PyTorch Basics/pytorch-datasets-1.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"pytorch-datasets-1.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"metadata":{"id":"bIAYbYajk1w9","colab_type":"text"},"cell_type":"markdown","source":["# Generating Data in PyTorch\n","- Generating data from NumPy array\n","- Generating data using custom DataSet and DataLoaders"]},{"metadata":{"id":"GVU5-yp3N89I","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":105},"outputId":"88348d80-9804-4c3b-d18e-951fecb2de4a","executionInfo":{"status":"ok","timestamp":1538721493611,"user_tz":420,"elapsed":3858,"user":{"displayName":"Buomsoo Kim","photoUrl":"","userId":"18268696804115368229"}}},"cell_type":"code","source":["!pip3 install torch torchvision"],"execution_count":39,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (0.4.1)\n","Requirement already satisfied: torchvision in /usr/local/lib/python3.6/dist-packages (0.2.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.11.0)\n","Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision) (5.3.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.14.6)\n"],"name":"stdout"}]},{"metadata":{"id":"8yy37hEYOEiQ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"7c452566-27df-4050-b6ac-51b1b59eb608","executionInfo":{"status":"ok","timestamp":1538720530647,"user_tz":420,"elapsed":7366,"user":{"displayName":"Buomsoo Kim","photoUrl":"","userId":"18268696804115368229"}}},"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import torch, torchvision\n","torch.__version__"],"execution_count":2,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'0.4.1'"]},"metadata":{"tags":[]},"execution_count":2}]},{"metadata":{"id":"WVZTmJ4CY8fM","colab_type":"text"},"cell_type":"markdown","source":["## 1. Generating data from NumPy array\n","- Import data using Pandas or NumPy and convert into Torch tensors"]},{"metadata":{"id":"7K9iC17g-N2E","colab_type":"text"},"cell_type":"markdown","source":["### Import data"]},{"metadata":{"id":"wJX5td0owoB-","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":198},"outputId":"1392e1fd-0640-4153-a3cf-f5e3759b7bdf","executionInfo":{"status":"ok","timestamp":1538679078875,"user_tz":420,"elapsed":362,"user":{"displayName":"Buomsoo Kim","photoUrl":"","userId":"18268696804115368229"}}},"cell_type":"code","source":["# retrive iris dataset from UCI repository using read_table() in Pandas\n","url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n","data = pd.read_table(url, header = None, sep = \",\")\n","data.columns = [\"sepal_len\", \"sepal_wid\", \"petal_len\", \"petal_wid\", \"class\"]\n","data.head()"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sepal_len</th>\n","      <th>sepal_wid</th>\n","      <th>petal_len</th>\n","      <th>petal_wid</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","      <td>1.3</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   sepal_len  sepal_wid  petal_len  petal_wid        class\n","0        5.1        3.5        1.4        0.2  Iris-setosa\n","1        4.9        3.0        1.4        0.2  Iris-setosa\n","2        4.7        3.2        1.3        0.2  Iris-setosa\n","3        4.6        3.1        1.5        0.2  Iris-setosa\n","4        5.0        3.6        1.4        0.2  Iris-setosa"]},"metadata":{"tags":[]},"execution_count":16}]},{"metadata":{"id":"9Yb_HdI1xGst","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":198},"outputId":"3cb7e2a7-1631-41d4-a075-2cb8414b6c88","executionInfo":{"status":"ok","timestamp":1538679125648,"user_tz":420,"elapsed":346,"user":{"displayName":"Buomsoo Kim","photoUrl":"","userId":"18268696804115368229"}}},"cell_type":"code","source":["# first convert class into categorical values ({0, 1, 2})\n","data[\"class\"] = data[\"class\"].astype(\"category\").cat.codes\n","data.head()"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sepal_len</th>\n","      <th>sepal_wid</th>\n","      <th>petal_len</th>\n","      <th>petal_wid</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","      <td>1.3</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   sepal_len  sepal_wid  petal_len  petal_wid  class\n","0        5.1        3.5        1.4        0.2      0\n","1        4.9        3.0        1.4        0.2      0\n","2        4.7        3.2        1.3        0.2      0\n","3        4.6        3.1        1.5        0.2      0\n","4        5.0        3.6        1.4        0.2      0"]},"metadata":{"tags":[]},"execution_count":17}]},{"metadata":{"id":"peGq6XBDyg2S","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":36},"outputId":"f607c936-d549-45a1-e61c-c5d840e9d896","executionInfo":{"status":"ok","timestamp":1538679562868,"user_tz":420,"elapsed":248,"user":{"displayName":"Buomsoo Kim","photoUrl":"","userId":"18268696804115368229"}}},"cell_type":"code","source":["X_data = data[[\"sepal_len\", \"sepal_wid\", \"petal_len\", \"petal_wid\"]].values.astype(\"float32\")\n","y_data = data[\"class\"].values.astype(\"int32\")\n","\n","print(X_data.shape, y_data.shape)"],"execution_count":34,"outputs":[{"output_type":"stream","text":["(150, 4) (150,)\n"],"name":"stdout"}]},{"metadata":{"id":"IFD0FeRccdgJ","colab_type":"text"},"cell_type":"markdown","source":["### Generating tensors\n","- Generating tensors directly"]},{"metadata":{"id":"jvRy7IJzbVMB","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":54},"outputId":"b1e92b7e-bf1e-4672-8f8c-cd89ecb8fdfa","executionInfo":{"status":"ok","timestamp":1538679564113,"user_tz":420,"elapsed":307,"user":{"displayName":"Buomsoo Kim","photoUrl":"","userId":"18268696804115368229"}}},"cell_type":"code","source":["# using from_numpy(): infers data type implicitly from numpy array\n","X_tensor = torch.from_numpy(X_data)\n","y_tensor = torch.from_numpy(y_data)\n","\n","print(X_tensor.type(), y_tensor.type())\n","print(X_tensor.size(), y_tensor.size())"],"execution_count":35,"outputs":[{"output_type":"stream","text":["torch.FloatTensor torch.IntTensor\n","torch.Size([150, 4]) torch.Size([150])\n"],"name":"stdout"}]},{"metadata":{"id":"X7GNYw4fbn9H","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":54},"outputId":"51d779e3-45e7-40aa-c10a-0fff1256fd3e","executionInfo":{"status":"ok","timestamp":1538679966434,"user_tz":420,"elapsed":259,"user":{"displayName":"Buomsoo Kim","photoUrl":"","userId":"18268696804115368229"}}},"cell_type":"code","source":["# assigning data type explicitly\n","X_tensor = torch.FloatTensor(X_data)\n","y_tensor = torch.LongTensor(y_data)\n","\n","print(X_tensor.type(), y_tensor.type())\n","print(X_tensor.size(), y_tensor.size())"],"execution_count":43,"outputs":[{"output_type":"stream","text":["torch.FloatTensor torch.LongTensor\n","torch.Size([150, 4]) torch.Size([150])\n"],"name":"stdout"}]},{"metadata":{"id":"yVWh8UoNhUWe","colab_type":"code","colab":{}},"cell_type":"code","source":["# logistic regression model\n","model = torch.nn.Linear(X_data.shape[-1], len(set(y_data)))\n","criterion = torch.nn.CrossEntropyLoss()  \n","optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)  \n","model"],"execution_count":0,"outputs":[]},{"metadata":{"id":"pYo3RuXjdKT6","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":203},"outputId":"34b2296e-8a4d-45e9-b572-ec7652a7db83","executionInfo":{"status":"ok","timestamp":1538680046559,"user_tz":420,"elapsed":320,"user":{"displayName":"Buomsoo Kim","photoUrl":"","userId":"18268696804115368229"}}},"cell_type":"code","source":["# model training: whole dataset at a time\n","for epoch in range(100):\n","  outputs = model(X_tensor)\n","  loss = criterion(outputs, y_tensor)\n","  optimizer.zero_grad()\n","  loss.backward()\n","  optimizer.step()\n","  \n","  if (epoch + 1) % 10 == 0:\n","    print(\"Epoch: {}, Loss: {:.5f}\".format(epoch + 1, loss.item()))"],"execution_count":52,"outputs":[{"output_type":"stream","text":["Epoch: 10, Loss: 0.67772\n","Epoch: 20, Loss: 0.67183\n","Epoch: 30, Loss: 0.66608\n","Epoch: 40, Loss: 0.66047\n","Epoch: 50, Loss: 0.65499\n","Epoch: 60, Loss: 0.64963\n","Epoch: 70, Loss: 0.64440\n","Epoch: 80, Loss: 0.63928\n","Epoch: 90, Loss: 0.63428\n","Epoch: 100, Loss: 0.62938\n"],"name":"stdout"}]},{"metadata":{"id":"Tgf96It4fhr9","colab_type":"text"},"cell_type":"markdown","source":["## 2. Generating data using custom DataSet and DataLoaders\n","- Using customized dataset and dataloaders makes it easier to manage training process (e.g., implementing mini-batch SGD)"]},{"metadata":{"id":"SFXadkSTeN4r","colab_type":"code","colab":{}},"cell_type":"code","source":["class IrisDataset(torch.utils.data.Dataset):\n","  def __init__(self):\n","    # import and initialize dataset\n","    url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n","    data = pd.read_table(url, header = None, sep = \",\")\n","    data.columns = [\"sepal_len\", \"sepal_wid\", \"petal_len\", \"petal_wid\", \"class\"]\n","    data[\"class\"] = data[\"class\"].astype(\"category\").cat.codes\n","    \n","    self.X = data[[\"sepal_len\", \"sepal_wid\", \"petal_len\", \"petal_wid\"]].values\n","    self.Y = data[\"class\"].values[:, np.newaxis].astype(np.int32)\n","    \n","  def __getitem__(self, idx):\n","    # get item by index\n","    return self.X[idx], self.Y[idx]\n","  \n","  def __len__(self):\n","    # returns length of data\n","    return len(self.X)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"Iv30GdmFg1om","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":52},"outputId":"6bca4e99-ac9e-42a8-8aab-09f9debbb6bd","executionInfo":{"status":"ok","timestamp":1538721091572,"user_tz":420,"elapsed":1479,"user":{"displayName":"Buomsoo Kim","photoUrl":"","userId":"18268696804115368229"}}},"cell_type":"code","source":["# create dataset instance\n","irisdataset = IrisDataset()\n","\n","print(type(irisdataset))\n","print(len(irisdataset))"],"execution_count":25,"outputs":[{"output_type":"stream","text":["<class '__main__.IrisDataset'>\n","150\n"],"name":"stdout"}]},{"metadata":{"id":"m56oNfIDg37o","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"f8607ce6-5921-410d-ec40-42ee044c157f","executionInfo":{"status":"ok","timestamp":1538721092739,"user_tz":420,"elapsed":1079,"user":{"displayName":"Buomsoo Kim","photoUrl":"","userId":"18268696804115368229"}}},"cell_type":"code","source":["# create dataloader instance\n","# set batch size to 32 (mini-batch SGD) and shuffle before training \n","# if batch_size is set to 1, stochastic gradient descent is implemented \n","dataloader = torch.utils.data.DataLoader(irisdataset, batch_size = 32, shuffle = True)\n","dataloader"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<torch.utils.data.dataloader.DataLoader at 0x7f90865702e8>"]},"metadata":{"tags":[]},"execution_count":26}]},{"metadata":{"id":"VIfIswG4o6Mt","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"1c3e9268-1ba6-4f8a-e663-5a54c2f61b39","executionInfo":{"status":"ok","timestamp":1538721627737,"user_tz":420,"elapsed":988,"user":{"displayName":"Buomsoo Kim","photoUrl":"","userId":"18268696804115368229"}}},"cell_type":"code","source":["# logistic regression model\n","model = torch.nn.Linear(4, 3).double()\n","criterion = torch.nn.CrossEntropyLoss()  \n","optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)  \n","model"],"execution_count":44,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Linear(in_features=4, out_features=3, bias=True)"]},"metadata":{"tags":[]},"execution_count":44}]},{"metadata":{"id":"H-chn-XA4tvv","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":193},"outputId":"5ff1caf5-a705-47df-817c-c26dc7330c61","executionInfo":{"status":"ok","timestamp":1538721632640,"user_tz":420,"elapsed":1412,"user":{"displayName":"Buomsoo Kim","photoUrl":"","userId":"18268696804115368229"}}},"cell_type":"code","source":["steps = len(dataloader)\n","\n","for epoch in range(100):\n","  for i, (x, y) in enumerate(dataloader):\n","    outputs = model(x)\n","    # to match outputs, y should be converted into LongTensor and size should be reduced to 1-d\n","    # this is because CrossEntropyLoss requires y_target to be 1-d\n","    loss = criterion(outputs, y.type(torch.LongTensor).view(-1))\n","    optimizer.zero_grad()\n","    loss.backward()\n","    optimizer.step()\n","\n","  if (epoch + 1) % 10 == 0:\n","    print(\"Epoch: {}, Loss: {:.5f}\".format(epoch + 1, loss.item()))"],"execution_count":45,"outputs":[{"output_type":"stream","text":["Epoch: 10, Loss: 1.28169\n","Epoch: 20, Loss: 1.14514\n","Epoch: 30, Loss: 1.11257\n","Epoch: 40, Loss: 1.04069\n","Epoch: 50, Loss: 0.98725\n","Epoch: 60, Loss: 0.93442\n","Epoch: 70, Loss: 0.88709\n","Epoch: 80, Loss: 0.79886\n","Epoch: 90, Loss: 0.82748\n","Epoch: 100, Loss: 0.80539\n"],"name":"stdout"}]}]}