feat: Better visualizability of first convolutional layer with onehot encoding

c1c0b313 · TheRiPtide · 465fcb01 · c1c0b313 · c1c0b313 · c1c0b313
Commit c1c0b313 authored Dec 22, 2021 by TheRiPtide
--- a/models/internal_priming.pth
+++ b/models/internal_priming.pth
--- a/notebooks/internal_priming.ipynb
+++ b/notebooks/internal_priming.ipynb
@@ -22,7 +22,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 26,
   "outputs": [],
   "source": [
    "# importing the libraries\n",
@@ -52,7 +52,7 @@
    "\n",
    "        self.cnn_layers = Sequential(\n",
    "            # Defining a 1D convolution layer\n",
-    "            Conv1d(1, 4, kernel_size=3, stride=1, padding=1),\n",
+    "            Conv1d(4, 4, kernel_size=3, stride=1, padding=1),\n",
    "            BatchNorm1d(4),\n",
    "            ReLU(inplace=True),\n",
    "            MaxPool1d(kernel_size=2, stride=2),\n",
@@ -128,13 +128,13 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 27,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
-      "100%|██████████| 20000/20000 [00:00<00:00, 27099.07it/s]\n"
+      "100%|██████████| 20000/20000 [00:00<00:00, 23948.83it/s]\n"
     ]
    }
   ],
@@ -185,7 +185,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 28,
   "outputs": [],
   "source": [
    "# TODO: reshape shape from [n, l] to [n, 1, l]\n",
@@ -199,8 +199,8 @@
    "train_shape = train_x.shape\n",
    "val_shape = val_x.shape\n",
    "\n",
-    "train_x = train_x.reshape(train_shape[0], 1, train_shape[1], 4)\n",
-    "val_x = val_x.reshape(val_shape[0], 1, val_shape[1], 4)\n",
+    "train_x = train_x.reshape(train_shape[0], 4, train_shape[1])\n",
+    "val_x = val_x.reshape(val_shape[0], 4, val_shape[1])\n",
    "\n",
    "train_x  = torch.from_numpy(train_x)\n",
    "train_y = torch.from_numpy(train_y)\n",
@@ -229,32 +229,13 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 29,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
-      "  0%|          | 0/25 [00:00<?, ?it/s]\n"
-     ]
-    },
-    {
-     "ename": "RuntimeError",
-     "evalue": "Expected 3-dimensional input for 3-dimensional weight [4, 1, 3], but got 4-dimensional input of size [18000, 1, 200, 4] instead",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mRuntimeError\u001B[0m                              Traceback (most recent call last)",
-      "\u001B[1;32m~\\AppData\\Local\\Temp/ipykernel_14744/999922600.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m     24\u001B[0m \u001B[1;31m# training the model\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     25\u001B[0m \u001B[1;32mfor\u001B[0m \u001B[0mepoch\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mtqdm\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mrange\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mn_epochs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 26\u001B[1;33m     \u001B[0mtrain_loss\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mval_loss\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtrain\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     27\u001B[0m     \u001B[0mtrain_losses\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtrain_loss\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     28\u001B[0m     \u001B[0mval_losses\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mval_loss\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\AppData\\Local\\Temp/ipykernel_14744/2669949571.py\u001B[0m in \u001B[0;36mtrain\u001B[1;34m()\u001B[0m\n\u001B[0;32m     67\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     68\u001B[0m     \u001B[1;31m# prediction for training and validation set\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 69\u001B[1;33m     \u001B[0moutput_train\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mmodel\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mx_train\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     70\u001B[0m     \u001B[0moutput_val\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mmodel\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mx_val\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     71\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32mc:\\users\\gzaug\\onedrive\\dokumente\\uni\\programming in life sciences\\scrna-seq-simulation\\venv\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001B[0m in \u001B[0;36m_call_impl\u001B[1;34m(self, *input, **kwargs)\u001B[0m\n\u001B[0;32m   1100\u001B[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001B[0;32m   1101\u001B[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001B[1;32m-> 1102\u001B[1;33m             \u001B[1;32mreturn\u001B[0m \u001B[0mforward_call\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0minput\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1103\u001B[0m         \u001B[1;31m# Do not call functions when jit is used\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1104\u001B[0m         \u001B[0mfull_backward_hooks\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnon_full_backward_hooks\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\AppData\\Local\\Temp/ipykernel_14744/2669949571.py\u001B[0m in \u001B[0;36mforward\u001B[1;34m(self, x)\u001B[0m\n\u001B[0;32m     43\u001B[0m     \u001B[1;31m# Defining the forward pass\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     44\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mforward\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 45\u001B[1;33m         \u001B[0mx\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcnn_layers\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mx\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     46\u001B[0m         \u001B[0mx\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mview\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mx\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msize\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m-\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     47\u001B[0m         \u001B[0mx\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mlinear_layers\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mx\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32mc:\\users\\gzaug\\onedrive\\dokumente\\uni\\programming in life sciences\\scrna-seq-simulation\\venv\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001B[0m in \u001B[0;36m_call_impl\u001B[1;34m(self, *input, **kwargs)\u001B[0m\n\u001B[0;32m   1100\u001B[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001B[0;32m   1101\u001B[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001B[1;32m-> 1102\u001B[1;33m             \u001B[1;32mreturn\u001B[0m \u001B[0mforward_call\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0minput\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1103\u001B[0m         \u001B[1;31m# Do not call functions when jit is used\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1104\u001B[0m         \u001B[0mfull_backward_hooks\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnon_full_backward_hooks\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32mc:\\users\\gzaug\\onedrive\\dokumente\\uni\\programming in life sciences\\scrna-seq-simulation\\venv\\lib\\site-packages\\torch\\nn\\modules\\container.py\u001B[0m in \u001B[0;36mforward\u001B[1;34m(self, input)\u001B[0m\n\u001B[0;32m    139\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mforward\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0minput\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    140\u001B[0m         \u001B[1;32mfor\u001B[0m \u001B[0mmodule\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 141\u001B[1;33m             \u001B[0minput\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mmodule\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0minput\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    142\u001B[0m         \u001B[1;32mreturn\u001B[0m \u001B[0minput\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    143\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32mc:\\users\\gzaug\\onedrive\\dokumente\\uni\\programming in life sciences\\scrna-seq-simulation\\venv\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001B[0m in \u001B[0;36m_call_impl\u001B[1;34m(self, *input, **kwargs)\u001B[0m\n\u001B[0;32m   1100\u001B[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001B[0;32m   1101\u001B[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001B[1;32m-> 1102\u001B[1;33m             \u001B[1;32mreturn\u001B[0m \u001B[0mforward_call\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0minput\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1103\u001B[0m         \u001B[1;31m# Do not call functions when jit is used\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1104\u001B[0m         \u001B[0mfull_backward_hooks\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnon_full_backward_hooks\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32mc:\\users\\gzaug\\onedrive\\dokumente\\uni\\programming in life sciences\\scrna-seq-simulation\\venv\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001B[0m in \u001B[0;36mforward\u001B[1;34m(self, input)\u001B[0m\n\u001B[0;32m    299\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    300\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mforward\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0minput\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mTensor\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m->\u001B[0m \u001B[0mTensor\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 301\u001B[1;33m         \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_conv_forward\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0minput\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mweight\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mbias\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    302\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    303\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32mc:\\users\\gzaug\\onedrive\\dokumente\\uni\\programming in life sciences\\scrna-seq-simulation\\venv\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001B[0m in \u001B[0;36m_conv_forward\u001B[1;34m(self, input, weight, bias)\u001B[0m\n\u001B[0;32m    295\u001B[0m                             \u001B[0mweight\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mbias\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mstride\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    296\u001B[0m                             _single(0), self.dilation, self.groups)\n\u001B[1;32m--> 297\u001B[1;33m         return F.conv1d(input, weight, bias, self.stride,\n\u001B[0m\u001B[0;32m    298\u001B[0m                         self.padding, self.dilation, self.groups)\n\u001B[0;32m    299\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;31mRuntimeError\u001B[0m: Expected 3-dimensional input for 3-dimensional weight [4, 1, 3], but got 4-dimensional input of size [18000, 1, 200, 4] instead"
+      "100%|██████████| 25/25 [00:19<00:00,  1.25it/s]\n"
     ]
    }
   ],
@@ -309,8 +290,19 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
+   "execution_count": 30,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "train_losses_list = [train_loss.item() for train_loss in train_losses]\n",
    "val_losses_list = [val_loss.item() for val_loss in val_losses]\n",
@@ -330,8 +322,17 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
+   "execution_count": 31,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9966111111111111\n",
+      "0.998\n"
+     ]
+    }
+   ],
   "source": [
    "# prediction for training set\n",
    "with torch.no_grad():\n",
@@ -375,8 +376,68 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
+   "execution_count": 32,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cnn_layers.0.weight tensor([[[-0.0161, -0.1727, -0.0706],\n",
+      "         [-0.1442, -0.2347, -0.1731],\n",
+      "         [ 0.0937, -0.2319,  0.0297],\n",
+      "         [ 0.4707,  1.2928, -0.7642]],\n",
+      "\n",
+      "        [[ 0.6057,  0.1347, -0.0041],\n",
+      "         [ 0.0967, -0.9823, -0.6446],\n",
+      "         [ 0.5267,  0.5432,  0.1329],\n",
+      "         [-0.0482, -0.0046, -0.0177]],\n",
+      "\n",
+      "        [[-0.3372, -0.1560, -0.0196],\n",
+      "         [ 0.1110, -0.3352, -0.1544],\n",
+      "         [-0.4499, -0.2293,  0.0253],\n",
+      "         [ 0.9977, -0.9843, -1.0379]],\n",
+      "\n",
+      "        [[-0.1263, -0.5871, -0.0511],\n",
+      "         [-0.1365, -0.1569,  0.0979],\n",
+      "         [-0.3475, -0.3901,  0.0927],\n",
+      "         [-0.6534, -0.9329,  0.6516]]])\n",
+      "cnn_layers.0.bias tensor([-0.0021, -0.1551, -1.0721,  0.4632])\n",
+      "cnn_layers.1.weight tensor([1.8590, 0.1158, 1.1549, 0.8114])\n",
+      "cnn_layers.1.bias tensor([ 0.7813, -0.7185,  0.1945,  0.2015])\n",
+      "cnn_layers.4.weight tensor([[[-1.0589,  0.3008, -1.0521],\n",
+      "         [ 0.2243,  0.5245,  0.1523],\n",
+      "         [ 0.0767,  0.6713, -0.4829],\n",
+      "         [-0.6312, -0.4684, -0.3525]],\n",
+      "\n",
+      "        [[ 0.6076,  0.0118,  0.3328],\n",
+      "         [-0.6541,  0.2015,  0.1579],\n",
+      "         [-0.8182,  0.1377, -0.8822],\n",
+      "         [ 0.5961, -0.2152,  0.7089]],\n",
+      "\n",
+      "        [[ 0.5840,  0.3963, -0.3982],\n",
+      "         [ 0.4481,  0.1088,  0.2149],\n",
+      "         [ 0.4938,  0.3682,  0.5467],\n",
+      "         [-0.1666,  0.2545,  0.4419]],\n",
+      "\n",
+      "        [[ 0.2782, -0.2773, -0.6268],\n",
+      "         [ 0.1686,  0.1611, -0.3611],\n",
+      "         [-0.9431, -0.2470, -0.1781],\n",
+      "         [-0.2127,  0.1223, -0.0467]]])\n",
+      "cnn_layers.4.bias tensor([ 0.0243,  0.1496, -0.2523, -0.1505])\n",
+      "cnn_layers.5.weight tensor([0.9917, 1.0135, 0.2734, 0.0942])\n",
+      "cnn_layers.5.bias tensor([-0.2346, -0.1730, -0.6458, -0.8736])\n",
+      "linear_layers.0.weight tensor([[ 0.2819,  0.3333,  0.3363,  ...,  0.3874,  0.3519,  0.2827],\n",
+      "        [ 0.3381,  0.3392,  0.2918,  ...,  0.3641,  0.2983,  0.3425],\n",
+      "        [-0.3373, -0.3675, -0.4146,  ..., -0.3503, -0.4156, -0.3663],\n",
+      "        ...,\n",
+      "        [-0.3867, -0.3346, -0.3592,  ..., -0.4135, -0.3362, -0.3592],\n",
+      "        [-0.3415, -0.3677, -0.3740,  ..., -0.4074, -0.3575, -0.3526],\n",
+      "        [-0.4087, -0.3892, -0.3258,  ..., -0.3189, -0.4211, -0.3985]])\n",
+      "linear_layers.0.bias tensor([ 0.1986,  0.3250, -0.4212, -0.3442, -0.3814, -0.3203, -0.3380, -0.4000,\n",
+      "        -0.3805, -0.4522])\n"
+     ]
+    }
+   ],
   "source": [
    "torch.save(model.state_dict(), '../models/internal_priming.pth')\n",
    "\n",


 %% Cell type:markdown id: tags:

 # Issue 21: Inferring the code of internal priming by deep learning

 In real data sets we would like to distinguish poly(A) sites from internal priming sites. To do this, we want to construct a classifier that uses the sequence flanking the sites. As a deep learning architecture we can use a convolutional neural network, for e.g. from a numpy implementation, https://pypi.org/project/numpycnn/)

 Reference: https://www.analyticsvidhya.com/blog/2019/10/building-image-classification-models-cnn-pytorch/

 Input: sequences of bona fide and internally-primed poly(A) sites (#16)

 Output: classifier based on the nucleotide sequence around the sites

 %% Cell type:code id: tags:

 ``` python
 # importing the libraries
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt

 # for creating validation set
 from sklearn.model_selection import train_test_split

 # for evaluating the model
 from sklearn.metrics import accuracy_score
 from tqdm import tqdm

 # PyTorch libraries and modules
 import torch
 from torch.autograd import Variable
 from torch.nn import Linear, ReLU, CrossEntropyLoss, Sequential, MaxPool1d, Module, Softmax, BatchNorm1d, Dropout, Conv1d
 from torch.optim import Adam, SGD
 from torchsummary import summary


 # adding the nn
 class Net(Module):
    def __init__(self):
        super(Net, self).__init__()

        self.cnn_layers = Sequential(
            # Defining a 1D convolution layer
-            Conv1d(1, 4, kernel_size=3, stride=1, padding=1),
+            Conv1d(4, 4, kernel_size=3, stride=1, padding=1),
            BatchNorm1d(4),
            ReLU(inplace=True),
            MaxPool1d(kernel_size=2, stride=2),
            # Defining another 1D convolution layer
            Conv1d(4, 4, kernel_size=3, stride=1, padding=1),
            BatchNorm1d(4),
            ReLU(inplace=True),
            MaxPool1d(kernel_size=2, stride=2),
        )

        self.linear_layers = Sequential(
            Linear(4 * 50, 10)
        )

    # Defining the forward pass
    def forward(self, x):
        x = self.cnn_layers(x)
        x = x.view(x.size(0), -1)
        x = self.linear_layers(x)
        return x

 # defining training function
 def train():
    model.train()
    tr_loss = 0
    # getting the training set
    x_train, y_train = Variable(train_x), Variable(train_y)
    # getting the validation set
    x_val, y_val = Variable(val_x), Variable(val_y)
    # converting the data into GPU format
    if torch.cuda.is_available():
        x_train = x_train.cuda()
        y_train = y_train.cuda()
        x_val = x_val.cuda()
        y_val = y_val.cuda()

    # clearing the Gradients of the model parameters
    optimizer.zero_grad()

    # prediction for training and validation set
    output_train = model(x_train)
    output_val = model(x_val)

    # computing the training and validation loss
    loss_train = criterion(output_train, y_train)
    loss_val = criterion(output_val, y_val)

    # computing the updated weights of all the model parameters
    loss_train.backward()
    optimizer.step()
    tr_loss = loss_train.item()

    return loss_train, loss_val
 ```

 %% Cell type:markdown id: tags:

 ## Load data

 %% Cell type:code id: tags:

 ``` python
 enum = {
    'A': [1, 0, 0, 0],
    'U': [0, 1, 0, 0],
    'G': [0, 0, 1, 0],
    'C': [0, 0, 0, 1]
 }

 # TODO: Get test data from issues 25 and 26

 internal_ends = []
 real_ends = []

 with open('../inputs/mock_internal_ends.txt', 'r') as file:
    while line := file.readline().rstrip():
        line_list = list(line)
        internal_ends.append(line_list)

 with open('../inputs/mock_real_ends.txt', 'r') as file:
    while line := file.readline().rstrip():
        line_list = list(line)
        real_ends.append(line_list)

 y_real = [1] * len(real_ends)
 y_internal = [0] * len(internal_ends)

 all_ends = real_ends + internal_ends
 train_y = y_real + y_internal

 train_x = [None] * len(all_ends)

 for i in tqdm(range(len(all_ends))):

    enum_list = [enum[key] for key in all_ends[i]]
    train_x[i] = enum_list

 train_x, val_x, train_y, val_y = train_test_split(train_x, train_y, test_size = 0.1)
 ```

 %% Output

-    100%|██████████| 20000/20000 [00:00<00:00, 27099.07it/s]
+    100%|██████████| 20000/20000 [00:00<00:00, 23948.83it/s]

 %% Cell type:code id: tags:

 ``` python
 # TODO: reshape shape from [n, l] to [n, 1, l]

 train_x = np.array(train_x, dtype=np.float32)
 train_y = np.array(train_y, dtype=np.int64)

 val_x = np.array(val_x, dtype=np.float32)
 val_y = np.array(val_y, dtype=np.int64)

 train_shape = train_x.shape
 val_shape = val_x.shape

-train_x = train_x.reshape(train_shape[0], 1, train_shape[1], 4)
-val_x = val_x.reshape(val_shape[0], 1, val_shape[1], 4)
+train_x = train_x.reshape(train_shape[0], 4, train_shape[1])
+val_x = val_x.reshape(val_shape[0], 4, val_shape[1])

 train_x  = torch.from_numpy(train_x)
 train_y = torch.from_numpy(train_y)

 val_x  = torch.from_numpy(val_x)
 val_y = torch.from_numpy(val_y)
 ```

 %% Cell type:markdown id: tags:

 # Model call and loss function definition

 %% Cell type:code id: tags:

 ``` python
 # defining the model
 model = Net()

 # defining the optimizer
 optimizer = Adam(model.parameters(), lr=0.07)

 # defining the loss function
 criterion = CrossEntropyLoss()

 # checking if GPU is available
 if torch.cuda.is_available():
    model = model.cuda()
    criterion = criterion.cuda()

 # defining the number of epochs
 n_epochs = 25

 # empty list to store training losses
 train_losses = []

 # empty list to store validation losses
 val_losses = []

 # training the model
 for epoch in tqdm(range(n_epochs)):
    train_loss, val_loss = train()
    train_losses.append(train_loss)
    val_losses.append(val_loss)
 ```

 %% Output

-      0%|          | 0/25 [00:00<?, ?it/s]
-
-    ---------------------------------------------------------------------------
-    RuntimeError                              Traceback (most recent call last)
-    ~\AppData\Local\Temp/ipykernel_14744/999922600.py in <module>
-         24 # training the model
-         25 for epoch in tqdm(range(n_epochs)):
-    ---> 26     train_loss, val_loss = train()
-         27     train_losses.append(train_loss)
-         28     val_losses.append(val_loss)
-    ~\AppData\Local\Temp/ipykernel_14744/2669949571.py in train()
-         67
-         68     # prediction for training and validation set
-    ---> 69     output_train = model(x_train)
-         70     output_val = model(x_val)
-         71
-    c:\users\gzaug\onedrive\dokumente\uni\programming in life sciences\scrna-seq-simulation\venv\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
-       1100         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
-       1101                 or _global_forward_hooks or _global_forward_pre_hooks):
-    -> 1102             return forward_call(*input, **kwargs)
-       1103         # Do not call functions when jit is used
-       1104         full_backward_hooks, non_full_backward_hooks = [], []
-    ~\AppData\Local\Temp/ipykernel_14744/2669949571.py in forward(self, x)
-         43     # Defining the forward pass
-         44     def forward(self, x):
-    ---> 45         x = self.cnn_layers(x)
-         46         x = x.view(x.size(0), -1)
-         47         x = self.linear_layers(x)
-    c:\users\gzaug\onedrive\dokumente\uni\programming in life sciences\scrna-seq-simulation\venv\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
-       1100         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
-       1101                 or _global_forward_hooks or _global_forward_pre_hooks):
-    -> 1102             return forward_call(*input, **kwargs)
-       1103         # Do not call functions when jit is used
-       1104         full_backward_hooks, non_full_backward_hooks = [], []
-    c:\users\gzaug\onedrive\dokumente\uni\programming in life sciences\scrna-seq-simulation\venv\lib\site-packages\torch\nn\modules\container.py in forward(self, input)
-        139     def forward(self, input):
-        140         for module in self:
-    --> 141             input = module(input)
-        142         return input
-        143
-    c:\users\gzaug\onedrive\dokumente\uni\programming in life sciences\scrna-seq-simulation\venv\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
-       1100         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
-       1101                 or _global_forward_hooks or _global_forward_pre_hooks):
-    -> 1102             return forward_call(*input, **kwargs)
-       1103         # Do not call functions when jit is used
-       1104         full_backward_hooks, non_full_backward_hooks = [], []
-    c:\users\gzaug\onedrive\dokumente\uni\programming in life sciences\scrna-seq-simulation\venv\lib\site-packages\torch\nn\modules\conv.py in forward(self, input)
-        299
-        300     def forward(self, input: Tensor) -> Tensor:
-    --> 301         return self._conv_forward(input, self.weight, self.bias)
-        302
-        303
-    c:\users\gzaug\onedrive\dokumente\uni\programming in life sciences\scrna-seq-simulation\venv\lib\site-packages\torch\nn\modules\conv.py in _conv_forward(self, input, weight, bias)
-        295                             weight, bias, self.stride,
-        296                             _single(0), self.dilation, self.groups)
-    --> 297         return F.conv1d(input, weight, bias, self.stride,
-        298                         self.padding, self.dilation, self.groups)
-        299
-    RuntimeError: Expected 3-dimensional input for 3-dimensional weight [4, 1, 3], but got 4-dimensional input of size [18000, 1, 200, 4] instead
+    100%|██████████| 25/25 [00:19<00:00,  1.25it/s]

 %% Cell type:markdown id: tags:

 ## Model Performance

 %% Cell type:code id: tags:

 ``` python
 train_losses_list = [train_loss.item() for train_loss in train_losses]
 val_losses_list = [val_loss.item() for val_loss in val_losses]

 # plotting the training and validation loss
 plt.plot(train_losses_list, label='Training loss')
 plt.plot(val_losses_list, label='Validation loss')
 plt.legend()
 plt.show()
 ```

+%% Output
+
+
+
 %% Cell type:code id: tags:

 ``` python
 # prediction for training set
 with torch.no_grad():
    output = model(train_x.cpu())

 softmax = torch.exp(output).cpu()
 prob = list(softmax.numpy())
 predictions = np.argmax(prob, axis=1)

 # accuracy on training set
 print(accuracy_score(train_y, predictions))

 with torch.no_grad():
    output = model(val_x.cpu())

 softmax = torch.exp(output).cpu()
 prob = list(softmax.numpy())
 predictions = np.argmax(prob, axis=1)

 # accuracy on validation set
 print(accuracy_score(val_y, predictions))
 ```

+%% Output
+
+    0.9966111111111111
+    0.998
+
 %% Cell type:markdown id: tags:

 ## Save Model

 %% Cell type:code id: tags:

 ``` python
 torch.save(model.state_dict(), '../models/internal_priming.pth')

 for name, param in model.named_parameters():
    if param.requires_grad:
        print(name, param.data)
 ```
+
+%% Output
+
+    cnn_layers.0.weight tensor([[[-0.0161, -0.1727, -0.0706],
+             [-0.1442, -0.2347, -0.1731],
+             [ 0.0937, -0.2319,  0.0297],
+             [ 0.4707,  1.2928, -0.7642]],
+    
+            [[ 0.6057,  0.1347, -0.0041],
+             [ 0.0967, -0.9823, -0.6446],
+             [ 0.5267,  0.5432,  0.1329],
+             [-0.0482, -0.0046, -0.0177]],
+    
+            [[-0.3372, -0.1560, -0.0196],
+             [ 0.1110, -0.3352, -0.1544],
+             [-0.4499, -0.2293,  0.0253],
+             [ 0.9977, -0.9843, -1.0379]],
+    
+            [[-0.1263, -0.5871, -0.0511],
+             [-0.1365, -0.1569,  0.0979],
+             [-0.3475, -0.3901,  0.0927],
+             [-0.6534, -0.9329,  0.6516]]])
+    cnn_layers.0.bias tensor([-0.0021, -0.1551, -1.0721,  0.4632])
+    cnn_layers.1.weight tensor([1.8590, 0.1158, 1.1549, 0.8114])
+    cnn_layers.1.bias tensor([ 0.7813, -0.7185,  0.1945,  0.2015])
+    cnn_layers.4.weight tensor([[[-1.0589,  0.3008, -1.0521],
+             [ 0.2243,  0.5245,  0.1523],
+             [ 0.0767,  0.6713, -0.4829],
+             [-0.6312, -0.4684, -0.3525]],
+    
+            [[ 0.6076,  0.0118,  0.3328],
+             [-0.6541,  0.2015,  0.1579],
+             [-0.8182,  0.1377, -0.8822],
+             [ 0.5961, -0.2152,  0.7089]],
+    
+            [[ 0.5840,  0.3963, -0.3982],
+             [ 0.4481,  0.1088,  0.2149],
+             [ 0.4938,  0.3682,  0.5467],
+             [-0.1666,  0.2545,  0.4419]],
+    
+            [[ 0.2782, -0.2773, -0.6268],
+             [ 0.1686,  0.1611, -0.3611],
+             [-0.9431, -0.2470, -0.1781],
+             [-0.2127,  0.1223, -0.0467]]])
+    cnn_layers.4.bias tensor([ 0.0243,  0.1496, -0.2523, -0.1505])
+    cnn_layers.5.weight tensor([0.9917, 1.0135, 0.2734, 0.0942])
+    cnn_layers.5.bias tensor([-0.2346, -0.1730, -0.6458, -0.8736])
+    linear_layers.0.weight tensor([[ 0.2819,  0.3333,  0.3363,  ...,  0.3874,  0.3519,  0.2827],
+            [ 0.3381,  0.3392,  0.2918,  ...,  0.3641,  0.2983,  0.3425],
+            [-0.3373, -0.3675, -0.4146,  ..., -0.3503, -0.4156, -0.3663],
+            ...,
+            [-0.3867, -0.3346, -0.3592,  ..., -0.4135, -0.3362, -0.3592],
+            [-0.3415, -0.3677, -0.3740,  ..., -0.4074, -0.3575, -0.3526],
+            [-0.4087, -0.3892, -0.3258,  ..., -0.3189, -0.4211, -0.3985]])
+    linear_layers.0.bias tensor([ 0.1986,  0.3250, -0.4212, -0.3442, -0.3814, -0.3203, -0.3380, -0.4000,
+            -0.3805, -0.4522])


--- a/src/polyA_classifier/polyA_classifier.py
+++ b/src/polyA_classifier/polyA_classifier.py
@@ -15,7 +15,7 @@ class Net(Module):

        self.cnn_layers = Sequential(
            # Defining a 1D convolution layer
-            Conv1d(1, 4, kernel_size=3, stride=1, padding=1),
+            Conv1d(4, 4, kernel_size=3, stride=1, padding=1),
            BatchNorm1d(4),
            ReLU(inplace=True),
            MaxPool1d(kernel_size=2, stride=2),
@@ -42,11 +42,11 @@ class PolyAClassifier:
    """Classifier object using the state-dict of a pretrained pytorch model."""

    enum = {
-        'A': 0.0,
-        'U': 1 / 3,
-        'T': 1 / 3,
-        'G': 2 / 3,
-        'C': 1.0
+        'A': [1, 0, 0, 0],
+        'U': [0, 1, 0, 0],
+        'T': [0, 1, 0, 0],
+        'G': [0, 0, 1, 0],
+        'C': [0, 0, 0, 1]
    }

    def __init__(self, model=Net, state_dict_path: str = './models/internal_priming.pth'):
@@ -103,7 +103,7 @@ class PolyAClassifier:
            raise ValueError('Not all sequences of length 200')

        test_shape = test.shape
-        test = test.reshape(test_shape[0], 1, test_shape[1])
+        test = test.reshape(test_shape[0], 4, test_shape[1])

        if test_shape[1] != 200:
            raise ValueError('Sequences not of length 200')


--- a/tests/resources/internal_priming_test_model.pth
+++ b/tests/resources/internal_priming_test_model.pth