{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "from local.test import *\n",
    "from local.data.all import *\n",
    "from local.optimizer import *\n",
    "from local.learner import *\n",
    "from torch.utils.data import TensorDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default_exp test_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synthetic Learner\n",
    "\n",
    "> For quick testing of the training loop and Callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "from torch.utils.data import TensorDataset\n",
    "\n",
    "def synth_dbunch(a=2, b=3, bs=16, n_train=10, n_valid=2, cuda=False):\n",
    "    def get_data(n):\n",
    "        x = torch.randn(bs*n, 1)\n",
    "        return TensorDataset(x, a*x + b + 0.1*torch.randn(bs*n, 1))\n",
    "    train_ds = get_data(n_train)\n",
    "    valid_ds = get_data(n_valid)\n",
    "    tfms = Cuda() if cuda else None\n",
    "    train_dl = TfmdDL(train_ds, bs=bs, shuffle=True, after_batch=tfms, num_workers=0)\n",
    "    valid_dl = TfmdDL(valid_ds, bs=bs, after_batch=tfms, num_workers=0)\n",
    "    return DataBunch(train_dl, valid_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class RegModel(Module):\n",
    "    def __init__(self): self.a,self.b = nn.Parameter(torch.randn(1)),nn.Parameter(torch.randn(1))\n",
    "    def forward(self, x): return x*self.a + self.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export\n",
    "@delegates(Learner.__init__)\n",
    "def synth_learner(n_trn=10, n_val=2, cuda=False, lr=1e-3, data=None, **kwargs):\n",
    "    if data is None: data = synth_dbunch(n_train=n_trn,n_valid=n_val, cuda=cuda)\n",
    "    return Learner(data, RegModel(), lr=lr, loss_func=MSELossFlat(),\n",
    "                   opt_func=partial(SGD, mom=0.9), **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## - Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converted 00_test.ipynb.\n",
      "Converted 01_core.ipynb.\n",
      "Converted 01a_torch_core.ipynb.\n",
      "Converted 02_script.ipynb.\n",
      "Converted 03_dataloader.ipynb.\n",
      "Converted 04_transform.ipynb.\n",
      "Converted 05_data_core.ipynb.\n",
      "Converted 06_data_transforms.ipynb.\n",
      "Converted 07_vision_core.ipynb.\n",
      "Converted 08_pets_tutorial.ipynb.\n",
      "Converted 09_vision_augment.ipynb.\n",
      "Converted 11_layers.ipynb.\n",
      "Converted 11a_vision_models_xresnet.ipynb.\n",
      "Converted 12_optimizer.ipynb.\n",
      "Converted 13_learner.ipynb.\n",
      "Converted 14_callback_schedule.ipynb.\n",
      "Converted 14a_callback_data.ipynb.\n",
      "Converted 15_callback_hook.ipynb.\n",
      "Converted 16_callback_progress.ipynb.\n",
      "Converted 17_callback_tracker.ipynb.\n",
      "Converted 18_callback_fp16.ipynb.\n",
      "Converted 19_callback_mixup.ipynb.\n",
      "Converted 20_metrics.ipynb.\n",
      "Converted 21_tutorial_imagenette.ipynb.\n",
      "Converted 22_vision_learner.ipynb.\n",
      "Converted 23_tutorial_transfer_learning.ipynb.\n",
      "Converted 30_text_core.ipynb.\n",
      "Converted 31_text_data.ipynb.\n",
      "Converted 32_text_models_awdlstm.ipynb.\n",
      "Converted 33_text_models_core.ipynb.\n",
      "Converted 34_callback_rnn.ipynb.\n",
      "Converted 35_tutorial_wikitext.ipynb.\n",
      "Converted 36_text_models_qrnn.ipynb.\n",
      "Converted 37_text_learner.ipynb.\n",
      "Converted 38_tutorial_ulmfit.ipynb.\n",
      "Converted 40_tabular_core.ipynb.\n",
      "Converted 41_tabular_model.ipynb.\n",
      "Converted 42_tabular_rapids.ipynb.\n",
      "Converted 50_data_block.ipynb.\n",
      "Converted 90_notebook_core.ipynb.\n",
      "Converted 91_notebook_export.ipynb.\n",
      "Converted 92_notebook_showdoc.ipynb.\n",
      "Converted 93_notebook_export2html.ipynb.\n",
      "Converted 94_notebook_test.ipynb.\n",
      "Converted 95_index.ipynb.\n",
      "Converted 96_data_external.ipynb.\n",
      "Converted 97_utils_test.ipynb.\n",
      "Converted notebook2jekyll.ipynb.\n",
      "Converted tmp.ipynb.\n"
     ]
    }
   ],
   "source": [
    "#hide\n",
    "from local.notebook.export import *\n",
    "notebook2script(all_fs=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}