{"cells":[{"metadata":{},"cell_type":"markdown","source":"MNIST is a classic toy dataset for image recognition. The dataset consists of handwritten images that have served as the basis for benchmarking classification algorithms\n\n**Table of Contents**:\n\n- Imports\n- Preparing Data\n- Explore Data\n- Model\n    - Simple Feed Forward NN (Test Accuracy of 96%)\n    - **CNN based on LeNet5** (Test Accuracy of 98%)\n- Preparing Test Data\n- Looking at Prediction\n- Exporting Data\n- Ensembles (Test Accuracy of 98%)\n- Commit History"},{"metadata":{},"cell_type":"markdown","source":"# Imports"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# Basic Torch\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nimport torchvision.transforms as transforms\nimport torchvision\nfrom torch.utils.data import TensorDataset\nfrom torch.optim import Adam, SGD\n\n# Basic Numeric Computation\nimport numpy as np\nimport pandas as pd\n\n# Look at data\nfrom matplotlib import pyplot\n\n# Easy way to split train data\nfrom sklearn.model_selection import train_test_split\n\n# Looking at directory\nimport os\nbase_dir = \"../input\"\nprint(os.listdir(base_dir))\n\ndevice = torch.device(\"cpu\")# if torch.cuda.is_available() else torch.device(\"cpu\")\ndevice\nepochs=12","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Preparing Data\n## Extract Transform Load (ETL)"},{"metadata":{},"cell_type":"markdown","source":"### 1. Extract"},{"metadata":{"trusted":true},"cell_type":"code","source":"train = pd.read_csv(base_dir + '/train.csv')\ntest = pd.read_csv(base_dir + '/test.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 2. Transform"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Convert Dataframe into format ready for training\ndef createImageData(raw: pd.DataFrame):\n    y = raw['label'].values\n    y.resize(y.shape[0],1)\n    x = raw[[i for i in raw.columns if i != 'label']].values\n    x = x.reshape([-1,1, 28, 28])\n    y = y.astype(int).reshape(-1)\n    x = x.astype(float)\n    return x, y\n\n## Convert to One Hot Encoding\ndef one_hot_embedding(labels, num_classes=10):\n    y = torch.eye(num_classes) \n    return y[labels] ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train, y_train = createImageData(train)\n#x_train, x_val, y_train, y_val = train_test_split(x,y, test_size=0.02)\n\n#x_train.shape, y_train.shape, x_val.shape, y_val.shape\nx_train.shape, y_train.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Normalization\nmean = x_train.mean()\nstd = x_train.std()\nx_train = (x_train-mean)/std\n#x_val = (x_val-mean)/std\n\n# Numpy to Torch Tensor\nx_train = torch.from_numpy(np.float32(x_train)).to(device)\ny_train = torch.from_numpy(y_train.astype(np.long)).to(device)\ny_train = one_hot_embedding(y_train)\n#x_val = torch.from_numpy(np.float32(x_val))\n#y_val = torch.from_numpy(y_val.astype(np.long))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 3. Load"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Convert into Torch Dataset\ntrain_ds = TensorDataset(x_train, y_train)\n#val_ds = TensorDataset(x_val,y_val)","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true,"_kg_hide-output":true},"cell_type":"code","source":"# Make Data Loader\ntrain_dl = DataLoader(train_ds, batch_size=64)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"> # Explore Data\n\nIt is always benifition to look at your data before building your model. This helps to understand what the model will be dealing with and removes assumptions you might have induce unknowingly into your model"},{"metadata":{"trusted":true},"cell_type":"code","source":"index = 1\npyplot.imshow(x_train.cpu()[index].reshape((28, 28)), cmap=\"gray\")\nprint(y_train[index])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Model\n\nBelow are helper functions. \nInitially these were written as normal Pytorch functions but latter abstracted to make code clean and easily reuse later."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Helper Functions\n\n## Initialize weight with xavier_uniform\ndef init_weights(m):\n    if type(m) == nn.Linear:\n        torch.nn.init.xavier_uniform(m.weight)\n        m.bias.data.fill_(0.01)\n\n## Flatten Later\nclass Flatten(nn.Module):\n    def forward(self, input):\n        return input.view(input.size(0), -1)\n\n# Train the network and print accuracy and loss overtime\ndef fit(train_dl, model, loss, optim, epochs=10):\n    model = model.to(device)\n    print('Epoch\\tAccuracy\\tLoss')\n    accuracy_overtime = []\n    loss_overtime = []\n    for epoch in range(epochs):\n        avg_loss = 0\n        correct = 0\n        total=0\n        for x, y in train_dl: # Iterate over Data Loder\n    \n            # Forward pass\n            yhat = model(x) \n            l = loss(y, yhat)\n            \n            #Metrics\n            avg_loss+=l.item()\n            \n            # Backward pass\n            optim.zero_grad()\n            l.backward()\n            optim.step()\n            \n            # Metrics\n            _, original =  torch.max(y, 1)\n            _, predicted = torch.max(yhat.data, 1)\n            total += y.size(0)\n            correct = correct + (original == predicted).sum().item()\n            \n        accuracy_overtime.append(correct/total)\n        loss_overtime.append(avg_loss/len(train_dl))\n        print(epoch,accuracy_overtime[-1], loss_overtime[-1], sep='\\t')\n    return accuracy_overtime, loss_overtime\n\n# Plot Accuracy and Loss of Model\ndef plot_accuracy_loss(accuracy, loss):\n    f = pyplot.figure(figsize=(15,5))\n    ax1 = f.add_subplot(121)\n    ax2 = f.add_subplot(122)\n    ax1.title.set_text(\"Accuracy over epochs\")\n    ax2.title.set_text(\"Loss over epochs\")\n    ax1.plot(accuracy)\n    ax2.plot(loss, 'r:')\n\n# Take an array and show what model predicts \ndef predict_for_index(array, model, index):\n    testing = array[index].view(1,28,28)\n    pyplot.imshow(x_train[index].reshape((28, 28)), cmap=\"gray\")\n    print(x_train[index].shape)\n    a = model(testing.float())\n    print('Prediction',torch.argmax(a,1))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Feed Forward Neural Network with 2 hidden layers"},{"metadata":{},"cell_type":"markdown","source":"A feed forward neural network is not ideal for Image Classification. The reason is: **Parameters are not shared**.\n\nAlso, if you increase epoch beyond 10, you will find the model quickly overfits.\n\nInitially, I made a dumb model and focused on just getting through the complete flow. This is because Deep Learning is a iterative process and spending too much time on building the perfect model can lead to a lot of wasted time with no output.\n\nI will be making Convolutional NN later which will require better hyperparameter setting.\n\nThe input to this feed forward NN is 784 features. The target labels to predict is 10. I choose 100 as number of hidden units in layer intuitively for being a middle ground between 784 and 10.\n\n(m, 784) -> (m, 100) -> (m,10)\n\nm: number of items in mini-batch"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Define the model\n\nff_model = nn.Sequential(\n    Flatten(),\n    nn.Linear(28*28, 100),\n    nn.ReLU(),\n    nn.Linear(100, 10),\n    nn.Softmax(1),\n).to(device)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Initialize model with xavier initialization which is recommended for ReLu\nff_model.apply(init_weights)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Define Hyperparameters and Train"},{"metadata":{"trusted":true},"cell_type":"code","source":"optim = Adam(ff_model.parameters())\nloss = nn.MSELoss()\noutput = fit(train_dl, ff_model, loss, optim, epochs)\nplot_accuracy_loss(*output)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Looking at prediction the model makes"},{"metadata":{"trusted":true},"cell_type":"code","source":"index = 4\npredict_for_index(x_train, ff_model, index)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Convolutional NN"},{"metadata":{},"cell_type":"markdown","source":"A CNN is perfect for images because the parameters used to detect something in one part of image are same as once used in other part of the image.\n\nInitially, I started with a simple CNN which was not accurate and would often overfit. I had to rewatch videos from Andrew Ng's Convolutional Neural Network Course and re-learned that it is always better to take inspiration from other successful models.\n\nThis model has accuracy of **0.98942**.\n\n![image.png](attachment:image.png)\n\nSources for creating model:\n- [LeNet-5 – A Classic CNN Architecture](https://engmrk.com/lenet-5-a-classic-cnn-architecture/) (Image Reference)\n- [DeepLearning.ai-Summary / Convolutional Neural Networks](https://github.com/mbadry1/DeepLearning.ai-Summary/tree/master/4-%20Convolutional%20Neural%20Networks#classic-networks)","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{"trusted":true},"cell_type":"code","source":"# A too simple NN taken from pytorch.org/tutorials\nclass Mnist_CNN(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1)\n        self.conv2 = nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1)\n        self.conv3 = nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1)\n\n    def forward(self, xb):\n        xb = xb.view(-1, 1, 28, 28)\n        xb = F.relu(self.conv1(xb))\n        xb = F.relu(self.conv2(xb))\n        xb = F.relu(self.conv3(xb))\n        xb = F.avg_pool2d(xb, 4)\n        return xb.view(-1, xb.size(1))\n\nclass LeNet5(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1)\n        self.average1 = nn.AvgPool2d(2, stride=2)\n        self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1)\n        self.average2 = nn.AvgPool2d(2, stride=2)\n        self.conv3 = nn.Conv2d(16, 120, kernel_size=4, stride=1)\n        \n        self.flatten = Flatten()\n        \n        self.fc1 = nn.Linear(120, 82)\n        self.fc2 = nn.Linear(82,10)\n\n    def forward(self, xb):\n        xb = xb.view(-1, 1, 28, 28)\n        xb = F.tanh(self.conv1(xb))\n        xb = self.average1(xb)\n        xb = F.tanh(self.conv2(xb))\n        xb = self.average2(xb)\n        xb = F.tanh(self.conv3(xb))\n        xb = xb.view(-1, xb.shape[1])\n        xb = F.relu(self.fc1(xb))\n        xb = F.relu(self.fc2(xb))\n        return xb","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"conv_model = LeNet5()\nconv_model.apply(init_weights)\nloss = nn.MSELoss()\noptim = SGD(conv_model.parameters(), lr=0.1, momentum=0.9)\nplot_accuracy_loss(*fit(train_dl, conv_model,loss,optim,epochs))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Preparing Test Data"},{"metadata":{},"cell_type":"markdown","source":"**Normalization**: We need to normalize with the same values used to normalize train data else it will lead to uneven distribution between train and test set."},{"metadata":{"trusted":true},"cell_type":"code","source":"x_test = test.values\nx_test = x_test.reshape([-1, 28, 28]).astype(float)\nx_test = (x_test-mean)/std\nx_test = torch.from_numpy(np.float32(x_test))\nx_test.shape","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Looking at prediction\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"index = 7\npredict_for_index(x_test, ff_model, index)\npredict_for_index(x_test, conv_model, index)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Exporting Data for Submission"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Export data to CSV in format of submission\ndef export_csv(model_name, predictions, commit_no):\n    df = pd.DataFrame(prediction.tolist(), columns=['Label'])\n    df['ImageId'] = df.index + 1\n    file_name = f'submission_{model_name}_v{commit_no}.csv'\n    print('Saving ',file_name)\n    df[['ImageId','Label']].to_csv(file_name, index = False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# just to make output easier to read\ncommit_no = 17","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ff_test_yhat = ff_model(x_test.float())\nprediction = torch.argmax(ff_test_yhat,1)\nprint('Prediction',prediction)\nexport_csv('ff_model',prediction, commit_no=commit_no)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cn_train_yhat = conv_model(x_test)\nprediction = torch.argmax(cn_train_yhat,1)\nyo = torch.argmax(y_train,1)\nexport_csv('lenet_model',prediction, commit_no=commit_no)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Ensemble\n\nEnsemble is a popular technique using usually in competition which allow combining results of different models to product higher accuracy.\n\nIn this particular case, it seems like LeNet was much better than the feed-forward network that is why the accuracy of the ensemble on test data is the same as LeNet5 – **0.98942**"},{"metadata":{"trusted":true},"cell_type":"code","source":"models = []\noptims = []\nloss = nn.MSELoss()\nensembles = 15\nimport sys\nfor i in range(ensembles):\n    sys.stdout.write(f'Ensemble No {i+1}\\n')\n    model = LeNet5()\n    model.apply(init_weights)\n    #optim = Adam(model.parameters())\n    optim = SGD(model.parameters(), lr=0.1, momentum=0.9)\n\n    accuracy, _ = fit(train_dl, model,loss,optim,epochs)\n    if accuracy[-1] > 95:\n        models.append(model)\n        optims.append(optim)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ensemble = cn_train_yhat\n\nfor model in models:\n    ensemble+=model(x_test)\n\nensemble_one_hot = torch.argmax(ensemble,1) # Find argmax\nexport_csv(f'ensemble_{ensembles}_LeNets',ensemble_one_hot, commit_no=commit_no)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ensemble_one_hot","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ensemble = ff_test_yhat + cn_train_yhat # Add probabilities of individual predictions\nensemble_one_hot = torch.argmax(y_train,1) # Find argmax\nexport_csv('ensemble',ensemble_one_hot, commit_no=commit_no)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Commit History\n\n**v3:** Corrected Input Shape: There was different shape between y and yhat\n\n**v4:** Changed output layer to Softmax: It was previously LogSoftmax. I knew it was wrong but for some reason code was not working then with Softmax.\n\n**v5:** Clean Code using Helper functions and plotting\n\n**v6:** Bug fixes\n\n**v7:** Adding Documentation. Verbose saving.\n\n**v9:** Implementation of LeNet5 architecture.\n\n**v10:** Updating results from LeNet5. Table of Contents. And LetNet5 architecture and link to sources.\n\n**v11:** Ensemble 15 LeNets\n\n**v12:** Trying out a suggestion (via comment) by using learning rate of 0.001. Instead of directly changing LR, switching to Adam.\n\n**v13:** Some ensembles were not even fit to training set well.\n\nv14: Reverting to using SGD\n\nv17: Lr 0.1\n\n***\n\nIf you have any tips, suggestion or any interesting dataset, you can get in touch with the comment section below."},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}