diff --git a/notebooks/c09_Autoencoders/Autoencoders.ipynb b/notebooks/c09_Autoencoders/Autoencoders.ipynb
index 3cdcf31..f7c436f 100644
--- a/notebooks/c09_Autoencoders/Autoencoders.ipynb
+++ b/notebooks/c09_Autoencoders/Autoencoders.ipynb
@@ -62,8 +62,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:37:25.815620Z",
-     "start_time": "2020-10-12T06:37:24.333305Z"
+     "end_time": "2020-10-16T07:27:31.675362Z",
+     "start_time": "2020-10-16T07:27:30.532618Z"
     }
    },
    "outputs": [],
@@ -118,15 +118,28 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:37:25.828473Z",
-     "start_time": "2020-10-12T06:37:25.818203Z"
+     "end_time": "2020-10-16T07:27:31.766026Z",
+     "start_time": "2020-10-16T07:27:31.676874Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "Dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T07:27:31.801155Z",
+     "start_time": "2020-10-16T07:27:31.767942Z"
     }
    },
    "outputs": [],
    "source": [
     "path = Path(\"../../data/processed/MNIST/\")\n",
     "\n",
-    "\n",
     "class DigitsDataset(Dataset):\n",
     "    def __init__(self, path, transform=None):\n",
     "\n",
@@ -134,35 +147,24 @@
     "        self.transform = transform\n",
     "        data = pd.read_csv(path)\n",
     "        if \"label\" in data.columns:\n",
-    "            self.x = data.drop(columns=[\"label\"]).values\n",
+    "            self.x = data.drop(columns=[\"label\"]).values.reshape((-1, 28, 28))\n",
     "            self.y = data[\"label\"].values\n",
     "        else:\n",
-    "            self.x = data.values\n",
+    "            self.x = data.values.reshape((-1, 28, 28))\n",
     "\n",
     "    def __len__(self):\n",
+    "        \"\"\"Python method for length\"\"\"\n",
     "        return len(self.x)\n",
     "\n",
     "    def __getitem__(self, idx):\n",
-    "        if torch.is_tensor(idx):\n",
-    "            idx = idx.tolist()\n",
-    "\n",
-    "        output = self.x[idx] / 255\n",
+    "        \"\"\"Python method for square brackets\"\"\"\n",
+    "        output = self.x[int(idx)] / 255\n",
     "        if self.transform:\n",
     "            output = self.transform(output)\n",
     "        return output\n",
     "\n",
     "    def show(self, idx):\n",
-    "#         plt.figure(figsize=(2, 2))\n",
-    "        plt.imshow(self.x[idx].reshape((28, 28)), \"gray\")\n",
-    "\n",
-    "    def sample(self, n):\n",
-    "        idx = np.random.randint(0, len(self), n)\n",
-    "        return self[idx]\n",
-    "\n",
-    "\n",
-    "class ToTensor(object):\n",
-    "    def __call__(self, data):\n",
-    "        return torch.FloatTensor(data)"
+    "        plt.imshow(self.x[idx], \"gray\")\n"
    ]
   },
   {
@@ -181,45 +183,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T07:27:36.476137Z",
+     "start_time": "2020-10-16T07:27:31.802583Z"
+    },
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "ds_train = DigitsDataset(path / \"train.csv\", transform=transforms.ToTensor())\n",
+    "ds_test = DigitsDataset(path / \"test.csv\", transform=transforms.ToTensor())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:37:31.457047Z",
-     "start_time": "2020-10-12T06:37:25.831089Z"
+     "end_time": "2020-10-16T07:27:36.482222Z",
+     "start_time": "2020-10-16T07:27:36.477566Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<__main__.DigitsDataset at 0x7f52142ce390>"
+       "(torch.Size([1, 28, 28]), torch.float64)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ds_train = DigitsDataset(path / \"train.csv\", transform=ToTensor())\n",
-    "ds_test = DigitsDataset(path / \"test.csv\", transform=ToTensor())\n",
-    "ds_train"
+    "x= ds_train[0]\n",
+    "x.shape, x.dtype"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T01:18:49.378554Z",
+     "start_time": "2020-10-16T01:18:49.367362Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:37:32.349527Z",
-     "start_time": "2020-10-12T06:37:31.459489Z"
+     "end_time": "2020-10-16T07:27:37.043208Z",
+     "start_time": "2020-10-16T07:27:36.483525Z"
     },
+    "lines_to_next_cell": 2,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 16 Axes>"
       ]
@@ -235,27 +265,26 @@
     "        ds_train.show(i*4+j)\n",
     "        plt.xticks([])\n",
     "        plt.yticks([])\n",
-    "plt.show()\n",
-    "    "
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:37:32.378943Z",
-     "start_time": "2020-10-12T06:37:32.352080Z"
+     "end_time": "2020-10-16T07:27:37.048080Z",
+     "start_time": "2020-10-16T07:27:37.044598Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "torch.Size([784])"
+       "torch.Size([1, 28, 28])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -275,21 +304,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:37:32.389720Z",
-     "start_time": "2020-10-12T06:37:32.381486Z"
+     "end_time": "2020-10-16T07:27:37.114529Z",
+     "start_time": "2020-10-16T07:27:37.050312Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<torch.utils.data.dataloader.DataLoader at 0x7f519ad77e50>"
+       "<torch.utils.data.dataloader.DataLoader at 0x7f67e6ff6d68>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -321,11 +350,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T07:27:37.159767Z",
+     "start_time": "2020-10-16T07:27:37.116171Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# nn.Module??"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:37:32.412786Z",
-     "start_time": "2020-10-12T06:37:32.391804Z"
+     "end_time": "2020-10-16T07:27:37.207305Z",
+     "start_time": "2020-10-16T07:27:37.161086Z"
     }
    },
    "outputs": [],
@@ -346,13 +389,14 @@
     "            nn.Sigmoid()\n",
     "        )\n",
     "    def encode(self, x):\n",
-    "        return self.encoder(x)\n",
+    "        x = x.reshape((-1, 1, 28 * 28))\n",
+    "        return self.encoder(x).reshape((-1, 2))\n",
     "\n",
     "    def decode(self, z):\n",
-    "        return self.decoder(z)\n",
+    "        return self.decoder(z).reshape((-1, 28 * 28))\n",
     "\n",
     "    def forward(self, x):\n",
-    "        z = self.encode(x.view(-1, 784))\n",
+    "        z = self.encode(x)\n",
     "        return self.decode(z)"
    ]
   },
@@ -365,11 +409,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:37:32.453091Z",
-     "start_time": "2020-10-12T06:37:32.417932Z"
+     "end_time": "2020-10-16T07:27:37.267882Z",
+     "start_time": "2020-10-16T07:27:37.208616Z"
     }
    },
    "outputs": [
@@ -379,7 +423,7 @@
        "device(type='cuda')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -398,11 +442,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:37:35.338050Z",
-     "start_time": "2020-10-12T06:37:32.456493Z"
+     "end_time": "2020-10-16T07:27:38.608283Z",
+     "start_time": "2020-10-16T07:27:37.269138Z"
     }
    },
    "outputs": [
@@ -424,7 +468,7 @@
        ")"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -436,11 +480,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:37:35.727602Z",
-     "start_time": "2020-10-12T06:37:35.340324Z"
+     "end_time": "2020-10-16T07:27:38.781048Z",
+     "start_time": "2020-10-16T07:27:38.609630Z"
     }
    },
    "outputs": [
@@ -451,9 +495,9 @@
       "==================================================================\n",
       "                    Kernel Shape Output Shape    Params Mult-Adds\n",
       "Layer                                                            \n",
-      "0_encoder.Linear_0    [784, 400]     [2, 400]    314.0k    313.6k\n",
-      "1_encoder.ReLU_1               -     [2, 400]         -         -\n",
-      "2_encoder.Linear_2      [400, 2]       [2, 2]     802.0     800.0\n",
+      "0_encoder.Linear_0    [784, 400]  [2, 1, 400]    314.0k    313.6k\n",
+      "1_encoder.ReLU_1               -  [2, 1, 400]         -         -\n",
+      "2_encoder.Linear_2      [400, 2]    [2, 1, 2]     802.0     800.0\n",
       "3_decoder.Linear_0      [2, 400]     [2, 400]      1.2k     800.0\n",
       "4_decoder.ReLU_1               -     [2, 400]         -         -\n",
       "5_decoder.Linear_2    [400, 784]     [2, 784]  314.384k    313.6k\n",
@@ -473,7 +517,7 @@
        "1"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -494,11 +538,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:37:35.734064Z",
-     "start_time": "2020-10-12T06:37:35.730003Z"
+     "end_time": "2020-10-16T07:27:38.784861Z",
+     "start_time": "2020-10-16T07:27:38.782393Z"
     }
    },
    "outputs": [],
@@ -515,11 +559,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:37:35.743168Z",
-     "start_time": "2020-10-12T06:37:35.737127Z"
+     "end_time": "2020-10-16T07:27:38.831573Z",
+     "start_time": "2020-10-16T07:27:38.786146Z"
     }
    },
    "outputs": [],
@@ -539,11 +583,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:37:35.758604Z",
-     "start_time": "2020-10-12T06:37:35.745614Z"
+     "end_time": "2020-10-16T07:27:38.895226Z",
+     "start_time": "2020-10-16T07:27:38.832886Z"
     }
    },
    "outputs": [],
@@ -553,7 +597,7 @@
     "    train_loss = 0\n",
     "    for batch_idx, data in enumerate(tqdm(train_loader, leave=False, desc='train')):\n",
     "        # We make sure the data is in the right device (cpu or gpu)\n",
-    "        data = data.to(device)\n",
+    "        data = data.to(device).float()\n",
     "        \n",
     "        # We make sure that any saved gradient (derivative) is zeroed.\n",
     "        optimizer.zero_grad()\n",
@@ -587,7 +631,7 @@
     "    test_loss = 0\n",
     "    with torch.no_grad():\n",
     "        for i, data in enumerate(tqdm(test_loader, leave=False, desc='test')):\n",
-    "            data = data.to(device)\n",
+    "            data = data.to(device).float()\n",
     "            recon_batch = model(data)\n",
     "            test_loss += loss_function(recon_batch, data).item()\n",
     "\n",
@@ -597,24 +641,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:37:36.091253Z",
-     "start_time": "2020-10-12T06:37:35.761085Z"
+     "end_time": "2020-10-16T07:27:39.021041Z",
+     "start_time": "2020-10-16T07:27:38.896557Z"
     }
    },
    "outputs": [
     {
      "data": {
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O9ATx56UzQwPMnz8/0nWI/O233x7ZdJtse2wmoboIFY7aUs3nzQQ2BqCPBB6uneY4BYb3cwaoiotwCvAy0FNEikXkFGAsMEREFgJDEt1pwHg/Z5dKlwMhhBHlmAaX87jTAPF+zi55DRtu3759dKRSr1t1Wi0oXRlnl112SWV7BFivD99+++3IZtfROsRYV/CB0hV/5s2bl8o6LRrA4sWLI12nH7MboHZPQPvpbYozndJMH5+G0llpzz777FR+5ZVXItv+++8f6TrOobi4mIZOUVFRpNvPXMcC2DV3RXECNtzaFrTV+ykWG8dx/vnnl3tP3SYbg3LOOedEut2XqG08bNhxMo4PAo6TcfK6HIB42t+uXbtU/vDDD6PrVq9eHen6RJ1+HsTTJZ05CODEE0+MdO0ibNOmTWSzRTxXrVqVyjb82BYZXbduXSpr1yKUnqqfddZZqWynqtdff325NlsEVbtb7fJJhyZDHFY8adKkVLaZkRsKNsT7mmuuifQ///nPqWzdiRVhQ77t9F+/ll1yXXvttZFuT4FqdAZqm3XIvre6xmcCjpNxfBBwnIzjg4DjZJy8ViDq3bt3eOyxx1L9sssuS2XtuoO4UhDE62NbmFMf67XZgnSILsDw4cNT2VZzsSHHf/tbyalaG6r8hz/8IdJPPvnkVLYZjvXeAsSVhC688MLIpjMV26pH1lWk35vNlmOzNesQ5K+//jqVx40bR3FxcaOrQKRdyjbku6LvvN2L0t9RgEGDBqWyLm5bFh999FEq33bbbZHNZjvKB16ByHGcMvFBwHEyjg8CjpNx8ron0KtXrzBx4sRUf/TRR1PZhk727Nkz0nX6runTp0e2Ll26pLKt8msrEulqM9Z/bKsB7bbbbqlssws//vjjka4rDdvQ5UMOOSTSZ8+enco2dFkfJbbrUx2LAHH6sw4dOkQ2GwI9eHDJEQAdb/DJJ5+wbt26RrcnoNExAxDHaWwqOpR7xYr4ZPXVV18d6ToNnq1YVR/4noDjOGXig4DjZJy8Lgc6deoUzjjjjFTXU36bWejiiy+O9PXr16fys88+G9l0YQd76uull16KdL080KfwAJ577rlI15mJ7evo6T9AixYtUtkWL9VtB9hmm5LM3Tb0VJ+Q1C4miDMGQ/xebRGRrbbaKtJ1eLBe5gwaNIi5c+c26uWAzd60ZMmSar+Wzgikl7YQZxKC0kVy6htfDjiOUyY+CDhOxvFBwHEyTl6PEjdr1izKsjNt2rRUHjBgQHStdRlqdEFNiMNnW7ZsGdlsRmG9jjvvvPMiW0Vr97322iuy2WO+OiONriIEpV172vW3aNGiyKaLT9rMQnaPoHfv3qls9yFsSHTfvn1TWR8lbgxZhspCVyQ64ogjIpvdB9P1MKxLWe/1QNzvQ4cOjWxdu3aNdNu3hYrPBBwn4/gg4DgZxwcBx8k4ed0TCCFE6cX++Mc/prI9lmmrsGg/uK72A3H4rF1H2yzBOsTYpnGy60G9n6BTeQEcfPDBka5jCmxqL5vGTFeZ3XnnnSObPgptQ4ptddp//OMfqWzDhu2eymmnnZbK+rO1n1dDwR6d1pWpgaj6tQ0Pf/rppyNdpxSzVZ9syLF+LZvmzu4F+Z6A4zgNAh8EHCfj5HU50KJFiyjji56GjRw5stS1Gj212nrrrSObDgO1U++777470ps2LXnLDz30UGTTywqIw4hteK99rs4mZE+X2eIj2tW3cuXKyKZDXHVm5LLaoN2hc+fOjWz77LNPpOuiljqr8oMPPkhDZL/99ov0IUOGRLoOk9ZFZKB0gU9tt9faLFE2nF3Tr19ckPmJJ54o99pCwmcCjpNxfBBwnIxTlarEXUXkGRFZICLvicj5yeNFIvKEiCxM/m5T2Ws5hYv3c3apyp7ABmBUCGGeiLQG5orIE8DJwFMhhLEiMhoYDVxSweuwZs2aKCOPdqvpvQIovTbT6+xu3bpFNh32aSvxWLeNziCsw2cBXnzxxUjX7kTrlly2bFmk6/WfzYpkQ3N19SJ7zFVnFLZrTLvfoV2crVu3jmx2LatDZfUxY+UWrbV+rit0KLB12dpKSvpous6qBLB27doq39Nmiq4Iexy+oVDpTCCEsDSEMC+RvwEWAJ2Bo4GNv6JJwLA6aqOTB7yfs8sm7QmISHdgD+BVoEMIYSnkvkBA+3Kec7qIzBGROfpwjFO41LSf89ZQp1aosotQRFoB04ALQghrqhppFkIYD4wHaNu2bdDTaD2FtckfbdGHjh07prLN8qMj4MaMGRPZrDtIn/SyUz1bzFQX8JgwYUJks4Ur9Yk+e8LwwAMPjHTtFrSFTbU70b4XW/BSL6FsMdUbbrgh0nXkpC7GYZdPtdHPdZVZSH/mNgrQJoLVJwc3ZfpvsW5ZnVnI9nNDpUozARFpRu6LMTmEsNFBvlxEOib2jsCK8p7vNAy8n7NJVbwDAvwFWBBCuEmZZgIbI3xGAg/XfvOcfOH9nF2qshzYBzgReEdE3kweuxQYCzwgIqcAnwLH1EkLnXzh/ZxRKh0EQggvAuUtDAeX83iZFBUVcfzxx6e6XoPbE3K2GKdeO+v1N8TFQG2o7QEHHBDp+mSgzlIMpcOGdeFT63KzYc1vvPFGKttMxLoIK8SuUXtPnc1o1KhRkW3GjBmRrkOFBw4cGNl04VWI9wj0HsVG12Ft9nNtYfdLtIvUZgeaNWtWpOt9APs61h2tOfHEEyPdfn/0PkA+M3XXJR4x6DgZxwcBx8k4Pgg4TsbJ61Hi5s2bR356vVa2WYFt9ly9HrRBR/o4rD1Se+SRR0a6Xrvr7DMQ+88hrvBz0003RTZbXUaH4o4dOzay2axJ+ujznnvuGdl0dSWbWcgeD9ZhzbZA6sKFCyP9pJNOSuW2bdumsg2HLiS0Tx5KV1nSnHvuuZGu9z1sTIE9hlxddLg6bFqIcSHhMwHHyTg+CDhOxsnrcmDdunVRkQ6dIWj+/PnRtXZ5oAuD2AIjOqS4e/fukc2ertPuonvuuSey2WmjxhYDta4j3aaioqLIpk+/QXxScMqUKZFt0KBBqayLlEBpl9RBBx2UyjZDkE2CqROP6vepE78WGjoLFMTfEfv90N8Bq9vQ55q49k499dRUtqHKDSWxqMVnAo6TcXwQcJyM44OA42ScvO4JrFq1Ksrmo11cNhTY7hH88pe/TGW7Vtb7DLrIBsDDD8fnXSpyM9nCoXqd36dPn8hmXXs6e5Bdn9q1o7bbDEV6n8S6O7XLEuJsOjbDcadOnSJdu1z18WF7lLiQsEeAL7jgglTWhWUBRowYEen6aLrdQ9J7MhBnm7Lccccdka6L3zYWfCbgOBnHBwHHyTg+CDhOxpF8Hofs1KlT0Gt2faRTh/NC6XW1Dst9++23I5s+fmszvtrClbrS0fTp0yObXXPr/QTtH4bSx4O1T3vNmjWRzYb76n2JM844I7LptGp9+/aNbDasWacps0eH7b6JPr6sjyRPmTKF5cuX12pV0rpKL+bUjBBCmf3sMwHHyTg+CDhOxsmri7Bp06ZROKsO4V28eHF0batWrSJdZ4+dPHlyZNOuI71sgNJTc13UxLoLbVYinbHIFjY95pg4y5Z2wdmiIVOnTo10vQSwpxP1CUSbpdhmyNFhw9ZlZrMbzZ49O5V1KLUqPuJkFJ8JOE7G8UHAcTKODwKOk3Hy6iIUkS+AxUBbYGUll+eTLLdn+xBCu8ovqzrez1WmIPo5r4NAelOROSGEfpVfmR+8PXVDob0Pb0/Z+HLAcTKODwKOk3HqaxAYX0/3LQ9vT91QaO/D21MG9bIn4DhO4eDLAcfJOD4IOE7GyesgICJDReR9EVkkIqPzeW/VhntEZIWIvKseKxKRJ0RkYfJ3m4peo5bb01VEnhGRBSLynoicX99tqin13c/ex5tG3gYBEWkC3A4cBuwCjBCR8mtE1x0TgaHmsdHAUyGEHsBTiZ4vNgCjQgi9gIHAOcnnUp9tqjYF0s8T8T6uOiGEvPwDBgF/V/oYYEy+7m/a0h14V+nvAx0TuSPwfn20K7n/w8CQQmpTQ+xn7+Oq/8vncqAz8JnSi5PHCoEOIYSlAMnf9vXRCBHpDuwBvFoobaoGhdrPBfF5FmIf53MQKCu1kfsnE0SkFTANuCCEsKay6wsY7+dyKNQ+zucgUAx0VXoX4PM83r8ilotIR4Dk74pKrq9VRKQZuS/H5BDCQ4XQphpQqP3sfVwO+RwEXgd6iMgOItIcOA6Ymcf7V8RMYGMG0pHk1mx5QXLVMv8CLAgh6DRD9damGlKo/ex9XB553hA5HPgA+BC4rJ42ZaYAS4EfyP2vdQqwLbnd2YXJ36I8tmdfctPlt4E3k3+H12ebGno/ex9v2j8PG3acjOMRg46TcXwQcJyM44OA42QcHwQcJ+P4IOA4GccHAcfJOD4IOE7G+X9cQ5gdQr+o5wAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -625,18 +667,22 @@
     "def show_prediction(idx, title='', ds=ds_train):\n",
     "    \"\"\"Show a predict vs actual\"\"\"\n",
     "    model.eval()\n",
-    "    original = ds[[idx]]\n",
+    "    original = ds[idx].float()\n",
     "    result = model(original.to(device))\n",
-    "    img = cvt2image(result[0])\n",
+    "    img = cvt2image(result[0]).squeeze()\n",
     "    \n",
     "    plt.figure(figsize=(4, 2))\n",
     "    plt.subplot(1, 2, 1)\n",
     "    plt.imshow(img, \"gray\")\n",
     "    plt.title(\"Predicted\")\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])\n",
     "\n",
     "    plt.subplot(1, 2, 2)\n",
-    "    ds.show(idx)\n",
+    "    plt.imshow(original.squeeze(), 'gray')\n",
     "    plt.title(\"Actual\")\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])\n",
     "    \n",
     "    plt.suptitle(title)\n",
     "    plt.show()\n",
@@ -653,18 +699,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:39:03.510640Z",
-     "start_time": "2020-10-12T06:37:36.093311Z"
-    }
+     "end_time": "2020-10-16T07:28:21.550367Z",
+     "start_time": "2020-10-16T07:27:39.022440Z"
+    },
+    "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e30b75eeec074474870b766c7a220d3e",
+       "model_id": "5c0e59fb0fb64f1fa3fef106f90f8121",
        "version_major": 2,
        "version_minor": 0
       },
@@ -677,14 +724,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -705,7 +750,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#1 Train loss: 178.2493\tBatch Loss: 173.468216  \n"
+      "#1 Train loss: 178.3538\tBatch Loss: 168.613846  \n"
      ]
     },
     {
@@ -726,19 +771,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#1 Test loss: 163.5345\n"
+      "#1 Test loss: 164.9124\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -759,7 +802,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#2 Train loss: 160.2049\tBatch Loss: 162.289246  \n"
+      "#2 Train loss: 160.6247\tBatch Loss: 152.052567  \n"
      ]
     },
     {
@@ -780,19 +823,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#2 Test loss: 157.5076\n"
+      "#2 Test loss: 157.3779\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -813,7 +854,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#3 Train loss: 155.8522\tBatch Loss: 160.536362  \n"
+      "#3 Train loss: 155.8542\tBatch Loss: 144.605682  \n"
      ]
     },
     {
@@ -834,19 +875,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#3 Test loss: 154.9373\n"
+      "#3 Test loss: 154.8878\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -867,7 +906,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#4 Train loss: 153.3094\tBatch Loss: 129.708893  \n"
+      "#4 Train loss: 153.1930\tBatch Loss: 134.469849  \n"
      ]
     },
     {
@@ -888,19 +927,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#4 Test loss: 152.7504\n"
+      "#4 Test loss: 152.8370\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -921,7 +958,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#5 Train loss: 151.4798\tBatch Loss: 155.446121  \n"
+      "#5 Train loss: 151.3649\tBatch Loss: 154.656097  \n"
      ]
     },
     {
@@ -942,19 +979,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#5 Test loss: 151.0343\n"
+      "#5 Test loss: 151.2472\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -975,7 +1010,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#6 Train loss: 150.1137\tBatch Loss: 152.872360  \n"
+      "#6 Train loss: 150.0777\tBatch Loss: 147.247498  \n"
      ]
     },
     {
@@ -996,19 +1031,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#6 Test loss: 149.7643\n"
+      "#6 Test loss: 150.2152\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -1029,7 +1062,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#7 Train loss: 149.0917\tBatch Loss: 144.135498  \n"
+      "#7 Train loss: 149.0814\tBatch Loss: 157.059296  \n"
      ]
     },
     {
@@ -1050,19 +1083,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#7 Test loss: 148.8236\n"
+      "#7 Test loss: 149.3303\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -1083,7 +1114,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#8 Train loss: 148.1586\tBatch Loss: 162.126541  \n"
+      "#8 Train loss: 148.1392\tBatch Loss: 151.582336  \n"
      ]
     },
     {
@@ -1104,19 +1135,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#8 Test loss: 148.6431\n"
+      "#8 Test loss: 148.7061\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -1137,7 +1166,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#9 Train loss: 147.3867\tBatch Loss: 149.165192  \n"
+      "#9 Train loss: 147.3177\tBatch Loss: 141.647736  \n"
      ]
     },
     {
@@ -1158,19 +1187,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#9 Test loss: 148.0795\n"
+      "#9 Test loss: 148.3968\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -1191,7 +1218,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#10 Train loss: 146.6183\tBatch Loss: 151.356247  \n"
+      "#10 Train loss: 146.6584\tBatch Loss: 147.833496  \n"
      ]
     },
     {
@@ -1212,30 +1239,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#10 Test loss: 146.9857\n",
+      "#10 Test loss: 148.3187\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "epochs = 10\n",
     "for epoch in tqdm(range(1, epochs + 1)):\n",
-    "    show_prediction(10, title=f\"epoch={epoch}\")\n",
+    "    show_prediction(3, title=f\"epoch={epoch}\")\n",
     "    train(epoch, loss_bce)\n",
     "    test(epoch, loss_bce)\n",
-    "show_prediction(10, title=f\"epoch={epoch}\")"
+    "show_prediction(3, title=f\"epoch={epoch}\")"
    ]
   },
   {
@@ -1248,24 +1273,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:39:03.754044Z",
-     "start_time": "2020-10-12T06:39:03.512427Z"
+     "end_time": "2020-10-16T07:28:21.622657Z",
+     "start_time": "2020-10-16T07:28:21.551729Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1303,51 +1326,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-10-12T06:15:54.893417Z",
-     "start_time": "2020-10-12T06:15:54.867697Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:39:03.773776Z",
-     "start_time": "2020-10-12T06:39:03.756375Z"
+     "end_time": "2020-10-16T07:28:21.634907Z",
+     "start_time": "2020-10-16T07:28:21.623967Z"
     }
    },
    "outputs": [],
    "source": [
     "# Scatter plot\n",
     "\n",
-    "def traverse(ds=ds_train, model=model, y=3, xmin=-5, xmax=5):\n",
-    "    res = model.encode(ds_train[:1000].to(device))\n",
+    "def traverse(ds=ds_train, model=model, y=None, xmin=None, xmax=None):\n",
+    "    # Get the first 1000 images\n",
+    "    n = min(1000, len(ds))\n",
+    "    x = torch.stack([ds[i] for i in range(n)], 0)\n",
+    "    print(x.shape)\n",
+    "    x = x.to(device).float().reshape((-1, 1, 28,28))\n",
+    "    print(x.shape)\n",
+    "    \n",
+    "    res = model.encode(x)\n",
+    "    \n",
+    "    # If we output a distribution, use the mean\n",
     "    if isinstance(res, Normal):\n",
     "        res = res.loc\n",
+    "    \n",
+    "    # to numpy\n",
     "    res = res.detach().cpu().numpy()\n",
-    "    res.shape\n",
     "\n",
-    "    for i in range(10):\n",
-    "        idx = ds.y[:1000] == i\n",
-    "        plt.scatter(res[idx, 0], res[idx, 1], label=i)\n",
+    "    classes = pd.Series(ds.y).unique()\n",
+    "    for i, cls in enumerate(classes):\n",
+    "        idx = ds.y[:n] == cls\n",
+    "        plt.scatter(res[idx, 0], res[idx, 1], label=cls)\n",
     "    plt.title('the latent space')\n",
     "    plt.xlabel('latent variable 1')\n",
     "    plt.ylabel('latent variable 2')\n",
-    "\n",
-    "    # change these numbers, to change where we travel\n",
-    "    y=3\n",
-    "    xmin=-5\n",
-    "    xmax=5\n",
+    "    \n",
+    "    if xmin is None:\n",
+    "        xmin, xmax = plt.xlim()\n",
+    "        xrange = xmax-xmin\n",
+    "        xmin -= xrange/2\n",
+    "        xmax += xrange/2\n",
+    "    if y is None:\n",
+    "        ymin, ymax = plt.ylim()\n",
+    "        y = (ymin+ymax)/2\n",
     "\n",
     "    plt.hlines(y, xmin, xmax, color='r', lw=2, label='traversal')\n",
     "    plt.legend()\n",
     "    plt.show()\n",
+    "    \n",
+    "    \n",
     "\n",
     "    # Do out traversal\n",
     "    plt.figure(figsize=(12, 12))\n",
@@ -1355,7 +1383,7 @@
     "    xs = np.linspace(xmin, xmax, n_steps)\n",
     "    for xi, x in enumerate(xs):\n",
     "        # Decode image at x,y\n",
-    "        z = torch.tensor([x, y])[None :].float().to(device)\n",
+    "        z = torch.tensor([x, y]).float().to(device)[None, :]\n",
     "        img = model.decode(z).cpu().detach().numpy()\n",
     "        img = (img.reshape((28, 28)) * 255).astype(np.uint8)\n",
     "        \n",
@@ -1369,17 +1397,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T04:17:00.551274Z",
+     "start_time": "2020-10-16T04:17:00.547259Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:39:04.944917Z",
-     "start_time": "2020-10-12T06:39:03.776116Z"
+     "end_time": "2020-10-16T07:28:22.262602Z",
+     "start_time": "2020-10-16T07:28:21.636164Z"
     }
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1000, 1, 28, 28])\n",
+      "torch.Size([1000, 1, 28, 28])\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1391,7 +1439,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x864 with 10 Axes>"
       ]
@@ -1401,9 +1449,21 @@
     }
    ],
    "source": [
-    "traverse(model=model, y=3, xmin=-5, xmax=5)"
+    "traverse(model=model, ds=ds_train)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T07:24:17.282876Z",
+     "start_time": "2020-10-16T07:24:17.279495Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1456,12 +1516,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:39:04.956706Z",
-     "start_time": "2020-10-12T06:39:04.947169Z"
-    }
+     "end_time": "2020-10-16T07:28:22.271725Z",
+     "start_time": "2020-10-16T07:28:22.263986Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -1485,6 +1546,7 @@
     "\n",
     "    def encode(self, x):\n",
     "        \"\"\"Takes in image, output distribution\"\"\"\n",
+    "        x = x.reshape((-1, 28*28))\n",
     "        h = self.encoder(x)\n",
     "        # first few features are mean\n",
     "        mean = h[:, :2]\n",
@@ -1496,7 +1558,7 @@
     "\n",
     "    def decode(self, z):\n",
     "        \"\"\"Takes in latent vector and produces image.\"\"\"\n",
-    "        return self.decoder(z)\n",
+    "        return self.decoder(z).reshape((-1, 28 * 28))\n",
     "\n",
     "    def forward(self, x):\n",
     "        \"\"\"Combine the above methods\"\"\"\n",
@@ -1505,6 +1567,29 @@
     "        return self.decode(z), dist"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T07:28:22.319313Z",
+     "start_time": "2020-10-16T07:28:22.274602Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Normal's\n",
+    "d = Normal(torch.tensor([0, 1]), torch.Tensor([2, 3]))\n",
+    "# d.rsample()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1512,33 +1597,58 @@
     "ExecuteTime": {
      "end_time": "2020-10-11T23:51:16.012733Z",
      "start_time": "2020-10-11T23:51:15.910665Z"
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 24,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:39:04.996485Z",
-     "start_time": "2020-10-12T06:39:04.959802Z"
+     "end_time": "2020-10-16T07:28:22.369602Z",
+     "start_time": "2020-10-16T07:28:22.320901Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VAE(\n",
+       "  (encoder): Sequential(\n",
+       "    (0): Linear(in_features=784, out_features=400, bias=True)\n",
+       "    (1): ReLU()\n",
+       "    (2): Linear(in_features=400, out_features=4, bias=True)\n",
+       "  )\n",
+       "  (decoder): Sequential(\n",
+       "    (0): Linear(in_features=2, out_features=400, bias=True)\n",
+       "    (1): ReLU()\n",
+       "    (2): Linear(in_features=400, out_features=784, bias=True)\n",
+       "    (3): Sigmoid()\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model = VAE().to(device)\n",
-    "optimizer = optim.Adam(model.parameters(), lr=1e-3)"
+    "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n",
+    "model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 25,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:39:05.043344Z",
-     "start_time": "2020-10-12T06:39:04.998761Z"
+     "end_time": "2020-10-16T07:28:22.449297Z",
+     "start_time": "2020-10-16T07:28:22.370823Z"
     }
    },
    "outputs": [
@@ -1571,7 +1681,7 @@
        "1"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1610,11 +1720,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 26,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:39:05.049577Z",
-     "start_time": "2020-10-12T06:39:05.045179Z"
+     "end_time": "2020-10-16T07:28:22.472328Z",
+     "start_time": "2020-10-16T07:28:22.450557Z"
     }
    },
    "outputs": [],
@@ -1625,17 +1735,17 @@
     "    # KL-divergence between a diagonal multivariate normal,\n",
     "    # and a standard normal distribution (with zero mean and unit variance)\n",
     "    # In other words, we are punishing it if it's distribution moves away from a standard normal dist\n",
-    "    KLD = -0.5 * torch.sum(1 + p.scale.log() - p.loc.pow(2) - p.scale)\n",
+    "    KLD = -0.5 * torch.sum(1 + dist.scale.log() - dist.loc.pow(2) - dist.scale)\n",
     "    return BCE + KLD"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 27,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:39:05.446758Z",
-     "start_time": "2020-10-12T06:39:05.056785Z"
+     "end_time": "2020-10-16T07:28:22.688441Z",
+     "start_time": "2020-10-16T07:28:22.473709Z"
     }
    },
    "outputs": [
@@ -1643,13 +1753,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/wassname/anaconda/envs/deep_ml_curriculum/lib/python3.7/site-packages/torch/distributions/distribution.py:134: UserWarning: sample_n will be deprecated. Use .sample((n,)) instead\n",
+      "/anaconda/envs/py37_pytorch/lib/python3.7/site-packages/torch/distributions/distribution.py:134: UserWarning: sample_n will be deprecated. Use .sample((n,)) instead\n",
       "  warnings.warn('sample_n will be deprecated. Use .sample((n,)) instead', UserWarning)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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aDlRXhmrqM9TYe3vQuRWquXNNRK4QkeBf4L/GVVWtxbVU+4P3PgVaVubgruWGcw6w1Pt++StwmXd9aB3umtyPcdXsCzlQGmrJ8bsKlxSW4aoSX8Crlo3n59NLTufhGg4ERHSuJm2HkhIydobf8bSGiHwIfF/jeANktIjIo8D/qepbfsfiJxH5BLhWVZcETSsBTlHVEr/iagtxtwGMVdUf+h1LS4nICcD3vCqupCUi38dV1/0saNoh52rYdZM1qRiTqNp7UjHJrV1UfxmTZP5CZK3BjEk4VlIxxhgTNTFtViquP52/AqnAo4012RSRo3AXxi5V1Rdasm6w3NxcLSgoiFL0xhjT8c2fP79cVfOitb2YJRVx3YHcj7srvhSYKyIzVHVZmOXuxrXeadG6oQoKCpg3r9W9uRhjTNIRkbXNLxW5WF5TORoo8rqOqMH1LTQlzHLfB17E3ejY0nWNMcYkkFgmlQEcfJ9AKQd3aRLo2O9i3B2mLVo3aBvXi+s1dF5ZWVmbgzbGGNN6sUwq4bohCG0V8Bdc53L1IdMjWddNVH1EVQtVtTAvL2rVgsYYY1ohlhfqS3F3TQfk4+6iDVYITBPX/UwucJ6I1EW4rjHGJITa2lpKS0vZt2+f36E0qnPnzuTn55Oenh7T/cQyqcwFRojIEFyHcZcR0ueNqg4J/C0iTwCvqerL4jo7bHJdY4xJFKWlpWRmZlJQUIC0uUP16FNVKioqKC0tZciQIc2v0AYxq/7yOrO7AdeqaznwvKouFZGpIjK1NevGKlZjjGmLffv2kZOTk5AJBUBEyMnJiUtJKqb3qajqTFyvpcHTQi/KB6Zf3dy6xhiTqBI1oQTEKz7rpsWYxjSEth8xxjQn1gM1GZP4dlfAZ09C5WrXxnBvJWxZAtvWQ84wuPA+GHy831Ea0y5YUjHJrWI1/ONc2LUFuveFlFTolAn5R8HhX4Ol0+GZr8B350Cvwc1vz5gkZ0nFJK+GBnjxWqivhW+/D/1Ch34HjrwaHjgO3v4VXPpM3EM0JlIlJSWcc845HHPMMSxYsICRI0fy1FNP0bVr17jGYUnFJK9lL8PGBXDxw+ETCkDPgXD0/8BHf4Ft66BnY4PuGeP85tWlLNu4I6rbHNu/B7++oPmh6lesWMFjjz3G5MmTueaaa3jggQf4yU9+EtVYmmMX6k3ymvc49BriqrmaUniNe17wz6aXM8ZnAwcOZPLkyQBceeWVfPjhh3GPwUoqJjltL4WSD+GUmyGlmd9WPQfCwGPgi9fh1F/EJz7TbkVSooiV0GbDfjRztpKKSU4r3wQUxl0S2fKjz4cti6GqJJZRGdMm69atY86cOQA899xznHDCCXGPwZKKSU5rZkHWIMgZHtnyI89xz6v/E7OQjGmrMWPG8OSTTzJ+/HgqKyv5zne+E/cYrPrLJJ+Geih+H8ZcCJFWD+QMh8x+UPzBgWssxiSYlJQUHnoobKcl8YvB170b44ety2Hfdig4MfJ1RKDgBHcdRsOOwmCMwZKKSUYbF7jnAUe2bL3Bk2H3VqhcE/2YjGmjgoIClixZ4ncYllRMEtr4GXTqAdlDW7ZeIAkFkpIx5hCWVEzy2bgA+k9svilxqN5jILWTJRVjmmBJxSSXhnrYsgz6jm/5uqnp0HccbFwY9bCM6SgsqZjksm0d1FdD3qjWrd//CNj0ues3zBhzCEsqJrmUr3LPuSNbt37/I6Bmp+sm3xhzCEsqJrmUr3DPbUkqYNdVjGlETJOKiJwjIitEpEhEbg4zf4qILBKRhSIyT0ROCJpXIiKLA/NiGadJIuUroWsudM1u3fq5oyCtiyUVk3DuvPNORo0axRlnnMHll1/OPffc40scMbujXkRSgfuBM4FSYK6IzFDVZUGLvQvMUFUVkfHA88DooPmnqmp5rGI0Sah8VetLKQCpae5i/aZF0YvJdCxv3AybF0d3m30Ph3PvanT2/PnzmTZtGgsWLKCuro5JkyZx5JEtvA8rSmJZUjkaKFLVNapaA0wDpgQvoKq7VPffntwNN5irMbFTvhJyR7RtG3mjoeyL6MRjTBR88MEHXHzxxXTt2pUePXpw4YUX+hZLLPv+GgCsD3pdChwTupCIXAz8HugNnB80S4G3RUSBh1X1kXA7EZHrgesBBg2yAZRME3ZXwJ6K1rf8Cug9BhY8DbvLoVtudGIzHUcTJYpY8qOb+3BiWVIJ9x8eUhJR1emqOhq4CLg9aNZkVZ0EnAt8T0ROCrcTVX1EVQtVtTAvLy8KYZsOq3yle25L9RccSEpWWjEJ4qSTTmL69Ons3buXnTt38uqrr/oWSyyTSikwMOh1PrCxsYVV9X1gmIjkeq83es9bgem46jRjWm9/Umlr9dcY97x1edu2Y0yUTJo0iUsvvZSJEydyySWXcOKJLegsNcpimVTmAiNEZIiIZACXATOCFxCR4eKV2URkEpABVIhINxHJ9KZ3A84C/O8pzbRvFatcNytZbawm7dHf9R1WtiI6cRkTBb/61a9YsWIFb7/9tq+XAmJ2TUVV60TkBuAtIBV4XFWXishUb/5DwCXAVSJSC+wFLvVagvUBpnv5Jg14VlXfjFWsJklUlUCvwS3v8yuUiKsCs+ovYw4R00G6VHUmMDNk2kNBf98N3B1mvTXAhFjGZpJQ1VroVRCdbeWN9oYkNibx3Hbbbb7t2+6oN8mjai30HBydbeWNht1lrkWZMYAm+OBt8YrPkopJDnuroHp79Eoqvb17dMvsYr2Bzp07U1FRkbCJRVWpqKigc+fOMd+XjVFvkkNViXvuFcWSCrgWZQUnNL2s6fDy8/MpLS2lrKzM71Aa1blzZ/Lz82O+H0sqJjlUrXXP0SqpZPaH9K4Hej02SS09PZ0hQ4b4HUZCsOovkxwCJZVoXVNJSYGc4QfufTHGAJZUTLKoKoEu2dC5R/S2mTvSkooxISypmOSwLYrNiQNyR8K29VCzJ7rbNaYds6RikkPgxsdoyh0BqI0CaUwQSyqm42uodyWKWJRUwC7WGxPEkorp+HZshIba6F2kD8gZBoglFWOCWFIxHd+2KDcnDkjvAj0H2cV6Y4JYUjEd3/4bHwuiv21rAWbMQSypmI6vqgQkBbJicDdx7gioKIKGhuhv25h2yJKK6fiq1rqEkpoe/W3njoDaPbBjQ/S3bUw7ZEnFdHxVJdG/SB+wvwWYVYEZA5ZUTDLYtjb696gEWLNiYw5iScV0bDW7YdcW6BWjzv665UHnLCupGOOxpGI6tspi95w9NDbbF3GllQorqRgDMU4qInKOiKwQkSIRuTnM/CkiskhEForIPBE5IdJ1jYlIVSCpxLBb8tyRVv1ljCdmSUVEUoH7gXOBscDlIjI2ZLF3gQmqOhG4Bni0Besa07xASSVW1V/gWoDt3AT7dsRuH8a0E7EsqRwNFKnqGlWtAaYBU4IXUNVdemD8zW6ARrquMRGpKoYuvaBLz9jtI3Cx3qrAjIlpUhkArA96XepNO4iIXCwiXwCv40orEa/rrX+9V3U2L5GH8jQ+qSyObSkFrAWYMUFimVQkzDQ9ZILqdFUdDVwE3N6Sdb31H1HVQlUtzMvLa22spqOqXBO7i/QBvQogJc1agBlDbJNKKTAw6HU+sLGxhVX1fWCYiOS2dF1jwqqvhe2lsb1ID+5O/V5DLKkYQ2yTylxghIgMEZEM4DJgRvACIjJcRMT7exKQAVREsq4xzdq2DrQ+9tVfYC3AjPGkxWrDqlonIjcAbwGpwOOqulREpnrzHwIuAa4SkVpgL3Cpd+E+7LqxitV0UPFoThyQOwJWvQ31dZAas4+VMQkvpme/qs4EZoZMeyjo77uBuyNd15gWiUdz4oDckW4gsG1rvcG7jElOdke96biqSiCtC2T2jf2+rGNJYwBLKqYjq1zjqr4kXGPCKMsd4Z4tqZgkZ0nFdFzxuEcloEtP6N4HyiypmORmScV0TKqu+iseF+kDckdC+Yr47c+YBGRJxXRMOzdD3d7YjEvfmNyRrqSiYe/TNSYpWFIxHVM8mxMH5I2C6u2wa2v89mlMgrGkYjqmyjXuOdZdtATb3wLMqsBM8rKkYjqmymKQVMga2Pyy0ZI3yj2XWVIxycuSiumYqoqh50DXL1e8ZPaDjExrVmySmiUV0zHFszlxgIi7X8VKKiaJWVIxHVNVcXwv0gfkjbKOJU1Ss6RiOp69Ve4Rz4v0AbkjYedGG1rYJC1LKqbjiWdHkqFsFEiT5CypmI7Hj3tUAgItwKxZsUlSllRMx7O/pFIQ/333GgIp6Xax3iQtSyqm46kqdp07ZnSL/75T09x4Klb9ZZKUJRXT8fjRnDhY7gir/jJJy5KK6Xgqi/1p+RWQO8rFUFfjXwzG+CSmSUVEzhGRFSJSJCI3h5l/hYgs8h6zRWRC0LwSEVksIgtFZF4s4zQdSO1e16TXj4v0AXmjQOuhcrV/MRjjk5iNUS8iqcD9wJlAKTBXRGao6rKgxYqBk1W1SkTOBR4Bjgmaf6qqlscqRtMBVa11z75Wf3nNistWQO8x/sVhjA9iWVI5GihS1TWqWgNMA6YEL6Cqs1W1ynv5MZAfw3hMMvCzOXGADS1sklgsk8oAYH3Q61JvWmOuBd4Ieq3A2yIyX0Sub2wlEbleROaJyLyysrI2BWw6AD9vfAzI6AY98qGiyL8YjPFJzKq/AAkzLeyQeCJyKi6pnBA0ebKqbhSR3sA7IvKFqr5/yAZVH8FVm1FYWGhD7iW7yjXQKQu6ZvsbR85QqLBrKib5xLKkUgoED2aRD2wMXUhExgOPAlNUtSIwXVU3es9bgem46jRjmlZVDNkFrsdgP2UPswv1JinFMqnMBUaIyBARyQAuA2YELyAig4CXgG+o6sqg6d1EJDPwN3AWsCSGsZqOwu97VAJyhrlOLfdU+h2JMXEVs+ovVa0TkRuAt4BU4HFVXSoiU735DwG3AjnAA+J+WdapaiHQB5juTUsDnlXVN2MVq+kgGuph2zoYe6HfkbiSCrgqML+r4oyJo1heU0FVZwIzQ6Y9FPT3dcB1YdZbA0wInW5Mk7aXQkNt4pRUwFWBDTzK31iMiSO7o950HJVr3LOfzYkDehWApNjFepN0LKmYjmP/PSo+dtESkNYJsgbaxXqTdCypmI6jshhSO0Fmf78jcXKGWUnFJJ2IkoqIvCgi54uIJSGTuKqKoddgSEmQ0zR7mKuSU7t9yiSPSD99DwJfB1aJyF0iMjqGMRnTOpUliXGRPiBnGFTvgN3WfZ1JHhElFVX9t6peAUwCSnB3uM8WkW+JSHosAzQmIqquVJAIF+kDsoNagBmTJCKuJxCRHOBqXBPgBcBfcUnmnZhEZkxL7C6D2t2JcZE+ICfoXhVjkkRE96mIyEvAaOBp4AJV3eTN+peNdWISQiJ0JBmq5yCQVCupmKQS6c2Pj3o3Mu4nIp1Utdq7A94YfyVCl/ehUtNdwwErqZgkEmn11x1hps2JZiDGtEllMSCudJBIrGNJk2SaLKmISF/cGChdROQIDnRn3wPoGuPYjIlc5RrIync3HSaSnGGwdrZrSOB3z8nGxEFz1V9n4y7O5wN/Cpq+E/hljGIypuWqihOr6isge5hrQLBrC2T29TsaY2KuyaSiqk8CT4rIJar6YpxiMqblKoth9Pl+R3Go4BZgllRMEmiu+utKVX0GKBCRm0Lnq+qfwqxmTHxV74Q95YlZUtmfVIqgYLK/sRgTB81Vf3XznrvHOhBjWi0RmxMHZA2E1Ay7WG+SRnPVXw97z7+JTzjGtEIidXkfKiXVdYNvzYpNkoi0Q8n/FZEeIpIuIu+KSLmIXBnr4IyJSFUCl1TgQMeSxiSBSO9TOUtVdwBfAkqBkcBPm1tJRM4RkRUiUiQiN4eZf4WILPIes0VkQqTrGrNfZTF0zYXOPfyOJLwcL6k0NPgdiTExF2lSCXQaeR7wnKpWNreCiKQC9wPnAmOBy0VkbMhixcDJqjoeuB14pAXrGuMkanPigOyhULcPdm70OxJjYi7SpPKqiHwBFALvikgesK+ZdY4GilR1jarWANOAKcELqOpsVa3yXn6Mux8monWN2S/RurwPZR1LmiQSadf3NwPHAYWqWgvspvkv+QHA+qDXpd60xlwLvNHKdU2yqquGHaUJXlKxLvBN8oi0Q0mAMbj7VYLXeaqJ5cP1SRF2CDwRORWXVE5oxbrXA9cDDBqUYP0+mdjbtg60IWFKKlW7a5i9uoLVZbuoa1CG5nbj6IJs+qd1tpKKSQqRdn3/NDAMWAjUe5OVppNKKTAw6HU+cEilsoiMBx4FzlXVipasC6Cqj+BdiyksLLRxW5NN4B4Vn8dRKa3aw33vFvHiZ6XUNRx8GorAh937krlhBQnalMCYqIm0pFIIjFVt0WDbc4ERIjIE2ABchhuSeD8RGQS8BHxDVVe2ZF1jAN+7vFdVnv10HXe+vpy6BuWKYwYx5YgBjOufRYrAii07eWPxZlbMyWNgyTL++MoSfnHeGDqnp/oSrzGxFmlSWQL0BTY1t2CAqtaJyA3AW0Aq8LiqLhWRqd78h4BbgRzgAXE9uNapamFj60a6b5NEKoshvRt0y4v7rqvr6vnFS4t56bMNnDA8l7suOZz8Xgd33n1Y/ywO659Fdf0xpM59mKfnFDNvbRUPXXkkA7Oto2/T8USaVHKBZSLyKVAdmKiqFza1kjew18yQaQ8F/X0dbnjiiNY15hCB5sRx7la+cncNU5+ez6cllfzojJHcePpwpIkYOvUZAVrL01/pz3deK+dL933IXy6dyKmje8cxamNiL9KkclssgzCm1SrXQO7IuO6yaOsurn1yLpu27+Pey4/gwgn9m1/JawE2udd2Xvv+iXz7mflc8+RcfnPhYVx1XEFsAzYmjiJtUvweUAKke3/PBT6LYVzGNK+hAarWxvUi/Yeryrn4gY/YXV3HtOuPjSyhwEH3qgzK6cpL3zme00f34dZXlvLHt1fQssuVxiSuSPv++h/gBeBhb9IA4OUYxWRMZHZuhPrquF2k/+cna/nmPz6lf1YXXv7eZCYN6hX5ypn9IL3r/j7AumSk8tCVk7i0cCD3/aeIX7y0mPoGSyym/Yu0+ut7uLvcPwFQ1VUiYpXBxl9x6vK+vkG58/XlPP5RMaeMyuO+y48gs3N68ysGE3ElqqB7VdJSU7jrksPJzczg/v+uprZe+cNXxpOSYsMOm/Yr0qRSrao1gQuR3g2Q9rPK+CsOzYn31NRx43ML+PfyrVx9fAG3nD+GtNRIezcKkT0Uti47aJKI8NOzR5ORmsqf/72StBTh918+3BKLabciTSrvicgvgS4icibwXeDV2IVlTAQq10BKGvTIb37Z1mx+dw3XPDGXRaXb+O2UKFxQzxkGK2ZCfR2kHvzR+8EZI6hvaODe/xSRkiL87uJxTbYmMyZRRZpUbsZ1o7IY+Dauqe+jsQrKmIhUFkPPQYd8QUfD+so9fPPxT9mwbS8PXnkkZx8WhfHls4dBQx1sXxe2ccGPzhxJXYPywKzV5HTL4Cdnj2r7Po2Js4g+jaraICIvAy+rallsQzImQlXFMWn5tb5yD199aA57a+v553XHUFiQHZ0N728BtiZs3K4qbBRVe2r423+LyO/VhcuOtv7sTPvSZOWwOLeJSDnwBbBCRMpE5Nb4hGdMI1Rj0uX91p37uPKxT9hbW8+064+NXkKBiHorFhFunzKOk0fm8f9eWcK8kmaHLjImoTR3xfGHwGTgKFXNUdVs4Bhgsoj8KNbBGdOovVVQvT2qF+mr6+qZ+vR8tu6o5h/fOoox/aLc/WP33pDRvdneitNSU7j3siPo37ML3/nnZ2zd0dzQRcYkjuaSylXA5apaHJigqmuAK715xvgjMOZ7FEsqv3l1GZ+t28YfvzahZfegRErEJcEIxlXJ6prOI98oZMfeWn76wiK7OdK0G80llXRVLQ+d6F1XaWFDfWOiqDK6zYlnLt7Es5+sY+rJwzjv8H5R2WZY2cMiHldlVN9MfnX+GN5bWcbTH6+NXUzGRFFzSaWmlfOMia3APSq9Ctq8qc3b9/HL6YuZkJ/Fj8+KcT9iOcPcwGL1tREt/o1jB3PyyDzufH05RVt3xjY2Y6KguaQyQUR2hHnsBA6PR4DGhFVZDJn9Ib1Lmzajqtzy8mL21dbz50snkt7aGxsjlT0MtN4llgiICH/46ni6dUrjB9MWUlPXENv4jGmjJj9Bqpqqqj3CPDJV1aq/jH8CXd630ZtLNvPv5Vu56cyRDM3rHoXAmhHUsWSkemd25vdfPpylG3fwt/8WxSgwY6Ijxj/LjImRyuI2X6Tfsa+WX89Yyth+PbhmcpxGjoygWXE4Zx/Wl4sm9ufBWUV8sXlHDAIzJjosqZj2p2Y37NoM2QVt2swf3lxB+a5qfv/lw1vfn1dLdcuFTj1aVFIJuPWCw8jsnM7PX1hkPRqbhGVJxbQ/VSXuuQ1303+2ropnPlnLN48vYMLAnlEJKyKB3opbWFIByO6WwW0XHsbnpdv5x0fFza9gjA9imlRE5BwRWSEiRSJyc5j5o0VkjohUi8hPQuaViMhiEVkoIvNiGadpZ9rY5X1NXQM3v7iIvj068+OzfOhfK2c4lLfu2sgF4/txxpje3PP2CtZW7I5yYMa0XcySioikAvcD5wJjgctFZGzIYpXAjcA9jWzmVFWdqKqFsYrTtENt7PL+ofdWs3LLLu64aBzdO0W/M8pm5Y12nUrWtDwpiAi3XzSO9JQUbn5xsd0UaRJOLEsqRwNFqrpGVWuAacCU4AVUdauqzgUia7RvDLiSSuee0KXld70Xbd3J3/5TxAUT+nP6mD7Rjy0Sed69MOUrW7V6v6wu/OK8McxZU8G/5q6PYmDGtF0sk8oAIPiML/WmRUqBt0Vkvohc39hCInK9iMwTkXllZdaBclKoXNOqUkpDg3Lzi4vp2imVX18QWmiOo7zR7rmsdUkF4LKjBnLs0GzunLmcLdY3mEkgsUwq4UYYaklZfbKqTsJVn31PRE4Kt5CqPqKqhapamJeX15o4TXtT1brmxE9/vJZ5a6u45fyx5HbvFIPAIpQ91A0uVvZFqzeRkiLc9eXx1NQ1cMvLS6wazCSMWCaVUmBg0Ot8YGOkK6vqRu95KzAdV51mkl19LWxb3+KSyorNO/ndzOWcPDKPSya1pMAcA6np7n6VshVt2kxBbjd+fNZI3lm2hZmLN0cpOGPaJpZJZS4wQkSGiEgGcBkwI5IVRaSbiGQG/gbOApbELFLTflSVuG5OckZEvMremnq+/9xnZHZO556vTkiMYXrzRkF525IKwDWThzA+P4tfz1hC1W7rjs/4L2ZJRVXrgBuAt4DlwPOqulREporIVAAR6SsipcBNwC0iUioiPYA+wIci8jnwKfC6qr4Zq1hNO1K+yj3nRp5Ubn99GSu37OJPX5tAXqaP1V7B8ka5a0N11W3aTFpqCndfMp5te2q5/bVlUQrOmNaLaXtKVZ2JG88+eNpDQX9vxlWLhdoBTIhlbKadqvCSSs7wiBZ/w+vS/tsnDeWkkQl0zS1vNGiDu7O+T9saDYzp14Pvnjqce99dxVmH9eWccX2jFKQxLWd31Jv2pXwldMuDLj2bXbS0ag8/f3GR16W9Dzc5NiXXa1bchov1wb5/2nAOH5DFL6cvZutOaw1m/GNJxbQv5UURXU+pq2/gh9MW0qBw7+VHkJGWYKd67ghAWn2vSqj01BT+fOkEdlfX2U2RxlcJ9kkzphkVqyC3+aqvh99fw7y1Vdxx0TgG53SLQ2AtlN4Feg2OWkkFYHjvTH5x7mj+88VWnvvUboo0/rCkYtqPPZWwp+JA1VEjlm3cwV/+vZLzx/fjoiN8bj7clLzRbboBMpyrjivgxBG53P7aMkrKrW8wE3+WVEz7UeF1wthE9VdNXQM3Pb+QrC4Z3D5lXJwCa6W8Ua7kVV8XtU2mpAh/+MoEMtJS+NHzC6mrt5EiTXxZUjHtR+D6QxPNie/7zyq+2LyT33/5cLK7ZcQpsFbKHQX1NQe68o+SvlmdueOicSxYt42H318T1W0b0xxLKqb9KF8FKenQc3DY2Us2bOeBWau5ZFI+Z471qbPIlugd6ANsedQ3fcGE/px3eF/ufXcV6yr2RH37xjTGkoppP8pWuDHeUw+9vUpVufWVJfTsks6tfnYW2RJ5Y0BSYHNsOou49UuHkZYi3DrD+gYz8WNJxbQfW5ZAn8PCzpq+YAOfrdvGz88ZTVaX9DgH1koZXV0fYFtik1T6ZnXmprNGMWtFGW8usb7BTHxYUjHtw94q2L4e+hx68X1vTT13v/kFE/Kz+MqR4TpoSGB9DotZUgH45nGDGduvB795dRm7qqPXIMCYxlhSMe3DFq9fq76HHzLrH7OL2bKjml+eN4aUlAToLLIl+o5zF+r37YjJ5tNSU7jj4nFs2bmPe99dFZN9GBPMkoppHwK/5kNKKtv21PDgrNWcPro3xwzN8SGwNurjJcmt0b9YHzBpUC8umZTPEx+VUFplF+1NbFlSMe3D5sXQJRsyD+4s8dEPitlVXcfPzhntU2BtFLhGtGVxTHdz05kjQeBP70T3ZktjQllSMe3DliWuqihoLJTte2p5YnYJ543rx6i+mT4G1wZZ+dA5C7Ysjelu+vfswreOL2D6gg0s3xSbqjZjwJKKaQ8a6l31UJ+Dr6f8Y7YrpdxwWmTd4CckEVelF6NmxcG+c8owMjulcfeb0etvzJhQllRM4qtYDXX7XEnFs3NfLY9/WMxZY/swpl8PH4OLgj7jXEmlIbZdqvTsmsF3Tx3OrBVlzF5dHtN9meRlScUkvo0L3HPf8fsnPTVnLTv21XHj6ZGPAJmw+hwGtbthW0nMd3X18QX0y+rM/765wm6INDFhScUkvg3zIb0b9B4DwO7qOh79YA2nje7NuAFZPgcXBYESWByqwDqnp3Lj6SNYuH4b7y7fGvP9meQT06QiIueIyAoRKRKRm8PMHy0ic0SkWkR+0pJ1TRLZMA/6T4SUVACe+XgtVXtq+X57vpYSLNBdS4wv1gd85ch8Bud05Z63V9DQYKUVE10xSyoikgrcD5wLjAUuF5HQTpkqgRuBe1qxrkkGddWuOfGAIwF39/zfP1jDiSNyOWJQL5+Di5KMrpAzHDZ9HpfdpaemcNOZI/li805eW7wpLvs0ySOWJZWjgSJVXaOqNcA0YErwAqq6VVXnArUtXdckic1LXPfwXlJ59tN1lO+q6RjXUoL1n+Sq+eJ0neOC8f0Z1SeTv7yz0sZcMVEVy6QyAAge07TUmxbVdUXkehGZJyLzysrKWhWoSWAb5rvn/EL21dbz8HurOXZoNkcVZPsbV7TlF8LurbC9NC67S0kRbjprJGvKd/PSZxvisk+THGKZVMJ1whTpz7CI11XVR1S1UFUL8/LyIg7OtBMb5kH3PtBjAM/PW8/WndUdr5QC+0tibJgXt12eNbYPE/Kz+Ou7q6iuq4/bfk3HFsukUgoMDHqdD2yMw7qmIymdBwMKqa5v4MFZqzmqoBfHtcc+vprTZxykdnL/b5yICD8+axQbtu1l2qfrm1/BmAjEMqnMBUaIyBARyQAuA2bEYV3TUezaCpWrYdAxvDh/A5u27+P7p41ApJ31RByJtAzoN/5AdV+cnDgil2OGZPO3/xaxt8ZKK6btYpZUVLUOuAF4C1gOPK+qS0VkqohMBRCRviJSCtwE3CIipSLSo7F1YxWrSVDrPgagLv8YHphVxISBPTlxRK7PQcXQgELYuBDq4zfuiYjw07NHUbazmr9/YOPZm7Y7dFzWKFLVmcDMkGkPBf29GVe1FdG6JsmsmwNpnXl5S29Kq5bz2ymHdcxSSkB+IXzyIGxdCv0mxG23hQXZnHd4Xx6ctZpLjxpInx6d47Zv0/HYHfUmca2bgw44kvveW8u4AT04dVRvvyOKrYFHu2evhBZPN58zhvoG5Q9vrYj7vk3HYknFJKbqXbBpESs6Hcbaij0d91pKsJ6D3KPkg7jvelBOV751QgEvzC9lcen2uO/fdByWVExi2jAPtJ7H1/djdN9MzhzTx++I4mPwCbB2dtxuggx2w6nDye2ewe2vLbPOJk2rWVIxiWntHJQUZlYN5IbThre/sedbq2Ay7KmAsviPeZLZOZ2bzhzFpyWVvLrIum8xrWNJxSSkhnVzKEoZTP8+vTl3XD+/w4mfghPcc8mHvuz+0qMGMj4/i9++uozte0J7TzKmeZZUTOKpr6V+3ad8VDOCn5w1itRkKaUA9BwMPfJ9SyqpKcLvLj6cqj013PXmcl9iMO2bJRWTcKpLF5Jev5ey7EmcOTZJrqUEiLjSytqPfLmuAjBuQBbXnjCE5z5dz6fFlb7EYNovSyom4Sz44HUATj3zoo7f4iucgsmwuwzKV/oWwg/PGMGAnl345fTF1i+YaRFLKiah7NhXS03Re2xMy6fw8DF+h+OP/ddV4t+0OKBrRhp3XDyOoq27uO/dIt/iMO2PJRWTUP4+ayVH6HI6DT/F71D802sIZPb37bpKwKmjevOVI/N5YFYRn62r8jUW035YUjEJY33lHmZ/+F8yZS85407zOxz/iMCQE6H4A2jwdwCtWy8YS7+sLvz4+c+tw0kTEUsqJmHc+fpyjk1Z5l4MPsHfYPw29BTYUw5bl/kaRo/O6fzhq+MpLt/NXW9YazDTPEsqJiHMLirnzaWb+WpOCeSOhMwka/UVasjJ7rn4PX/jAI4flss1k4fw5Jy1vLfSRlc1TbOkYnxXV9/Ab15dxuCeGQze/TkUnOh3SP7LGgA5w2GN/0kF4GfnjGJkn+7c9K+FbNmxz+9wTAKzpGJ898zHa1mxZSd3Hd+A1Ow60Pop2Q052d2vUu//ne2d01O5/+uT2FNTz43PLaCu3t9rPSZxWVIxviqt2sMf3lrBiSNyOTbFq7O3pOIMPRlqdsGGz/yOBIARfTK546JxfFJcyb3vrvI7HJOgLKkY36gqv3hpMQr87uLDkeL3IHcUdO/g46ZEquBEQBLiukrAJUfm89Uj87nvv0V2fcWEZUnF+OZfc9fzwapyfnHuaAZ2B0o+guGn+x1W4uia7catT5DrKgG/nTKOUX0yueHZz1hdtsvvcEyCiWlSEZFzRGSFiBSJyM1h5ouI3OvNXyQik4LmlYjIYhFZKCLzYhmnib9N2/e6JsRDs7nimMHuRr/6ahh+ht+hJZYhJ0Ppp1Cz2+9I9uuSkcrfryokPTWF/3lynvVmbA4Ss6QiIqnA/cC5wFjgchEZG7LYucAI73E98GDI/FNVdaKqFsYqThN/9Q3KT/9vEXUNyt2XjHdjpRS9A+ldYfBkv8NLLMNPh/oaWDPL70gOMjC7Kw9/40jWV+3he89+Rq1duDeeWJZUjgaKVHWNqtYA04ApIctMAZ5S52Ogp4gk0eAZyelv/yniw6Jybr1gLINzurmJq95x1xDSO/sbXKIZPBk6ZcGKN/yO5BBHFWRz58WH82FRObfNWGqjRRogtkllALA+6HWpNy3SZRR4W0Tmi8j1je1ERK4XkXkiMq+szC4cJrqPisr5y7sr+fIRA7jsqIFuYsVqqCq2qq9wUtNdaWXlW7532RLO1woHMvXkYfzzk3U8MGu13+GYBBDLpBKuz/LQnzJNLTNZVSfhqsi+JyInhduJqj6iqoWqWpiXl9f6aE3Mbdmxjx9MW8DwvO7ccfG4A93aL3/VPY88y7/gEtmoc2H3VtiYGE2LQ/3s7FFMmdifP7y1gpc+K/U7HOOzWCaVUmBg0Ot8YGOky6hq4HkrMB1XnWbaqX219Xznmfnsrq7ngSsm0TUj7cDMpS9B/0nQq8C3+BLa8DNAUmHFTL8jCSslRfjfr4znuKE5/OyFRXy4qtzvkIyPYplU5gIjRGSIiGQAlwEzQpaZAVzltQI7FtiuqptEpJuIZAKISDfgLGBJDGM1MdTQoPzoXwtZsH4bf/zaBEb0yTwws2I1bPocxn3ZvwATXddsN3DX0um+jQbZnE5pqTz0jSMZltedqc/MZ9nGHX6HZHwSs6SiqnXADcBbwHLgeVVdKiJTRWSqt9hMYA1QBPwd+K43vQ/woYh8DnwKvK6qb8YqVhNbd85czhtLNvOr88Zw3uEh7TCWTnfPYy+Ke1ztyoTLoXINrPvY70galdUlnSeuOYrundL41hOfsmHbXr9DMj6QjtRio7CwUOfNs1taEsljHxZz+2vLuPr4An59wdiDhwdWhQcnQ0Y3uO4d/4JsD2p2wz0j4bCLYcrf/I6mSV9s3sFXH5xD36zOvDD1eLK6pvsdkmmCiMyP5m0bdke9iZmn5pRw+2vLOHdcX/7fl8YeOt586VzYuhQmXOZPgO1JRjdXmls6PaFuhAxndN8ePPyNIymp2M31T8+zMe6TjCUVExNPzSnh1leWcubYPvz1siNITQnT0G/uo5CRCeO/Fv8A26MjrnAdTC550e9ImnX88Fzu+eoEPimu5MfPf05DQ8epETFNs6Riou6Jj4r3J5T7vz6JjLQwp9muMvere+Ll0Cnz0PnmUIOOgz6Hw5z7E/aCfbApEwdw87mjeW3RJu568wu/wzFxYknFRI2q8se3V3Dbq8s4q6mEAvDZE677kcJr4xpjuyYCx98AZV9A0b/9jiYi3z5pKFcdN5hH3l/DPz4q9jscEweWVExU1NU38PMXF3Hff4q4tHAgD1zRREKp2Q0fPwjDTofeo+MbaHt32Jchsx/MvtfvSCIiIvz6gsM4a2wffvvaMt5YvMnvkEyMWVIxbbZzXy3XPz2f5+eVcuNpw7nrksNJS23i1Jr/JOypgJN/Fr8gO4q0DDj2u1D8Pqz/1O9oIpKaItx7+REcMbAnP/jXQuaVVPodkokhSyqmTdaU7eKi+z/ivZVl3HHROG46a9ShrbyC1VW7X9mDT4BBx8Yv0I7kqGuhaw7M+r3fkUSsc3oqj37zKAb07MJ1T82jaKuNw9JRWVIxrfbfL7Yy5f6PqNpTyzPXHsOVxw5ufqW5j8HOTXDyT2MfYEeV0Q0m/wBW/wfWfeJ3NBHL7pbBk986mrQU4cpHP7EBvjooSyqmxfbV1nPbjKV864m5DOzVlRk3TOa4YTkRrLgDPrgHhp7iHqb1jroOuuXBO7e2i5ZgAYNyuvL0tcdQW9/ApQ/PYXHpdr9DMlFmScW0yOLS7Zx/7wc8MbuEq48v4KXvHk9+r66RrTz7Pnct5YzbYhpjUsjoBqffCus/hsUv+B1Ni4zp14Pnpx5Hp7RUvvrwbF5bFNrPrGnPLKmYiGzbU8OvX1nClPs/ZHd1PU9fezS3XXgYndNTI9tAxWr46K8w7hLof0Rsg00WE6+EfhNdaWVf++rAcVhed17+3mTG9uvBDc8u4BcvLWZ3dZ3fYZkosKRimlRb38A/P1nLqffM4umP13LFMYN564cnceKIFoxdowozfwKpGXDWnbELNtmkpMD5f4RdW+DVH7SrajCAvMxOTLv+OKaePIxpc9dx+h/f45WFG6i3u+/bNUsqJqz6BuWlz0o540/v8avpSxjRJ5PXvn8it180ruUdBC581l1UPu1X0MNGi46q/EJ3XJe+5Lq9aWcy0lK4+dzRvDD1eLK7ZfCDaQs540/vMe3TddZnWDtlvRSbg1TX1fPKgo08/P5qVpftZky/Htx05kjOGNO76abCjdm8GB49AwYUwlWvQGpa8+uYlmlogOcudXfZX/wIjP+q3xG1Sn2D8tbSzTw4azWLN2wnt3sGXxrfnykT+zNxYM/WnX+mWdHupdiSigGgbGc1z89bzxOzSyjbWc3ovpncePoIzjmsLynhOoOMRGUxPHUh1NfBt9+D7r2jG7Q5oGYPPPs1WPuRqxIrvMbviFpNVfmwqJxnP1nHu19spaaugfxeXThlVB4nj+zNccNy6N7JfpxEiyWVJlhSaZna+gY+XFXOtLnreHf5VuoalJNG5nH9iUOZPDynbb8MS+fBtK+7/r2ufAkGTIpe4Ca8mt3w/Deh6B3X5PicuyC1fY9lsmNfLW8t2cxbSzcze3UFe2rqSU8VCgdnM3l4DkcVZDNhYM/IG4yYQ1hSaYIllebtqaljzuoK3liymXeWbWH73lpyumVwyZH5fK1wIMN7d2/bDratc6285j4GPQbAN16CvFHRCd40r6Ee/n2b67Vg4LHw5YehV4HfUUVFdV0989dW8d7KMt5bUcYXm3cCkJGawoSBWRxVkM1RQ7I5cnAvenRu38k0niypNMGSyqHKdlazdON2Fq7fxuzVFSxYV0VtvZLZOY0zx/ThnHF9OWVU78Y7f2yOqmsuXPQOrHwLit9z04+6Dk67BTpnRe+fMZFb9H/w2o8AhZN+Csd8G9K7+B1VVFXtrmHe2irmllTySXElSzdsp65BSRE3UNjEQT2ZkJ/F+PyejOjdven+6JJYu0oqInIO8FcgFXhUVe8KmS/e/POAPcDVqvpZJOuGkyxJpaFB2bmvju17a9m2t4Zte2rZtreW7XtrKd9ZTWnVXkqr9lBcvputO6sB12v6uP5ZHD88hxOG53LMkJzWJZLafbB5EWyY76q4Sj91pROAnBEwdgoceTX0HBi9f9i0zrZ18PqPYdXb0K03jPuy68kgZwR0z4NOPdyJ0UHsqaljwbptfFpcyby1lSwq3c7Ofe7el87pKYzrn8Xh+VmM6pPJ0LzuDM3rRk63jKRvANBukoqIpAIrgTOBUmAucLmqLgta5jzg+7ikcgzwV1U9JpJ1w4lFUlFVGhQaVGlQRb2/6xuUhgao9/5W1f1/B6Y3qNLQ4NZX3HTFbWP/dlTZU13P7po6dlfXsbum3j1Xe0kjKGFs31PDtr217NhbS2NN+UWUft0zKMjOYFCvDMb27sqYPl0Z1Tfz4CqBsO+7gjZAfS001LnHjo1QUQRbl8OGebB5CTTUusV7DIABR8KQk2DEmR2mmqXDKfkQPnkIVr4N9dUHpqekQZds6JrtnjP7QM5wyB4GvQZDelfv0RlS0t31mZQ090hNd9NSEvfXf0ODUlKxm0Wl2/m8dBuLS7ezZON29tU27F8mIzWFnO4Z5HTPILd7J7K7ZtAlI5Uu6al0Tk+lS4Z77pyeQpf0VDqlpZKeKqSnpZCRmkJ6aop7nZpCRlrKgecUISVFEFz3/ykCgiApIECKCCLumZDXbh3iluyinVRi2YTiaKBIVdcAiMg0YAoQnBimAE+py2wfi0hPEekHFESwbtQcefs77KmpPyhpNHjJxC8pAj26pNOzSzpZXTPI6pLO4Oyu9OyaTlYX9+jpTZ+w8NfkrpmONNQjDbVQC2zxHtEacC+ju7sT/rjvuXsjBhTaPSftRcEJ7lG9C7Yuc9WVe8pdlzl7KmFvJeypgo0LYdkM0BbcHyIpXqJJh+vegT6HxezfaKmUFPFKJN256IgBgGu2vHHbXlaX7aK4fDdbdlRTvquail3VlO+qYdWWXVTX1bO3pp69tfW+fgeA+x44KCmJl3A4kHBEILd7J97/2ak+RnpALJPKAGB90OtSXGmkuWUGRLguACJyPXC993KXiKxoQ8wBuUB5FLYTD3GKdQewEXi9LRux4xobiRHrLeMiWSoxYo1Me4oV+Xmr442ge/HIxTKphCu7heb9xpaJZF03UfUR4JGWhdY0EZkXzeJgLFmssWGxxobFGjuJEm8sk0opEHy1Nh/3UzeSZTIiWNcYY0yCieVVtrnACBEZIiIZwGXAjJBlZgBXiXMssF1VN0W4rjHGmAQTs5KKqtaJyA3AW7hmwY+r6lIRmerNfwiYiWv5VYRrUvytptaNVaxhRLU6LcYs1tiwWGPDYo2dhIi3Q938aIwxxl+J28jcGGNMu2NJxRhjTNRYUgFE5F8istB7lIjIwkaWKxGRxd5yvvQHIyK3iciGoHjPa2S5c0RkhYgUicjN8Y7Ti+EPIvKFiCwSkeki0rOR5Xw7rs0dJ68Ryb3e/EUi4kt3yyIyUET+KyLLRWSpiPwgzDKniMj2oHPjVj9i9WJp8j1NoOM6Kuh4LRSRHSLyw5BlfDuuIvK4iGwVkSVB07JF5B0RWeU992pkXX++A1TVHkEP4I/ArY3MKwFyfY7vNuAnzSyTCqwGhuKaZ38OjPUh1rOANO/vu4G7E+m4RnKccA1J3sDdO3Us8IlP73s/YJL3dyauG6PQWE8BXvMjvpa+p4lyXMOcD5uBwYlyXIGTgEnAkqBp/wvc7P19c7jPlZ/fAVZSCeJ1cPk14Dm/Y2mj/V3kqGoNEOjmJq5U9W1VrfNefoy73yiRRHKc9nclpKofA4GuhOJKVTep19mqqu4EluN6nmivEuK4hjgdWK2qa32OYz9VfR+oDJk8BXjS+/tJ4KIwq/r2HWBJ5WAnAltUdVUj8xV4W0Tme93D+OUGr8rg8UaKvo11f+Ona3C/TMPx67hGcpwS7liKSAFwBPBJmNnHicjnIvKGiPjZEVdz72nCHVfc/XCN/aBMlOMK0Efd/Xx4z+GGVPXt+CbNmJwi8m+gb5hZv1LVV7y/L6fpUspkVd0oIr2Bd0TkC++XRNxiBR4Ebsd9aG/HVdeFjh0bcTc3bRXJcRWRXwF1wD8b2UxcjmsYbelKyBci0h14Efihqu4Imf0Zrupml3et7WVgRJxDDGjuPU2045oBXAj8IszsRDqukfLt+CZNUlHVM5qaLyJpwJeBI5vYxkbveauITMcVMaP+5ddcrAEi8nfgtTCzIukiJyoiOK7fBL4EnK5eZW+YbcTluIbRlq6E4k5E0nEJ5Z+q+lLo/OAko6ozReQBEclV1bh3ihjBe5owx9VzLvCZqm4JnZFIx9WzRUT6qeomr8pwa5hlfDu+Vv11wBnAF6paGm6miHQTkczA37iL0EvCLRtLIfXOFzcSQ0J0cyNuoLWfAxeq6p5GlvHzuLalK6G48q73PQYsV9U/NbJMX285RORo3Oe7In5R7o8jkvc0IY5rkEZrKRLluAaZAXzT+/ubwCthlvHvO8CPFg2J+ACeAKaGTOsPzPT+HoprQfE5sBRXveNHnE8Di4FF3knSLzRW7/V5uBZCq32MtQhXr7vQezyUaMc13HECpgbOBVw1wv3e/MVAoU/H8gRc9cWioON5XkisN3jH8HNcw4jjfYo17HuaiMfVi6UrLklkBU1LiOOKS3SbcKMklQLXAjnAu8Aq7znbWzYhvgOsmxZjjDFRY9VfxhhjosaSijHGmKixpGKMMSZqLKkYY4yJGksqxhhjosaSijHGmKixpGKMMSZq/j+rhIzGK3zOAQAAAABJRU5ErkJggg==\n",
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8LV4M1wbtqz/D/T5K8aonhnxnKUHzXwu8H/R+KEH7uTesBJjsvb4Yd/lnj/dd/CWwLUOXj7sssiHo+wod39hvKOx3G27fCvO9NLavXQEsC3qfDjzsfR/bgO8HjUvz1j2ygfU8gbuisRd3lP3FoHEn4WpA7va+i2tasP264Q4+SnC/3c+Ay+Lw+8zwYujV2L4a7tHmG96TkLb74x1PS4jI+8C3NIY3wkWKiDwI/FNV58Q7lngSkbnA9aq6NGhYMXCqqhbHK67WEFf9fLSqfjfesTSXiEwGvqnu0lGHJSLfwl0GuyVoWL19Ney8bT05GJOo2npyMB1bm29byZgEdi/+ai8Zk3DszMEYY0w9CdWCYEBubq7m5+fHOwxjjGkzFixYsENV8yK1vIRMDvn5+cyf3+JWNowxpsMRkfVNT+WflTkYY4ypx5KDMcaYeiw5GGOMqSchyxyMMSYeqqurKSkpobKyMt6hNCgjI4P+/fuTmpoa1fX4Sg7ielf6I+62+Ae1gYbNRORYXNPcl6rqc82Z1xhj4q2kpISsrCzy8/ORVjcgHXmqSllZGSUlJRQUFER1XU1eVhLXqcx9uPbJRwOXi8joBqa7C5jT3HmNMSYRVFZWkpOTk5CJAUBEyMnJicmZjZ8yh4m4RuXWqmoVrqepaWGm+xbwPK4xq+bOa4wxCSFRE0NArOLzkxz6cXgHIyUc3jFOoLvGC3Htmzdr3qBl3CiuT9f5paWlPsIypplU3cMY0yQ/ySFcmgr9hd2L6zKwNmS4n3ndQNWZqjpBVSfk5UXsJj9jnHkPwh+OgJmnwIGd8Y7GmITnJzmU4HofC+iP6wkq2ATgaa8VyouB+8V1vO1nXmOia/NCmP1DyOoN25bDM1fZGYQxTfCTHOYBw7zeqdJwnWTPCp5AVQtUNV9V84HngG+o6r/8zGtM1L1+K3TKhitfgLPuhOL3YOPceEdlTFjFxcWMHDmSa665hrFjx3LxxRezf//+mMfRZFVWVa3xup6bg6uO+rCqLhOR6d740HKGJueNTOjG+LBtOWz4CM7+FXTqDkdfCf/9FXwyEwYeF+/oTAL7+cvLWL55d0SXObpvV/7v/Ka7ki4sLOShhx7ixBNP5LrrruP+++/n5ptvjmgsTfF1h7SqzlbV4ao6RFV/6Q2bES4xqOq1gXscGprXmJhZ9BQkpcCRl7j36V1cglj+kpU9mIQ1YMAATjzRdQl/5ZVX8v7778c8BrtD2rRfdXWw5J8w9EzoElTJYcyX4OP7YPW/YeyX4xefSWh+jvCjJbS6ajyq11rbSqb92vwZ7NnikkGwvsdAZk8onB2fuIxpwoYNG/joo48AeOqpp5g8eXLMY7DkYNqv1W8AAkPPOHx4UhIMPxuK/g01VXEJzZjGjBo1iscee4yxY8dSXl7O17/+9ZjHYJeVTPu1eg70PxY6Z9cfN/xs+OzvsGk+DDoh9rEZ04ikpCRmzGiwrk9sYojr2o2Jlr2l7rLSsLPCjx90IiBQHPuCPmPaAksOpn1a7/3pDz41/PjO2dBrjLvnwZgEkp+fz9KlS+MdhiUH004VfwCpmdB3XMPTFJwEGz+BmoOxi8uYNsKSg2mf1n8AAydBciMdouRPhppK2LQgdnEZ00ZYcjDtz74y2L7cK1doxMDjsXIHY8Kz5GDanw0fuuf8JuqGd86G3lbuYEw4lhxM+1P8AaR0cje7NSXfyh2MCceSg2l/1r8PA46FlLSmpw2UO5TMj35cxrQhlhxM+3JgJ2xdCoN8Njcw8Hj3bE14mwTxy1/+khEjRnDGGWdw+eWXc/fdd8clDrtD2rQvG+YCCvlNFEYHdM6G7MFWY8nU99qtsHVJZJfZ+0iY+psGRy9YsICnn36azz77jJqaGo455hjGjx8f2Rh8suRg2peSeSDJ/sobAvpNgHXvut7hErxzedO+vffee1x44YV07twZgAsuuCBusVhyMO3LpvnQ6whI6+x/nv4TYMmzsHsTdOsfvdhM29LIEX40xaN57nCszMG0H3V1sOkz6NfM0/B+E9yzXVoycXbyySfz4osvcuDAAfbs2cPLL78ct1h8JQcROUdECkWkSERuDTN+mogsFpGFIjJfRCYHjSsWkSWBcZEM3pjDlBXBwQp3JtAcvcdAcprVWDJxd8wxx3DppZcybtw4LrroIk466aS4xdJkchCRZOA+YCowGrhcREaHTPYf4ChVHQdcBzwYMn6Kqo5T1Wb+ao1phsCRf3PPHFLSofdYO3MwCeGnP/0phYWFvPHGGwwcODBucfg5c5gIFKnqWlWtAp4GpgVPoKp7VVW9t5mAYkysbZoPaVmQO7z58/af4Jr4rq2JfFzGtEF+CqT7ARuD3pcAk0InEpELgV8DPYHzgkYp8IaIKPCAqs4MtxIRuRG4EYhrtjRt2KYFrhXWpOTmz9tvPMydAaUrXHVDYxLA7bffHrd1+zlzCFd0Xu/MQFVfVNWRwBeBO4JGnaiqx+AuS31TRE4OtxJVnamqE1R1Ql5eXrhJjGlYdaW7+a255Q0BgUtRVu7Q4X1+ESQxxSo+P8mhBBgQ9L4/sLmhiVX1XWCIiOR67zd7z9uBF3GXqYyJrK1LoK66+eUNAdmDoVMPd2nJdFgZGRmUlZUlbIJQVcrKysjIyIj6uvxcVpoHDBORAmATcBnwP8ETiMhQYI2qqogcA6QBZSKSCSSp6h7v9VnALyL6CYwBV94An1dLbS4R1zPc9uWRi8m0Of3796ekpITS0tJ4h9KgjIwM+veP/v04TSYHVa0RkZuAOUAy8LCqLhOR6d74GcBFwNUiUg0cAC71EkUv4EXvpo4U4ElVfT1Kn8V0ZFsWQZfe0LVPy5fRczR89oS7XyLJbgHqiFJTUykoKIh3GAnB1x3SqjobmB0ybEbQ67uAu8LMtxY4qpUxGtO0bUvd/Qqt0Ws0VO+DXesh2/4gTMdmh0em7authtJC12xGa/TykotdWjLGkoNpB8qKoLYKerYyOeSNdM/blrU+JmPaOEsOpu0L/Jm39swhvQv0yLfkYAyWHEx7sG0ZJKW07M7oUFZjyRjAkoNpD7Ytg9wR/roFbUrP0e4yVfWB1i/LmDbMkoNp+7Yta/0lpYBeo0HrXAG3MR2YJQfTth3YCbtLIpgcvBpLVu5gOjhLDqZt2+aVD/Rq5T0OAdmDISXDyh1Mh2fJwbRth2oqhXYx0kJJyZA3ws4cTIdnycG0bduXuQbzslrRbEaovFGwY1XklmdMG2TJwbRt25a7m98i2Sl77jDYvQkO7oncMo1pYyw5mLatrAhyh0Z2mYH7JcqKIrtcY9oQSw6m7TqwEw6UQ/aQyC43kBx2rI7sco1pQyw5mLarbK17zolwcsguAEm2cgfToVlyMG1X+Rr3HOkzh5R018aSJQfTgVlyMG1X2RpA3B95pOUOhx1W5mA6LksOpu0qXwPdBkBqFPrTzR3qCqTraiO/bGPaAF/JQUTOEZFCESkSkVvDjJ8mIotFZKGIzBeRyX7nNabFytZAzuDoLDt3ONQehF0borN8YxJck8lBRJKB+4CpwGjgchEJvR31P8BRqjoOuA54sBnzGtN8qu7MIdLlDQFWY8l0cH7OHCYCRaq6VlWrgKeBacETqOpeVVXvbSagfuc1pkX2l0NlReRrKgUcSg5WKG06Jj/JoR+wMeh9iTfsMCJyoYisBF7FnT34nteb/0bvktT80tJSP7GbjixaNZUCOmdD5xxLDqbD8pMcwrVLoPUGqL6oqiOBLwJ3NGdeb/6ZqjpBVSfk5eX5CMt0aGVecojWmQN4NZbsspLpmPwkhxJgQND7/sDmhiZW1XeBISKS29x5jfGtfA1IEnQfFL115A6zMwfTYflJDvOAYSJSICJpwGXArOAJRGSoiGv5TESOAdKAMj/zGtMiZWug+8DIdA3akNzhsH+HK98wpoNJaWoCVa0RkZuAOUAy8LCqLhOR6d74GcBFwNUiUg0cAC71CqjDzhulz2I6kmjWVAoIrrE0cFJ012VMgmkyOQCo6mxgdsiwGUGv7wLu8juvMa2i6tpVGhDlP+wcr7XXMksOpuOxO6RN27OvFKr2RP/MofsgSE6zcgfTIVlyMG1PLGoqASSnuARkNZZMB2TJwbQ9h+5xiFLTGcGsxpLpoCw5mLanbA0kpUS3GmtA7nAoXwc1VdFflzEJxJKDaXvK13jlAb7qU7RO7jDQWthZHP11GZNALDmYtqdsbfTLGwIChd6BS1nGdBCWHEzbogrla6NfUykgkITKrOMf07FYcjBty56tUL0vdmcOnbOhU4/Pa0gZ00FYcjBtSyxrKgVkD7HLSqbDseRg2pZY3eMQLGeonTmYDseSg2lbyte4u5a7DWh62kjJGQK7N0HV/tit05g4s+Rg2payNdAjH5KSY7fOwCWsnetit05j4sySg2lbYllTKeBQA3xWY8l0HJYcTNtRV+eSQyzLGyCoOquVO5iOw5KDaTv2bIaaytjWVAJIz4LMnlZjyXQolhxM2xGPmkoBOUPszMF0KJYcTNtx6B4HSw7GRJuv5CAi54hIoYgUicitYcZfISKLvceHInJU0LhiEVkiIgtFZH4kgzcdTNkaSMmArv1iv+7sIbBvO1Tujv26jYmDJpu1FJFk4D7gTKAEmCcis1R1edBk64BTVHWniEwFZgLB/SpOUdUdEYzbdETla6FHASTF4YQ3J6gBvr5Hx379xsSYn1/ZRKBIVdeqahXwNDAteAJV/VBVd3pvPwb6RzZMY3BnDvEob4Cg6qx2acl0DH6SQz9gY9D7Em9YQ64HXgt6r8AbIrJARG5saCYRuVFE5ovI/NLSUh9hmQ6lrtbdhBbrmkoBPQrcc/na+KzfmBjz01uKhBmmYScUmYJLDpODBp+oqptFpCfwpoisVNV36y1QdSbuchQTJkwIu3zTgVWUQG1V/M4c0jq7sg47czAdhJ8zhxIguCGb/sDm0IlEZCzwIDBNVcsCw1V1s/e8HXgRd5nKmOaJZ02lgJwhdpe06TD8JId5wDARKRCRNOAyYFbwBCIyEHgBuEpVVwUNzxSRrMBr4CxgaaSCNx1IPO9xCLCmu00H0uRlJVWtEZGbgDlAMvCwqi4Tkene+BnAz4Ac4H4RAahR1QlAL+BFb1gK8KSqvh6VT2Lat/K1kNoZsvrEL4acIXBgJ+wvd50AGdOO+eqhXVVnA7NDhs0Ien0DcEOY+dYCR4UON6bZyta4wmgJVwQWI9lBbSxZcjDtnN0hbdqG8jXxq6kUEKjOapeWTAdgycEkvtoa2Fkc3/IGcP1ISJLVWDIdgiUHk/gqNkBdTXxrKgGkeD3QWY0l0wFYcjCJr8y78SzeZw7gLi3ZZSXTAVhyMIkvEe5xCMgZ4pKV2n2apn2z5GASX9kaSMuCLj3jHYlLUFV7YO/2eEdiTFRZcjCJr6zIHbHHsxprgNVYMh2EJQeT+MqKPv9Tjrccrzqt1Vgy7ZwlB5PYag7Crg2Jkxy6DYSkVKuxZNo9Sw4msZWvAzRxkkNyCnQf4O67MKYds+RgElvgCD0RqrEG9ChwfUsY045ZcjCJLRGTQ3aBnTmYds+Sg0lsZUWQ2RMyusU7ks/1KIDKCtc6qzHtlCUHk9jK1iROeUNAj3z3bJeWTDtmycEktsA9Dokk2+tP2i4tmXbMkoNJXJUVsG974p45lNuZg2m/LDmYxHWoa9AESw5pma4cxC4rmXbMV3IQkXNEpFBEikTk1jDjrxCRxd7jQxE5yu+8xjQoUZMDeDWW1sc7CmOipsnkICLJwH3AVGA0cLmIjA6ZbB1wiqqOBe4AZjZjXmPCKysC5PNr/ImkR4FdVjLtmp8zh4lAkaquVdUq4GlgWvAEqvqhqu703n4M9Pc7rzENKiuC7gMhJT3ekdSXXQC7N7nmPYxph/wkh37AxqD3Jd6whlwPvNbceUXkRhGZLyLzS0tLfYRl2r1EanAvVI98QF27T8a0Q36SQ7h2ksP2dCIiU3DJ4UfNnVdVZ6rqBFWdkJeX5yMs066pJuY9DgE9vEtddmnJtFMpPqYpAQYEve8PbA6dSETGAg8CU1W1rDnzGlPP3u2uU51ETQ6H7nWw5GDaJz/JYR4wTEQKgE3AZcD/BE8gIgOBF4CrVHVVc+mApokAACAASURBVOY1JqwEalOpbO9BVm7dQ22dkpWRQr/uneiZlQepmXYjnGm3mkwOqlojIjcBc4Bk4GFVXSYi073xM4CfATnA/eJ666rxLhGFnTdKn8W0J4eSQ+zPHOrqlHnF5by0aDNvrdjO1t2V9aY5Nr8HM9P7klW21tcRljFtja/9WlVnA7NDhs0Ien0DcIPfeY1pUlkRJKdDt/5NTxshu/ZX8ez8jfz94/VsLD9Ap9RkThvZk6MHdmd0n66kpSSxp7KGpZsqeGXxFj6p6MbQfctYtmgz54/tgyRCN6bGRIgd9JjEVLYGsgdDUnLUV7Wl4gAz313LU59soLK6jkkF2dx81gjOHN2Lzmn1fyJTRvbkW6cPY+uzR9F9+ROc/tSnPDsvj3suOYqeXTOiHq8xsWDJwSSmsiLIHRbVVeyprObef6/m8Y+KUYVp4/pxw0kFjOrT1df8vQeNhOUH+d05ffjf/2xn6h/f4/eXHMWpI3pGNW5jYsGSg0k8dbVQvhZGTI3aKl5fuoX/m7WM7XsOcumEAXxzylAGZHdu3kK8Bvi+PKSWcaMmc9OTn3HtI/P44dkj+MapQ+wyk2nTrOE9k3h2bYC66qgURqsq97y5iulPfEpeVjovfuNEfnPR2OYnBoAeg9zzzvUM65XFSzedyLRxffndnEK++8xCKqtrIxu8MTFkZw4m8USpwb3q2jpufX4Jz39awpfH9+dXXzqS1ORWHB91H+ieveqsGanJ3HvpOIb3yuJ3cwopLtvP364ab+UQpk2yMweTeKJQjVVV+dlLS3n+0xK+d8Zwfnvx2NYlBoDUTtCl92H3OogI35wylAeuGs/qbXu44C8fsKSkonXrMSYOLDmYxFNWBOndIDM3Yot8Yu4GnvpkI984dQjfOWNY5MoDeuTDrvpNd599RG+em34CSQKXPPAR766y9sJM22LJwSSeQNegEfoDn7u2jJ/PWsZpI3vyg7NGRGSZh/TIb/Au6dF9u/Kvm04kPzeT6x+bx6uLt0R23cZEkSUHk3gi2G/0nspqvvP0QgZmd+bey8aRnBThGkQ9BkFFCdRUhR3dMyuDp288jnEDunPTU5/y/IKSyK7fmCix5GASy8G9ULERciNzhP+7OYVs21PJ7y85iq4ZqRFZ5mECTXdXbGxwkm6dUnn8ukmcOCSXHz63iJcWbop8HMZEmCUHk1jKVrvnvOGtXtSC9Tv5+8frueb4fI4e2KPVywvLu9ehqQb4OqUl87erJzCxIJvvP7uI15faJSaT2Cw5mMSyw0sOua1LDtW1dfz4hcX06ZrBzWdHuJwhWHfvXocwhdKhOqUl89A1xzJuQHe+/fRC5heXRy8uY1rJkoNJLKWFIMmQ3boyhyc+Xs+qbXv5+bQxdEmP4u08WX0gOc13092Z6Sk8ePUE+nXvxFcfn0/xjn3Ri82YVrDkYBLLjkLXkU5KWosXUbG/mj/+ZzWTh+Zyxqgot3OUlOTOHprRr0OPzDQeufZYRISvPDqPigPV0YvPmBay5GASy47Vrb6k9Oe3VlNxoJqfnDsqNu0b9RgEO5u+rBQsPzeTmVeNZ2P5fm55bhGqYXvPNSZuLDmYxFFb45rOaEVyKN6xj8c+KuaS8QMY3ddf66qt1si9Do2ZkJ/NrVNHMmfZNh75oPnzGxNNlhxM4ti5zjW4l9fyAuS7Xl9JanISPzir9bWdfOs+CCp3wYFdzZ71+skFnDGqF79+bQULNzZ/fmOixVdyEJFzRKRQRIpE5NYw40eKyEciclBEbg4ZVywiS0RkoYjMj1Tgph3a4XU/3sIzh3nF5by2dCvTTxkS28buAtVZfdRYCiUi/P7LR5HXJZ2b/7nIWnI1CaPJ5CAiycB9wFRgNHC5iIwOmawc+DZwdwOLmaKq41R1QmuCNe1caaF7bkEnP3V1yp2vLKd31wy+etLgCAfWBJ/3OjSkW+dUfn3RWIq27+XPb62OWFjGtIafM4eJQJGqrlXVKuBpYFrwBKq6XVXnAVbtwrTcjlWuamhGt2bP+vLizSwqqeDms0fQKS36XYse5lC/DsUtXsQpw/P48vj+zHhnLUs3WSuuJv78JId+QHDbACXeML8UeENEFojIjQ1NJCI3ish8EZlfWmotWHZIO1a16KyhsrqW375eyJh+XfnS0c3ZNSMkoxt06tHsGkuhbjtvNNmZadzy3GJqausiFJwxLeMnOYSrC9icencnquoxuMtS3xSRk8NNpKozVXWCqk7Iy8trxuJNu6AKpata1KbSzHfXsmnXAW47bzRJkW5Yz68W1lgK1q1zKreffwTLt+zmqXkNt9VkTCz4SQ4lwICg9/2BzX5XoKqbveftwIu4y1TGHG7PFqja0+yaSlsrKvnr22s498jeHDc4J0rB+dDMG+Eacu6RvZlUkM09bxSya3/4ll6NiQU/yWEeMExECkQkDbgMmOVn4SKSKSJZgdfAWcDSlgZr2rEW1lS66/WV1Kry46mjohBUM/TIdy2z1rWutpGIcPsFR1BxoJp7/22F0yZ+mkwOqloD3ATMAVYAz6rqMhGZLiLTAUSkt4iUAN8HbhOREhHpCvQC3heRRcAnwKuq+nq0Poxpw0qbnxw+3bCTFz/bxFdPKmBAducoBeZTj3yorYLdrW+Oe1SfrvzPpIH8/eP1rN62p/WxGdMCvlokU9XZwOyQYTOCXm/FXW4KtRs4qjUBmg5iRyGkd4Ws3r4mr6tTfv7ycnpmpfONUyPX13SLBfq7LlsD3Qe2enHfP3MEL322mbvfKOSBq6wGuIk9u0PaJIYdq9xZg8+2kP61cBOLNu7ilnNGkhnNVlf9OpQciiKyuOzMNL568mDmLNtmd06buLDkYBJD6Srfl5T2HazhrtdXclT/bvGpuhpOVm9IzXRnDhFy3eQCcjLTuHtOYcSWaYxflhxM/FVWwN6tvnt/m/HOGrbtPsjPzj8iflVXQ4m4fq8jdOYA0CU9hW9MGcr7RTv4sGhHxJZrjB+WHEz8Her9relqrNt2V/K399Zy/lF9GT8oSl1/tlTOECiP3JkDwBWTBtKnWwa/nVNozXqbmLLkYOLvUJtKTZ853Pvv1dTWKT88K4pdf7ZUzlB3l3RN5O5PyEhN5junD2Phxl28uXxbxJZrTFMsOZj421HoutoMNGDXgDWle3l2/kaumDSIgTlxrroaTs5Q0NqI3AwX7OLx/SnIzeT3b6yits7OHkxsWHIw8bdjteszOrnxWkd3zykkIyWJm05LgKqr4QTOfHZEtgA5JTmJ7585nMJte5i1qPX3URjjhyUHE3+lhU02uPfphp28tnQrN548hNwu6TEKrJkCyWH7yogv+rwj+zC6T1fueXMVVTXWKJ+JPksOJr5qDroe4BppU0lV+c1rK8ntksYNJxXEMLhmSu8C3QZCaeSTQ1KS8MNzRrCx/ADPzNsQ8eUbE8qSg4mv8rWgdY3WVHq7sJRP1pXzndOHJcYNb43pOTIqyQHg1OF5TMzP5k9vFbG/qiYq6zAmwJKDia/AH2kDl5Vq65S7Xl9Jfk5nLpvY+mYpoi5vhCtDqY38n7eIcMs5Iyjdc5BHPiiO+PKNCWbJwcTX9pUgSQ1eVvrXZ5tYuXUPN589gtTkNrC75o2E2oMRr7EUMCE/mzNG9eSBd9ZQsd86XjTR0wZ+baZd274cehRAaqd6oyqra7nnzVUc2a8b547pE4fgWiDPazp8+/KoreLms0ew52ANf30nsjfcGRPMkoOJr9KV0DN8XwxPfLyeTbsOcOvUkYnTTEZTeo5yZ0LbotdtycjeXfniuH488sE6tu2ujNp6TMdmycHET81B11BdmOSwu7Kav/y3iJOG5XLi0Nw4BNdCaZ0hZxhsWRzV1XzvjOHUqfKn/1iHQCY6LDmY+Nmx2t1RnDey3qgH3lnDrv3V/Oic+uMSXp+xsHVJVFcxMKczl08cyDPzNlK8Y19U12U6JksOJn62r3DPPUcfNnjb7koeen8d08b1ZUy/bnEIrJV6Hwm7S2B/eVRXc9NpQ0lNTuL3b66K6npMx+QrOYjIOSJSKCJFInJrmPEjReQjETkoIjc3Z17TgZWugKSUzzvK8QQa1/vBmQnYuJ4fvce6563RvbTUMyuDG04q4OVFm/l0w86orst0PE0mBxFJBu4DpgKjgctFZHTIZOXAt4G7WzCv6ai2r3CJISXt0KCi7QneuJ4ffbyecTd/FvVVTT9lCD2z0vnFy8ups0b5TAT5OXOYCBSp6lpVrQKeBqYFT6Cq21V1HhBa8brJeU0Htn1FvfKGhG9cz4/O2ZA9GErmR31Vmekp3HLOSBZu3MVL1iifiSA/yaEfsDHofYk3zI/WzGvas6p97kaxoPKGTzfs5PVlCd64nl/9J8LGTyAGHfR86eh+HNW/G3e9VmjNapiI8ZMcwlUw97vH+55XRG4UkfkiMr+0tNTn4k2bVVoIqGuLiDbUuJ5f/SfAvu2wK/qN5CUlCT87fzRbd1cy4227Mc5Ehp/kUAIMCHrfH9jsc/m+51XVmao6QVUn5OXl+Vy8abMCbSp5Zw5tqnE9PwZMdM8l82KyuvGDsjn/qL488O5aNu06EJN1mvbNT3KYBwwTkQIRSQMuA2b5XH5r5jXt2bZlkJwOPQraXuN6fvQ8AlIzYf2HMVvlrVNHIgK/eS06rcKajqXJ5KCqNcBNwBxgBfCsqi4TkekiMh1ARHqLSAnwfeA2ESkRka4NzRutD2PakC2LoPcYSE5pe43r+ZGcAoNOgHXvxmyV/bp34saTh/Dyos3ML47uPRam/fP1S1TV2ao6XFWHqOovvWEzVHWG93qrqvZX1a6q2t17vbuheU0HV1fnkkOfozhQVcvdbxS2rcb1/Bp8CpStht1+r8K23vRTBtO7awa3v7zM+ps2rdJODtNMm7JzHRzcDX3GMfPdtWypqOR/vzC67TSu51fBye45hmcPndNS+PG5I1m6aTfPzNvY9AzGNMCSg4m9LYsAKM0ayYx31nDekX2YWJAd56CioNeR0Ckb1r4T09VecFRfJhZk87s5K9m1vyqm6zbthyUHE3tbFkJSKr/9NIlaVW6d2gYb1/MjKQkKToJ178TkfocAEeH284+g4kA191i7S6aFLDmY2NuyiH09RvDPhdu5fnIBA7LbaDMZfhScArs3ub6yY2h0365cedwgnvh4Pcs3747puk37YMnBxJYqumUR7+zuR99uGdw0pQ03k+FHwSnuee3bMV/1988cTvfOadw+axkawzMX0z5YcjCxtWsDcmAnH+zvx8+njWkfN7w1JmcIdO3nLi3FWPfOafzw7BF8UlzOrEWxqzFl2gdLDiamdqz+BIDOg8Zz5uhecY4mBkRg8KmuULquNuarv2TCAI7s141fzV7BvoPW7pLxz5KDiRlV5YP3/kO1JvOVL50X73BiZ/AUqNzlCuJjLDlJ+Pm0I9i2+yB/fqso5us3bZclBxMzT32ykR67lrKn61D65vaIdzixM/hU97zmv3FZ/TEDe3DRMf156P21rC3dG5cYTNtjycHERMnO/fz61aVMSFlDj+EnxDuc2OqS57oOjUOhdMCPpo4gPSWZX7yy3AqnjS+WHEzU1dUptzy3mKFspLPuRwYeF++QYm/wFNjwMRyMz5F7z6wMvnvGMN4uLOU/K7bHJQbTtlhyMFH3t/fW8uGaMn56pFfffsCk+AYUD0NOg7rqmLbSGuqaE/IZ1rMLP39lGQeqYl84btoWSw4mqhaX7OJ3cwqZOqY345NWQZde0CM/3mHF3sDjISUD1san3AEgNTmJX0wbw8byA/z5rdVxi8O0DZYcTNTsO1jDt5/6jLysdH594Rhkw0furEHaWQN7fqRmuAQRp0LpgOOH5HDx+P7MfHctq7btiWssJrFZcjBR83+zlrG+fD/3XjqO7gc3Q8XGz1sq7YiGTIHSFTFtwjucn5w7iqyMFH7ywhLqrFlv0wBLDiYqZi3azHMLSrhpylAmDc75vNnqQHMSHdGQ09xzHGstAWRnpvGTc0cxf/1O/v7x+rjGYhKXJQcTcRvL9/PTF5ZwzMDufOf0YW7gunehS2/IHRbf4OKp5xGQmRf3S0sAF4/vzynD8/jNaytZt2NfvMMxCciSg4mogzW1fOupz1Dgj5cdTUpykuv5bd27rvnqjljeEJCU5DWl8bbbJnEkItx10VhSk4Wb/7nIeo0z9fhKDiJyjogUikiRiNwaZryIyJ+88YtF5JigccUiskREForI/EgGbxLPz19ezsKNu/jdxWM/b4p7y0LYtx2GnhHf4BLB4CluW2yPf1fqvbtl8ItpY1iwficz3lkT73BMgmkyOYhIMnAfMBUYDVwuIqNDJpsKDPMeNwJ/DRk/RVXHqeqE1odsEtUz8zbw5NwNTD9lCFOPDOoPevUbgFhyAFcoDQlxaQlg2ri+nHdkH+55cxXzisvjHY5JIH7OHCYCRaq6VlWrgKeBaSHTTAMeV+djoLuItLPe4k1jFpfs4n9fWsbkobncfNbww0eumgP9j4XM3PgEl0i69oW8kXG93yGYiPDri45kQI9O3PTkp+zYezDeIZkE4Sc59AOCeyov8Yb5nUaBN0RkgYjc2NBKRORGEZkvIvNLS0t9hGUSRdneg0z/+wLyuqTzp8u9coaA3Zth86cw/Kz4BZhoBk9xd0pXV8Y7EgC6ZqRy3xXHsHN/Nd99eiE1tfEtDzGJwU9yCFeCGFp61dg0J6rqMbhLT98UkbAV3VV1pqpOUNUJeXl5PsIyiaCmto5vP/0ZO/ZVMePK8WRnph0+wbJ/uefRF8Y+uEQ19HSoqYTi9+IdySFH9O3GndPG8H7RDu58dUW8wzEJwE9yKAEGBL3vD4TexdPgNKoaeN4OvIi7TGXaiV/NXskHRWXc+cUxHNm/W/0Jlj4PvcdCbjvvDrQ5Ck6GtC6w8tV4R3KYS44dwA2TC3j0w2Ie/6g43uGYOPOTHOYBw0SkQETSgMuAWSHTzAKu9motHQdUqOoWEckUkSwAEckEzgKWRjB+E0ePf1TMwx+s49oT8rlkwoD6E5SvhU3zYcyXYh5bQktJd4XzhbPjXqU11I/PHcUZo3px+6xlvLVyW7zDMXHUZHJQ1RrgJmAOsAJ4VlWXich0EZnuTTYbWAsUAX8DvuEN7wW8LyKLgE+AV1X19Qh/BhMH/125ndtnLeOMUT353y+EVl7zLHgMJBnGXhrb4NqCkV+AvdugZF68IzlMcpLwx8vGcUTfbnz9iU/5eG1ZvEMycSKJ2PHHhAkTdP58uyUiUX22YSdXPDiXwXmZPHPj8WSmp9SfqKYK7hkFA4+Dy/4R+yATXeVuuHsYHH0VnHd3vKOpp3xfFZc88BFbKyp58quTGNu/e7xDMk0QkQWRvF3A7pA2zbJq2x6ufWQeeVnpPHztseETA7iyhv07YMJXYhtgW5HRFUZMdduptjre0dSTnZnGE9dPonvnVK566BMWbdwV75BMjFlyML5tKNvPVQ/NJSM1iSeun0TPrIzwE9bVwft/gF5jYMjpsQ2yLRl7KRwoh9VvxjuSsHp3y+Cprx5H104pXPHgXObbTXIdiiUH40vxjn1cOvMjqmrqePy6SZ83jRHOilmwoxAmf69jt6XUlKFnuM6P5j8c70gaNCC7M89+7Xh6ZqVz9cOf8N9C62K0o7DkYJq0bsc+Lpv5MQdr6njyq8cxondWwxPXVsNbd0DuCBj9xdgF2RYlp8L4a6Ho365mV4Lq060TT3/tOPJzMrnhsfk8OXdDvEMyMWDJwTRqSUkFX57xIVW1dTz51UmM6tO18Rk+fQzKiuCM2yG5gfII87nx10JSMnwc2hxZYumZlcGz04/n5GG5/OTFJdzxynKq7U7qds2Sg2nQu6tKuWzmR6SnJPPs145nZO8mEsP+cnjrThg02RW2mqZ17QtHXeaq/e7ZGu9oGtUlPYW/XT2Ba0/I56H313HF3+ayfXdiNAFiIs+Sg6lHVXnkg3Vc9+g8BuZk8sI3TmBozy5Nz/jWnVBZAVPvsrKG5jjpZqirgXd/F+9ImpSSnMTtFxzBvZeOY8mmCs790/v8e7ndLNceWXIwhzlQVcv3nlnIz19ezqkjevLs146jV9cGaiUF27IYFjwCx94AvcdEP9D2JLsAJlznCqY3fxbvaHz54tH9+Nc3TyS3Sxo3PD6fm/+5iN2ViVcl17ScJQdzyOKSXZz35/d4adFmfnDmcGZeNZ6sjNSmZ6yrhVd/ABndYcpPoh9oe3TabdA5F175ntuebcCI3lnMumkyN00ZyguflnDOH97l/dU74h2WiRBLDoaDNbX84c1VfOn+DzlQVcsT10/iW6cPIynJ56WhT/4GJZ/A2b+CTj2iG2x71am7236bP0voqq2h0lKSuPnsETz/9RPISEvmyofm8pMXl1Cx384i2jprPqOD+6BoB//7r6Ws3bGPaeP68osLxtCts4+zhYCdxXD/8TDoBLjiOStraA1V+PsXoWQ+fO1dyBkS74iapbK6lrvnFPLwB+vo0TmNW6eO5KJj+vs/yDCtYs1nmIhYvW0PNzw2nysenEutKo9dN5E/XnZ08xJDXS3M+jZIEnzhXksMrSUCF/zFVW19/gbXPlUbkpGazG1fGM3L35rMoJzO/PC5xVzywEcs37w73qGZFrDk0MEUbd/DD55dxNn3vsvctWXcfNZw5nz3ZE4Z3oIOlv77S1j3jrsc0j1Mk92m+boPgAv+7HrP++8v4x1NixzRtxvPTT+B3148lrU79nH+X97npy8uYUvFgXiHZprB7lLqAGrrlPdWl/KPuRt4c/k2MlKT+MqJBXxzytD6Pbf59dH98N7v4Zir3cNEzuhp7ua4D+6FAZNg5LnxjqjZkpKESyYM4KzRvbjnzVU89ckG/rmghMuPHcC1JxZQkJsZ7xBNE6zMoZ1SVZZu2s1rS7fw0sLNbNp1gOzMNK48bhDXnpDf8qRQtQ/e/D+Y9zf3J3bRQ64ZCBNZ1Qfg4XOgbA185VXoc1S8I2qVjeX7+fNbq3nh003U1CknDcvlwqP7cfqoXnTrZPtPJES6zMGSQzuyc18Vc9eV8c6qHby7qpRNuw6QnCScMCSHS48dwJmje5GektyyhdfWwKKn3KWOPVvg+Ju8JjLshx01FZvgobOg5gBc+Tz0PTreEbXa9j2VPPPJRp76ZAObKypJTRYmFmQzqSCHiQXZjBvQnYzUFu6jHZwlB3PI9t2VzCveydx1ZcxdW07htj2Aa+bghCE5nD6qJ2eN7k2Plp4lAOzZBp8+7m5w270J+h8LZ94Bg46P0KcwjdpR5Gow7SuFKT+FiTdCqo+bEhNcXZ2ysGQXry/dynurd7By625UIS05iaMGdOPogT0Y068bY/p2JT8n02o8+RCX5CAi5wB/BJKBB1X1NyHjxRt/LrAfuFZVP/UzbziWHD6nqlQcqGbzrkqKSveyfPNulm/ZzYotuyndcxCAzmnJjB/Ug0kF2UwanMO4Ad1JTW5FXYPKClg1B5a/BKted007DJ4CE78KI861WkmxtrcUXvomrJ4D6d0gfzL0PhJ6joLswe4O6/RGWsptAyr2VzN/fTmfrCtn7rpylm/ZTVWNa9ivS3oKo/t2ZUheF/JzOjMoJ5P83M4MzO5M5zQrNg2IeXIQkWRgFXAmUALMAy5X1eVB05wLfAuXHCYBf1TVSX7mDSceySGwHVRBvfd6aBwoSmBTHXr2hmnw/EBtrbK/upb9B2vYVxX0XFXDvoMhz1U17D9Y656ratlfVcu+g4HXNeyprOFgzeetX6YmC8N6ZjGqT1dG9cli/CB3hJWanOQCq612f+a1VVBzEGoPuueaSvdcV+M9at2z1rlyhIoS12x0ySewbZkbntUHxlzkmnZoY3Xu26Xi92Hhk67f6R2rgaDfbmZPyBsBeSO95xHu+8vo5h4p6XELuyWqa+tYvW0vSzdVsHRzBUs3VVBctp/yfYdX7+2UmkyPzqn0yEyjR+c0unVKJT0libSUpKDnZNK81ylJQkqSkJz8+euUZCE5yb1PPjTs8PfuOYmU5MPfJycLqd775CRBREgSSBIhSQQRdywVeJ8kIFE6uIp0cvCTdicCRaq61gvgaWAaEPwHPw14XN0/5Mci0l1E+gD5PuaNmPF3vMn+qtrD/rQBUOoNC/z5x/OqWkZqEplpKXRKSyYzLYXO6e45JzONzPQUOqclk5meQs+sdPp278SgnM4M65lFWop3VlBZAfeM9hJCtftDb430ru669kk3w7Azod8ESLLazgkjf7J7gEvoZWtg5zqX1MuKoLQQFj8DB8PcVyDJ7v4JSXKvr5/jzj4SVGpyEqP7dmV0365cwufVpCsOVLOhbD/FZfvYuHM/O/dVUb6vmp37q9i5v4rNFQeoqqmjqqaOg4eea6lLsKvnnycMEOTQ+7ysdN69ZUq8wwP8JYd+wMag9yW4s4Ompunnc14ARORG4Ebv7V4RKfQRWyTkAoneIEyMYtyN+4peBm5r7sxtYTtC24gz+jHeNra1S7DtGBmHxbgSkB+1eFmDIhDPIX6SQ7hzoNA83NA0fuZ1A1VnAjN9xBNRIjI/kqdi0WAxRk5biNNijAyLsXX8JIcSIPj21/7AZp/TpPmY1xhjTILxc0F5HjBMRApEJA24DJgVMs0s4GpxjgMqVHWLz3mNMcYkmCbPHFS1RkRuAubgqqM+rKrLRGS6N34GMBtXU6kIV5X1K43NG5VP0nIxv5TVAhZj5LSFOC3GyLAYWyEhb4IzxhgTX1ZP0RhjTD2WHIwxxtTT4ZKDiDwjIgu9R7GILGxgumIRWeJNF9PbtUXkdhHZFBRn2DabReQcESkUkSIRuTXGMf5ORFaKyGIReVFEujcwXcy3Y1Pbxas48Sdv/GIROSYWcQWtf4CI/FdEVojIMhH5TphpThWRiqB94GexjDEojka/vwTYliOCttFCEdktIt8NmSbm21JEHhaRlk9hUgAAA5pJREFU7SKyNGhYtoi8KSKrveewferG83d9GFXtsA/g98DPGhhXDOTGKa7bgZubmCYZWAMMxlUZXgSMjmGMZwEp3uu7gLsSYTv62S64yhOv4e7DOQ6YG+Pvtw9wjPc6C9fETGiMpwKvxGP/a873F+9tGea73woMive2BE4GjgGWBg37LXCr9/rWcL+ZeP+ugx8d7swhwGss8BLgqXjH0kKHmjVR1Sog0DRJTKjqG6pa4739GHcPSyLws10ONfeiqh8DgeZeYkJVt6jXMKWq7gFW4FoTaIviui1DnA6sUdX1cVr/Iar6LlAeMnga8Jj3+jHgi2FmjevvOliHTQ7AScA2VV3dwHgF3hCRBV7THrF2k3ea/nADp58NNVkSD9fhjh7DifV29LNdEmbbiUg+cDQwN8zo40VkkYi8JiJHxDSwzzX1/SXMtsTdR9XQwV4ibMte6u7/wnvuGWaahNme7bK9WxH5N9A7zKifqupL3uvLafys4URV3SwiPYE3RWSldzQQ9RiBvwJ34H6Yd+Auf10Xuogw80a0XrKf7SgiPwVqgH80sJiobscwWtPcS0yJSBfgeeC7qhraWt6nuMsje70yp38Bw2IdI01/f4myLdOAC4AfhxmdKNvSj4TYntBOk4OqntHYeBFJAb4EjG9kGZu95+0i8iLudC9if2pNxRggIn8DXgkzyk+zJq3iYzteA3wBOF29C6ZhlhHV7RhGa5p7iRkRScUlhn+o6guh44OTharOFpH7RSRXVWPakJyP7y/u29IzFfhUVbeFjkiUbQlsE5E+qrrFu/S2Pcw0ibI9O+xlpTOAlapaEm6kiGSKSFbgNa7wdWm4aaMh5JrthQ2sO65Nk4jrxOlHwAWqur+BaeKxHVvT3EtMeOVdDwErVPWeBqbp7U2HiEzE/VbLYhWjt14/319ct2WQBq8EJMK29MwCrvFeXwO8FGaaxGlyKB6l4PF+AI8C00OG9QVme68H42oJLAKW4S6jxDK+vwNLgMW4HaNPaIze+3NxNV3WxCHGIty10YXeY0aibMdw2wWYHvjOcafu93njlwATYrztJuMuFSwO2n7nhsR4k7fNFuEK/E+IZYyNfX+JtC29GDrj/uy7BQ2L67bEJaotQDXubOB6IAf4D7Dae872pk2Y33Xww5rPMMYYU09HvaxkjDGmEZYcjDHG1GPJwRhjTD2WHIwxxtRjycEYY0w9lhyMMcbUY8nBGGNMPf8PU2LPAqLg28kAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1710,12 +1820,67 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train"
-   ]
-  },
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T07:28:22.925195Z",
+     "start_time": "2020-10-16T07:28:22.690034Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor(0.) tensor(50.)\n",
+      "close is lower? tensor(True)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Part 1\n",
+    "p = Normal(0, 1)\n",
+    "kld_close = torch.distributions.kl.kl_divergence(p, Normal(0, 1))\n",
+    "kld_far = torch.distributions.kl.kl_divergence(p, Normal(10, 1))\n",
+    "print(kld_close, kld_far)\n",
+    "print('close is lower?', kld_close<kld_far)\n",
+    "\n",
+    "# Part 2: Plot the KLD as you vary the mean of q\n",
+    "p = Normal(0, 1)\n",
+    "means = np.arange(-10, 11)\n",
+    "\n",
+    "klds=[]\n",
+    "for mean in means:\n",
+    "    q = Normal(mean, 1)\n",
+    "    kld = torch.distributions.kl.kl_divergence(p, q)\n",
+    "    klds.append(kld)\n",
+    "\n",
+    "plt.plot(means, klds)\n",
+    "plt.ylabel('KLD(p|q) loss [lower in better]')\n",
+    "plt.xlabel('distance between distributions')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1725,20 +1890,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 29,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:39:05.577626Z",
-     "start_time": "2020-10-12T06:39:05.449637Z"
+     "end_time": "2020-10-16T07:28:22.934466Z",
+     "start_time": "2020-10-16T07:28:22.926596Z"
     }
    },
    "outputs": [],
    "source": [
-    "def train(epoch, loss_function, log_interval=50):\n",
+    "def train_vae(epoch, loss_function, model, train_loader, log_interval=50):\n",
     "    model.train()\n",
     "    train_loss = 0\n",
     "    for batch_idx, data in enumerate(tqdm(train_loader, leave=False)):\n",
-    "        data = data.to(device)\n",
+    "        data = data.to(device).float()\n",
     "        optimizer.zero_grad()\n",
     "        recon_batch, dist = model(data)\n",
     "        loss = loss_function(recon_batch, data, dist)\n",
@@ -1756,12 +1921,12 @@
     "    print('#{} Train loss: {:.4f}'.format(epoch, train_loss / len(train_loader.dataset)))\n",
     "\n",
     "\n",
-    "def test(epoch, loss_function, log_interval=50):\n",
+    "def test_vae(epoch, loss_function, model, test_loader, log_interval=50):\n",
     "    model.eval()\n",
     "    test_loss = 0\n",
     "    with torch.no_grad():\n",
     "        for i, data in enumerate(test_loader):\n",
-    "            data = data.to(device)\n",
+    "            data = data.to(device).float()\n",
     "            recon_batch, dist = model(data)\n",
     "            test_loss += loss_function(recon_batch, data, dist).item()\n",
     "\n",
@@ -1771,11 +1936,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 30,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:39:05.612985Z",
-     "start_time": "2020-10-12T06:39:05.579434Z"
+     "end_time": "2020-10-16T07:28:23.016177Z",
+     "start_time": "2020-10-16T07:28:22.935746Z"
     }
    },
    "outputs": [
@@ -1808,7 +1973,7 @@
        "1"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1821,30 +1986,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:40:40.325413Z",
-     "start_time": "2020-10-12T06:39:05.615864Z"
+     "end_time": "2020-10-16T03:43:29.737765Z",
+     "start_time": "2020-10-16T03:43:29.734448Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T07:29:13.624055Z",
+     "start_time": "2020-10-16T07:28:23.017556Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "74b0ce2ac56d4d6a9792267815aa79f4",
+       "model_id": "4c62e139b673463dbb0559bf3e1a50d5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1873,20 +2048,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#1 Train loss: 182.1748Loss: 154.050079  \n",
-      "#1 Test loss: 166.7879\n"
+      "#1 Train loss: 185.0811Loss: 170.045776  \n",
+      "#1 Test loss: 168.0937\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -1907,20 +2080,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#2 Train loss: 162.6419Loss: 166.730240  \n",
-      "#2 Test loss: 159.6808\n"
+      "#2 Train loss: 164.9095Loss: 158.547150  \n",
+      "#2 Test loss: 162.5043\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -1941,20 +2112,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#3 Train loss: 158.2025Loss: 157.931046  \n",
-      "#3 Test loss: 156.9358\n"
+      "#3 Train loss: 160.9141Loss: 168.935593  \n",
+      "#3 Test loss: 160.0993\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -1975,20 +2144,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#4 Train loss: 155.6275Loss: 148.499344  \n",
-      "#4 Test loss: 154.4997\n"
+      "#4 Train loss: 158.7383Loss: 165.017349  \n",
+      "#4 Test loss: 157.9522\n"
      ]
     },
     {
      "data": {
-      "image/png": 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4Badu3QEbIhvPDrQrCMcbOFoz3W4cGptwK1euTMrsbK14WNCa7TZsOL7Wzvazm0fEKwL16NEjKYvvXb9+fVK2devWJO+bkFaGDef91re+1eZnxcO0dsXpGTNmJHm7OnS94paA4xQcVwKOU3BcCThOwanbPgEbIhsP0dmw3Hiqbq9evZIyuxlo7KfZYRvry8d+v22PXT0o7rOw19r+gyOOOCJPW78+Hra0Q5i2b8HDhksT70h01llnJWV2Cnn8jrS0g1X8nY8fPz4pi1eKBliyZEkrWlw73BJwnILjSsBxCo4rAccpOHXbJ2B97tg3s30CNjYg5u23307y8Ri99altf0Lsy1vf0F4bt+kTn/hEUmZ3zI2vjXc5gnRVY7vyrR13bvQVhcthw8FvueWWJH/uuefmabu82JNPPpnk4yXF7NTzeJky+yw7nXzYsGFJ3vsEHMfpErgScJyCU7fugB2qiU1zu5pvHBpshwStyR+bc3bGmDXj42ut+W/DiONNQ0aOHJmU2Xvj2YEvvvhiUhabqtactGHDRWbs2LFJfty4cUk+difnzp2blNkNPuNye61dFerKK68s2aYxY8Yk+VmzZpW8tp5wS8BxCo4rAccpOJXsSjxYRP4sIgtEZL6ITAnn+4jILBFZHP4e2NKznPrF5VxcKukT2A5crqpzRWRfYI6IzAIuAp5Q1RtEZBowDZha5jmtYtu2bSXLbHhvPB3X+t/x6r2QhnaWC/21z7VDhPHqQDZv+yHiVYsBnn/++Tz92GOPJWVPP/10nq5ymHBN5Nwa4qHWBx54ICmzspw9e3aePv30dGPl1qzQZIeYyxHX2ZVo0RJQ1dWqOjektwALgEHAl4B7w2X3Aud0UhudKuByLi6tGh0QkaHAscCLQH9VXQ3ZCyQi/UrcczFwcTvb6VQRl3OxqFgJiEhv4HfAd1V1c6Wr2qjqdGB6eEbFIW52s48tW7bk6U2bNiVlsetg22WjxeLhRbtRqF3lx87+K1UnpMN5CxcuTMpeffXVJP/MM8/k6XjTU0hXN7KrIlWDasu5NcSRfVau8XcK6czB9izQajcdjWeINsoszopGB0SkB9mLMVNVfx9OrxWRAaF8ALCu1P1O18DlXEwqGR0Q4C5ggareFBU9AkwM6YnAwx3fPKdauJyLSyXuwGeAC4HXROTlcO77wA3Ar0Xkm8By4LxOaaFTLVzOBaVFJaCqzwKlHMPTS5xvN7ZPIPbrVq1alZTFvrv14234cbyCr92Q1PYJxCsP2VWI4j4KgMWLF+fpeOML2H02YFyvHe6slZ9ZKzmXw8ojDvO2MyjtUGv8vtjn2E1gYy688MIkf9pppyX5WD6NMovTIwYdp+C4EnCcguNKwHEKTt1OJbb+VjyN1sYJxD73hg0bkjK7yWgcRmw3GbWhwBs3bszTdlUfG07a1NSUp995552SbYfGGV/ubOyqzTasO2by5MlJ/rOf/WyetjEFdhpyW7HT1lsTYlxPuCXgOAXHlYDjFJy6dQcssQltQ3bj4US7yajd1DOebWZnnlkzPa7HltkZfrHJ3yhDR7XGDvfGQ6929aZ4U1qbt6HP7ZHPpEmT8rQNVe4qC4ta3BJwnILjSsBxCo4rAccpOFJN/7Wzppi2UGeb7y2Kb6+qbf+SmqEach49enSSnzBhQpK/9NJL87RdbWrdunQi5H333VeynjvvvDPJL126tBWtrC9KydktAccpOK4EHKfguBJwnILT8H0CTst0xT4Bp/V4n4DjOM3iSsBxCo4rAccpOK4EHKfguBJwnILjSsBxCk61pxKvB5YBB4d0vVDk9hzWCc90OVdGXci5qnECeaUis1V1TNUrLoG3p3Oot8/h7Wkedwccp+C4EnCcglMrJTC9RvWWwtvTOdTb5/D2NENN+gQcx6kf3B1wnILjSsBxCk5VlYCIjBeRRSKyRESmVbPuqA0zRGSdiMyLzvURkVkisjj8PbCK7RksIn8WkQUiMl9EptS6Te2l1nJ2GbeOqikBEekO3A6cAYwCJohI6T2iO497gPHm3DTgCVUdATwR8tViO3C5qo4ETgQuC99LLdvUZupEzvfgMq4cVa3KAZwE/DHKXwVcVa36TVuGAvOi/CJgQEgPABbVol2h/oeBcfXUpq4oZ5dx5Uc13YFBQFOUXxHO1QP9VXU1QPjbrxaNEJGhwLHAi/XSpjZQr3Kui++zHmVcTSXQ3NJGPj4ZEJHewO+A76rq5lq3px24nEtQrzKuphJYAQyO8ocCq6pYfznWisgAgPB3XQvXdygi0oPs5Zipqr+vhza1g3qVs8u4BNVUAn8FRojIMBHpCZwPPFLF+svxCDAxpCeS+WxVQbLdUe4CFqjqTfXQpnZSr3J2GZeiyh0iZwKvA38Hrq5Rp8wvgdXANrJfrW8CB5H1zi4Of/tUsT2nkJnLrwIvh+PMWrapq8vZZdy6w8OGHafgeMSg4xQcVwKOU3BcCThOwXEl4DgFx5WA4xQcVwKOU3BcCThOwfl/53ZglKqvXKwAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -2009,20 +2176,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#5 Train loss: 153.6454Loss: 151.353165  \n",
-      "#5 Test loss: 152.8403\n"
+      "#5 Train loss: 157.2109Loss: 158.479919  \n",
+      "#5 Test loss: 156.6860\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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njmaRjoG0IuqJhcgcGbEQmdOU/YmrFoGr2sPXX7ejfW7TWVDppm2+zLfBu7L+2NQt9tIgPdZ/Ti8V0uucy+6zeG+oJxYic2TEQmSOjFiIzGlKiKlqQ+2Oph+m51ZpYChrUK+J/aqEaaioarUOKD8t5TV7Gsryn8Vr5PS6HW0yLpqH30j8tddeq6X9k3TNQD2xEJkjIxYic2TEQmROU1b26GiHhapjq86r2qnB62WvT/0qHCn+EUe/OVtKOkXPP3JZtZG44sSti//upPf4uuuuK5U99NBDPdKmFPXEQmSOjFiIzGmKO+1Jwyne5axytX2ZD/dUhaP8dMk05OSP9dM7d+zYUUv7J5xSF9qHJnzoquopJtE6+Huc7kG9ZMmSnm7OGagnFiJzZMRCZI6MWIjMsUa0mJm1tHCr0sgdhaPSvNfW/th0lUq/Emaqw31oomqB+PeiiUMIXbYjeavf43OYV0MIk9srUE8sRObIiIXIHBmxEJnTEnHirsLrSh/vPVuq9HNVmZ9KqamVojtQTyxE5siIhcicXuVOdxfeTdcUSdFKqCcWInNkxEJkjoxYiMxpVBPvAjZ0R0PEWXN5F19P97g1qXufG5o7LYRoPeROC5E5MmIhMkdGLETmyIiFyBwZsRCZIyMWInNkxEJkjoxYiMyREQuROf8PoKHiMqlJjHMAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -2043,20 +2208,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#6 Train loss: 151.9474Loss: 155.882004  \n",
-      "#6 Test loss: 151.6404\n"
+      "#6 Train loss: 155.9319Loss: 158.190308  \n",
+      "#6 Test loss: 155.9110\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -2077,20 +2240,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#7 Train loss: 150.5245Loss: 154.039017  \n",
-      "#7 Test loss: 150.3561\n"
+      "#7 Train loss: 154.7547Loss: 162.252594  \n",
+      "#7 Test loss: 154.4885\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -2111,20 +2272,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#8 Train loss: 149.3793Loss: 154.810287  \n",
-      "#8 Test loss: 149.9096\n"
+      "#8 Train loss: 153.7207Loss: 147.029648  \n",
+      "#8 Test loss: 153.7610\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -2145,20 +2304,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#9 Train loss: 148.2509Loss: 144.554245  \n",
-      "#9 Test loss: 148.9081\n"
+      "#9 Train loss: 152.8651Loss: 165.493423  \n",
+      "#9 Test loss: 153.0809\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -2179,20 +2336,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "#10 Train loss: 147.3035Loss: 135.425735  \n",
-      "#10 Test loss: 148.2795\n"
+      "#10 Train loss: 152.0754Loss: 146.243683  \n",
+      "#10 Test loss: 152.0925\n"
      ]
     },
     {
      "data": {
-      "image/png": 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mvi0fh4HG4b++HyC2QbM23l8s/Gm9t9xyS5A3ffr0nPfFy8/FU4D958tvc4Crr746kOfNm5em49WgKwnrCRhGxjElYBgZp2rMAX8V3rjL7HfR/M07oOlKs/7GE4ceemiQF8v+vfEKRR999FEg+ysPxV38tWvXBrK/Oo11/4tPHM570UUXtfuzfPPy0UcfDfLmzp0byP4s1ErGegKGkXFMCRhGxjElYBgZp2J9AvFQny/Hw37+cF28wky8etDIkSPT9Pjx44O8eKNKfxWiuD6+XQ/htN64fvEKyEbx8XckOvnkk4O82A/jrzod+5R69uwZyH47T5o0KciLh5j9aeGVjPUEDCPjmBIwjIxjSsAwMk7V+AR8Ozu22/zw39gHMG7cuEA+7LDD0nQcChyP5/vLgMW7FcU7Dfu7H8cr4Q4YMCCQ42XDjLYT+29uvPHGQD7jjDPSdBwe/uSTTwayv6SY/3xAuExZ/Flxu+63336BbD4BwzCqAlMChpFxKtYciIfZfNkPIYZwWHDo0KFB3tixYwPZXwE2XjF4yZIlgexvDNmrV68gr6amJpD9zT/icNF4iLClWZBGfsSrQE2cODGQ/WHjeEXneINPPz++dvjw4YF8+eWX56zT4YcfHsgLFizIeW0lYT0Bw8g4pgQMI+PksytxrYj8RUTqROQNEbnEne8nIgtEZJn727f41TWKhbVzdsnHJ7ADmKmqi0VkN+AlEVkAfA94QlWvE5HZwGxgVguf0ybi0E7fJxCv6uPb+WPGjAny4lWCfXt89erVQV68U5DvMxg2bFjOz4FwanG8iky8arDvI4g3Eo13UCohZWnntuCHAt9zzz1BXvxMLFq0KE0fd1y4sXIc8t0SGzduzPtav8xqotWegKquUdXFLv0xUAcMBk4D7nSX3QmcXqQ6GiXA2jm7tGl0QESGAYcBC4FBqroGkgdIRAbmuOcC4IIO1tMoIdbO2SJvJSAifYD7gUtVdUu+w1yqOgeY4z4j72V0WtrPPY4A81cAiofyYtm/Np4h1r9//0D2NyQdMmRIkBdHrG3atClNx/vJx118v+saDyf6pkM5Vh0qdTu3BT+yL34GnnnmmUD2Zw62pfsfE2866pul/jNZzeQ1OiAi3UgejHmq+oA7vU5Ealx+DbA+1/1GdWDtnE3yGR0Q4LdAnape72XNB6a69FTgocJXzygV1s7ZJR9z4CjgPOB1EXnFnfsRcB3wBxE5H6gHzixKDY1SYe2cUVpVAqr6LJDLMDwux/kOE9vDvq0cr/zr23yxnRaHBvft+9Uw90EHHRTkxSGifnhyPHMxHjryVxuOZ4/FsxP9+lfKasPlaueWaGkz2fj/Fm/66j8T8ef4m5XGnHfeeYF87LHHBrL/fFVK23UUixg0jIxjSsAwMo4pAcPIOBU7lbil8el43N1fFbihoSHIGzgwjG3x4wZ8/wA03YzStytju/7pp58OZD9kdOnSpUFeHDfg+zfi79JZ7MxC0JZVm2fMmBHI3/zmN9N0HFMQT0NuL/FqU20JMa4krCdgGBnHlIBhZJyqMQf8oZmWzIHHHnssyIs3Dq2rq0vTvXv3DvLiUGB/KM9fZQigvr4+kFesWJGm425hPKQZzyo0mideQerNN99M06NGjQry4pWefDl+ljpick2bNi1Nx6HK1bKwaIz1BAwj45gSMIyMY0rAMDKOlHJIqiNTTH27LrbxfNsxHlaKr/Xt8bZMBY2v7UxDeapa0OWPizWV2CdeQWrKlCmBPH369DQd+37Wrw8nQt511105y7n11lsD+b333mtDLSuLXO1sPQHDyDimBAwj45gSMIyMUzU+AaN4VKNPwGg75hMwDKNZTAkYRsYxJWAYGceUgGFkHFMChpFxTAkYRsYp9VTiDcBKoL9LVwpZrs/QInymtXN+VEQ7lzROIC1UZJGqHl7ygnNg9SkOlfY9rD7NY+aAYWQcUwKGkXHKpQTmlKncXFh9ikOlfQ+rTzOUxSdgGEblYOaAYWQcUwKGkXFKqgREZJKIvCUiy0VkdinL9uowV0TWi8gS71w/EVkgIsvc374tfUaB61MrIn8RkToReUNELil3nTpKudvZ2rhtlEwJiEhX4GbgRGA0MEVEcu8RXTzuACZF52YDT6jqCOAJJ5eKHcBMVR0FjAcudv+Xctap3VRIO9+BtXH+qGpJDuAI4DFP/iHww1KVH9VlGLDEk98Caly6BnirHPVy5T8ETKykOlVjO1sb53+U0hwYDKzy5AZ3rhIYpKprANzfga1cXxREZBhwGLCwUurUDiq1nSvi/1mJbVxKJdDc0kY2PukQkT7A/cClqrql3PXpANbOOajUNi6lEmgAaj15X2B1CctviXUiUgPg/q5v5fqCIiLdSB6Oear6QCXUqQNUajtbG+eglErgRWCEiOwnIt2Bs4D5JSy/JeYDU116KonNVhIk2R3lt0Cdql5fCXXqIJXaztbGuSixQ+Qk4G3gHeCKMjll7gXWAF+QvLXOB/Yi8c4uc3/7lbA+R5N0l18DXnHHSeWsU7W3s7Vx2w4LGzaMjGMRg4aRcUwJGEbGMSVgGBnHlIBhZBxTAoaRcUwJGEbGMSVgGBnn/wHO1q729ZeT9gAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
@@ -2205,11 +2360,11 @@
    ],
    "source": [
     "epochs = 10\n",
-    "show_prediction(10, title=f\"epoch={0}\")\n",
+    "show_prediction(3, title=f\"epoch={0}\")\n",
     "for epoch in tqdm(range(1, epochs + 1)):\n",
-    "    train(epoch, loss_bce_kld)\n",
-    "    test(epoch, loss_bce_kld)\n",
-    "    show_prediction(10, title=f\"epoch={epoch}\")"
+    "    train_vae(epoch, loss_bce_kld, model, train_loader)\n",
+    "    test_vae(epoch, loss_bce_kld, model, test_loader)\n",
+    "    show_prediction(3, title=f\"epoch={epoch}\")"
    ]
   },
   {
@@ -2223,11 +2378,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 32,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:40:40.330932Z",
-     "start_time": "2020-10-12T06:40:40.327574Z"
+     "end_time": "2020-10-16T07:29:13.627736Z",
+     "start_time": "2020-10-16T07:29:13.625453Z"
     }
    },
    "outputs": [],
@@ -2237,11 +2392,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 33,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:40:40.345843Z",
-     "start_time": "2020-10-12T06:40:40.334161Z"
+     "end_time": "2020-10-16T07:29:13.679449Z",
+     "start_time": "2020-10-16T07:29:13.629054Z"
     }
    },
    "outputs": [],
@@ -2252,11 +2407,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 34,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:40:40.366440Z",
-     "start_time": "2020-10-12T06:40:40.348416Z"
+     "end_time": "2020-10-16T07:29:13.792227Z",
+     "start_time": "2020-10-16T07:29:13.680776Z"
     }
    },
    "outputs": [],
@@ -2275,24 +2430,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 35,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:40:40.738597Z",
-     "start_time": "2020-10-12T06:40:40.369008Z"
+     "end_time": "2020-10-16T07:29:13.933300Z",
+     "start_time": "2020-10-16T07:29:13.793521Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 288x144 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -2310,44 +2463,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 36,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:40:40.764195Z",
-     "start_time": "2020-10-12T06:40:40.740466Z"
+     "end_time": "2020-10-16T07:29:14.595937Z",
+     "start_time": "2020-10-16T07:29:13.934662Z"
     }
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(1000, 2)"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dist = model.encode(ds_train[:1000].to(device))\n",
-    "res = dist.loc.cpu().detach().numpy()\n",
-    "res.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-10-12T06:40:41.939887Z",
-     "start_time": "2020-10-12T06:40:40.766137Z"
-    }
-   },
-   "outputs": [
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1000, 1, 28, 28])\n",
+      "torch.Size([1000, 1, 28, 28])\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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c8GWMBLMwysgnqFvU2fRn9kLj1OK5+ZsM6xZBe0vi8XR3K1Yd78QBU75hZGhnQw/vMKeVA4ZimPfmPOpb6lEo6lvqmffmvJwoiDCffPIJa9euZerUqTkbU6OxJJMIoy0vYVn1FLpmMgxnM8c7ku12l4vGbMcz+w/w04PZKwbo8R3mtM8BuPfde/EFfDHHfAEf9757LxeNuSjr8Zubm5kzZw733HMP/fr1y3o8jSYlmUQYpbqnK0rbm2Uzh8nkGflM1Ovh1Zz1zgFoaGlI63g6dHR0MGfOHK666ipmz56d9XiabkKhbdGW9nZlLU8qO3+uJsNkn02us5nz+f/Qw6s5a+UADO0zNK3jdlFK8Y1vfINJkybxgx/8IKuxNN2IYrBFW9nbwVqeVPkDmSiceLrys7HzrGyUR56qORcLWjkA1510HWXOsphjZc4yrjspu7DTFStW8Ne//pVly5Zx4okncuKJJ/L88/nteKopAorBFh1jbzfBTJ5UUUmZKJxo6hYZPaKTfTZWJbpTle42I9X/Q7aKqhBRXF2I9jlAxK9w77v30tDSwNA+Q7nupOuy9jecccYZKKUTbXodxWKLDtvb51VhmvBlJk8yG31MJVaTnIhk/ofwRBzuEW0ly+d/BUuujc1edpYYx9OlK3woPbias1YOIS4ac1FOnM8aTdKEskKQS3kyUThgPhGbyZLLUuCp3jsbJW5VpLAHoc1KGk2uKTZbdD7kSdcZm2zCjZclnFMxr9H4N9NJN1MfSiqlWQw+pS5AKwdNj2Lj8ldZeO3XuPvKmSy89mtsXP5q1wtRbLbofMiTrsKxmnDFmb/PJv69PQPA5elM4Bt3XmZKsxh8Sl2ANitpegwbl7/KSwvvx9/eBsDhfXt5aeH9AEw6c3rXClNstuhcy5Ou+ceqPlO+lWb4vc16Urz3dyMrfMtL6ZmHisWnlGe0ctD0GJY//peIYgjjb29j+eN/6Xrl0BtIR+HkqU+7baxW+1teMkxX6VBsPqU8oZWDpsdweP++tI5ruphC7qZyudq3W6W2mzuttXLIIz6fj7POOou2tjb8fj+XXXYZP/vZzwotVpeycfmrLH/8Lxzev4++Awdx5pVX520V33fgIA7v22t6vKtl0YSInyDHnZe+GScX5DpiC5JP/MXWWjUDtEM6j5SWlrJs2TLee+891q1bxz//+U9WrlxZaLG6jLAP4PC+vaAUh/ft5fn77+aVPz6Yl+edeeXVuEpiWz+6Sko588qrTWV5aeH9hXFY9xbMonpW/29honwyjdiyyqCuudy4t3KEoSBqb499jx7gtNY7hxBNS5eyZ8E9+OvrcVVXM+SG66mcOTOrMUWEiooKwKix1NHRgYikuKvnYOYDAHjv5ecZPmFSZNVutaJPd6UfPmd2z8Jrv6b9EV1NqtwGyH3hPisy8XkkW/1D8p1BD3BaF1Q5iMifgIuBPUqp40LHBgBPAKOBT4DLlVIH8ylH09Kl1P/kNpTPqMzq372b+p8YK4psFUQgEODkk0/mo48+4tprr+1VJbuT2frDk7JVhNGuzRv54PXatCOPJp053fS89kcUALsTYVdNmOn6PFKt/pNlV/cAp3WhzUqPABfEHbsZqFVKjQNqQz/nlT0L7okohjDK52PPgnuyHtvpdLJu3Tp27tzJ22+/zfr1aUZGdGPCtn4zwpOyVYRRXe0/LVf6uZQlmYyaLLE7EaYzYXZltdtkq3/Lczuyy6EoIgqqHJRSbwDxtXhnAX8Off9n4JJ8y+Gvr0/reCZUVVVxzjnn8M9//jNnYxY7Z155teW58KRs5kAGUMGg6fFMV/rJ/BGaPJGsUF+YdCZMu5nJuVIgyTKokym06ByKYkmEzIBC7xzMOEIpVQ8Q+neI2UUiMldEVovI6r17zScYu7iqq9M6bpe9e/fS2NgIgNfr5ZVXXmHixIlZjdmdmHTmdE4498KE49FOYivEYf6rmelKf9KZ0zlv7vfpO2gwiNB30GDOm/v9rP0NRZGRnQ5dufI2y8ye8o3MJ8wXbkrt5M1laYtkTuxx5yW/NzqHItsyIAWi2zqklVILgYUAU6ZMyar06ZAbro/xOQBIWRlDbrg+Kxnr6+u55pprCAQCBINBLr/8ci6++OKsxuxufO6b32P4hEmWTmIramZcEONzgOxX+lb+iEwpqoxsOxQivDJXuQ11i6wb/kSbeHLZrS6ZE9tO1FE3cj6bUYzK4VMRqVZK1YtINbAn3w8MO51zHa1UU1PD2rVrcyFityZdJzEkVyrFQrfLyO6KNp/5ItlkHG3iyXWUkJVyszNeN3I+m1GMyuEZ4BrgztC/S7rioZUzZ2atDDTpYZm0NmgwkPuVfjS5SIjrdhFQ3Tm8MpmM0T6LrooSsnpOmG7mfDajoD4HEXkMeAuYICI7ReQbGErhXBHZApwb+lnTAymUkzhXCXHdLgKqO/c8tpLRMyB2ZW832S1b34upsz2Uw9QNnc9mFHTnoJT6ksWpGV0qiKYgJEtayye5MgedeeXVMT4HKPIIKLs1gYoRK9njO8RlW9oi1b3pPKebU4xmJU0vIp+mIytyZQ4qlHLLmO48oaUjeyonuJXv5YWbwO+177DPxNnejYrxaeWg6XWkKtCXDtkot/qGJWzbehe+tnrKSqsZM/ZGqofOymgs2xRbn4l0yJXsVv4Ls2ioXDrsu1kxvmLMc9Bo8koxJMTVNyxh06Zb8bXtBhS+tt1s2nQr9Q1dEn9ROLoyz8KKdH0suXLYJ4sWK4bPJY6kykFE+onIWJPjNfkTqecRCASYPHlyr8txKFbylRCXDtu23kUwGDtRBINetm29q8tk6HKKpfeyncztaDJ12MdP+FbRTU07jNalhf5c4rA0K4nI5cA9wB4RcQNfVUq9Ezr9CHBS3qXrIdx7771MmjSJQ4cOFVoUTYhC+Dqi8bWZl2axOt4jKJY8i/CznvoOqEDyazN12JuZkBDAKl837ngR5J8k8zn8GDg5lIx2KvBXEfmxUmoxkZitnsOHqxp4a8lWmg+0UTGglNNnjWX81KFZj7tz506ee+45br31Vn7729/mQFJNNhRLw5+y0uqQSSnxeI+lmPIsai43VuvJ8AwwoqEymaBNy5UrkiuIOAqcf5LMrOSMqnH0NjAduFVE/h+236578OGqBl59dBPNB4yQxOYDbbz66CY+XNWQ9djXX389v/71r3FY1ArSdB3F1PBnzNgbcThiTRsOh4cxY2/sclm6jGLLs0j13JI+mSmGukVJEuRUqLaUDQqcf5Jsxjoc7W8IKYpzMKqmHptnubqUt5Zsxd8eWwXU3x7krSVbsxr32WefZciQIZx88slZjaPJnOjCeC88uCCnZcCzoXroLCZO/AVlpcMAoax0GBMn/iL/0UqFJNNubF0pTzSZrNzD5iQrKkcaRfg8A5KPUwT5J8nMSt8lznyklDosIhcAxRd3lQXhHYPd43ZZsWIFzzzzDM8//zw+n49Dhw7xla98hb/97W9ZjauxR3xhPKXMN7yFKndRPXRWz1YG8RRbnkUq30MmK/dk3e/CE37dImhvth6jcmRR5D9YKgel1HsWxzuAR/MmUQGoGFBqqggqBpSaXG2f+fPnM3/+fABee+017rrrLq0YuhCrNqXxFG25i55IseVZhGXJVeZ4st1GuKTGguMg0J54XhxgsYApBDoJDjh91lhefXRTjGnJVeLg9FkJUbyaPBLtLC6rqEApaGtpznlhvGgKUe6iWJzimhC53NFYFv4b2TmelQJRofmnSJLjtHKASFRSPqKVwpxzzjmcc845ORuvu2I1McabgHyHD0fuybRPglUmtDgcKKUKMjF3ux4QvYVkOxq7JS/qFkF7S+Lx+F1IqoquUPShrBFExAOMUkptzrM8BWP81KE5VQaaRJJNjKlMQLksjJcs4S3fq/pu1wMixxSkZEg22C15EX9dGLNwWLMigmYUOJQ1pXIQkZnAXUAJcJSInAjcrpT6Qp5l0xQ56U6kySZGOyagfBfG64pVfbfrAZFDwiVDwpnh4ZIhQO4VRK4K3NlN3LNyRJuFw9ZcDttXwuo/kTQroMChrHZ2DvOAU4HXAJRS60RkdP5E0nQHMplIk02MViagaPJdGK8rVvW5LPrX3UhWMqR66Kzc7SpyWeDObuJeugl+W14iqWIoglBWO5lZfqVUU94l0XQrkk2kViRrjmNWDC+arnAcd8WqvhiK/hWKZCVDclqIMNlqP13sJu6lm+CXzGRUORJO+LIhbwEL8dlRDutF5MuAU0TGicjvgDfzLJemyMlkIk02McYXwyvr25fSir5dWhivKzq7FUPRv0yob1jCihVnUrvsaFasODOjSduqNEhZaXVuCxHmskyH3cS9dBP8LJVJKMfhvb8XvBCfHbPSfwK3Am3AY8CLwB35FEpT/GRiHknlAyh0Mbyu6uxW6PdM11eUK1/BmLE3xowDnSVDNmz4oek9GRUizGUfabthrumGwybrypdq59NFSYRilTXanZgyZYpavXp1zLGNGzcyadKkAknUyejRo+nbty9OpxOXy0W8nMVMss8w3ucAqSOBig2zSRK6UWe3DMjk/23FijMtigQOY9q05UmfF+9HGDBwOgf2v5rgV8jmGQmYRQ65PcXX19nKaT6vCkt/hMMNwY7Yny95MOP3EpE1SqkpZueSlexeai0h6Ggl+7z66qsMGtSzHI7F2CIznRWxlUP9vLnfZ+4DD3el2FmTzntn4nTPtLy42Y6joWFxQg2p+oYl+P2tCfdnXIiw2Mp0WGGVW2GZByGxigGMn1+4KS/vlsys1IO7jiSis1bTp9DmkWjSjZ7qjvkGZtE8jVv6pfXemfiKUpUXr6urY82aBxlyxJuUlrbgdA5m4sRbUkYnhd8p3tQE4HL1Z/z4n2Qe4lpMZTrSDasddx6s/l+TExZrdbP2pjkgWW2l18Pfi0gJMBFDus1KKZPCILlFRD4BDgMBjIgp061PLshnfLuIcN555yEifPvb32bu3BQ15DUZYTXZv/DgAiDx/7G75RuYrcI3briZT5YNxt/eL+baZEouE19RMl9BXV0db755D2PG/gun0yheFwzuZcOGWzDclIlE7zjMFAiAy+nJKvehaJLtMgmr3fJS18iWgpTRSiJyEbAVuA+4H/hIRD6fb8FCTFdKnZhPxQCZhWXaZcWKFbz77ru88MILPPDAA7zxxhtZj6npJFyS2ypHQgWDpj0buiIyKZeYTaKKdqpP+dT0eisll0kobbLy4rW1tYwctTqiGDppA5ym40VHLeWjI15R9ee2ci6/cJN1z+h0o6pSlf/OEDuhrHdjTNLnKKXOxmj6syAv0hSIfK4ihw0bBsCQIUO49NJLefvtt7MeU2MQ07wnCWaKvrvlG1hNlu4Kv+lxKyWXaSht9dBZTJu2nBmf/Yhp05ZHVuFNTU2UlprUEwIgkLKhkXXnO8Xrr5/Mxk23WYbQNi1dypbPzmDjpGPY8tkZNC1dChRZf26rid57wDpUNZ2oKmeJUZ4jD9gJZd2jlPoo6udtwJ68SBOLAl4SEQX8Xim1MPqkiMwF5gKMGjUqqwflK2u1paWFYDBI3759aWlp4aWXXuK22wqb9diTsFuSG0j4/821Q71l7R4OvfgJgcY2nFWl9Dt/NH0mD8loLDOs7P4dzYl/wqmUXC59RZWVlfg7SnGXJP4/hP0Gycw7ZiarMP5AI7t3d3YHiA6hLX/HQf1PbqPluBYOfzdAYMAn7Dp4A6OeX42vrIj6c9spsgexJTlm3AaLv5X6nmzamNogWbTS7NC3H4jI88AijAn7i8A7eZEmlmlKqd0iMgR4WUQ2KaUiNpmQslgIRihrNg/KV3z7p59+yqWXXgqA3+/ny1/+MhdccEFWY2o6SWdnJyZtWnM1Sbas3UPj4i2oDqPkcqCxjcbFWwBypiDMJtFgh7B7Vez44nB0aTjxjBkz2L17oem5QMAXUQRhBRFevYePh//dsOFHGO7F5IR3AEMWlNByXAtNVwVQoQ1gYIDik/ZHcbmq8PsPJtxbkP7cdovsQecuo+Zyw+yUytGcaRtTmyTbOcyM+v5T4OzQ93uB/nmTKIRSanfo3z0i8hRGfae8GOzzFZY5ZswY3nvPtGeSJgfYqccURgWDqS8K0bR0KXsW3IO/vh5XdTVDbrieypkzLa8/9OInEcUQeV5HkEMvfpIz5RA/yTrpz44VFTRu7RP73GAwYkKL//3N9e6mvmEJhw/fhbvEPD5FKS8bN91GQ8PiGEf6hg0/ZMOGH1BWOiyyk7BKgjPD11aPv76Epus6FUPkmSUKlMLh8MQoUmkXqn2fTf8ls8UsrLa9xXzijzYnff5XqZVKnqu2JotW+lpen5wEEekDOEJtSfsA5wEZFEZJzsHWdj5t8tEeCFJy9Ml88TfT6F9ekuvHWD/T6eCIyrK8P7OnYrbjs6LvoMG2xmxaupT6n9yG8vkA8O/eTf1PDFOglYIINJo/3+p4psS3Fd3ofpXl+/+SoCDNou1yvbuxCkGNZ+euR3FI/FFjox+tKOI6EielrLQa3+faURXmn68/0MRRvqvY3voogf4K5wHou8RB+/pnaApMSaro80J8WK1Vee/2FuNc9PXJdhB5rtpqp2R3GfAN4FigLHxcKfX1PMp1BPCUiIAh49+VUv/M5QMOtraz66CXYChDvD0QZNdB4z8rX5N1rp7Z2xSMVQ6K2Y5vzORT+OD12oxNhHsW3BNRDGGUz8eeBfdYTirOqlJTReCsyq7NbCrCn4FZtFZ8OKvV7qZp6daMlINVCGo8qad8FfdvqvFKGDP2Rj6adYfl4GWl1QR+/i+O2O2Oe5KPbS/9nNYBv8bXVk9JsIqKJU7KXjlsa4eYM6wmfu+BxDBXf4p+1HnEjkP6r8Am4HyM1ftVwMZ8CqWU2gackM9nfNrki0zSYYJK8WmTL28TbS6eWQilVkhS5aCY+Q2GT5iUsYnQX2/utLQ6DtDv/NExq3IAcTvod/5oW8/MFjvRdla7mGCrn5a1e9JWEF3l3FUKAj4nzrIAHc0uPl0zlGp3P9odiT6FTtl2s+u70HeJk/LVneG0rVMCNF24DxX6KNodBzl4IVQedFK+OvUOMafUXG6YmuJ3BdGOaaseEeLsklIgdkJZj1ZK/QRoUUr9GbgIOD6vUnUB7QFzG7TV8a545sHWdjbVH6JuZyOb6g9xsDXRlptMweSKsBy7DnqZducynl67K2djp0smOSiTzpzO3Ace5oePL2XuAw+n5TtyVZs7La2Og2GWqZo9LrJTcFaVUjV7XE6jlZJhJ2cj2S7m0IufpP1Mu85dsW8tSnr/ewsnsXvVEI44eTe72r+ZcqMRGAhNVwVondLp5D48y8RHUWocB2OHuPtH/xUTFptXUlWPTdZruguyv+3sHMLFPBpF5DigARidN4m6iBKnw3SyLnHa0Ze5f6bdHUG+lVq0HArY1ejllsXvA3DJ5OE5eUY6dHUm85Abro/xOQBIWRlDbrg+6X19Jg+xrQyydQzHm9nsmNL6nT+ag0+Yd/nNxDeSLAQ11zjLAgz/TD0DJzbhcNsPTAxP/OHdQ8AiVyz+uB0/U05IVT02l9VlM8DOTLhQRPoDPwGeATYAv86rVF3AEZVlOOKWNQ4Rjqgss7gjv8+0uyOwUl65Umpmcng7AvzmxcK0D893JvPTa3cx7c5lHHXzc0y7cxmvjjiJ6jtuxzVsGIjgGjaM6jtuz9kkEXYMhyfksGO4Za291KGYxD+lOLxvLx+8XsuxZ8/IuEeEeMwzmVvW7qH+zrfZefNy6u98O0bGcNa0kwEoBe2HXTR9NNT42dZT7SMCg45rTEsxhPEPgF1VFSCC08Kva3Y87GfKK6l6QKTbIyLHpNw5KKX+GPr2dWBMfsXpOsKr8Xw7dhsbG/nmN7/J+vXrERHuefD3HDnxxIRn7jiQWJUSEncER1SWxewwILdKzWoHsrsx/ytEMzLJQbG7Mn967S5uWfw+3g7DrBDeJc2ffRKXLKvN/cuQfdhrvJltVJ9J1PQ/m/It/Rg/4Dj8fdpwSynepS1s2fUa4648J/JcK5Q3kOB3sBPd1LilH+89PAp/+xGR+3YsL+W4r+W+EFym5qmOZhcbRw2B7Xvov8QfkxcBIG2Gb8KMZH6mnJCqemyBq8smS4L7ilLqbyLyA7PzSqnf5k+srqF/eUnenbjXXXcdF1xwAf/4xz9ob2+ntbWVqqp+CdelY+YSIWJzdTmE6ipPTt7jYGs7gqBM1n7Dqjwmd+SfdHNQ0gnZ/M2LmyOKIUx4l5QvE1q2Ya/R5rRRfSZxyqDP43IYUTnSASUOY5FQLhX43+1gC4aCSDX+wf8zdoZ9Jg+hZe0eDi7anGDXj1diVv6gjpZS3H1yG8abEgXBIDii5vlwkqASYXP1AD67uhkwTE2BAYRCXGOd1tEk8zNlhFV11mSTfQGryybbOYSza/p2hSCFJh/lDw4dOsQbb7zBI488AkBJSQklJeaTuJ0dQbxfAiCYoz18eGwzxeBxO/nR+RNy86AMSCeTOZ2VefRuaNzhD/nMwVX0DTRz2FnBxuWSlyzjbMNeoxP/avqfHVEMZrgcbtrfbYYrrZ8bIQiNz3zE/sWbkHajmrAZ0WNY+X12rRzImHMPdok/AoyIptUfns0jR36FRmcVA9nHHO/jHLliC41bKwHwuY2prny1tTKIJ5WfKS0yqc5aYCwN1Uqp34uIEziklPpZ/FcXyph3srUDW7Ft2zYGDx7M1772NSZPnsw3v/lNWlrMi5T1Ly9heH9PZKdQ4nQwvH/sjiCfkUpmYwM4RZg/+/iUK+l4232hIpzSWZmHd0PjDn/IjP2v0y/QjAD9As2mlVyzfcen1+5iQVsL3jgFnE7Ya3TBwHJX4g40Ho8Ya7x+548GZ3LbTNDrx9EhlooBwFHuivghZo76HqP6JHYKrK4ci2pz2k1dSCDd5pQrOIMHJ8yl0TMAxMF+GcKf3N/hTTkzck1ZR1yBQht2Kks/U90i64qqVqRq/VmEJPViKqUCQI/v+JZstZkNfr+fd999l+9+97usXbuWPn36cOedd1pe37+8hInV/agZUcXE6n4JpqJ8RCqFw1atxggqZUsx3LL4fXY1emMinAqhIKxW4GbHf3T+BDxuJ585uAq3ip084sNls33H8P2LvK38Ci8NBAmiaCt3pRX2Gl1VtTVwKOX1rUHDlNJn8hD6XzY+aVaapEpZcwpBnz+iaMsdFZwy6PMxCmLc8ZWUTV6FcjdbPMs4GMSdthKw4v/kKtol1ufmd5ewfOq5xg/BIBPqo/wgTicohWvYMJxVVaZjukLVlBMI7wCsKqpakSpstQixE+LypojcLyJnishJ4a+8S9aF5Kv8wYgRIxgxYgRTp04F4LLLLuPdd9/NeLxcRyqFTUnJlIsdX0My231X0+/80Yg79vOwWplfMnk482cfT99As+lY0WaTbN8x+v5X8HMZzZzFYa4u8aZtvgzncYy46tSEd43GH+zgscC+GAXm8LhMTYe2CCiI+1VxOdycMOizkSipypO2oJwmtZaU0Q/6mGPuxjf6PpTqsFy8p+t83od55NqhiiocgSCuYJD3Rg1h2aRRRuRSwPh/8O/eTaC5GXHHmuaShi5nugOwCj/torDUTLCT5/CZ0L/Rb6+AAlSxyg/5Kn8wdOhQRo4cyebNm5kwYQK1tbUcc8wxGY+X60glK1NSGAGmTxzMtDuXsbvRy7AqDz86f0LCTsIqkmlXozflvbnmZTp409XO5R0OhiB0lLsZOnOs5QR8yeThLBw0OGXJdqt3tBvFZff+p9fu4jcvbk75mYV9ZKojaPxHKWhWfpzBDsocZTQHDvP3jn381TOI4S9u5lzcEUd9yh1CmpQ7Kvjh40bSWG3tWMvrpk1bznPbnuPH//oxd2fya6CMiSdeeQxiH/tI/P8d2LgfBPxOY5rzlbh5f6RRY2t4Y2hB4PdDVRWu8nJ7hRYz3QGYVWftwrDUTLATylqcDXVzSD7LH/zud7/jqquuor29nTFjxvDww+k3r4+upeRyGDbhQFBlHX6byhwlAk+u2ZUQ6gmxCXHDqjzsMpn8JHRPsntzSXRoaniT7+lwMp8OLklyn51wWat3tBvFZed+q9BaiP3M4iOyUMbv690d7bzs9AMdxl+2y1Buuxu9pqZTgKahb7Jv3JP4y/bj8g1k0JY5VDZ8JuG6ZDQNfZOtz/8Qf+l+UA6QxOc42vrz3LbnmPfmPIIqSBCrPnHmKBV6T0fsMRG4nEf5o/pujGnJ1dHOaauXEYwr1R50ONhcPaBTOQCqqYlxK9+yJ0imiWkxYak7jBIY0TuOInRK29k5hFuFxhfeK15PSpqEV5X5aNZy4oknsnr16ozvj49Q8gcVDhFGDijPOnzVKnw2TFBhK9TzR+dPiJnUILKYNb0XsLU6TpdMQ1PthMuavWN0FFf8in/6xMG8umlvzM/Rijb6/vAu4KRGH3/Fw0P4eAV/gvzh6/yNbQlrf9UR5Hvi4WV1OOH9hlV5THfGTUPf5NNjH4mYgfye/Xx67CMAthVE/BhIMLS8j7oo6GTV3iNY1novvoARPLGi2cGZFUF7JiRlhKU6S+Ic+WIoiGnyLwAWqavYxyCqfI1MW/EKx2ytMx0uHLkUoSyNnXc2O4CwAugmUUt2qrI+BJRjtAf9I3AZ0ON6XaZT/qAryVWxPrNkPzMzlR3iTSHhiTd6cjRbJUPnajjV6tiMVCaXbEw/qcJlzd4x/HyzFf/fVm6Peecn3t4RMxGKwJyTh8eYexwIQxFuwgN4Iwpid6M3ZrdgNZ8OUsKTVPA/UcrF43Yyf+IwWJloNts37skE/4BytrNv3JO2lYPZGPECBgiyqbSBhpaOyLHFjWWcWWGe+BkjjzKGs8qOFjFKDX1G/sUpzSvZvWpIJHw1mvZ+A2gfPBzlLsHR0ca/G/0cuT30f+T1suWzM+xVZc02MS2Zz6K7KQfgM0qpGhGpU0r9TETuBhbnWzCNQbYRSnZqNtU3evHHJUw4RCwjH81MKZdMHh4zUU+7c5m5qUns7UbisWNyydb0k4r4dwxjtmOJpyPu81UKnnhnB1+ra6Y0ztzjQfgOZbyCYfoYVuWxNAtFI8AROLgZD4KXDVVu5k8cxtg1+01d0P6y/abjWB3P9FqnQ3F5H6GjYRp1/Y1V/rQPAjj7QNCk3lF4rSLS6V9IusEQozCfFe39BtBWfWQkQy5YUsY7p54CEFEQ/t272f2j/2L3j/4L17BhyRVFNolp3ShqyU6YS/ivrVVEhmEU4jsqfyJposk2QilVbkT/8hKOGVbJyAHlCTkWleVuPO5Yy7CdhLin1+6itT2x8b3bKZbhi6lW91Ymox8uei8SjRMOTU1X3mzJtLRIR0Dhbu0wPTckNB2G5U8ncq4M4eZyIweifOWnlkrF5RuY1vFsri0tP8j8+i/x/MYHeGTLHXxr4yn0e9qJ6oid9gMBJx0d7rQillSK7qLtg4fHpk4DAZeLuhNqTK8PF97LS2XWbhS1ZGeGeVZEqoDfAO8CnwCP5VEmTRTZFgi0u/Mwy7EoL3Exf/bxDK/yIMDwKk/KhLjwCv9g3KRX5XHTp8R6ozqsypM0ycxqAg4oFck3CIempiNvLshmZ7LHIqx0DwqnCHNONnYrVpFzykLblrR0UL7jvYiSMWPQljlIINY0KYESBm2ZY1N68zHMcPkG4hBBEI7wD2TIhP+gX/3p9P+rA9XkQinw+fqwZcMU3G5zhWmFhOb9qrFNHPPlLZwwdyPHfHkLVWObAFBuc/lay8stx8xb4b0CF9NLBzvRSneEvn1SRJ4FypRSTfkVSxMm2wKB2ZYmtzKlWGFlYjns8xNI4tuYPnFwUrNRMj9GtFkqXXntkszhXFXuxu2QBNORHRZ5glznd8Ws7r0oHsJHQCn+tnI7f1u5nc/h4mY8lEVN9v5gB/5gB2WuxEmu1X+IGftf50Cf4xnkMFcsYb9CNtFK8WM4OioIurzg6PwdWBE8h3+4vsne88o4wqe49sM2Pt8ApcdeyqF1QT5Z8gXaSgfQ1rQQpQ4ZoS9pRttWjW1i1Nn1Ed9ESV8/o842Cuc1d7SjShI/g/LW5D6PvBTeK3AxvXQQq5VH5AKR94AngCeUUlu7RKo0mTJlioqPCNq4cSOTJlnbIXsLZvWYHCIJpTnMMPsMU0XlWE3gYB7BFMYpYqo8hld5WHHzZxN8DmZjf3znRUnfJ1NSPRsMk1mfEhdN3o6Ez6XS4+aQryOhDpbbKfzmshM4FzeHXvyEjkYfe0KKIexQjuZzuPgOZQxBaPEfZv2B1wBiiu+BoTTe2fcC21s2MrjvZE4fdC6eHOc2JCM6PPatjs/zB/fXaIsy65QFFLeu93FBfQfPHmgh6DQmbn/bRvytL3PC3PfTMyspCHaA0+TXWQUBgba2cj75eDJ79xqFpZ1+P6e8/U6nU9oE17BhjMtTdd5iQUTWKKWmmJ6zoRyOBK4IfQUxFMUipZT1p9rFFKty2Lx5M1dccUXk523btnH77bdz/fXXd6kcmfacjv8M7UySyRSAnfNm14cn/afX7uKHi95LqkTygZVzPR0Znl67i3nPfECj1zCZ9C9389OZx8bsco66+bmUn80nv7rYttyaXkIWdUiSKQc7ZqV/YzT3+bWIjMNo+vMr0sth6ZVMmDCBdevWARAIBBg+fDiXXnppl8uRq9LkdqJywiHuVr+uCmMStTPZQqw9PzyRmuVUhLOxzXIm7GYdW5FtFnRYdqtnhuXLdZMcjSYb7CbBjQYux9g9BID/yqNMBaGuro7a2lqampqorKxkxowZ1NSYRzNkQm1tLWPHjuXII4/M2Zhdjd1JUpHaTGRnlQyGLyKa8AT7s6UfRJze4XF2NXq5/ol1XP/EOoaHlACQ4Mv40f+9x61PvU9Lu3GsyuNm3heOjeQsxCuSVOayMJk4pu3sxqIZfdOzpsfDlWWjCwheNPI7VLgSY/4VKpShnNp2o4Dfefxc7jXKkRxCUYHgsmmmuvisPjR4Ev1bQ1sDLH2jJbKY8AZhgy9AXeVKgq42Bg/extixq3G527LuQx3/QsOuLYn8MOy0RipH+2BeYw4f0jNI6ZUUkVUYeQ0O4ItKqVOVUnfnWzARuUBENovIRyJycz6fVVdXx9KlS2lqMvzsTU1NLF26lLo68wzLTHj88cf50pe+lLPxCoHdyW94lYe7Lz8haVip3bFe3ZSYvAXgSxHzH3Zoz3vmg4SJtyOoIooBoNHbwY/+7z3+++n3TSuvTp84OOFd4hHIKGTWzm7MDlv6jqd24NkcclaggEPOCvo4zUt6K+AOvAmlw83YQ5BF3tZIocCLaWYJ7baL9137YRtlgdhrywKKM/YGmHl2BVPP78vMsyt4fbibyeVOgs42tgwezn1Hf5OvlvyN6+V/WMEZtp5lh9iWoMKeur5FGUZaDNjZOVyjlNqUd0miCPWReAA4F9gJvCMizyilNuTjebW1tXR0xIbPdXR0UFtbm5PdQ3t7O8888wzz58/PeqxCYlZCIp6wAkiWURwe64Yn1qWcYsx2K3YnVG9HwPbE2xFUPLZqR8Jux9sR4NVNe5k/+3hLfwcYE2665qyn1+6ybV6zw5a+49nSd3zk55kohpqs8PegQg5vb8TBfQhFOUJJ1PVeFP9DYq+QabhtF+/7fIMf8PHA+FI+LROO8CnO2OPn2RFufKEsywaP8IvjjNDsj10jeX3CCZFiefsYwoPqeh7kOgaxj8t5lGn8K+VzlTLyHxxRM1ywQ6haErse9rc6izKMtBiw43PoUsUQ4lTgI6XUNgAReRyYBeRFOYR3DHaPp8sLL7zASSedxBFHHJH64iLGbMKPj1aKngCT2dkvmTyc659Yl/KZZjuMfPWztpr4dzd6uWTycG5IIu/wODlTZXSHz+eTh/BxE56YSKVwmCwYpcPDWdgQGw2VLGoqWe6EGZ9v8IeUhMHFZ/WJKIYwPqdw73gXPpkUUQwRRABhH0P4o/ouQEoFEfA52bniCIZN3YO7wk9Hs4vdqwYzYnVsRrdrUFVRhpEWA7Z8DgVgOBBd+nAnMDVfD6usrDRVBJWVifbaTHjssce6vUkpTC7zCFI5pq2ym+36AMCICvJ1BG3tIKz8JGEFlaz6bLycqYoA5sqcZMXUoe9w7rhn+XfZQRy+gQzZMgdfw2mWEz4kKgsr9ljsSIIqVA48UvLCXIl8WmZ+fF+Zi1RTUruUsUhdlVQ5KAU7VxxB49bK2DpLSgGdykHKyhhy061Jn9ebseNzSMgeMTuWY8x+e2L+akVkroisFpHVe/ea26XtMmPGDNxxDT/cbjczZszIalyA1tZWXn75ZWbPnp31WD0Ns3IX4f/4ZNnNZveZ4XE7+enMY2Oypqs8bpyOxF8vt0P40tSRSf0kVs9VGMrATkZ3+HguzUnxTB36Dtcc+ziDPAeNwnSe/Ww/9mF+M/RVS8WQDg/hS/BX+KSdXw97hAuOvpXp/d7gdqzf7wifhTExuphSEqya+0RQmBbfi24V6qyqovqO21MX2uvF2Nk5vAXEd34zO5ZLdgIjo34eAeyOvkAptRBYCEaeQzYPC/sV8hGtVF5ezv799guZ9SZS+SXs3lfpMWrxHGztiKz+h5uYuMI8vXZXTLRTdLTSlCMHWMoT/dxdjd6YkF27Gd3hMiHp5nukw+xxz1LqjPWhlTo7mD3uWVY1nJL1+PH+ij0oHlJ+Xqm/LHLNhio35ROH0rqyIeH+az9s4xfHlSWYluwyiH3JLxBwBIMxvRwcUa1Cq750JdU//WlGz+5NWCbBichQDPPO34Av07mo6wc8pJSamDehRFzAh8AMYBfwDvBlpdQHZtcXaxJcd0d/htZYJcYly+j2uJ3Mn318RLnkiz+e+/9MF+BKwTdfvi/t8dxOARVbWVaAq04bxZQjB8S858Q2J2e3uekXFCoGlPLZoHmJ8eeHOrlvvDtkSsJ8xxBdnjWEU7XzbR5IalZy7gf/3QPYXD0An9tFWYefCfUHGN7YrBVDHJkmwZ0PfBVj1f7bqOOHgR/nTDoTlFJ+Efk+8CJGst2frBSDRlMIUpmNwruMxx57mok7l9M30Iy73wAmNH8tZw71PiVOWtsDkbyS08b055P9Xvb7+jPIczDheuU8guFVnpjdVmOrebmP8Lm+Yyvxj+vHARXE1RZANjUxykfCDu83L26m36ftXOArwRWa05sPtOHt66LcZIdwVMM2rji4jT+f9jmayywK4JkojFRRUkpB36Vujr71Nk4B9iy4p7P95623aTNSGlgqB6XUn4E/i8gcpdSTXShT+PnPA8939XM1GjvY6R0xoflDPlO/DH/AKLftP3SAlxbez6nVn2WVY3TCvWblN5LtQKzMb/UNP2HTplsJBjvlczg8HDPxJlacY7/EyJMNB7hx8w68QSOnxF/qxDN5IF929mX/37bywO83R679eh8XHYFEh/4GX4ATy524oiZ6f7CDt0s+JCAOmkvTSxz0i5uHlNE5zWz3EPAKR3/xN1TOnJmfktu9CDs+h2dF5MvA6Ojre1KbUI0mXVK1DQWj7Wh0X2oAf3sbnzm4irqhY5PeGyYdv8yHqxp4a8lWmg/0ZfCkaxhS8xQBtYey0mrGjL2R6qGzItduXP5qpC2qp6I/Ts8Z+DuOpmJAKafPGsv4qUOZv60eb1y1QG9Qcd/hg/y/lljHtq/F3NG9q0NBa4Bjypx4HEYm9HsHXqVjhOEPqGjzWu8cLAiK0zSkNdgh9N3/OSqvMRRD/U9uQ/mMsN1wjwZA7x5sYqfw3j+BJmANRukMALoiS9ou2ueQH9L9DPNdgqRYqG9Ywratd+Ftq6eprT//t/kidvjO4qxzjuTloI9dbR0ML3VzwnN/55iP3kscQISxNz3Eoic3c9z+IP2UA1eFi89+cTzjpw7NSKYPVzXw6qOb8Ld3Zo67ShxMv2oi46cO5cNVDSxf9CG+Fr9R/dT7MqjoCd2Fq/xcXKWd/993XN7f0hfwk0WJZiu7+Ns28vZkB6vGndC5c8igRsZAtZfftn0fZ4kff2spg/v+ByefcwsAWz47A//u3Qn3xFdafW7bc9z77r00tDQwtM9QrjvpOi4ak5/qvsVIVoX3gBFKqQtyLJOmhxEuQRLONA+XIAF6lIKob1gSMdkIUFV6gG+f+A+2VU/j5w3NkZX2zrYOPj3nEkBxzEexZVj6DhzEMe1Ozj7kwB9amwWa/bz88AbqtzZy9peTx3p07hDaIiv9t5ZsjVEMAP72IG8t2Ur91kbWv9E5Ufp9/4pTDAB+/L5/xSiHytYgTX0SQ3crW+21qI3n/VElvDi5HG9pqF9ElkWT9jOIdXVfp6SkJLQgKcM9oI6amhrLXgzRx5/b9hzz3pyHL2DsLupb6pn35jyAXqUgrLCjHN4UkeOVUvlN5+yhLFiwgD/+8Y+ICMcffzwPP/wwZWX2urh1J/JdgqRY2Lb1rhhbPkAw6GXBbmdC7H+Hy83y086LUQ6uklLOvPJq08kciEzin6zfHzP5h3cU8TuE5gNtCTuGaJoPtMUoBkPgw+YvFzzM+6NKeLXGQ1O5A0+bwhlQBKIcym6/YnqduUM9+t7K1iDT67wcv709cm7pqX1ixsqWijYvXq/xBbELEk91tfnOobo68v29794bUQxhfAEf9757r1YO2GsTegawJlQEr05E3heR3FWk68Hs2rWL++67j9WrV7N+/XoCgQCPP/54ocXKC/kuQVIs+NrMV6T7VJXp8cMVlfQdNBhE6DtoMOfN/T6TzpxO8wHrntDr39gdOR+e/D9cZeQLvLVkK2uHurjv4kruuLw/911cydqh9godvD+qhPsuruQ3376D31/1QzYcHau0N4w7ledO6WPsFkTwljlQgMcXBKWobAlw8ZoWanYmtvF8f1RJzL1NfZw8d0of3h9lVEB9tcaTnWKIM3+7An6mbksMYAwvSIbccD0StwiTsjKG3HB95OeGlsQcjGTHext2fqs+n3cpioCwHdnXVm/qwMsUv9+P1+vF7XbT2trKsGHDciBt8ZHvEiTFQllpNb62xBXpIGlkHwMSjg8vLeGcq+fzxqLNtLUEWPao4s2n36C0j5O2FnvlM8LmofFTh/JWheK5U/rQ4TIm2vAkDERW6WaEJ+/wfYf69ufFsy8BCO1sXCyfel7kfJigU3C3dXDjos6yGspkjn+1xpNwb4dLePq0PpHdRKa4/Ypjtx/ko2FlNJd6qGjzMnXbB4zbu8v0+qampojTOSaU9YbrY5zRQ/sMpb4lUdkP7ZOZ36enkfJ/LNTsZyTw2dD3rXbu606E7cjGH73C17abTZtupb5hSVbjDh8+nBtvvJFRo0ZRXV1NZWUl5513Xm6ELjLyWYKkmBgz9kYcjtjwS4fDww3DAnjiynJ4HMLXgh5e+cuGGEXga/HT3ppeXaXwTuK1E8tNJ+FXa5KHhJpN3n53CcunnguOvrjKz+VQH/OqOIc8bnxln3YeMIlhsZz8Q7uIjFCKCl8rN3squfQ9B195s5bvvLGEr6x6yVIxQOeCpHLmTMYtq2XSxg2MW1abEKV03UnXUeaM3V2UOcu47qTrMpO3h2GnttJPgZuAW0KH3BhZ0z0GKzvytq13ZTXuwYMHWbJkCR9//DG7d++mpaWFv/2tR310EWpqapg5c2bnH2ZlJTNnzuxSf8OHqxr4849X8MB3lvHnH6+ImGJySfXQWUyc+AvKSocBQlnpMCZO/AXfmHAud00YyYhSNwIcIQ6+8J4X/vYJykQPpNvZsWKAMXE3mTTOgSSTc4rzhyqqGHTU93GVTrJ0NFe0eWmp+CTp+Cmd1CLpvXQwyIyNq7l20yquPWMM06+aCI7UCjWdBclFYy5i3mfmUd2nGkGo7lPNvM/M0/6GEHbMSpcCk4F3AZRSu0Wkb16l6mKs7MhWx+3yyiuvcNRRRzF4sNHNbPbs2bz55pt85StfyWrcYqWmpqZgzmcrRy2QcXioFdVDZ5maHOcMHcCcoQNMw0rtIA449oxhbFrZkHBvR5ufD1c1UOV0cDCQOG5pRzu+sk8p85mXhbeKPKryKa755TQe+M4yzl7zEc9PGxtTMjts2w86E30k0Q5oFMbknyoCyc41gAOF0+VixpnGRD9+6lAq3zQ3XYoISqmMwqcvGnORVgYW2FEO7UopJSIKQET65FmmLsfKjlxWWm1ytX1GjRrFypUraW1txePxUFtby5QppiHFmixJFsqZK+VgFkJqNrZVJFIqVBDO/vJEqsdWRXISwrS1BHj54Q34ZlVBWeIuQICWik8slcP0Om+MzwEMW/5/9qsCwOX+iAkbnqel//mR/INo274jUIrP8yktfT4h6Gzjo0GjeW1iDX6Xo1OAyIskUQA2w1eDDifrJk6OmehnzJgREy4Nxk6hq3eovQU7ymGRiPweqBKRbwFfB/6YX7G6ljFjbzQtNzBm7I1ZjTt16lQuu+wyTjrpJFwuF5MnT2bu3LnZiqsxwSr6J1lUUDqkszPJ9Jlh09H4qUN5a8lWMMk69paaT64+dwlBZxvHnTUsEgYrDkPhVAwo5UujB+KqO8DLE0ppKndQ5VVcEWxj7c4NHH24geZLT6Fv60Q+s245X3nrBbYcMYpVY46ldtIUlk08GSUSUhbNjNu7i5VHj+9UDPFkqRjC7I3TrzU1NWzfvp01a9aglEJEOOGEE7RiyBN2OsHdJSLnAoeACcBtSqmX8y5ZFxI2EeQjWulnP/sZP/vZz7Iep9gpdHZ0xYBS00k5POFmSzo7EytZUnH6rLGR763uF2UeLSRKUepyc/aXJ3J26Fi4RMa+bftoaxrEtVdezf1nGglodXV13PXWeywbe3zEjHS4Tz9qp57HpK3r2Xh053EVmtSby8p5fcJk43s7NZFMqqqmQ992L/PmzaOyspJx48bxwQcfRHIajOEV7733HqNGjdIKIg+kVA4i8iul1E3AyybHegxWdmRNaoohO/r0WWNNy0dET7jZkM7OxEoWhSLQbu2UjVYyVgrGTDEYx4WHTzqL+15dx/BSN9e0HyT4v/dHajsd3reXlxbeD8CkM6dTW1vLmxOnJrTk9LvcrB9Xg3KYRxj5nS7+dbTN/9N0lEKcKcoV8HPKR+sB4/cpvjxOmJ6YaFks2AlJPdfkWK/IfdDYI1l2dFcxfupQpl81MbJTqBhQGqkrlAusdiBmx81kmXjaUFPFEE5Mu+Py/kx8/l2+88t/8ecfr2D0cQNxlST+eSaLCjpU3heFUbrjF8E+vHDK52LO+9vbWP74XwBjwrVa/StJPi20uUuyLn0RjyfgpV+7UZKkX7uXszevTRquGk1PS7QsFix3DiLyXeB7wJi4jOi+wIp8C6bpPhRLdvT4qUOzVgZWTud0dybxsvz5x4l/MvGJaY0eMRLa3mnBt7KBiacNjfgPwkyv8/L0aX0SJ2eTn9cddxrDP90RU77j8P59fLiqAUegNKOKqPnA4xDuOn4ic4YOoK6ujsWLX0zr/lSJls8++2yMn+Lkk0/m4osvzkbkXkEys9LfgReA+cDNUccPK6UO5FUqTbeiq7Kjk0UL2Y0kSjV+Kqdzps8wMxFZZRW/WuPh+O1NfLJ+P9f8choAf/zh67S1BDh+e7uhHOwgwvKp58Yoh74DB/HWkq30aR3N1K0beX3iCbGmpRShpq6AH5TC73JbXmMHJ0aJ5xGlbm4ZUx1RDEsz6MGQLK/h2WefjTFJKaUiP2sFkZxkzX6aMEp1fwlARIYAZUCFiFQopbZ3jYiaYscqxDCX2dHJJm4gJzkOqZzO2exMKgaUsq91Oy0VRiioI1BKU7l5seOmcgf+to3s+/hf3H3lnfQdOIijT5nFlner8LcHLXMWzDhUURX5ftP4ybz92Uv4VDnwtE1ASRC/wxmrEJIoBgkqLnZXsCToSzypFKCMZA0LPA7hrgkjmTM0scwImJsnU+HxeJL6G9asWWN5XCuH5NhxSM/EaBM6DNgDHAlsBI7Nr2ia7kL4jzOf0UrJJu7w92bn0pnM8xkOO3RKkI/f2wJiyBl0tVmadfq1tOFvfRkwQlkP79vL+7V/5fgZ/8GODwdz9K4O1oxz2LL7Vx5uBKXYctRxvHjmTNpxgoC3TEi7Co5DWO0OoMw+DhEq1GHaVQntElWSIhSxVOHzcdFHwvFl7WDxX5LMDHnUUUfx8ccfxxxzu918/vPJ3Z9W/WqUUgWPsCt27OQ5/Bw4DXhFKTVZRKYT2k1oUnPvvffyhz/8AaUU3/rWt7j++usLLVJeyHd2dCYTd/OBtrTMTfkMh93w79URxRBm1L56NgwfEzPJu/2KM1e9RFgxhPG3t/HRO0sY+JO72bB5B0R1aBNgvKeED73tMWWPStva+P4//sbn6rZx5eXX0+4uyeodhpe62dVmvbJvpoJvdvye/+Mqmtx9YwvkKRj86Vk8t/11nn51B62+5oQJOZl58pprrsloMg9nT5sdL3SEXbFjRzl0KKX2i4hDRBxKqVdF5Fd5l6wHsH79ev7whz/w9ttvU1JSwgUXXMBFF13EuHHjCi1at6NiQClvVaiEfgGnN4di8E0m9dI+zrTMTfkMh42f9LYMHs6H1UfGrv6VomZbG8dsedt0jMP79rLQpHWnAlqCivsnjWL+tnp2+doZcmAfp72/hj/OupJffu1aFNlFF7kDilvGVDN/Wz07LRREZaui9J2j+JIsSzjnCJTiK/uUw+VbwGd8vvETcirzZCYLkJNPPtk0DNblcvWK/iPZYGdf2SgiFcAbwKMici/xy5oewJMNB5jy5gdUv7qOKW9+wJMN2fvcN27cyGmnnUZ5eTkul4uzzz6bp556KgfS9j6aL6w27RfQfGE1p88amxD26SpxIEhSU1Q8+QyHjXfOrxpzbEKOASJ8enxfo/+DCeLoZ7ly39XWwZyhA1j9mWPZ1LKTbz6/mH+ePp1PBw42QlMzDT1VCgkqara2MWfoAG4ZU42ZK9otwi+nHMWsORcmVOcl6KBP82ijeJ8jrmZUVMhzuHijx9MZYuty2etVYcXFF1/MlClTkND7iwhTpkyx9G3osNhO7HzyswAfcANwFVAJ3J5PobqaJxsOcOPmHTEtHm/cvAPA0nlmh+OOO45bb72V/fv34/F4eP7553VtpQx52OE1jex52OHl2qljgMRIopcf3mA6VjJTVC7CYcM82XDAWMm3dTD4pOmcuGktY+r/bchgkWOwq62DM6+8mucfuDehx7OzbBqV3iCNJpVZh5d2TsiVM2fycHk1bY7sJlYARFACdWNLebLhQOTv4b8/3BkpANjf5eTn44Yb50Lna2traWpswhEopU/zaADT4n3QOSHX1dXxwgsvxGRBe71eli5dyvbt29myZUva/oG6ujq2bNmSUJgvPFY8fUtKjP7TFj0gehN2yme0RP345zzKAoCIzAO+BewNHfqxUur5fD5zvslW3RtUzN9Wn5VymDRpEjfddBPnnnsuFRUVnHDCCVmvhHorVqaM8Eo6flL/cFVDpLZQPLkqqZGM+AXHniC8Pn4yyu9g7J6PqfD5aPYkKojhpW4mfWY6L/1pA/7WZUBoQhXj9+acda28OK1vzO+rxyHcMia2SGR9LhRDFB1Oifw9hL+sCJt/whFmzY56DvfbgpVly+Px8Ktf/SpGKcQ8u6MjxjRk1z9glrm/ePFitm/fbmrCcolw7Io3I+1F/bt3U/+T2wB6pYKwNCuJyGEROWTydVhEDuVZrgVKqRNDX3lVDEDSrXq2fOMb3+Ddd9/ljTfeYMCAAdrfkAFPNhywtJhHr5jDhCclM8WQy5IayfjvD3cmLDjagFXHnMBjp86kuawsob9BeJJ/suEAD86Zwm++/d+d7TyVD3/ry0yu3xDpGwFGvkB4IRNtCjX7XLIlnb+Huro6nnvzMeoHvMbhys0J5qQwDoeD9vZ2S8VghZ0MfKvQ2LCiie8/cuqGjRy5NdbkqHw+9iy4Jy3ZegrJ8hx6VM+GZAwvdZuuTHPxB7Znzx6GDBnC9u3bWbx4MW+99VbWY/Y25m+rN2s+hkDCihmsS2aLg5yW1LDiyYYDpj0XwKheSXlUjkIoxyCcDAYYO45QV7ZDffvz/DmzWTbtIrxl5VS2HOaXGO+dzBQafz6e/k4HvjYfXmfod9yGT8Lu30P8ij2ZL7y0tDRtxRDGzCwUHdGUjNraWm644YaYncfGSceYXuuvz66vS3elWG0c3xeRq4HVwA+VUgfjLxCRucBcMPomZIPZH5LZVj0T5syZw/79+3G73TzwwAP0798/6zF7G1YrVoW5T8jKp6CC5lFK0b6B4VEZu1ZEXz9EHJxT18r4Da0RX8f8wH57LwYRxbD6M0ba0JQ3P0iMRnK58IbMkU0V/bhx8w7KRJKaQiO+gS27OOiP7aDmcQg/Hz+CcXt2snTpUmpHTWTjsKMi1VfNFEU6fw92k9msQlftEu/kT1BKSTB7rqu6OmJSij/eGymIchCRVzBPhbkV+B/gDoy//TuAuzF6SMSglFoILASYMmVKmk0XYwn/IaUzQdhl+fLlWY/R27Ha2Y2wWMlaVjQddIAFCxbEODW3DBmRVjBCvC/hUxVk8aRSLmr2c/x2I1R216VVab3fzrYOprz5AbeMqbZluvEGFV7TvVTnWOHf45+PGw5Y/G6H3q+8tpaztr5vTLbTpvOo3/i840tcjNuzkwWP/TmlU9jOhB8OUbWzyk92fzTpZFiblXYZcsP11P/kNpSvMwNcysoYcsP1acvXEyiIclBKfS71VSAifwCezbM4ACmdbJrCke7Ozixfob1iL80lWwg0GRFAYafmE2dchDfOAhS9Ao/fVbQEggkr9s56SO1GeQuLiCJRqnN1HkdYKVW5nAkr/XQJK9LwmHdNGBnZmcRjljtwg8l16ZRlt9oRWLXztLva93g8eL1eS8VkV8lYlXYJO533LLhHRytRhGYlEalWSoWNfJcC6wspj6bwpLuzMyuS92m/HQR8sek5HR0d7AmYF5rb1dZhGuJsRVN5pzIwiyhy+40Et7oxpQkhuWG8QUWZKDyORJNRpniDilve28z7D72YVYmIZGXZ48dLp51nfOkVj8dDW1sbwWCsxp4yZUrKWkjJzFSpFEtkjJkze60yiKfolAPwaxE5EcOs9Anw7YJKoykK0t3ZxYe2zptn3rywtKOdtpLE0NYqp8M0xNmK6D4LpzcL504YGVFmld4g56xr5fjt7Yzc749keZsppcZAsDPTua2DKqeD5qCiQ8XumlxBxWFbksEht1HrKJsSEemUZU+31paZggBsTebRzJgxg8WLF5ueKykp4aabjP5kdXV1CeZFnRWdSNEpB6XUfxRaBk3Pw2pV6XBY9zu2G7rp9ium1xkRN+FQ2fFRyuyB73SWkzh+ezvHb2/nvosrTSurDi91JyjCeNPWVa4O1q5dG9PiMxkVbZ3RQJmWiEi3LHs6pS7iTVZerxe3283s2bPTkrOmpsZSOUQn2umaSvZIsyyjRtM9mTFjRkJZB7fbjc9lXoyu0R+wDN3s73QwotSNAEeIg9kb2zh+e7tluQ2zpLvpdV7cAfM8h3jCZTFeHOjgqlUv0fT804yp/zdnb15Lha8VlKLC18qJe3fgiVN2roCfqds+iDmWiQPY6vPLRVn2XHYStFJW4ePF0LWwu1B0OweNJh9YmTqeanZa5rhYOcJ/Pn5ErInrnOTPNnOQT27wc3xZJQ87vAl+FLNKsj7PngQ7/ri9uxJaaX7rrGmRXUbfdi+nfLQ+4ZpMmjDlsyx7LjsJpireVyxdC7sDWjnkkcbGRv7+97/zve99r9CiWPLII4+wevVq7r///kKLknfMTB23xDmdoXMFn6sQ52Rd5K4NXRM2HX1/43b6tQaYXqE4/kBnJdmmYatTRvRUVlbGmKTq6upYunoP0Xc5nU7a29uZN29e2hN8vsqy57KTYColFnZM5+JZPR2tHPJIY2MjDz74YIJyCAQCOJ32OnmlSz7HLgbSTVhLRSoFkKsQ52QF/R741zbu9DXR4TRMQk3lRsVZIBIe2+ptTpppbGbiiZ8oRYRAIBCZHIvF3p7rToJWSqyuro729vaE4w6HI6ddC3sKWjnkkZtvvpmtW7dy4okn4na7qaiooLq6mnXr1rFhwwYuueQSduzYgc/n47rrrmPu3Ln8z//8Dx9//DG//vWvAWNlv2bNGn73u9/xt7/9jfvuu4/29namTp3Kgw8+iNPppKKigh/84Ae8+OKL3H333Tz77LM888wzuFwuzjvvPO666y6WLl3Kz3/+c9rb2xk4cCCPPvooRxxxRIE/ofTIV/XcXCmATBTXh6sauK/pIB3lsQo9OncCjH4IQZd55neyHUCqXIJ89jCw25wnG5NVOg2AamtrCQQSc0hKS0u1M9qE3qMcMq1nb4VF+8Fo7rzzTtavX8+6det47bXXuOiii1i/fj1HHXUUAH/6058YMGAAXq+XU045hTlz5nDZZZdx+umnR5TDE088wa233srGjRt54oknWLFiBW63m+9973s8+uijXH311bS0tHDcccdx++23c+DAAb7xjW+wadMmRITGxkYAzjjjDFauXImI8Mc//pFf//rX3H333bn9TPJMvqrn5oJMFddbS7bS9LnEVqEQmzsxgHE0uTfbyh2IJ1XmcK7s7dETtcfjob29PTIZp9qlZGKyShZ5BInKxuo9M63t1NPpPcqhCDj11FMjigHgvvvuizT/2bFjB1u2bOG0005jzJgxrFy5knHjxrF582amTZvGAw88wJo1azjllFMA4xd6yJAhgGFHnjNnDgD9+vWjrKyMb37zm1x00UWRxKGdO3dyxRVXUF9fT3t7e4wc3YV8Vs/NlkwVV/OBNipby0zDWsO5E64SB9NnnoHPMz6j1XWqyT8X9nazcNR4cr1LsYo8Wrx4MU6nM0ExaX9DevQe5WBjpZ9v+vTpE/n+tdde45VXXuGtt96ivLycc845B1+opssVV1zBokWLmDhxIpdeemmk7MA111zD/PnzE8YtKyuL+BlcLhdvv/02tbW1PP7449x///0sW7aM//zP/+QHP/gBX/jCF3jttdeYN29el7xzLsmmeu7G5a+y/PG/cHj/PvoOHMSZV17NpDOn50y2TBVXxYBSptd5ee6UPjGZ0+Hcidi+14bPIqwgojuoJSNZ5nA+w1HNyGVUULKx4s1HHR0duFwu3G53znwbPR2d55BH+vbty+HD5nmsTU1N9O/fn/LycjZt2sTKlSsj52bPns3TTz/NY489xhVXXAEYTrt//OMf7NmzB4ADBw7w73//O2Hc5uZmmpqauPDCC7nnnntYt25d5HnDhxtF2P7857z3bMoLt4ypTojjt1MtdOPyV3lp4f0c3rcXlOLwvr28tPB+Ni5/NWeyWSmoVIrr9Fljmdzg56J3WqhsCYBSVLYGuNlTyUM/PoNrfjkt4sgOr87Dk2J4RVxXV5f0GWY5CmBE7tgxS9nB7qTv8XhYsGAB8+bNY8GCBSllT0a6K36v15vQwyFX798T6T07hwIwcOBApk2bxnHHHYfH44lxAF9wwQU89NBD1NTUMGHCBE477bTIuf79+3PMMcewYcMGTj31VACOOeYYfv7zn3PeeecRDAYjJcCPPPLImGcePnyYWbNm4fP5UEqxYMECAObNm8cXv/hFhg8fzmmnncbHH39s+z3M4u7z3RPBjExDS5c//hf87YYzd8PRNSyfei6HKqp4sOkwv4xqfZkO8c7nGQP7sqjhYNpl38OfY9mSrRz/bFPSzzeZGeW+Ne/z9phj2Rsk4XPJZ45CmPDuNhVerzdn0VJmUU7JqKyszFs4bk9E7PyHFjtTpkxR0W0EATZu3MikSZMKJFHPYOPGjTgP9U9I4HKVOLqkaY5dUkWs3H3lTFCKDUfX8OLZl+B3d2ZFexzCXRNGpqUg4p3P4XEuH9qf2v2Hc172PayIdvraqWjzMnXbBzGJbVsGD+f1CZNjSmlk8l7ZkI2ZsrKykhtuMKsFm5q6ujrLkhnR2HXe9zZEZI1SyrSxvd45aJJi1lXN3x7krSVbi0I52KmV03fgIA7v28vyqefGKAbILNrJyvlcu/+wZWnsTIlRRCI0l5Xz+oTJABEFsWrMsQk1lvIRxZVMCadKZEtmdsrGD1FTU8MLL7xg6mi2KhGusYf2OWiSYtVVzep4V2OnVs6ZV16Nq6SUQxVVpmOkG+3UlVFTZorI73SxakynEmou9eRdnlT+jmS1l+xM/tn4H6xCUZVSzJs3L6EdqMYePVo59ASTWaEIf3ZmReOSHe9q7NTK2TDuBP709R9bjpFur/BMnc+ZYDXBRyuE6Kqr+ZInlRKuqamxdPbacRzbda6bkarYniYzeqxyKCsrY//+/VpBZIBSiv3791NWVsbps8biKon9NQmXpS4GUk0MYbPMpziz7o0cJtOoqXSpq6szqq6aMMQpkZX61G0f4ArENjLKtTx2lHBNTQ033HBDZLUORPom2CHT6qj5rBjbm+mxPocRI0awc+dO9u7dW2hRuiVlZWWMGDEC91HGH10xRCuZMWPGDO56aw1vjppAc6mHijYvn9m+mRtPPxkwN8tEU+ZIf32Uz57j0dTW1nJqSUWCs9kVDPDTSUcxrv9MamtrGbd3F57ycstopVyQbnG8eF+QXTLxP3RFNFZvpMcqB7fb3S2zgIuRZEXjCs2WISN4fXyQsAekuayc18dPZtaQEdSQ2u5+0B/IqD5TsnpM6dT7saKuro6mpibGYUyWq8YcG1F+U7d9wJwZJ8PQAV02AaZbHM9uUlw8mZqCdIhq7umxykHTO5i/rZ5413hb6PicoQMss6qjyWVkTy46jYXHCBPft6EQtnSz1fm4ceOora1l8eLFCUowkx2ANgUVF1o5aLo1qSKHzBr2pDNOuiRz3NpVDslW3YWcQKNX53V1dTz99NMEg0aYc1NTE08//XTkumQlO8zQpqDiQysHTbcmVb2leP+AA0gs2py7yJ5cdBpLdm2xJHItXbo0ohjCBINBXnjhBWpqatLKXu6Odb56Az02WknTO7ATORTuwVw//UTumzQqr5FGuQirTDZGMSiGuro6y0k/nHMQH9oqFiXzdbhp8VIQ5SAiXxSRD0QkKCJT4s7dIiIfichmETm/EPJp8sOTDQeY8uYHVL+6jilvfsCTDQeyHnPO0AHcNWEkI0rdCDCi1J20bES616dLLsIqiz000264aXRo66WXXlrU76RJpCC1lURkEhAEfg/cqJRaHTp+DPAYcCowDHgFGK+UMrMERDCrraQpLqzqEXVV/Z9cRBCZjRldusHj8fD5z38eyD6sMh/y5opkZqBkJSuK+Z16K8lqKxW08J6IvEascrgFQCk1P/Tzi8A8pdRbycbRyqH4mfLmB6a+gRGl7pzXI4rHLOY+m0Js8UohGofDwSWXXNKjJz27iW35KHanFUxu6U6F94YDK6N+3hk6loCIzAXmAowaNSr/kmnSJrqstdUSpCu6uKUbQZRsAkqV3BV2yvbkCcyusznXnd9yESassU/elIOIvEK4dVUstyqllljdZnLMdF5RSi0EFoKxc8hISE3eMDMjmZGPekTxpBNBZDYBLVmyJLJTsNO3IJc9CzIh36trs5yHXERppSIXYcIa++RNOSilPpfBbTuBkVE/jwB250YiTVeSqmwF5KcekRnplH4wm4ACgUBkss/EDNuVE5jd1XW2CiQ+I9nK1JTLaKSuUECaTorNrPQM8HcR+S2GQ3oc8HZhRdJkQjJzkZCf+j9WpFP6IV8TjZ1xc7Hit7O6Tsc8Y1cmu59xNu+Ybn0nTXYUKpT1UhHZCZwOPBdyPKOU+gBYBGwA/glcmypSSZM/sgk9tTIXjSh1Uz/9RFZ/5tgu61KWrJx0PNlMNB6PB4/HvLdCqnEz7Q8dj53VtZ0eGOnKZOczzvYdiz3Et6dRkJ2DUuop4CmLc78AftG1EmniifcZ7GzrSKtAnVnZiq4yI5lhtzCbXWdrkCAOHBEfhIjg9XrxeDw4nU4Cgc41TaoJrK6ujqeeeirBZJWJOcrO6tqueSZdG3+qzzhbn4Guvtq1FJtZSVMkWLXCtFugrqvKWuea+Amow9mBI+DAiTNyjV/87K3ay6jmUZHJLjyxe71eHA4HHo8Hr9ebcgILr6atfBlWTnOrCdKOeceueSbXNv5cjKerr3YdWjloTMlFK8xkZa2zJTpM1o7iScfWHT0BPbftORb+cyHj94+nPFBOq7OVDwd+yNSWqbR3tJveHwwGKSkp4aabbkr5HqlKW8ebqVL5C+ysru36B3Jt49c+g+6FVg4aU1IVtCsk6Zq8somPv2jMRXAB3PvuvTS0NDC0z1CuO+k63vnLO0nvs7saTnVde3s7dXV1MZO+mWnmqaeeiimdHe7EZoZd80y6PRxSkevxNPlFKweNKcXmM4gmXZNXtrbui8ZcZCiJKD6s/DDpxG53NZyqtHUgEIiR0+rasFnKruKzY57JtY1f+wy6F1o5aEwpZp9BuiavfMTHz5gxg8WLFyc9b3ecVA7waDnt9EnIZV5Frm382mfQfdDKQWNJPn0G2ZCuySsftu6amhq2b9+OWU2vKVOm2J4A41fTZkTLaTeaSieGabJF93PQdDvs9HCIJl/x8RdffDGzZ8+Oie2fPXs2F198cVrjhEtbz549O6Wcuk+CpqvQOwdNtyNdk1c+bd25NJPYlTO+Xad28mryQUFLducKXbJb0xuwCsfVZaw1mdKdSnZrNBoT7OY3aDS5QvscNFmTj/afmljs1kPSaHKF3jlosiLbGkxdRXc3vehy1ZquRu8cNFmRLCGtWDCrBrp48WJ+9atfpV31tFBYRR/pqCRNvtDKQZMVuajBlG+s6hd5vd6MymIXAl2uWtPVaOWgyQqrxLNiqMEUJpnppbvY7dPpSaHR5ALtc9BkRTHXYAqTquREd7Hb66gkTVeidw6arJgzdAB3TRjJiFI3gtHp7a4JI4vKGW1mkolG2+01mkT0zkGTNcVagykal8tl6nfQdnuNxhytHDQ9GrPyEmG6Y0irRtNVaOWg6dFYRSpVVlYmbYij0fR2tM9B06PRyWMaTWYURDmIyBdF5AMRCYrIlKjjo0XEKyLrQl8PFUI+Tc9BJ49pNJlRqJ3DemA28IbJua1KqRNDX9/pYrk0PQydPKbRZEZBfA5KqY1g3ahEo8kVum+xRpMZxeiQPkpE1gKHgP9WSi03u0hE5gJzAUaNGtWF4mm6Gzp5TKNJn7wpBxF5BRhqcupWpdQSi9vqgVFKqf0icjLwtIgcq5Q6FH+hUmohsBCMZj+5kluj0Wg0eVQOSqnPZXBPG9AW+n6NiGwFxgO6zZtGo9F0IUUVyioig0XEGfp+DDAO2FZYqTQajab3UahQ1ktFZCdwOvCciLwYOnUWUCci7wH/AL6jlNJtxTQajaaLKVS00lPAUybHnwSe7HqJNBqNRhONKNX9fbkishf4d6HlyDGDgH2FFqJI0J9FJ/qzMNCfQyfZfBZHKqUGm53oEcqhJyIiq5VSU1Jf2fPRn0Un+rMw0J9DJ/n6LIrKIa3RaDSa4kArB41Go9EkoJVD8bKw0AIUEfqz6ER/Fgb6c+gkL5+F9jloNBqNJgG9c9BoNBpNAlo5aDQajSYBrRyKDN0IycDqcwidu0VEPhKRzSJyfqFkLAQiMk9EdkX9HlxYaJm6GhG5IPR//5GI3FxoeQqJiHwiIu+HfhdyWoOuGEt293bCjZB+b3Juq1LqxK4Vp2CYfg4icgxwJXAsMAx4RUTGK6UCXS9iwViglLqr0EIUglDttQeAc4GdwDsi8oxSakNhJSso05VSOU8I1DuHIkMptVEptbnQchSaJJ/DLOBxpVSbUupj4CPg1K6VTlNATgU+UkptU0q1A49j/E5ocoxWDt2Lo0RkrYi8LiJnFlqYAjEc2BH1887Qsd7E90WkTkT+JCL9Cy1MF6P//2NRwEsisibUAC1naLNSAch3I6TuQoafg1lv2R4Vj53scwH+B7gD453vAO4Gvt510hWcHv//nybTlFK7RWQI8LKIbFJKvZGLgbVyKAC6EZJBJp8DxkpxZNTPI4DduZGoOLD7uYjIH4Bn8yxOsdHj///TQSm1O/TvHhF5CsPslhPloM1K3QTdCCnCM8CVIlIqIkdhfA5vF1imLkNEqqN+vBTDcd+beAcYJyJHiUgJRnDCMwWWqSCISB8R6Rv+HjiPHP4+6J1DkSEilwK/AwZjNEJap5Q6H6MR0u0i4gcC9PBGSFafg1LqAxFZBGwA/MC1vSxS6dciciKGKeUT4NsFlaaLUUr5ReT7wIuAE/iTUuqDAotVKI4AnhIRMObyvyul/pmrwXX5DI1Go9EkoM1KGo1Go0lAKweNRqPRJKCVg0aj0WgS0MpBo9FoNAlo5aDRaDSaBLRy0HR7RKQ5xfkqEflels/4qogMy2aMJGN/R0SuTnHNIyJymcnxc0QkrUQ4EfmniDSme5+md6GVg6Y3UAVkpRyAr2JUgc0pIuJSSj2klPpLrsdOwm+A/+jC52m6IVo5aHoMIlIhIrUi8m6oxn24WuedwNhQzfvfhK79kYi8Eypg97PQsdEislFE/hDqJfGSiHhCK/YpwKOhMTxRz5wkIm9H/TxaROpC398WesZ6EVkooWwlEXlNRH4pIq8D14V6NNwYOvet0D3viciTIlIe9YqfE5HlIvKhiFxs8v59QsX43gkVaDStVqqUqgUOZ/xBa3oFWjloehI+4FKl1EnAdODu0IR8M6FeGEqpH4nIeRhlN04FTgROFpGzQmOMAx5QSh0LNAJzlFL/wKhhdVVoDG/4gUqpjUBJqKQJwBXAotD39yulTlFKHQd4gOgJvUopdbZS6u64d1gcuucEYCPwjahzo4GzgYuAh0SkLO7eW4FlSqlTQu//m1BZBY0mbbRy0PQkBPhlaOX+CkYp5yNMrjsv9LUWeBeYiKEUAD5WSq0Lfb8GY0JOxSLg8tD3VwBPhL6fLiKrROR94LMYDYrCPIE5x4V2B+8DV8Xds0gpFVRKbcGoqzXR5L1uFpF1wGtAGTDKhvwaTQK6tpKmJ3EVRi2mk5VSHSLyCcYEGY8A85VS8V3mRhOqfBsigLHiT8UTwP+JyGJAKaW2hFb1DwJTlFI7RGRenCwtFmM9AlyilHpPRL4KnBN1Lr7WTfzPgrHT6fXNojTZo3cOmp5EJbAnpBimA0eGjh8G+kZd9yLwdRGpABCR4aF6+MmIHyOCUmorhiL5CZ07grAi2Bd6TkKkkQV9gXoRcWMou2i+KCIOERkLjAHilcCLwH9G+TYm23ymRpOA3jloehKPAkvFaLS+DtgEEGqQtEJE1gMvhPwOk4C3QvNoM/AVjAneikcw7Pxe4PRov0OIJzCigI4KPbMx1G/hfYzqqe/YfIefAKuAf4fujVZIm4HXMUxl31FK+ULyh7kDuAeoCymIT4j1cwAgIssxTFIVIrIT+IZS6kWb8ml6Cboqq0aj0WgS0GYljUaj0SSglYNGo9FoEtDKQaPRaDQJaOWg0Wg0mgS0ctBoNBpNAlo5aDQajSYBrRw0Go1Gk8D/B22HzhXeMumMAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2359,7 +2493,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x864 with 10 Axes>"
       ]
@@ -2369,36 +2503,7 @@
     }
    ],
    "source": [
-    "# Scatter plot\n",
-    "for i in range(10):\n",
-    "    idx = ds_train.y[:1000] == i\n",
-    "    plt.scatter(res[idx, 0], res[idx, 1], label=i)\n",
-    "plt.title('the latent space')\n",
-    "plt.xlabel('latent variable 1')\n",
-    "plt.ylabel('latent variable 2')\n",
-    "\n",
-    "# change these numbers, to change where we travel\n",
-    "y=1\n",
-    "xmin=-5\n",
-    "xmax=5\n",
-    "plt.hlines(y, xmin, xmax, color='r', lw=2, label='traversal')\n",
-    "plt.legend()\n",
-    "plt.show()\n",
-    "\n",
-    "# Do out traversal\n",
-    "plt.figure(figsize=(12, 12))\n",
-    "model.to(device)\n",
-    "n_ims = 10\n",
-    "xs = np.linspace(xmin, xmax, 10)\n",
-    "for xi, x in enumerate(xs):\n",
-    "    plt.subplot(1, 10, xi+1)\n",
-    "    z = torch.tensor([x, y])[None :].float().to(device)\n",
-    "    img = model.decode(z).cpu().detach().numpy()\n",
-    "    img = (img.reshape((28, 28)) * 255).astype(np.uint8)\n",
-    "    plt.imshow(img, cmap='gray')\n",
-    "    plt.title(f'{x:2.1f}, {y:2.1f}')\n",
-    "    plt.xticks([])\n",
-    "    plt.yticks([])"
+    "traverse(model=model, ds=ds_train)"
    ]
   },
   {
@@ -2420,50 +2525,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 37,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:40:41.945749Z",
-     "start_time": "2020-10-12T06:40:41.942505Z"
+     "end_time": "2020-10-16T07:29:14.599357Z",
+     "start_time": "2020-10-16T07:29:14.597319Z"
     }
    },
    "outputs": [],
    "source": [
     "# Create the model definition\n",
-    "# YOUR CODE HERE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-10-12T06:40:41.954910Z",
-     "start_time": "2020-10-12T06:40:41.948056Z"
-    }
-   },
-   "outputs": [],
-   "source": [
+    "# YOUR CODE HERE\n",
+    "\n",
     "# # Training logic\n",
     "# epochs = 10\n",
     "# show_prediction(10, title=f\"epoch={0}\")\n",
     "# for epoch in tqdm(range(1, epochs + 1)):\n",
     "#     train(epoch, loss_bce_kld)\n",
     "#     test(epoch, loss_bce_kld)\n",
-    "#     show_prediction(10, title=f\"epoch={epoch}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-10-12T06:40:41.965193Z",
-     "start_time": "2020-10-12T06:40:41.957337Z"
-    }
-   },
-   "outputs": [],
-   "source": [
+    "#     show_prediction(10, title=f\"epoch={epoch}\")\n",
+    "\n",
     "# # Visualise the results\n",
     "# idx = np.random.randint(0, len(ds_test))\n",
     "# show_prediction(idx)\n",
@@ -2472,13 +2553,6 @@
     "# traverse(model=model, y=3, xmin=-5, xmax=5)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2520,25 +2594,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 38,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:40:42.542015Z",
-     "start_time": "2020-10-12T06:40:41.967445Z"
-    }
+     "end_time": "2020-10-16T07:29:15.094581Z",
+     "start_time": "2020-10-16T07:29:14.600622Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "img_loss=83.68, random_loss=1858.73\n",
+      "img_loss=109.35, random_loss=2230.30\n",
       "anomoly detected=True\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -2551,16 +2626,16 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5198485910>"
+       "<matplotlib.image.AxesImage at 0x7f67e6856208>"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -2572,7 +2647,7 @@
     }
    ],
    "source": [
-    "img = ds_train[11].to(device)\n",
+    "img = ds_train[11].to(device).float()\n",
     "\n",
     "# First try to reconstruct a real image\n",
     "img_recon, _ = model(img)\n",
@@ -2602,18 +2677,6 @@
     "plt.imshow(cvt2image(rand_recon), cmap=\"gray\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-10-12T06:28:57.201707Z",
-     "start_time": "2020-10-12T06:28:56.584158Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2632,16 +2695,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 39,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:40:42.552551Z",
-     "start_time": "2020-10-12T06:40:42.543802Z"
+     "end_time": "2020-10-16T07:29:15.102988Z",
+     "start_time": "2020-10-16T07:29:15.096134Z"
     }
    },
    "outputs": [],
    "source": [
-    "img = ds_train[11].to(device)\n",
+    "img = ds_train[11].to(device).float()\n",
     "\n",
     "# First try to reconstruct a real image\n",
     "img_recon, _ = model(img)\n",
@@ -2661,17 +2724,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 40,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T06:40:43.090111Z",
-     "start_time": "2020-10-12T06:40:42.554520Z"
+     "end_time": "2020-10-16T07:29:15.601127Z",
+     "start_time": "2020-10-16T07:29:15.104248Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -2684,16 +2747,16 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f519a9aa850>"
+       "<matplotlib.image.AxesImage at 0x7f67e66db780>"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -2911,18 +2974,501 @@
     "</details>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# (extra) Conv\n",
+    "\n",
+    "Encoding and Decoding images is much easier with convolutions because they are aware that pixels are nearby. Lets try with convolutions and see if the loss is lower."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T06:00:46.553987Z",
+     "start_time": "2020-10-16T05:59:59.363Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-10-16T07:27:30.745Z"
+    },
+    "lines_to_next_cell": 0,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================================================================================\n",
+      "                                  Kernel Shape     Output Shape    Params  \\\n",
+      "Layer                                                                       \n",
+      "0_encoder.Conv2d_0               [1, 32, 4, 4]  [1, 32, 13, 13]     544.0   \n",
+      "1_encoder.BatchNorm2d_1                   [32]  [1, 32, 13, 13]      64.0   \n",
+      "2_encoder.ReLU_2                             -  [1, 32, 13, 13]         -   \n",
+      "3_encoder.Conv2d_3              [32, 64, 4, 4]    [1, 64, 5, 5]   32.832k   \n",
+      "4_encoder.BatchNorm2d_4                   [64]    [1, 64, 5, 5]     128.0   \n",
+      "5_encoder.ReLU_5                             -    [1, 64, 5, 5]         -   \n",
+      "6_encoder.Conv2d_6             [64, 128, 5, 5]   [1, 128, 1, 1]  204.928k   \n",
+      "7_encoder.ReLU_7                             -   [1, 128, 1, 1]         -   \n",
+      "8_encoder.Flatten_8                          -         [1, 128]         -   \n",
+      "9_fc11                                [128, 2]           [1, 2]     258.0   \n",
+      "10_fc12                               [128, 2]           [1, 2]     258.0   \n",
+      "11_fc2                                [2, 128]         [1, 128]     384.0   \n",
+      "12_decoder.ConvTranspose2d_0  [128, 128, 3, 3]   [1, 128, 3, 3]  147.584k   \n",
+      "13_decoder.BatchNorm2d_1                 [128]   [1, 128, 3, 3]     256.0   \n",
+      "14_decoder.ReLU_2                            -   [1, 128, 3, 3]         -   \n",
+      "15_decoder.ConvTranspose2d_3   [64, 128, 3, 3]    [1, 64, 7, 7]   73.792k   \n",
+      "16_decoder.BatchNorm2d_4                  [64]    [1, 64, 7, 7]     128.0   \n",
+      "17_decoder.ReLU_5                            -    [1, 64, 7, 7]         -   \n",
+      "18_decoder.ConvTranspose2d_6    [32, 64, 2, 2]  [1, 32, 14, 14]    8.224k   \n",
+      "19_decoder.BatchNorm2d_7                  [32]  [1, 32, 14, 14]      64.0   \n",
+      "20_decoder.ReLU_8                            -  [1, 32, 14, 14]         -   \n",
+      "21_decoder.ConvTranspose2d_9     [1, 32, 2, 2]   [1, 1, 28, 28]     129.0   \n",
+      "22_decoder.Sigmoid_10                        -   [1, 1, 28, 28]         -   \n",
+      "\n",
+      "                              Mult-Adds  \n",
+      "Layer                                    \n",
+      "0_encoder.Conv2d_0              86.528k  \n",
+      "1_encoder.BatchNorm2d_1            32.0  \n",
+      "2_encoder.ReLU_2                      -  \n",
+      "3_encoder.Conv2d_3               819.2k  \n",
+      "4_encoder.BatchNorm2d_4            64.0  \n",
+      "5_encoder.ReLU_5                      -  \n",
+      "6_encoder.Conv2d_6               204.8k  \n",
+      "7_encoder.ReLU_7                      -  \n",
+      "8_encoder.Flatten_8                   -  \n",
+      "9_fc11                            256.0  \n",
+      "10_fc12                           256.0  \n",
+      "11_fc2                            256.0  \n",
+      "12_decoder.ConvTranspose2d_0  1.327104M  \n",
+      "13_decoder.BatchNorm2d_1          128.0  \n",
+      "14_decoder.ReLU_2                     -  \n",
+      "15_decoder.ConvTranspose2d_3  3.612672M  \n",
+      "16_decoder.BatchNorm2d_4           64.0  \n",
+      "17_decoder.ReLU_5                     -  \n",
+      "18_decoder.ConvTranspose2d_6  1.605632M  \n",
+      "19_decoder.BatchNorm2d_7           32.0  \n",
+      "20_decoder.ReLU_8                     -  \n",
+      "21_decoder.ConvTranspose2d_9   100.352k  \n",
+      "22_decoder.Sigmoid_10                 -  \n",
+      "------------------------------------------------------------------------------------\n",
+      "                         Totals\n",
+      "Total params           469.573k\n",
+      "Trainable params       469.573k\n",
+      "Non-trainable params        0.0\n",
+      "Mult-Adds             7.757376M\n",
+      "====================================================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x144 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95acf84005fe498cbd134635f2344523",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "657adacc00e64900b1663bc0ec791605",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=657.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#1 [6400/42000 (15%)]\tLoss: 209.216248  \r"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's create our model. Same as before the model has three main sections:\n",
+    "class CVAE(nn.Module):\n",
+    "    def __init__(self, n_latent=2):\n",
+    "        super(CVAE, self).__init__()\n",
+    "        # After each layer in the encoder we decrease the size of output and increase the number of channels. \n",
+    "        self.encoder = nn.Sequential(\n",
+    "            nn.Conv2d(1, 32, kernel_size=4, stride=2),\n",
+    "            nn.BatchNorm2d(32),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Conv2d(32, 64, kernel_size=4, stride=2),\n",
+    "            nn.BatchNorm2d(64),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Conv2d(64, 128, kernel_size=5, stride=2),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Flatten(),\n",
+    "        )\n",
+    "\n",
+    "        # It acts as opposite of encoder. At each layer we increase the size of output and decrease the number of channels.\n",
+    "        self.decoder = nn.Sequential(\n",
+    "            nn.ConvTranspose2d(128, 128, kernel_size=3, stride=2),\n",
+    "            nn.BatchNorm2d(128),\n",
+    "            nn.ReLU(),\n",
+    "            nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2),\n",
+    "            nn.BatchNorm2d(64),\n",
+    "            nn.ReLU(),\n",
+    "            nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2),\n",
+    "            nn.BatchNorm2d(32),\n",
+    "            nn.ReLU(),\n",
+    "            nn.ConvTranspose2d(32, 1, kernel_size=2, stride=2),\n",
+    "            nn.Sigmoid(),\n",
+    "        )\n",
+    "\n",
+    "        self.fc11 = nn.Linear(128, n_latent)\n",
+    "        self.fc12 = nn.Linear(128, n_latent)\n",
+    "        self.fc2 = nn.Linear(n_latent, 128)\n",
+    "\n",
+    "    def encode(self, x):\n",
+    "        x = x.reshape((-1, 1, 28, 28))\n",
+    "        h = self.encoder(x)\n",
+    "        mu, logvar = self.fc11(h), self.fc12(h)\n",
+    "        return Normal(mu, torch.exp(logvar))\n",
+    "\n",
+    "    def decode(self, z):\n",
+    "        z = self.fc2(z)\n",
+    "        z = z.view(z.size(0), 128, 1, 1)\n",
+    "        z = self.decoder(z)\n",
+    "        z = z.reshape((-1, 28*28))\n",
+    "        return z\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        dist = self.encode(x)\n",
+    "        z = dist.rsample()\n",
+    "        z = self.decode(z)\n",
+    "        return z, dist\n",
+    "    \n",
+    "model = CVAE().to(device)\n",
+    "from deep_ml_curriculum.torchsummaryX import summary\n",
+    "x = torch.randn(1, 1, 28, 28).to(device)\n",
+    "summary(model, x)\n",
+    "\n",
+    "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n",
+    "\n",
+    "# training loop\n",
+    "epochs = 10\n",
+    "show_prediction(3, title=f\"epoch={0}\")\n",
+    "for epoch in tqdm(range(1, epochs + 1)):\n",
+    "    train_vae(epoch, loss_bce_kld, model, train_loader)\n",
+    "    test_vae(epoch, loss_bce_kld, model, test_loader)\n",
+    "    show_prediction(3, title=f\"epoch={epoch}\")\n",
+    "    \n",
+    "traverse(model=model, ds=ds_train, y=3, xmin=-5, xmax=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T07:27:05.397902Z",
+     "start_time": "2020-10-16T07:27:04.841088Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T23:39:34.490191Z",
-     "start_time": "2020-10-11T23:37:39.751506Z"
+     "end_time": "2020-10-16T03:35:39.149181Z",
+     "start_time": "2020-10-16T03:35:38.491225Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T00:53:01.943011Z",
+     "start_time": "2020-10-16T00:53:01.939633Z"
     }
    },
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-15T03:07:51.397762Z",
+     "start_time": "2020-10-15T03:07:51.394444Z"
+    },
+    "lines_to_next_cell": 2
+   },
+   "source": [
+    "# (extra) Dataset: Fossils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T04:22:39.074354Z",
+     "start_time": "2020-10-16T04:22:39.069600Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-10-16T07:27:30.762Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "class FossilDataset(Dataset):\n",
+    "    def __init__(self, path, transform=None, split='train'):\n",
+    "\n",
+    "        self.root_dir = Path(path)\n",
+    "        self.transform = transform\n",
+    "        self.x = (np.load(path/f'X_{split}.npy') * 255).astype(np.uint8)[:, 0]\n",
+    "        self.y = np.load(path/f'y_{split}.npy')\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.x)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        if torch.is_tensor(idx):\n",
+    "            idx = idx.tolist()\n",
+    "\n",
+    "        output = self.x[idx]\n",
+    "        if self.transform:\n",
+    "            output = self.transform(output)\n",
+    "        return output\n",
+    "\n",
+    "    def show(self, idx):\n",
+    "        plt.imshow(self.x[idx], \"gray\")\n",
+    "\n",
+    "    def sample(self, n):\n",
+    "        idx = np.random.randint(0, len(self), n)\n",
+    "        return self[idx]\n",
+    "\n",
+    "path = Path(\"../../data/processed/fossil_image_classification/\")\n",
+    "transform = transforms.Compose([\n",
+    "    transforms.ToPILImage(),\n",
+    "    transforms.RandomVerticalFlip(),\n",
+    "    transforms.RandomHorizontalFlip(),    \n",
+    "    transforms.Resize((28, 28)),\n",
+    "    transforms.RandomResizedCrop((28, 28)),\n",
+    "    transforms.ToTensor(),\n",
+    "])\n",
+    "ds_train = FossilDataset(path, split='train', transform=transform)\n",
+    "transform = transforms.Compose([\n",
+    "    transforms.ToPILImage(),\n",
+    "    transforms.Resize((28, 28)),\n",
+    "    transforms.ToTensor(),\n",
+    "])\n",
+    "ds_test = FossilDataset(path, split='val', transform=transform)\n",
+    "print(len(ds_train))\n",
+    "print(len(ds_test))\n",
+    "\n",
+    "batch_size=10\n",
+    "train_loader = torch.utils.data.DataLoader(ds_train, batch_size, shuffle=True)\n",
+    "test_loader = torch.utils.data.DataLoader(ds_test, batch_size)\n",
+    "\n",
+    "plt.imshow(ds_train[10][0], 'gray')\n",
+    "ds_train[1].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-10-16T07:27:30.767Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "    \n",
+    "model = CVAE().to(device)\n",
+    "from deep_ml_curriculum.torchsummaryX import summary\n",
+    "x = torch.randn(1, 1, 28, 28).to(device)\n",
+    "summary(model, x)\n",
+    "\n",
+    "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n",
+    "\n",
+    "# training loop\n",
+    "epochs = 100\n",
+    "show_prediction(3, title=f\"epoch={0}\", ds=ds_test)\n",
+    "for epoch in tqdm(range(1, epochs + 1)):\n",
+    "    train_vae(epoch, loss_bce_kld, model, train_loader)\n",
+    "    test_vae(epoch, loss_bce_kld, model, test_loader)\n",
+    "    if epoch%10==0:\n",
+    "        show_prediction(3, title=f\"epoch={epoch}\", ds=ds_test)\n",
+    "    \n",
+    "traverse(model=model, ds=ds_test, y=1, xmin=-2, xmax=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-10-16T07:27:30.771Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "traverse(model=model, ds=ds_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# (extra) DataSet: Micro-CT DeepRockSR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-10-16T07:27:30.777Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Rocks(datasets.ImageFolder):\n",
+    "    def __init__(self, *args, **kwargs):\n",
+    "        super().__init__(*args, **kwargs)\n",
+    "        self.y = np.array([ds_test.classes[i] for i in self.targets])\n",
+    "        \n",
+    "    def __getitem__(self, idx):\n",
+    "        x, y = super().__getitem__(idx)\n",
+    "        return x[:1]\n",
+    "    \n",
+    "transform = transforms.Compose([\n",
+    "    transforms.Resize((28, 28)),\n",
+    "    transforms.ToTensor(),\n",
+    "])\n",
+    "        \n",
+    "ds_train = Rocks(\n",
+    "    \"../../data/processed/deep-rock-sr/DeepRockSR-2D/\",\n",
+    "    is_valid_file=lambda f: (\"train_LR_default_X4\" in f) and not (\"shuffle\" in f),\n",
+    "    transform=transform\n",
+    ")\n",
+    "ds_test = Rocks(\n",
+    "    \"../../data/processed/deep-rock-sr/DeepRockSR-2D/\",\n",
+    "    is_valid_file=lambda f: (\"test_LR_default_X4\" in f) and not (\"shuffle\" in f),\n",
+    "    transform=transform\n",
+    ")\n",
+    "batch_size=32\n",
+    "train_loader = torch.utils.data.DataLoader(ds_train, batch_size, shuffle=True)\n",
+    "test_loader = torch.utils.data.DataLoader(ds_test, batch_size)\n",
+    "ds_train[0].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T04:25:01.874745Z",
+     "start_time": "2020-10-16T04:25:01.871470Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-10-16T07:27:30.785Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model = CVAE().to(device)\n",
+    "from deep_ml_curriculum.torchsummaryX import summary\n",
+    "x = torch.randn(1, 1, 28, 28).to(device)\n",
+    "summary(model, x)\n",
+    "\n",
+    "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n",
+    "\n",
+    "# training loop\n",
+    "epochs = 10\n",
+    "show_prediction(3, title=f\"epoch={0}\", ds=ds_test)\n",
+    "for epoch in tqdm(range(1, epochs + 1)):\n",
+    "    train_vae(epoch, loss_bce_kld, model, train_loader)\n",
+    "    test_vae(epoch, loss_bce_kld, model, test_loader)\n",
+    "    show_prediction(3, title=f\"epoch={epoch}\", ds=ds_test)\n",
+    "    \n",
+    "traverse(model=model, ds=ds_test, y=1, xmin=-2, xmax=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-10-16T07:27:30.789Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "traverse(model=model, ds=ds_train)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2943,6 +3489,13 @@
     "- [Understanding Variation Autoencoders](https://towardsdatascience.com/understanding-variational-autoencoders-vaes-f70510919f73)\n",
     "- [Visualizing MNIST using a variational autoencoder](https://www.kaggle.com/rvislaywade/visualizing-mnist-using-a-variational-autoencoder)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -2965,7 +3518,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.0"
   },
   "toc": {
    "base_numbering": 1,
@@ -2980,7 +3533,7 @@
     "height": "calc(100% - 180px)",
     "left": "10px",
     "top": "150px",
-    "width": "384px"
+    "width": "383.984px"
    },
    "toc_section_display": true,
    "toc_window_display": true
diff --git a/notebooks/c09_Autoencoders/Autoencoders.py b/notebooks/c09_Autoencoders/Autoencoders.py
index 7a38871..2488fc6 100644
--- a/notebooks/c09_Autoencoders/Autoencoders.py
+++ b/notebooks/c09_Autoencoders/Autoencoders.py
@@ -88,8 +88,10 @@
 # - `sample`: which returns a random sample of the data.
 
 # +
-path = Path("../../data/processed/MNIST/")
+# Dataset?
 
+# +
+path = Path("../../data/processed/MNIST/")
 
 class DigitsDataset(Dataset):
     def __init__(self, path, transform=None):
@@ -98,35 +100,25 @@ def __init__(self, path, transform=None):
         self.transform = transform
         data = pd.read_csv(path)
         if "label" in data.columns:
-            self.x = data.drop(columns=["label"]).values
+            self.x = data.drop(columns=["label"]).values.reshape((-1, 28, 28))
             self.y = data["label"].values
         else:
-            self.x = data.values
+            self.x = data.values.reshape((-1, 28, 28))
 
     def __len__(self):
+        """Python method for length"""
         return len(self.x)
 
     def __getitem__(self, idx):
-        if torch.is_tensor(idx):
-            idx = idx.tolist()
-
-        output = self.x[idx] / 255
+        """Python method for square brackets"""
+        output = self.x[int(idx)] / 255
         if self.transform:
             output = self.transform(output)
         return output
 
     def show(self, idx):
-#         plt.figure(figsize=(2, 2))
-        plt.imshow(self.x[idx].reshape((28, 28)), "gray")
+        plt.imshow(self.x[idx], "gray")
 
-    def sample(self, n):
-        idx = np.random.randint(0, len(self), n)
-        return self[idx]
-
-
-class ToTensor(object):
-    def __call__(self, data):
-        return torch.FloatTensor(data)
 
 
 # -
@@ -135,9 +127,14 @@ def __call__(self, data):
 
 # Now that we have a `Dataset` class, we can create a training and test dataset.
 
-ds_train = DigitsDataset(path / "train.csv", transform=ToTensor())
-ds_test = DigitsDataset(path / "test.csv", transform=ToTensor())
-ds_train
+ds_train = DigitsDataset(path / "train.csv", transform=transforms.ToTensor())
+ds_test = DigitsDataset(path / "test.csv", transform=transforms.ToTensor())
+
+
+x= ds_train[0]
+x.shape, x.dtype
+
+
 
 for i in range(4):
     for j in range(4):
@@ -146,7 +143,7 @@ def __call__(self, data):
         plt.xticks([])
         plt.yticks([])
 plt.show()
-    
+
 
 # Both of these are the same
 ds_train.__getitem__(1).shape
@@ -168,6 +165,10 @@ def __call__(self, data):
 #
 # Now we need to create the model. The architecture we are going to use here is made of two linear layers for the encoder and two linear layers for the decoder.
 
+# +
+# # nn.Module??
+# -
+
 class AE(nn.Module):
     def __init__(self):
         super(AE, self).__init__()
@@ -184,13 +185,14 @@ def __init__(self):
             nn.Sigmoid()
         )
     def encode(self, x):
-        return self.encoder(x)
+        x = x.reshape((-1, 1, 28 * 28))
+        return self.encoder(x).reshape((-1, 2))
 
     def decode(self, z):
-        return self.decoder(z)
+        return self.decoder(z).reshape((-1, 28 * 28))
 
     def forward(self, x):
-        z = self.encode(x.view(-1, 784))
+        z = self.encode(x)
         return self.decode(z)
 
 
@@ -230,7 +232,7 @@ def train(epoch, loss_function, log_interval=50):
     train_loss = 0
     for batch_idx, data in enumerate(tqdm(train_loader, leave=False, desc='train')):
         # We make sure the data is in the right device (cpu or gpu)
-        data = data.to(device)
+        data = data.to(device).float()
         
         # We make sure that any saved gradient (derivative) is zeroed.
         optimizer.zero_grad()
@@ -264,7 +266,7 @@ def test(epoch, loss_function, log_interval=50):
     test_loss = 0
     with torch.no_grad():
         for i, data in enumerate(tqdm(test_loader, leave=False, desc='test')):
-            data = data.to(device)
+            data = data.to(device).float()
             recon_batch = model(data)
             test_loss += loss_function(recon_batch, data).item()
 
@@ -279,18 +281,22 @@ def cvt2image(tensor):
 def show_prediction(idx, title='', ds=ds_train):
     """Show a predict vs actual"""
     model.eval()
-    original = ds[[idx]]
+    original = ds[idx].float()
     result = model(original.to(device))
-    img = cvt2image(result[0])
+    img = cvt2image(result[0]).squeeze()
     
     plt.figure(figsize=(4, 2))
     plt.subplot(1, 2, 1)
     plt.imshow(img, "gray")
     plt.title("Predicted")
+    plt.xticks([])
+    plt.yticks([])
 
     plt.subplot(1, 2, 2)
-    ds.show(idx)
+    plt.imshow(original.squeeze(), 'gray')
     plt.title("Actual")
+    plt.xticks([])
+    plt.yticks([])
     
     plt.suptitle(title)
     plt.show()
@@ -302,10 +308,10 @@ def show_prediction(idx, title='', ds=ds_train):
 
 epochs = 10
 for epoch in tqdm(range(1, epochs + 1)):
-    show_prediction(10, title=f"epoch={epoch}")
+    show_prediction(3, title=f"epoch={epoch}")
     train(epoch, loss_bce)
     test(epoch, loss_bce)
-show_prediction(10, title=f"epoch={epoch}")
+show_prediction(3, title=f"epoch={epoch}")
 
 # ## Results
 # Now let's check out the model.
@@ -315,6 +321,7 @@ def show_prediction(idx, title='', ds=ds_train):
 # show this row of the data
 show_prediction(idx)
 
+
 # <font color='blue' size='4rem'>Run the cell above a few times and compare the predicted and actual images.</font>
 
 # ## Latent space
@@ -325,33 +332,48 @@ def show_prediction(idx, title='', ds=ds_train):
 #
 # We can also traverse the latent space and see how the reconstructed image changes in meaningfull ways. This is a usefull property and means the model has learnt how to vary images.
 
-
-
 # +
 # Scatter plot
 
-def traverse(ds=ds_train, model=model, y=3, xmin=-5, xmax=5):
-    res = model.encode(ds_train[:1000].to(device))
+def traverse(ds=ds_train, model=model, y=None, xmin=None, xmax=None):
+    # Get the first 1000 images
+    n = min(1000, len(ds))
+    x = torch.stack([ds[i] for i in range(n)], 0)
+    print(x.shape)
+    x = x.to(device).float().reshape((-1, 1, 28,28))
+    print(x.shape)
+    
+    res = model.encode(x)
+    
+    # If we output a distribution, use the mean
     if isinstance(res, Normal):
         res = res.loc
+    
+    # to numpy
     res = res.detach().cpu().numpy()
-    res.shape
 
-    for i in range(10):
-        idx = ds.y[:1000] == i
-        plt.scatter(res[idx, 0], res[idx, 1], label=i)
+    classes = pd.Series(ds.y).unique()
+    for i, cls in enumerate(classes):
+        idx = ds.y[:n] == cls
+        plt.scatter(res[idx, 0], res[idx, 1], label=cls)
     plt.title('the latent space')
     plt.xlabel('latent variable 1')
     plt.ylabel('latent variable 2')
-
-    # change these numbers, to change where we travel
-    y=3
-    xmin=-5
-    xmax=5
+    
+    if xmin is None:
+        xmin, xmax = plt.xlim()
+        xrange = xmax-xmin
+        xmin -= xrange/2
+        xmax += xrange/2
+    if y is None:
+        ymin, ymax = plt.ylim()
+        y = (ymin+ymax)/2
 
     plt.hlines(y, xmin, xmax, color='r', lw=2, label='traversal')
     plt.legend()
     plt.show()
+    
+    
 
     # Do out traversal
     plt.figure(figsize=(12, 12))
@@ -359,7 +381,7 @@ def traverse(ds=ds_train, model=model, y=3, xmin=-5, xmax=5):
     xs = np.linspace(xmin, xmax, n_steps)
     for xi, x in enumerate(xs):
         # Decode image at x,y
-        z = torch.tensor([x, y])[None :].float().to(device)
+        z = torch.tensor([x, y]).float().to(device)[None, :]
         img = model.decode(z).cpu().detach().numpy()
         img = (img.reshape((28, 28)) * 255).astype(np.uint8)
         
@@ -369,11 +391,12 @@ def traverse(ds=ds_train, model=model, y=3, xmin=-5, xmax=5):
         plt.title(f'{x:2.1f}, {y:2.1f}')
         plt.xticks([])
         plt.yticks([])
+# -
 
 
-# -
 
-traverse(model=model, y=3, xmin=-5, xmax=5)
+traverse(model=model, ds=ds_train)
+
 
 
 # Each color represents a number. Despite most numbers overlapping, we can still see some distictions, for instance between $1$ and other numbers. 
@@ -417,6 +440,7 @@ def __init__(self):
 
     def encode(self, x):
         """Takes in image, output distribution"""
+        x = x.reshape((-1, 28*28))
         h = self.encoder(x)
         # first few features are mean
         mean = h[:, :2]
@@ -428,19 +452,28 @@ def encode(self, x):
 
     def decode(self, z):
         """Takes in latent vector and produces image."""
-        return self.decoder(z)
+        return self.decoder(z).reshape((-1, 28 * 28))
 
     def forward(self, x):
         """Combine the above methods"""
         dist = self.encode(x.view(-1, 784))
         z = dist.rsample() # sample, with gradient
         return self.decode(z), dist
+
+
 # -
+# Normal's
+d = Normal(torch.tensor([0, 1]), torch.Tensor([2, 3]))
+# d.rsample()
+
+
+
 
 
 
 model = VAE().to(device)
 optimizer = optim.Adam(model.parameters(), lr=1e-3)
+model
 
 # We can view the shape of our model and number of params
 x = torch.rand((1, 784)).to(device)
@@ -464,7 +497,7 @@ def loss_bce_kld(recon_x, x, dist):
     # KL-divergence between a diagonal multivariate normal,
     # and a standard normal distribution (with zero mean and unit variance)
     # In other words, we are punishing it if it's distribution moves away from a standard normal dist
-    KLD = -0.5 * torch.sum(1 + p.scale.log() - p.loc.pow(2) - p.scale)
+    KLD = -0.5 * torch.sum(1 + dist.scale.log() - dist.loc.pow(2) - dist.scale)
     return BCE + KLD
 
 
@@ -483,8 +516,6 @@ def loss_bce_kld(recon_x, x, dist):
 plt.title(f"KLD(p|q) = {kld:2.2f}\nKLD({p}|{q})")
 plt.legend()
 plt.show()
-
-
 # -
 
 # ## Exercise 1: KLD
@@ -509,16 +540,42 @@ def loss_bce_kld(recon_x, x, dist):
 # # YOUR code here, plot locs vs klds
 # ```
 
+# +
+# Part 1
+p = Normal(0, 1)
+kld_close = torch.distributions.kl.kl_divergence(p, Normal(0, 1))
+kld_far = torch.distributions.kl.kl_divergence(p, Normal(10, 1))
+print(kld_close, kld_far)
+print('close is lower?', kld_close<kld_far)
+
+# Part 2: Plot the KLD as you vary the mean of q
+p = Normal(0, 1)
+means = np.arange(-10, 11)
+
+klds=[]
+for mean in means:
+    q = Normal(mean, 1)
+    kld = torch.distributions.kl.kl_divergence(p, q)
+    klds.append(kld)
+
+plt.plot(means, klds)
+plt.ylabel('KLD(p|q) loss [lower in better]')
+plt.xlabel('distance between distributions')
+plt.show()
+
+
+# -
+
 # ## Train
 
 # We also need to slightly adjust the training loop since the loss function now takes four inputs.
 
 # +
-def train(epoch, loss_function, log_interval=50):
+def train_vae(epoch, loss_function, model, train_loader, log_interval=50):
     model.train()
     train_loss = 0
     for batch_idx, data in enumerate(tqdm(train_loader, leave=False)):
-        data = data.to(device)
+        data = data.to(device).float()
         optimizer.zero_grad()
         recon_batch, dist = model(data)
         loss = loss_function(recon_batch, data, dist)
@@ -536,12 +593,12 @@ def train(epoch, loss_function, log_interval=50):
     print('#{} Train loss: {:.4f}'.format(epoch, train_loss / len(train_loader.dataset)))
 
 
-def test(epoch, loss_function, log_interval=50):
+def test_vae(epoch, loss_function, model, test_loader, log_interval=50):
     model.eval()
     test_loss = 0
     with torch.no_grad():
         for i, data in enumerate(test_loader):
-            data = data.to(device)
+            data = data.to(device).float()
             recon_batch, dist = model(data)
             test_loss += loss_function(recon_batch, data, dist).item()
 
@@ -555,12 +612,14 @@ def test(epoch, loss_function, log_interval=50):
 summary(model, torch.rand((1, 784)).to(device))
 1
 
+
+
 epochs = 10
-show_prediction(10, title=f"epoch={0}")
+show_prediction(3, title=f"epoch={0}")
 for epoch in tqdm(range(1, epochs + 1)):
-    train(epoch, loss_bce_kld)
-    test(epoch, loss_bce_kld)
-    show_prediction(10, title=f"epoch={epoch}")
+    train_vae(epoch, loss_bce_kld, model, train_loader)
+    test_vae(epoch, loss_bce_kld, model, test_loader)
+    show_prediction(3, title=f"epoch={epoch}")
 
 # ## Saving and Loading Model
 #
@@ -582,42 +641,7 @@ def test(epoch, loss_function, log_interval=50):
 
 # One property of a latent space is that you can travese it, and get meaningful varations of outputs.
 
-dist = model.encode(ds_train[:1000].to(device))
-res = dist.loc.cpu().detach().numpy()
-res.shape
-
-# +
-# Scatter plot
-for i in range(10):
-    idx = ds_train.y[:1000] == i
-    plt.scatter(res[idx, 0], res[idx, 1], label=i)
-plt.title('the latent space')
-plt.xlabel('latent variable 1')
-plt.ylabel('latent variable 2')
-
-# change these numbers, to change where we travel
-y=1
-xmin=-5
-xmax=5
-plt.hlines(y, xmin, xmax, color='r', lw=2, label='traversal')
-plt.legend()
-plt.show()
-
-# Do out traversal
-plt.figure(figsize=(12, 12))
-model.to(device)
-n_ims = 10
-xs = np.linspace(xmin, xmax, 10)
-for xi, x in enumerate(xs):
-    plt.subplot(1, 10, xi+1)
-    z = torch.tensor([x, y])[None :].float().to(device)
-    img = model.decode(z).cpu().detach().numpy()
-    img = (img.reshape((28, 28)) * 255).astype(np.uint8)
-    plt.imshow(img, cmap='gray')
-    plt.title(f'{x:2.1f}, {y:2.1f}')
-    plt.xticks([])
-    plt.yticks([])
-# -
+traverse(model=model, ds=ds_train)
 
 # If we compare this plot with the similar plot for normal autoencoder, we can see that VAE did a better job at creating clusters. The points for each digits are closer together compared to previous model. However, there is still room for improvement. 
 
@@ -630,7 +654,6 @@ def test(epoch, loss_function, log_interval=50):
 # Create the model definition
 # YOUR CODE HERE
 
-# +
 # # Training logic
 # epochs = 10
 # show_prediction(10, title=f"epoch={0}")
@@ -639,7 +662,6 @@ def test(epoch, loss_function, log_interval=50):
 #     test(epoch, loss_bce_kld)
 #     show_prediction(10, title=f"epoch={epoch}")
 
-# +
 # # Visualise the results
 # idx = np.random.randint(0, len(ds_test))
 # show_prediction(idx)
@@ -648,8 +670,6 @@ def test(epoch, loss_function, log_interval=50):
 # traverse(model=model, y=3, xmin=-5, xmax=5)
 # -
 
-
-
 # ## Exercise 3: Wider
 # Create a new VAE but this time use a more than two parameters for the latent space. This will reduce the loss
 
@@ -660,7 +680,7 @@ def test(epoch, loss_function, log_interval=50):
 # The model will reconstruct normal data well, and fail to reconstruct anomolies. This means we can use it for anomoly detection
 
 # +
-img = ds_train[11].to(device)
+img = ds_train[11].to(device).float()
 
 # First try to reconstruct a real image
 img_recon, _ = model(img)
@@ -689,9 +709,6 @@ def test(epoch, loss_function, log_interval=50):
 plt.subplot(1, 2, 2)
 plt.imshow(cvt2image(rand_recon), cmap="gray")
 # -
-
-
-
 # # Application: Denoising
 #
 # Since the model only keep the important information, noise ends up being discarded. This can not only let us compress data, but denoise it.
@@ -699,7 +716,7 @@ def test(epoch, loss_function, log_interval=50):
 # In the example below we add some artifacts, and the autoencoder discards them during reconstruction.
 
 # +
-img = ds_train[11].to(device)
+img = ds_train[11].to(device).float()
 
 # First try to reconstruct a real image
 img_recon, _ = model(img)
@@ -893,7 +910,233 @@ def test(epoch, loss_function, log_interval=50):
 #
 # </details>
 
+# # (extra) Conv
+#
+# Encoding and Decoding images is much easier with convolutions because they are aware that pixels are nearby. Lets try with convolutions and see if the loss is lower.
+
+
+
+# +
+# Let's create our model. Same as before the model has three main sections:
+class CVAE(nn.Module):
+    def __init__(self, n_latent=2):
+        super(CVAE, self).__init__()
+        # After each layer in the encoder we decrease the size of output and increase the number of channels. 
+        self.encoder = nn.Sequential(
+            nn.Conv2d(1, 32, kernel_size=4, stride=2),
+            nn.BatchNorm2d(32),
+            nn.ReLU(),
+            nn.Conv2d(32, 64, kernel_size=4, stride=2),
+            nn.BatchNorm2d(64),
+            nn.ReLU(),
+            nn.Conv2d(64, 128, kernel_size=5, stride=2),
+            nn.ReLU(),
+            nn.Flatten(),
+        )
+
+        # It acts as opposite of encoder. At each layer we increase the size of output and decrease the number of channels.
+        self.decoder = nn.Sequential(
+            nn.ConvTranspose2d(128, 128, kernel_size=3, stride=2),
+            nn.BatchNorm2d(128),
+            nn.ReLU(),
+            nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2),
+            nn.BatchNorm2d(64),
+            nn.ReLU(),
+            nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2),
+            nn.BatchNorm2d(32),
+            nn.ReLU(),
+            nn.ConvTranspose2d(32, 1, kernel_size=2, stride=2),
+            nn.Sigmoid(),
+        )
+
+        self.fc11 = nn.Linear(128, n_latent)
+        self.fc12 = nn.Linear(128, n_latent)
+        self.fc2 = nn.Linear(n_latent, 128)
+
+    def encode(self, x):
+        x = x.reshape((-1, 1, 28, 28))
+        h = self.encoder(x)
+        mu, logvar = self.fc11(h), self.fc12(h)
+        return Normal(mu, torch.exp(logvar))
+
+    def decode(self, z):
+        z = self.fc2(z)
+        z = z.view(z.size(0), 128, 1, 1)
+        z = self.decoder(z)
+        z = z.reshape((-1, 28*28))
+        return z
+
+    def forward(self, x):
+        dist = self.encode(x)
+        z = dist.rsample()
+        z = self.decode(z)
+        return z, dist
+    
+model = CVAE().to(device)
+from deep_ml_curriculum.torchsummaryX import summary
+x = torch.randn(1, 1, 28, 28).to(device)
+summary(model, x)
+
+optimizer = optim.Adam(model.parameters(), lr=1e-3)
+
+# training loop
+epochs = 10
+show_prediction(3, title=f"epoch={0}")
+for epoch in tqdm(range(1, epochs + 1)):
+    train_vae(epoch, loss_bce_kld, model, train_loader)
+    test_vae(epoch, loss_bce_kld, model, test_loader)
+    show_prediction(3, title=f"epoch={epoch}")
+    
+traverse(model=model, ds=ds_train, y=3, xmin=-5, xmax=5)
+# -
+
+
+
+
+
+
+
+
+# # (extra) Dataset: Fossils
+
+
+
+
+# +
+
+
+class FossilDataset(Dataset):
+    def __init__(self, path, transform=None, split='train'):
+
+        self.root_dir = Path(path)
+        self.transform = transform
+        self.x = (np.load(path/f'X_{split}.npy') * 255).astype(np.uint8)[:, 0]
+        self.y = np.load(path/f'y_{split}.npy')
+
+    def __len__(self):
+        return len(self.x)
+
+    def __getitem__(self, idx):
+        if torch.is_tensor(idx):
+            idx = idx.tolist()
+
+        output = self.x[idx]
+        if self.transform:
+            output = self.transform(output)
+        return output
+
+    def show(self, idx):
+        plt.imshow(self.x[idx], "gray")
+
+    def sample(self, n):
+        idx = np.random.randint(0, len(self), n)
+        return self[idx]
+
+path = Path("../../data/processed/fossil_image_classification/")
+transform = transforms.Compose([
+    transforms.ToPILImage(),
+    transforms.RandomVerticalFlip(),
+    transforms.RandomHorizontalFlip(),    
+    transforms.Resize((28, 28)),
+    transforms.RandomResizedCrop((28, 28)),
+    transforms.ToTensor(),
+])
+ds_train = FossilDataset(path, split='train', transform=transform)
+transform = transforms.Compose([
+    transforms.ToPILImage(),
+    transforms.Resize((28, 28)),
+    transforms.ToTensor(),
+])
+ds_test = FossilDataset(path, split='val', transform=transform)
+print(len(ds_train))
+print(len(ds_test))
+
+batch_size=10
+train_loader = torch.utils.data.DataLoader(ds_train, batch_size, shuffle=True)
+test_loader = torch.utils.data.DataLoader(ds_test, batch_size)
+
+plt.imshow(ds_train[10][0], 'gray')
+ds_train[1].shape
 
+# +
+    
+model = CVAE().to(device)
+from deep_ml_curriculum.torchsummaryX import summary
+x = torch.randn(1, 1, 28, 28).to(device)
+summary(model, x)
+
+optimizer = optim.Adam(model.parameters(), lr=1e-3)
+
+# training loop
+epochs = 100
+show_prediction(3, title=f"epoch={0}", ds=ds_test)
+for epoch in tqdm(range(1, epochs + 1)):
+    train_vae(epoch, loss_bce_kld, model, train_loader)
+    test_vae(epoch, loss_bce_kld, model, test_loader)
+    if epoch%10==0:
+        show_prediction(3, title=f"epoch={epoch}", ds=ds_test)
+    
+traverse(model=model, ds=ds_test, y=1, xmin=-2, xmax=3)
+# -
+
+traverse(model=model, ds=ds_train)
+
+
+# # (extra) DataSet: Micro-CT DeepRockSR
+
+# +
+class Rocks(datasets.ImageFolder):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.y = np.array([ds_test.classes[i] for i in self.targets])
+        
+    def __getitem__(self, idx):
+        x, y = super().__getitem__(idx)
+        return x[:1]
+    
+transform = transforms.Compose([
+    transforms.Resize((28, 28)),
+    transforms.ToTensor(),
+])
+        
+ds_train = Rocks(
+    "../../data/processed/deep-rock-sr/DeepRockSR-2D/",
+    is_valid_file=lambda f: ("train_LR_default_X4" in f) and not ("shuffle" in f),
+    transform=transform
+)
+ds_test = Rocks(
+    "../../data/processed/deep-rock-sr/DeepRockSR-2D/",
+    is_valid_file=lambda f: ("test_LR_default_X4" in f) and not ("shuffle" in f),
+    transform=transform
+)
+batch_size=32
+train_loader = torch.utils.data.DataLoader(ds_train, batch_size, shuffle=True)
+test_loader = torch.utils.data.DataLoader(ds_test, batch_size)
+ds_train[0].shape
+# -
+
+
+
+# +
+model = CVAE().to(device)
+from deep_ml_curriculum.torchsummaryX import summary
+x = torch.randn(1, 1, 28, 28).to(device)
+summary(model, x)
+
+optimizer = optim.Adam(model.parameters(), lr=1e-3)
+
+# training loop
+epochs = 10
+show_prediction(3, title=f"epoch={0}", ds=ds_test)
+for epoch in tqdm(range(1, epochs + 1)):
+    train_vae(epoch, loss_bce_kld, model, train_loader)
+    test_vae(epoch, loss_bce_kld, model, test_loader)
+    show_prediction(3, title=f"epoch={epoch}", ds=ds_test)
+    
+traverse(model=model, ds=ds_test, y=1, xmin=-2, xmax=3)
+# -
+
+traverse(model=model, ds=ds_train)
 
 # # References
 # - [Pytorch examples for VAE](https://github.com/pytorch/examples/tree/master/vae)
@@ -905,3 +1148,5 @@ def test(epoch, loss_function, log_interval=50):
 # - [Understanding Semantic Segmentation](https://towardsdatascience.com/understanding-semantic-segmentation-with-unet-6be4f42d4b47)
 # - [Understanding Variation Autoencoders](https://towardsdatascience.com/understanding-variational-autoencoders-vaes-f70510919f73)
 # - [Visualizing MNIST using a variational autoencoder](https://www.kaggle.com/rvislaywade/visualizing-mnist-using-a-variational-autoencoder)
+
+