Class 5 - Pytorch and Images_solution .ipynb

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work"],"metadata":{"id":"BE3M-5mHbRdU"}},{"cell_type":"markdown","source":["# Neural Network Python versus Pytorch"],"metadata":{"id":"T5LxOrR6bjFY"}},{"cell_type":"code","source":["import pandas as pd\n","from sklearn.model_selection import train_test_split\n","import matplotlib.pyplot as plt"],"metadata":{"id":"TmnDoKifpFZ7","executionInfo":{"status":"ok","timestamp":1712738281212,"user_tz":-120,"elapsed":1630,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["#load the pickle file in OLAT\n","#df_dummies_model.to_pickle('df_dummies_model.pkl')\n","\n","df_dummies_model=pd.read_pickle('/content/drive/MyDrive/dlss24/dlss24/df_dummies_model.pkl')"],"metadata":{"id":"eOaGLl-Lbxjf","executionInfo":{"status":"ok","timestamp":1712738342451,"user_tz":-120,"elapsed":656,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["df_dummies_model"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"id":"N1JiJSClKR2M","executionInfo":{"status":"ok","timestamp":1712738348177,"user_tz":-120,"elapsed":7,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"2c6f4667-1f5f-426e-f7c0-b8faa90127e0"},"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       District  Wild MSW number  Wild MSW weight (kg)  Wild 1SW number  \\\n","0      non-with            107.0                 400.5              0.0   \n","1      non-with           6606.0               24856.9              0.0   \n","2      non-with            185.0                 703.1              0.0   \n","3      non-with           7008.0               26151.9              1.0   \n","4      non-with            484.0                1912.3              0.0   \n","...       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Y_train) * np.log(1 - y_predicted+ epsilon))\n","    costs.append(cost)\n","\n","\n","\n","\n","    # Backward propagation\n","    dz = y_predicted - Y_train\n","    dw = 1/m_train * np.dot(X_train, dz.T)\n","    db = 1/m_train * np.sum(dz)\n","    dw=np.array(dw, dtype=float)\n","    # Update parameters\n","    w -= learning_rate * dw\n","    b -= learning_rate * db\n","\n","    # Print the cost every 100 training iterations\n","    if print_cost and i % 100 == 0:\n","        print (\"Cost after iteration %i: %f\" % (i, cost))\n","\n","\n","dw=np.array(w, dtype=float)\n","# Predict train/test set examples\n","Y_prediction_train = (sigmoid(np.array(np.dot(w.T, X_train),dtype=float)  + b) > 0.5).astype(int)\n","Y_prediction_test = (sigmoid(np.array(np.dot(w.T, X_test),dtype=float) + b) > 0.5).astype(int)\n","\n","# Print train/test Errors\n","train_accuracy = np.mean(Y_prediction_train == Y_train) * 100\n","test_accuracy = np.mean(Y_prediction_test == Y_test) * 100\n","print(\"Train accuracy: {} %\".format(train_accuracy))\n","print(\"Test accuracy: {} %\".format(test_accuracy))\n","\n","d = {\"costs\": costs,\n","      \"Y_prediction_train\": Y_prediction_train,\n","      \"Y_prediction_test\": Y_prediction_test,\n","      \"w\": w,\n","      \"b\": b,\n","      \"learning_rate\": learning_rate,\n","      \"num_iterations\": num_iterations,\n","      \"train_accuracy\": train_accuracy,\n","      \"test_accuracy\": test_accuracy}\n","\n","\n","\n","\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mcNJf6iSbKPs","executionInfo":{"status":"ok","timestamp":1712733575947,"user_tz":-120,"elapsed":1439,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"f1a91b22-e872-4a41-8e1b-986bbb1c8435"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Train accuracy: 63.08216584627036 %\n","Test accuracy: 62.93494704992436 %\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"AmByBhdh4GLm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import torch\n","import numpy as np\n","\n","def sigmoid(z):\n","    \"\"\"\n","    Compute the sigmoid function for an input tensor.\n","\n","    Arguments:\n","    z -- A scalar or PyTorch tensor of any size.\n","\n","    Returns:\n","    s -- Sigmoid of z, same shape as z.\n","    \"\"\"\n","    return 1 / (1 + torch.exp(-z))\n","\n","def model(X_train, Y_train, X_test, Y_test, num_iterations=2000, learning_rate=0.5, print_cost=False):\n","    \"\"\"\n","    Build the logistic regression model using PyTorch.\n","\n","    Arguments:\n","    X_train -- Training set represented by a PyTorch tensor of shape (num_features, m_train).\n","    Y_train -- Training labels represented by a PyTorch tensor (vector) of shape (1, m_train).\n","    X_test -- Test set represented by a PyTorch tensor of shape (num_features, m_test).\n","    Y_test -- Test labels represented by a PyTorch tensor (vector) of shape (1, m_test).\n","    num_iterations -- Number of iterations of the optimization loop.\n","    learning_rate -- Learning rate of the gradient descent update rule.\n","    print_cost -- If True, print the cost every 100 iterations.\n","\n","    Returns:\n","    d -- Dictionary containing information about the model.\n","    \"\"\"\n","    m_train = X_train.shape[1]\n","    m_test = X_test.shape[1]\n","    num_features = X_train.shape[0]\n","\n","    # Initialize parameters\n","    w = torch.zeros(num_features, 1, dtype=torch.float64)\n","    b = torch.tensor(0, dtype=torch.float64)\n","\n","    # Gradient descent\n","    costs = []\n","    for i in range(num_iterations):\n","        # Forward propagation\n","        z_train = torch.mm(w.T, X_train) + b\n","        y_predicted = sigmoid(z_train)\n","\n","        epsilon = 1e-15  # Small constant to avoid division by zero\n","        # Compute cost\n","        cost = -1/m_train * torch.sum(Y_train * torch.log(y_predicted + epsilon) + (1 - Y_train) * torch.log(1 - y_predicted + epsilon))\n","        costs.append(cost.item())\n","\n","        # Backward propagation\n","        dz = y_predicted - Y_train\n","        dw = 1/m_train * torch.mm(X_train, dz.T)\n","        db = 1/m_train * torch.sum(dz)\n","\n","        # Update parameters\n","        w -= learning_rate * dw\n","        b -= learning_rate * db\n","\n","        # Print the cost every 100 training iterations\n","        if print_cost and i % 100 == 0:\n","            print (\"Cost after iteration %i: %f\" % (i, cost))\n","\n","    # Predict train/test set examples\n","    Y_prediction_train = (sigmoid(torch.mm(w.T, X_train) + b) > 0.5).int()\n","    Y_prediction_test = (sigmoid(torch.mm(w.T, X_test) + b) > 0.5).int()\n","\n","    # Print train/test Errors\n","    train_accuracy = torch.mean((Y_prediction_train == Y_train).float()) * 100\n","    test_accuracy = torch.mean((Y_prediction_test == Y_test).float()) * 100\n","    print(\"Train accuracy: {} %\".format(train_accuracy))\n","    print(\"Test accuracy: {} %\".format(test_accuracy))\n","\n","    d = {\"costs\": costs,\n","         \"Y_prediction_train\": Y_prediction_train.numpy(),\n","         \"Y_prediction_test\": Y_prediction_test.numpy(),\n","         \"w\": w.numpy(),\n","         \"b\": b.numpy(),\n","         \"learning_rate\": learning_rate,\n","         \"num_iterations\": num_iterations,\n","         \"train_accuracy\": train_accuracy.item(),\n","         \"test_accuracy\": test_accuracy.item()}\n","\n","    return d\n","\n","# Convert data to NumPy arrays\n","X_train = X_train.astype(np.float64)\n","Y_train = Y_train.astype(np.float64)\n","X_test = X_test.astype(np.float64)\n","Y_test = Y_test.astype(np.float64)\n","\n","# Convert data to PyTorch tensors\n","X_train_tensor = torch.tensor(X_train, dtype=torch.float64)\n","Y_train_tensor = torch.tensor(Y_train, dtype=torch.float64)\n","X_test_tensor = torch.tensor(X_test, dtype=torch.float64)\n","Y_test_tensor = torch.tensor(Y_test, dtype=torch.float64)\n","\n","# Example usage:\n","model_results = model(X_train_tensor, Y_train_tensor, X_test_tensor, Y_test_tensor, num_iterations=2000, learning_rate=0.5, print_cost=True)\n"],"metadata":{"id":"E_6QZhbgpb0l","executionInfo":{"status":"aborted","timestamp":1712733565977,"user_tz":-120,"elapsed":8,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Out of the Box Pytorch\n","1. Convert the NumPy arrays extracted from the DataFrame into PyTorch tensors using torch.tensor.\n","2. Create DataLoader objects for both the training and test sets using TensorDataset and DataLoader.\n","3. Initialize the logistic regression model (LogisticRegression).\n","4. Define the loss function (BCELoss) and the optimizer (SGD).\n","5. Train the model for a specified number of epochs, iterating through the batches in the training DataLoader.\n","6. Evaluate the trained model's performance on the test set by calculating the accuracy."],"metadata":{"id":"1I2DJneWvfF9"}},{"cell_type":"code","source":["import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","from torch.utils.data import DataLoader, TensorDataset\n","from sklearn.model_selection import train_test_split\n","\n","# Define your model architecture\n","class LogisticRegression(nn.Module):\n","    def __init__(self, input_size):\n","        super(LogisticRegression, self).__init__()\n","        self.linear = nn.Linear(input_size, 1)\n","        self.sigmoid = nn.Sigmoid()\n","\n","    def forward(self, x):\n","        out = self.linear(x)\n","        out = self.sigmoid(out)\n","        return out\n"],"metadata":{"id":"VyLKT4IDtJDQ","executionInfo":{"status":"ok","timestamp":1712741627642,"user_tz":-120,"elapsed":4394,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["X_train, X_test, y_train, y_test = train_test_split(df_dummies_model.drop(columns=['District', 'y']), df_dummies_model[['y']], test_size=0.2, random_state=42)\n","X_train, X_test, y_train, y_test =X_train*1, X_test*1, y_train*1, y_test*1"],"metadata":{"id":"IchX38dMu_cV","executionInfo":{"status":"ok","timestamp":1712741627643,"user_tz":-120,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["X_train_tensor.shape"],"metadata":{"id":"Qly3WguN0fgP","colab":{"base_uri":"https://localhost:8080/","height":158},"executionInfo":{"status":"error","timestamp":1712741630777,"user_tz":-120,"elapsed":312,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"f060cfa4-e325-4f88-e3e5-cb3043459a5b"},"execution_count":11,"outputs":[{"output_type":"error","ename":"NameError","evalue":"name 'X_train_tensor' is not defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-11-d5372e1550f3>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_train_tensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'X_train_tensor' is not defined"]}]},{"cell_type":"code","source":["X_train.shape[1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZPzdkecWUBwh","executionInfo":{"status":"ok","timestamp":1712741672565,"user_tz":-120,"elapsed":258,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"9e9002a6-e6a6-4778-9748-d0cd95abc9d0"},"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["19"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["X_train"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"id":"6cr1435zW-rU","executionInfo":{"status":"ok","timestamp":1712741677436,"user_tz":-120,"elapsed":397,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"350aeada-2729-4a6d-dbc0-90979f0d6618"},"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       Wild MSW number  Wild MSW weight (kg)  Wild 1SW number  \\\n","21955            136.0                 652.7            518.0   \n","69              5278.0               18606.4              0.0   \n","22016             14.0                  62.9              0.0   \n","21909             13.0                  64.4             55.0   \n","19995             45.0                 188.7            368.0   \n","...                ...                   ...              ...   \n","2830             388.0                1761.3           2998.0   \n","2865             982.0                4026.5              0.0   \n","3029             126.0                 497.6              0.0   \n","860              127.0                 645.4              5.0   \n","22194              9.0                  31.5              0.0   \n","\n","       Wild 1SW weight (kg)  Sea trout number  Sea trout weight (kg)  \\\n","21955                1317.8              61.0                   79.7   \n","69                      0.0            1609.0                 2047.1   \n","22016                   0.0              61.0                   76.2   \n","21909                 149.7               2.0                    2.3   \n","19995                 825.5              35.0                   21.3   \n","...                     ...               ...                    ...   \n","2830                 6799.3             965.0                  656.3   \n","2865                    0.0             140.0                  125.6   \n","3029                    0.0               0.0                    0.0   \n","860                    15.5             613.0                 1378.5   \n","22194                   0.0              46.0                   59.2   \n","\n","       Finnock number  Finnock weight (kg)  Farmed MSW number  \\\n","21955             0.0                  0.0                0.0   \n","69                0.0                  0.0                0.0   \n","22016             0.0                  0.0                0.0   \n","21909             0.0                  0.0                0.0   \n","19995             0.0                  0.0                0.0   \n","...               ...                  ...                ...   \n","2830              0.0                  0.0                0.0   \n","2865              0.0                  0.0                0.0   \n","3029              0.0                  0.0                0.0   \n","860               0.0                  0.0                0.0   \n","22194             0.0                  0.0                0.0   \n","\n","       Farmed MSW weight (kg)  Farmed 1SW number  Month_August  \\\n","21955                     0.0                0.0             0   \n","69                        0.0                0.0             0   \n","22016                     0.0                0.0             0   \n","21909                     0.0                0.0             0   \n","19995                     0.0                0.0             0   \n","...                       ...                ...           ...   \n","2830      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],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Finnock weight (kg)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0511372002807726,\n        \"min\": 0.0,\n        \"max\": 2.3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.9,\n          2.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Farmed MSW number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10087366339566971,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          1.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Farmed MSW weight (kg)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4708044534464034,\n        \"min\": 0.0,\n        \"max\": 10.4,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          3.9,\n          2.8,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Farmed 1SW number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.314764394219501,\n        \"min\": 0.0,\n        \"max\": 15.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0,\n          2.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month_August\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month_February\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month_July\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month_June\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month_March\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month_May\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month_November\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month_September\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["# Convert NumPy arrays to PyTorch tensors\n","X_train_tensor = torch.tensor(X_train.values, dtype=torch.float32)\n","Y_train_tensor = torch.tensor(Y_train, dtype=torch.float32)\n","\n","X_test_tensor = torch.tensor(X_test.values, dtype=torch.float32)\n","Y_test_tensor = torch.tensor(Y_test, dtype=torch.float32)"],"metadata":{"id":"OBs2oaaOUinh","executionInfo":{"status":"ok","timestamp":1712741639591,"user_tz":-120,"elapsed":234,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":13,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"UEU-_ZQJUpSx","executionInfo":{"status":"aborted","timestamp":1712733565977,"user_tz":-120,"elapsed":7,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X_test_tensor"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9xQ0aHcjUp4z","executionInfo":{"status":"ok","timestamp":1712741691648,"user_tz":-120,"elapsed":224,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"06841cbc-ec4b-4b43-a4e1-69f3fca81251"},"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([[6.8400e+02, 2.3691e+03, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n","         0.0000e+00],\n","        [3.4000e+01, 9.2200e+01, 3.5000e+01,  ..., 0.0000e+00, 0.0000e+00,\n","         0.0000e+00],\n","        [3.1900e+02, 1.5250e+03, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n","         0.0000e+00],\n","        ...,\n","        [3.3000e+01, 1.4300e+02, 7.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n","         0.0000e+00],\n","        [5.0000e+00, 2.7200e+01, 2.8000e+01,  ..., 0.0000e+00, 0.0000e+00,\n","         0.0000e+00],\n","        [3.0000e+00, 1.4500e+01, 0.0000e+00,  ..., 1.0000e+00, 0.0000e+00,\n","         0.0000e+00]])"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["# tensor syntax\n","# (num_sample, (size one sample), batch)\n","\n","#numerical exercise: (2600, (1,19), 10)\n","\n","#image exercise: (10 000, (224,224,3), 10)"],"metadata":{"id":"JXZX7LQNUpJd","executionInfo":{"status":"ok","timestamp":1712741615410,"user_tz":-120,"elapsed":367,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# Create DataLoader for training and testing\n","train_dataset = TensorDataset(X_train_tensor, Y_train_tensor.reshape(-1,1))\n","train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n","\n","test_dataset = TensorDataset(X_test_tensor, Y_test_tensor.reshape(-1,1))\n","test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)"],"metadata":{"id":"mmkwdSLfUjnl","executionInfo":{"status":"ok","timestamp":1712741644485,"user_tz":-120,"elapsed":3,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["train_dataset"],"metadata":{"id":"i03-1p9LUxOk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712741703458,"user_tz":-120,"elapsed":335,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"3963510b-1029-44e3-ed55-a6910878be3f"},"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<torch.utils.data.dataset.TensorDataset at 0x7c84298c24d0>"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["for x_train,label in train_dataset:\n","  print(x_train,label )\n","  break"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-8aOJOvtXHxH","executionInfo":{"status":"ok","timestamp":1712741739578,"user_tz":-120,"elapsed":6,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"11bd8821-83eb-4f50-e481-2dd6ba25a1c6"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([1.3600e+02, 6.5270e+02, 5.1800e+02, 1.3178e+03, 6.1000e+01, 7.9700e+01,\n","        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n","        0.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n","        0.0000e+00]) tensor([1.])\n"]}]},{"cell_type":"markdown","source":["## CODE: See whats inside datasets"],"metadata":{"id":"RbzfwAH6dgip"}},{"cell_type":"code","source":[],"metadata":{"id":"wK7o8ciUdjEp","executionInfo":{"status":"aborted","timestamp":1712733565977,"user_tz":-120,"elapsed":6,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"8_XHdZIhdjCD","executionInfo":{"status":"aborted","timestamp":1712733565978,"user_tz":-120,"elapsed":6,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"maTd3J3Odi_i","executionInfo":{"status":"aborted","timestamp":1712733565978,"user_tz":-120,"elapsed":6,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"fP4F7Ptudi9F","executionInfo":{"status":"aborted","timestamp":1712733565978,"user_tz":-120,"elapsed":6,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i,j in test_dataset:\n","  print(i,j)\n","  break"],"metadata":{"id":"iQP1DY-0UxFB","executionInfo":{"status":"aborted","timestamp":1712733565978,"user_tz":-120,"elapsed":6,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"ABhCRUYvUxCI","executionInfo":{"status":"aborted","timestamp":1712733565978,"user_tz":-120,"elapsed":6,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"XmR9Q_wfVAY4","executionInfo":{"status":"aborted","timestamp":1712733565978,"user_tz":-120,"elapsed":5,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Initialize the model\n","input_size = X_train.shape[1]\n","model = LogisticRegression(input_size)\n","\n","# Define loss function and optimizer\n","criterion = nn.BCELoss()  # Binary Cross Entropy Loss\n","optimizer = optim.SGD(model.parameters(), lr=0.01)\n","\n","#https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html\n","#https://pytorch.org/docs/stable/generated/torch.optim.SGD.html"],"metadata":{"id":"SuuuQ0MPVAVp","executionInfo":{"status":"ok","timestamp":1712741977425,"user_tz":-120,"elapsed":2716,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":20,"outputs":[]},{"cell_type":"markdown","source":["In logistic regression, we model the probability \\( P(Y=1|X) \\), the probability of the positive class given the input features \\( X \\), using the logistic (sigmoid) function:\n","\n","$$ \\ P(Y=1|X) = \\frac{1}{1 + e^{-z}} \\ $$\n","\n","\n","\n","Where $$ \\ z = wX^T + b \\ $$ is the linear combination of the input features \\( X \\) and the model parameters \\( w \\) (weights) and \\( b \\) (bias).\n","\n","During training, we aim to find the parameters \\( w \\) and \\( b \\) that maximize the likelihood of the observed data. The likelihood function \\( L(w, b) \\) represents the probability of observing the given data under the current model parameters. For binary classification, where we have two classes (0 and 1), the likelihood function can be written as:\n","\n","$$ \\ L(w, b) = \\prod_{i=1}^{N} (y_i \\cdot P(Y=1|X_i) + (1 - y_i) \\cdot (1 - P(Y=1|X_i))) \\ $$\n","\n","Where \\( N \\) is the number of samples, \\( y_i \\) is the actual label for the \\( i \\)th sample, and \\( P(Y=1|X_i) \\) is the predicted probability of the positive class for the \\( i \\)th sample.\n","\n","Maximizing the likelihood function \\( L(w, b) \\) is equivalent to maximizing its logarithm, known as the log-likelihood function:\n","\n","$$ \\log L(w, b) = \\sum_{i=1}^{N} (y_i \\cdot \\log(P(Y=1|X_i)) + (1 - y_i) \\cdot \\log(1 - P(Y=1|X_i))) \\ $$\n","\n","And if we scale the log-likelihood by \\(-1/N\\), we get the cross-entropy loss function:\n","\n","$$ \\ J(w, b) = -\\frac{1}{N} \\sum_{i=1}^{N} (y_i \\cdot \\log(P(Y=1|X_i)) + (1 - y_i) \\cdot \\log(1 - P(Y=1|X_i))) \\ $$\n","\n","So, the cross-entropy loss in logistic regression can be interpreted as the negative log-likelihood, scaled by the number of samples, and is used as the cost function to be minimized during training to find the optimal parameters \\( w \\) and \\( b \\). Therefore, the connection to likelihood is fundamental in understanding the theoretical underpinnings of the cross-entropy loss in logistic regression."],"metadata":{"id":"lTR8c-R3WK6Z"}},{"cell_type":"markdown","source":["## CODE: See whats inside parameters"],"metadata":{"id":"K_j5EfuMdI1J"}},{"cell_type":"code","source":[],"metadata":{"id":"u2pK-6lyVAS5","executionInfo":{"status":"aborted","timestamp":1712733565979,"user_tz":-120,"elapsed":26447,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"9SwgL124dOok","executionInfo":{"status":"aborted","timestamp":1712733565979,"user_tz":-120,"elapsed":26447,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"bAOTTm5hdOl3","executionInfo":{"status":"aborted","timestamp":1712733565979,"user_tz":-120,"elapsed":26447,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"Ju-KcENzdOg0","executionInfo":{"status":"aborted","timestamp":1712733565979,"user_tz":-120,"elapsed":26447,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"S0zFnGEFdOeK","executionInfo":{"status":"aborted","timestamp":1712733565979,"user_tz":-120,"elapsed":26446,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"zYxCwPWHdOba","executionInfo":{"status":"aborted","timestamp":1712733565979,"user_tz":-120,"elapsed":26446,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in model.parameters():\n","  print(i)\n","  break"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NfuF_MafVCPx","executionInfo":{"status":"ok","timestamp":1712741985420,"user_tz":-120,"elapsed":229,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"df16c4da-4f95-4ddc-d205-34c73f67f437"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["Parameter containing:\n","tensor([[-0.1741, -0.2282, -0.0777, -0.1332,  0.0129,  0.0896, -0.2005, -0.1849,\n","         -0.0834, -0.1678, -0.1028,  0.1952, -0.0876, -0.1384,  0.0394,  0.1858,\n","         -0.0834, -0.2177, -0.1544]], requires_grad=True)\n"]}]},{"cell_type":"code","source":["optimizer"],"metadata":{"id":"KTuCKc_7VCNA","executionInfo":{"status":"aborted","timestamp":1712733565979,"user_tz":-120,"elapsed":26436,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"v7Wp0SUbXYdC","executionInfo":{"status":"aborted","timestamp":1712733565979,"user_tz":-120,"elapsed":26435,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# A forward and backward example without updating the weights\n","\n","model_simple = nn.Sigmoid()\n","loss_function = nn.BCELoss() #binary cross entropy loss\n","input = torch.randn(3, 2, requires_grad=True)\n","target = torch.rand(3, 2, requires_grad=False)\n","\n","output_=model_simple(input)\n","loss_res = loss_function(output_, target) #forward\n","loss_res.backward()"],"metadata":{"id":"FleRBinLXYGJ","executionInfo":{"status":"aborted","timestamp":1712733565979,"user_tz":-120,"elapsed":26435,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"Nq0f7g3tXrMr","executionInfo":{"status":"aborted","timestamp":1712733565979,"user_tz":-120,"elapsed":26435,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"3wxBSf3IXYCo","executionInfo":{"status":"aborted","timestamp":1712733565979,"user_tz":-120,"elapsed":26435,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## CODE: Create LogReg in Pytorch"],"metadata":{"id":"nndIwN86dSSc"}},{"cell_type":"code","source":["# Training the model\n","num_epochs = 100\n","losses = []\n","for epoch in range(num_epochs):\n","    model.train()  # Set the model to training mode\n","    total_loss = 0.0\n","    for inputs, labels in train_loader: #this goes automatically for each batch\n","        optimizer.zero_grad()  # Zero the gradients (a check)\n","\n","        # YOUR CODE STARTS HERE\n","\n","        outputs = ... # Forward pass\n","        loss = ...  # Compute the loss\n","        ....  # Backward pass\n","\n","        # YOUR CODE ENDS HERE\n","\n","        optimizer.step()  # Update the parameters\n","        total_loss += loss.item() * inputs.size(0)\n","\n","    epoch_loss = total_loss / len(train_loader.dataset)\n","    losses.append(epoch_loss)  # Store the loss value for this epoch\n","    print(f\"Epoch {epoch+1}/{num_epochs}, Loss: {epoch_loss:.4f}\")\n","\n","# Evaluate the model\n","model.eval()  # Set the model to evaluation mode\n","correct = 0\n","total = 0\n","with torch.no_grad():\n","    for inputs, labels in test_loader:\n","        outputs = model(inputs)\n","        predicted = (outputs >= 0.5).float()  # Convert probabilities to binary predictions\n","        total += labels.size(0)\n","        correct += (predicted == labels).sum().item()\n","\n","accuracy = correct / total * 100\n","print(f\"Test Accuracy: {accuracy:.2f}%\")"],"metadata":{"id":"IkDnUC3WYsKO","executionInfo":{"status":"aborted","timestamp":1712733565980,"user_tz":-120,"elapsed":26435,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"olTgTGqEYsHv","executionInfo":{"status":"aborted","timestamp":1712733565980,"user_tz":-120,"elapsed":26435,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Training the model\n","num_epochs = 100\n","losses = []\n","for epoch in range(num_epochs):\n","    model.train()  # Set the model to training mode\n","    total_loss = 0.0\n","    for inputs, labels in train_loader: #this goes automatically for each batch\n","        optimizer.zero_grad()  # Zero the gradients (a check)\n","        outputs = model(inputs)  # Forward pass\n","        loss = criterion(outputs, labels)  # Compute the loss\n","        loss.backward()  # Backward pass\n","        optimizer.step()  # Update the parameters\n","        total_loss += loss.item() * inputs.size(0)\n","    epoch_loss = total_loss / len(train_loader.dataset)\n","    losses.append(epoch_loss)  # Store the loss value for this epoch\n","    print(f\"Epoch {epoch+1}/{num_epochs}, Loss: {epoch_loss:.4f}\")\n","\n","# Evaluate the model\n","model.eval()  # Set the model to evaluation mode\n","correct = 0\n","total = 0\n","with torch.no_grad():\n","    for inputs, labels in test_loader:\n","        outputs = model(inputs)\n","        predicted = (outputs >= 0.5).float()  # Convert probabilities to binary predictions\n","        total += labels.size(0)\n","        correct += (predicted == labels).sum().item()\n","\n","accuracy = correct / total * 100\n","print(f\"Test Accuracy: {accuracy:.2f}%\")\n"],"metadata":{"id":"kKwnxxfeq3o1","executionInfo":{"status":"aborted","timestamp":1712733565980,"user_tz":-120,"elapsed":26430,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"Pz0OhalrY_iN","executionInfo":{"status":"aborted","timestamp":1712733565980,"user_tz":-120,"elapsed":26430,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# YOUR CODE STARTS HERE\n","\n","# Plot the loss values\n","...\n","\n","# YOUR CODE ENDS HERE"],"metadata":{"id":"u5itLQOpY_fr","executionInfo":{"status":"aborted","timestamp":1712733566245,"user_tz":-120,"elapsed":5,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"vmB8DuysY_dS","executionInfo":{"status":"aborted","timestamp":1712733566245,"user_tz":-120,"elapsed":5,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## CODE: Plot the loss values"],"metadata":{"id":"4RbzgAKOdWnk"}},{"cell_type":"code","source":[],"metadata":{"id":"y1sEtWDCdZbv","executionInfo":{"status":"aborted","timestamp":1712733566246,"user_tz":-120,"elapsed":6,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"yTdB1L8odZZH","executionInfo":{"status":"aborted","timestamp":1712733566246,"user_tz":-120,"elapsed":6,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"olaoxECPdZWg","executionInfo":{"status":"aborted","timestamp":1712733566246,"user_tz":-120,"elapsed":5,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"KjrySFvsdZRi","executionInfo":{"status":"aborted","timestamp":1712733566246,"user_tz":-120,"elapsed":5,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"MrMfmWzmdZPG","executionInfo":{"status":"aborted","timestamp":1712733566246,"user_tz":-120,"elapsed":5,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Plot the loss values\n","plt.plot(losses)\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.title('Training Loss')\n","plt.show()"],"metadata":{"id":"VDFbSMDFry0M","executionInfo":{"status":"aborted","timestamp":1712733566246,"user_tz":-120,"elapsed":5,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Hugging Face demo"],"metadata":{"id":"4fIsLVBhc454"}},{"cell_type":"code","source":["#https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb"],"metadata":{"id":"tNBI_TNr2R0w","executionInfo":{"status":"aborted","timestamp":1712733566246,"user_tz":-120,"elapsed":5,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!pip install accelerate -U"],"metadata":{"id":"6vHqFGRsZKGg","executionInfo":{"status":"aborted","timestamp":1712733566246,"user_tz":-120,"elapsed":5,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!pip install datasets\n"],"metadata":{"id":"XVe24oeb2Kgn","executionInfo":{"status":"aborted","timestamp":1712733566246,"user_tz":-120,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[" df_dummies_model.drop(columns=['District', 'y'])[0:int(df_dummies_model.shape[0]*0.7)]"],"metadata":{"id":"gl9tcTeBa1u5","executionInfo":{"status":"aborted","timestamp":1712733566246,"user_tz":-120,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from datasets import Dataset\n","\n","\n","# Convert DataFrame to dictionary\n","train_data = df_dummies_model.drop(columns=['District', 'y'])[0:int(df_dummies_model.shape[0]*0.7)].values\n","train_data_dict = {f\"feature_{i}\": train_data[:, i] for i in range(train_data.shape[1])}\n","\n","# Create Dataset object\n","train_dataset = Dataset.from_dict(train_data_dict)\n","\n","# Convert DataFrame to dictionary\n","test_data = df_dummies_model.drop(columns=['District', 'y'])[int(df_dummies_model.shape[0]*0.7)+1:].values\n","test_data_dict = {f\"feature_{i}\": test_data[:, i] for i in range(test_data.shape[1])}\n","\n","# Create Dataset object\n","test_dataset = Dataset.from_dict(test_data_dict)"],"metadata":{"id":"BQOHEar61nK-","executionInfo":{"status":"aborted","timestamp":1712733566246,"user_tz":-120,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test_dataset"],"metadata":{"id":"tGNhGpZWbcS7","executionInfo":{"status":"aborted","timestamp":1712733566246,"user_tz":-120,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!pip install transformers[torch]"],"metadata":{"id":"EEXpummSblus","executionInfo":{"status":"aborted","timestamp":1712733566246,"user_tz":-120,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!pip install pytorch-tabnet"],"metadata":{"id":"UZvzZyZXcqgq","executionInfo":{"status":"aborted","timestamp":1712733566246,"user_tz":-120,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from transformers import Trainer, TrainingArguments\n","import matplotlib.pyplot as plt\n","from transformers import BertForSequenceClassification, Trainer, TrainingArguments\n","from pytorch_tabnet.tab_model import TabNetClassifier\n","\n","# Load pre-trained BERT model for sequence classification\n","#model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=2)  # Assuming binary classification\n","\n","# Instantiate the TabNetClassifier model\n","model = TabNetClassifier()\n","\n","\n","# Define the training arguments\n","training_args = TrainingArguments(\n","    output_dir='./results',          # Output directory\n","    num_train_epochs=3,              # Total number of epochs to train\n","    per_device_train_batch_size=8,   # Batch size per device during training\n","    logging_dir='./logs',            # Directory for storing logs\n","    logging_steps=10,                # Log every X updates steps\n","    save_steps=500,                  # Save checkpoint every X updates steps\n","    evaluation_strategy=\"epoch\"      # Evaluate at the end of each epoch\n",")\n","\n","# Instantiate the Trainer\n","trainer = Trainer(\n","    model=model,                     # The model to be trained\n","    args=training_args,              # Training arguments\n","    train_dataset=train_dataset,    # Training dataset\n","    eval_dataset=test_dataset       # Evaluation dataset\n",")\n","\n","# Train the model\n","trainer.train()\n","\n","# Plot the loss values\n","train_loss = trainer.state.log_history['loss']\n","plt.plot(train_loss)\n","plt.xlabel('Training Steps')\n","plt.ylabel('Loss')\n","plt.title('Training Loss')\n","plt.show()\n"],"metadata":{"id":"hGBVL0WutW92","executionInfo":{"status":"aborted","timestamp":1712733566246,"user_tz":-120,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Images"],"metadata":{"id":"xLnilysKc_rI"}},{"cell_type":"code","source":["import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import torchvision\n","\n"],"metadata":{"id":"2YkwRiALd6s3","executionInfo":{"status":"ok","timestamp":1712742110242,"user_tz":-120,"elapsed":551,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])\n","\n","trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n","testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)\n","\n","batch_size = 32\n","trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)\n","testloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mSptEG9Rd6p-","executionInfo":{"status":"ok","timestamp":1712742122798,"user_tz":-120,"elapsed":9074,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"3bd16cd4-0d38-431e-dd9a-c78e457a1af2"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 170498071/170498071 [00:03<00:00, 42630100.22it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Extracting ./data/cifar-10-python.tar.gz to ./data\n","Files already downloaded and verified\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"GIIvrLy3eEwh","executionInfo":{"status":"aborted","timestamp":1712733566247,"user_tz":-120,"elapsed":5,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","plt.imshow(trainset.data[3])\n","plt.show()"],"metadata":{"id":"6h3_L73-eEts","executionInfo":{"status":"aborted","timestamp":1712733566247,"user_tz":-120,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class CIFAR10Model(nn.Module):\n","    def __init__(self):\n","        super().__init__()\n","        self.conv1 = nn.Conv2d(3, 32, kernel_size=(3,3), stride=1, padding=1) #INPUT CHANNELs\n","        self.act1 = nn.ReLU()\n","        self.drop1 = nn.Dropout(0.3)\n","\n","        self.conv2 = nn.Conv2d(32, 32, kernel_size=(3,3), stride=1, padding=1)\n","        self.act2 = nn.ReLU()\n","        self.pool2 = nn.MaxPool2d(kernel_size=(2, 2))\n","\n","        self.flat = nn.Flatten()\n","\n","        self.fc3 = nn.Linear(8192, 512)\n","        self.act3 = nn.ReLU()\n","        self.drop3 = nn.Dropout(0.5)\n","\n","        self.fc4 = nn.Linear(512, 10) #(dim, N_CLASSES)\n","\n","    def forward(self, x):\n","        # input 3x32x32, output 32x32x32 (32 filters)\n","        x = self.act1(self.conv1(x))\n","        x = self.drop1(x)\n","        # input 32x32x32, output 32x32x32\n","        x = self.act2(self.conv2(x))\n","        # input 32x32x32, output 32x16x16 (max pool)\n","        x = self.pool2(x)\n","        # input 32x16x16, output 8192 (flat)\n","        x = self.flat(x)\n","        # input 8192, output 512\n","        x = self.act3(self.fc3(x))\n","        x = self.drop3(x)\n","        # input 512, output 10\n","        x = self.fc4(x)\n","        return x\n","\n","model = CIFAR10Model()"],"metadata":{"id":"UL-wzYvQeUjH","executionInfo":{"status":"aborted","timestamp":1712733566247,"user_tz":-120,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X = torch.tensor([trainset.data[7]], dtype=torch.float32).permute(0,3,1,2)\n","model.eval()\n","with torch.no_grad():\n","    feature_maps = model.conv1(X)"],"metadata":{"id":"ksUZeXNxeUgo","executionInfo":{"status":"aborted","timestamp":1712733566247,"user_tz":-120,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["fig, ax = plt.subplots(4, 8, sharex=True, sharey=True, figsize=(16,8))\n","for i in range(0, 32):\n","    row, col = i//8, i%8\n","    ax[row][col].imshow(feature_maps[0][i])\n","plt.show()"],"metadata":{"id":"rrvc_Yf1eUdl","executionInfo":{"status":"aborted","timestamp":1712733566247,"user_tz":-120,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"_bFaJo3AeuKL","executionInfo":{"status":"aborted","timestamp":1712733566247,"user_tz":-120,"elapsed":26638,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X = torch.tensor([trainset.data[7]], dtype=torch.float32).permute(0,3,1,2)\n","\n","model.eval()\n","with torch.no_grad():\n","    feature_maps = model.act1(model.conv1(X))\n","    feature_maps = model.drop1(feature_maps)\n","    feature_maps = model.conv2(feature_maps)\n","\n","fig, ax = plt.subplots(4, 8, sharex=True, sharey=True, figsize=(16,8))\n","for i in range(0, 32):\n","    row, col = i//8, i%8\n","    ax[row][col].imshow(feature_maps[0][i])\n","plt.show()"],"metadata":{"id":"YnKn3QNteuHw","executionInfo":{"status":"aborted","timestamp":1712733566247,"user_tz":-120,"elapsed":26633,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"aWUoIvU5euEj","executionInfo":{"status":"aborted","timestamp":1712733566247,"user_tz":-120,"elapsed":26629,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])\n","\n","trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n","testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)\n","\n","batch_size = 32\n","trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)\n","testloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)\n","\n","\n","loss_fn = nn.CrossEntropyLoss()\n","optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n","\n","n_epochs = 20\n","for epoch in range(n_epochs):\n","    for inputs, labels in trainloader:\n","        # forward, backward, and then weight update\n","        y_pred = model(inputs)\n","        loss = loss_fn(y_pred, labels)\n","        optimizer.zero_grad()\n","        loss.backward()\n","        optimizer.step()\n","\n","    acc = 0\n","    count = 0\n","    for inputs, labels in testloader:\n","        y_pred = model(inputs)\n","        acc += (torch.argmax(y_pred, 1) == labels).float().sum()\n","        count += len(labels)\n","    acc /= count\n","    print(\"Epoch %d: model accuracy %.2f%%\" % (epoch, acc*100))\n","\n","torch.save(model.state_dict(), \"cifar10model.pth\")"],"metadata":{"id":"_qGdY_H3wbeq","executionInfo":{"status":"aborted","timestamp":1712733566247,"user_tz":-120,"elapsed":26624,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## CODE: Do this on GPU T4\n","\n","Follow this example: https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html"],"metadata":{"id":"JmO2Pidxic58"}},{"cell_type":"code","source":["device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n","\n","# Assuming that we are on a CUDA machine, this should print a CUDA device:\n","\n","print(device)"],"metadata":{"id":"lXjcowOMjHDD","executionInfo":{"status":"aborted","timestamp":1712733566247,"user_tz":-120,"elapsed":26624,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import torch\n","import torchvision\n","import torchvision.transforms as transforms"],"metadata":{"id":"ePHnAPUJi4Ha","executionInfo":{"status":"aborted","timestamp":1712733566247,"user_tz":-120,"elapsed":26624,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["transform = transforms.Compose(\n","    [transforms.ToTensor(),\n","     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n","\n","batch_size = 4\n","\n","trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n","                                        download=True, transform=transform)\n","trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n","                                          shuffle=True, num_workers=2)\n","\n","testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n","                                       download=True, transform=transform)\n","testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n","                                         shuffle=False, num_workers=2)\n","\n","classes = ('plane', 'car', 'bird', 'cat',\n","           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"],"metadata":{"id":"zWViBC1ti0Es","executionInfo":{"status":"aborted","timestamp":1712733566247,"user_tz":-120,"elapsed":26619,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","# functions to show an image\n","\n","\n","def imshow(img):\n","    img = img / 2 + 0.5     # unnormalize\n","    npimg = img.numpy()\n","    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n","    plt.show()\n","\n","\n","# get some random training images\n","dataiter = iter(trainloader)\n","images, labels = next(dataiter)\n","\n","# show images\n","imshow(torchvision.utils.make_grid(images))\n","# print labels\n","print(' '.join(f'{classes[labels[j]]:5s}' for j in range(batch_size)))"],"metadata":{"id":"IiUgQjxNi0CM","executionInfo":{"status":"aborted","timestamp":1712733566247,"user_tz":-120,"elapsed":26614,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import torch.nn as nn\n","import torch.nn.functional as F\n","\n","\n","class Net(nn.Module):\n","    def __init__(self):\n","        super().__init__()\n","        self.conv1 = nn.Conv2d(3, 6, 5)\n","        self.pool = nn.MaxPool2d(2, 2)\n","        self.conv2 = nn.Conv2d(6, 16, 5)\n","        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n","        self.fc2 = nn.Linear(120, 84)\n","        self.fc3 = nn.Linear(84, 10)\n","\n","    def forward(self, x):\n","        x = self.pool(F.relu(self.conv1(x)))\n","        x = self.pool(F.relu(self.conv2(x)))\n","        x = torch.flatten(x, 1) # flatten all dimensions except batch\n","        x = F.relu(self.fc1(x))\n","        x = F.relu(self.fc2(x))\n","        x = self.fc3(x)\n","        return x\n","\n","\n","net = Net()\n","net.to(device)"],"metadata":{"id":"MHhxg0AZiz-y","executionInfo":{"status":"aborted","timestamp":1712733566247,"user_tz":-120,"elapsed":26610,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import torch.optim as optim\n","\n","criterion = nn.CrossEntropyLoss()\n","optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"],"metadata":{"id":"pU32Bin8jEcc","executionInfo":{"status":"aborted","timestamp":1712733566248,"user_tz":-120,"elapsed":26611,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for epoch in range(2):  # loop over the dataset multiple times\n","\n","    running_loss = 0.0\n","    for i, data in enumerate(trainloader, 0):\n","        # get the inputs; data is a list of [inputs, labels]\n","        #inputs, labels = data\n","        inputs, labels = data[0].to(device), data[1].to(device)\n","        # zero the parameter gradients\n","        optimizer.zero_grad()\n","\n","        # forward + backward + optimize\n","        outputs = net(inputs)\n","        loss = criterion(outputs, labels)\n","        loss.backward()\n","        optimizer.step()\n","\n","        # print statistics\n","        running_loss += loss.item()\n","        if i % 2000 == 1999:    # print every 2000 mini-batches\n","            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')\n","            running_loss = 0.0\n","\n","print('Finished Training')"],"metadata":{"id":"a2YfWqktjEYn","executionInfo":{"status":"aborted","timestamp":1712733566248,"user_tz":-120,"elapsed":26607,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["PATH = './cifar_net.pth'\n","torch.save(net.state_dict(), PATH)"],"metadata":{"id":"2srBXVPbeyKv","executionInfo":{"status":"aborted","timestamp":1712733566248,"user_tz":-120,"elapsed":26606,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataiter = iter(testloader)\n","images, labels = next(dataiter)\n","\n","# print images\n","imshow(torchvision.utils.make_grid(images))\n","print('GroundTruth: ', ' '.join(f'{classes[labels[j]]:5s}' for j in range(4)))"],"metadata":{"id":"ry-frRGJjWz4","executionInfo":{"status":"aborted","timestamp":1712733566248,"user_tz":-120,"elapsed":26602,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["net = Net()\n","net.load_state_dict(torch.load(PATH))"],"metadata":{"id":"6vsFuZhFjWxM","executionInfo":{"status":"aborted","timestamp":1712733566248,"user_tz":-120,"elapsed":26597,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["outputs = net(images)"],"metadata":{"id":"a8Mb1h0bjWts","executionInfo":{"status":"aborted","timestamp":1712733566248,"user_tz":-120,"elapsed":26597,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["_, predicted = torch.max(outputs, 1)\n","\n","print('Predicted: ', ' '.join(f'{classes[predicted[j]]:5s}'\n","                              for j in range(4)))"],"metadata":{"id":"n9QFXt-_jzhx","executionInfo":{"status":"aborted","timestamp":1712733566248,"user_tz":-120,"elapsed":26593,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["correct = 0\n","total = 0\n","# since we're not training, we don't need to calculate the gradients for our outputs\n","with torch.no_grad():\n","    for data in testloader:\n","        images, labels = data\n","        # calculate outputs by running images through the network\n","        outputs = net(images)\n","        # the class with the highest energy is what we choose as prediction\n","        _, predicted = torch.max(outputs.data, 1)\n","        total += labels.size(0)\n","        correct += (predicted == labels).sum().item()\n","\n","print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')"],"metadata":{"id":"shkATN8Pj1me","executionInfo":{"status":"aborted","timestamp":1712733566248,"user_tz":-120,"elapsed":26588,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prepare to count predictions for each class\n","correct_pred = {classname: 0 for classname in classes}\n","total_pred = {classname: 0 for classname in classes}\n","\n","# again no gradients needed\n","with torch.no_grad():\n","    for data in testloader:\n","        images, labels = data\n","        outputs = net(images)\n","        _, predictions = torch.max(outputs, 1)\n","        # collect the correct predictions for each class\n","        for label, prediction in zip(labels, predictions):\n","            if label == prediction:\n","                correct_pred[classes[label]] += 1\n","            total_pred[classes[label]] += 1\n","\n","\n","# print accuracy for each class\n","for classname, correct_count in correct_pred.items():\n","    accuracy = 100 * float(correct_count) / total_pred[classname]\n","    print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')"],"metadata":{"id":"74rOlkUVj5zA","executionInfo":{"status":"aborted","timestamp":1712733566248,"user_tz":-120,"elapsed":26584,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"z89ZN0NIj_f2","executionInfo":{"status":"aborted","timestamp":1712733566248,"user_tz":-120,"elapsed":26583,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"K64syxonk_OF","executionInfo":{"status":"aborted","timestamp":1712733566248,"user_tz":-120,"elapsed":26583,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Real world example\n","\n","https://github.com/pytorch/examples/blob/main/imagenet/main.py\n"],"metadata":{"id":"lk20aZuElAuh"}},{"cell_type":"code","source":[],"metadata":{"id":"eW2KQ-Vhk_Kq","executionInfo":{"status":"aborted","timestamp":1712733566248,"user_tz":-120,"elapsed":26583,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"execution_count":null,"outputs":[]}]}