using a simple Sequential model in Tabular Learner

iris01.ipynb

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     "ename": "ModuleNotFoundError",
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      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
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      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'fastai'"
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   "source": [
    "from fastai.tabular.all import *\n",
    "import torch.nn as nn\n",
    "\n",
    "df = pd.read_csv('iris.csv')\n",
    "split = TrainTestSplitter(random_state=42)(df)\n",
    "df.species = pd.Categorical(df.species)\n",
    "\n",
    "dls = TabularPandas(df, splits=split, procs=[Normalize], cat_names=[], cont_names=list(df.columns[:-1]), y_names='species', y_block=CategoryBlock()).dataloaders(bs=8)\n",
    "\n",
    "class NNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(NNet, self).__init__()\n",
    "        self.nnet = nn.Sequential(\n",
    "            nn.Linear(4,10),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(10,3),\n",
    "            nn.Softmax()\n",
    "        )\n",
    "    def forward(self, x, _):\n",
    "        return self.nnet(x.view(-1,4))\n",
    "\n",
    "model = NNet()\n",
    "learn = Learner(dls, model=model, metrics=accuracy, loss_func=CrossEntropyLossFlat)\n",
    "learn.fit(10, lr=0.1)"
   ]
  }
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