20230328 public 0.95725 with dropout .25

ML2023_hw04.ipynb

@@ -61,70 +61,14 @@
     "id": "U6Y1cfpDfpON",
     "outputId": "6ba26637-5c7b-48a9-be0b-1f10ba76590a"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TERM_PROGRAM=Apple_Terminal\r\n",
-      "SHELL=/bin/zsh\r\n",
-      "TERM=xterm-color\r\n",
-      "TMPDIR=/var/folders/_h/h6dll3ns6lvc3ljhm8wlnp7h0000gn/T/\r\n",
-      "TERM_PROGRAM_VERSION=447\r\n",
-      "TERM_SESSION_ID=643AED00-1886-4C0F-8047-3E99B4468B4D\r\n",
-      "USER=sam\r\n",
-      "SSH_AUTH_SOCK=/private/tmp/com.apple.launchd.H0LZFasLIa/Listeners\r\n",
-      "PATH=/Users/sam/my_venv/ml/bin:/opt/homebrew/opt/bison/bin:/opt/homebrew/opt/cbc/bin:/Users/sam/flutter/bin:/Library/Frameworks/Python.framework/Versions/3.10/bin:/opt/homebrew/bin:/opt/homebrew/sbin:/usr/local/bin:/System/Cryptexes/App/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/opt/X11/bin:/Library/Apple/usr/bin:/Applications/Wireshark.app/Contents/MacOS:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/local/bin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/bin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/appleinternal/bin\r\n",
-      "LaunchInstanceID=5020678A-B21F-4CCA-B898-3F054FE219CA\r\n",
-      "__CFBundleIdentifier=com.apple.Terminal\r\n",
-      "PWD=/Users/sam/Documents/ML_2023_Spring/HW4\r\n",
-      "XPC_FLAGS=0x0\r\n",
-      "XPC_SERVICE_NAME=0\r\n",
-      "SHLVL=1\r\n",
-      "HOME=/Users/sam\r\n",
-      "LOGNAME=sam\r\n",
-      "DISPLAY=/private/tmp/com.apple.launchd.jaQhA1Fj8r/org.xquartz:0\r\n",
-      "SECURITYSESSIONID=186ac\r\n",
-      "OLDPWD=/Users/sam/Documents/ML_2023_Spring/HW4\r\n",
-      "HOMEBREW_PREFIX=/opt/homebrew\r\n",
-      "HOMEBREW_CELLAR=/opt/homebrew/Cellar\r\n",
-      "HOMEBREW_REPOSITORY=/opt/homebrew\r\n",
-      "MANPATH=/opt/homebrew/share/man::\r\n",
-      "INFOPATH=/opt/homebrew/share/info:\r\n",
-      "ZSH=/Users/sam/.oh-my-zsh\r\n",
-      "PAGER=cat\r\n",
-      "LESS=-R\r\n",
-      "LSCOLORS=Gxfxcxdxbxegedabagacad\r\n",
-      "VIRTUAL_ENV=/Users/sam/my_venv/ml\r\n",
-      "PS1=(ml) %(?:%{\u001b[01;32m%}➜ :%{\u001b[01;31m%}➜ ) %{$fg[cyan]%}%c%{$reset_color%} $(git_prompt_info)\r\n",
-      "VIRTUAL_ENV_PROMPT=(ml) \r\n",
-      "MONGODB_HOSTNAME=192.168.255.5\r\n",
-      "MONGODB_PORT=27717\r\n",
-      "MONGODB_USERNAME=sam\r\n",
-      "MONGODB_PASSWORD=pc152pc152\r\n",
-      "PYTORCH_ENABLE_MPS_FALLBACK=1\r\n",
-      "LANG=zh_TW.UTF-8\r\n",
-      "__CF_USER_TEXT_ENCODING=0x1F5:0x2:0x35\r\n",
-      "PYDEVD_USE_FRAME_EVAL=NO\r\n",
-      "JPY_PARENT_PID=5926\r\n",
-      "CLICOLOR=1\r\n",
-      "FORCE_COLOR=1\r\n",
-      "CLICOLOR_FORCE=1\r\n",
-      "GIT_PAGER=cat\r\n",
-      "MPLBACKEND=module://matplotlib_inline.backend_inline\r\n",
-      "_=/usr/bin/printenv\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "#!tar zxf Dataset.tar.gz\n",
-    "!export PYTORCH_ENABLE_MPS_FALLBACK=1\n",
-    "!printenv"
+    "#!tar zxf Dataset.tar.gz"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "id": "E6burzCXIyuA"
    },
@@ -187,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "id": "KpuGxl4CI2pr"
    },
@@ -216,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "id": "B7c2gZYoJDRS"
    },
@@ -292,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "id": "iXZ5B0EKJGs8"
    },
@@ -304,7 +248,7 @@
     "import torchaudio\n",
     "\n",
     "class Classifier(nn.Module):\n",
-    "    def __init__(self, d_model=160, n_spks=600, dropout=0.1):\n",
+    "    def __init__(self, d_model=160, n_spks=600, dropout=0.25):\n",
     "        super().__init__()\n",
     "        # Project the dimension of features from that of input into d_model.\n",
     "        self.prenet = nn.Linear(40, d_model)\n",
@@ -315,7 +259,7 @@
     "        #    d_model=d_model, dim_feedforward=256, nhead=8\n",
     "        #)\n",
     "        #self.encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=2)\n",
-    "        self.encoder = torchaudio.models.Conformer(d_model, 8, 256, 2, 31) # From torchaudio\n",
+    "        self.encoder = torchaudio.models.Conformer(d_model, 8, 256, 2, 31, dropout=dropout) # From torchaudio\n",
     "        \n",
     "        # Project the the dimension of features from d_model into speaker nums.\n",
     "        self.pred_layer = nn.Linear(d_model, n_spks)\n",
@@ -364,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {
     "id": "ykt0N1nVJJi2"
    },
@@ -519,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -541,19 +485,205 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Train:   1% 20/2000 [00:02<03:09, 10.46 step/s, accuracy=0.00, loss=6.52, step=20]"
+      "Train: 100% 2000/2000 [06:35<00:00,  5.05 step/s, accuracy=0.28, loss=3.44, step=2000]  \n",
+      "Valid: 100% 5664/5667 [00:56<00:00, 100.29 uttr/s, accuracy=0.25, loss=3.53]\n",
+      "Train: 100% 2000/2000 [06:25<00:00,  5.19 step/s, accuracy=0.53, loss=2.23, step=4000]  \n",
+      "Valid: 100% 5664/5667 [00:58<00:00, 96.06 uttr/s, accuracy=0.44, loss=2.51] \n",
+      "Train: 100% 2000/2000 [06:37<00:00,  5.04 step/s, accuracy=0.59, loss=1.77, step=6000] \n",
+      "Valid: 100% 5664/5667 [00:59<00:00, 95.68 uttr/s, accuracy=0.53, loss=2.01] \n",
+      "Train: 100% 2000/2000 [07:21<00:00,  4.53 step/s, accuracy=0.66, loss=1.39, step=8000] \n",
+      "Valid: 100% 5664/5667 [00:55<00:00, 101.48 uttr/s, accuracy=0.63, loss=1.60]\n",
+      "Train:   0% 0/2000 [00:00<?, ? step/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 8000, best model saved. (accuracy=0.6324)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Train: 100% 2000/2000 [06:26<00:00,  5.17 step/s, accuracy=0.78, loss=0.82, step=1e+4]  \n",
+      "Valid: 100% 5664/5667 [00:56<00:00, 99.78 uttr/s, accuracy=0.65, loss=1.50] \n",
+      "Train: 100% 2000/2000 [05:41<00:00,  5.85 step/s, accuracy=0.75, loss=1.21, step=12000]  \n",
+      "Valid: 100% 5664/5667 [00:56<00:00, 100.25 uttr/s, accuracy=0.69, loss=1.33]\n",
+      "Train: 100% 2000/2000 [06:58<00:00,  4.78 step/s, accuracy=0.66, loss=1.26, step=14000] \n",
+      "Valid: 100% 5664/5667 [01:02<00:00, 90.35 uttr/s, accuracy=0.71, loss=1.22] \n",
+      "Train: 100% 2000/2000 [07:45<00:00,  4.29 step/s, accuracy=0.75, loss=1.04, step=16000] \n",
+      "Valid: 100% 5664/5667 [00:55<00:00, 102.93 uttr/s, accuracy=0.72, loss=1.19]\n",
+      "Train:   0% 0/2000 [00:00<?, ? step/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 16000, best model saved. (accuracy=0.7225)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Train: 100% 2000/2000 [06:32<00:00,  5.10 step/s, accuracy=0.81, loss=0.62, step=18000]  \n",
+      "Valid: 100% 5664/5667 [01:00<00:00, 93.13 uttr/s, accuracy=0.75, loss=1.13] \n",
+      "Train: 100% 2000/2000 [07:12<00:00,  4.62 step/s, accuracy=0.91, loss=0.32, step=2e+4]   \n",
+      "Valid: 100% 5664/5667 [01:02<00:00, 91.35 uttr/s, accuracy=0.73, loss=1.14] \n",
+      "Train: 100% 2000/2000 [07:24<00:00,  4.50 step/s, accuracy=0.88, loss=0.53, step=22000] \n",
+      "Valid: 100% 5664/5667 [01:00<00:00, 93.62 uttr/s, accuracy=0.78, loss=1.00] \n",
+      "Train: 100% 2000/2000 [07:27<00:00,  4.47 step/s, accuracy=0.88, loss=0.32, step=24000] \n",
+      "Valid: 100% 5664/5667 [00:56<00:00, 101.13 uttr/s, accuracy=0.79, loss=0.93]\n",
+      "Train:   0% 0/2000 [00:00<?, ? step/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 24000, best model saved. (accuracy=0.7890)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Train: 100% 2000/2000 [06:23<00:00,  5.21 step/s, accuracy=0.97, loss=0.16, step=26000]  \n",
+      "Valid: 100% 5664/5667 [00:57<00:00, 98.43 uttr/s, accuracy=0.78, loss=1.00] \n",
+      "Train: 100% 2000/2000 [06:04<00:00,  5.49 step/s, accuracy=0.84, loss=0.44, step=28000]  \n",
+      "Valid: 100% 5664/5667 [00:59<00:00, 95.85 uttr/s, accuracy=0.80, loss=0.90] \n",
+      "Train: 100% 2000/2000 [20:59<00:00,  1.59 step/s, accuracy=0.81, loss=0.52, step=3e+4]     \n",
+      "Valid: 100% 5664/5667 [04:36<00:00, 20.49 uttr/s, accuracy=0.80, loss=0.91]  \n",
+      "Train: 100% 2000/2000 [22:10<00:00,  1.50 step/s, accuracy=0.94, loss=0.22, step=32000]    \n",
+      "Valid: 100% 5664/5667 [00:51<00:00, 109.33 uttr/s, accuracy=0.80, loss=0.91] \n",
+      "Train:   0% 0/2000 [00:00<?, ? step/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 32000, best model saved. (accuracy=0.7991)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Train: 100% 2000/2000 [04:22<00:00,  7.61 step/s, accuracy=0.91, loss=0.28, step=34000]  \n",
+      "Valid: 100% 5664/5667 [08:57<00:00, 10.54 uttr/s, accuracy=0.81, loss=0.87]  \n",
+      "Train: 100% 2000/2000 [04:29<00:00,  7.42 step/s, accuracy=0.94, loss=0.21, step=36000]  \n",
+      "Valid: 100% 5664/5667 [00:53<00:00, 106.10 uttr/s, accuracy=0.82, loss=0.84]\n",
+      "Train: 100% 2000/2000 [04:33<00:00,  7.31 step/s, accuracy=0.88, loss=0.69, step=38000] \n",
+      "Valid: 100% 5664/5667 [00:54<00:00, 103.30 uttr/s, accuracy=0.83, loss=0.79]\n",
+      "Train: 100% 2000/2000 [05:15<00:00,  6.34 step/s, accuracy=0.88, loss=0.27, step=4e+4]  \n",
+      "Valid: 100% 5664/5667 [00:52<00:00, 107.15 uttr/s, accuracy=0.83, loss=0.78] \n",
+      "Train:   0% 0/2000 [00:00<?, ? step/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 40000, best model saved. (accuracy=0.8314)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Train: 100% 2000/2000 [04:30<00:00,  7.40 step/s, accuracy=0.81, loss=0.66, step=42000]  \n",
+      "Valid: 100% 5664/5667 [00:54<00:00, 104.40 uttr/s, accuracy=0.83, loss=0.78]\n",
+      "Train: 100% 2000/2000 [04:53<00:00,  6.83 step/s, accuracy=0.91, loss=0.29, step=44000]  \n",
+      "Valid: 100% 5664/5667 [00:58<00:00, 97.09 uttr/s, accuracy=0.83, loss=0.78] \n",
+      "Train: 100% 2000/2000 [06:14<00:00,  5.34 step/s, accuracy=0.94, loss=0.32, step=46000] \n",
+      "Valid: 100% 5664/5667 [01:00<00:00, 94.26 uttr/s, accuracy=0.84, loss=0.75] \n",
+      "Train: 100% 2000/2000 [07:06<00:00,  4.69 step/s, accuracy=1.00, loss=0.07, step=48000] \n",
+      "Valid: 100% 5664/5667 [00:54<00:00, 103.14 uttr/s, accuracy=0.84, loss=0.71]\n",
+      "Train:   0% 0/2000 [00:00<?, ? step/s]"
      ]
     },
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[8], line 110\u001b[0m\n\u001b[1;32m    105\u001b[0m     train_log({\n\u001b[1;32m    106\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstatus\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcompleted\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    107\u001b[0m     })\n\u001b[1;32m    109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 110\u001b[0m     \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparse_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[0;32mIn[8], line 64\u001b[0m, in \u001b[0;36mmain\u001b[0;34m(data_dir, save_path, batch_size, n_workers, valid_steps, warmup_steps, total_steps, save_steps)\u001b[0m\n\u001b[1;32m     61\u001b[0m     batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(train_iterator)\n\u001b[1;32m     63\u001b[0m loss, accuracy \u001b[38;5;241m=\u001b[39m model_fn(batch, model, criterion, device)\n\u001b[0;32m---> 64\u001b[0m batch_loss \u001b[38;5;241m=\u001b[39m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     65\u001b[0m batch_accuracy \u001b[38;5;241m=\u001b[39m accuracy\u001b[38;5;241m.\u001b[39mitem()\n\u001b[1;32m     67\u001b[0m \u001b[38;5;66;03m# Updata model\u001b[39;00m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 48000, best model saved. (accuracy=0.8448)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Train: 100% 2000/2000 [06:08<00:00,  5.43 step/s, accuracy=0.94, loss=0.40, step=5e+4]   \n",
+      "Valid: 100% 5664/5667 [00:59<00:00, 95.57 uttr/s, accuracy=0.85, loss=0.72] \n",
+      "Train: 100% 2000/2000 [06:04<00:00,  5.48 step/s, accuracy=0.94, loss=0.15, step=52000] \n",
+      "Valid: 100% 5664/5667 [00:58<00:00, 96.03 uttr/s, accuracy=0.85, loss=0.69] \n",
+      "Train: 100% 2000/2000 [06:10<00:00,  5.39 step/s, accuracy=0.97, loss=0.17, step=54000] \n",
+      "Valid: 100% 5664/5667 [00:57<00:00, 98.99 uttr/s, accuracy=0.86, loss=0.66] \n",
+      "Train: 100% 2000/2000 [06:30<00:00,  5.13 step/s, accuracy=0.97, loss=0.18, step=56000] \n",
+      "Valid: 100% 5664/5667 [00:54<00:00, 104.36 uttr/s, accuracy=0.86, loss=0.68]\n",
+      "Train:   0% 0/2000 [00:00<?, ? step/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 56000, best model saved. (accuracy=0.8619)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Train: 100% 2000/2000 [06:18<00:00,  5.29 step/s, accuracy=0.97, loss=0.16, step=66000]  \n",
+      "Valid: 100% 5664/5667 [00:59<00:00, 94.93 uttr/s, accuracy=0.88, loss=0.60] \n",
+      "Train: 100% 2000/2000 [05:48<00:00,  5.73 step/s, accuracy=1.00, loss=0.04, step=68000] \n",
+      "Valid: 100% 5664/5667 [00:58<00:00, 96.95 uttr/s, accuracy=0.88, loss=0.62] \n",
+      "Train: 100% 2000/2000 [05:43<00:00,  5.82 step/s, accuracy=1.00, loss=0.03, step=7e+4]  \n",
+      "Valid: 100% 5664/5667 [00:58<00:00, 97.21 uttr/s, accuracy=0.87, loss=0.61] \n",
+      "Train: 100% 2000/2000 [06:33<00:00,  5.09 step/s, accuracy=1.00, loss=0.03, step=72000]  \n",
+      "Valid: 100% 5664/5667 [00:54<00:00, 104.50 uttr/s, accuracy=0.88, loss=0.58]\n",
+      "Train:   0% 0/2000 [00:00<?, ? step/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 72000, best model saved. (accuracy=0.8810)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Train: 100% 2000/2000 [05:44<00:00,  5.80 step/s, accuracy=1.00, loss=0.03, step=74000]  \n",
+      "Valid: 100% 5664/5667 [00:56<00:00, 99.98 uttr/s, accuracy=0.89, loss=0.58] \n",
+      "Train: 100% 2000/2000 [05:47<00:00,  5.76 step/s, accuracy=1.00, loss=0.02, step=76000] \n",
+      "Valid: 100% 5664/5667 [00:58<00:00, 97.10 uttr/s, accuracy=0.88, loss=0.57] \n",
+      "Train: 100% 2000/2000 [05:54<00:00,  5.64 step/s, accuracy=1.00, loss=0.06, step=78000] \n",
+      "Valid: 100% 5664/5667 [00:58<00:00, 96.74 uttr/s, accuracy=0.88, loss=0.60] \n",
+      "Train: 100% 2000/2000 [06:37<00:00,  5.03 step/s, accuracy=1.00, loss=0.02, step=8e+4]   \n",
+      "Valid: 100% 5664/5667 [00:54<00:00, 103.68 uttr/s, accuracy=0.89, loss=0.58]\n",
+      "Train:   0% 0/2000 [00:00<?, ? step/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 80000, best model saved. (accuracy=0.8868)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Train:   0% 0/2000 [00:12<?, ? step/s]\n"
      ]
     }
    ],
@@ -574,8 +704,8 @@
     "        \"n_workers\": 8,\n",
     "        \"valid_steps\": 2000,\n",
     "        \"warmup_steps\": 1000,\n",
-    "        \"save_steps\": 10000,\n",
-    "        \"total_steps\": 200000,\n",
+    "        \"save_steps\": 8000,\n",
+    "        \"total_steps\": 80000,\n",
     "    }\n",
     "\n",
     "    return config\n",
@@ -672,13 +802,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import gc\n",
     "\n",
-    "del train_loader, valid_loader\n",
+    "#del train_loader, valid_loader\n",
     "gc.collect()"
    ]
   },
@@ -695,7 +836,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "metadata": {
     "id": "efS4pCmAJXJH"
    },
@@ -722,7 +863,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -757,7 +898,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eec3d680f9594fc1a23cbd885723c9b6",
+       "model_id": "397ac09d6c52481cbac427496d266b3e",
        "version_major": 2,
        "version_minor": 0
       },