{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "49f8a125",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "71c3904e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/CEO/miniconda3/envs/py37/bin/python'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.executable\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "320a71c6",
   "metadata": {},
   "source": [
    "# Dogs v Cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "41471e63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "from fastai.vision.all import *\n",
    "import gradio as gr\n",
    "\n",
    "def is_cat(x):\n",
    "    return x[0].isupper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f2e432ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "PILImage mode=RGB size=192x192"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = PILImage.create('dog.jpeg')\n",
    "im.thumbnail((192, 192))\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "20cf1910",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e80fd71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[False, True]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LABELS = learn.dls.vocab\n",
    "LABELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "91133a4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs\n",
      "Wall time: 4.77 µs\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "('False', tensor(0), tensor([9.9996e-01, 4.0008e-05]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time\n",
    "learn.predict(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fbfb35f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "categories = ('Dog', 'Cat')\n",
    "\n",
    "def classify_image(img):\n",
    "    pred, idx, probs = learn.predict(img)\n",
    "    return dict(zip(categories, map(float, probs)))\n",
    "#     return {LABELS[i]: float(probs[i]) for i, _ in enumerate(LABELS)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "607bdfc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'Dog': 0.9999599456787109, 'Cat': 4.000756598543376e-05}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_image(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "abd33e96",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/CEO/miniconda3/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: `width` is deprecated in `Interface()`, please use it within `launch()` instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Users/CEO/miniconda3/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: `height` is deprecated in `Interface()`, please use it within `launch()` instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMPORTANT: You are using gradio version 3.34.0, however version 4.29.0 is available, please upgrade.\n",
      "--------\n",
      "Running on local URL:  http://127.0.0.1:7861\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#|export\n",
    "# image = gr.Image(shape=(192, 192))\n",
    "image = gr.Image(width=192, height=192)\n",
    "label = gr.Label(num_top_classes=2)\n",
    "examples = ['dog.jpeg', 'cat.jpeg', 'bear.jpeg', 'dog2.jpeg']\n",
    "\n",
    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
    "intf.launch(inline=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f0d25829",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = learn.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c75b8ca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = list(m.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a1bd98e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([ 2.3446e-01,  2.6666e-01, -5.1096e-08,  5.1791e-01,  3.4404e-09,\n",
       "         2.2356e-01,  4.2253e-01,  1.3153e-07,  2.5060e-01,  1.5152e-06,\n",
       "         3.1662e-01,  2.4885e-01,  3.7893e-01,  1.0862e-05,  2.7682e-01,\n",
       "         2.3648e-01,  2.4049e-01,  3.9556e-01,  4.6903e-01,  2.9108e-01,\n",
       "         2.7211e-01,  2.7887e-01,  2.9064e-01,  2.0798e-01,  2.5996e-01,\n",
       "         2.7882e-01,  2.9270e-01,  3.1609e-01,  3.8658e-01,  3.0299e-01,\n",
       "         2.6654e-01,  2.0914e-01,  2.8644e-01,  3.3198e-01,  4.2674e-01,\n",
       "         3.7236e-01,  7.4804e-08,  1.8950e-01,  1.4740e-08,  2.2213e-01,\n",
       "         1.7989e-01,  2.5041e-01,  2.7378e-01,  2.5928e-01,  2.9401e-01,\n",
       "         3.0001e-01,  2.2539e-01,  2.6472e-01,  2.2001e-08,  2.6542e-01,\n",
       "         2.2080e-01,  2.8344e-01,  3.3052e-01,  2.2611e-01,  3.6713e-01,\n",
       "         2.1187e-01,  2.3922e-01,  2.4936e-01,  5.2365e-01,  2.4862e-01,\n",
       "         2.9524e-01,  2.5939e-01,  4.8368e-01,  2.6573e-01],\n",
       "       requires_grad=True)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9a09b2fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 3, 7, 7])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a05ca56d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([[[[-1.0403e-02, -6.1059e-03, -1.7828e-03,  ...,  5.6663e-02,\n",
       "            1.7138e-02, -1.2623e-02],\n",
       "          [ 1.1092e-02,  9.5664e-03, -1.0988e-01,  ..., -2.7117e-01,\n",
       "           -1.2901e-01,  3.8199e-03],\n",
       "          [-6.9203e-03,  5.9129e-02,  2.9551e-01,  ...,  5.1974e-01,\n",
       "            2.5637e-01,  6.3646e-02],\n",
       "          ...,\n",
       "          [-2.7530e-02,  1.6054e-02,  7.2580e-02,  ..., -3.3282e-01,\n",
       "           -4.2056e-01, -2.5779e-01],\n",
       "          [ 3.0617e-02,  4.0962e-02,  6.2847e-02,  ...,  4.1387e-01,\n",
       "            3.9361e-01,  1.6608e-01],\n",
       "          [-1.3745e-02, -3.6802e-03, -2.4084e-02,  ..., -1.5065e-01,\n",
       "           -8.2196e-02, -5.7471e-03]],\n",
       "\n",
       "         [[-1.1395e-02, -2.6615e-02, -3.4640e-02,  ...,  3.2556e-02,\n",
       "            7.0995e-04, -2.5679e-02],\n",
       "          [ 4.5684e-02,  3.3616e-02, -1.0451e-01,  ..., -3.1248e-01,\n",
       "           -1.6046e-01, -1.2144e-03],\n",
       "          [-8.2223e-04,  9.8438e-02,  4.0211e-01,  ...,  7.0789e-01,\n",
       "            3.6890e-01,  1.2462e-01],\n",
       "          ...,\n",
       "          [-5.5921e-02, -5.2112e-03,  2.7074e-02,  ..., -4.6175e-01,\n",
       "           -5.7078e-01, -3.6550e-01],\n",
       "          [ 3.2875e-02,  5.5583e-02,  9.9668e-02,  ...,  5.4640e-01,\n",
       "            4.8277e-01,  1.9869e-01],\n",
       "          [ 5.2993e-03,  6.6902e-03, -1.7253e-02,  ..., -1.4816e-01,\n",
       "           -7.7206e-02,  7.5589e-04]],\n",
       "\n",
       "         [[-1.9938e-03, -9.1294e-03,  2.1235e-02,  ...,  8.9241e-02,\n",
       "            3.3733e-02, -2.0005e-02],\n",
       "          [ 1.5439e-02, -1.8605e-02, -1.2586e-01,  ..., -2.5335e-01,\n",
       "           -1.2972e-01, -2.7871e-02],\n",
       "          [ 9.9035e-03,  4.9094e-02,  2.1702e-01,  ...,  3.4876e-01,\n",
       "            1.0439e-01,  1.8506e-02],\n",
       "          ...,\n",
       "          [-2.8313e-02,  1.8451e-02,  9.8680e-02,  ..., -1.1734e-01,\n",
       "           -2.5757e-01, -1.5445e-01],\n",
       "          [ 2.0823e-02, -2.5783e-03, -3.7771e-02,  ...,  2.4148e-01,\n",
       "            2.4350e-01,  1.1801e-01],\n",
       "          [ 7.9165e-04,  8.2017e-04, -9.9933e-03,  ..., -1.4855e-01,\n",
       "           -1.1746e-01, -3.8282e-02]]],\n",
       "\n",
       "\n",
       "        [[[-4.3537e-03, -3.9669e-03,  3.2271e-03,  ..., -3.7015e-02,\n",
       "           -2.5125e-02, -4.7889e-02],\n",
       "          [ 5.1425e-02,  5.3548e-02,  8.0564e-02,  ...,  1.4487e-01,\n",
       "            1.4296e-01,  1.2323e-01],\n",
       "          [-7.2202e-03,  2.3226e-03,  3.7716e-02,  ...,  6.1615e-02,\n",
       "            8.0429e-02,  1.1725e-01],\n",
       "          ...,\n",
       "          [-2.6641e-02, -1.2284e-01, -1.3641e-01,  ..., -1.4061e-01,\n",
       "           -1.1147e-01, -4.9434e-02],\n",
       "          [ 2.3586e-02, -1.7199e-02, -1.1044e-02,  ..., -1.8758e-02,\n",
       "           -2.3235e-02, -2.9358e-02],\n",
       "          [ 2.8731e-02,  2.1723e-02,  4.7948e-02,  ...,  2.5570e-02,\n",
       "            3.5430e-02,  1.1392e-02]],\n",
       "\n",
       "         [[ 4.9134e-04,  1.2219e-02,  4.2072e-02,  ...,  4.6392e-02,\n",
       "            4.0426e-02, -1.4416e-02],\n",
       "          [ 4.3534e-02,  6.8883e-02,  1.3276e-01,  ...,  2.8608e-01,\n",
       "            2.6910e-01,  2.0940e-01],\n",
       "          [-5.7558e-02, -2.2548e-02,  3.0627e-02,  ...,  1.3767e-01,\n",
       "            1.6542e-01,  1.7951e-01],\n",
       "          ...,\n",
       "          [-1.0810e-01, -2.5218e-01, -2.9736e-01,  ..., -2.8502e-01,\n",
       "           -2.1492e-01, -1.0315e-01],\n",
       "          [ 4.0728e-02, -3.2728e-02, -6.3426e-02,  ..., -9.2359e-02,\n",
       "           -6.9866e-02, -4.9801e-02],\n",
       "          [ 8.2942e-02,  8.7593e-02,  1.0112e-01,  ...,  5.2732e-02,\n",
       "            6.0986e-02,  4.1232e-02]],\n",
       "\n",
       "         [[-1.6388e-02, -1.3831e-02,  5.3002e-03,  ...,  4.3678e-02,\n",
       "            2.2695e-02, -4.5986e-02],\n",
       "          [ 3.3237e-02,  4.2077e-02,  9.3555e-02,  ...,  2.6162e-01,\n",
       "            2.2971e-01,  1.6695e-01],\n",
       "          [-4.5954e-02, -1.6319e-02,  2.6849e-02,  ...,  1.4952e-01,\n",
       "            1.3217e-01,  1.3579e-01],\n",
       "          ...,\n",
       "          [-7.2091e-02, -1.8898e-01, -2.3386e-01,  ..., -1.9040e-01,\n",
       "           -1.5612e-01, -7.5969e-02],\n",
       "          [ 5.1169e-02, -2.5805e-02, -6.9355e-02,  ..., -5.9031e-02,\n",
       "           -6.1584e-02, -4.4560e-02],\n",
       "          [ 1.1174e-01,  7.8965e-02,  6.5827e-02,  ...,  3.1595e-02,\n",
       "            2.5195e-02,  7.4148e-03]]],\n",
       "\n",
       "\n",
       "        [[[-7.0824e-08, -6.4305e-08, -7.3805e-08,  ..., -9.7998e-08,\n",
       "           -1.0904e-07, -8.3420e-08],\n",
       "          [-6.1124e-09,  2.0612e-09, -8.0921e-09,  ..., -4.9840e-08,\n",
       "           -4.3835e-08, -3.0537e-09],\n",
       "          [ 7.1952e-08,  7.5615e-08,  5.9281e-08,  ..., -9.7507e-09,\n",
       "           -1.0951e-09,  4.2442e-08],\n",
       "          ...,\n",
       "          [ 9.5887e-08,  1.0039e-07,  7.9816e-08,  ..., -1.7490e-08,\n",
       "           -4.7665e-08, -1.3265e-08],\n",
       "          [ 1.2904e-07,  1.4761e-07,  1.7476e-07,  ...,  1.3232e-07,\n",
       "            1.0628e-07,  9.3314e-08],\n",
       "          [ 1.2558e-07,  1.3644e-07,  1.8431e-07,  ...,  2.1398e-07,\n",
       "            1.7709e-07,  1.7166e-07]],\n",
       "\n",
       "         [[-1.2690e-07, -9.6137e-08, -1.0372e-07,  ..., -1.1808e-07,\n",
       "           -1.3309e-07, -1.0819e-07],\n",
       "          [-5.7412e-08, -2.5054e-08, -3.0114e-08,  ..., -7.2921e-08,\n",
       "           -6.7021e-08, -2.2574e-08],\n",
       "          [ 2.1813e-08,  4.8608e-08,  3.1221e-08,  ..., -1.8694e-08,\n",
       "           -7.9589e-09,  3.9749e-08],\n",
       "          ...,\n",
       "          [ 5.6012e-08,  7.5524e-08,  4.4495e-08,  ..., -4.4127e-08,\n",
       "           -5.9929e-08, -1.8247e-08],\n",
       "          [ 7.7612e-08,  9.8346e-08,  1.0455e-07,  ...,  6.3270e-08,\n",
       "            4.1780e-08,  4.5900e-08],\n",
       "          [ 5.9832e-08,  7.1005e-08,  9.0435e-08,  ...,  1.1654e-07,\n",
       "            8.7549e-08,  9.8835e-08]],\n",
       "\n",
       "         [[-4.3809e-08,  1.3270e-08,  7.8274e-09,  ..., -5.8803e-09,\n",
       "           -2.6217e-08, -1.5649e-08],\n",
       "          [ 4.1699e-08,  1.0777e-07,  1.0946e-07,  ...,  7.6402e-08,\n",
       "            7.1449e-08,  9.7613e-08],\n",
       "          [ 1.0436e-07,  1.6585e-07,  1.5933e-07,  ...,  1.3517e-07,\n",
       "            1.3487e-07,  1.6448e-07],\n",
       "          ...,\n",
       "          [ 9.8762e-08,  1.5072e-07,  1.2546e-07,  ...,  6.8314e-08,\n",
       "            6.8381e-08,  1.1367e-07],\n",
       "          [ 9.1433e-08,  1.3576e-07,  1.3793e-07,  ...,  1.1678e-07,\n",
       "            1.1723e-07,  1.4394e-07],\n",
       "          [ 6.2181e-08,  8.8183e-08,  1.0456e-07,  ...,  1.3941e-07,\n",
       "            1.3332e-07,  1.5844e-07]]],\n",
       "\n",
       "\n",
       "        ...,\n",
       "\n",
       "\n",
       "        [[[-6.1976e-02, -3.0308e-02,  1.9123e-02,  ...,  4.3621e-02,\n",
       "           -2.2100e-02, -4.2172e-02],\n",
       "          [-3.8177e-02,  5.9459e-03,  4.5669e-02,  ...,  9.5934e-02,\n",
       "            5.9216e-02,  2.9943e-02],\n",
       "          [-2.9781e-02,  2.6732e-03,  2.0352e-02,  ...,  5.9749e-02,\n",
       "            4.1401e-02,  2.3126e-02],\n",
       "          ...,\n",
       "          [ 1.1831e-02,  4.5634e-02,  4.4846e-02,  ...,  4.7426e-02,\n",
       "            2.2350e-02, -5.4249e-03],\n",
       "          [-3.2575e-02, -1.2293e-02,  2.1950e-02,  ...,  5.8065e-02,\n",
       "           -7.4235e-03, -5.9627e-02],\n",
       "          [-4.3436e-02, -2.8281e-02, -6.0446e-03,  ...,  8.8412e-02,\n",
       "            8.5168e-03, -4.9918e-02]],\n",
       "\n",
       "         [[-6.1363e-02, -1.4077e-02,  1.7147e-02,  ...,  1.8318e-02,\n",
       "           -3.2657e-02, -4.0967e-02],\n",
       "          [-3.1599e-02,  2.4375e-02,  4.5427e-02,  ...,  6.6755e-02,\n",
       "            4.6713e-02,  3.3301e-02],\n",
       "          [-3.2290e-02,  2.0666e-02,  2.3289e-02,  ...,  3.5263e-02,\n",
       "            3.6563e-02,  3.1372e-02],\n",
       "          ...,\n",
       "          [ 1.7710e-02,  6.1062e-02,  4.8311e-02,  ...,  3.8001e-02,\n",
       "            2.9188e-02,  1.4229e-02],\n",
       "          [-1.0908e-02,  2.2127e-02,  4.2803e-02,  ...,  6.0450e-02,\n",
       "            1.6496e-02, -1.2192e-02],\n",
       "          [-2.2292e-02,  1.3241e-02,  3.0910e-02,  ...,  1.0416e-01,\n",
       "            4.0400e-02, -5.0415e-03]],\n",
       "\n",
       "         [[-8.5361e-02, -4.2671e-02,  6.7213e-03,  ...,  3.0726e-02,\n",
       "           -3.4789e-02, -4.9888e-02],\n",
       "          [-2.9258e-02,  1.8122e-02,  5.1027e-02,  ...,  9.0176e-02,\n",
       "            5.3463e-02,  4.0212e-02],\n",
       "          [-3.9955e-02, -1.1219e-03,  9.5912e-03,  ...,  2.4120e-02,\n",
       "            2.6361e-02,  2.5553e-02],\n",
       "          ...,\n",
       "          [-3.1843e-03,  3.0489e-02,  1.6360e-02,  ...,  5.6436e-03,\n",
       "           -6.0768e-03, -8.2807e-03],\n",
       "          [-2.2981e-02, -2.7779e-03,  2.3229e-02,  ...,  3.6008e-02,\n",
       "           -1.4121e-02, -3.2208e-02],\n",
       "          [-9.8683e-03,  7.0917e-03,  1.0676e-02,  ...,  7.0586e-02,\n",
       "            1.3168e-02, -8.1228e-03]]],\n",
       "\n",
       "\n",
       "        [[[-7.7550e-03,  1.9973e-02,  3.4262e-02,  ...,  2.8783e-02,\n",
       "            1.2898e-02,  1.8205e-02],\n",
       "          [ 8.9931e-03, -3.2746e-02, -3.5626e-02,  ...,  7.2632e-02,\n",
       "            4.5972e-02,  5.2455e-02],\n",
       "          [-3.5933e-02, -1.1872e-01, -1.3759e-01,  ...,  3.3931e-02,\n",
       "            3.7889e-02,  2.7015e-02],\n",
       "          ...,\n",
       "          [ 1.7452e-02,  4.0411e-03, -8.1246e-03,  ...,  2.8410e-03,\n",
       "            1.8367e-02,  1.6079e-02],\n",
       "          [-8.3471e-04,  1.6525e-02,  1.7248e-02,  ...,  3.4479e-03,\n",
       "            2.2920e-02,  6.9118e-04],\n",
       "          [ 6.2839e-03,  2.7214e-02,  1.4440e-02,  ...,  7.6273e-03,\n",
       "            1.8790e-02,  1.5626e-02]],\n",
       "\n",
       "         [[-1.3320e-02, -4.6847e-04,  8.1130e-03,  ..., -6.0008e-03,\n",
       "            9.3258e-03,  1.5826e-02],\n",
       "          [-1.8147e-02, -6.7857e-02, -7.0646e-02,  ...,  2.9978e-02,\n",
       "            2.6368e-02,  2.3859e-02],\n",
       "          [-5.4171e-02, -1.4661e-01, -1.6213e-01,  ...,  1.1907e-02,\n",
       "            3.2571e-02,  1.2054e-02],\n",
       "          ...,\n",
       "          [ 9.4340e-04, -1.7483e-02, -1.9428e-02,  ..., -4.0498e-03,\n",
       "            2.4708e-02,  1.2915e-02],\n",
       "          [-5.6158e-04,  1.1856e-02,  2.4877e-02,  ...,  6.1880e-03,\n",
       "            3.9277e-02,  9.7009e-03],\n",
       "          [-7.1135e-03,  6.7278e-03,  5.3565e-03,  ..., -7.5771e-03,\n",
       "            2.7267e-02,  1.7734e-02]],\n",
       "\n",
       "         [[-1.3360e-04, -4.9030e-03,  2.3169e-03,  ..., -4.7853e-02,\n",
       "           -2.6052e-02, -2.3444e-02],\n",
       "          [-1.5510e-04, -5.1400e-02, -5.9962e-02,  ..., -1.7250e-02,\n",
       "           -2.3242e-02, -3.7229e-02],\n",
       "          [-2.2534e-02, -9.9448e-02, -1.1188e-01,  ..., -1.1634e-02,\n",
       "           -8.3022e-03, -4.0546e-02],\n",
       "          ...,\n",
       "          [ 1.1539e-02, -7.9946e-03, -1.4023e-03,  ..., -3.4080e-02,\n",
       "           -8.6820e-03, -2.3502e-02],\n",
       "          [ 3.0382e-03,  7.6015e-04,  2.0017e-02,  ..., -2.1932e-02,\n",
       "            1.4864e-02, -1.4465e-02],\n",
       "          [-1.9023e-02, -2.9396e-02, -2.3191e-02,  ..., -4.8553e-02,\n",
       "           -1.3043e-02, -2.4390e-02]]],\n",
       "\n",
       "\n",
       "        [[[-3.6213e-02,  7.3102e-03,  1.9222e-02,  ...,  1.9744e-02,\n",
       "            1.5022e-02, -1.7145e-02],\n",
       "          [-1.0993e-02,  8.5747e-02,  1.2677e-01,  ...,  1.3870e-02,\n",
       "            8.8419e-05, -3.0021e-02],\n",
       "          [ 1.1329e-01,  1.8643e-01,  5.0772e-02,  ..., -1.7319e-01,\n",
       "           -7.1887e-02, -6.2321e-02],\n",
       "          ...,\n",
       "          [-5.3010e-02, -2.5772e-01, -2.6737e-01,  ...,  2.6792e-01,\n",
       "            1.4359e-01,  5.5292e-02],\n",
       "          [-2.0945e-02, -2.9885e-02,  1.0252e-01,  ...,  2.0853e-01,\n",
       "           -4.0336e-03, -3.7998e-02],\n",
       "          [-2.2088e-02,  1.2474e-02,  8.4386e-02,  ..., -4.4907e-02,\n",
       "           -1.4678e-01, -9.0778e-02]],\n",
       "\n",
       "         [[-5.3225e-03,  3.2908e-02,  1.5611e-02,  ..., -7.5756e-03,\n",
       "            3.2004e-03,  1.3050e-03],\n",
       "          [ 6.1781e-02,  1.4907e-01,  1.4656e-01,  ..., -2.8729e-02,\n",
       "           -2.0040e-02, -9.0057e-03],\n",
       "          [ 1.6152e-01,  2.0895e-01, -2.5457e-02,  ..., -2.7259e-01,\n",
       "           -1.0715e-01, -6.2781e-02],\n",
       "          ...,\n",
       "          [-1.3718e-01, -4.0854e-01, -3.8539e-01,  ...,  4.0861e-01,\n",
       "            2.6220e-01,  1.3509e-01],\n",
       "          [-5.9329e-02, -6.1106e-02,  1.4205e-01,  ...,  3.5792e-01,\n",
       "            9.1046e-02, -1.5938e-03],\n",
       "          [ 7.9290e-03,  5.8498e-02,  1.5347e-01,  ...,  4.7145e-02,\n",
       "           -1.0083e-01, -9.7783e-02]],\n",
       "\n",
       "         [[-5.6097e-03,  1.3533e-02, -2.6339e-02,  ...,  4.6575e-03,\n",
       "            2.2503e-03,  1.4092e-02],\n",
       "          [ 6.6522e-03,  4.5265e-02,  6.0375e-02,  ...,  1.4536e-02,\n",
       "           -4.8766e-03,  4.2413e-03],\n",
       "          [ 5.5326e-02,  1.2407e-01,  4.3324e-02,  ..., -1.4467e-01,\n",
       "           -7.4274e-02, -5.7331e-02],\n",
       "          ...,\n",
       "          [-3.1482e-02, -1.6326e-01, -1.5783e-01,  ...,  2.2922e-01,\n",
       "            1.2038e-01,  7.2202e-02],\n",
       "          [-1.0412e-02, -1.0449e-03,  8.4668e-02,  ...,  1.5762e-01,\n",
       "            2.2313e-02, -9.9166e-03],\n",
       "          [-4.8027e-03, -4.9094e-03,  3.6432e-02,  ..., -2.4261e-02,\n",
       "           -7.1066e-02, -6.6630e-02]]]], requires_grad=True)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "991de068",
   "metadata": {},
   "source": [
    "# export -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "416da297",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nbdev\n",
    "import nbdev.export\n",
    "\n",
    "# dir(nbdev.export) # shows that no notebook2script, now nb_export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a0d2f5c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from nbdev.export import notebook2script\n",
    "# notebook2script('app.ipynb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4d5c5a92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# nbdev.export.nb_export('app.ipynb', 'app')\n",
    "# print('Export successful')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3debd079",
   "metadata": {},
   "outputs": [],
   "source": [
    "notebook_name = \"app.ipynb\"\n",
    "export_destination = \".\" # the root directory\n",
    "nbdev.export.nb_export(notebook_name, export_destination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "359ae4e2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7 (py37)",
   "language": "python",
   "name": "py37"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.16"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}