PyTorch Concepts(Fashion Mnist Example).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision import datasets, transforms\n",
    "import helper\n",
    "\n",
    "# Define a transform to normalize the data\n",
    "transform = transforms.Compose([transforms.ToTensor(),\n",
    "                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "# Download and load the training data\n",
    "trainset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
    "\n",
    "# Download and load the test data\n",
    "testset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=False, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1c6c569160>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1c6c630b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "images,label = next(iter(trainloader))\n",
    "plt.imshow(images[0].numpy().squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#building a network\n",
    "from torch import nn,optim\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = nn.Sequential(\n",
    "                    nn.Linear(784,128),\n",
    "                    nn.ReLU(),\n",
    "                    nn.Linear(128,64),\n",
    "                    nn.ReLU(),\n",
    "                    nn.Linear(64,10),\n",
    "                    nn.LogSoftmax(dim=1)\n",
    ")\n",
    "#defining the loss\n",
    "criterion = nn.NLLLoss()\n",
    "optimizer = optim.Adam(model.parameters(),lr=0.003)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model\n",
    "%config IPCompleter.greedy=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.5013802529080336\n",
      "Training loss: 0.3856514115005668\n",
      "Training loss: 0.3525104767509869\n",
      "Training loss: 0.3313788785132518\n",
      "Training loss: 0.314835078648921\n"
     ]
    }
   ],
   "source": [
    "#training the network\n",
    "epochs = 5\n",
    "for e in range(epochs):\n",
    "    running_loss = 0\n",
    "    for images,labels in trainloader:\n",
    "        images = images.view(images.shape[0],-1)\n",
    "        output = model(images)\n",
    "        loss = criterion(output,labels)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        running_loss+= loss.item()\n",
    "    else:\n",
    "        print(f\"Training loss: {running_loss/len(trainloader)}\")\n",
    "#after the completion of the for loop it goes to the else block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1c6c3d6e80>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 204,
       "width": 423
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the results\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import helper\n",
    "\n",
    "dataiter = iter(testloader)\n",
    "images,labels = dataiter.next()\n",
    "img = images[0]\n",
    "\n",
    "img = img.view(img.shape[0],-1)\n",
    "#as we used log soft max so the porbability calculation\n",
    "ps = torch.exp(model(img))\n",
    "helper.view_classify(img, ps, version='Fashion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}