age_detion.ipynb

{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "Python 3.7.9 64-bit ('ai5': conda)",
   "display_name": "Python 3.7.9 64-bit ('ai5': conda)",
   "metadata": {
    "interpreter": {
     "hash": "e52054b27c3089f063d6cf3c59a4bec9947fac0d44525b69ec87aa44b6cd8db5"
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import os\n",
    "import cv2\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_SEED = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('dataset/train.csv')\n",
    "test_df = pd.read_csv('dataset/test.csv')\n",
    "#sample = pd.read_csv('datatset/sample.csv')\n",
    "\n",
    "# Loading the data files into pandas dataframe type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "le_class = LabelEncoder()\n",
    "train_df['Class'] = le_class.fit_transform(train_df['Class'])\n",
    "\n",
    "# Giving different class name a numerical variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images_path = os.path.join('dataset/'+'Train/')\n",
    "train_images = os.listdir(train_images_path)\n",
    "\n",
    "test_images_path = os.path.join('dataset/'+'Test/')\n",
    "test_images = os.listdir(test_images_path)\n",
    "\n",
    "# Listing train and test images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_train_data(train_images_path,train_images,train_df):\n",
    "    train_img,target = [],[]\n",
    "\n",
    "    for image in tqdm(train_images):\n",
    "        img = cv2.imread(train_images_path+image)\n",
    "        img = cv2.resize(img,(128,128))\n",
    "\n",
    "        train_img.append(img)\n",
    "\n",
    "        label = train_df.loc[train_df['ID']==image,'Class'].values[0]\n",
    "        target.append(label)\n",
    "\n",
    "    return np.array(train_img),np.array(target)                ##  creating a trainable dataset (numpy) for model training\n",
    "\n",
    "def create_test_data(test_images_path,test_images,test_df):\n",
    "    test_img = []\n",
    "\n",
    "    for image in tqdm(test_images):\n",
    "        img = cv2.imread(test_images_path+image)\n",
    "        img = cv2.resize(img,(128,128))\n",
    "\n",
    "        test_img.append(img)\n",
    "\n",
    "    return np.array(test_img)            ## preparing images for testing similar to train images\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|██████████| 19906/19906 [00:48<00:00, 407.22it/s]\n",
      "100%|██████████| 6636/6636 [00:05<00:00, 1235.60it/s]\n"
     ]
    }
   ],
   "source": [
    "x_images_train,target = create_train_data(train_images_path,train_images,train_df)\n",
    "x_test_images = create_test_data(test_images_path,test_images,test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Train Images: 19906\nTarget Labels: 19906\nTest Images: 6636\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Images:\",len(x_images_train))\n",
    "print(\"Target Labels:\",len(target))\n",
    "print(\"Test Images:\",len(x_test_images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x_images_train,target,test_size=0.2,random_state=RANDOM_SEED,stratify=target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(rescale=1/255,      \n",
    "                                    horizontal_flip=True,\n",
    "                                    shear_range=0.2,\n",
    "                                    zoom_range=0.2)\n",
    "test_datagen = ImageDataGenerator(rescale=1/255)\n",
    "\n",
    "#creating Data Generators for our Test and Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen.fit(x_train)\n",
    "test_datagen.fit(x_test)\n",
    "test_datagen.fit(x_test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = train_datagen.flow(x_train,\n",
    "                            to_categorical(y_train,3),\n",
    "                            batch_size=256)\n",
    "test_set = test_datagen.flow(x_test,\n",
    "                            to_categorical(y_test,3),\n",
    "                            batch_size=256)                            \n",
    "\n",
    "#final preparation of data for model training                            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications import ResNet50V2,VGG16\n",
    "from keras.layers import Dense,Dropout,BatchNormalization,Flatten\n",
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_tr = VGG16(include_top=False,\n",
    "                    weights='imagenet',\n",
    "                    input_shape=(128,128,3),\n",
    "                    pooling=None)\n",
    "model_tr.trainable = False                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(model_tr)\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512,activation='relu'))\n",
    "model.add(Dense(512,activation='relu'))\n",
    "model.add(Dense(3,activation='softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',metrics=['accuracy'],optimizer='adam')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from livelossplot import PlotLossesKeras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 864x576 with 2 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"567.274375pt\" version=\"1.1\" viewBox=\"0 0 856.690625 567.274375\" width=\"856.690625pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2020-10-07T19:04:20.871603</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.3.1, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  </rdf:RDF>\r\n </metadata>\r\n <defs>\r\n  <style type=\"text/css\">*{stroke-linecap:butt;stroke-linejoin:round;}</style>\r\n </defs>\r\n <g id=\"figure_1\">\r\n  <g id=\"patch_1\">\r\n   <path d=\"M 0 567.274375 \r\nL 856.690625 567.274375 \r\nL 856.690625 0 \r\nL 0 0 \r\nz\r\n\" style=\"fill:none;\"/>\r\n  </g>\r\n  <g id=\"axes_1\">\r\n   <g id=\"patch_2\">\r\n    <path d=\"M 36.465625 529.718125 \r\nL 422.890625 529.718125 \r\nL 422.890625 22.318125 \r\nL 36.465625 22.318125 \r\nz\r\n\" style=\"fill:#ffffff;\"/>\r\n   </g>\r\n   <g id=\"matplotlib.axis_1\">\r\n    <g id=\"xtick_1\">\r\n     <g id=\"line2d_1\">\r\n      <defs>\r\n       <path d=\"M 0 0 \r\nL 0 3.5 \r\n\" id=\"m5f4d796edd\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n      </defs>\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"54.030398\" xlink:href=\"#m5f4d796edd\" y=\"529.718125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_1\">\r\n      <!-- 0 -->\r\n      <g transform=\"translate(50.849148 544.316562)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 31.78125 66.40625 \r\nQ 24.171875 66.40625 20.328125 58.90625 \r\nQ 16.5 51.421875 16.5 36.375 \r\nQ 16.5 21.390625 20.328125 13.890625 \r\nQ 24.171875 6.390625 31.78125 6.390625 \r\nQ 39.453125 6.390625 43.28125 13.890625 \r\nQ 47.125 21.390625 47.125 36.375 \r\nQ 47.125 51.421875 43.28125 58.90625 \r\nQ 39.453125 66.40625 31.78125 66.40625 \r\nz\r\nM 31.78125 74.21875 \r\nQ 44.046875 74.21875 50.515625 64.515625 \r\nQ 56.984375 54.828125 56.984375 36.375 \r\nQ 56.984375 17.96875 50.515625 8.265625 \r\nQ 44.046875 -1.421875 31.78125 -1.421875 \r\nQ 19.53125 -1.421875 13.0625 8.265625 \r\nQ 6.59375 17.96875 6.59375 36.375 \r\nQ 6.59375 54.828125 13.0625 64.515625 \r\nQ 19.53125 74.21875 31.78125 74.21875 \r\nz\r\n\" id=\"DejaVuSans-48\"/>\r\n       </defs>\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_2\">\r\n     <g id=\"line2d_2\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"132.096054\" xlink:href=\"#m5f4d796edd\" y=\"529.718125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_2\">\r\n      <!-- 2 -->\r\n      <g transform=\"translate(128.914804 544.316562)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 19.1875 8.296875 \r\nL 53.609375 8.296875 \r\nL 53.609375 0 \r\nL 7.328125 0 \r\nL 7.328125 8.296875 \r\nQ 12.9375 14.109375 22.625 23.890625 \r\nQ 32.328125 33.6875 34.8125 36.53125 \r\nQ 39.546875 41.84375 41.421875 45.53125 \r\nQ 43.3125 49.21875 43.3125 52.78125 \r\nQ 43.3125 58.59375 39.234375 62.25 \r\nQ 35.15625 65.921875 28.609375 65.921875 \r\nQ 23.96875 65.921875 18.8125 64.3125 \r\nQ 13.671875 62.703125 7.8125 59.421875 \r\nL 7.8125 69.390625 \r\nQ 13.765625 71.78125 18.9375 73 \r\nQ 24.125 74.21875 28.421875 74.21875 \r\nQ 39.75 74.21875 46.484375 68.546875 \r\nQ 53.21875 62.890625 53.21875 53.421875 \r\nQ 53.21875 48.921875 51.53125 44.890625 \r\nQ 49.859375 40.875 45.40625 35.40625 \r\nQ 44.1875 33.984375 37.640625 27.21875 \r\nQ 31.109375 20.453125 19.1875 8.296875 \r\nz\r\n\" id=\"DejaVuSans-50\"/>\r\n       </defs>\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_3\">\r\n     <g id=\"line2d_3\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"210.161711\" xlink:href=\"#m5f4d796edd\" y=\"529.718125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_3\">\r\n      <!-- 4 -->\r\n      <g transform=\"translate(206.980461 544.316562)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 37.796875 64.3125 \r\nL 12.890625 25.390625 \r\nL 37.796875 25.390625 \r\nz\r\nM 35.203125 72.90625 \r\nL 47.609375 72.90625 \r\nL 47.609375 25.390625 \r\nL 58.015625 25.390625 \r\nL 58.015625 17.1875 \r\nL 47.609375 17.1875 \r\nL 47.609375 0 \r\nL 37.796875 0 \r\nL 37.796875 17.1875 \r\nL 4.890625 17.1875 \r\nL 4.890625 26.703125 \r\nz\r\n\" id=\"DejaVuSans-52\"/>\r\n       </defs>\r\n       <use xlink:href=\"#DejaVuSans-52\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_4\">\r\n     <g id=\"line2d_4\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"288.227367\" xlink:href=\"#m5f4d796edd\" y=\"529.718125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_4\">\r\n      <!-- 6 -->\r\n      <g transform=\"translate(285.046117 544.316562)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 33.015625 40.375 \r\nQ 26.375 40.375 22.484375 35.828125 \r\nQ 18.609375 31.296875 18.609375 23.390625 \r\nQ 18.609375 15.53125 22.484375 10.953125 \r\nQ 26.375 6.390625 33.015625 6.390625 \r\nQ 39.65625 6.390625 43.53125 10.953125 \r\nQ 47.40625 15.53125 47.40625 23.390625 \r\nQ 47.40625 31.296875 43.53125 35.828125 \r\nQ 39.65625 40.375 33.015625 40.375 \r\nz\r\nM 52.59375 71.296875 \r\nL 52.59375 62.3125 \r\nQ 48.875 64.0625 45.09375 64.984375 \r\nQ 41.3125 65.921875 37.59375 65.921875 \r\nQ 27.828125 65.921875 22.671875 59.328125 \r\nQ 17.53125 52.734375 16.796875 39.40625 \r\nQ 19.671875 43.65625 24.015625 45.921875 \r\nQ 28.375 48.1875 33.59375 48.1875 \r\nQ 44.578125 48.1875 50.953125 41.515625 \r\nQ 57.328125 34.859375 57.328125 23.390625 \r\nQ 57.328125 12.15625 50.6875 5.359375 \r\nQ 44.046875 -1.421875 33.015625 -1.421875 \r\nQ 20.359375 -1.421875 13.671875 8.265625 \r\nQ 6.984375 17.96875 6.984375 36.375 \r\nQ 6.984375 53.65625 15.1875 63.9375 \r\nQ 23.390625 74.21875 37.203125 74.21875 \r\nQ 40.921875 74.21875 44.703125 73.484375 \r\nQ 48.484375 72.75 52.59375 71.296875 \r\nz\r\n\" id=\"DejaVuSans-54\"/>\r\n       </defs>\r\n       <use xlink:href=\"#DejaVuSans-54\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_5\">\r\n     <g id=\"line2d_5\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"366.293024\" xlink:href=\"#m5f4d796edd\" y=\"529.718125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_5\">\r\n      <!-- 8 -->\r\n      <g transform=\"translate(363.111774 544.316562)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 31.78125 34.625 \r\nQ 24.75 34.625 20.71875 30.859375 \r\nQ 16.703125 27.09375 16.703125 20.515625 \r\nQ 16.703125 13.921875 20.71875 10.15625 \r\nQ 24.75 6.390625 31.78125 6.390625 \r\nQ 38.8125 6.390625 42.859375 10.171875 \r\nQ 46.921875 13.96875 46.921875 20.515625 \r\nQ 46.921875 27.09375 42.890625 30.859375 \r\nQ 38.875 34.625 31.78125 34.625 \r\nz\r\nM 21.921875 38.8125 \r\nQ 15.578125 40.375 12.03125 44.71875 \r\nQ 8.5 49.078125 8.5 55.328125 \r\nQ 8.5 64.0625 14.71875 69.140625 \r\nQ 20.953125 74.21875 31.78125 74.21875 \r\nQ 42.671875 74.21875 48.875 69.140625 \r\nQ 55.078125 64.0625 55.078125 55.328125 \r\nQ 55.078125 49.078125 51.53125 44.71875 \r\nQ 48 40.375 41.703125 38.8125 \r\nQ 48.828125 37.15625 52.796875 32.3125 \r\nQ 56.78125 27.484375 56.78125 20.515625 \r\nQ 56.78125 9.90625 50.3125 4.234375 \r\nQ 43.84375 -1.421875 31.78125 -1.421875 \r\nQ 19.734375 -1.421875 13.25 4.234375 \r\nQ 6.78125 9.90625 6.78125 20.515625 \r\nQ 6.78125 27.484375 10.78125 32.3125 \r\nQ 14.796875 37.15625 21.921875 38.8125 \r\nz\r\nM 18.3125 54.390625 \r\nQ 18.3125 48.734375 21.84375 45.5625 \r\nQ 25.390625 42.390625 31.78125 42.390625 \r\nQ 38.140625 42.390625 41.71875 45.5625 \r\nQ 45.3125 48.734375 45.3125 54.390625 \r\nQ 45.3125 60.0625 41.71875 63.234375 \r\nQ 38.140625 66.40625 31.78125 66.40625 \r\nQ 25.390625 66.40625 21.84375 63.234375 \r\nQ 18.3125 60.0625 18.3125 54.390625 \r\nz\r\n\" id=\"DejaVuSans-56\"/>\r\n       </defs>\r\n       <use xlink:href=\"#DejaVuSans-56\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"text_6\">\r\n     <!-- epoch -->\r\n     <g transform=\"translate(214.45 557.994687)scale(0.1 -0.1)\">\r\n      <defs>\r\n       <path d=\"M 56.203125 29.59375 \r\nL 56.203125 25.203125 \r\nL 14.890625 25.203125 \r\nQ 15.484375 15.921875 20.484375 11.0625 \r\nQ 25.484375 6.203125 34.421875 6.203125 \r\nQ 39.59375 6.203125 44.453125 7.46875 \r\nQ 49.3125 8.734375 54.109375 11.28125 \r\nL 54.109375 2.78125 \r\nQ 49.265625 0.734375 44.1875 -0.34375 \r\nQ 39.109375 -1.421875 33.890625 -1.421875 \r\nQ 20.796875 -1.421875 13.15625 6.1875 \r\nQ 5.515625 13.8125 5.515625 26.8125 \r\nQ 5.515625 40.234375 12.765625 48.109375 \r\nQ 20.015625 56 32.328125 56 \r\nQ 43.359375 56 49.78125 48.890625 \r\nQ 56.203125 41.796875 56.203125 29.59375 \r\nz\r\nM 47.21875 32.234375 \r\nQ 47.125 39.59375 43.09375 43.984375 \r\nQ 39.0625 48.390625 32.421875 48.390625 \r\nQ 24.90625 48.390625 20.390625 44.140625 \r\nQ 15.875 39.890625 15.1875 32.171875 \r\nz\r\n\" id=\"DejaVuSans-101\"/>\r\n       <path d=\"M 18.109375 8.203125 \r\nL 18.109375 -20.796875 \r\nL 9.078125 -20.796875 \r\nL 9.078125 54.6875 \r\nL 18.109375 54.6875 \r\nL 18.109375 46.390625 \r\nQ 20.953125 51.265625 25.265625 53.625 \r\nQ 29.59375 56 35.59375 56 \r\nQ 45.5625 56 51.78125 48.09375 \r\nQ 58.015625 40.1875 58.015625 27.296875 \r\nQ 58.015625 14.40625 51.78125 6.484375 \r\nQ 45.5625 -1.421875 35.59375 -1.421875 \r\nQ 29.59375 -1.421875 25.265625 0.953125 \r\nQ 20.953125 3.328125 18.109375 8.203125 \r\nz\r\nM 48.6875 27.296875 \r\nQ 48.6875 37.203125 44.609375 42.84375 \r\nQ 40.53125 48.484375 33.40625 48.484375 \r\nQ 26.265625 48.484375 22.1875 42.84375 \r\nQ 18.109375 37.203125 18.109375 27.296875 \r\nQ 18.109375 17.390625 22.1875 11.75 \r\nQ 26.265625 6.109375 33.40625 6.109375 \r\nQ 40.53125 6.109375 44.609375 11.75 \r\nQ 48.6875 17.390625 48.6875 27.296875 \r\nz\r\n\" id=\"DejaVuSans-112\"/>\r\n       <path d=\"M 30.609375 48.390625 \r\nQ 23.390625 48.390625 19.1875 42.75 \r\nQ 14.984375 37.109375 14.984375 27.296875 \r\nQ 14.984375 17.484375 19.15625 11.84375 \r\nQ 23.34375 6.203125 30.609375 6.203125 \r\nQ 37.796875 6.203125 41.984375 11.859375 \r\nQ 46.1875 17.53125 46.1875 27.296875 \r\nQ 46.1875 37.015625 41.984375 42.703125 \r\nQ 37.796875 48.390625 30.609375 48.390625 \r\nz\r\nM 30.609375 56 \r\nQ 42.328125 56 49.015625 48.375 \r\nQ 55.71875 40.765625 55.71875 27.296875 \r\nQ 55.71875 13.875 49.015625 6.21875 \r\nQ 42.328125 -1.421875 30.609375 -1.421875 \r\nQ 18.84375 -1.421875 12.171875 6.21875 \r\nQ 5.515625 13.875 5.515625 27.296875 \r\nQ 5.515625 40.765625 12.171875 48.375 \r\nQ 18.84375 56 30.609375 56 \r\nz\r\n\" id=\"DejaVuSans-111\"/>\r\n       <path d=\"M 48.78125 52.59375 \r\nL 48.78125 44.1875 \r\nQ 44.96875 46.296875 41.140625 47.34375 \r\nQ 37.3125 48.390625 33.40625 48.390625 \r\nQ 24.65625 48.390625 19.8125 42.84375 \r\nQ 14.984375 37.3125 14.984375 27.296875 \r\nQ 14.984375 17.28125 19.8125 11.734375 \r\nQ 24.65625 6.203125 33.40625 6.203125 \r\nQ 37.3125 6.203125 41.140625 7.25 \r\nQ 44.96875 8.296875 48.78125 10.40625 \r\nL 48.78125 2.09375 \r\nQ 45.015625 0.34375 40.984375 -0.53125 \r\nQ 36.96875 -1.421875 32.421875 -1.421875 \r\nQ 20.0625 -1.421875 12.78125 6.34375 \r\nQ 5.515625 14.109375 5.515625 27.296875 \r\nQ 5.515625 40.671875 12.859375 48.328125 \r\nQ 20.21875 56 33.015625 56 \r\nQ 37.15625 56 41.109375 55.140625 \r\nQ 45.0625 54.296875 48.78125 52.59375 \r\nz\r\n\" id=\"DejaVuSans-99\"/>\r\n       <path d=\"M 54.890625 33.015625 \r\nL 54.890625 0 \r\nL 45.90625 0 \r\nL 45.90625 32.71875 \r\nQ 45.90625 40.484375 42.875 44.328125 \r\nQ 39.84375 48.1875 33.796875 48.1875 \r\nQ 26.515625 48.1875 22.3125 43.546875 \r\nQ 18.109375 38.921875 18.109375 30.90625 \r\nL 18.109375 0 \r\nL 9.078125 0 \r\nL 9.078125 75.984375 \r\nL 18.109375 75.984375 \r\nL 18.109375 46.1875 \r\nQ 21.34375 51.125 25.703125 53.5625 \r\nQ 30.078125 56 35.796875 56 \r\nQ 45.21875 56 50.046875 50.171875 \r\nQ 54.890625 44.34375 54.890625 33.015625 \r\nz\r\n\" id=\"DejaVuSans-104\"/>\r\n      </defs>\r\n      <use xlink:href=\"#DejaVuSans-101\"/>\r\n      <use x=\"61.523438\" xlink:href=\"#DejaVuSans-112\"/>\r\n      <use x=\"125\" xlink:href=\"#DejaVuSans-111\"/>\r\n      <use x=\"186.181641\" xlink:href=\"#DejaVuSans-99\"/>\r\n      <use x=\"241.162109\" xlink:href=\"#DejaVuSans-104\"/>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"matplotlib.axis_2\">\r\n    <g id=\"ytick_1\">\r\n     <g id=\"line2d_6\">\r\n      <defs>\r\n       <path d=\"M 0 0 \r\nL -3.5 0 \r\n\" id=\"m1a0d6a10fa\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n      </defs>\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"36.465625\" xlink:href=\"#m1a0d6a10fa\" y=\"499.767892\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_7\">\r\n      <!-- 0.62 -->\r\n      <g transform=\"translate(7.2 503.567111)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 10.6875 12.40625 \r\nL 21 12.40625 \r\nL 21 0 \r\nL 10.6875 0 \r\nz\r\n\" id=\"DejaVuSans-46\"/>\r\n       </defs>\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\r\n       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-54\"/>\r\n       <use x=\"159.033203\" xlink:href=\"#DejaVuSans-50\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_2\">\r\n     <g id=\"line2d_7\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"36.465625\" xlink:href=\"#m1a0d6a10fa\" y=\"430.676433\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_8\">\r\n      <!-- 0.64 -->\r\n      <g transform=\"translate(7.2 434.475652)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\r\n       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-54\"/>\r\n       <use x=\"159.033203\" xlink:href=\"#DejaVuSans-52\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_3\">\r\n     <g id=\"line2d_8\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"36.465625\" xlink:href=\"#m1a0d6a10fa\" y=\"361.584973\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_9\">\r\n      <!-- 0.66 -->\r\n      <g transform=\"translate(7.2 365.384192)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\r\n       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-54\"/>\r\n       <use x=\"159.033203\" xlink:href=\"#DejaVuSans-54\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_4\">\r\n     <g id=\"line2d_9\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"36.465625\" xlink:href=\"#m1a0d6a10fa\" y=\"292.493514\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_10\">\r\n      <!-- 0.68 -->\r\n      <g transform=\"translate(7.2 296.292733)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\r\n       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-54\"/>\r\n       <use x=\"159.033203\" xlink:href=\"#DejaVuSans-56\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_5\">\r\n     <g id=\"line2d_10\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"36.465625\" xlink:href=\"#m1a0d6a10fa\" y=\"223.402055\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_11\">\r\n      <!-- 0.70 -->\r\n      <g transform=\"translate(7.2 227.201273)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 8.203125 72.90625 \r\nL 55.078125 72.90625 \r\nL 55.078125 68.703125 \r\nL 28.609375 0 \r\nL 18.3125 0 \r\nL 43.21875 64.59375 \r\nL 8.203125 64.59375 \r\nz\r\n\" id=\"DejaVuSans-55\"/>\r\n       </defs>\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\r\n       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-55\"/>\r\n       <use x=\"159.033203\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_6\">\r\n     <g id=\"line2d_11\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"36.465625\" xlink:href=\"#m1a0d6a10fa\" y=\"154.310595\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_12\">\r\n      <!-- 0.72 -->\r\n      <g transform=\"translate(7.2 158.109814)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\r\n       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-55\"/>\r\n       <use x=\"159.033203\" xlink:href=\"#DejaVuSans-50\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_7\">\r\n     <g id=\"line2d_12\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"36.465625\" xlink:href=\"#m1a0d6a10fa\" y=\"85.219136\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_13\">\r\n      <!-- 0.74 -->\r\n      <g transform=\"translate(7.2 89.018355)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\r\n       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-55\"/>\r\n       <use x=\"159.033203\" xlink:href=\"#DejaVuSans-52\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"line2d_13\">\r\n    <path clip-path=\"url(#p5e5e9b4afd)\" d=\"M 54.030398 281.877872 \r\nL 93.063226 230.077653 \r\nL 132.096054 168.88553 \r\nL 171.128883 211.534554 \r\nL 210.161711 163.322498 \r\nL 249.194539 103.984789 \r\nL 288.227367 264.38223 \r\nL 327.260196 254.183578 \r\nL 366.293024 84.514484 \r\nL 405.325852 78.951657 \r\n\" style=\"fill:none;stroke:#1f77b4;stroke-linecap:square;stroke-width:1.5;\"/>\r\n   </g>\r\n   <g id=\"line2d_14\">\r\n    <path clip-path=\"url(#p5e5e9b4afd)\" d=\"M 54.030398 506.654489 \r\nL 93.063226 297.077632 \r\nL 132.096054 228.059748 \r\nL 171.128883 217.325733 \r\nL 210.161711 153.199414 \r\nL 249.194539 131.150721 \r\nL 288.227367 87.053543 \r\nL 327.260196 113.070917 \r\nL 366.293024 82.423276 \r\nL 405.325852 45.381761 \r\n\" style=\"fill:none;stroke:#ff7f0e;stroke-linecap:square;stroke-width:1.5;\"/>\r\n   </g>\r\n   <g id=\"patch_3\">\r\n    <path d=\"M 36.465625 529.718125 \r\nL 36.465625 22.318125 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_4\">\r\n    <path d=\"M 422.890625 529.718125 \r\nL 422.890625 22.318125 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_5\">\r\n    <path d=\"M 36.465625 529.718125 \r\nL 422.890625 529.718125 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_6\">\r\n    <path d=\"M 36.465625 22.318125 \r\nL 422.890625 22.318125 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"text_14\">\r\n    <!-- accuracy -->\r\n    <g transform=\"translate(202.606875 16.318125)scale(0.12 -0.12)\">\r\n     <defs>\r\n      <path d=\"M 34.28125 27.484375 \r\nQ 23.390625 27.484375 19.1875 25 \r\nQ 14.984375 22.515625 14.984375 16.5 \r\nQ 14.984375 11.71875 18.140625 8.90625 \r\nQ 21.296875 6.109375 26.703125 6.109375 \r\nQ 34.1875 6.109375 38.703125 11.40625 \r\nQ 43.21875 16.703125 43.21875 25.484375 \r\nL 43.21875 27.484375 \r\nz\r\nM 52.203125 31.203125 \r\nL 52.203125 0 \r\nL 43.21875 0 \r\nL 43.21875 8.296875 \r\nQ 40.140625 3.328125 35.546875 0.953125 \r\nQ 30.953125 -1.421875 24.3125 -1.421875 \r\nQ 15.921875 -1.421875 10.953125 3.296875 \r\nQ 6 8.015625 6 15.921875 \r\nQ 6 25.140625 12.171875 29.828125 \r\nQ 18.359375 34.515625 30.609375 34.515625 \r\nL 43.21875 34.515625 \r\nL 43.21875 35.40625 \r\nQ 43.21875 41.609375 39.140625 45 \r\nQ 35.0625 48.390625 27.6875 48.390625 \r\nQ 23 48.390625 18.546875 47.265625 \r\nQ 14.109375 46.140625 10.015625 43.890625 \r\nL 10.015625 52.203125 \r\nQ 14.9375 54.109375 19.578125 55.046875 \r\nQ 24.21875 56 28.609375 56 \r\nQ 40.484375 56 46.34375 49.84375 \r\nQ 52.203125 43.703125 52.203125 31.203125 \r\nz\r\n\" id=\"DejaVuSans-97\"/>\r\n      <path d=\"M 8.5 21.578125 \r\nL 8.5 54.6875 \r\nL 17.484375 54.6875 \r\nL 17.484375 21.921875 \r\nQ 17.484375 14.15625 20.5 10.265625 \r\nQ 23.53125 6.390625 29.59375 6.390625 \r\nQ 36.859375 6.390625 41.078125 11.03125 \r\nQ 45.3125 15.671875 45.3125 23.6875 \r\nL 45.3125 54.6875 \r\nL 54.296875 54.6875 \r\nL 54.296875 0 \r\nL 45.3125 0 \r\nL 45.3125 8.40625 \r\nQ 42.046875 3.421875 37.71875 1 \r\nQ 33.40625 -1.421875 27.6875 -1.421875 \r\nQ 18.265625 -1.421875 13.375 4.4375 \r\nQ 8.5 10.296875 8.5 21.578125 \r\nz\r\nM 31.109375 56 \r\nz\r\n\" id=\"DejaVuSans-117\"/>\r\n      <path d=\"M 41.109375 46.296875 \r\nQ 39.59375 47.171875 37.8125 47.578125 \r\nQ 36.03125 48 33.890625 48 \r\nQ 26.265625 48 22.1875 43.046875 \r\nQ 18.109375 38.09375 18.109375 28.8125 \r\nL 18.109375 0 \r\nL 9.078125 0 \r\nL 9.078125 54.6875 \r\nL 18.109375 54.6875 \r\nL 18.109375 46.1875 \r\nQ 20.953125 51.171875 25.484375 53.578125 \r\nQ 30.03125 56 36.53125 56 \r\nQ 37.453125 56 38.578125 55.875 \r\nQ 39.703125 55.765625 41.0625 55.515625 \r\nz\r\n\" id=\"DejaVuSans-114\"/>\r\n      <path d=\"M 32.171875 -5.078125 \r\nQ 28.375 -14.84375 24.75 -17.8125 \r\nQ 21.140625 -20.796875 15.09375 -20.796875 \r\nL 7.90625 -20.796875 \r\nL 7.90625 -13.28125 \r\nL 13.1875 -13.28125 \r\nQ 16.890625 -13.28125 18.9375 -11.515625 \r\nQ 21 -9.765625 23.484375 -3.21875 \r\nL 25.09375 0.875 \r\nL 2.984375 54.6875 \r\nL 12.5 54.6875 \r\nL 29.59375 11.921875 \r\nL 46.6875 54.6875 \r\nL 56.203125 54.6875 \r\nz\r\n\" id=\"DejaVuSans-121\"/>\r\n     </defs>\r\n     <use xlink:href=\"#DejaVuSans-97\"/>\r\n     <use x=\"61.279297\" xlink:href=\"#DejaVuSans-99\"/>\r\n     <use x=\"116.259766\" xlink:href=\"#DejaVuSans-99\"/>\r\n     <use x=\"171.240234\" xlink:href=\"#DejaVuSans-117\"/>\r\n     <use x=\"234.619141\" xlink:href=\"#DejaVuSans-114\"/>\r\n     <use x=\"275.732422\" xlink:href=\"#DejaVuSans-97\"/>\r\n     <use x=\"337.011719\" xlink:href=\"#DejaVuSans-99\"/>\r\n     <use x=\"391.992188\" xlink:href=\"#DejaVuSans-121\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"legend_1\">\r\n    <g id=\"patch_7\">\r\n     <path d=\"M 334.65625 292.19625 \r\nL 415.890625 292.19625 \r\nQ 417.890625 292.19625 417.890625 290.19625 \r\nL 417.890625 261.84 \r\nQ 417.890625 259.84 415.890625 259.84 \r\nL 334.65625 259.84 \r\nQ 332.65625 259.84 332.65625 261.84 \r\nL 332.65625 290.19625 \r\nQ 332.65625 292.19625 334.65625 292.19625 \r\nz\r\n\" style=\"fill:#ffffff;opacity:0.8;stroke:#cccccc;stroke-linejoin:miter;\"/>\r\n    </g>\r\n    <g id=\"line2d_15\">\r\n     <path d=\"M 336.65625 267.938437 \r\nL 356.65625 267.938437 \r\n\" style=\"fill:none;stroke:#1f77b4;stroke-linecap:square;stroke-width:1.5;\"/>\r\n    </g>\r\n    <g id=\"line2d_16\"/>\r\n    <g id=\"text_15\">\r\n     <!-- validation -->\r\n     <g transform=\"translate(364.65625 271.438437)scale(0.1 -0.1)\">\r\n      <defs>\r\n       <path d=\"M 2.984375 54.6875 \r\nL 12.5 54.6875 \r\nL 29.59375 8.796875 \r\nL 46.6875 54.6875 \r\nL 56.203125 54.6875 \r\nL 35.6875 0 \r\nL 23.484375 0 \r\nz\r\n\" id=\"DejaVuSans-118\"/>\r\n       <path d=\"M 9.421875 75.984375 \r\nL 18.40625 75.984375 \r\nL 18.40625 0 \r\nL 9.421875 0 \r\nz\r\n\" id=\"DejaVuSans-108\"/>\r\n       <path d=\"M 9.421875 54.6875 \r\nL 18.40625 54.6875 \r\nL 18.40625 0 \r\nL 9.421875 0 \r\nz\r\nM 9.421875 75.984375 \r\nL 18.40625 75.984375 \r\nL 18.40625 64.59375 \r\nL 9.421875 64.59375 \r\nz\r\n\" id=\"DejaVuSans-105\"/>\r\n       <path d=\"M 45.40625 46.390625 \r\nL 45.40625 75.984375 \r\nL 54.390625 75.984375 \r\nL 54.390625 0 \r\nL 45.40625 0 \r\nL 45.40625 8.203125 \r\nQ 42.578125 3.328125 38.25 0.953125 \r\nQ 33.9375 -1.421875 27.875 -1.421875 \r\nQ 17.96875 -1.421875 11.734375 6.484375 \r\nQ 5.515625 14.40625 5.515625 27.296875 \r\nQ 5.515625 40.1875 11.734375 48.09375 \r\nQ 17.96875 56 27.875 56 \r\nQ 33.9375 56 38.25 53.625 \r\nQ 42.578125 51.265625 45.40625 46.390625 \r\nz\r\nM 14.796875 27.296875 \r\nQ 14.796875 17.390625 18.875 11.75 \r\nQ 22.953125 6.109375 30.078125 6.109375 \r\nQ 37.203125 6.109375 41.296875 11.75 \r\nQ 45.40625 17.390625 45.40625 27.296875 \r\nQ 45.40625 37.203125 41.296875 42.84375 \r\nQ 37.203125 48.484375 30.078125 48.484375 \r\nQ 22.953125 48.484375 18.875 42.84375 \r\nQ 14.796875 37.203125 14.796875 27.296875 \r\nz\r\n\" id=\"DejaVuSans-100\"/>\r\n       <path d=\"M 18.3125 70.21875 \r\nL 18.3125 54.6875 \r\nL 36.8125 54.6875 \r\nL 36.8125 47.703125 \r\nL 18.3125 47.703125 \r\nL 18.3125 18.015625 \r\nQ 18.3125 11.328125 20.140625 9.421875 \r\nQ 21.96875 7.515625 27.59375 7.515625 \r\nL 36.8125 7.515625 \r\nL 36.8125 0 \r\nL 27.59375 0 \r\nQ 17.1875 0 13.234375 3.875 \r\nQ 9.28125 7.765625 9.28125 18.015625 \r\nL 9.28125 47.703125 \r\nL 2.6875 47.703125 \r\nL 2.6875 54.6875 \r\nL 9.28125 54.6875 \r\nL 9.28125 70.21875 \r\nz\r\n\" id=\"DejaVuSans-116\"/>\r\n       <path d=\"M 54.890625 33.015625 \r\nL 54.890625 0 \r\nL 45.90625 0 \r\nL 45.90625 32.71875 \r\nQ 45.90625 40.484375 42.875 44.328125 \r\nQ 39.84375 48.1875 33.796875 48.1875 \r\nQ 26.515625 48.1875 22.3125 43.546875 \r\nQ 18.109375 38.921875 18.109375 30.90625 \r\nL 18.109375 0 \r\nL 9.078125 0 \r\nL 9.078125 54.6875 \r\nL 18.109375 54.6875 \r\nL 18.109375 46.1875 \r\nQ 21.34375 51.125 25.703125 53.5625 \r\nQ 30.078125 56 35.796875 56 \r\nQ 45.21875 56 50.046875 50.171875 \r\nQ 54.890625 44.34375 54.890625 33.015625 \r\nz\r\n\" id=\"DejaVuSans-110\"/>\r\n      </defs>\r\n      <use xlink:href=\"#DejaVuSans-118\"/>\r\n      <use x=\"59.179688\" xlink:href=\"#DejaVuSans-97\"/>\r\n      <use x=\"120.458984\" xlink:href=\"#DejaVuSans-108\"/>\r\n      <use x=\"148.242188\" xlink:href=\"#DejaVuSans-105\"/>\r\n      <use x=\"176.025391\" xlink:href=\"#DejaVuSans-100\"/>\r\n      <use x=\"239.501953\" xlink:href=\"#DejaVuSans-97\"/>\r\n      <use x=\"300.78125\" xlink:href=\"#DejaVuSans-116\"/>\r\n      <use x=\"339.990234\" xlink:href=\"#DejaVuSans-105\"/>\r\n      <use x=\"367.773438\" xlink:href=\"#DejaVuSans-111\"/>\r\n      <use x=\"428.955078\" xlink:href=\"#DejaVuSans-110\"/>\r\n     </g>\r\n    </g>\r\n    <g id=\"line2d_17\">\r\n     <path d=\"M 336.65625 282.616562 \r\nL 356.65625 282.616562 \r\n\" style=\"fill:none;stroke:#ff7f0e;stroke-linecap:square;stroke-width:1.5;\"/>\r\n    </g>\r\n    <g id=\"line2d_18\"/>\r\n    <g id=\"text_16\">\r\n     <!-- training -->\r\n     <g transform=\"translate(364.65625 286.116562)scale(0.1 -0.1)\">\r\n      <defs>\r\n       <path d=\"M 45.40625 27.984375 \r\nQ 45.40625 37.75 41.375 43.109375 \r\nQ 37.359375 48.484375 30.078125 48.484375 \r\nQ 22.859375 48.484375 18.828125 43.109375 \r\nQ 14.796875 37.75 14.796875 27.984375 \r\nQ 14.796875 18.265625 18.828125 12.890625 \r\nQ 22.859375 7.515625 30.078125 7.515625 \r\nQ 37.359375 7.515625 41.375 12.890625 \r\nQ 45.40625 18.265625 45.40625 27.984375 \r\nz\r\nM 54.390625 6.78125 \r\nQ 54.390625 -7.171875 48.1875 -13.984375 \r\nQ 42 -20.796875 29.203125 -20.796875 \r\nQ 24.46875 -20.796875 20.265625 -20.09375 \r\nQ 16.0625 -19.390625 12.109375 -17.921875 \r\nL 12.109375 -9.1875 \r\nQ 16.0625 -11.328125 19.921875 -12.34375 \r\nQ 23.78125 -13.375 27.78125 -13.375 \r\nQ 36.625 -13.375 41.015625 -8.765625 \r\nQ 45.40625 -4.15625 45.40625 5.171875 \r\nL 45.40625 9.625 \r\nQ 42.625 4.78125 38.28125 2.390625 \r\nQ 33.9375 0 27.875 0 \r\nQ 17.828125 0 11.671875 7.65625 \r\nQ 5.515625 15.328125 5.515625 27.984375 \r\nQ 5.515625 40.671875 11.671875 48.328125 \r\nQ 17.828125 56 27.875 56 \r\nQ 33.9375 56 38.28125 53.609375 \r\nQ 42.625 51.21875 45.40625 46.390625 \r\nL 45.40625 54.6875 \r\nL 54.390625 54.6875 \r\nz\r\n\" id=\"DejaVuSans-103\"/>\r\n      </defs>\r\n      <use xlink:href=\"#DejaVuSans-116\"/>\r\n      <use x=\"39.208984\" xlink:href=\"#DejaVuSans-114\"/>\r\n      <use x=\"80.322266\" xlink:href=\"#DejaVuSans-97\"/>\r\n      <use x=\"141.601562\" xlink:href=\"#DejaVuSans-105\"/>\r\n      <use x=\"169.384766\" xlink:href=\"#DejaVuSans-110\"/>\r\n      <use x=\"232.763672\" xlink:href=\"#DejaVuSans-105\"/>\r\n      <use x=\"260.546875\" xlink:href=\"#DejaVuSans-110\"/>\r\n      <use x=\"323.925781\" xlink:href=\"#DejaVuSans-103\"/>\r\n     </g>\r\n    </g>\r\n   </g>\r\n  </g>\r\n  <g id=\"axes_2\">\r\n   <g id=\"patch_8\">\r\n    <path d=\"M 463.065625 529.718125 \r\nL 849.490625 529.718125 \r\nL 849.490625 22.318125 \r\nL 463.065625 22.318125 \r\nz\r\n\" style=\"fill:#ffffff;\"/>\r\n   </g>\r\n   <g id=\"matplotlib.axis_3\">\r\n    <g id=\"xtick_6\">\r\n     <g id=\"line2d_19\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"480.630398\" xlink:href=\"#m5f4d796edd\" y=\"529.718125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_17\">\r\n      <!-- 0 -->\r\n      <g transform=\"translate(477.449148 544.316562)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_7\">\r\n     <g id=\"line2d_20\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"558.696054\" xlink:href=\"#m5f4d796edd\" y=\"529.718125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_18\">\r\n      <!-- 2 -->\r\n      <g transform=\"translate(555.514804 544.316562)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_8\">\r\n     <g id=\"line2d_21\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"636.761711\" xlink:href=\"#m5f4d796edd\" y=\"529.718125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_19\">\r\n      <!-- 4 -->\r\n      <g transform=\"translate(633.580461 544.316562)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-52\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_9\">\r\n     <g id=\"line2d_22\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"714.827367\" xlink:href=\"#m5f4d796edd\" y=\"529.718125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_20\">\r\n      <!-- 6 -->\r\n      <g transform=\"translate(711.646117 544.316562)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-54\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_10\">\r\n     <g id=\"line2d_23\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"792.893024\" xlink:href=\"#m5f4d796edd\" y=\"529.718125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_21\">\r\n      <!-- 8 -->\r\n      <g transform=\"translate(789.711774 544.316562)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-56\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"text_22\">\r\n     <!-- epoch -->\r\n     <g transform=\"translate(641.05 557.994687)scale(0.1 -0.1)\">\r\n      <use xlink:href=\"#DejaVuSans-101\"/>\r\n      <use x=\"61.523438\" xlink:href=\"#DejaVuSans-112\"/>\r\n      <use x=\"125\" xlink:href=\"#DejaVuSans-111\"/>\r\n      <use x=\"186.181641\" xlink:href=\"#DejaVuSans-99\"/>\r\n      <use x=\"241.162109\" xlink:href=\"#DejaVuSans-104\"/>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"matplotlib.axis_4\">\r\n    <g id=\"ytick_8\">\r\n     <g id=\"line2d_24\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"463.065625\" xlink:href=\"#m1a0d6a10fa\" y=\"484.342125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_23\">\r\n      <!-- 0.60 -->\r\n      <g transform=\"translate(433.8 488.141344)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\r\n       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-54\"/>\r\n       <use x=\"159.033203\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_9\">\r\n     <g id=\"line2d_25\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"463.065625\" xlink:href=\"#m1a0d6a10fa\" y=\"418.148563\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_24\">\r\n      <!-- 0.65 -->\r\n      <g transform=\"translate(433.8 421.947781)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 10.796875 72.90625 \r\nL 49.515625 72.90625 \r\nL 49.515625 64.59375 \r\nL 19.828125 64.59375 \r\nL 19.828125 46.734375 \r\nQ 21.96875 47.46875 24.109375 47.828125 \r\nQ 26.265625 48.1875 28.421875 48.1875 \r\nQ 40.625 48.1875 47.75 41.5 \r\nQ 54.890625 34.8125 54.890625 23.390625 \r\nQ 54.890625 11.625 47.5625 5.09375 \r\nQ 40.234375 -1.421875 26.90625 -1.421875 \r\nQ 22.3125 -1.421875 17.546875 -0.640625 \r\nQ 12.796875 0.140625 7.71875 1.703125 \r\nL 7.71875 11.625 \r\nQ 12.109375 9.234375 16.796875 8.0625 \r\nQ 21.484375 6.890625 26.703125 6.890625 \r\nQ 35.15625 6.890625 40.078125 11.328125 \r\nQ 45.015625 15.765625 45.015625 23.390625 \r\nQ 45.015625 31 40.078125 35.4375 \r\nQ 35.15625 39.890625 26.703125 39.890625 \r\nQ 22.75 39.890625 18.8125 39.015625 \r\nQ 14.890625 38.140625 10.796875 36.28125 \r\nz\r\n\" id=\"DejaVuSans-53\"/>\r\n       </defs>\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\r\n       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-54\"/>\r\n       <use x=\"159.033203\" xlink:href=\"#DejaVuSans-53\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_10\">\r\n     <g id=\"line2d_26\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"463.065625\" xlink:href=\"#m1a0d6a10fa\" y=\"351.955\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_25\">\r\n      <!-- 0.70 -->\r\n      <g transform=\"translate(433.8 355.754218)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\r\n       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-55\"/>\r\n       <use x=\"159.033203\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_11\">\r\n     <g id=\"line2d_27\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"463.065625\" xlink:href=\"#m1a0d6a10fa\" y=\"285.761437\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_26\">\r\n      <!-- 0.75 -->\r\n      <g transform=\"translate(433.8 289.560655)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\r\n       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-55\"/>\r\n       <use x=\"159.033203\" xlink:href=\"#DejaVuSans-53\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_12\">\r\n     <g id=\"line2d_28\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"463.065625\" xlink:href=\"#m1a0d6a10fa\" y=\"219.567874\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_27\">\r\n      <!-- 0.80 -->\r\n      <g transform=\"translate(433.8 223.367092)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\r\n       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-56\"/>\r\n       <use x=\"159.033203\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_13\">\r\n     <g id=\"line2d_29\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"463.065625\" xlink:href=\"#m1a0d6a10fa\" y=\"153.374311\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_28\">\r\n      <!-- 0.85 -->\r\n      <g transform=\"translate(433.8 157.17353)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\r\n       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-56\"/>\r\n       <use x=\"159.033203\" xlink:href=\"#DejaVuSans-53\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_14\">\r\n     <g id=\"line2d_30\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"463.065625\" xlink:href=\"#m1a0d6a10fa\" y=\"87.180748\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_29\">\r\n      <!-- 0.90 -->\r\n      <g transform=\"translate(433.8 90.979967)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 10.984375 1.515625 \r\nL 10.984375 10.5 \r\nQ 14.703125 8.734375 18.5 7.8125 \r\nQ 22.3125 6.890625 25.984375 6.890625 \r\nQ 35.75 6.890625 40.890625 13.453125 \r\nQ 46.046875 20.015625 46.78125 33.40625 \r\nQ 43.953125 29.203125 39.59375 26.953125 \r\nQ 35.25 24.703125 29.984375 24.703125 \r\nQ 19.046875 24.703125 12.671875 31.3125 \r\nQ 6.296875 37.9375 6.296875 49.421875 \r\nQ 6.296875 60.640625 12.9375 67.421875 \r\nQ 19.578125 74.21875 30.609375 74.21875 \r\nQ 43.265625 74.21875 49.921875 64.515625 \r\nQ 56.59375 54.828125 56.59375 36.375 \r\nQ 56.59375 19.140625 48.40625 8.859375 \r\nQ 40.234375 -1.421875 26.421875 -1.421875 \r\nQ 22.703125 -1.421875 18.890625 -0.6875 \r\nQ 15.09375 0.046875 10.984375 1.515625 \r\nz\r\nM 30.609375 32.421875 \r\nQ 37.25 32.421875 41.125 36.953125 \r\nQ 45.015625 41.5 45.015625 49.421875 \r\nQ 45.015625 57.28125 41.125 61.84375 \r\nQ 37.25 66.40625 30.609375 66.40625 \r\nQ 23.96875 66.40625 20.09375 61.84375 \r\nQ 16.21875 57.28125 16.21875 49.421875 \r\nQ 16.21875 41.5 20.09375 36.953125 \r\nQ 23.96875 32.421875 30.609375 32.421875 \r\nz\r\n\" id=\"DejaVuSans-57\"/>\r\n       </defs>\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\r\n       <use x=\"95.410156\" xlink:href=\"#DejaVuSans-57\"/>\r\n       <use x=\"159.033203\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"line2d_31\">\r\n    <path clip-path=\"url(#pe637c3f973)\" d=\"M 480.630398 316.622146 \r\nL 519.663226 321.974298 \r\nL 558.696054 417.554726 \r\nL 597.728883 377.515968 \r\nL 636.761711 421.392854 \r\nL 675.794539 326.974596 \r\nL 714.827367 276.687863 \r\nL 753.860196 336.89573 \r\nL 792.893024 426.261295 \r\nL 831.925852 477.959786 \r\n\" style=\"fill:none;stroke:#1f77b4;stroke-linecap:square;stroke-width:1.5;\"/>\r\n   </g>\r\n   <g id=\"line2d_32\">\r\n    <path clip-path=\"url(#pe637c3f973)\" d=\"M 480.630398 45.381761 \r\nL 519.663226 290.784514 \r\nL 558.696054 351.367372 \r\nL 597.728883 368.333939 \r\nL 636.761711 402.385126 \r\nL 675.794539 425.801989 \r\nL 714.827367 458.393036 \r\nL 753.860196 451.03594 \r\nL 792.893024 474.460775 \r\nL 831.925852 506.654489 \r\n\" style=\"fill:none;stroke:#ff7f0e;stroke-linecap:square;stroke-width:1.5;\"/>\r\n   </g>\r\n   <g id=\"patch_9\">\r\n    <path d=\"M 463.065625 529.718125 \r\nL 463.065625 22.318125 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_10\">\r\n    <path d=\"M 849.490625 529.718125 \r\nL 849.490625 22.318125 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_11\">\r\n    <path d=\"M 463.065625 529.718125 \r\nL 849.490625 529.718125 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_12\">\r\n    <path d=\"M 463.065625 22.318125 \r\nL 849.490625 22.318125 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"text_30\">\r\n    <!-- Loss -->\r\n    <g transform=\"translate(643.1175 16.318125)scale(0.12 -0.12)\">\r\n     <defs>\r\n      <path d=\"M 9.8125 72.90625 \r\nL 19.671875 72.90625 \r\nL 19.671875 8.296875 \r\nL 55.171875 8.296875 \r\nL 55.171875 0 \r\nL 9.8125 0 \r\nz\r\n\" id=\"DejaVuSans-76\"/>\r\n      <path d=\"M 44.28125 53.078125 \r\nL 44.28125 44.578125 \r\nQ 40.484375 46.53125 36.375 47.5 \r\nQ 32.28125 48.484375 27.875 48.484375 \r\nQ 21.1875 48.484375 17.84375 46.4375 \r\nQ 14.5 44.390625 14.5 40.28125 \r\nQ 14.5 37.15625 16.890625 35.375 \r\nQ 19.28125 33.59375 26.515625 31.984375 \r\nL 29.59375 31.296875 \r\nQ 39.15625 29.25 43.1875 25.515625 \r\nQ 47.21875 21.78125 47.21875 15.09375 \r\nQ 47.21875 7.46875 41.1875 3.015625 \r\nQ 35.15625 -1.421875 24.609375 -1.421875 \r\nQ 20.21875 -1.421875 15.453125 -0.5625 \r\nQ 10.6875 0.296875 5.421875 2 \r\nL 5.421875 11.28125 \r\nQ 10.40625 8.6875 15.234375 7.390625 \r\nQ 20.0625 6.109375 24.8125 6.109375 \r\nQ 31.15625 6.109375 34.5625 8.28125 \r\nQ 37.984375 10.453125 37.984375 14.40625 \r\nQ 37.984375 18.0625 35.515625 20.015625 \r\nQ 33.0625 21.96875 24.703125 23.78125 \r\nL 21.578125 24.515625 \r\nQ 13.234375 26.265625 9.515625 29.90625 \r\nQ 5.8125 33.546875 5.8125 39.890625 \r\nQ 5.8125 47.609375 11.28125 51.796875 \r\nQ 16.75 56 26.8125 56 \r\nQ 31.78125 56 36.171875 55.265625 \r\nQ 40.578125 54.546875 44.28125 53.078125 \r\nz\r\n\" id=\"DejaVuSans-115\"/>\r\n     </defs>\r\n     <use xlink:href=\"#DejaVuSans-76\"/>\r\n     <use x=\"53.962891\" xlink:href=\"#DejaVuSans-111\"/>\r\n     <use x=\"115.144531\" xlink:href=\"#DejaVuSans-115\"/>\r\n     <use x=\"167.244141\" xlink:href=\"#DejaVuSans-115\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"legend_2\">\r\n    <g id=\"patch_13\">\r\n     <path d=\"M 761.25625 292.19625 \r\nL 842.490625 292.19625 \r\nQ 844.490625 292.19625 844.490625 290.19625 \r\nL 844.490625 261.84 \r\nQ 844.490625 259.84 842.490625 259.84 \r\nL 761.25625 259.84 \r\nQ 759.25625 259.84 759.25625 261.84 \r\nL 759.25625 290.19625 \r\nQ 759.25625 292.19625 761.25625 292.19625 \r\nz\r\n\" style=\"fill:#ffffff;opacity:0.8;stroke:#cccccc;stroke-linejoin:miter;\"/>\r\n    </g>\r\n    <g id=\"line2d_33\">\r\n     <path d=\"M 763.25625 267.938437 \r\nL 783.25625 267.938437 \r\n\" style=\"fill:none;stroke:#1f77b4;stroke-linecap:square;stroke-width:1.5;\"/>\r\n    </g>\r\n    <g id=\"line2d_34\"/>\r\n    <g id=\"text_31\">\r\n     <!-- validation -->\r\n     <g transform=\"translate(791.25625 271.438437)scale(0.1 -0.1)\">\r\n      <use xlink:href=\"#DejaVuSans-118\"/>\r\n      <use x=\"59.179688\" xlink:href=\"#DejaVuSans-97\"/>\r\n      <use x=\"120.458984\" xlink:href=\"#DejaVuSans-108\"/>\r\n      <use x=\"148.242188\" xlink:href=\"#DejaVuSans-105\"/>\r\n      <use x=\"176.025391\" xlink:href=\"#DejaVuSans-100\"/>\r\n      <use x=\"239.501953\" xlink:href=\"#DejaVuSans-97\"/>\r\n      <use x=\"300.78125\" xlink:href=\"#DejaVuSans-116\"/>\r\n      <use x=\"339.990234\" xlink:href=\"#DejaVuSans-105\"/>\r\n      <use x=\"367.773438\" xlink:href=\"#DejaVuSans-111\"/>\r\n      <use x=\"428.955078\" xlink:href=\"#DejaVuSans-110\"/>\r\n     </g>\r\n    </g>\r\n    <g id=\"line2d_35\">\r\n     <path d=\"M 763.25625 282.616562 \r\nL 783.25625 282.616562 \r\n\" style=\"fill:none;stroke:#ff7f0e;stroke-linecap:square;stroke-width:1.5;\"/>\r\n    </g>\r\n    <g id=\"line2d_36\"/>\r\n    <g id=\"text_32\">\r\n     <!-- training -->\r\n     <g transform=\"translate(791.25625 286.116562)scale(0.1 -0.1)\">\r\n      <use xlink:href=\"#DejaVuSans-116\"/>\r\n      <use x=\"39.208984\" xlink:href=\"#DejaVuSans-114\"/>\r\n      <use x=\"80.322266\" xlink:href=\"#DejaVuSans-97\"/>\r\n      <use x=\"141.601562\" xlink:href=\"#DejaVuSans-105\"/>\r\n      <use x=\"169.384766\" xlink:href=\"#DejaVuSans-110\"/>\r\n      <use x=\"232.763672\" xlink:href=\"#DejaVuSans-105\"/>\r\n      <use x=\"260.546875\" xlink:href=\"#DejaVuSans-110\"/>\r\n      <use x=\"323.925781\" xlink:href=\"#DejaVuSans-103\"/>\r\n     </g>\r\n    </g>\r\n   </g>\r\n  </g>\r\n </g>\r\n <defs>\r\n  <clipPath id=\"p5e5e9b4afd\">\r\n   <rect height=\"507.4\" width=\"386.425\" x=\"36.465625\" y=\"22.318125\"/>\r\n  </clipPath>\r\n  <clipPath id=\"pe637c3f973\">\r\n   <rect height=\"507.4\" width=\"386.425\" x=\"463.065625\" y=\"22.318125\"/>\r\n  </clipPath>\r\n </defs>\r\n</svg>\r\n",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "accuracy\n\tvalidation       \t (min:    0.683, max:    0.742, cur:    0.742)\n\ttraining         \t (min:    0.618, max:    0.752, cur:    0.752)\nLoss\n\tvalidation       \t (min:    0.605, max:    0.757, cur:    0.605)\n\ttraining         \t (min:    0.583, max:    0.932, cur:    0.583)\n"
     ]
    }
   ],
   "source": [
    "h = model.fit(train_set,\n",
    "            validation_data = test_set,\n",
    "            steps_per_epoch=train_set.n//256,\n",
    "            validation_steps=test_set.n//256,\n",
    "            callbacks=[PlotLossesKeras()],\n",
    "            epochs=10\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test_images)\n",
    "y_pred = [np.argmax(k) for k in y_pred]\n",
    "y_pred = le_class.inverse_transform(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.DataFrame({'Class':y_pred,'ID':test_images})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub.to_csv('Submission.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}