CNN_SMOTE_100.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"CNN_SMOTE_100.ipynb","provenance":[],"collapsed_sections":[],"machine_shape":"hm","authorship_tag":"ABX9TyMCe+03w/xBfQs8W9jXf+T6"},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"Zwecc6kFZCMz"},"source":["# **Connect to Google Drive**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I7cmjYtcqtHf","executionInfo":{"status":"ok","timestamp":1619136948132,"user_tz":-180,"elapsed":19913,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"803b95ed-72f1-4872-ea82-c7ada89d64e7"},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2DbK7LxsvmpW","executionInfo":{"status":"ok","timestamp":1619136950372,"user_tz":-180,"elapsed":806,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"d403c924-2ae7-4f2f-a469-c013b85834fb"},"source":["cd gdrive/My\\ Drive/Colab\\ Notebooks/"],"execution_count":2,"outputs":[{"output_type":"stream","text":["/content/gdrive/My Drive/Colab Notebooks\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"7C6t60rGSRwf","executionInfo":{"status":"ok","timestamp":1619136950373,"user_tz":-180,"elapsed":524,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"RiV7e8P5SUiQ","executionInfo":{"status":"ok","timestamp":1619136950845,"user_tz":-180,"elapsed":770,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":2,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FhHTjk0pZZB4"},"source":["# **Preprocessing the dataset**\n","\n","- Load the data from Google Drive.\n","- In Google Drive, have 3 directories: train_data - valid_data - test_data.\n","- In each folder, data is stored in h5py format.\n","- Standardization (z-score)."]},{"cell_type":"code","metadata":{"id":"hg58Jdv_wnQ7","executionInfo":{"status":"ok","timestamp":1619136951222,"user_tz":-180,"elapsed":655,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["import numpy as np\n","import h5py"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"ISqT2YpNHn0t","executionInfo":{"status":"ok","timestamp":1619136951518,"user_tz":-180,"elapsed":687,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["def preprocessing_dataset(num_file, filename_prefix):\n","    X = []\n","    y = []\n","\n","    # Load the data from h5py\n","    for i in range(1, num_file):\n","        \n","        filename = filename_prefix + str(i) + '.hdf5'\n","        print('Loading batch ' + str(i) + ' ...')\n","\n","        with h5py.File(filename, 'r') as f:\n","            for j in f['X'][:]:\n","                X.append(j)\n","\n","            for j in f['y'][:]:\n","                y.append(j)\n","\n","    # Convert to numpy array\n","    X = np.array(X)\n","    y = np.array(y)\n","\n","    # Calculate the mean of each channel\n","    mean_0 = X[:, 0, :, :].mean()\n","    mean_1 = X[:, 1, :, :].mean()\n","    mean_2 = X[:, 2, :, :].mean()\n","\n","    # Calculate the standard deviation of each channel\n","    std_0 = X[:, 0, :, :].std()\n","    std_1 = X[:, 1, :, :].std()\n","    std_2 = X[:, 2, :, :].std()\n","\n","    # Standardization\n","    X[:, 0, :, :] = (X[:, 0, :, :] - mean_0) / std_0\n","    X[:, 1, :, :] = (X[:, 1, :, :] - mean_1) / std_1\n","    X[:, 2, :, :] = (X[:, 2, :, :] - mean_2) / std_2\n","\n","    return X, y"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"9ShtHx4ETpfp","executionInfo":{"status":"ok","timestamp":1619136951847,"user_tz":-180,"elapsed":740,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":4,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iihRe7PLJtWa"},"source":["**Configure the dataset**"]},{"cell_type":"code","metadata":{"id":"PJvsDiR5JrFa","executionInfo":{"status":"ok","timestamp":1619136952963,"user_tz":-180,"elapsed":677,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["train_num_file = 10\n","train_filename_prefix = 'train_data/X_train_'\n","\n","valid_num_file = 4\n","valid_filename_prefix = 'valid_data/X_valid_'\n","\n","test_num_file = 4\n","test_filename_prefix = 'test_data/X_test_'"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"id":"dt9M5zOKSYeZ","executionInfo":{"status":"ok","timestamp":1619136953475,"user_tz":-180,"elapsed":832,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"fK1yHloTqq0M"},"source":["### **Load the training set**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6Tr-B93DyS2g","executionInfo":{"status":"ok","timestamp":1619137040924,"user_tz":-180,"elapsed":87798,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"7b7e4022-bfb4-4701-9c7b-395000b3f9f5"},"source":["X_train, y_train = preprocessing_dataset(train_num_file, train_filename_prefix)"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Loading batch 1 ...\n","Loading batch 2 ...\n","Loading batch 3 ...\n","Loading batch 4 ...\n","Loading batch 5 ...\n","Loading batch 6 ...\n","Loading batch 7 ...\n","Loading batch 8 ...\n","Loading batch 9 ...\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NbGvwTXfzuIE","executionInfo":{"status":"ok","timestamp":1619137040925,"user_tz":-180,"elapsed":87543,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"09003707-a254-4952-c59b-969103dc8de6"},"source":["print(X_train.shape)\n","print(y_train.shape)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["(165133, 3, 50, 50)\n","(165133,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"r-e26mJtSZH1","executionInfo":{"status":"ok","timestamp":1619137040927,"user_tz":-180,"elapsed":87357,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"dv0w0DlUrEpX"},"source":["### **Load the validation set**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"54FTpMpF7P3k","executionInfo":{"status":"ok","timestamp":1619137066042,"user_tz":-180,"elapsed":111980,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"c23e5160-8d41-46e6-dd89-b88805f6fd17"},"source":["X_valid, y_valid = preprocessing_dataset(valid_num_file, valid_filename_prefix)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Loading batch 1 ...\n","Loading batch 2 ...\n","Loading batch 3 ...\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SN9_Tb7NQjww","executionInfo":{"status":"ok","timestamp":1619137066044,"user_tz":-180,"elapsed":111693,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"6d5f7eb0-5e43-4606-e218-49f4b5882db1"},"source":["print(X_valid.shape)\n","print(y_valid.shape)"],"execution_count":9,"outputs":[{"output_type":"stream","text":["(55044, 3, 50, 50)\n","(55044,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"EJY7KP0pSZqM","executionInfo":{"status":"ok","timestamp":1619137066045,"user_tz":-180,"elapsed":111444,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":9,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8sh-TSIjnIhU"},"source":["### **Load the test set**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3el2jGb-nOQs","executionInfo":{"status":"ok","timestamp":1619137096669,"user_tz":-180,"elapsed":141409,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"99b80c7d-4386-4b58-b6e5-2ef555adfcae"},"source":["X_test, y_test = preprocessing_dataset(test_num_file, test_filename_prefix)"],"execution_count":10,"outputs":[{"output_type":"stream","text":["Loading batch 1 ...\n","Loading batch 2 ...\n","Loading batch 3 ...\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zgJ4jkibnOqj","executionInfo":{"status":"ok","timestamp":1619137096671,"user_tz":-180,"elapsed":141075,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"62ef5efc-d103-479b-c583-e6df27b109d0"},"source":["print(X_test.shape)\n","print(y_test.shape)"],"execution_count":11,"outputs":[{"output_type":"stream","text":["(55045, 3, 50, 50)\n","(55045,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ZqVc1Y3YSaQw","executionInfo":{"status":"ok","timestamp":1619137096672,"user_tz":-180,"elapsed":140706,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"id":"3WeOXNGYSbIB","executionInfo":{"status":"ok","timestamp":1619137096673,"user_tz":-180,"elapsed":140343,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":11,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"46SO5TPuHCqT"},"source":["# **Building Model Convolution Neural Network**"]},{"cell_type":"code","metadata":{"id":"xZT0mpOOQ-q2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619137100343,"user_tz":-180,"elapsed":143308,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"5ffcab9b-ff59-4758-8b36-9d057fa7a9cb"},"source":["import math\n","import time\n","import random\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sn\n","import torch\n","import torch.nn as nn\n","from torch import optim\n","from imblearn.over_sampling import SMOTE\n","import collections\n","\n","from warnings import simplefilter, filterwarnings\n","# ignore all future warnings\n","simplefilter(action='ignore', category=FutureWarning)\n","filterwarnings(\"ignore\")"],"execution_count":12,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n","  \"(https://pypi.org/project/six/).\", FutureWarning)\n","/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n","  warnings.warn(message, FutureWarning)\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"9n2NO9hbxwbB"},"source":["### **Parameters of the model**"]},{"cell_type":"code","metadata":{"id":"J8DbgXlox9DK","executionInfo":{"status":"ok","timestamp":1619137100344,"user_tz":-180,"elapsed":142662,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["EPOCHS = 80\n","BATCH_SIZE = 128\n","LR = 1e-6"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"id":"FCfrIllISdGu","executionInfo":{"status":"ok","timestamp":1619137100345,"user_tz":-180,"elapsed":142302,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":13,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"IKQeUCpDVokL"},"source":["### **Defining the Model**"]},{"cell_type":"markdown","metadata":{"id":"F9oKDoeJXV8d"},"source":["**Model description:**\n","\n","- Input model is RGB image data (3 color channels, 50 pixels height, 50 pixels width)\n","- Transform input image to multi-dimensional matrix with size 3 - 50 - 50.\n","\n","\n","**Activation function**\n","\n","- The ReLU is the most used activation function in the world right now. Since, it is used in almost all the convolutional neural networks or deep learning. \n","- Computationally efficient—allows the network to converge very quickly.\n","- Non-linear—although it looks like a linear function, ReLU has a derivative function and allows for backpropagation.\n","\n","![ReLU-activation-function.png](data:image/png;base64,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)\n","\n","**Avoid overfitting**\n","\n","To avoid overfitting in neural networks, use two methods of Dropout and Batch normalization.\n","- **Dropout**: is one of the most commonly used and the most powerful regularization techniques used in deep learning. Dropout is applied to intermediate layers of the model during the training time. Let's look at an example of how dropout is applied on a linear layer's output that generates 10 values:\n","\n","  + The figure shows what happens when dropout is applied to the linear layer output with a threshold value of 0.2. It randomly masks or zeros 20% of data, so that the model will not be dependent on a particular set of weights or patterns, thus overfitting.\n","\n","![a.png](data:image/png;base64,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)\n","\n","- **Batch normalization**: (also known as batch norm) is a method used to make artificial neural networks faster and more stable through normalization of the input layer by re-centering and re-scaling. To increase the stability of a neural network, batch normalization normalizes the output of a previous activation layer by subtracting the batch mean and dividing by the batch standard deviation.\n"]},{"cell_type":"code","metadata":{"id":"f-PHjODbRNb1","executionInfo":{"status":"ok","timestamp":1619137100346,"user_tz":-180,"elapsed":140485,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["class CNN(nn.Module):\n","  \n","    def __init__(self):\n","        super(CNN, self).__init__()\n","\n","        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=2)\n","        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=2)\n","        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=2)\n","        self.conv4 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=2)\n","        self.conv5 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=2)\n","\n","        self.bn1 = nn.BatchNorm2d(32)\n","        self.bn2 = nn.BatchNorm2d(64)\n","        self.bn3 = nn.BatchNorm2d(128)\n","        self.bn4 = nn.BatchNorm2d(256)\n","        self.bn5 = nn.BatchNorm2d(512)\n","\n","        self.mp = nn.MaxPool2d(kernel_size=2)\n","        self.relu = nn.ReLU()\n","        self.do = nn.Dropout()\n","\n","        self.fc1 = nn.Linear(4608, 1024)\n","        self.fc2 = nn.Linear(1024, 256)\n","        self.fc3 = nn.Linear(256, 64)\n","        self.fc4 = nn.Linear(64, 16)\n","        self.fc5 = nn.Linear(16, 2)\n","\n","    def forward(self, x):\n","        x = self.mp(self.relu(self.bn1(self.conv1(x))))\n","        x = self.mp(self.relu(self.bn2(self.conv2(x))))\n","        x = self.mp(self.relu(self.bn3(self.conv3(x))))\n","        x = self.mp(self.relu(self.bn4(self.conv4(x))))\n","        x = self.do(self.mp(self.relu(self.bn5(self.conv5(x)))))\n","\n","        x = x.view(x.shape[0], -1)\n","\n","        x = self.relu(self.fc1(x))\n","        x = self.relu(self.fc2(x))\n","        x = self.relu(self.fc3(x))\n","        x = self.relu(self.fc4(x))\n","        out = self.fc5(x)\n","\n","        return out"],"execution_count":14,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ABX8AhN1RRpQ","executionInfo":{"status":"ok","timestamp":1619137100347,"user_tz":-180,"elapsed":140095,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"7285a0bf-87e0-4819-ed5c-61e8fbc0a551"},"source":["model = CNN()\n","print(model)"],"execution_count":15,"outputs":[{"output_type":"stream","text":["CNN(\n","  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2))\n","  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2))\n","  (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2))\n","  (conv4): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2))\n","  (conv5): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2))\n","  (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (bn3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (bn4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (bn5): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (mp): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  (relu): ReLU()\n","  (do): Dropout(p=0.5, inplace=False)\n","  (fc1): Linear(in_features=4608, out_features=1024, bias=True)\n","  (fc2): Linear(in_features=1024, out_features=256, bias=True)\n","  (fc3): Linear(in_features=256, out_features=64, bias=True)\n","  (fc4): Linear(in_features=64, out_features=16, bias=True)\n","  (fc5): Linear(in_features=16, out_features=2, bias=True)\n",")\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gJAFxg_eSusn","executionInfo":{"status":"ok","timestamp":1619137100349,"user_tz":-180,"elapsed":139595,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":15,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"j298Jstjk3Hf"},"source":["**Calculate the number of trainable parameters (weights and biases)**"]},{"cell_type":"code","metadata":{"id":"-53ysjW9RUhL","executionInfo":{"status":"ok","timestamp":1619137100351,"user_tz":-180,"elapsed":138794,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["def count_parameters(model):\n","    return sum(p.numel() for p in model.parameters() if p.requires_grad)"],"execution_count":16,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Uwng3UiXRW-5","executionInfo":{"status":"ok","timestamp":1619137100353,"user_tz":-180,"elapsed":138264,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"11b3ca85-a8a5-4b05-afcb-bae8286f9162"},"source":["print(f'The model has {count_parameters(model):,} trainable parameters')"],"execution_count":17,"outputs":[{"output_type":"stream","text":["The model has 6,570,098 trainable parameters\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"_EMFt1yKSxFQ","executionInfo":{"status":"ok","timestamp":1619137100354,"user_tz":-180,"elapsed":137435,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":17,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PkoWZdg1lk1E"},"source":["**Optimization Algorithm**\n","\n","- Steps to optimization\n","\n","  + Pass a batch of data through your model.\n","  + Calculate the loss of your batch by comparing your model's predictions against the actual labels.\n","  + Calculate the gradient of each of your parameters with respect to the loss.\n","  + Update each of your parameters by subtracting their gradient multiplied by a small learning rate parameter."]},{"cell_type":"code","metadata":{"id":"Vy7JDLV3RZJY","executionInfo":{"status":"ok","timestamp":1619137100355,"user_tz":-180,"elapsed":136519,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["optimizer = optim.Adam(model.parameters(), lr=LR)"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"id":"uwqIpOEsSy_4","executionInfo":{"status":"ok","timestamp":1619137100356,"user_tz":-180,"elapsed":136024,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":18,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"lL6ZwLSFna9u"},"source":["**Loss Fuction**\n","\n","- **Cross-entropy** can be used to define a loss function in machine learning and optimization."]},{"cell_type":"code","metadata":{"id":"Ellkdw4mRbIR","executionInfo":{"status":"ok","timestamp":1619137100357,"user_tz":-180,"elapsed":135100,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["loss_fn = nn.CrossEntropyLoss()"],"execution_count":19,"outputs":[]},{"cell_type":"code","metadata":{"id":"0LxlbzOzSzss","executionInfo":{"status":"ok","timestamp":1619137100358,"user_tz":-180,"elapsed":134713,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"HnAa7POyomp4"},"source":["**Calculate the accuracy** \n","- This takes the index of the highest value for your prediction and compares it against the actual class label. Then divide how many our model got correct by the amount in the batch to calculate accuracy across the batch."]},{"cell_type":"code","metadata":{"id":"Q2C1ydL2RjDm","executionInfo":{"status":"ok","timestamp":1619137100359,"user_tz":-180,"elapsed":133929,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["def calculate_accuracy(y_pred, y):\n","    top_pred = y_pred.argmax(1, keepdim = True)\n","    correct = top_pred.eq(y.view_as(top_pred)).sum()\n","    acc = correct.float() / y.shape[0]\n","    return acc"],"execution_count":20,"outputs":[]},{"cell_type":"code","metadata":{"id":"uCnsmr96S0d4","executionInfo":{"status":"ok","timestamp":1619137100361,"user_tz":-180,"elapsed":133358,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OJN5Z-YYo0KM"},"source":["**Create mini-batch**"]},{"cell_type":"code","metadata":{"id":"PtW_l5uGRlPL","executionInfo":{"status":"ok","timestamp":1619137100362,"user_tz":-180,"elapsed":132484,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["def next_batch(X, y, batch_size):\n","    # Mini-batch\n","    for i in np.arange(0, X.shape[0], batch_size):\n","        yield X[i:i + batch_size], y[i:i + batch_size]"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"Qw-P-Qt0S1Mg","executionInfo":{"status":"ok","timestamp":1619137100723,"user_tz":-180,"elapsed":132274,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":21,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ohXGSlXgoJAc"},"source":["**Transform the model and data to GPU**"]},{"cell_type":"code","metadata":{"id":"5aA6REEXRdIe","executionInfo":{"status":"ok","timestamp":1619137100725,"user_tz":-180,"elapsed":131316,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"id":"bYkiWamORfNN","executionInfo":{"status":"ok","timestamp":1619137105739,"user_tz":-180,"elapsed":135708,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["model = model.to(device)\n","loss_fn = loss_fn.to(device)"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"id":"rsSTDNtxS1uH","executionInfo":{"status":"ok","timestamp":1619137105740,"user_tz":-180,"elapsed":135125,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"id":"YKabQCgtS10v","executionInfo":{"status":"ok","timestamp":1619137105740,"user_tz":-180,"elapsed":134621,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":23,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"VWN6jKWulMmz"},"source":["### **Training the Model**"]},{"cell_type":"markdown","metadata":{"id":"dJjXwVq3OV8_"},"source":["**Define Traning step**\n","- Put the model into train mode: **model.train()**\n","- Iterate over batches of data.\n","- Transform the batch to GPU mode.\n","- Clear the gradients calculated from the last batch.\n","- Impliment model for batch of images: x, get predictions, y_pred.\n","- Calculate the loss between prediction labels and the actual labels.\n","- Calculate the accuracy between prediction labels and the actual labels.\n","- Calculate the gradients of each parameter.\n","- Update the parameters by taking optimizer step.\n","- Return the loss and the accuracy of epoch."]},{"cell_type":"code","metadata":{"id":"uQag77GvRoIo","executionInfo":{"status":"ok","timestamp":1619137105741,"user_tz":-180,"elapsed":132837,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["def train(model, X, y, optimizer, loss_fn, device, batch_size):\n","    epoch_loss = 0\n","    epoch_acc = 0\n","    \n","    # Impliment train mode\n","    model.train()\n","    \n","    for (batchX, batchY) in next_batch(X, y, batch_size):\n","        # SMOTE\n","        x_tmp = batchX.reshape(batchX.shape[0], 3 * 50 * 50)\n","        y_tmp = batchY\n","\n","        count = collections.Counter(y_tmp)\n","\n","        x_smote, y_smote = SMOTE(sampling_strategy={0: count[0], 1: count[0]}, k_neighbors=3).fit_resample(x_tmp, y_tmp)\n","        x_smote = x_smote.reshape(x_smote.shape[0], 3, 50, 50)\n","\n","        # Create tensor\n","        x = torch.from_numpy(x_smote).to(device)\n","        y = torch.from_numpy(y_smote).to(device)\n","        \n","        # Clear the gradients calculated from the last batch\n","        optimizer.zero_grad()\n","                \n","        y_pred = model(x)\n","\n","        loss = loss_fn(y_pred, y)\n","        acc = calculate_accuracy(y_pred, y)\n","        \n","        # Calculate the gradients of each parameter\n","        loss.backward()\n","        \n","        # Update the parameters\n","        optimizer.step()\n","        \n","        epoch_loss += loss.item()\n","        epoch_acc += acc.item()\n","        \n","    num_of_batchs = math.ceil(len(X) / batch_size)\n","    return epoch_loss / num_of_batchs, epoch_acc / num_of_batchs"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"id":"DzOgNwIYS3_h","executionInfo":{"status":"ok","timestamp":1619137105741,"user_tz":-180,"elapsed":130779,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":24,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"slsqH7YBQZrU"},"source":["**Define Evaluation step**\n","\n","- Similar the training step. The differences are:\n","  + Put the model into evalution mode: **model.eval()**\n","  + Don't calculate the gradients of each parameter.\n","  + Don't update the parameters by taking optimizer step.\n","\n","- **torch.no_grad()** ensures that gradients are not calculated for whatever is inside the with block. As our model will not have to calculate gradients it will be faster and use less memory."]},{"cell_type":"code","metadata":{"id":"DASp2ZTYSVzh","executionInfo":{"status":"ok","timestamp":1619137105742,"user_tz":-180,"elapsed":129377,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["def evaluate(model, X, y, loss_fn, device, batch_size):\n","    epoch_loss = 0\n","    epoch_acc = 0\n","    \n","    # Impliment evaluation mode\n","    model.eval()\n","    \n","    with torch.no_grad():\n","        for (batchX, batchY) in next_batch(X, y, batch_size):\n","            x = torch.from_numpy(batchX).to(device)\n","            y = torch.from_numpy(batchY).to(device)\n","\n","            y_pred = model(x)\n","\n","            loss = loss_fn(y_pred, y)\n","            acc = calculate_accuracy(y_pred, y)\n","\n","            epoch_loss += loss.item()\n","            epoch_acc += acc.item()\n","        \n","    num_of_batchs = math.ceil(len(X) / batch_size)\n","    return epoch_loss / num_of_batchs, epoch_acc / num_of_batchs"],"execution_count":25,"outputs":[]},{"cell_type":"code","metadata":{"id":"o9o2ZxaQS42-","executionInfo":{"status":"ok","timestamp":1619137105742,"user_tz":-180,"elapsed":128879,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":25,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3D-urAR5RrK3"},"source":["**Calculate the time need to run each epoch**"]},{"cell_type":"code","metadata":{"id":"gPKz1vGhSiNH","executionInfo":{"status":"ok","timestamp":1619137105743,"user_tz":-180,"elapsed":127951,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["def epoch_time(start_time, end_time):\n","    elapsed_time = end_time - start_time\n","    elapsed_mins = int(elapsed_time / 60)\n","    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n","    return elapsed_mins, elapsed_secs"],"execution_count":26,"outputs":[]},{"cell_type":"code","metadata":{"id":"qJatZ8upS5ZD","executionInfo":{"status":"ok","timestamp":1619137105744,"user_tz":-180,"elapsed":127478,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":26,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zuP4bbb8R4ki"},"source":["**Training the Model**\n","- Iterate over epochs.\n","- In each epochs:\n","  - Training the Model with training set.\n","  - Evaluate the Model with validation set.\n","  - Check the best validation loss and save the model parameters\n","  - Print the result metrics: Loss and Accuracy."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_-pyaes-SlCC","executionInfo":{"status":"ok","timestamp":1619143064287,"user_tz":-180,"elapsed":6084911,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"8c8c27e2-69fe-4e8f-9e1b-bdcfdda93ec7"},"source":["best_valid_loss = float('inf')\n","train_loss_list = list()\n","valid_loss_list = list()\n","\n","for epoch in range(EPOCHS):\n","    start_time = time.monotonic()\n","\n","    train_loss, train_acc = train(model, X_train, y_train, optimizer, loss_fn, device, batch_size=BATCH_SIZE)\n","    valid_loss, valid_acc = evaluate(model, X_valid, y_valid, loss_fn, device, batch_size=BATCH_SIZE)\n","\n","    train_loss_list.append(train_loss)\n","    valid_loss_list.append(valid_loss)\n","    \n","    if valid_loss < best_valid_loss:\n","        best_valid_loss = valid_loss\n","        torch.save(model.state_dict(), 'smote_v1.pt')\n","    \n","    end_time = time.monotonic()\n","\n","    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n","    \n","    print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n","    print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n","    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')"],"execution_count":27,"outputs":[{"output_type":"stream","text":["Epoch: 01 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.468 | Train Acc: 78.58%\n","\t Val. Loss: 0.402 |  Val. Acc: 82.54%\n","Epoch: 02 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.342 | Train Acc: 85.48%\n","\t Val. Loss: 0.387 |  Val. Acc: 83.09%\n","Epoch: 03 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.326 | Train Acc: 86.11%\n","\t Val. Loss: 0.381 |  Val. Acc: 83.52%\n","Epoch: 04 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.315 | Train Acc: 86.61%\n","\t Val. Loss: 0.366 |  Val. Acc: 84.15%\n","Epoch: 05 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.308 | Train Acc: 87.01%\n","\t Val. Loss: 0.363 |  Val. Acc: 84.38%\n","Epoch: 06 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.302 | Train Acc: 87.29%\n","\t Val. Loss: 0.358 |  Val. Acc: 84.60%\n","Epoch: 07 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.297 | Train Acc: 87.56%\n","\t Val. Loss: 0.357 |  Val. Acc: 84.68%\n","Epoch: 08 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.294 | Train Acc: 87.69%\n","\t Val. Loss: 0.353 |  Val. Acc: 84.87%\n","Epoch: 09 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.291 | Train Acc: 87.84%\n","\t Val. Loss: 0.347 |  Val. Acc: 85.14%\n","Epoch: 10 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.288 | Train Acc: 88.04%\n","\t Val. Loss: 0.344 |  Val. Acc: 85.31%\n","Epoch: 11 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.286 | Train Acc: 88.11%\n","\t Val. Loss: 0.347 |  Val. Acc: 85.21%\n","Epoch: 12 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.284 | Train Acc: 88.24%\n","\t Val. Loss: 0.342 |  Val. Acc: 85.42%\n","Epoch: 13 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.282 | Train Acc: 88.32%\n","\t Val. Loss: 0.342 |  Val. Acc: 85.42%\n","Epoch: 14 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.281 | Train Acc: 88.39%\n","\t Val. Loss: 0.340 |  Val. Acc: 85.57%\n","Epoch: 15 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.279 | Train Acc: 88.49%\n","\t Val. Loss: 0.338 |  Val. Acc: 85.62%\n","Epoch: 16 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.277 | Train Acc: 88.57%\n","\t Val. Loss: 0.342 |  Val. Acc: 85.46%\n","Epoch: 17 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.276 | Train Acc: 88.65%\n","\t Val. Loss: 0.335 |  Val. Acc: 85.80%\n","Epoch: 18 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.274 | Train Acc: 88.70%\n","\t Val. Loss: 0.340 |  Val. Acc: 85.60%\n","Epoch: 19 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.273 | Train Acc: 88.71%\n","\t Val. Loss: 0.337 |  Val. Acc: 85.78%\n","Epoch: 20 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.272 | Train Acc: 88.75%\n","\t Val. Loss: 0.336 |  Val. Acc: 85.73%\n","Epoch: 21 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.271 | Train Acc: 88.81%\n","\t Val. Loss: 0.333 |  Val. Acc: 85.86%\n","Epoch: 22 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.269 | Train Acc: 88.93%\n","\t Val. Loss: 0.335 |  Val. Acc: 85.76%\n","Epoch: 23 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.268 | Train Acc: 88.96%\n","\t Val. Loss: 0.332 |  Val. Acc: 85.95%\n","Epoch: 24 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.267 | Train Acc: 88.96%\n","\t Val. Loss: 0.335 |  Val. Acc: 85.79%\n","Epoch: 25 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.267 | Train Acc: 89.02%\n","\t Val. Loss: 0.333 |  Val. Acc: 85.92%\n","Epoch: 26 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.264 | Train Acc: 89.11%\n","\t Val. Loss: 0.333 |  Val. Acc: 85.89%\n","Epoch: 27 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.263 | Train Acc: 89.13%\n","\t Val. Loss: 0.330 |  Val. Acc: 86.04%\n","Epoch: 28 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.263 | Train Acc: 89.19%\n","\t Val. Loss: 0.329 |  Val. Acc: 86.03%\n","Epoch: 29 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.262 | Train Acc: 89.26%\n","\t Val. Loss: 0.328 |  Val. Acc: 86.13%\n","Epoch: 30 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.262 | Train Acc: 89.24%\n","\t Val. Loss: 0.328 |  Val. Acc: 86.11%\n","Epoch: 31 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.260 | Train Acc: 89.28%\n","\t Val. Loss: 0.326 |  Val. Acc: 86.16%\n","Epoch: 32 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.259 | Train Acc: 89.31%\n","\t Val. Loss: 0.327 |  Val. Acc: 86.15%\n","Epoch: 33 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.258 | Train Acc: 89.37%\n","\t Val. Loss: 0.329 |  Val. Acc: 86.08%\n","Epoch: 34 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.257 | Train Acc: 89.41%\n","\t Val. Loss: 0.330 |  Val. Acc: 86.02%\n","Epoch: 35 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.257 | Train Acc: 89.46%\n","\t Val. Loss: 0.327 |  Val. Acc: 86.16%\n","Epoch: 36 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.256 | Train Acc: 89.48%\n","\t Val. Loss: 0.325 |  Val. Acc: 86.26%\n","Epoch: 37 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.255 | Train Acc: 89.54%\n","\t Val. Loss: 0.325 |  Val. Acc: 86.21%\n","Epoch: 38 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.254 | Train Acc: 89.54%\n","\t Val. Loss: 0.325 |  Val. Acc: 86.25%\n","Epoch: 39 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.253 | Train Acc: 89.61%\n","\t Val. Loss: 0.324 |  Val. Acc: 86.29%\n","Epoch: 40 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.252 | Train Acc: 89.64%\n","\t Val. Loss: 0.324 |  Val. Acc: 86.31%\n","Epoch: 41 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.252 | Train Acc: 89.69%\n","\t Val. Loss: 0.328 |  Val. Acc: 86.10%\n","Epoch: 42 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.251 | Train Acc: 89.69%\n","\t Val. Loss: 0.323 |  Val. Acc: 86.34%\n","Epoch: 43 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.250 | Train Acc: 89.79%\n","\t Val. Loss: 0.322 |  Val. Acc: 86.39%\n","Epoch: 44 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.248 | Train Acc: 89.79%\n","\t Val. Loss: 0.322 |  Val. Acc: 86.37%\n","Epoch: 45 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.248 | Train Acc: 89.79%\n","\t Val. Loss: 0.323 |  Val. Acc: 86.38%\n","Epoch: 46 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.247 | Train Acc: 89.84%\n","\t Val. Loss: 0.322 |  Val. Acc: 86.41%\n","Epoch: 47 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.247 | Train Acc: 89.89%\n","\t Val. Loss: 0.322 |  Val. Acc: 86.38%\n","Epoch: 48 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.246 | Train Acc: 89.86%\n","\t Val. Loss: 0.322 |  Val. Acc: 86.39%\n","Epoch: 49 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.245 | Train Acc: 89.92%\n","\t Val. Loss: 0.322 |  Val. Acc: 86.36%\n","Epoch: 50 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.244 | Train Acc: 89.96%\n","\t Val. Loss: 0.320 |  Val. Acc: 86.41%\n","Epoch: 51 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.243 | Train Acc: 89.95%\n","\t Val. Loss: 0.324 |  Val. Acc: 86.23%\n","Epoch: 52 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.242 | Train Acc: 90.09%\n","\t Val. Loss: 0.321 |  Val. Acc: 86.43%\n","Epoch: 53 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.241 | Train Acc: 90.08%\n","\t Val. Loss: 0.320 |  Val. Acc: 86.48%\n","Epoch: 54 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.241 | Train Acc: 90.10%\n","\t Val. Loss: 0.321 |  Val. Acc: 86.35%\n","Epoch: 55 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.240 | Train Acc: 90.21%\n","\t Val. Loss: 0.320 |  Val. Acc: 86.45%\n","Epoch: 56 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.239 | Train Acc: 90.23%\n","\t Val. Loss: 0.320 |  Val. Acc: 86.41%\n","Epoch: 57 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.238 | Train Acc: 90.25%\n","\t Val. Loss: 0.319 |  Val. Acc: 86.42%\n","Epoch: 58 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.238 | Train Acc: 90.19%\n","\t Val. Loss: 0.319 |  Val. Acc: 86.46%\n","Epoch: 59 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.236 | Train Acc: 90.32%\n","\t Val. Loss: 0.320 |  Val. Acc: 86.44%\n","Epoch: 60 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.236 | Train Acc: 90.31%\n","\t Val. Loss: 0.319 |  Val. Acc: 86.47%\n","Epoch: 61 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.235 | Train Acc: 90.31%\n","\t Val. Loss: 0.319 |  Val. Acc: 86.48%\n","Epoch: 62 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.234 | Train Acc: 90.40%\n","\t Val. Loss: 0.319 |  Val. Acc: 86.46%\n","Epoch: 63 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.234 | Train Acc: 90.44%\n","\t Val. Loss: 0.319 |  Val. Acc: 86.46%\n","Epoch: 64 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.233 | Train Acc: 90.45%\n","\t Val. Loss: 0.320 |  Val. Acc: 86.51%\n","Epoch: 65 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.232 | Train Acc: 90.56%\n","\t Val. Loss: 0.321 |  Val. Acc: 86.47%\n","Epoch: 66 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.231 | Train Acc: 90.54%\n","\t Val. Loss: 0.318 |  Val. Acc: 86.52%\n","Epoch: 67 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.230 | Train Acc: 90.60%\n","\t Val. Loss: 0.321 |  Val. Acc: 86.46%\n","Epoch: 68 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.231 | Train Acc: 90.53%\n","\t Val. Loss: 0.320 |  Val. Acc: 86.45%\n","Epoch: 69 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.230 | Train Acc: 90.62%\n","\t Val. Loss: 0.320 |  Val. Acc: 86.41%\n","Epoch: 70 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.229 | Train Acc: 90.60%\n","\t Val. Loss: 0.320 |  Val. Acc: 86.47%\n","Epoch: 71 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.227 | Train Acc: 90.73%\n","\t Val. Loss: 0.321 |  Val. Acc: 86.44%\n","Epoch: 72 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.228 | Train Acc: 90.66%\n","\t Val. Loss: 0.322 |  Val. Acc: 86.39%\n","Epoch: 73 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.227 | Train Acc: 90.71%\n","\t Val. Loss: 0.320 |  Val. Acc: 86.41%\n","Epoch: 74 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.225 | Train Acc: 90.77%\n","\t Val. Loss: 0.319 |  Val. Acc: 86.54%\n","Epoch: 75 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.224 | Train Acc: 90.86%\n","\t Val. Loss: 0.320 |  Val. Acc: 86.53%\n","Epoch: 76 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.224 | Train Acc: 90.85%\n","\t Val. Loss: 0.319 |  Val. Acc: 86.62%\n","Epoch: 77 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.223 | Train Acc: 90.91%\n","\t Val. Loss: 0.320 |  Val. Acc: 86.47%\n","Epoch: 78 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.222 | Train Acc: 90.89%\n","\t Val. Loss: 0.322 |  Val. Acc: 86.47%\n","Epoch: 79 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.221 | Train Acc: 90.94%\n","\t Val. Loss: 0.321 |  Val. Acc: 86.44%\n","Epoch: 80 | Epoch Time: 1m 14s\n","\tTrain Loss: 0.220 | Train Acc: 91.01%\n","\t Val. Loss: 0.321 |  Val. Acc: 86.51%\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"AO_4rnTlS6qM","executionInfo":{"status":"ok","timestamp":1619143064288,"user_tz":-180,"elapsed":6084133,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":27,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rclO3ZcaT3I_"},"source":["**Load the parameters of the best model**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"W3pRCywIld27","executionInfo":{"status":"ok","timestamp":1619143064289,"user_tz":-180,"elapsed":6083386,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"042638eb-3bde-4fd2-c12f-98d9989e4102"},"source":["model.load_state_dict(torch.load('smote_v1.pt'))"],"execution_count":28,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<All keys matched successfully>"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"code","metadata":{"id":"s7jDC-emS7SI","executionInfo":{"status":"ok","timestamp":1619143064290,"user_tz":-180,"elapsed":6083156,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":28,"outputs":[]},{"cell_type":"code","metadata":{"id":"Lxo29pkTS7bU","executionInfo":{"status":"ok","timestamp":1619143064290,"user_tz":-180,"elapsed":6082857,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":28,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rP-QoefNUOJL"},"source":["### **Examining the Model**"]},{"cell_type":"code","metadata":{"id":"vsuC-nhhTIIy","executionInfo":{"status":"ok","timestamp":1619143064291,"user_tz":-180,"elapsed":6082364,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["from sklearn.metrics import confusion_matrix, accuracy_score, balanced_accuracy_score, f1_score, precision_score, recall_score"],"execution_count":29,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C8eXQ8b7CQRe"},"source":["**Visualizing loss of train set and valid set**"]},{"cell_type":"code","metadata":{"id":"8EnfxhZ2Cffe","executionInfo":{"status":"ok","timestamp":1619143064291,"user_tz":-180,"elapsed":6081874,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["epoch_list = np.arange(1, EPOCHS + 1)"],"execution_count":30,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"nHpukHOwC85C","executionInfo":{"status":"ok","timestamp":1619143064292,"user_tz":-180,"elapsed":6081638,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"6bfe79d3-3aa3-42dc-e9a4-e037770b3020"},"source":["fig, ax = plt.subplots(figsize=(14,8))\n","\n","ax.plot(epoch_list, train_loss_list, label='train loss')\n","ax.plot(epoch_list, valid_loss_list, label='validate loss')\n","ax.set_xlabel('Epoch')\n","ax.set_ylabel('Loss')\n","\n","ax.legend()\n","plt.show()"],"execution_count":31,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"9eC3_r9dS82t","executionInfo":{"status":"ok","timestamp":1619143064293,"user_tz":-180,"elapsed":6081383,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":31,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xcSAID4QUZEH"},"source":["**Get the predictions**"]},{"cell_type":"code","metadata":{"id":"LcWx6hIj8Ucv","executionInfo":{"status":"ok","timestamp":1619143064294,"user_tz":-180,"elapsed":6080954,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["def get_predictions(model, X, y, device, batch_size):\n","    \n","    model.eval()\n","    y_predict = []\n","\n","    with torch.no_grad():\n","        for (batchX, batchY) in next_batch(X, y, batch_size):\n","            X = torch.from_numpy(batchX).to(device)\n","\n","            y_tmp = model(X)\n","            top_pred = y_tmp.argmax(1, keepdim = True)\n","\n","            for i in top_pred:\n","                y_predict.append(i.item())\n","\n","    return np.array(y_predict)"],"execution_count":32,"outputs":[]},{"cell_type":"code","metadata":{"id":"OYDZ8I7E_B1m","executionInfo":{"status":"ok","timestamp":1619143068233,"user_tz":-180,"elapsed":6084629,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["y_predict = get_predictions(model, X_test, y_test, device, batch_size=BATCH_SIZE)"],"execution_count":33,"outputs":[]},{"cell_type":"code","metadata":{"id":"UKGucRbAS9on","executionInfo":{"status":"ok","timestamp":1619143068234,"user_tz":-180,"elapsed":6084439,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":33,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"D6cEYYL2Uir5"},"source":["**Confusion matrix**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I3199D2tCKGK","executionInfo":{"status":"ok","timestamp":1619143068234,"user_tz":-180,"elapsed":6083943,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"ce80ef26-d010-4a3b-d092-4e77308c7761"},"source":["cm = confusion_matrix(y_test, y_predict)\n","print(cm)"],"execution_count":34,"outputs":[{"output_type":"stream","text":["[[35582  3846]\n"," [ 3393 12224]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"2zOKEZraEiZp","executionInfo":{"status":"ok","timestamp":1619143068235,"user_tz":-180,"elapsed":6083704,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["pd_cm = pd.DataFrame(cm, index = ['negative', 'positive'], columns = ['negative', 'positive'])"],"execution_count":35,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"48rztXHME2Uu","executionInfo":{"status":"ok","timestamp":1619143068992,"user_tz":-180,"elapsed":6084199,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"f79a44e5-6eb0-4c78-d60f-44ccf319b2b1"},"source":["plt.figure(figsize=(14,10))\n","ax = plt.axes()\n","sn.heatmap(pd_cm, annot=True, fmt='d', cmap='Blues')\n","ax.set_xlabel('Predict label')\n","ax.set_ylabel('True label')\n","\n","plt.show()"],"execution_count":36,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x720 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"4A0px8nbTSY_","executionInfo":{"status":"ok","timestamp":1619143068993,"user_tz":-180,"elapsed":6084021,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":36,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"F-OYpxThG_n5"},"source":["**Quantifying the quality of predictions**"]},{"cell_type":"code","metadata":{"id":"OhAg9GrbHPSX","executionInfo":{"status":"ok","timestamp":1619143068994,"user_tz":-180,"elapsed":6083540,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":["def show_metrics_scores(y_test, y_predict):\n","    print(f'Accuracy score\\t\\t: {accuracy_score(y_test, y_predict):.4f}\\n')\n","    print(f'Balanced accuracy score\\t: {balanced_accuracy_score(y_test, y_predict):.4f}\\n')\n","    print(f'F1 score\\t\\t: {f1_score(y_test, y_predict):.4f}\\n')\n","    print(f'Recall score\\t\\t: {recall_score(y_test, y_predict):.4f}')"],"execution_count":37,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YEtRmFG7lx5l","executionInfo":{"status":"ok","timestamp":1619143068994,"user_tz":-180,"elapsed":6083297,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}},"outputId":"ae4c7312-ee80-4bce-fc58-a67f7ae148b8"},"source":["show_metrics_scores(y_test, y_predict)"],"execution_count":38,"outputs":[{"output_type":"stream","text":["Accuracy score\t\t: 0.8685\n","\n","Balanced accuracy score\t: 0.8426\n","\n","F1 score\t\t: 0.7715\n","\n","Recall score\t\t: 0.7827\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"2OuGt43Tap63","executionInfo":{"status":"ok","timestamp":1619143068995,"user_tz":-180,"elapsed":6083193,"user":{"displayName":"Trung Lương","photoUrl":"","userId":"02044496887486952450"}}},"source":[""],"execution_count":38,"outputs":[]}]}