+   ],
+   "source": [
+    "d1.dataset.data[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -49,7 +137,7 @@
        "torch.Tensor"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -60,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -69,8 +157,8 @@
      "text": [
       "EMNIST Dataset\n",
       "Num classes: 80\n",
-      "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M', 23: 'N', 24: 'O', 25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X', 34: 'Y', 35: 'Z', 36: 'a', 37: 'b', 38: 'c', 39: 'd', 40: 'e', 41: 'f', 42: 'g', 43: 'h', 44: 'i', 45: 'j', 46: 'k', 47: 'l', 48: 'm', 49: 'n', 50: 'o', 51: 'p', 52: 'q', 53: 'r', 54: 's', 55: 't', 56: 'u', 57: 'v', 58: 'w', 59: 'x', 60: 'y', 61: 'z', 62: ' ', 63: '!', 64: '\"', 65: '#', 66: '&', 67: \"'\", 68: '(', 69: ')', 70: '*', 71: '+', 72: ',', 73: '-', 74: '.', 75: '/', 76: ':', 77: ';', 78: '?', 79: '_'}\n",
       "Input shape: [28, 28]\n",
+      "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M', 23: 'N', 24: 'O', 25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X', 34: 'Y', 35: 'Z', 36: 'a', 37: 'b', 38: 'c', 39: 'd', 40: 'e', 41: 'f', 42: 'g', 43: 'h', 44: 'i', 45: 'j', 46: 'k', 47: 'l', 48: 'm', 49: 'n', 50: 'o', 51: 'p', 52: 'q', 53: 'r', 54: 's', 55: 't', 56: 'u', 57: 'v', 58: 'w', 59: 'x', 60: 'y', 61: 'z', 62: ' ', 63: '!', 64: '\"', 65: '#', 66: '&', 67: \"'\", 68: '(', 69: ')', 70: '*', 71: '+', 72: ',', 73: '-', 74: '.', 75: '/', 76: ':', 77: ';', 78: '?', 79: '_'}\n",
       "\n"
      ]
     }
@@ -81,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,17 +182,17 @@
     "        ax.imshow(x, cmap='gray')\n",
     "        ax.set_xticks([])\n",
     "        ax.set_yticks([])\n",
-    "        ax.set_title(dataset.translator(int(y)))"
+    "        ax.set_title(dataset.mapper(int(y)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 648x648 with 9 Axes>"
       ]
@@ -119,12 +207,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 648x648 with 9 Axes>"
       ]
@@ -139,43 +227,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 28, 28])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset[0][0].shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.int64"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset[0][1].dtype"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",