Understanding pyTorch regression model quickly.

torch_regressor_cheatsheet.ipynb

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   "source": [
    "### 1. What is this code?\n",
    "* A simple NeuralNet model using pyTorch.\n",
    "### 2. How can I use this?\n",
    "* Just run and see the output, and modify as you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
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   "source": [
    "import torch\n",
    "from sklearn.datasets import make_regression\n",
    "\n",
    "class NeuralNet(torch.nn.Module):\n",
    "    def __init__(self, input_size, hidden_size):\n",
    "        super(NeuralNet, self).__init__()\n",
    "        self.input_size = input_size\n",
    "        self.hidden_size  = hidden_size\n",
    "        self.linear_1 = torch.nn.Linear(self.input_size, self.hidden_size)\n",
    "        self.relu = torch.nn.ReLU()\n",
    "        self.linear_2 = torch.nn.Linear(self.hidden_size, 1)\n",
    "        \n",
    "    def forward(self, input_tensor):\n",
    "        linear1 = self.linear_1(input_tensor)\n",
    "        relu = self.relu(linear1)\n",
    "        linear2 = self.linear_2(relu)\n",
    "        return linear2\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [1] Data preparation\n",
    "X, y = make_regression(\n",
    "    n_samples=5000, # 5000 data\n",
    "    n_features=50, # 50 columns = 50 dimensions\n",
    "    noise=1, # randomness(standard deviation)\n",
    "    random_state=42\n",
    "    )\n",
    "\n",
    "X, y = torch.FloatTensor(X), torch.FloatTensor(y)\n",
    "\n",
    "X_train = X[:4000]\n",
    "X_test = X[4000:]\n",
    "y_train = y[:4000]\n",
    "y_test = y[4000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [2] Hyperparameters\n",
    "number_of_x_features = X.shape[1]\n",
    "model = NeuralNet(number_of_x_features, 128)\n",
    "learning_rate = 0.03\n",
    "criterion = torch.nn.MSELoss()\n",
    "epochs = 2000\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 0.03)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Training, test loss is 35319.47265625\n",
      "Train loss at 0 is 38226.875\n",
      "Train loss at 500 is 1.2112720012664795\n",
      "Train loss at 1000 is 0.18726365268230438\n",
      "Train loss at 1500 is 0.04877258837223053\n"
     ]
    }
   ],
   "source": [
    "# [3] Train\n",
    "model.eval()\n",
    "test_loss_before = criterion(model(X_test).squeeze(), y_test)\n",
    "print('Before Training, test loss is {}'.format(test_loss_before.item()))\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    optimizer.zero_grad()\n",
    "    train_output = model(X_train)\n",
    "    train_loss = criterion(train_output.squeeze(), y_train)\n",
    "    if epoch % 500 == 0:\n",
    "        print('Train loss at {} is {}'.format(epoch, train_loss.item()))\n",
    "    train_loss.backward()\n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After Training, test loss is 51.258384704589844\n"
     ]
    }
   ],
   "source": [
    "# [4] Evaluate\n",
    "model.eval()\n",
    "test_loss = criterion(torch.squeeze(model(X_test)), y_test)\n",
    "print('After Training, test loss is {}'.format(test_loss.item()))"
   ]
  }
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