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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
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"id": "a677dbb1",
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"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib as mpl\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "markdown",
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"id": "49ba8631",
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"metadata": {},
"source": [
"# Fixed Learning Rate\n",
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"80/10/10 Split, 100/200 epochs\n",
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"\n",
"## Index\n",
"0. learning rate\n",
"1. top-1 accuracy\n",
"2. top-5 accuracy\n",
"3. last val loss\n",
"4. last val accuracy"
]
},
{
"cell_type": "code",
"execution_count": 2,
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"id": "4d96109f",
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"metadata": {},
"outputs": [],
"source": [
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"fixed_results_200e = np.array([\n",
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" [1e-6, 0.31, 2.84, 5.28, 0.67],\n",
" [1e-5, 0.8, 2.59, 5.28, 0.55],\n",
" [1e-4, 6.98, 17.23, 4.6, 7.41],\n",
" [1e-3, 21.56, 44.72, 4.97, 26.9],\n",
" [5e-3, 39.35, 66.83, 3.34, 43.5],\n",
" [1e-2, 13.65, 30.02, 4.15, 17.46],\n",
" [5e-2, 1.79, 6.73, 5.13, 1.78],\n",
" [1e-1, 0.8, 2.78, 5.29, 0.55]\n",
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"])\n",
"\n",
"fixed_results_100e = np.array([\n",
" [1e-6, 0.31, 2.9, 5.28, 0.67],\n",
" [1e-5, 0.8, 2.1, 5.28, 0.55],\n",
" [1e-4, 2.35, 8.28, 5.00, 2.63],\n",
" [1e-3, 18.47, 40.09, 4.55, 23.41],\n",
" [5e-3, 35.52, 63.19, 3.33, 40.93],\n",
" [1e-2, 22.42, 47.19, 3.59, 27.02],\n",
" [5e-2, 2.47, 9.02, 5.07, 2.14],\n",
" [1e-1, 0.8, 2.53, 5.28, 0.55]\n",
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"])"
]
},
{
"cell_type": "code",
"execution_count": 3,
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"id": "345d4c52",
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"plt.plot(fixed_results_200e[:, 0], fixed_results_200e[:, 1], 'x-', label=\"Top-1 Accuracy\")\n",
"plt.plot(fixed_results_200e[:, 0], fixed_results_200e[:, 2], 'x-', label=\"Top-5 Accuracy\")\n",
"plt.plot(fixed_results_200e[:, 0], fixed_results_200e[:, 4], 'x-', label=\"Final Val. Accuracy\")\n",
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"\n",
"plt.ylim(0)\n",
"\n",
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"plt.title('Model Accuracy for Fixed Learning Rates')\n",
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"plt.ylabel('% Accuracy')\n",
"plt.xlabel('Learning Rate')\n",
"\n",
"plt.legend()\n",
"plt.xscale('log')\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
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"id": "4fa68d02",
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"plt.plot(fixed_results_200e[:, 0], fixed_results_200e[:, 3], 'x-', label=\"Final Validation Loss\")\n",
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"\n",
"# plt.ylim(0)\n",
"\n",
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"plt.title('Final Validation Loss for Fixed Learning Rates')\n",
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"plt.ylabel('Loss')\n",
"plt.xlabel('Learning Rate')\n",
"\n",
"# plt.legend()\n",
"plt.xscale('log')\n",
"plt.grid()\n",
"plt.show()"
]
},
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{
"cell_type": "markdown",
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"id": "e57035e6",
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"metadata": {},
"source": [
"# Step-Down\n",
"80/10/10 Split, 100 epochs\n",
"\n",
"## Index\n",
"0. learning rate\n",
"1. step size\n",
"2. gamma\n",
"3. top-1 accuracy\n",
"4. top-5 accuracy\n",
"5. last val loss\n",
"6. last val accuracy"
]
},
{
"cell_type": "code",
"execution_count": 5,
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"id": "b7c2a026",
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"metadata": {},
"outputs": [],
"source": [
"step_down_results = np.array([\n",
" [1e-2, 0.33, 0.1, 43.79, 70.85, 2.79, 47.24],\n",
" [1e-2, 0.33, 0.25, 45.52, 73.07, 2.90, 49.88],\n",
" [1e-2, 0.33, 0.5, 45.71, 71.71, 2.89, 49.39],\n",
" [1e-2, 0.33, 0.75, 40.09, 68.19, 3.00, 46.38]\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 6,
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"id": "53e815da",
2021-04-07 22:40:14 +01:00
"metadata": {},
"outputs": [
{
"data": {
2021-04-09 13:04:40 +01:00
"image/png": "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
2021-04-07 22:40:14 +01:00
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(step_down_results[:, 2], step_down_results[:, 3], 'x-', label=\"Top-1 Accuracy\")\n",
"plt.plot(step_down_results[:, 2], step_down_results[:, 4], 'x-', label=\"Top-5 Accuracy\")\n",
"plt.plot(step_down_results[:, 2], step_down_results[:, 6], 'x-', label=\"Final Val. Accuracy\")\n",
"\n",
"plt.ylim(0)\n",
"\n",
"plt.title('Model Accuracy for Step Down Learning Rates')\n",
"plt.ylabel('% Accuracy')\n",
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"plt.xlabel('Gamma')\n",
"\n",
"plt.legend()\n",
"# plt.xscale('log')\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a37bab2d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(step_down_results[:, 2], step_down_results[:, 5], 'x-', label=\"Final Validation Loss\")\n",
"\n",
"# plt.ylim(0)\n",
"\n",
"plt.title('Final Validation Loss for Fixed Learning Rates')\n",
"plt.ylabel('Loss')\n",
"plt.xlabel('Gamma')\n",
"\n",
"# plt.legend()\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "080c7fcb",
"metadata": {},
"source": [
"# Exponential Decay\n",
"80/10/10 Split, 100 epochs\n",
"\n",
"## Index\n",
"0. learning rate\n",
"1. decay rate\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": 8,
"id": "ab27ce49",
"metadata": {},
"outputs": [],
"source": [
"exp_results = np.array([\n",
" [1e-2, 0.70, 2.35, 8.09, 4.97, 2.75],\n",
" [1e-2, 0.80, 5.0, 15.57, 4.61, 7.23],\n",
" [1e-2, 0.90, 25.88, 52.5, 3.28, 29.17],\n",
" [1e-2, 0.925, 37.43, 63.37, 3.13, 40.81],\n",
" [1e-2, 0.95, 44.1, 71.22, 2.99, 48.84],\n",
" [1e-2, 0.98, 44.41, 71.83, 3.04, 47.61],\n",
" [1e-2, 0.99, 42.43, 69.55, 3.25, 45.47],\n",
" \n",
" [1e-1, 0.85, 12.91, 34.96, 3.85, 14.89],\n",
" [1e-1, 0.9, 0.8, 2.29, 5.29, 0.55]\n",
"])\n",
"two_results = 7"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "684e1744",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(exp_results[:two_results, 1], exp_results[:two_results, 2], 'x-', label=\"Top-1 Accuracy\")\n",
"plt.plot(exp_results[:two_results, 1], exp_results[:two_results, 3], 'x-', label=\"Top-5 Accuracy\")\n",
"plt.plot(exp_results[:two_results, 1], exp_results[:two_results, 5], 'x-', label=\"Final Val. Accuracy\")\n",
"\n",
"plt.ylim(0)\n",
"\n",
"plt.title('Model Accuracy for Exponential Learning Rates')\n",
"plt.ylabel('% Accuracy')\n",
"plt.xlabel('Decay Rate')\n",
2021-04-07 22:40:14 +01:00
"\n",
"plt.legend()\n",
"# plt.xscale('log')\n",
"plt.grid()\n",
"plt.show()"
]
},
2021-04-09 13:04:40 +01:00
{
"cell_type": "code",
"execution_count": 10,
"id": "87ee3e82",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAA72ElEQVR4nO3dd3hUZfbA8e9JDwRCSQiBUBM6BkIQC6sSbCgI664KCq4de3dVVtfC6m9ddZuiCGsHNWJdwIKFtooKBEjoSC8B6SX0wPn9cS84hJSZJJOZTM7neebJnVvPmTszJ/e+d+4rqooxxhjjrbBAB2CMMaZ6scJhjDHGJ1Y4jDHG+MQKhzHGGJ9Y4TDGGOMTKxzGGGN8YoWjCohIgYi0roT1PCEiYysjpiLrvVZEvvN4XmK8Rectx7a+EJFryrt8MBKRp0Rkq4hs8uM2mrv7JbyS11uh/VkVRORPIvJqoOMwv4oIdAChRERWA0nAEY/RbVU1zs/bbQqsAdqp6ooi0z4BVqjqA96ur7LiFZEngDRVHeKx7osqY93FbOtNYL2qPuqP9Zey3ebA/UALVd1cSetUYB9w7EdWhapaD/Dr+6iYOFoCq4BIVS2sym17UtX/89e6i7zWu4D3gT+q6pFSF6T493dNYUccle8SVY3zeOT7e4OqugH4Frjac7yINAAuBt7ydww1WHNgW3mKhoiU9o9bF4/3UL1yRxfkyngNqkoX95+lc4CBwPUBjifoWeGoAiKiIpLmDr8pIi+JyGciskdEfhKRVI95/y0i60Rkt4jkiMhZXm7mLYoUDmAQsEhV54vIwyKywt3mIhG51Mt4G4rIeDeemUBqkXmLjVdE+gB/Aga6p1hy3fFTReRGdzhMRB4VkTUisllE3haReHdaSzeOa0RkrXsq6BEvX4ui+dwkIstFZLubSxN3vIjIP91t7xaR+SLS2Z12sfs67RGRDSJy0hGbiJwHfA00cXN80x3fX0QWishON98OHsusFpGHRCQP2OvtF6fH6xEhIg1EZL2IXOJOi3Pz+4P7vL2IfO3mu1RErvBYT6n704fXNF5EXhORje7r89Sx02gikioik0Vkm7vf3hGReqW8Bmml7WvxOEVb1vtCRGJF5C0R2SEii0XkQRFZ701Oqroc+B7o6rE+X9/fpb0uaSIyTUR2uXG/X57XPiioqj0q6QGsBs4rZrziHNICvAlsA3rgnCp8B8j2mHcI0NCddj+wCYhxpz0BjC1h27E4h9q/8Rj3A3CPO3w50ATnn4WBwF4g2Z12LfBdCfFmA+OA2kBnYEOReX2KF5gK3OgOXw8sB1rjnIb5GBjjTmvpxvEfN7cuwEGgQwn5vwk8Vcz43sBWoBsQDbwITHenXQjkAPUAATp4vCYbgbPc4fpAtxK22wvnFNmx523d1/Z8IBJ40M0xyuM9Mg9oBsSWsM7jr7/HuGOvR4T7/AL3tW7kvkYfuuNrA+uA69x9kuHm39Gb/VnaNotM+wQY5a6nETATuNmdlubmHw0kAtOBfxX5nBx/Dcra13i8j7yY9xlgmrvPUoA8z/1Txmezvbvf763A+7u01+U94BGcz2AMHp/V6vYIeACh9HA/EAXATvfxqTu+aOF41WOZi4ElpaxzB86hdLFv1CLzvgqMdofbAIeARiXMOw8Y4A5fSzGFAwgHDgPtPab9HyV80XgTLycWjm+B2zymtXO3F+HxBZHiMX0mMKiE7b5J8YXjNeBZj+dx7jZa4hSVZcDpQFiR5dYCNwN1y9jnvTixcPwZGOfxPAzny7mXx3vk+jLWqcBuj/fRCxTzJY5TBOe762/ojhsI/K/I+kYBj/u6P4vbpjs+CefLOtZj3JXAlBLW81tgbpHPyfXFbKfYfU3xhaOkeVcCF3pMu5GyC8dunGKvOF/u0eV5f5f1ugBvA6M9Y6+uDztVVfl+q6r13MdvS5jH8+qbfXg0eorIA+4h9i4R2QnEAwlebvst4HIRicE5bTVJ3XPvIvIHEZnnnj7ZifPfZlnrTcT5El/nMW6N5wwVjLdJkfWtcbeX5DGuxNeqPNtQ1QKcI76mqjoZGAG8BGwWkdEiUted9fc4RX2Ne3rhjHJu7yjO69fUY551RRcqRjeP99FdJcwzGmc/vqmq29xxLYDTju1nd58MBhrjxf70Uguco6mNHtsYhfMfNiKSJCLZ7qma3cBYTn5PFPca+LKvS5q3SZF1e/Vau8sPBE7DOVoAfH5/l/q64Bx9CjDTPZVZbdtSrHAEEff86YPAFUB9dRpFd+G82bzxHbAdGIBziP2Wu94WOIf2d+D8Z1oPWODFercAhTinFI5p7kO8Wsb683E+bJ7rLgR+KWM5X5ywDRGpjXPqYQOAqr6gqplAR5zTTH90x89S1QE4H/pPcU7vlGd7gvP6bfCYp6zXpUzuefPROP/F3iZumxTOF+U0j6JTT50G9lspY3/6YB3Of9YJHtuoq6qd3On/h5PjKapaF+e9WPS9VuHXoAQbcU5RHdOspBk9qWMczundx6Bc7+9SXxdV3aSqN6lqE5yj2Zc99lu1YoUjuNTB+WBvASJE5DGgbumL/Eqd4+G3gb/hnLef4E6qjfMm3wIgItfh/Kda1vqO4LQ7PCEitUSkI3CND/H+ArQUkZLeZ+8B94pIKxGJw/nCeV/Lf+lnuIjEeDyi3G1cJyJdRSTa3cZPqrpaRE4VkdNEJBLnVMUB4KiIRInIYBGJV9XDOKcyjnoZwzigr4ic6673fpwvkxnlzKkkf8LZp9cDzwFvu8VkItBWRK4WkUj3caqIdPBif5Yk2vN1xdmvXwF/F5G64lzkkCoi57jz18E5ZbtLnEvF/1ipmZduHDBMROq7277Dx+WfAW4Skcb4+P5W1Y2U8rqIyOUicqyo7cDZf96+r4KKFY7gMgn4Eue8+xqcLzJvDrU9vY3zX+T7qnoQQFUXAX/H+W/qF+AUnKtHvHEHzmH8Jpx2hDd8iPcD9+82EZlTzLpfB8bgNJ6ucpe/08u4ivMwsN/jMVlVv8Fpd/gI57/RVJyrzcD5EvgPzod4Dc4prOfcaVcDq91TLbfgnO4pk6ouxfkP+0WcRulLcC7RPlSBvE4gIpnAfcAf3GLwN5wvoYdVdQ9Ow/kgnKOfTe70aHfx0vZnSQo48XXtDfwBiAIW4bx+HwLJ7vxP4pz+2QV8hlOsqspwYD3O++kbN66D3i6sqvNx3o9/pHzv79Jel1OBn0SkABgP3K2qK33MLyiI22hjjDEhR0RuxWk4P6fMmY3X7IjDGBMyRCRZRHq6p4na4Zwq/CTQcYWaYPjVpjHGVJYonCuZWuFcypwNvBzIgEKRnaoyxhjjEztVZYwxxichc6oqISFBW7ZsWe7l9+7dS+3atcuesZoItXwg9HIKtXwg9HIKtXzg5JxycnK2qmqiL+sImcLRsmVLZs+eXe7lp06dSq9evSovoAALtXwg9HIKtXwg9HIKtXzg5JxExOe7B9ipKmOMMT6xwmGMMcYnfi0c4tx3f757c72TziOJ4wVx+hLIE5FuHtOuEZGf3Yc3t0UwxhhTBaqijSNLVbeWMO0inNt/t8G5K+VInDt7NsC5DXR3nFsp5IjIeFXdUQXxGmOMKUWgT1UNAN5270z5I1BPRJJxOtj5WlW3u8Xia6BPIAM1xhjj8OsPAEVkFb/eBXKUqo4uMn0i8Iyqfuc+/xZ4CKdznBhVfcod/2dgv6o+X2T5ocBQgKSkpMzs7Gyf4vt85SFaxYfToWE4BQUFxMXFsXjbEVbtOsLFraPKkXHwOJZPKAm1nEItHwi9nEItHzg5p6ysrBxV7e7LOvx9quo3qrpBRBoBX4vIElWdXlkrdwvRaIDu3burr5fNRTXbyh3vzmXEVV2IW7eAqGad+c//5jLiqm6cmeptX0TBqSZcRljdhVo+EHo5hVo+UDk5+bVwqOqxznI2i8gnOP1sexaODZzY0UqKO24DzlGH5/iplR3fmakJjLgyg+vfnEVGgrBk+hxeGlz9i4YxxviT39o4RKS2iNQ5NozTR8CCIrONB/7gXl11OrDL7QxlEnCB2xlLfXfZSf6IM6V+LY4cUX7YeIQ
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(exp_results[:two_results, 1], exp_results[:two_results, 4], 'x-', label=\"Final Validation Loss\")\n",
"\n",
"# plt.ylim(0)\n",
"\n",
"plt.title('Final Validation Loss for Fixed Learning Rates')\n",
"plt.ylabel('Loss')\n",
"plt.xlabel('Decay Rate')\n",
"\n",
"# plt.legend()\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "ce1b8019",
"metadata": {},
"source": [
"# Sigmoid Decay\n",
"80/10/10 Split, 100 epochs\n",
"\n",
"## Index\n",
"0. learning rate\n",
"1. step size\n",
"2. gamma\n",
"3. top-1 accuracy\n",
"4. top-5 accuracy\n",
"5. last val loss\n",
"6. last val accuracy"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "c4c39704",
"metadata": {},
"outputs": [],
"source": [
"sig_results = np.array([\n",
" [1e-2, 50, 0.05, 46.94, 72.88, 2.79, 52.94],\n",
" [1e-2, 50, 0.1, 45.95, 73.63, 2.65, 51.29],\n",
" [1e-2, 50, 0.15, 41.94, 68.56, 2.94, 47.49],\n",
" [1e-2, 50, 0.2, 41.82, 68.13, 2.82, 45.16]\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0a2e899f",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(sig_results[:, 2], sig_results[:, 3], 'x-', label=\"Top-1 Accuracy\")\n",
"plt.plot(sig_results[:, 2], sig_results[:, 4], 'x-', label=\"Top-5 Accuracy\")\n",
"plt.plot(sig_results[:, 2], sig_results[:, 6], 'x-', label=\"Final Val. Accuracy\")\n",
"\n",
"plt.ylim(0)\n",
"\n",
"plt.title('Model Accuracy for Sigmoid Learning Rates')\n",
"plt.ylabel('% Accuracy')\n",
"plt.xlabel('Gamma')\n",
"\n",
"plt.legend()\n",
"# plt.xscale('log')\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "53b1a3b8",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(sig_results[:, 2], sig_results[:, 5], 'x-', label=\"Final Validation Loss\")\n",
"\n",
"# plt.ylim(0)\n",
"\n",
"plt.title('Final Validation Loss for Fixed Learning Rates')\n",
"plt.ylabel('Loss')\n",
"plt.xlabel('Gamma')\n",
"\n",
"# plt.legend()\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "82bd3a32",
"metadata": {},
"source": [
"# Best\n",
"\n",
"100 Epochs\n",
"\n",
"top-1 accuracy indexes: 1, 3, 2, 3"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "c866fad2",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"best_top_1_results = list()\n",
"best_labels = list()\n",
"\n",
"# Fixed\n",
"b_fixed = fixed_results_100e[np.argmax(fixed_results_100e[:, 1])]\n",
"best_top_1_results.append(b_fixed[1:3])\n",
"best_labels.append(f'Fixed\\n{b_fixed[0]}')\n",
"\n",
"# Step Down\n",
"b_sd = step_down_results[np.argmax(step_down_results[:, 3])]\n",
"best_top_1_results.append(b_sd[3:5])\n",
"best_labels.append(f'Step Down\\n{b_sd[0]}, Step: {b_sd[1]}, Gamma: {b_sd[2]}')\n",
"\n",
"# Exp\n",
"b_exp = exp_results[np.argmax(exp_results[:, 2])]\n",
"best_top_1_results.append(b_exp[2:4])\n",
"best_labels.append(f'Exponential Decay\\n{b_exp[0]}, Rate: {b_exp[1]}')\n",
"\n",
"# Sig\n",
"b_sig = sig_results[np.argmax(sig_results[:, 3])]\n",
"best_top_1_results.append(b_sig[3:5])\n",
"best_labels.append(f'Sigmoid Decay\\n{b_sig[0]}, Step: {b_sig[1]}, Gamma: {b_sig[2]}')\n",
"\n",
"# print(best_top_1_results)\n",
"# print(best_labels)\n",
"# print(best_top_1_results)\n",
"plt.barh(range(len(best_labels)), [i[0] for i in best_top_1_results], tick_label=best_labels, label='Top-1')\n",
"plt.barh(range(len(best_labels)), [i[1] - i[0] for i in best_top_1_results], tick_label=best_labels, label='Top-5', left=[i[0] for i in best_top_1_results])\n",
"\n",
"plt.legend()\n",
"plt.grid(axis='x')\n",
"plt.title('Best Test Accuracy for Various Learning Schedule Policies')\n",
"plt.xlabel('% Test Accuracy')\n",
"plt.ylabel('Learning Schedule Policies')\n",
"plt.show()"
]
},
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{
"cell_type": "code",
"execution_count": null,
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"id": "39bd3540",
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"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",
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"version": "3.8.4"
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}
},
"nbformat": 4,
"nbformat_minor": 5
}
