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   "source": [
    "# Neural Nets Notebook\n",
    "\n",
    "## Exercise 2\n",
    "\n",
    "```python\n",
    "class MLP_drop(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim):\n",
    "        super().__init__()\n",
    "\n",
    "        self.input_fc = nn.Linear(input_dim, 250)\n",
    "        self.hidden_fc = nn.Linear(250, 100)\n",
    "        self.output_fc = nn.Linear(100, output_dim)\n",
    "        self.dropout = nn.Dropout(0.25)\n",
    "\n",
    "    def forward(self, x):\n",
    "\n",
    "        batch_size = x.shape[0]\n",
    "        x = x.view(batch_size, -1)\n",
    "        h_1 = F.relu(self.input_fc(x))\n",
    "        x = self.hidden_fc(h_1)\n",
    "        x = self.dropout(x)\n",
    "        h_2 = F.relu(x)\n",
    "        y_pred = self.output_fc(h_2)\n",
    "\n",
    "        return y_pred\n",
    "    \n",
    "\n",
    "model = MLP_drop(INPUT_DIM, OUTPUT_DIM)\n",
    "optimizer = optim.Adam(model.parameters())\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "EPOCHS = 10\n",
    "\n",
    "best_valid_loss = float('inf')\n",
    "\n",
    "for epoch in trange(EPOCHS):\n",
    "\n",
    "    start_time = time.monotonic()\n",
    "\n",
    "    train_loss, train_acc = train(model, train_iterator, optimizer, criterion, device)\n",
    "    valid_loss, valid_acc = evaluate(model, valid_iterator, criterion, device)\n",
    "\n",
    "    if valid_loss < best_valid_loss:\n",
    "        best_valid_loss = valid_loss\n",
    "        torch.save(model.state_dict(), 'mlp-model-drop.pt')\n",
    "\n",
    "    end_time = time.monotonic()\n",
    "\n",
    "    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
    "\n",
    "    print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n",
    "    print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n",
    "    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')\n",
    "```"
   ]
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   "cell_type": "markdown",
   "id": "4404d771",
   "metadata": {},
   "source": [
    "# Conv Nets Notebook \n",
    "\n",
    "## Set up the network\n",
    "\n",
    "```python\n",
    "    def forward(self, x):\n",
    "\n",
    "        x = self.conv1(x)\n",
    "        x = F.max_pool2d(x, kernel_size=2)\n",
    "        x = F.relu(x)\n",
    "        x = self.conv2(x)\n",
    "        x = F.max_pool2d(x, kernel_size=2)\n",
    "        x = F.relu(x)\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        x = self.fc_1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc_2(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc_3(x)\n",
    "\n",
    "        return x\n",
    "```"
   ]
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