scratchpad.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-17 15:09:13.384599: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-01-17 15:09:14.414425: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2023-01-17 15:09:14.414541: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2023-01-17 15:09:14.414556: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    },
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'primitives' from 'neurallogic' (/workspaces/neural-logic/neurallogic/__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mjax\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mjnp\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mflax\u001b[39;00m \u001b[39mimport\u001b[39;00m linen \u001b[39mas\u001b[39;00m nn\n\u001b[0;32m----> 5\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mneurallogic\u001b[39;00m \u001b[39mimport\u001b[39;00m neural_logic_net, harden, harden_layer, hard_or, hard_and, hard_not, primitives, symbolic_primitives\n\u001b[1;32m      6\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtests\u001b[39;00m \u001b[39mimport\u001b[39;00m test_mnist\n\u001b[1;32m      7\u001b[0m tf\u001b[39m.\u001b[39mconfig\u001b[39m.\u001b[39mexperimental\u001b[39m.\u001b[39mset_visible_devices([], \u001b[39m\"\u001b[39m\u001b[39mGPU\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'primitives' from 'neurallogic' (/workspaces/neural-logic/neurallogic/__init__.py)"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "from flax import linen as nn\n",
    "from neurallogic import neural_logic_net, harden, harden_layer, hard_or, hard_and, hard_not, symbolic_primitives\n",
    "from tests import test_mnist\n",
    "tf.config.experimental.set_visible_devices([], \"GPU\")\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clear the GPU memory\n",
    "from numba import cuda\n",
    "cuda.select_device(0)\n",
    "cuda.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sandpit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval('1+2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "eval() arg 1 must be a string, bytes or code object",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39meval\u001b[39;49m([\u001b[39m'\u001b[39;49m\u001b[39m1+2\u001b[39;49m\u001b[39m'\u001b[39;49m])\n",
      "\u001b[0;31mTypeError\u001b[0m: eval() arg 1 must be a string, bytes or code object"
     ]
    }
   ],
   "source": [
    "eval(['1+2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1+2']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval(\"['1+2']\")"
   ]
  }
 ],
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