{"cells":[{"metadata":{},"cell_type":"markdown","source":"<h1 style=\"font-family:verdana;\"> <center>📚 Introduction 📚</center> </h1>\n\n***\n![image.png](attachment:image.png)\n<br>\n<br>\n\n<div class=\"alert alert-block alert-info\" style=\"font-size:15px; font-family:verdana; line-height: 1.4em;\">\n    <center>\n        <span style=\"color:crimson;\">📌Classification of Numbers(MNIST)</span> \n       <br> \n  \n\n\n<br>\n \nIn this kernel, we will visualize the MNIST dataset using Matplotlib and Plotly. This is a classic Machine Learning Problem, I will demonstrate how this problem can be approached using PyTorch Library. \n    </center> \n</div>\n\n***","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{},"cell_type":"markdown","source":"<center><h2>Importing Libraries</h2></center>"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import warnings\nwarnings.filterwarnings('ignore')\nimport numpy as np\nimport pandas as pd\nimport cv2\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport seaborn as sns\nimport plotly.express as px\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\n\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader, Dataset\nfrom torch.optim import Adam\nfrom torchvision import transforms\nimport torch.nn.functional as F\n\nfrom sklearn.model_selection import train_test_split","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<center><h2>Loading Data</h2></center>"},{"metadata":{},"cell_type":"markdown","source":"<center> Defining Paths </center>"},{"metadata":{"trusted":true},"cell_type":"code","source":"PATH = '../input/digit-recognizer/'\nTRAIN_PATH = PATH + 'train.csv'\nTEST_PATH = PATH + 'test.csv'","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<center>Reading Train & Test csv files</center>"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df = pd.read_csv(TRAIN_PATH)\ntest_df = pd.read_csv(TEST_PATH)\n\ndisplay(train_df.head())\ndisplay(test_df.head())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<center><h3>Merging pixel columns & Reshaping to 2D Matrix</h3></center>"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_pixels = train_df[train_df.columns[1:]].values\ntest_pixels = test_df[test_df.columns[:]].values\ntrain_labels = train_df['label'].values\nprint('Shape of sample data before Reshaping : ',train_pixels[0].shape)\n\n\n\"\"\" Converting vector of shape 784 to matrix of shape 28*28 \"\"\"\ntrain_images = np.array([x.reshape(28,28) for x in train_pixels])\ntest_images = np.array([y.reshape(28,28) for y in test_pixels])\n\nprint('Shape of sample data after Reshaping : ',np.shape(train_images[0]))\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<center><h3>Function to Display Images & Labels</center>"},{"metadata":{"trusted":true},"cell_type":"code","source":"def display_images():\n    rows, col = 2, 5\n    #rows, col = 2, 2\n    fig = plt.figure(figsize=(15,7))\n    for index in range(10):\n        ax = fig.add_subplot(rows, col, index+1)\n        ax.imshow(train_images[index])\n        ax.set_title(train_labels[index])\n        ax.axis('off')\n        \n    plt.show()\n    \ndisplay_images()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<center><h3>Distribution of Numbers in the Dataset<center>"},{"metadata":{"trusted":true},"cell_type":"code","source":"px.bar(pd.Series(train_labels).value_counts().reset_index(name='Digit'))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<center>\nWe have almost even distribution of all the numbers, <br>the samples are sufficient for Spliting data into Train & Test samples"},{"metadata":{},"cell_type":"markdown","source":"<center><h3> Creating Training & Test Samples"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_images, test_images, train_labels, test_labels = train_test_split(train_images, train_labels,\n                                                                       test_size=0.2, stratify = train_labels, random_state=101)\nprint('Distribution of Numbers in Train Sample')\nprint(pd.Series(train_labels).value_counts())\n\nprint(\"\")\nprint('Distribution of Numbers in Test Sample')\nprint(pd.Series(test_labels).value_counts())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<center> We have used stratified Sampling strategy on labels, <br>Numbers are evenly distributed in bot Training and Test samples"},{"metadata":{},"cell_type":"markdown","source":"<center><h3> Creating Datasets(Tensors) for PyTorch</h3></center>\n<br>\n    <ol>\n    <li>We will be transforming our individual input sample before sending for training.\n        <ul>\n            <li> Normalizing pixels- We have only one Channel(B&W), so we will use normalize Height and Width using (value),(value)\n                Incase of RGB(3 channels), our normalizing values would be (r_heig,g_heigh,b_heigh),(r_width,g_width,b_width)\n         <li>  To_Tensor - This will convert the images to Tesnor Values and will a new dimension to value. Shape - (1,28,28)     \n        </ul>\n        <li> Batching - In the Data_loader library we will use batch size of 100 samples to be passed so that the weigths are updated for 100 sample at the same time\n            After Batching, the input tensor size would be (100,1,28,28)\n            100 is the batch size, 1 is the number of channels(Black & white)\n            28*28 is the height and widht of each image\n    </ol>\n    \n    One mental anchor we have when feeding tensors into convolutional is that the first dimension is always batch size (N).\n    It’s important to know how PyTorch expects its tensors to be shaped— because you might be perfectly satisfied that your 28 x 28 pixel image shows up as a tensor of torch.Size([28, 28]). \n    Whereas PyTorch on the other hand, thinks you want it to be looking at your 28 batches of 28 feature vectors. \n    \n    >>> torch.Size([16, 3, 28, 28])\n    # 4d: [batch_size, channels, height, width]\n    # use for nn.Conv2d() input.\n    \n    So, if you wanted to load a grey scale, 28 x 28 pixel image into a Conv2d network layer, \n    find the layer type in the example above. \n    Since it wants a 4d tensor, and you already have a 2d tensor with height and width, just add batch_size, and channels to pad out the extra dimensions, like so: [1, 1, 28, 28]."},{"metadata":{"trusted":true},"cell_type":"code","source":"class CreateDataset(Dataset):\n    def __init__(self, X, y, transform=None):\n        self.X = X\n        self.y = y\n        self.transform = transform\n    \n    def __len__(self):\n        return len(self.X)\n    \n    def __getitem__(self, idx):\n        inp = self.X[idx].astype(np.float32)\n        out = self.y[idx]\n        \n        if self.transform:\n            inp = self.transform(inp)\n        \n        return inp,out","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"t = transforms.Compose([transforms.ToTensor(),\n                                transforms.Normalize((0.1307), (0.3081))])\n\ntrain_dataset = CreateDataset(train_images, train_labels, transform=t)\ntrain_loader = DataLoader(train_dataset, shuffle=True , batch_size=100) ## num_workers\n\ntest_dataset = CreateDataset(test_images, test_labels, transform=t)\ntest_loader = DataLoader(test_dataset, shuffle=True , batch_size=100)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<center><h3>Creating Network</center>"},{"metadata":{"trusted":true},"cell_type":"code","source":"class Net(nn.Module):\n    def __init__(self, num_classes=10):\n        super(Net, self).__init__()\n        # Inchannel = 1, for B&W images, 3 for RGB, out_channels is of our choice\n        self.conv1 = nn.Conv2d(in_channels=1,out_channels=32,kernel_size=3) \n        # Out channel from above is used as in channel in below conv layer\n        self.conv2 = nn.Conv2d(in_channels=32,out_channels=64,kernel_size=3)\n        \n        self.pool = nn.MaxPool2d(2,2)\n        self.relu = nn.ReLU()\n        \n        self.fc1 = nn.Linear(1600, 100)\n        self.fc2 = nn.Linear(100, 10)\n        #self.drop = nn.Dropout2d()\n        #self.softmax = nn.Softmax(dim=1)\n        \n    def forward(self, x):\n        x = self.relu(self.pool(self.conv1(x)))\n        x = self.relu(self.pool(self.conv2(x)))\n        \n        x = x.view(100,-1)\n        x = self.relu(self.fc1(x))\n        #x = self.drop(x)\n        x = self.fc2(x)\n        return F.log_softmax(x)\n                ","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"    Use view() to change your tensor’s dimensions.\n    image = image.view(batch_size, -1)\n    You supply your batch_size as the first number, and then “-1” basically tells Pytorch, \n    “you figure out this other number for me… please.” \n    Your tensor will now feed properly into any linear layer\n\nsoftmax:\nexp(x_i) / exp(x).sum()\n\nlog_softmax:\nlog( exp(x_i) / exp(x).sum() )"},{"metadata":{},"cell_type":"markdown","source":"![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{},"cell_type":"markdown","source":"<center> <h3> Some Parameters, loss, optimizer"},{"metadata":{"trusted":true},"cell_type":"code","source":"EPOCHS = 15\nnet = Net()\ncriterion = nn.CrossEntropyLoss()\noptimizer = Adam(net.parameters(), lr=0.001)        ","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<center><h3> Training Model"},{"metadata":{"trusted":true},"cell_type":"code","source":"for epoch in range(EPOCHS):\n    net.train()\n    for data in train_loader:\n        # Get inputs\n        X_train, y_train = data\n        # Zero the parameter gradients\n        optimizer.zero_grad()\n        \n        #Forward + backward + optimize\n        y_pred = net(X_train)\n        y_pred = y_pred.squeeze(0)\n\n        train_loss = criterion(y_pred, y_train)\n        train_loss.backward()\n        optimizer.step()\n    print(f\"Epoch :{epoch+1}, Train Loss = {train_loss.item()}\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<center> <h3>Evaluation"},{"metadata":{"trusted":true},"cell_type":"code","source":"correct, total = 0, 0\n\npred_probabs = []\npred_y = []\ntest_y = []\ntest_x = []\nnet.eval()\n\nfor data in test_loader:\n    X_test, y_test = data\n    output = net(X_test)\n    _, predicted = torch.max(output.data,1)\n    total += y_test.size(0)\n    correct += (predicted == y_test).sum().item()\n    \n    pred_probabs.append(output)\n    test_x.append(X_test)\n    pred_y.append(predicted)\n    test_y.append(y_test)\n\nprint('Test Accuracy : ',100*correct/total)\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<center><h3> Converting Tensor(GPU) data into Numpy Arrays "},{"metadata":{"trusted":true},"cell_type":"code","source":"pred_y = np.array([x.cpu().detach().numpy() for x in pred_y])\n\ntest_y = np.array([x.cpu().detach().numpy() for x in test_y])\n\ntest_x = np.array([x.cpu().detach().numpy() for x in test_x])\n\npred_probabs = np.array([x.cpu().detach().numpy() for x in pred_probabs])\n\n\n'''Reshaping the arrays as they are present in batch format'''\npred_y = pred_y.reshape(-1)\ntest_y = test_y.reshape(-1)\ntest_x = test_x.reshape(8400,28,28)\npred_probabs = pred_probabs.reshape(8400,10)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"    x.cpu() will do nothing at all if your Tensor is \n    already on the cpu and otherwise create a new Tensor on the cpu with the same content as x.\n\n    y = x.detach() breaks the graph between x and y. \n    But y will actually be a view into x and share memory with it."},{"metadata":{"trusted":true},"cell_type":"code","source":"pred_df = pd.DataFrame(zip(pred_y, test_y), columns=['Test','Pred'])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<center><h3> Plotting Predicted outputs"},{"metadata":{"trusted":true},"cell_type":"code","source":"def display_images():\n    rows, col = 4, 5\n    #rows, col = 2, 2\n    fig = plt.figure(figsize=(15,7))\n    for index in range(20):\n        ax = fig.add_subplot(rows, col, index+1)\n        ax.imshow(test_x[index])\n        ax.set_title(pred_y[index])\n        ax.axis('off')\n        \n    plt.show()\n    \ndisplay_images()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<center> <h3>Test Labels vs Predicted Labels"},{"metadata":{"trusted":true},"cell_type":"code","source":"_ = plt.figure(figsize=(10,5))\n_ = sns.lineplot(data=pred_df, x='Test', y='Pred')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The accuracy (98%) is so high that the test and pred labels form a straight line at 45 degrees"},{"metadata":{},"cell_type":"markdown","source":"<center><h3> Samples that were predicted wrongly"},{"metadata":{"trusted":true},"cell_type":"code","source":"pred_df[pred_df['Test'] != pred_df['Pred']]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<center><h3> Distribution of Numbers that were predicted wrong\n    </h3></center>\n   <center> Number VS Count of wrong predictions"},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.DataFrame(pred_df[pred_df['Test'] != pred_df['Pred']]['Test'].value_counts().reset_index())\ndf.columns = ['Number','Wrong Prediction Count']\ndisplay(df)\npx.bar(pred_df[pred_df['Test'] != pred_df['Pred']]['Test'].value_counts())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<center><h3> Predicted Softmax Probabilities"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Output Probabilities of First Four samples\n# Exp is used as the pred_prob was an output of log_softmax, \n# hence reversing/ using anti log \nnp.round(np.exp(pred_probabs[:4]), decimals=4)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(14,20))\n\n\nfor i in range(4):\n    plt.subplot(4,2,2*i+1)\n    plt.imshow(test_x[i+10])\n    plt.title(\"\") \n    plt.xlabel(\"\") \n    plt.ylabel(\"\") \n    plt.xticks([]) \n    plt.yticks([])\n\n    plt.subplot(4,2,2*i+2)\n    sns.barplot(x=[0,1,2,3,4,5,6,7,8,9],y=np.round(np.exp(pred_probabs[i+10]), decimals=4)) \n    plt.title(\"Pred Proba\", fontsize=16) \n    plt.xlabel(\"Number\", fontsize=16) \n    plt.ylabel(\"Prob\", fontsize=16) \n    plt.xticks(fontsize=14)\n\nplt.subplots_adjust(hspace = 0.6, wspace = 0.1)\n\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The Probabilities of above samples are one, which means that our model was not confused even a littled for above samples"}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}