smart-iss-posts/notebooks/test_model_class.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(os.getcwd() + '/..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from iss.tools.config import Config\n",
    "from iss.models.SimpleAutoEncoder import SimpleAutoEncoder\n",
    "from iss.models.ModelTrainer import ModelTrainer\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import time\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfg = Config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SimpleAutoEncoder(cfg.get('models')['simple'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 45, 80, 3)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 10800)             0         \n",
      "_________________________________________________________________\n",
      "enc_1 (Dense)                (None, 2000)              21602000  \n",
      "_________________________________________________________________\n",
      "enc_2 (Dense)                (None, 100)               200100    \n",
      "_________________________________________________________________\n",
      "enc_3 (Dense)                (None, 30)                3030      \n",
      "_________________________________________________________________\n",
      "dec_1 (Dense)                (None, 100)               3100      \n",
      "_________________________________________________________________\n",
      "dec_2 (Dense)                (None, 2000)              202000    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10800)             21610800  \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 45, 80, 3)         0         \n",
      "=================================================================\n",
      "Total params: 43,621,030\n",
      "Trainable params: 43,621,030\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collection Manager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from iss.data.CollectionManager import CollectionManagerFromDirectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "collection = CollectionManagerFromDirectory(config = cfg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from iss.models.DataLoader import ImageDataGeneratorWrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 9537 images belonging to 1 classes.\n",
      "Found 2725 images belonging to 1 classes.\n"
     ]
    }
   ],
   "source": [
    "data_loader = ImageDataGeneratorWrapper(cfg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = ModelTrainer(model, data_loader, cfg.get('models')['simple'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "10/10 [==============================] - 30s 3s/step - loss: 4.0362 - val_loss: 3.8519\n",
      "ok\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=80x45 at 0x7F16E9F40CF8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFAAAAAtCAIAAAC2z3vlAAAAIUlEQVR4nO3BMQEAAADCoPVP7WMMoAAAAAAAAAAAAADgBipdAAErWIFjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=80x45 at 0x7F16E9F40CC0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2\n",
      "10/10 [==============================] - 20s 2s/step - loss: 3.9729 - val_loss: 3.9454\n",
      "ok\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=80x45 at 0x7F16E9F40C50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFAAAAAtCAIAAAC2z3vlAAAAIUlEQVR4nO3BMQEAAADCoPVP7WMMoAAAAAAAAAAAAADgBipdAAErWIFjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=80x45 at 0x7F16E9F40D68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from iss.tools.tools import Tools "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = data_loader.get_test_generator().next()[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict_one(x = sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=80x45 at 0x7F16E9F1A400>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tools.display_one_picture_scaled(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFAAAAAtCAIAAAC2z3vlAAAAIUlEQVR4nO3BMQEAAADCoPVP7WMMoAAAAAAAAAAAAADgBipdAAErWIFjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=80x45 at 0x7F16E9F1A128>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tools.display_index_picture_scaled(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[  9.29346570e-05,   8.98255530e-05,   9.10840463e-05],\n",
       "         [  9.24957130e-05,   8.69057039e-05,   9.48583547e-05],\n",
       "         [  9.25923814e-05,   9.37460136e-05,   9.16876670e-05],\n",
       "         ..., \n",
       "         [  9.72924754e-05,   9.01218809e-05,   9.07215144e-05],\n",
       "         [  8.95589183e-05,   9.17836296e-05,   9.02524989e-05],\n",
       "         [  9.32235635e-05,   9.47016451e-05,   9.11756142e-05]],\n",
       "\n",
       "        [[  9.44539206e-05,   9.08145885e-05,   9.35679709e-05],\n",
       "         [  9.06047280e-05,   8.93754986e-05,   9.36847937e-05],\n",
       "         [  9.12203177e-05,   9.15631317e-05,   9.22772961e-05],\n",
       "         ..., \n",
       "         [  9.03476830e-05,   9.41506369e-05,   9.19400118e-05],\n",
       "         [  9.54698262e-05,   9.56243603e-05,   9.01497115e-05],\n",
       "         [  9.08325746e-05,   9.22526378e-05,   9.67690648e-05]],\n",
       "\n",
       "        [[  9.75354778e-05,   9.62107879e-05,   9.33410265e-05],\n",
       "         [  9.07929862e-05,   9.04788467e-05,   9.17322686e-05],\n",
       "         [  9.03079417e-05,   9.44491185e-05,   9.32097464e-05],\n",
       "         ..., \n",
       "         [  9.64030842e-05,   9.21978717e-05,   9.25744753e-05],\n",
       "         [  9.29698217e-05,   9.10726303e-05,   9.48995221e-05],\n",
       "         [  9.55267460e-05,   9.14257762e-05,   9.13872645e-05]],\n",
       "\n",
       "        ..., \n",
       "        [[  9.43425621e-05,   8.91427480e-05,   9.57405864e-05],\n",
       "         [  9.73207134e-05,   9.43527630e-05,   9.58818273e-05],\n",
       "         [  9.58043602e-05,   9.36572978e-05,   8.99789084e-05],\n",
       "         ..., \n",
       "         [  9.33905685e-05,   9.14029151e-05,   9.26123321e-05],\n",
       "         [  9.01690219e-05,   9.53693379e-05,   9.11882526e-05],\n",
       "         [  9.13038821e-05,   8.71358206e-05,   9.15639248e-05]],\n",
       "\n",
       "        [[  9.18796868e-05,   9.30848983e-05,   9.20263992e-05],\n",
       "         [  8.88295326e-05,   9.11370516e-05,   9.00926607e-05],\n",
       "         [  9.69637622e-05,   8.97215723e-05,   9.01956664e-05],\n",
       "         ..., \n",
       "         [  9.22475519e-05,   9.00504892e-05,   9.20245875e-05],\n",
       "         [  8.98239523e-05,   9.11542447e-05,   9.47700828e-05],\n",
       "         [  9.55717333e-05,   9.17145953e-05,   9.02576721e-05]],\n",
       "\n",
       "        [[  9.10112358e-05,   9.42766128e-05,   8.98375292e-05],\n",
       "         [  9.17554207e-05,   9.20126913e-05,   9.06156056e-05],\n",
       "         [  9.30430760e-05,   9.51249895e-05,   9.26310167e-05],\n",
       "         ..., \n",
       "         [  9.62113336e-05,   9.31859176e-05,   9.16533099e-05],\n",
       "         [  9.47599619e-05,   9.40426253e-05,   9.60465622e-05],\n",
       "         [  8.99810038e-05,   9.24791093e-05,   9.42508268e-05]]]], dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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