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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of samples: [ 16. 32. 64. 128. 256.] (x2)\n",
"Number of samples: [ 8. 16. 32. 64. 128.] (x4)\n",
"Number of samples: [ 4. 8. 16. 32. 64.] (x14)\n",
"Number of samples: [ 4. 8. 16. 32. 64.] (x20)\n",
"Number of samples: [ 2. 4. 8. 16. 32.] (x77)\n"
]
}
],
"source": [
"# This is equivalent to round factor / log2(num_classes) to the nearest power of 2\n",
"factor = np.array([16, 32, 64, 128, 256])\n",
"for num_classes in [2, 4, 14, 20, 77]:\n",
" scale = factor / np.log2(num_classes)\n",
" nearest_power_of_2 = 2 ** np.round(np.log2(scale)) # round to nearest power of 2\n",
" print(\"Number of samples: \", nearest_power_of_2, f\"(x{num_classes})\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llmcal",
"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": "3.11.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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