Output.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('models/grayscale_model_epochs_100_conv_5_by_5_dropout.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open('model_data/grayscale_test_images_data.pck', 'rb')\n",
    "\n",
    "test_images_data = pickle.load(file)\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12500"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_images_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_images_data = test_images_data.reshape([-1, 64, 64, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.3177494 ],\n",
       "       [0.4937685 ],\n",
       "       [0.6297013 ],\n",
       "       ...,\n",
       "       [0.15498883],\n",
       "       [0.9962691 ],\n",
       "       [0.1848764 ]], dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(test_images_data) #list(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[117],\n",
       "        [116],\n",
       "        [111],\n",
       "        ...,\n",
       "        [137],\n",
       "        [138],\n",
       "        [135]],\n",
       "\n",
       "       [[106],\n",
       "        [110],\n",
       "        [118],\n",
       "        ...,\n",
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       "        [145],\n",
       "        [144]],\n",
       "\n",
       "       [[106],\n",
       "        [106],\n",
       "        [116],\n",
       "        ...,\n",
       "        [160],\n",
       "        [135],\n",
       "        [147]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[146],\n",
       "        [128],\n",
       "        [138],\n",
       "        ...,\n",
       "        [153],\n",
       "        [122],\n",
       "        [151]],\n",
       "\n",
       "       [[145],\n",
       "        [134],\n",
       "        [142],\n",
       "        ...,\n",
       "        [147],\n",
       "        [143],\n",
       "        [122]],\n",
       "\n",
       "       [[145],\n",
       "        [115],\n",
       "        [148],\n",
       "        ...,\n",
       "        [138],\n",
       "        [159],\n",
       "        [137]]], dtype=uint8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_images = os.listdir('data/test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'11522.jpg'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = model.predict(test_images_data).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       " ...]"
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "result"
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  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.31774941086769104]"
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     "execution_count": 14,
     "metadata": {},
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   "source": [
    "result[0]"
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.31774941086769104"
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  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_result_list = [x[0] for x in result]"
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  {
   "cell_type": "code",
   "execution_count": 17,
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = [x.replace('.jpg', '') for x in test_images]"
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  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(list(zip(filenames, new_result_list)), \n",
    "               columns =['id', 'label'])"
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  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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    "df.sort_values(by=['id'], inplace=True)"
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   "execution_count": 23,
   "metadata": {},
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       "         id     label\n",
       "1942      1  0.655696\n",
       "7820     10  0.002453\n",
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       "2726   1000  0.566591\n",
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     "metadata": {},
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   "execution_count": 24,
   "metadata": {},
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   "source": [
    "df.to_csv('data/submission.csv', index=False)"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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