01. Intro To Deep Learning/04. Multilayer Neural Networks/04. Multilayer Neural Networks - Pytorch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer Neural Networks - Pytorch (From Scratch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Chosen device: cuda\n"
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torchvision import datasets\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu' # will select 'cuda' is gpu is available otherwise 'cpu'\n",
    "print('Chosen device:', device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform = lambda image: torch.FloatTensor(image.getdata())/255.0\n",
    "# or \n",
    "def transform(image):\n",
    "    return torch.FloatTensor(image.getdata())/255.0\n",
    "\n",
    "train_mnist = datasets.FashionMNIST(root = '~/data', train = True, transform = transform, download = True)\n",
    "test_mnist = datasets.FashionMNIST(root = '~/data', train = False, transform = transform, download = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "torch.Size([784]) 9\n"
    }
   ],
   "source": [
    "image, label = train_mnist[0]\n",
    "print(image.shape, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.imshow(image.reshape(28, 28), cmap = 'gray')\n",
    "plt.title(label)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating data loader for training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "No of batches for batch size 64 is 938\n"
    }
   ],
   "source": [
    "batch_size = 64\n",
    "train_data = torch.utils.data.DataLoader(train_mnist, batch_size = batch_size, shuffle = True)\n",
    "test_data = torch.utils.data.DataLoader(test_mnist, batch_size = batch_size, shuffle = False)\n",
    "\n",
    "print('No of batches for batch size {} is {}'.format(batch_size, len(train_data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initializing parameters with mean of 0 and stddev of 0.1\n",
    "\n",
    "W1 = nn.Parameter(torch.normal(0, 0.1, [784, 256], device = device))\n",
    "b1 = nn.Parameter(torch.normal(0, 0.1, [256], device = device))\n",
    "\n",
    "W2 = nn.Parameter(torch.normal(0, 0.1, [256, 128], device = device))\n",
    "b2 = nn.Parameter(torch.normal(0, 0.1, [128], device = device))\n",
    "\n",
    "W3 = nn.Parameter(torch.normal(0, 0.1, [128, 10], device = device))\n",
    "b3 = nn.Parameter(torch.normal(0, 0.1, [10], device = device))\n",
    "\n",
    "params = [W1, b1, W2, b2, W3, b3]\n",
    "\n",
    "def relu(linear):\n",
    "    linear[linear < 0] = 0\n",
    "    return linear\n",
    "\n",
    "def softmax(linear):\n",
    "    exp = torch.exp(linear - torch.max(linear, axis = 1)[0].reshape(-1, 1))\n",
    "    # subtracting max for numerical statbility... (it does not affect the outputs)\n",
    "    norms = torch.sum(exp, axis = 1).reshape(-1, 1)\n",
    "    return exp / norms\n",
    "\n",
    "def neuralnetworks(inputs):\n",
    "    layer1 = relu(torch.mm(inputs, W1) + b1)\n",
    "    layer2 = relu(torch.mm(layer1, W2) + b2)\n",
    "    return softmax(torch.mm(layer2, W3) + b3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining our objective function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categoricalcrossentropy(predictions, labels):\n",
    "    return -torch.sum(labels * torch.log(predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining our optimizer (Gradient Descent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sgd(params, lr):\n",
    "    with torch.no_grad():\n",
    "        # doing all the parameter updated inside torch.no_grad, because we don't want to record any computation of sgd for backward pass....\n",
    "        for param in params:\n",
    "            param -= lr * param.grad\n",
    "            param.grad.zero_()\n",
    "            # zeroing the gradient of after each update so that it would't affect the gradients of for the next update"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining our accuracy function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(predictions, labels):\n",
    "    return (predictions.argmax(1) == labels.argmax(1)).float().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding one hot representation\n",
    "\n",
    "- If the label is 0 then it's one hot vector is [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
    "\n",
    "- If the label is 1 then it's one hot vector is [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]\n",
    "\n",
    "- If the label is 2 then it's one hot vector is [0, 0, 1, 0, 0, 0, 0, 0, 0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "\n",
    "def onehot(label):\n",
    "    return F.one_hot(label, 10).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Epoch: 0 Loss: 36.7842903137207 Accuracy: 0.8006396889686584\nEpoch: 1 Loss: 26.418485641479492 Accuracy: 0.853611409664154\nEpoch: 2 Loss: 23.678926467895508 Accuracy: 0.8672541379928589\nEpoch: 3 Loss: 22.025747299194336 Accuracy: 0.8761993646621704\nEpoch: 4 Loss: 20.6820068359375 Accuracy: 0.8821129202842712\nEpoch: 5 Loss: 19.62018585205078 Accuracy: 0.8879430890083313\nEpoch: 6 Loss: 18.873184204101562 Accuracy: 0.8910248279571533\nEpoch: 7 Loss: 17.991788864135742 Accuracy: 0.8968050479888916\nEpoch: 8 Loss: 17.24404525756836 Accuracy: 0.9014192819595337\nEpoch: 9 Loss: 16.631868362426758 Accuracy: 0.9046841859817505\nEpoch: 10 Loss: 16.075313568115234 Accuracy: 0.9082822799682617\nEpoch: 11 Loss: 15.572749137878418 Accuracy: 0.9106143712997437\nEpoch: 12 Loss: 15.081428527832031 Accuracy: 0.9141791462898254\nEpoch: 13 Loss: 14.567258834838867 Accuracy: 0.9172941446304321\nEpoch: 14 Loss: 14.04989242553711 Accuracy: 0.9203425049781799\nEpoch: 15 Loss: 13.69704532623291 Accuracy: 0.920792281627655\nEpoch: 16 Loss: 13.312600135803223 Accuracy: 0.9233908653259277\nEpoch: 17 Loss: 12.91247272491455 Accuracy: 0.9260561466217041\nEpoch: 18 Loss: 12.642915725708008 Accuracy: 0.9269390106201172\nEpoch: 19 Loss: 12.179080963134766 Accuracy: 0.9289212822914124\n"
    }
   ],
   "source": [
    "epochs = 20\n",
    "num_batches = len(train_data)\n",
    "learning_rate = .001 # you can change it to 0.01 for faster training\n",
    "losses = []\n",
    "accs = []\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    cumulative_loss = 0\n",
    "    cumulative_acc = 0\n",
    "    for features, labels in train_data:\n",
    "        features = features.to(device) # .to(device) put features stored in cpu to device(which we have initialized above)\n",
    "        labels = onehot(labels.to(device)) # .to(device) put labels stored in cpu to device(which we have initialized above)\n",
    "        \n",
    "        outputs = neuralnetworks(features)\n",
    "        loss = categoricalcrossentropy(outputs, labels)\n",
    "        loss.backward()\n",
    "\n",
    "        sgd(params, learning_rate)\n",
    "        \n",
    "        cumulative_loss += loss        \n",
    "        cumulative_acc += accuracy(outputs, labels)\n",
    "\n",
    "    print(f'Epoch: {epoch} Loss: {cumulative_loss/num_batches} Accuracy: {cumulative_acc/num_batches}')\n",
    "    losses.append(cumulative_loss/num_batches)\n",
    "    accs.append(cumulative_acc/num_batches)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets see test accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Test Accuracy: 0.8798766136169434\n"
    }
   ],
   "source": [
    "acc = 0.0\n",
    "test_batches = len(test_data)\n",
    "for features, labels in test_data:\n",
    "    features = features.to(device) # .to(device) put features stored in cpu to device(which we have initialized above)\n",
    "    labels = onehot(labels.to(device)) # .to(device) put labels stored in cpu to device(which we have initialized above)\n",
    "    outputs = neuralnetworks(features)\n",
    "    acc += accuracy(outputs, labels)\n",
    "    \n",
    "print(f'Test Accuracy: {acc/test_batches}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(losses = losses, accs = accs, model = neuralnetworks):\n",
    "    samples = 16\n",
    "    plt.plot(losses, 'r')\n",
    "    plt.title('Loss during Training')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.show()\n",
    "    \n",
    "    plt.plot(accs, 'g')\n",
    "    plt.title('Accuracy during Training')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.show()\n",
    "    \n",
    "    plt.figure(figsize = (15, 15))\n",
    "\n",
    "    for i, example in enumerate(test_mnist, 1):\n",
    "        features = example[0].to(device).reshape(1, -1)\n",
    "        labels = example[1]\n",
    "        outputs = model(features).argmax(1)\n",
    "        plt.subplot(4, 4, i)\n",
    "        plt.imshow(features[0].cpu().reshape(28, 28), cmap = 'gray')\n",
    "        plt.axis('off')\n",
    "        plt.title(f'Predicted: {outputs.item()}, Target: {labels}')\n",
    "        if i == samples:\n",
    "            break\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.5 64-bit",
   "language": "python",
   "name": "python37564bit78abdacf64e443609f57f8757402df21"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}