Cloth_fashion_NN.ipynb

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  "metadata": {
    "colab": {
      "name": "Cloth-fashion NN.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Vishwaak/Time-for-Machine/blob/master/Cloth_fashion_NN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zL3QjV1nfQ-F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "import helper\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision import datasets , transforms\n",
        "import torch.nn.functional as F\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZ28lfZifyLZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "3520455c-9aa8-4f2a-89a2-a16b4122b6ed"
      },
      "source": [
        "# importing th data\n",
        "\n",
        "\n",
        "# Define a transform to normalize the data\n",
        "transform = transforms.Compose([transforms.ToTensor(),\n",
        "                                transforms.Normalize((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)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r0it [00:00, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /root/.pytorch/F_MNIST_data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "26427392it [00:01, 14884222.41it/s]                              \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /root/.pytorch/F_MNIST_data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r0it [00:00, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /root/.pytorch/F_MNIST_data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "32768it [00:00, 102087.64it/s]           \n",
            "0it [00:00, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /root/.pytorch/F_MNIST_data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /root/.pytorch/F_MNIST_data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "4423680it [00:01, 4357924.17it/s]                             \n",
            "0it [00:00, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /root/.pytorch/F_MNIST_data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /root/.pytorch/F_MNIST_data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "8192it [00:00, 32059.83it/s]            "
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /root/.pytorch/F_MNIST_data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bB-vBwFQhbh_",
        "colab_type": "code",
        "outputId": "7b9c9ce2-cd90-4f2d-8782-d358098019d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        }
      },
      "source": [
        "image, label = next(iter(trainloader))\n",
        "plt.imshow(image[2,:].numpy().squeeze() , cmap = 'Greys_r');"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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S/HiSSw+i5nOttfOXND4AsIKlBH1r7V+Dfcr/mwEA9zblyXinVNUrkpyU5DtJPt1a+/yE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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 253,
              "height": 250
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMnNLdtNhgjz",
        "colab_type": "code",
        "outputId": "fd35c5f4-d72e-4178-81de-009ba4d08ce9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(image.shape)\n",
        "print(label.shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([64, 1, 28, 28])\n",
            "torch.Size([64])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kd1cBb2QIj_4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn as nn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCPZItTqxviR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = nn.Sequential(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",
        "                     )\n",
        "critc = nn.NLLLoss()\n",
        "image = image.view(image.shape[0] , -1)\n",
        "\n",
        "logitics = model(image)\n",
        "\n",
        "loss = critc(logitics , label)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "neM1a_V1NB0w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u5YJuZNRNCBu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPOait6-xcQs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2f45787c-dbd6-4488-f9bf-ae0d1982982e"
      },
      "source": [
        "print(loss)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(2.3386, grad_fn=<NllLossBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XoP9J9CI4QDR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "3bb46666-3425-4dbc-bd81-025769082295"
      },
      "source": [
        "print(model[0].weight.grad)\n",
        "loss.backward()\n",
        "print(model[0].weight.grad)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "None\n",
            "tensor([[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  ...,  0.0000e+00,\n",
            "          0.0000e+00,  0.0000e+00],\n",
            "        [-1.9406e-03, -1.9406e-03, -1.9406e-03,  ..., -1.9406e-03,\n",
            "         -1.9406e-03, -1.9406e-03],\n",
            "        [-6.4096e-03, -6.4096e-03, -6.4096e-03,  ..., -6.7241e-03,\n",
            "         -7.0276e-03, -6.4096e-03],\n",
            "        ...,\n",
            "        [-5.2129e-03, -5.2129e-03, -5.2129e-03,  ..., -5.2117e-03,\n",
            "         -5.2129e-03, -5.2129e-03],\n",
            "        [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  ...,  0.0000e+00,\n",
            "          0.0000e+00,  0.0000e+00],\n",
            "        [-5.4081e-04, -5.4081e-04, -5.4081e-04,  ..., -5.0096e-05,\n",
            "         -1.8325e-04, -5.4081e-04]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sNwrP5m6nJcX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch import optim\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4UFtsWcW4VyP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "dfda508e-6861-4aad-a2ef-f4ba4707d8d9"
      },
      "source": [
        "print('Initial weights - ', model[0].weight)\n",
        "\n",
        "images, labels = next(iter(trainloader))\n",
        "images = images.view(images.shape[0], -1)\n",
        "\n",
        "# Clear the gradients, do this because gradients are accumulated\n",
        "optimizer.zero_grad()\n",
        "\n",
        "# Forward pass, then backward pass, then update weights\n",
        "output = model(images)\n",
        "loss = critc(output, labels)\n",
        "loss.backward()\n",
        "print('Gradient -', model[0].weight.grad)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Initial weights -  Parameter containing:\n",
            "tensor([[ 0.0248, -0.0286,  0.0113,  ..., -0.0159, -0.0100,  0.0002],\n",
            "        [ 0.0346,  0.0206, -0.0277,  ...,  0.0132,  0.0068,  0.0338],\n",
            "        [-0.0156,  0.0005, -0.0302,  ..., -0.0132,  0.0242, -0.0157],\n",
            "        ...,\n",
            "        [-0.0172,  0.0186,  0.0161,  ...,  0.0011, -0.0193, -0.0089],\n",
            "        [ 0.0187, -0.0225,  0.0198,  ..., -0.0150, -0.0300,  0.0071],\n",
            "        [-0.0208, -0.0172,  0.0103,  ...,  0.0009,  0.0068, -0.0313]],\n",
            "       requires_grad=True)\n",
            "Gradient - tensor([[ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
            "        [-0.0056, -0.0056, -0.0056,  ..., -0.0056, -0.0056, -0.0056],\n",
            "        [-0.0012, -0.0012, -0.0012,  ..., -0.0015, -0.0012, -0.0012],\n",
            "        ...,\n",
            "        [-0.0011, -0.0011, -0.0011,  ..., -0.0011, -0.0011, -0.0011],\n",
            "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
            "        [ 0.0007,  0.0007,  0.0007,  ...,  0.0008,  0.0007,  0.0007]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmmmvLYxnYic",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "outputId": "9aa799fd-3ce8-42ef-af5b-46d22e3939e1"
      },
      "source": [
        "image, label = next(iter(trainloader))\n",
        "plt.imshow(image[2,:].numpy().squeeze() , cmap = 'Greys_r');"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DJOckOX782PN7/QuSfLqqjplBXwDQnSU/o2+t3Z/k9/dZva6qLk5yU5IXJrkiyTufxHud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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 253,
              "height": 250
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5eWGnSya2qgG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "68f765b9-c2fa-4447-c4a0-7c32658e92fe"
      },
      "source": [
        "model = nn.Sequential(nn.Linear(784, 256),\n",
        "                      nn.ReLU(),\n",
        "                      nn.Linear(256, 128),\n",
        "                      nn.ReLU(),\n",
        "                      nn.Linear(128, 64),\n",
        "                      nn.ReLU(),\n",
        "                      nn.Linear(64, 10),\n",
        "                      nn.log_softmax(dim=1))\n",
        "\n",
        "criterion = nn.NLLLoss()\n",
        "optimizier = optim.SGD(model.parameters() , lr = 0.01)\n",
        "epoches = 5\n",
        "\n",
        "for e in range(epoches):\n",
        "  running_loss = 0\n",
        "  for image , label in trainloader:\n",
        "    image = image.view(image.shape[0] , -1)\n",
        "    output = model.forward(image)\n",
        "    loss = criterion(output , label)\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    \n",
        "    running_loss += loss.item()\n",
        "  else:\n",
        "     print(f\"Training loss: {running_loss/len(trainloader)}\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-81219c865e47>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m                       \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReLU\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m                       \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m                       nn.log_softmax(dim=1))\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mcriterion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNLLLoss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'torch.nn' has no attribute 'log_softmax'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "euLmsa6RL28V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn.functional as F"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T5KW9vfJ4tZq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "54b679e9-f819-4aa0-d145-3a7b232f48d5"
      },
      "source": [
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\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)\n",
        "dataiter = iter(testloader)\n",
        "\n",
        "images , labels = dataiter.next()\n",
        "img = images[1]\n",
        "img = img.view(img.shape[0] , -1)\n",
        "ps = torch.exp(model(img))\n",
        "\n",
        "view_classify(img , ps , version='Fashion')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-1cbaa4384717>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mview_classify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mps\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mversion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Fashion'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZCvf4HSEAEXn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def view_classify(img, ps, version=\"MNIST\"):\n",
        "    ''' Function for viewing an image and it's predicted classes.\n",
        "    '''\n",
        "    ps = ps.data.numpy().squeeze()\n",
        "\n",
        "    fig, (ax1, ax2) = plt.subplots(figsize=(6,9), ncols=2)\n",
        "    ax1.imshow(img.resize_(1, 28, 28).numpy().squeeze())\n",
        "    ax1.axis('off')\n",
        "    ax2.barh(np.arange(10), ps)\n",
        "    ax2.set_aspect(0.1)\n",
        "    ax2.set_yticks(np.arange(10))\n",
        "    if version == \"MNIST\":\n",
        "        ax2.set_yticklabels(np.arange(10))\n",
        "    elif version == \"Fashion\":\n",
        "        ax2.set_yticklabels(['T-shirt/top',\n",
        "                            'Trouser',\n",
        "                            'Pullover',\n",
        "                            'Dress',\n",
        "                            'Coat',\n",
        "                            'Sandal',\n",
        "                            'Shirt',\n",
        "                            'Sneaker',\n",
        "                            'Bag',\n",
        "                            'Ankle Boot'], size='small');\n",
        "    ax2.set_title('Class Probability')\n",
        "    ax2.set_xlim(0, 1.1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3N_L7NgpNF_t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Classifier(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.fc1 = nn.Linear(784, 256)\n",
        "        self.fc2 = nn.Linear(256, 128)\n",
        "        self.fc3 = nn.Linear(128, 64)\n",
        "        self.fc4 = nn.Linear(64, 10)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        # make sure input tensor is flattened\n",
        "        x = x.view(x.shape[0], -1)\n",
        "        \n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = F.relu(self.fc3(x))\n",
        "        x = F.log_softmax(self.fc4(x), dim=1)\n",
        "        \n",
        "        return x\n",
        "   "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGEDaR8MNGG6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Classifier()\n",
        "\n",
        "criterion = nn.NLLLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.003)\n",
        "images , labels = next(iter(trainloader))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DRdRvagNGQD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "d9f8f165-db42-4c4d-b8c1-b36214b55d85"
      },
      "source": [
        "epoches = 5\n",
        "\n",
        "for e in range(epoches):\n",
        "  running_loss = 0\n",
        "  for images , labels in trainloader:\n",
        "    log_ps = model(images)\n",
        "    loss = criterion(log_ps , labels)\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    \n",
        "    running_loss += loss.item()\n",
        "  else:\n",
        "    print(f\"Training loss: {running_loss/len(trainloader)}\")\n",
        "  "
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training loss: 0.39521421527049183\n",
            "Training loss: 0.36209806661679544\n",
            "Training loss: 0.34517337073649423\n",
            "Training loss: 0.331672255243701\n",
            "Training loss: 0.32178110368788115\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g5eo6VYRSgx7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "outputId": "e2eed9a6-4dc0-4cea-9402-9fbb980bb0a0"
      },
      "source": [
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "dataiter = iter(testloader)\n",
        "images , labels = dataiter.next()\n",
        "\n",
        "img = images[1]\n",
        "img = img.view(img.shape[0] , -1)\n",
        "\n",
        "ps = torch.exp(model(img))\n",
        "\n",
        "view_classify(img , ps , version='Fashion')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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APgecLGnfiHizQv12B36Qvn6kQn2YmbUbTsrNrN1ZG6upZgkHciSvMIn5zGZ3KjqxXEk1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\n",
            "text/plain": [
              "<Figure size 432x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 370,
              "height": 197
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-I6R_08NGZz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}