810_nn04_fashion_conv.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fashion MNIST mit Conv-Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.optim as optim\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = torchvision.datasets.FashionMNIST(\n",
    "    root='./data'\n",
    "    ,train=True\n",
    "    ,download=True\n",
    "    ,transform=transforms.Compose([\n",
    "        transforms.ToTensor()\n",
    "    ])\n",
    ")\n",
    "\n",
    "test_set = torchvision.datasets.FashionMNIST(\n",
    "    root='./data'\n",
    "    ,train=False\n",
    "    ,download=True\n",
    "    ,transform=transforms.Compose([\n",
    "        transforms.ToTensor()\n",
    "    ])\n",
    ")\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_set, batch_size=100)\n",
    "test_loader = torch.utils.data.DataLoader(test_set,batch_size=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 16, 5, padding=2)\n",
    "        self.conv2 = nn.Conv2d(16, 32, 5, padding=2)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.fc = nn.Linear(7*7*32, 10)\n",
    "        \n",
    "    def forward(self, t):\n",
    "        t = self.conv1(t)\n",
    "        t = F.relu(t)\n",
    "        t = self.pool(t)\n",
    "           \n",
    "        t = self.conv2(t)\n",
    "        t = F.relu(t)\n",
    "        t = self.pool(t)\n",
    "        \n",
    "        t = t.reshape(-1, 7*7*32)\n",
    "        t = self.fc(t)\n",
    "\n",
    "        return t\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0 total_correct: 50532 loss: 261.1365255266428\n",
      "epoch 1 total_correct: 53088 loss: 190.78211621940136\n",
      "Wall time: 3min 13s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "def get_num_correct(preds, labels):\n",
    "    return preds.argmax(dim=1).eq(labels).sum().item()\n",
    "\n",
    "net = Net()\n",
    "optimizer = optim.Adam(net.parameters(), lr=0.01)\n",
    "\n",
    "for epoch in range(2):\n",
    "\n",
    "    total_loss = 0\n",
    "    total_correct = 0\n",
    "\n",
    "    for batch in train_loader: # Get Batch\n",
    "        images, labels = batch \n",
    "\n",
    "        preds = net(images) # Pass Batch\n",
    "        loss = F.cross_entropy(preds, labels) # Calculate Loss\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward() # Calculate Gradients\n",
    "        optimizer.step() # Update Weights\n",
    "\n",
    "        total_loss += loss.item()\n",
    "        total_correct += get_num_correct(preds, labels)\n",
    "\n",
    "    print(\n",
    "        \"epoch\", epoch, \n",
    "        \"total_correct:\", total_correct, \n",
    "        \"loss:\", total_loss\n",
    "    )\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correct: 0.8778\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "for batch in test_loader:\n",
    "    images, labels = batch \n",
    "    preds = net(images)\n",
    "    \n",
    "    images.shape, labels\n",
    "    correct += get_num_correct(preds,labels)\n",
    "    \n",
    "print(\"correct:\",correct/len(test_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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