6_tutorial_nn_package.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://pytorch.org/tutorials/beginner/former_torchies/nn_tutorial.html#sphx-glr-beginner-former-torchies-nn-tutorial-py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class MNISTConvNet(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        # this is the place where you instantiate all your modules\n",
    "        # you can later access them using the same names you've given them in\n",
    "        # here\n",
    "        super(MNISTConvNet, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 10, 5)\n",
    "        self.pool1 = nn.MaxPool2d(2, 2)\n",
    "        self.conv2 = nn.Conv2d(10, 20, 5)\n",
    "        self.pool2 = nn.MaxPool2d(2, 2)\n",
    "        self.fc1 = nn.Linear(320, 50)\n",
    "        self.fc2 = nn.Linear(50, 10)\n",
    "\n",
    "    # it's the forward function that defines the network structure\n",
    "    # we're accepting only a single input in here, but if you want,\n",
    "    # feel free to use more\n",
    "    def forward(self, input):\n",
    "        x = self.pool1(F.relu(self.conv1(input)))\n",
    "        x = self.pool2(F.relu(self.conv2(x)))\n",
    "\n",
    "        # in your model definition you can go full crazy and use arbitrary\n",
    "        # python code to define your model structure\n",
    "        # all these are perfectly legal, and will be handled correctly\n",
    "        # by autograd:\n",
    "        # if x.gt(0) > x.numel() / 2:\n",
    "        #      ...\n",
    "        #\n",
    "        # you can even do a loop and reuse the same module inside it\n",
    "        # modules no longer hold ephemeral state, so you can use them\n",
    "        # multiple times during your forward pass\n",
    "        # while x.norm(2) < 10:\n",
    "        #    x = self.conv1(x)\n",
    "\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MNISTConvNet(\n",
      "  (conv1): Conv2d(1, 10, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv2): Conv2d(10, 20, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=320, out_features=50, bias=True)\n",
      "  (fc2): Linear(in_features=50, out_features=10, bias=True)\n",
      ")\n",
      "<class '__main__.MNISTConvNet'>\n"
     ]
    }
   ],
   "source": [
    "net = MNISTConvNet()\n",
    "print(net)\n",
    "print (type(net))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 10])\n"
     ]
    }
   ],
   "source": [
    "input = torch.randn(1, 1, 28, 28)\n",
    "out = net(input)\n",
    "print(out.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(2.2403)\n"
     ]
    }
   ],
   "source": [
    "target = torch.tensor([3], dtype=torch.long)\n",
    "loss_fn = nn.CrossEntropyLoss()  # LogSoftmax + ClassNLL Loss\n",
    "err = loss_fn(out, target)\n",
    "err.backward()\n",
    "\n",
    "print(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([10, 1, 5, 5])\n"
     ]
    }
   ],
   "source": [
    "print(net.conv1.weight.grad.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1.8021)\n",
      "tensor(0.4947)\n"
     ]
    }
   ],
   "source": [
    "print(net.conv1.weight.data.norm())  # norm of the weight\n",
    "print(net.conv1.weight.grad.data.norm())  # norm of the gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inside Conv2d forward\n",
      "\n",
      "input:  <class 'tuple'>\n",
      "input[0]:  <class 'torch.Tensor'>\n",
      "output:  <class 'torch.Tensor'>\n",
      "\n",
      "input size: torch.Size([1, 10, 12, 12])\n",
      "output size: torch.Size([1, 20, 8, 8])\n",
      "output norm: tensor(15.4226)\n"
     ]
    }
   ],
   "source": [
    "def printnorm(self, input, output):\n",
    "    # input is a tuple of packed inputs\n",
    "    # output is a Tensor. output.data is the Tensor we are interested\n",
    "    print('Inside ' + self.__class__.__name__ + ' forward')\n",
    "    print('')\n",
    "    print('input: ', type(input))\n",
    "    print('input[0]: ', type(input[0]))\n",
    "    print('output: ', type(output))\n",
    "    print('')\n",
    "    print('input size:', input[0].size())\n",
    "    print('output size:', output.data.size())\n",
    "    print('output norm:', output.data.norm())\n",
    "\n",
    "\n",
    "net.conv2.register_forward_hook(printnorm)\n",
    "\n",
    "out = net(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inside Conv2d forward\n",
      "\n",
      "input:  <class 'tuple'>\n",
      "input[0]:  <class 'torch.Tensor'>\n",
      "output:  <class 'torch.Tensor'>\n",
      "\n",
      "input size: torch.Size([1, 10, 12, 12])\n",
      "output size: torch.Size([1, 20, 8, 8])\n",
      "output norm: tensor(15.4226)\n",
      "Inside Conv2d backward\n",
      "Inside class:Conv2d\n",
      "\n",
      "grad_input:  <class 'tuple'>\n",
      "grad_input[0]:  <class 'torch.Tensor'>\n",
      "grad_output:  <class 'tuple'>\n",
      "grad_output[0]:  <class 'torch.Tensor'>\n",
      "\n",
      "grad_input size: torch.Size([1, 10, 12, 12])\n",
      "grad_output size: torch.Size([1, 20, 8, 8])\n",
      "grad_input norm: tensor(0.1033)\n"
     ]
    }
   ],
   "source": [
    "def printgradnorm(self, grad_input, grad_output):\n",
    "    print('Inside ' + self.__class__.__name__ + ' backward')\n",
    "    print('Inside class:' + self.__class__.__name__)\n",
    "    print('')\n",
    "    print('grad_input: ', type(grad_input))\n",
    "    print('grad_input[0]: ', type(grad_input[0]))\n",
    "    print('grad_output: ', type(grad_output))\n",
    "    print('grad_output[0]: ', type(grad_output[0]))\n",
    "    print('')\n",
    "    print('grad_input size:', grad_input[0].size())\n",
    "    print('grad_output size:', grad_output[0].size())\n",
    "    print('grad_input norm:', grad_input[0].norm())\n",
    "\n",
    "\n",
    "net.conv2.register_backward_hook(printgradnorm)\n",
    "\n",
    "out = net(input)\n",
    "err = loss_fn(out, target)\n",
    "err.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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