main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ab9744fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch as t\n",
    "import torchvision.datasets as datasets\n",
    "import torchvision.transforms as transforms\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "74c30637",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.1+cpu\n"
     ]
    }
   ],
   "source": [
    "print(t.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9349edc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74d297c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 503: Service Unavailable\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./data\\MNIST\\raw\\train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "583793a76d6d45f4acf271e11645e78f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/9912422 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data\\MNIST\\raw\\train-images-idx3-ubyte.gz to ./data\\MNIST\\raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data\\MNIST\\raw\\train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d19acc1dc424116b9e95a4694a9414f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/28881 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data\\MNIST\\raw\\train-labels-idx1-ubyte.gz to ./data\\MNIST\\raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data\\MNIST\\raw\\t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c96767f408b46b19996eee17fbc2f29",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1648877 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data\\MNIST\\raw\\t10k-images-idx3-ubyte.gz to ./data\\MNIST\\raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data\\MNIST\\raw\\t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "710c8939836d44009698d850af116893",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4542 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data\\MNIST\\raw\\t10k-labels-idx1-ubyte.gz to ./data\\MNIST\\raw\n",
      "\n",
      "Processing...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\princ\\anaconda3\\lib\\site-packages\\torchvision\\datasets\\mnist.py:502: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:143.)\n",
      "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# the data\n",
    "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)),])\n",
    "\n",
    "mnist_trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
    "train_loader = t.utils.data.DataLoader(mnist_trainset, batch_size=10, shuffle=True)\n",
    "\n",
    "mnist_testset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)\n",
    "test_loader = t.utils.data.DataLoader(mnist_testset, batch_size=10, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5c3fbc98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 10000\n"
     ]
    }
   ],
   "source": [
    "# train_len = len(mnist_trainset)\n",
    "# test_len = len(mnist_testset)\n",
    "# print(train_len, test_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5175db9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the model\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.linear1 = nn.Linear(28*28, 100) # input layer\n",
    "        self.linear2 = nn.Linear(100, 50) # hidden layer\n",
    "        self.final = nn.Linear(50, 10) # output layer\n",
    "        self.relu = nn.ReLU() # piecewise linear function\n",
    "    \n",
    "    # convert + flatten\n",
    "    def forward(self, img):\n",
    "        x = img.view(-1, 28*28) # reshape the image for the model\n",
    "        x = self.relu(self.linear1(x))\n",
    "        x = self.relu(self.linear2(x))\n",
    "        x = self.final(x)\n",
    "        return x\n",
    "\n",
    "net = Net()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c7060f5a",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_loader' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 9\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[39mfor\u001b[39;00m epoch \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(epoch):\n\u001b[1;32m      7\u001b[0m     net\u001b[39m.\u001b[39mtrain()\n\u001b[0;32m----> 9\u001b[0m     \u001b[39mfor\u001b[39;00m data \u001b[39min\u001b[39;00m train_loader:\n\u001b[1;32m     10\u001b[0m         x, y \u001b[39m=\u001b[39m data \u001b[39m# x=features, y=targets\u001b[39;00m\n\u001b[1;32m     11\u001b[0m         optimiser\u001b[39m.\u001b[39mzero_grad() \u001b[39m# set gradient to 0 before each loss calc\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train_loader' is not defined"
     ]
    }
   ],
   "source": [
    "# loss function\n",
    "cross_en_loss = nn.CrossEntropyLoss()\n",
    "optimiser = t.optim.Adam(net.parameters(), lr=0.001) # e-1\n",
    "epoch = 10\n",
    "\n",
    "for epoch in range(epoch):\n",
    "    net.train()\n",
    "    \n",
    "    for data in train_loader:\n",
    "        x, y = data # x=features, y=targets\n",
    "        optimiser.zero_grad() # set gradient to 0 before each loss calc\n",
    "        output = net(x.view(-1, 28*28)) # pass in reshaped batch\n",
    "        loss = cross_en_loss(output, y) # cal and grab the loss value\n",
    "        loss.backward() # apply loss back through the network's parameters\n",
    "        optimiser.step() # optimise weights to account for loss and gradients\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "618c9cdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.101\n"
     ]
    }
   ],
   "source": [
    "# evaluating our dataset\n",
    "correct = 0\n",
    "total = 0\n",
    "with t.no_grad():\n",
    "    for data in test_loader:\n",
    "        x, y = data\n",
    "        output = net(x.view(-1, 784))\n",
    "        for idx, i in enumerate(output):\n",
    "            if t.argmax(i) == y[idx]:\n",
    "                correct += 1\n",
    "            total += 1\n",
    "print(f\"accuracy: {round(correct/total, 3)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cc250cd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(3)\n"
     ]
    }
   ],
   "source": [
    "# visualization\n",
    "plt.imshow(x[3].view(28, 28))\n",
    "plt.show()\n",
    "print(t.argmax(net(x[3].view(-1, 784))[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69dd7994",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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