DIGITS-CNN/cars/architecture-investigations/architecture.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "682fef9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "fig_dpi = 200\n",
    "lw = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75cc3c1d",
   "metadata": {},
   "source": [
    "# Dense Layers\n",
    "\n",
    "Exponential LR Decay: 0.98\n",
    "\n",
    "100 Epochs\n",
    "\n",
    "## Index\n",
    "0. fc layers\n",
    "1. nodes per layer\n",
    "2. top-1 accuracy\n",
    "3. top-5 accuracy\n",
    "4. last val loss\n",
    "5. last val accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "85cb4e35",
   "metadata": {},
   "outputs": [],
   "source": [
    "fc_results = np.array([\n",
    "    [1, 256, 52.44, 79.86, 2.49, 57.66],\n",
    "    [1, 512, 49.29, 73.93, 2.95, 53.25],\n",
    "    [1, 1024, 40.7, 68.38, 3.66, 45.22],\n",
    "    [1, 2048, 32.12, 58.93, 4.66, 35.72],\n",
    "    [1, 4096, 24.03, 46.76, 5.61, 27.94],\n",
    "    [1, 8192, 19.70, 41.01, 6.42, 23.96],\n",
    "    \n",
    "    [2, 256, 54.48, 81.22, 1.86, 57.11],\n",
    "    [2, 512, 56.64, 82.46, 1.94, 60.23],\n",
    "    [2, 1024, 56.39, 81.53, 2.08, 60.91],\n",
    "    [2, 2048, 51.39, 79.00, 2.38, 56.74],\n",
    "    [2, 4096, 44.41, 71.83, 3.04, 47.61], # DEFAULT ALEXNET\n",
    "    [2, 8192, 37.74, 64.36, 3.60, 42.40],\n",
    "    \n",
    "    [3, 256, 0.80, 2.10, 5.29, 0.55],\n",
    "    [3, 512, 30.7, 65.16, 2.57, 30.82],\n",
    "    [3, 1024, 48.36, 76.65, 2.30, 49.88],\n",
    "    [3, 2048, 54.11, 80.48, 2.38, 58.21],\n",
    "    [3, 4096, 54.48, 82.09, 2.39, 57.17],\n",
    "    [3, 8192, 50.71, 78.57, 2.55, 55.88],\n",
    "    \n",
    "    [4, 256, 0.80, 2.29, 5.29, 0.55],\n",
    "    [4, 512, 0.80, 2.10, 5.29, 0.55],\n",
    "    [4, 1024, 0.80, 2.10, 5.29, 0.55],\n",
    "    [4, 2048, 25.45, 60.9, 2.84, 28.55],\n",
    "    [4, 4096, 41.14, 73.81, 2.81, 46.32],\n",
    "    [4, 8192, 49.85, 77.58, 2.97, 53.92]\n",
    "])\n",
    "\n",
    "fc_0_results = [0, 196, 28.91, 54.05, 6.52, 33.21]\n",
    "\n",
    "layers = [1, 2, 3, 4]\n",
    "nodes = [256, 512, 1024, 2048, 4096, 8192]\n",
    "\n",
    "fc_matrix = np.zeros((len(layers), len(nodes)))\n",
    "for i in fc_results:\n",
    "    fc_matrix[layers.index(i[0]), nodes.index(i[1])] = i[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "0fd9f416",
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "X, Y = np.meshgrid(layers, nodes)\n",
    "\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "# fig = plt.figure()\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "ax = plt.axes(projection='3d')\n",
    "\n",
    "surf = ax.plot_surface(X, Y, fc_matrix.T, cmap='viridis')\n",
    "\n",
    "ax.set_title('Accuracy For Different MLP Configurations')\n",
    "ax.set_xlabel('Fully Connected Layers')\n",
    "ax.set_ylabel('Nodes Per Layer')\n",
    "ax.set_zlabel('Top-1 % Test Accuracy')\n",
    "ax.set_xticks([1, 2, 3, 4])\n",
    "\n",
    "ax.view_init(40, -70)\n",
    "fig.colorbar(surf, location=\"left\", shrink=0.7, aspect=15)\n",
    "\n",
    "# plt.tight_layout()\n",
    "plt.savefig('fc-accuracy-surf.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "# fig.set_dpi(fig_dpi)\n",
    "\n",
    "sns.heatmap(fc_matrix.T, xticklabels=layers, yticklabels=nodes, cmap='inferno')\n",
    "\n",
    "plt.title(\"Accuracy For Different MLP Configurations\")\n",
    "plt.xlabel(\"Fully-Connected Layer\")\n",
    "plt.ylabel(\"Nodes Per Layer\")\n",
    "\n",
    "# plt.tight_layout()\n",
    "# plt.savefig('fc-accuracy-surf.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "dc9d04b1",
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 4))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "# plt.plot([196], fc_0_results[2], 'x-', label=f'0 Layers')\n",
    "\n",
    "for i in layers:\n",
    "    plt.plot(nodes, fc_matrix[i-1, :], '-', label=f'{i} Layers', ms=8, lw=lw)\n",
    "\n",
    "plt.plot([4096], [fc_matrix[1, 4]], \"x\", label=\"AlexNet\", ms=\"8\", c=(0, 0, 0))\n",
    "\n",
    "# plt.annotate('Standard\\nAlexNet', \n",
    "#              (4096, fc_matrix[layers.index(2), nodes.index(4096)]),\n",
    "#              textcoords=\"offset points\",\n",
    "#              xytext=(40, 10),\n",
    "#              ha='center',\n",
    "#              arrowprops={'arrowstyle': 'simple'}\n",
    "#             )\n",
    "    \n",
    "plt.title('Accuracy for Varied Dense Layer Shapes')\n",
    "plt.xlabel('Nodes Per Layer')\n",
    "plt.ylabel('Top-1 % Test Accuracy')\n",
    "\n",
    "plt.ylim(0, 60)\n",
    "\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('fc-accuracy.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "340c4eb8",
   "metadata": {},
   "source": [
    "# Convolutional Non-Linearity\n",
    "\n",
    "Exponential LR Decay: 0.98\n",
    "\n",
    "100 Epochs\n",
    "\n",
    "Taking conovlutional layers and distributing the standard number of filters into separate conv layers with ReLu nonlinearity\n",
    "\n",
    "## Index\n",
    "0. convolutional layer\n",
    "1. number of divisions\n",
    "2. top-1 accuracy\n",
    "3. top-5 accuracy\n",
    "4. last val loss\n",
    "5. last val accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "3426107c",
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_nonlin_results = np.array([\n",
    "#     [1, 1, 44.41, 71.83, 3.04, 47.61], # STANDARD ALEXNET\n",
    "#     [1, 2],\n",
    "#     [1, 4],\n",
    "    \n",
    "    [4, 1, 44.41, 71.83, 3.04, 47.61], # STANDARD ALEXNET\n",
    "    [4, 2, 42.31, 71.96, 3.07, 49.75],\n",
    "    [4, 4, 0.8, 2.47, 5.29, 0.55],\n",
    "    \n",
    "    [5, 1, 44.41, 71.83, 3.04, 47.61], # STANDARD ALEXNET\n",
    "    [5, 2, 46.08, 73.87, 3.00, 49.20],\n",
    "    [5, 4, 0.8, 2.47, 5.29, 0.55]\n",
    "])\n",
    "\n",
    "nonlin_layers = [4, 5]\n",
    "nonlin_div = [1, 2, 4]\n",
    "\n",
    "conv_nonlin_matrix = np.zeros((len(nonlin_layers), len(nonlin_div)))\n",
    "for i in conv_nonlin_results:\n",
    "    conv_nonlin_matrix[nonlin_layers.index(i[0]), nonlin_div.index(i[1])] = i[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "d7457e0e",
   "metadata": {},
   "outputs": [
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "for idx, i in enumerate(nonlin_layers):\n",
    "    plt.plot(nonlin_div, conv_nonlin_matrix[idx, :], 'x-', label=f'Layer {i}')\n",
    "\n",
    "plt.title('Accuracy for Varied Convolutional Non-Linearity')\n",
    "plt.xlabel('Layer Divisor')\n",
    "plt.ylabel('Top-1 % Test Accuracy')\n",
    "\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "# Convolutional Kernel Size\n",
    "\n",
    "Exponential LR Decay: 0.98\n",
    "\n",
    "100 Epochs\n",
    "\n",
    "## Index\n",
    "0. convolutional layer\n",
    "1. kernel size\n",
    "2. top-1 accuracy\n",
    "3. top-5 accuracy\n",
    "4. last val loss\n",
    "5. last val accuracy"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel_results = np.array([\n",
    "    [1, 3, 37.06, 64.73, 3.38, 44.36],\n",
    "    [1, 5, 43.73, 70.35, 3.19, 48.22],\n",
    "    [1, 7, 44.72, 71.16, 3.00, 48.96],\n",
    "    [1, 11, 44.41, 71.83, 3.04, 47.61], # DEFAULT ALEXNET\n",
    "    [1, 15, 43.17, 71.59, 3.09, 47.55],\n",
    "\n",
    "    [2, 3, 41.63, 67.63, 3.24, 45.53],\n",
    "    [2, 5, 44.41, 71.83, 3.04, 47.61], # DEFAULT ALEXNET\n",
    "    [2, 7, 45.15, 72.21, 2.97, 50.49],\n",
    "    [2, 9, 43.61, 71.34, 3.10, 47.37],\n",
    "    [2, 11, 39.35, 65.6, 3.36, 44.98],\n",
    "\n",
    "    [3, 3, 44.41, 71.83, 3.04, 47.61], # DEFAULT ALEXNET\n",
    "    [3, 5, 50.59, 75.91, 2.74, 53.80],\n",
    "    [3, 7, 47.13, 75.48, 3.19, 52.21],\n",
    "    [3, 9, 40.83, 69.73, 3.90, 47.00],\n",
    "    [3, 11, 33.29, 61.70, 5.27, 38.05],\n",
    "\n",
    "    [4, 3, 44.41, 71.83, 3.04, 47.61], # DEFAULT ALEXNET\n",
    "    [4, 5, 48.67, 74.74, 2.92, 51.84],\n",
    "    [4, 7, 49.54, 76.34, 2.98, 52.63],\n",
    "    [4, 9, 47.19, 74.24, 3.40, 50.37],\n",
    "    [4, 11, 43.55, 70.29, 3.98, 47.24],\n",
    "\n",
    "    [5, 3, 44.41, 71.83, 3.04, 47.61], # DEFAULT ALEXNET\n",
    "    [5, 5, 46.94, 74.31, 2.88, 51.53],\n",
    "    [5, 7, 48.24, 75.42, 2.87, 51.84],\n",
    "    [5, 9, 47.56, 74.68, 2.94, 53.43],\n",
    "    [5, 11, 44.1, 72.45, 3.60, 48.28],\n",
    "])\n",
    "\n",
    "default_kernel_sizes = [11, 5, 3, 3, 3]\n",
    "kernel_layers = {i[0] for i in kernel_results}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 3.5))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "kernel_parsed_results = [(list(), list()) for _ in kernel_layers]\n",
    "for row in kernel_results:\n",
    "    kernel_parsed_results[int(row[0]) - 1][0].append(row[2]) # top-1 accuracy\n",
    "    kernel_parsed_results[int(row[0]) - 1][1].append(row[1]) # kernel size (x value)\n",
    "\n",
    "for idx, l in enumerate(kernel_parsed_results):\n",
    "    plt.plot(l[1], l[0], '-', label=f'Layer {idx+1}', lw=lw)\n",
    "\n",
    "for idx, l in enumerate(kernel_parsed_results):\n",
    "    # print(l)\n",
    "    # print(l[0][l[1].index(default_kernel_sizes[idx])])\n",
    "\n",
    "    if idx == len(kernel_parsed_results) - 1:\n",
    "        label = 'AlexNet'\n",
    "    else:\n",
    "        label = None\n",
    "    plt.plot([default_kernel_sizes[idx]], [l[0][l[1].index(default_kernel_sizes[idx])]], \"x\", label=label, ms=6, c=(0, 0, 0))\n",
    "\n",
    "plt.title('Accuracy for Varied Convolutional Kernel Size')\n",
    "plt.xlabel(\"Kernel Size\")\n",
    "plt.ylabel('Top-1 % Test Accuracy')\n",
    "\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "\n",
    "plt.ylim(20, 60)\n",
    "plt.xlim()\n",
    "plt.xticks([3, 5, 7, 9, 11, 13, 15])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('kernel-accuracy.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "# Convolutional New Layers\n",
    "\n",
    "Exponential LR Decay: 0.98\n",
    "\n",
    "100 Epochs\n",
    "\n",
    "L1.5, Stride = 1\n",
    "\n",
    "## Index\n",
    "0. convolutional layer\n",
    "1. kernel size\n",
    "2. filters\n",
    "3. top-1 accuracy\n",
    "4. top-5 accuracy\n",
    "5. last val loss\n",
    "6. last val accuracy"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_results = [44.41, 71.83, 3.04, 47.61]\n",
    "\n",
    "layer1_kernel = np.array([\n",
    "    [1.5, 3, 176, 54.23, 80.73, 2.84, 55.76],\n",
    "    [1.5, 5, 176, 48.49, 78.38, 3.04, 52.76],\n",
    "    [1.5, 9, 176, 40.83, 68.75, 3.59, 43.26],\n",
    "    [1.5, 11, 176, 34.28, 66.15, 3.65, 39.22]\n",
    "])\n",
    "\n",
    "layer1_filter = np.array([\n",
    "    [1.5, 7, 96, 45.27, 74.80, 3.04, 49.75],\n",
    "    [1.5, 7, 176, 45.46, 75.17, 3.18, 49.75],\n",
    "    [1.5, 7, 256, 43.17, 72.82, 3.42, 50.06]\n",
    "])\n",
    "\n",
    "layer3_kernel = np.array([\n",
    "    [3.5, 3, 384, 45.58, 72.27, 2.92, 48.28],\n",
    "    [3.5, 5, 384, 52.56, 78.88, 2.74, 57.05],\n",
    "    [3.5, 9, 384, 44.78, 71.53, 4.07, 48.35]\n",
    "])\n",
    "\n",
    "layer3_filter = np.array([\n",
    "    [3.5, 3, 192, 46.20, 74.00, 2.81, 51.41],\n",
    "    [3.5, 3, 384, 45.58, 72.27, 2.92, 48.28], # repeated from above\n",
    "    [3.5, 3, 576, 44.84, 71.77, 2.98, 50.25]\n",
    "])\n",
    "\n",
    "layer1 = np.append(layer1_kernel, layer1_filter, axis=0)\n",
    "layer3 = np.append(layer3_kernel, layer3_filter, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 3))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "plt.plot(layer1_kernel[:, 1], layer1_kernel[:, 3], 'x-', label=\"Layer 1.5\\n176 Filters\", lw=lw)\n",
    "plt.plot(layer3_kernel[:, 1], layer3_kernel[:, 3], 'x-', label=\"Layer 3.5\\n384 Filters\", lw=lw)\n",
    "\n",
    "plt.title(\"Accuracy for Additional Convolutional Layers\")\n",
    "plt.xlabel(\"Kernel Size\")\n",
    "plt.ylabel('Top-1 % Test Accuracy')\n",
    "\n",
    "plt.xticks([3, 5, 7, 9, 11])\n",
    "plt.ylim(30, 60)\n",
    "\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('new-layer-kernel-accuracy.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 3))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "plt.plot(layer1_filter[:, 2], layer1_filter[:, 3], 'x-', label=\"Layer 1.5\\n7x7 Kernel\", lw=lw)\n",
    "plt.plot(layer3_filter[:, 2], layer3_filter[:, 3], 'x-', label=\"Layer 3.5\\n3x3 Kernel\", lw=lw)\n",
    "\n",
    "plt.title(\"Accuracy for Additional Convolutional Layers\")\n",
    "plt.xlabel(\"Layer Filters\")\n",
    "plt.ylabel('Top-1 % Test Accuracy')\n",
    "\n",
    "# plt.xticks([3, 5, 7, 9, 11])\n",
    "plt.xlim(0)\n",
    "plt.ylim(30, 60)\n",
    "\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('new-layer-filter-accuracy.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_results = [43.11, 71.22, 3.80, 47.06]\n",
    "\n",
    "standard_res = [44.41, 71.83, 3.04, 47.61]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "best_results = [standard_res]\n",
    "best_labels = ['Standard']\n",
    "\n",
    "# Dense\n",
    "b_dense = fc_results[np.argmax(fc_results[:, 2])]\n",
    "best_results.append(b_dense[2:])\n",
    "best_labels.append(f'Dense: {int(b_dense[0])} Layers\\nNodes: {int(b_dense[1])}')\n",
    "\n",
    "# Convolutional Kernel\n",
    "b_kernel = kernel_results[np.argmax(kernel_results[:, 2])]\n",
    "best_results.append(b_kernel[2:])\n",
    "best_labels.append(f'Kernel: Layer {int(b_kernel[0])}\\nSize: {int(b_kernel[1])}')\n",
    "\n",
    "# Layer 1.5 + 3.5\n",
    "for layer in [layer1, layer3]:\n",
    "    b_layer35 = layer[np.argmax(layer[:, 3])]\n",
    "    best_results.append(b_layer35[3:])\n",
    "    best_labels.append(f'Layer {b_layer35[0]}\\nK: {int(b_layer35[1])}, F: {int(b_layer35[2])}')\n",
    "\n",
    "best_results = best_results[::-1]\n",
    "best_labels = best_labels[::-1]\n",
    "\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "plt.barh(range(len(best_labels)), [i[0] for i in best_results], tick_label=best_labels, label='Top-1')\n",
    "plt.barh(range(len(best_labels)), [i[1] - i[0] for i in best_results], tick_label=best_labels, label='Top-5', left=[i[0] for i in best_results])\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(axis='x')\n",
    "plt.title('Best Accuracies for Architectures')\n",
    "plt.xlabel('% Test Accuracy')\n",
    "plt.ylabel('Architecture Accuracies')\n",
    "\n",
    "plt.xlim(0, 100)\n",
    "plt.xticks(np.linspace(0, 100, 11))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('best-barh.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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