D2L_Matrix_Factorization.ipynb

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        "<a href=\"https://colab.research.google.com/github/rajlm10/D2L-Torch/blob/main/D2L_Matrix_Factorization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "\u001b[K     |████████████████████████████████| 82 kB 766 kB/s \n",
            "\u001b[K     |████████████████████████████████| 15.7 MB 20.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 61 kB 9.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 9.9 MB 52.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 11.2 MB 48.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 930 kB 65.0 MB/s \n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.25.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q d2l torchinfo"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch import nn\n",
        "from d2l import torch as d2l\n",
        "from tqdm import tqdm\n",
        "import math"
      ],
      "metadata": {
        "id": "bBhEgCUvh5XR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d2l.DATA_HUB['ml-100k'] = (\n",
        "    'https://files.grouplens.org/datasets/movielens/ml-100k.zip',\n",
        "    'cd4dcac4241c8a4ad7badc7ca635da8a69dddb83')\n",
        "\n",
        "def read_data_ml100k():\n",
        "    data_dir = d2l.download_extract('ml-100k')\n",
        "    names = ['user_id', 'item_id', 'rating', 'timestamp']\n",
        "    data = pd.read_csv(os.path.join(data_dir, 'u.data'), '\\t', names=names,engine='python')\n",
        "    num_users = data.user_id.unique().shape[0]\n",
        "    num_items = data.item_id.unique().shape[0]\n",
        "    return data, num_users, num_items"
      ],
      "metadata": {
        "id": "nf5sWM_siWuU"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "data, num_users, num_items = read_data_ml100k()\n",
        "sparsity = 1 - len(data) / (num_users * num_items)\n",
        "print(f'number of users: {num_users}, number of items: {num_items}')\n",
        "print(f'matrix sparsity: {sparsity:f}')"
      ],
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        "colab": {
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        },
        "id": "CgtrmJEsieG6",
        "outputId": "9b796aad-a56c-46c0-d07c-dc681d93d599"
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      "outputs": [
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          "name": "stdout",
          "text": [
            "Downloading ../data/ml-100k.zip from https://files.grouplens.org/datasets/movielens/ml-100k.zip...\n",
            "number of users: 943, number of items: 1682\n",
            "matrix sparsity: 0.936953\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(5)"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100000 entries, 0 to 99999\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count   Dtype\n",
            "---  ------     --------------   -----\n",
            " 0   user_id    100000 non-null  int64\n",
            " 1   item_id    100000 non-null  int64\n",
            " 2   rating     100000 non-null  int64\n",
            " 3   timestamp  100000 non-null  int64\n",
            "dtypes: int64(4)\n",
            "memory usage: 3.1 MB\n"
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        "#Note that user_id and item_id start with 1\n",
        "data.describe()"
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          "height": 300
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              "            user_id        item_id         rating     timestamp\n",
              "count  100000.00000  100000.000000  100000.000000  1.000000e+05\n",
              "mean      462.48475     425.530130       3.529860  8.835289e+08\n",
              "std       266.61442     330.798356       1.125674  5.343856e+06\n",
              "min         1.00000       1.000000       1.000000  8.747247e+08\n",
              "25%       254.00000     175.000000       3.000000  8.794487e+08\n",
              "50%       447.00000     322.000000       4.000000  8.828269e+08\n",
              "75%       682.00000     631.000000       4.000000  8.882600e+08\n",
              "max       943.00000    1682.000000       5.000000  8.932866e+08"
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              "      <td>1.00000</td>\n",
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              "      <th>25%</th>\n",
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              "      <th>50%</th>\n",
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              "      <td>4.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>682.00000</td>\n",
              "      <td>631.000000</td>\n",
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              "      <td>8.882600e+08</td>\n",
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              "      <th>max</th>\n",
              "      <td>943.00000</td>\n",
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              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d2l.plt.hist(data['rating'], bins=5, ec='black')\n",
        "d2l.plt.xlabel('Rating')\n",
        "d2l.plt.ylabel('Count')\n",
        "d2l.plt.title('Distribution of Ratings in MovieLens 100K')\n",
        "d2l.plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "B7NGHJTdixcn",
        "outputId": "303f0b6f-9c17-4b83-f919-ca34eced6280"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Splitting the data\n",
        "\n",
        "We split the dataset into training and test sets. The following function provides two split modes including random and seq-aware. In the random mode, the function splits the 100k interactions randomly without considering timestamp and uses the 90% of the data as training samples and the rest 10% as test samples by default. In the seq-aware mode, we leave out the item that a user rated most recently for test, and users’ historical interactions as training set. User historical interactions are sorted from oldest to newest based on timestamp. This mode will be used in the sequence-aware recommendation section."
      ],
      "metadata": {
        "id": "SMVa-mhci7P-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#We have atleast 96 entries per user\n",
        "#If we had 1 entry only, we can't use it in the test set and hence can omit that user\n",
        "min(data.groupby('user_id')['user_id'].sum()) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TEYr1QIqkic5",
        "outputId": "de80105a-3b1b-4df6-e7ea-0fd0121c4376"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def split_data_ml100k(data, num_users, num_items,\n",
        "                      split_mode='random', test_ratio=0.1):\n",
        "    \"\"\"Split the dataset in random mode or seq-aware mode.\"\"\"\n",
        "    columns= list(data.columns)\n",
        "    if split_mode == 'seq-aware':\n",
        "        train_items, test_items, train_list = {}, {}, []\n",
        "        for line in tqdm(data.to_dict('records')):\n",
        "            u, i, rating, time = line['user_id'], line['item_id'], line['rating'], line['timestamp']\n",
        "            #key-> user_id value-> all instances of u\n",
        "            train_items.setdefault(u, []).append((u, i, rating, time))\n",
        "            #for every unique u make an entry and if u already exists\n",
        "            # replace the entry by most recent entry for u\n",
        "            if u not in test_items or test_items[u][-1] < time:\n",
        "                test_items[u] = (i, rating, time)\n",
        "        for u in range(1, num_users + 1):\n",
        "            #list of tuples\n",
        "            train_list.extend(sorted(train_items[u], key=lambda k: k[-1]))\n",
        "        test_data = [(key, *value) for key, value in test_items.items()]\n",
        "        train_data = [item for item in train_list if item not in test_data]\n",
        "        train_data = pd.DataFrame(train_data,columns=columns)\n",
        "        test_data = pd.DataFrame(test_data,columns=columns)\n",
        "    else:\n",
        "        mask = [True if x == 1 else False for x in np.random.uniform(\n",
        "            0, 1, (len(data))) < 1 - test_ratio]\n",
        "        neg_mask = [not x for x in mask]\n",
        "        train_data, test_data = data[mask], data[neg_mask]\n",
        "    return train_data, test_data"
      ],
      "metadata": {
        "id": "bpznb5t3i4ER"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Data\n",
        "\n",
        "After dataset splitting, we will convert the training set and test set into lists and dictionaries/matrix for the sake of convenience. The following function reads the dataframe line by line and enumerates the index of users/items start from zero. The function then returns lists of users, items, ratings and a dictionary/matrix that records the interactions. We can specify the type of feedback to either explicit or implicit"
      ],
      "metadata": {
        "id": "5VU3_OWqnD8I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def load_data_ml100k(data, num_users, num_items, feedback='explicit'):\n",
        "    users, items, scores = [], [], []\n",
        "    inter = np.zeros((num_items, num_users)) if feedback == 'explicit' else {}\n",
        "    for line in tqdm(data.to_dict('records')):\n",
        "        #subtract 1 since user_id & item_id start from 1\n",
        "        user_index, item_index = int(line['user_id'] - 1), int(line['item_id'] - 1)\n",
        "        score = int(line['rating']) if feedback == 'explicit' else 1\n",
        "        users.append(user_index) #len num interactions\n",
        "        items.append(item_index) #len num interactions\n",
        "        scores.append(score)     #len num interactions\n",
        "        if feedback == 'implicit':\n",
        "            #for each user, list of items interacted with\n",
        "            inter.setdefault(user_index, []).append(item_index)\n",
        "        else:\n",
        "            #num_items x num_users matrix with interaction scores at each position\n",
        "            inter[item_index, user_index] = score\n",
        "    return users, items, scores, inter"
      ],
      "metadata": {
        "id": "4_AEIqFjm_4h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_iterator(dataset,batch_size,is_train=True):\n",
        "  dataset=torch.utils.data.TensorDataset(*dataset)\n",
        "  return torch.utils.data.DataLoader(dataset,batch_size,shuffle=is_train)"
      ],
      "metadata": {
        "id": "1REa3Oa0oDRy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def split_and_load_ml100k(split_mode='seq-aware', feedback='explicit',test_ratio=0.1, batch_size=256):\n",
        "    data, num_users, num_items = read_data_ml100k()\n",
        "    train_data, test_data = split_data_ml100k(\n",
        "        data, num_users, num_items, split_mode, test_ratio)\n",
        "    train_u, train_i, train_r, _ = load_data_ml100k(\n",
        "        train_data, num_users, num_items, feedback)\n",
        "    test_u, test_i, test_r, _ = load_data_ml100k(\n",
        "        test_data, num_users, num_items, feedback)\n",
        "    \n",
        "    train_u, train_i, train_r = torch.tensor(train_u,dtype=torch.long),torch.tensor(train_i,dtype=torch.long),torch.tensor(train_r,dtype=torch.float32)\n",
        "    test_u, test_i, test_r = torch.tensor(test_u,dtype=torch.long),torch.tensor(test_u,dtype=torch.long),torch.tensor(test_r,dtype=torch.float32)    \n",
        "\n",
        "    train_iter= create_iterator ((train_u, train_i, train_r),batch_size=batch_size,is_train=True)\n",
        "    \n",
        "    test_iter= create_iterator ((test_u, test_i, test_r),batch_size=batch_size,is_train=False)\n",
        "    return num_users,num_items,train_iter, test_iter"
      ],
      "metadata": {
        "id": "MJS_EaGnn4-l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_users, num_items, train_iter, test_iter= split_and_load_ml100k(split_mode='seq-aware', feedback='explicit',test_ratio=0.1, batch_size=256)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jder7GfVwud_",
        "outputId": "52b10cf1-7d8e-451c-c2e4-805be6b278ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100000/100000 [00:00<00:00, 942068.13it/s]\n",
            "100%|██████████| 99057/99057 [00:00<00:00, 706124.48it/s]\n",
            "100%|██████████| 943/943 [00:00<00:00, 354157.30it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for batch in train_iter:\n",
        "  print(len(batch))\n",
        "  print(batch[0].shape)\n",
        "  print(batch[1].shape)\n",
        "  print(batch[2].shape)\n",
        "  break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p7_gAvtjq3bC",
        "outputId": "9be46e18-a491-48fc-9580-e4afe750b450"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n",
            "torch.Size([256])\n",
            "torch.Size([256])\n",
            "torch.Size([256])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Buikding the Model"
      ],
      "metadata": {
        "id": "Puldf34QgUPF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "uge6amyqgXLm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MatrixFactorization(nn.Module):\n",
        "  def __init__(self, num_factors, num_users, num_items, **kwargs):\n",
        "    super(MatrixFactorization,self).__init__(**kwargs)\n",
        "    self.P = nn.Embedding(num_users,num_factors)\n",
        "    self.Q = nn.Embedding(num_items,num_factors)\n",
        "    self.user_bias = nn.Embedding(num_users,1)\n",
        "    self.item_bias = nn.Embedding(num_items,1)\n",
        "  \n",
        "  def forward(self,user_id,item_id):\n",
        "    P_u = self.P(user_id) #batch_size,num_factors\n",
        "    Q_i = self.Q(item_id) #batch_size,num_factors\n",
        "    b_u = self.user_bias(user_id) #batch_size,1\n",
        "    b_i = self.item_bias(item_id) #batch_size,1\n",
        "\n",
        "    R = (P_u*Q_i).sum(axis=1) + b_u.view(-1) + b_i.view(-1)\n",
        "    return R.flatten()\n"
      ],
      "metadata": {
        "id": "_pbmVP83hPqD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def init_weights(m):\n",
        "  if type(m) == nn.Embedding:\n",
        "    nn.init.xavier_normal_(m.weight,0.01) \n"
      ],
      "metadata": {
        "id": "VDn4OHqPjGoR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_rmse_gpu(net, data_iter, device=None):\n",
        "  if isinstance(net, nn.Module):\n",
        "    net.eval() # Set the model to evaluation mode \n",
        "    if not device:\n",
        "      device = next(iter(net.parameters())).device \n",
        "\n",
        "  # No. of correct predictions, no. of predictions \n",
        "  metric = d2l.Accumulator(2)\n",
        "  with torch.no_grad():\n",
        "    for user_ids,label_ids, y in data_iter:\n",
        "      user_ids = user_ids.to(device)\n",
        "      label_ids = label_ids.to(device)\n",
        "      y = y.to(device) \n",
        "      metric.add(nn.MSELoss(reduction='sum')(net(user_ids,label_ids),y), y.shape[0])\n",
        "  return math.sqrt(metric[0] / metric[1])"
      ],
      "metadata": {
        "id": "a_r844dOjasc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_batch(net,X,y,loss,optimizer,devices):\n",
        "  X = [x.to(devices[0]) for x in X]\n",
        "  y = y.to(devices[0])\n",
        "  net.train()\n",
        "  optimizer.zero_grad()\n",
        "  pred = net(*X)\n",
        "  l = loss(pred, y)\n",
        "  l.sum().backward()\n",
        "  optimizer.step()\n",
        "  train_loss_sum = l.sum() \n",
        "  return train_loss_sum"
      ],
      "metadata": {
        "id": "FHQXFal-kP8f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(net, train_iter, test_iter, loss, trainer, num_epochs,devices=d2l.try_all_gpus()):\n",
        "  timer, num_batches = d2l.Timer(), len(train_iter)\n",
        "  net = nn.DataParallel(net, device_ids=devices).to(devices[0])\n",
        "  for epoch in range(num_epochs):\n",
        "    # Sum of training loss, sum of training squared error, no. of examples, no. of predictions\n",
        "    metric = d2l.Accumulator(2)\n",
        "    for user_ids,item_ids,labels in train_iter:\n",
        "      timer.start()\n",
        "      l=train_batch(net,(user_ids,item_ids),labels,loss,trainer,devices)\n",
        "      metric.add(l,labels.shape[0])\n",
        "      timer.stop()\n",
        "    test_rmse = evaluate_rmse_gpu(net, test_iter)\n",
        "    print(f'epoch {epoch+1}: RMSE loss {math.sqrt(metric[0] / metric[1]):.3f}, test RMSE {test_rmse:.3f}') \n",
        "  print(f'{metric[1] * num_epochs / timer.sum():.1f} examples/sec on 'f'{str(devices)}')"
      ],
      "metadata": {
        "id": "H3Ph5yJ0loqR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "devices = d2l.try_all_gpus()\n",
        "num_users, num_items, train_iter, test_iter = split_and_load_ml100k(test_ratio=0.1, batch_size=512)\n",
        "net = MatrixFactorization(32, num_users, num_items)\n",
        "net.apply(init_weights)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8TB_eV6DnDxG",
        "outputId": "97c8e083-bb2e-4eb0-b686-d34fd0e6d797"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100000/100000 [00:00<00:00, 1010493.04it/s]\n",
            "100%|██████████| 99057/99057 [00:00<00:00, 741202.57it/s]\n",
            "100%|██████████| 943/943 [00:00<00:00, 396117.04it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MatrixFactorization(\n",
              "  (P): Embedding(943, 32)\n",
              "  (Q): Embedding(1682, 32)\n",
              "  (user_bias): Embedding(943, 1)\n",
              "  (item_bias): Embedding(1682, 1)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchinfo import summary\n",
        "summary(net)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pgbYe5YrqRoV",
        "outputId": "978eb468-0cdb-4380-e992-b0574f224bc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "=================================================================\n",
              "Layer (type:depth-idx)                   Param #\n",
              "=================================================================\n",
              "MatrixFactorization                      --\n",
              "├─Embedding: 1-1                         30,176\n",
              "├─Embedding: 1-2                         53,824\n",
              "├─Embedding: 1-3                         943\n",
              "├─Embedding: 1-4                         1,682\n",
              "=================================================================\n",
              "Total params: 86,625\n",
              "Trainable params: 86,625\n",
              "Non-trainable params: 0\n",
              "================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr, num_epochs, wd= 0.002, 20, 1e-5\n",
        "optimizer = torch.optim.Adam(net.parameters(), lr=lr, weight_decay=wd)\n",
        "\n",
        "loss = nn.MSELoss(reduction='none')\n",
        "\n",
        "train(net, train_iter, test_iter, loss, optimizer, num_epochs,devices)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OB7AUOPrniMM",
        "outputId": "c79581e5-0861-46fc-fda0-d793a4a3e668"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1: RMSE loss 2.928, test RMSE 2.145\n",
            "epoch 2: RMSE loss 1.147, test RMSE 1.543\n",
            "epoch 3: RMSE loss 0.979, test RMSE 1.409\n",
            "epoch 4: RMSE loss 0.950, test RMSE 1.379\n",
            "epoch 5: RMSE loss 0.940, test RMSE 1.359\n",
            "epoch 6: RMSE loss 0.936, test RMSE 1.354\n",
            "epoch 7: RMSE loss 0.933, test RMSE 1.348\n",
            "epoch 8: RMSE loss 0.930, test RMSE 1.350\n",
            "epoch 9: RMSE loss 0.927, test RMSE 1.346\n",
            "epoch 10: RMSE loss 0.923, test RMSE 1.347\n",
            "epoch 11: RMSE loss 0.918, test RMSE 1.344\n",
            "epoch 12: RMSE loss 0.911, test RMSE 1.343\n",
            "epoch 13: RMSE loss 0.905, test RMSE 1.347\n",
            "epoch 14: RMSE loss 0.898, test RMSE 1.355\n",
            "epoch 15: RMSE loss 0.891, test RMSE 1.352\n",
            "epoch 16: RMSE loss 0.884, test RMSE 1.354\n",
            "epoch 17: RMSE loss 0.876, test RMSE 1.363\n",
            "epoch 18: RMSE loss 0.868, test RMSE 1.361\n",
            "epoch 19: RMSE loss 0.858, test RMSE 1.353\n",
            "epoch 20: RMSE loss 0.847, test RMSE 1.364\n",
            "336496.0 examples/sec on [device(type='cuda', index=0)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "net.eval()\n",
        "net(torch.tensor([20], dtype=torch.long, device=devices[0]),torch.tensor([30], dtype=torch.long, device=devices[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lyms3Xg2yVrg",
        "outputId": "70d5a338-1f64-4a26-8231-a8080bcd14e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([3.1830], device='cuda:0', grad_fn=<AddBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    }
  ]
}