04-spiral_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "import math\n",
    "from IPython import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from res.plot_lib import plot_data, plot_model, set_default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_default()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 12345\n",
    "random.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "N = 1000  # num_samples_per_class\n",
    "D = 2  # dimensions\n",
    "C = 3  # num_classes\n",
    "H = 100  # num_hidden_units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = torch.zeros(N * C, D).to(device)\n",
    "y = torch.zeros(N * C, dtype=torch.long).to(device)\n",
    "for c in range(C):\n",
    "    index = 0\n",
    "    t = torch.linspace(0, 1, N)\n",
    "    # When c = 0 and t = 0: start of linspace\n",
    "    # When c = 0 and t = 1: end of linpace\n",
    "    # This inner_var is for the formula inside sin() and cos() like sin(inner_var) and cos(inner_Var)\n",
    "    inner_var = torch.linspace(\n",
    "        # When t = 0\n",
    "        (2 * math.pi / C) * (c),\n",
    "        # When t = 1\n",
    "        (2 * math.pi / C) * (2 + c),\n",
    "        N\n",
    "    ) + torch.randn(N) * 0.2\n",
    "    \n",
    "    for ix in range(N * c, N * (c + 1)):\n",
    "        X[ix] = t[index] * torch.FloatTensor((\n",
    "            math.sin(inner_var[index]), math.cos(inner_var[index])\n",
    "        ))\n",
    "        y[ix] = c\n",
    "        index += 1\n",
    "\n",
    "print(\"Shapes:\")\n",
    "print(\"X:\", tuple(X.size()))\n",
    "print(\"y:\", tuple(y.size()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualise the data\n",
    "plot_data(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 1e-3\n",
    "lambda_l2 = 1e-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nn package to create our linear model\n",
    "# each Linear module has a weight and bias\n",
    "model = nn.Sequential(\n",
    "    nn.Linear(D, H),\n",
    "    nn.Linear(H, C)\n",
    ")\n",
    "model.to(device) #Convert to CUDA\n",
    "\n",
    "# nn package also has different loss functions.\n",
    "# we use cross entropy loss for our classification task\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "# we use the optim package to apply\n",
    "# stochastic gradient descent for our parameter updates\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, weight_decay=lambda_l2) # built-in L2\n",
    "\n",
    "# Training\n",
    "for t in range(1000):\n",
    "    \n",
    "    # Feed forward to get the logits\n",
    "    y_pred = model(X)\n",
    "    \n",
    "    # Compute the loss and accuracy\n",
    "    loss = criterion(y_pred, y)\n",
    "    score, predicted = torch.max(y_pred, 1)\n",
    "    acc = (y == predicted).sum().float() / len(y)\n",
    "    print(\"[EPOCH]: %i, [LOSS]: %.6f, [ACCURACY]: %.3f\" % (t, loss.item(), acc))\n",
    "    display.clear_output(wait=True)\n",
    "    \n",
    "    # zero the gradients before running\n",
    "    # the backward pass.\n",
    "    optimizer.zero_grad()\n",
    "    \n",
    "    # Backward pass to compute the gradient\n",
    "    # of loss w.r.t our learnable params. \n",
    "    loss.backward()\n",
    "    \n",
    "    # Update params\n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot trained model\n",
    "print(model)\n",
    "plot_model(X, y, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two-layered network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 1e-3\n",
    "lambda_l2 = 1e-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# nn package to create our linear model\n",
    "# each Linear module has a weight and bias\n",
    "\n",
    "model = nn.Sequential(\n",
    "    nn.Linear(D, H),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(H, C)\n",
    ")\n",
    "model.to(device)\n",
    "\n",
    "# nn package also has different loss functions.\n",
    "# we use cross entropy loss for our classification task\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "# we use the optim package to apply\n",
    "# ADAM for our parameter updates\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=lambda_l2) # built-in L2\n",
    "\n",
    "# e = 1.  # plotting purpose\n",
    "\n",
    "# Training\n",
    "for t in range(1000):\n",
    "    \n",
    "    # Feed forward to get the logits\n",
    "    y_pred = model(X)\n",
    "    \n",
    "    # Compute the loss and accuracy\n",
    "    loss = criterion(y_pred, y)\n",
    "    score, predicted = torch.max(y_pred, 1)\n",
    "    acc = (y == predicted).sum().float() / len(y)\n",
    "    print(\"[EPOCH]: %i, [LOSS]: %.6f, [ACCURACY]: %.3f\" % (t, loss.item(), acc))\n",
    "    display.clear_output(wait=True)\n",
    "    \n",
    "    # zero the gradients before running\n",
    "    # the backward pass.\n",
    "    optimizer.zero_grad()\n",
    "    \n",
    "    # Backward pass to compute the gradient\n",
    "    # of loss w.r.t our learnable params. \n",
    "    loss.backward()\n",
    "    \n",
    "    # Update params\n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot trained model\n",
    "print(model)\n",
    "plot_model(X, y, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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