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",
      "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([[[[0.0852918 , 0.44266819, 0.55968985],\n",
       "         [0.80456378, 0.7812197 , 0.93979271],\n",
       "         [0.60230819, 0.56047002, 0.96776284],\n",
       "         [0.6283819 , 0.8851144 , 0.84297738],\n",
       "         [0.32538761, 0.69431566, 0.41653711]],\n",
       "\n",
       "        [[0.88323994, 0.49007438, 0.52891214],\n",
       "         [0.75972182, 0.85386461, 0.64519541],\n",
       "         [0.7372128 , 0.34378423, 0.89210331],\n",
       "         [0.68795543, 0.15510637, 0.84101805],\n",
       "         [0.34090138, 0.49322424, 0.00737098]],\n",
       "\n",
       "        [[0.59636987, 0.80149115, 0.3530605 ],\n",
       "         [0.06466776, 0.40315287, 0.15753059],\n",
       "         [0.54568182, 0.95573263, 0.64114777],\n",
       "         [0.6558953 , 0.10547539, 0.82302922],\n",
       "         [0.60415623, 0.58044333, 0.46934783]],\n",
       "\n",
       "        [[0.89339205, 0.49514657, 0.66308424],\n",
       "         [0.23049011, 0.71922777, 0.19032885],\n",
       "         [0.23228222, 0.16731365, 0.89744304],\n",
       "         [0.64359666, 0.4594629 , 0.11503616],\n",
       "         [0.62930732, 0.09412582, 0.04021055]],\n",
       "\n",
       "        [[0.40104213, 0.9882606 , 0.20996853],\n",
       "         [0.44420542, 0.47306763, 0.98680773],\n",
       "         [0.95270149, 0.97320959, 0.54052338],\n",
       "         [0.04304848, 0.31208349, 0.9046649 ],\n",
       "         [0.00495649, 0.39177585, 0.67277488]]]])"
      ]
     },
     "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": [
    {
     "data": {
      "text/plain": [
       "array([[0.0852918 , 0.4426682 , 0.5596899 , 0.80456376, 0.7812197 ,\n",
       "        0.9397927 , 0.6023082 , 0.56047004, 0.9677628 , 0.6283819 ,\n",
       "        0.8851144 , 0.8429774 , 0.3253876 , 0.6943157 , 0.4165371 ,\n",
       "        0.8832399 , 0.4900744 , 0.5289121 , 0.7597218 , 0.8538646 ,\n",
       "        0.6451954 , 0.7372128 , 0.3437842 , 0.8921033 , 0.68795544,\n",
       "        0.15510637, 0.8410181 , 0.34090137, 0.49322423, 0.00737098,\n",
       "        0.59636986, 0.80149114, 0.35306048, 0.06466776, 0.40315288,\n",
       "        0.15753059, 0.54568183, 0.95573264, 0.6411478 , 0.6558953 ,\n",
       "        0.10547539, 0.8230292 , 0.60415626, 0.5804433 , 0.46934783,\n",
       "        0.893392  , 0.49514657, 0.6630842 , 0.2304901 , 0.7192278 ,\n",
       "        0.19032885, 0.23228222, 0.16731365, 0.89744306, 0.64359665,\n",
       "        0.4594629 , 0.11503616, 0.6293073 , 0.09412582, 0.04021055,\n",
       "        0.40104213, 0.98826057, 0.20996854, 0.44420543, 0.47306764,\n",
       "        0.9868077 , 0.9527015 , 0.9732096 , 0.54052335, 0.04304848,\n",
       "        0.31208348, 0.9046649 , 0.00495649, 0.39177585, 0.6727749 ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = model.predict(X)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_json = {\n",
    "    \"in\": (X*1000).round().astype(int).flatten().tolist()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_json = {\n",
    "    \"out\": (y*1000).round().astype(int).flatten().tolist()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"flatten2D_input.json\", \"w\") as f:\n",
    "    json.dump(in_json, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"flatten2D_output.json\", \"w\") as f:\n",
    "    json.dump(out_json, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf24",
   "language": "python",
   "name": "tf24"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}