pytorch3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0507aa74",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matt/.local/share/virtualenvs/Neural-Network-From-Scratch-JmgbtXSz/lib/python3.9/site-packages/torchvision/datasets/mnist.py:498: 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  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
      "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "from torchvision import transforms, datasets\n",
    "\n",
    "train = datasets.MNIST(\"\", train=True, download=True,\n",
    "                      transform=transforms.Compose([transforms.ToTensor()]))\n",
    "\n",
    "test = datasets.MNIST(\"\", train=False, download=True,\n",
    "                      transform=transforms.Compose([transforms.ToTensor()]))\n",
    "\n",
    "trainset = torch.utils.data.DataLoader(train, batch_size=10, shuffle=True)\n",
    "testset = torch.utils.data.DataLoader(test, batch_size=10, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "93d1c63b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn \n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4c61e10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (fc1): Linear(in_features=784, out_features=64, bias=True)\n",
      "  (fc2): Linear(in_features=64, out_features=64, bias=True)\n",
      "  (fc3): Linear(in_features=64, out_features=64, bias=True)\n",
      "  (fc4): Linear(in_features=64, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class Net(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(28*28, 64)\n",
    "        self.fc2 = nn.Linear(64, 64)\n",
    "        self.fc3 = nn.Linear(64, 64)\n",
    "        self.fc4 = nn.Linear(64, 10)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = F.relu(self.fc3(x))\n",
    "        x = self.fc4(x)\n",
    "        \n",
    "        return F.softmax(x, dim=1)\n",
    "    \n",
    "net = Net()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d421e33e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = torch.rand((28,28))\n",
    "X = X.view(-1, 28*28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eab21a21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.0874, 0.1044, 0.1044, 0.1094, 0.0951, 0.0985, 0.1082, 0.1039, 0.0899,\n",
       "         0.0990]], grad_fn=<SoftmaxBackward>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = net(X)\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "46a957d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-0.9000, grad_fn=<NllLossBackward>)\n",
      "tensor(-0.8041, grad_fn=<NllLossBackward>)\n",
      "tensor(-0.9719, grad_fn=<NllLossBackward>)\n"
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "optimizer = optim.Adam(net.parameters(), lr=1e-3)\n",
    "\n",
    "EPOCHS = 3\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "    for data in trainset:\n",
    "        # data is a batch of featuresets and labels\n",
    "        X, y = data\n",
    "        net.zero_grad()\n",
    "        output = net(X.view(-1,28*28))\n",
    "        loss = F.nll_loss(output, y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    print(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fb3d0176",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.942\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "\n",
    "with torch.no_grad():\n",
    "    for data in trainset:\n",
    "        X, y = data\n",
    "        output = net(X.view(-1, 784))\n",
    "        for idx, i in enumerate(output):\n",
    "            if torch.argmax(i) == y[idx]:\n",
    "                correct += 1\n",
    "            total += 1\n",
    "print(f'Accuracy: {round(correct/total, 3)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d3f67f51",
   "metadata": {},
   "outputs": [
    {
     "data": {
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PSbqlET3k9PU+Sb/Lbj2N7k3S/Ro6rOvX0HcbSyRNlbRGUq+kX0qa0kS9/VDSZkmbNBSsaQ3q7QINHaJvkrQxu81v9HuX6Ksu7xunywJB8AUdEARhB4Ig7EAQhB0IgrADQRB2IAjCDgTx/xlcS2ZCE9+3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(X[3].view(28,28))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "12faf774",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(8)\n"
     ]
    }
   ],
   "source": [
    "print(torch.argmax(net(X[3].view(-1, 784))[0]))"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Neural-Network-From-Scratch-JmgbtXSz",
   "language": "python",
   "name": "neural-network-from-scratch-jmgbtxsz"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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