circomlib-ml/models/flatten.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Input, Flatten\n",
    "from tensorflow.keras import Model\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = Input(shape=(5,5,3))\n",
    "x = Flatten()(inputs)\n",
    "model = Model(inputs, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 5, 5, 3)]         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 75)                0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 0\n",
      "Trainable params: 0\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[0.9191584 , 0.41015604, 0.0493302 ],\n",
       "         [0.20412956, 0.14984944, 0.71595293],\n",
       "         [0.57980447, 0.28233206, 0.30881941],\n",
       "         [0.98703541, 0.91977126, 0.89591016],\n",
       "         [0.29365768, 0.89541076, 0.97098122]],\n",
       "\n",
       "        [[0.28270309, 0.85760979, 0.12266525],\n",
       "         [0.2386079 , 0.93741419, 0.83312648],\n",
       "         [0.02935679, 0.68497567, 0.37248647],\n",
       "         [0.76807667, 0.72347087, 0.84375984],\n",
       "         [0.89233681, 0.87703334, 0.53846864]],\n",
       "\n",
       "        [[0.14028452, 0.61585222, 0.34271206],\n",
       "         [0.45404173, 0.26365195, 0.05140719],\n",
       "         [0.36253999, 0.51529482, 0.15006   ],\n",
       "         [0.82061228, 0.08937872, 0.65234282],\n",
       "         [0.31024437, 0.09785702, 0.40629764]],\n",
       "\n",
       "        [[0.75192339, 0.55825739, 0.86978978],\n",
       "         [0.76105885, 0.54160411, 0.72517187],\n",
       "         [0.28701856, 0.31868524, 0.46890464],\n",
       "         [0.0902    , 0.3022873 , 0.48529066],\n",
       "         [0.24453082, 0.93271481, 0.08555694]],\n",
       "\n",
       "        [[0.52171579, 0.22363436, 0.85212827],\n",
       "         [0.9823001 , 0.64424366, 0.96495129],\n",
       "         [0.61750385, 0.53921774, 0.75703119],\n",
       "         [0.57267588, 0.18643057, 0.26532282],\n",
       "         [0.22546175, 0.0340469 , 0.19259163]]]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.random.rand(1,5,5,3)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 95ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-23 17:09:53.715790: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.9191584 , 0.41015604, 0.0493302 , 0.20412956, 0.14984943,\n",
       "        0.71595293, 0.5798045 , 0.28233206, 0.3088194 , 0.9870354 ,\n",
       "        0.91977125, 0.89591014, 0.2936577 , 0.8954108 , 0.97098124,\n",
       "        0.2827031 , 0.8576098 , 0.12266525, 0.2386079 , 0.93741417,\n",
       "        0.8331265 , 0.02935679, 0.6849757 , 0.37248647, 0.76807666,\n",
       "        0.72347087, 0.84375983, 0.8923368 , 0.87703335, 0.53846866,\n",
       "        0.14028452, 0.61585224, 0.34271204, 0.45404172, 0.26365197,\n",
       "        0.05140718, 0.36253998, 0.5152948 , 0.15006   , 0.82061225,\n",
       "        0.08937872, 0.6523428 , 0.31024438, 0.09785703, 0.40629762,\n",
       "        0.7519234 , 0.5582574 , 0.8697898 , 0.76105887, 0.5416041 ,\n",
       "        0.72517186, 0.28701857, 0.31868523, 0.46890464, 0.0902    ,\n",
       "        0.3022873 , 0.48529068, 0.24453081, 0.9327148 , 0.08555695,\n",
       "        0.5217158 , 0.22363436, 0.85212827, 0.9823001 , 0.64424366,\n",
       "        0.9649513 , 0.6175039 , 0.5392177 , 0.7570312 , 0.5726759 ,\n",
       "        0.18643057, 0.26532283, 0.22546175, 0.0340469 , 0.19259164]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = model.predict(X)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_json = {\n",
    "    \"in\": (X*1e36).round().astype(int).flatten().tolist(),\n",
    "    \"out\": (X*1e36).round().astype(int).flatten().tolist()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"flatten2D_input.json\", \"w\") as f:\n",
    "    json.dump(in_json, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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