193 lines
96 KiB
Plaintext
193 lines
96 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "e156c77a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib as mpl\n",
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"from matplotlib import pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0456f690",
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"metadata": {},
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"source": [
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"# Dense Layers\n",
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"\n",
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"Exponential LR Decay: 0.98\n",
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"\n",
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"100 Epochs\n",
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"\n",
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"## Index\n",
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"0. fc layers\n",
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"1. nodes per layer\n",
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"2. top-1 accuracy\n",
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"3. top-5 accuracy\n",
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"4. last val loss\n",
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"5. last val accuracy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "da786662",
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"metadata": {},
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"outputs": [],
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"source": [
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"fc_results = np.array([\n",
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" [1, 512, 49.29, 73.93, 2.95, 53.25],\n",
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" [1, 1024, 40.7, 68.38, 3.66, 45.22],\n",
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" [1, 2048, 32.12, 58.93, 4.66, 35.72],\n",
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" [1, 4096, 24.03, 46.76, 5.61, 27.94],\n",
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" [1, 8192, 19.70, 41.01, 6.42, 23.96],\n",
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" \n",
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" [2, 512, 56.64, 82.46, 1.94, 60.23],\n",
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" [2, 1024, 56.39, 81.53, 2.08, 60.91],\n",
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" [2, 2048, 51.39, 79.00, 2.38, 56.74],\n",
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" [2, 4096, 44.41, 71.83, 3.04, 47.61], # DEFAULT ALEXNET\n",
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" [2, 8192, 37.74, 64.36, 3.60, 42.40],\n",
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" \n",
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" [3, 512, 30.7, 65.16, 2.57, 30.82],\n",
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" [3, 1024, 48.36, 76.65, 2.30, 49.88],\n",
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" [3, 2048, 54.11, 80.48, 2.38, 58.21],\n",
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" [3, 4096, 54.48, 82.09, 2.39, 57.17],\n",
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" [3, 8192, 50.71, 78.57, 2.55, 55.88],\n",
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" \n",
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" [4, 512, 0.8, 2.1, 5.29, 0.55],\n",
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" [4, 1024, 0.8, 2.1, 5.29, 0.55],\n",
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" [4, 2048, 25.45, 60.9, 2.84, 28.55],\n",
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" [4, 4096, 41.14, 73.81, 2.81, 46.32],\n",
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" [4, 8192, 49.85, 77.58, 2.97, 53.92]\n",
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"])\n",
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"\n",
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"fc_0_results = [0, 196, 28.91, 54.05, 6.52, 33.21]\n",
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"\n",
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"layers = [1, 2, 3, 4]\n",
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"nodes = [512, 1024, 2048, 4096, 8192]\n",
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"\n",
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"fc_matrix = np.zeros((4, 5))\n",
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"for i in fc_results:\n",
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" fc_matrix[layers.index(i[0]), nodes.index(i[1])] = i[2]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "c0064d43",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 2 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"X, Y = np.meshgrid(layers, nodes)\n",
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"\n",
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"# fig = plt.figure(figsize=(8, 5))\n",
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"fig = plt.figure()\n",
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"ax = plt.axes(projection='3d')\n",
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"\n",
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"surf = ax.plot_surface(X, Y, fc_matrix.T, cmap='viridis')\n",
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"\n",
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"ax.set_title('Accuracy with Different MLP Configurations')\n",
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"ax.set_xlabel('Fully Connected Layers')\n",
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"ax.set_ylabel('Nodes Per Layer')\n",
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"ax.set_zlabel('Top-1 % Test Accuracy')\n",
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"\n",
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"ax.view_init(50, -150)\n",
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"fig.colorbar(surf, shrink=0.3, aspect=6)\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "72a7412c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEWCAYAAABhffzLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAABmxElEQVR4nO2dd3gV1daH35V6UkgnIfQOoTfpQlAsFxFEbKBeEBH02q/eq167F3tBvXaKWEHhEwsoCkhEKdJLpJfQEkoq6eVkf3/MJKTnJDnJOUn2+zzzZGbPzJ7fnHOy9szaa68tSik0Go1G03hwcbQAjUaj0dQt2vBrNBpNI0Mbfo1Go2lkaMOv0Wg0jQxt+DUajaaRoQ2/RqPRNDK04dc4LSJyl4icEZE0EQl2tJ6SmLraV/NcJSId7a1Jo7EFbfjrKSISJSJJIuLpaC21gYi4A28AlyulfJVSCTWsb4WIPFdG+XgROS0iblWt09R1pCa6ysL8brNEJFVEzovIVhF51Nm+axGJEZHRjtahqTra8NdDRKQtcDGggHF1fO0qG8hqEgZYgL+qeqIYlPxtfwLcIiJSovxW4AulVF4V6q+Lz+AepVQTIBx4CLgJ+LEM/Y2WOvwtNji04a+f/B3YCCwAphTdISKtROQbETknIgki8k6RfXeIyF7zSXKPiPQzy4u5HURkgYjMMtcjReSkiDwiIqeBj0UkUESWmddIMtdbFjk/SEQ+FpFYc/+3Znm0iFxd5Dh3EYkXkb4l7qEzsN/cTBaRX83yoSKyWURSzL9Di5wTJSLPi8g6IAMo6YL5FgjGaDALzgkExgKfishAEdkgIskiEici74iIR5FjlYjcLSIHgYMlPzcR8RSR10TkuOme+kBEvIqc/y+z3lgRmVbqGy0HpVS6UioKo4EfAlxl1udivgUcNr/nr0UkyNzX1tQ2xdQTLyKPF9EyUES2mG8TZ0TkjSL7BovIevNz2CkikbZqLfq5lvf7EJHrRWRrieP/KSLfVfY5lvNbDDHrTxaRRBH5vYxGX1MSpZRe6tkCHAL+AfQHcoEws9wV2AnMBnwwnpiHm/uuB04BFwECdATamPsU0LFI/QuAWeZ6JJAHvAx4Al4YBnQi4A00ARYD3xY5fznwFRAIuAMjzfJ/A18VOW48sLuce2xr6nIzt4OAJIwndDdgkrkdbO6PAo4D3c397mXUOQeYW2R7JrDDXO8PDDbPbQvsBR4ocqwCVpo6vEp+buZn/r25vwnwA/Ciue9K4AzQw/xeviz5mZfQGQVML6N8LfCyuX4/RuPf0vxePgQWlvjs5pjfV28gG4gw928AbjXXfYHB5noLIAEYg/FQeJm53bQcnTHA6DLKy/19mFoTC7SYZduBiTZ8jpGU/i2+CHyA8Ttzx2jYxdH/o86+OFyAXqr4hcFwDGMfYm7vAx4014cA5zCNZYnzfgbuL6fOygx/DmCpQFMfIMlcDwfygcAyjmsOpAJ+5vYS4N/l1FlgvAoM/63AphLHbACmmutRwHM2fHbJBfcCrCv47Mo49gFgaYnP6JKyPjeMhjQd6FBk3xDgqLk+H3ipyL7OJT/zEvVGUbbhXwTMMdf3ApcW2Rdu/i4KGi4FtCyyfxNwk7m+Fni24DdU5JhHgM/K+N1MKUdnDGUY/op+H+b2+8Dz5np3jAbc04bPsdRvEXgO+K68z1IvZS/6laj+MQX4RSkVb25/yQV3TyvgmCrbX90KOFzNa55TSmUVbIiIt4h8KCLHROQ8hiEJEBFX8zqJSqmkkpUopWIxjO1EEQkA/gZ8YaOG5sCxEmXHMJ5SCzhRUQVKqT+AeOAaEekADMT4/BCRzqbL4LR5Ty8AISWqKK/+phhPt1tNl0MysMIsL9Be9NyS92ErLTCelgHaAEuLXG8vYMXoGyngdJH1DIyne4DbMRqffabLbGyROq8vqNOsdzhGo2Izlfw+wOhvmSwigtGgf62UyqbyzxFK/BaBVzHegH8RkSMi8mhVtDZWdOdIPcL0dd4AuJo+TjCelAJEpDeGcWktIm5lGP8TQIdyqs7A+IcroBlwssh2yRSuDwFdgEFKqdMi0gfjdV3M6wSJSIBSKrmMa30CTMf47W1QSp0q735LEIthmIrSGsMwlKezLD7F6CPpAvyslDpjlr9v3sMkpVSqiDwAXFfi3PLqjwcyge7l3E8cRoNYVHeVEJFWGO6ol82iE8A0pdS6Mo5tW1FdSqmDwCTTF34tsESMcNkTGE/8d1RVXwkq+n2glNooIjkYbpnJ5gKVf45Q4jtQSqWa13tIRHoAv4rIZqXU6hreQ4NGP/HXL67BeKrrhvH63AeIAH7HMGabMIzMSyLiIyIWERlmnjsXeFhE+otBRxEpMKQ7MJ7AXEXkSmBkJTqaYPyDJpsdik8X7FBKxQE/Ae+ZnXzuIjKiyLnfAv0wfNSfVuHefwQ6i8hkEXETkRvNz2FZFerAvOZo4A6MRqjoPZ0H0kSkK3CXrRUqpfIx/OmzRSQUQERaiMgV5iFfA1NFpJuIeFPk86oM8+l5JIY7YxPG5wCGX/v5gu9QRJqKyHgb67xFRJqaupPN4nzgc+BqEbnC/C1YzA7VluVWBu7mcQWLGxX8PorwKfAOkGu+idnyOZZ1L2PN37IAKRj/H/m2fA6NGkf7mvRi+4LxdPt6GeU3YLzWu2E8TX6L0SkXD7xd5Lg7MaJl0oBooK9ZPgAjbDIV+AxYSHEf/8kS12uO4YdOAw5gdJKW7Ij9BKNDMwn4psT5czF8ub4V3GvbonWaZcOBrRj/4FsxO67NfVGU4Rcvp+4oU5dnkbIRGP0laRgN6XPAH0X2l/LJU7xz14LhHjqC0YDsBe4rcuyj5ncUC0wrq74S+rLM7yMV42n5cYr7tl2Af5rfZyqGG++FCj67ws8Hw8CfNe/1L+CaIscNAn7DcCmdw+iob12OzhjzOkWXWZX9PsxzW2MY6GdL1Fnu50jZv8UHTR3pGG+pTzr6/7Q+LGJ+eBpNnSEiTwGdlVK3OFqLxjGYbsuzQD9luJ40dYj28WvqFPPV/3aMTj1N4+UuYLM2+o5BG35NnSEidwBvYnQgrnWwHI2DEJEYjI7eaxyrpPGiXT0ajUbTyNBRPRqNRtPIqBeunpCQENW2bVtHyyA9PR0fHx9HyygXra/6OLM20PpqgjNrg9rVt3Xr1nilVNNSOxwdVmTL0r9/f+UMrFmzxtESKkTrqz7OrE0pra8mOLM2pWpXH7BFlWFTtatHo9FoGhna8Gs0Gk0jQxt+jUajaWRow6/RaDSNDG34NRqNppHRMA3/H2/C0RIDQ4+uNco1Go2mkdMwDX+LfrB46gXjf3Stsd2iX/Xq0w2JRqNpQNSLAVxVpt0IuH4BLJwE4b0hdjv0nwrn42DvD+DuDR4+F/4WXXdxLV2f2ZAEdHoAiLzQkFy/oA5vSqPRaOxDwzT8YBh/31A4Zk5QtPE9285z9QQPb/DwNRsDb3D3gcB29Nr1LMR/CwmH4cbPjGtoNBpNPaPhGv6jayErBS5+GLbMh6vegPBekJMOuRmQkwY5GeZ6QZlZXrCem37hmPw8rK5euJzaatT/2yuQnQqdryz7LUGj0WiclIZp+Iu6YtqNgPYji29Xs0715c0w9D7YPBfO7YNFkyGwLQycCX1vAYuf3W5Bo9FoaouG2bl7altxI1/g8z+1rXr1mQ3Jnm7/gsv/C5O/ApUPIx+FJuHw82PwRjf46RHDDaTRaDROTMN84h/+QOmydiOq/7RvNiTJx/Iv1FXQkExbYfz98wPYPA/+/BC6/A0G3WkcJ1Ldu9BoNJpaoWE+8dub4Q+UbjTajbjQwLToB9d+BA9Gw4h/wYk/4dNx8P4w2PYZ5GbVtWJNNZgfPZ9NcZuKlW2K28T86PkOUqTR1A7a8NuTJs3gksfhwT0w/l3jaf/7e2B2N/h1lhFOWkvMj57PgawDxcq00aoaPYJ78PBvDxca/01xm3j4t4fpEdzDwco0GvvSMF09jsbdYnT29rkZYn6HjR/A2teMAV/dJ8Dgu6o/mKwcegT34P7t99Mnrg8DwwcWGq3XRr5m1+uURCmFVVmNJd9
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# 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, :], 'x-', label=f'{i} Layers')\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={\n",
|
||
|
|
" 'arrowstyle': 'simple'\n",
|
||
|
|
" }\n",
|
||
|
|
" )\n",
|
||
|
|
" \n",
|
||
|
|
"plt.title('Accuracy for Varied Dense Layers')\n",
|
||
|
|
"plt.xlabel('Nodes Per Layer')\n",
|
||
|
|
"plt.ylabel('Top-1 % Test Accuracy')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.grid()\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "6e359c36",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.9.2"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|