减小像素尺寸，识别率下降.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "### 导入依赖库\n",
    "import os, cv2, random\n",
    "import numpy as np\n",
    "\n",
    "# 画图工具\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from keras.layers import Input, Dense\n",
    "from keras.optimizers import Adam,RMSprop \n",
    "from keras.callbacks import Callback, EarlyStopping, TensorBoard\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.models import Model\n",
    "from keras.utils import np_utils\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from keras import backend as K\n",
    "K.set_image_data_format('channels_last') # 数据格式data_format设置为 NHWC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#下载数据压缩包，解压，然后清理压缩包。\n",
    "\n",
    "from modelarts.session import Session\n",
    "session = Session(username='***',  password='***', region_name='cn-north-4')\n",
    "\n",
    "if session.region_name == 'cn-north-1':\n",
    "    bucket_path=\"modelarts-labs/notebook/DL_image_recognition/image_recognition.tar.gz\"\n",
    "elif session.region_name == 'cn-north-4':\n",
    "    bucket_path=\"modelarts-labs-bj4/notebook/DL_image_recognition/image_recognition.tar.gz\"\n",
    "else:\n",
    "    print(\"请更换地区到北京一或北京四\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists('./data'):\n",
    "    session.download_data(\n",
    "    bucket_path= bucket_path,\n",
    "    path=\"./image_recognition.tar.gz\")\n",
    "\n",
    "    # 使用tar命令解压资源包\n",
    "    !tar xf ./image_recognition.tar.gz\n",
    "\n",
    "    # 清理压缩包\n",
    "    !rm -f ./image_recognition.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed 0 of 25000\n",
      "Processed 1000 of 25000\n",
      "Processed 2000 of 25000\n",
      "Processed 3000 of 25000\n",
      "Processed 4000 of 25000\n",
      "Processed 5000 of 25000\n",
      "Processed 6000 of 25000\n",
      "Processed 7000 of 25000\n",
      "Processed 8000 of 25000\n",
      "Processed 9000 of 25000\n",
      "Processed 10000 of 25000\n",
      "Processed 11000 of 25000\n",
      "Processed 12000 of 25000\n",
      "Processed 13000 of 25000\n",
      "Processed 14000 of 25000\n",
      "Processed 15000 of 25000\n",
      "Processed 16000 of 25000\n",
      "Processed 17000 of 25000\n",
      "Processed 18000 of 25000\n",
      "Processed 19000 of 25000\n",
      "Processed 20000 of 25000\n",
      "Processed 21000 of 25000\n",
      "Processed 22000 of 25000\n",
      "Processed 23000 of 25000\n",
      "Processed 24000 of 25000\n"
     ]
    }
   ],
   "source": [
    "DATA_DIR = './data/' # 数据集路径\n",
    "\n",
    "# 目标缩放尺寸\n",
    "ROWS = 32\n",
    "COLS = 32\n",
    "CHANNELS = 3\n",
    "\n",
    "image_file_names = [DATA_DIR+i for i in os.listdir(DATA_DIR)] \n",
    "dogs = [DATA_DIR+i for i in os.listdir(DATA_DIR) if 'dog' in i]\n",
    "cats = [DATA_DIR+i for i in os.listdir(DATA_DIR) if 'cat' in i]\n",
    "\n",
    "# 数据洗牌\n",
    "random.shuffle(image_file_names)\n",
    "\n",
    "def read_image(file_path):\n",
    "    img = cv2.imread(file_path, cv2.IMREAD_COLOR) # 彩色模式读取图像\n",
    "    return cv2.resize(img, (ROWS, COLS), interpolation=cv2.INTER_CUBIC)\n",
    "\n",
    "def prep_data(image_file_names):\n",
    "    count = len(image_file_names)\n",
    "    data = np.ndarray((count, ROWS, COLS, CHANNELS), dtype=np.uint8)\n",
    "    \n",
    "    for i, image_file in enumerate(image_file_names):\n",
    "        image = read_image(image_file)\n",
    "        data[i] = image\n",
    "        if i%1000 == 0: print('Processed {} of {}'.format(i, count))\n",
    "    \n",
    "    return data\n",
    "\n",
    "# 读取图片至内存\n",
    "images = prep_data(image_file_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_train_samples = len(image_file_names)\n",
    "num_classes = 2 # 类别数是2\n",
    "labels = []\n",
    "\n",
    "index = 0\n",
    "for filename in image_file_names:\n",
    "    if 'dog' in filename:\n",
    "        labels.append(1)\n",
    "    elif 'cat' in filename:\n",
    "        labels.append(0)\n",
    "        \n",
    "# 把类别标签转换为onehot编码\n",
    "labels = np_utils.to_categorical(labels, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data, train_label, test_label = train_test_split(images, labels, test_size=0.30, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_cats_and_dogs(idx):\n",
    "    cat = read_image(cats[idx])\n",
    "    dog = read_image(dogs[idx])\n",
    "    pair = np.concatenate((cat, dog), axis=1)\n",
    "    plt.figure(figsize=(10,5))\n",
    "    plt.imshow(pair)\n",
    "    plt.show()\n",
    "    \n",
    "for idx in range(0,3):\n",
    "    show_cats_and_dogs(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "##设置超参\n",
    "batch_size = 32 # 批大小\n",
    "learning_rate = 1e-4 # 设置学习率为1e-4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "optimizer = Adam(lr=learning_rate) # 优化器使用 RMSprop \n",
    "objective = 'binary_crossentropy' # loss 函数使用交叉熵\n",
    "def load_model():\n",
    "    # 这是一个模型参数随机初始化的模型，如果想要加载imagenet预训练模型，可以设置 weights='imagenet'\n",
    "    base_model = VGG16(include_top=False, weights=None, input_shape=(ROWS, COLS, CHANNELS), pooling='avg')\n",
    "    \n",
    "    # 修改base_model的模型输出层\n",
    "    prediction_layer = Dense(2, activation='softmax')(base_model.output)\n",
    "    \n",
    "    # 组装成新的模型\n",
    "    model = Model(inputs=base_model.input, outputs=prediction_layer)\n",
    "    \n",
    "    # 模型编译\n",
    "    model.compile(loss=objective, optimizer=optimizer, metrics=['accuracy'])\n",
    "    return model\n",
    "model = load_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 32, 32, 64)        1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 32, 32, 64)        36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 16, 16, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 16, 16, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 8, 8, 256)         295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 8, 8, 256)         590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 8, 8, 256)         590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 4, 4, 512)         1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 2, 2, 512)         0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 2, 2, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 2, 2, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 2, 2, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 1, 1, 512)         0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_1 ( (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 1026      \n",
      "=================================================================\n",
      "Total params: 14,715,714\n",
      "Trainable params: 14,715,714\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 13125 samples, validate on 4375 samples\n",
      "Epoch 1/15\n",
      "13125/13125 [==============================] - 57s 4ms/step - loss: 0.6902 - acc: 0.5290 - val_loss: 0.6828 - val_acc: 0.5611\n",
      "Epoch 2/15\n",
      "13125/13125 [==============================] - 31s 2ms/step - loss: 0.6577 - acc: 0.6029 - val_loss: 0.6498 - val_acc: 0.6046\n",
      "Epoch 3/15\n",
      "13125/13125 [==============================] - 41s 3ms/step - loss: 0.5599 - acc: 0.7150 - val_loss: 0.5390 - val_acc: 0.7241\n",
      "Epoch 4/15\n",
      "13125/13125 [==============================] - 36s 3ms/step - loss: 0.4857 - acc: 0.7660 - val_loss: 0.5247 - val_acc: 0.7449\n",
      "Epoch 5/15\n",
      "13125/13125 [==============================] - 40s 3ms/step - loss: 0.4175 - acc: 0.8095 - val_loss: 0.4592 - val_acc: 0.7874\n",
      "Epoch 6/15\n",
      "13125/13125 [==============================] - 34s 3ms/step - loss: 0.3493 - acc: 0.8424 - val_loss: 0.4905 - val_acc: 0.7630\n",
      "Epoch 7/15\n",
      "13125/13125 [==============================] - 42s 3ms/step - loss: 0.2759 - acc: 0.8846 - val_loss: 0.5831 - val_acc: 0.7769\n",
      "Epoch 8/15\n",
      "13125/13125 [==============================] - 45s 3ms/step - loss: 0.2075 - acc: 0.9157 - val_loss: 0.5388 - val_acc: 0.7819\n",
      "Epoch 9/15\n",
      "13125/13125 [==============================] - 57s 4ms/step - loss: 0.1343 - acc: 0.9489 - val_loss: 0.5812 - val_acc: 0.7945\n",
      "Epoch 10/15\n",
      "13125/13125 [==============================] - 39s 3ms/step - loss: 0.0964 - acc: 0.9634 - val_loss: 0.8080 - val_acc: 0.7959\n",
      "Epoch 00010: early stopping\n"
     ]
    }
   ],
   "source": [
    "epochs = 15 # 训练轮数\n",
    "\n",
    "# early stopping策略\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1, mode='auto')   \n",
    "\n",
    "def run_train():\n",
    "    \n",
    "    # 开始训练\n",
    "    history = model.fit(\n",
    "        train_data, \n",
    "        train_label, \n",
    "        batch_size=batch_size, \n",
    "        epochs=epochs,\n",
    "        validation_split=0.25,  # 训练数据中，抽取25%的数据作为验证数据\n",
    "        verbose=1,\n",
    "        shuffle=True,\n",
    "        callbacks=[early_stopping]) \n",
    "    return history\n",
    "\n",
    "history = run_train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('VGG-16 Loss Trend')\n",
    "plt.plot(loss, 'blue', label='Training Loss')\n",
    "plt.plot(val_loss, 'green', label='Validation Loss')\n",
    "plt.xticks(range(0, epochs)[0::2])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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epSwXL1YYiGREgSAxLTXV21/wxBPQqhXMnav+QyKZUSBIzNq+3WtGN2wY9O4NY8ZAkSJ+VyUSubQPQWLS8uXeElFyMowdC7ff7ndFIpFPMwSJOZ98AvXqeZ1Kv/5aYSASLAWCxAzn4OWXvQvXXH65t/P4qqv8rkokemjJSGLCoUNw993eSWbt2sHgwRCCa46LnFE0Q5Cot2ULNGrkhUGfPjBihMJAJCc0Q5ColpgIt9wCe/Z4vYluucXvikSil2YIErXGjIEGDbzWE/PnKwxETpcCQaJOejo8/zy0bQvx8bBoEVxxhd9ViUQ/LRlJVDlwAO66C8aN885A7t8fChTwuyqR2KBAkKjxyy/eyWYrVsBbb8HDD+vSliKhpECQqLBgAdx6q3d46aRJcNNNflckEnu0D0Ei3ogRkJAARYt6waAwEAkPBYJErPR06NkTOnb0rmi2cCFceqnfVYnELi0ZSUTavRs6dYKJE+G+++Cdd3RlM5Fw0wxBIs64cXDZZfD559Cvn3ckkcJAJPwUCBIxtm71LmLTujWUK+c1p3vgAb+rEjlzKBDEd87B++97s4IvvoBXX/VONqtVy+/KRM4s2ocgvtqwAbp1gxkzoGFDGDQILrnE76pEzkyaIYgv0tK8k8tq1PBmAwMGwMyZCgMRP2mGILlu5Urv2gWLF0Pz5t5O4woV/K5KRDRDkFyTmgrPPeddxWzjRu/6BRMnKgxEIoVmCJIr5s+Hrl1hzRpo3x7efhtKlfK7KhE5kWYIElb798M//gH163u3v/jCa0WhMBCJPAoECZupU72dxv36eecTrF6tPkQikUyBICG3c6d3zYJmzaBQIfj6a+jbF4oV87syEcmKAkFCxjkYO9Y7wWzUKHjmGfj2W6hXz+/KRCQY2qksIbF5M3Tv7h01FB8PX36py1qKRBvNEOS0pKfDwIHerODLL+HNN+GbbxQGItFIMwTJsU2bvBbVs2bB9dd7bScuusjvqkQkpxQIkiMLF3rXN05J8YLg7rt1fWORaKdAkGz7+GPo0AHKl4fZs6FaNb8rEpFQ0D4ECZpzXmvq22/32k8sWKAwEIklYQkEMxtiZvPN7JlM7i9uZl+Y2VwzGxCOGiS0Dh+Ge+6Bp56Ctm29dtXnnut3VSISSiEPBDNrCeR1ztUDyptZlQyGdQA+dM41AIqZWXyo65DQ2b3bO8N4yBCvOd2oUVCwoN9ViUiohWMfQgIwNnD7K6A+8MNJY3YCVc3sHOB84OeTn8TMugHdAC644IIwlCnB2LAB/vY3+PFHGDYMOnb0uyLJiaPpR0k9mkpqWmqmfx5OO0wey0NCpQS/yxWfhCMQigCbA7f3AhdnMOZr4G/AP4C1wO6TBzjnBgIDAeLj410Y6pRTmD8fbrnFO9dg+nTvimbij12HdjFn0xzmbJrDL3t/IfWo9wGe1Qf8iR/06S49qNcpW7QsW/+5NczvRiJVOAJhP1AocLsoGS9L9QHuc87tNbNHgc4EPvwlMoweDZ07wwUXwOefQ5WMFv4kbJIPJDNn0xxmb5rN7E2zWbl9JQ5HwXwFqXxOZQrkK0CBvAUokK8ARfIXoUTeEn/YViDv8dv58+b/4/YM/vx9TJH8Rfx+6+KjcATCErxlogXAlcD3GYwpDFxuZguAOsD0MNQhOeAcvPSSt6+gYUMYPx5KlvS7qti3ff9278N/oxcAq5NXA1A4rjD1zq/HC9e/QKOKjah9Xm0K5Cvgc7USq8IRCBOAuWZWHrgJaGtmLznnTjzi6BVgKFAR+AYYHYY6JJtSU70jiUaM8M4zGDQICmTx2bN9/3ZmbZzFzVVvplBcocwHyp9s2bfl2If/rI2z+H6n972pSFwR6l9Qn3aXt6NRpUbEl48nf978PlcrZ4qQB0JgGSgBaAy87pzbBiw/acwioHqoX1tybudOaNkS5syBF1+EXr2yPvP4263fcvNHN5O0N4kyRcrwz2v/yX3x91GsgHpcZ+SXPb8c+/CfvWk263etB+CsAmdR/4L6dKnVhUYVG3FVuauIyxvnc7VypjLnIn9/bXx8vEtMTPS7jJj1ww/ekUQ//wxDh8Idd2Q9fvya8XT4pAMlC5XkpRteYvjy4cz4aQbFCxbnoToP0aNOD0oUKpE7xUeojb9tPPbhP3vjbH767ScAzil4Dg0uaECjio1oVKkRNcvWJF8eNQyQ8DCzJc65oA/rVyCc4ebMgdtugzx5YMIEuO66zMc65+gztw/PzHyGOufVYULbCZQtWhaAhUkLeXnuy3y27jOK5i9K9/juPHrto5QpWiaX3knuOJx2mG37t7F131a27t/Kln1b/nh7/1Y2791M8sFkAEoUKkHDig29AKjYiCvKXEHePHl9fhdyplAgSNCGD/cufH/RRTBpUtadSlOOptB1YldGrhxJu8vbMfjmwRTM9+ez01ZsX0GfuX0Yu3osBfIVoGutrjx+3eNccHZkn0uScjTl2Af71n3HP9xP/nnHwR1/emwey0OZImUoV6wc5Yp6/11R5goSKiVQvXR18pg6xIg/FAhySs7B8897+wpuuMFrVle8eObjt+3fxq0f3crCzQt5+YaXear+U9gpWpuu27mOV79+lRErRgDQ8YqO9Kzfkyol/T1+dfv+7UzfMJ2ZG2eyac+mY9/wd6f86VQY8uXJR9miZb0P+WLlKF+0/LEP/fLFjt8uXaS0vvVLRFIgSJZSUqBLF+88gy5doH9/yJ/FQSzLti3j5tE3s/PQTkbcNoKWl7bM1uv9vOdn3pj3BoO/HczhtMO0qd6Gp+s/zeVlLj/NdxKclKMpzPt5HtN+nMa0DdNYtm0ZACULlaRKySreB3vgW325YuWO/1ysHKUKl9K3e4lqCgTJVHIy3Hqrdwbyq6/CE09kfSTRhLUTaDe+HSUKlWBi24nUKlcrx6+9bf823v7mbd5LfI/9h/dzc9Wb6dWgF7XPq53j58yIc47vkr87FgCzN87m0NFDxOWJo/4F9WlyUROaXNSEmmVr6sNeYp4CQTK0dq13JNGWLd55Bq1bZz7WOcdr817jqRlPUfu82kz4+wTKFSsXkjp2HdpF34V9+e/C/7I7ZTd/ufAv9GrQi0YVG51yGSozOw7uYPqG6Uz9cSrTfpzGln1bAKhWqhpNLvQCoFGlRhTNXzQk70EkWigQ5E+++gpatfKWhiZOhDp1Mh+bcjSFbp91Y8SKEbSt0Zb3b34/LCed7Uvdx4DEAfz7m3+z/cB26p1fj14NenHTxTedMhgOpx1m/i/zvVnAj9NYunUpDkeJQiX4y4V/ocmFTWh8UeOI35EtEm4KBPmDESO8fQVVq3pHElWqlPnY7fu3c9uY2/gm6RtevP5FejXoleNv7cE6dOQQ73/7Pq/Pf52f9/xMzbI1ebr+07S8tOWxHbXOOb7f+f2xAJi1cRYHjhwgX558XFvhWppe1JQmFzXhqnJXaeeuyAkUCHLMsGFeg7rrr/d6Ep19duZjV2xfQYvRLUg+kMzw24bT+rIs1pTC4EjaET5c8SGvznuVdTvXUa1UNe656h7WJK9h2oZp/LzH65BepUSVY/sBEiolcFaBs3K1TpFookAQ4HgY3Hijt0xUKItVn4nfT+TOcXdydsGzmdh2IleXvzr3Cj1JWnoa49aM4+W5L7Ni+wrOLnC2twx0URMaX9iYysUr+1abSLRRIAjDh0OnTqcOA+ccb8x/g57Te3J1+av5tO2nlC9WPldrzYxzjg27N1DxnIpq7SCSQ9kNBP2mxZgRI7wwuOEG+PTTzMMg9Wgq9066l2HLh9GmehuG3jKUwnGFc7XWrJgZF5XI4tRpEQm5oA7ENrOCv1/32MzuNjP1441AI0bAXXd5YTBxIhTO5PP91wO/cuPwGxm2fBi9G/Xmo1YfRVQYiIg/gj0zZyzH21WXAUaGpxzJqQ8/9MLg+uuzDoOV21dSe1BtlmxdwpjWY3g+4fmwH0kkItEh2EAo7pwbBuCc6wOUCl9Jkl0nhsFnn2UeBpPWTaLe+/U4nHaYOZ3m0KZ6m9wtVEQiWrD7EJLM7ElgEVAb+DV8JUl2jBzphUGjRpmHgXOOf3/zb5748glqlavFxLYTOe+s83K/WBGJaMHOEDoBB4HWwAGgY7gKkuCNGgUdO3phMGlSxmGQejSVuyfezeNfPk6ry1oxt/NchYGIZCjYGYIB3zjn+prZ3UDkH6sa40aN8q573LBhxjOD7fu3M+TbIQxcMpBNezbxbMNn6Z3QWw3dRCRTwQbCWGAckMjxncq3h6soydqJYTBpEhQp4m13zjF702z6J/Zn/JrxHE0/yo2Vb6T/3/pzU5Wb/C1aRCJesIHwh53KZjYzjDVJFkaP/nMY7D60m+HLhzNgyQDW7lhL8YLF6VG7B/defS9VS1X1u2QRiRLaqRxFPvoI2reHBg3gs88cq39bzIDpA/ho1UccOnqIuhXq8sEtH9CmepuwdCgVkdgWbCB0Ap4E/glMAf4TroIkYx99BO3awbUND3B7n9E0GtWfpVuXUiSuCB2v7Mi9V997WhewEREJNhDeAyoDJYA7gLZA/XAVJX80Zgzc+fBqynUewMqLhjNv2l5qlK7Bu399l/ZXtFfHTxEJiWADoSLQDPgQuBP4KmwVyTGpR1N57P1x9FswAO6fy468+WlTrQ33XX0f9c6vpzOMRSSkgg2EVODGwPjbgeJhq0jYsHsD/0v8H/0Xvs++tB0UPPciejV8nfvqdKZUYZ0kLiLhEWwgtAHKAY8AdwPdw1bRGepo+lE+X/c5/RP7M/XHqeQhL+lrb6bGofuYN+IvnFVM5w+ISHgFFQjOuQPA+sCPz4WvnDPTyBUj6TmjJ0l7kyhfrDytSz3P+Ge6Ur9GBSZPhqK6NryI5AJdD8Fne1L2cO+ke7mk5CW80+wdUle2oP2d+ah3LQoDEclVWofw2dBlQzlw5ACDWgwibfVttL8zH3XrwhdfKAxEJHcpEHyU7tJ5d/G7XFvhWn6afzVt20Ldut7MoFgxv6sTkTONAsFHU9ZPYf2u9dS1HgoDEfGdAsFHfRf1pUyRsgzr2YqrrlIYiIi/FAg+WbdzHVPWTyGe+9iVnJ8331QYiIi/FAg+eXfRu8TliWP1sHupXdtrWCci4icFgg/2pe5j6LKh1C12OxtXleWxx0BdKETEbzoPwQfDlw9n3+F97J7Tg8qV4bbb/K5IRCRMMwQzG2Jm883smUzuv9/MZgX+W2Zm/wtHHZHIOUe/xf249KxrWDWlDo8+CvkUyyISAUIeCGbWEsjrnKsHlDezKiePcc71d84lOOcSgLnAwFDXEammb5jO2h1rKbyqB8WLG507+12RiIgnHDOEBLxrMIPXJjvT6yaY2XlAGefckgzu62ZmiWaWmJycHIYy/dF3UV9KFijNkuFt6N79+PWQRUT8Fo5AKAJsDtzeC5TJYuwDQP+M7nDODXTOxTvn4s8999wQl+iPDbs3MGndJCru7Eb+PAV48EG/KxIROS4cgbAf+P2CvkUzew0zywNc75ybGYYaItJ7i98jb568rP7gPjp2hLJl/a5IROS4cATCEo4vE10JbMxkXANgYRhePyIdOHyAId8OoWpaS1J3nMejj/pdkYjIH4UjECYAHczsLbwL66w2s5cyGNcUmBOG149II1eO5LeU30ga34PmzeHSS/2uSETkj0J+wKNzbq+ZJQCNgdedc9uA5RmMezrUrx2pnHO8s/AdKuSrSdLK63i8n98ViYj8WViOgHfO7eb4kUZnvFkbZ7E6eTWlvxnCNdeY2lSISETSKVG5oO+ivhTLW5JfZ9xB35FqUyEikUm9jMJs02+b+PT7TzlrfVcqn1+Ili39rkhEJGMKhDDrn9gfHGyecD+PPKI2FSISufTxFEaHjhxi0NJBlPntFlLyVFSbChGJaAqEMBq9ajS7Du2CT3vQqzsULep3RSIimdOSUZg45+i7qC8ljtQgbnOC2lSISMTTDCFM5v0yj2XblpFvxgDu6mBqUyEiEU+BECZ9F/WlIOeQsqQ9/3zf72pERE5NS0ZhsHnvZsZ9Nw779m6aNy2iNhUiEhU0QwiDAYkDSHfpHJrTncc+8bsaEZHgKBBCLPVoKgOXDqRwUnMuu/hCGjb0uyIRkeAoEEJs7OrFP8TbAAAMtklEQVSx/HrgV/iqB4+9ojYVIhI9FAgh1ndRXwrtr0bp9L+oTYWIRBUFQggtTFrI4i2LYXY/Hn3E1KZCRKKKPrJCqO+ivuRLK0bRTR3p0sXvakREskeHnYbItv3bGLt6LEcTO9O9azG1qRCRqKMZQogMXDKQI+lHiPv2AR4c4nc1IiLZpxlCCBxOO8x7iwaQ58dmdPzbJZQr53dFIiLZpxlCCIxfM57tB7fCgsE8OsHvakREckaBEAL/XdCXvL9dTNNLmnHZZX5XIyKSM1oyOk1Lty5lweb5pC14gCce11+niEQvzRBO0zsL+2JHinAlndWmQkSimr7SnobkA8mMWjEat6wjPR8+W20qRCSqKRBOw+ClgzniUimf9CCtWvldjYjI6VEg5NDR9KP8d35/2HAjT3a5TG0qRCTqKRBy6NO1n7I95ReKrO6hNhUiEhMUCDn0+uy+sLsSPZo2V5sKEYkJWujIgRXbV7Do19nkXfoG/xiT1+9yRERCQjOEHHhjTl84Uoi2VbuoTYWIxAwFQjbtOrSLMd+NhBXtefqREn6XIyISMgqEbBqwcAhHOESDAj3UpkJEYooCIRvS0tN46+v3YGMjXnjgcr/LEREJKQVCNkz8fhI70zZy4Y4eNGrkdzUiIqGlQMiG3l/0hT3n82K7W9SmQkRijgIhSN8lf8eK/TM454f7adNaR+uKSOxRIATp2c/6wdECPHbDPWpTISIxSR9tQfhwxYd8smkw+b9vz0Pvl/K7HBGRsAjLDMHMhpjZfDN75hTj3jOzFuGoIRScczw38zk6fNIBt6k+Par+W20qRCRmhTwQzKwlkNc5Vw8ob2ZVMhnXACjrnPss1DWEQsrRFNqNb8eLc16k8PddqLp4Ci8+XdzvskREwiYcM4QEYGzg9ldA/ZMHmFkcMAjYaGa3ZPQkZtbNzBLNLDE5OTkMZWYu+UAyNw6/kdGrRlNzx6uk/t9gPhyWn0KFcrUMEZFcFY5AKAJsDtzeC5TJYExH4DvgdaC2mfU4eYBzbqBzLt45F3/uueeGocyMrUleQ53BdVi6dSmPVvg/lvV7kueeNeLjc60EERFfhCMQ9gO/f5cumslr1AIGOue2AR8C14ehjmybsWEG1w65loNHDjKu+Ww+eKI111wDTz3ld2UiIuEXjkBYwvFloiuBjRmMWQ9cGLgdD2wKQx3ZMmTpEJqNbEaFsyqw4O6FvPt0bQ4ehOHDIS7O7+pERMIvHIEwAehgZm8BbYDVZvbSSWOGANeb2RygO/BmGOoISrpL58kvn6TrZ125sfKNzOsyj2n/V5EvvoDXX4dq1fyqTEQkd5lzLvRPalYcaAzMCSwLnZb4+HiXmJh4+oWd5OCRg3T4pAPj14zn/vj7eeemd9j0Uz6uvBLq1oVp0yCPTt0TkShlZkucc0HvAQ3LiWnOud0cP9IoIm3dt5WbP7qZJVuW8HbTt3mozkOkpxt33QX58sHQoQoDETmznJFnKq/cvpK/jfobuw7t4tO2n9Kiqndu3Jtvwrx5MGIEnH++z0WKiOSyMy4QJv8wmb9//HeKFSjG3M5zqVWuFgArVsCzz0KrVtCunc9Fioj44IxaFHl30bs0H92ci0tczKKui46FQWoqtG8PJUrAgAGotbWInJHOiBlCWnoa/5z2T/678L+0uKQFo1qNomj+402Jnn8eVq6Ezz6DUupdJyJnqJgPhH2p+7hj3B18/sPnPFL3Ed5o/AZ58+Q9dv/XX3uHl3btCs2b+1ioiIjPYjoQkvYm0XxUc1b9uor3/voe919z/x/u37cP7roLKlWCt97yp0YRkUgRs4GwZMsSWoxuwf7D+/n8zs9penHTP4157DH46SeYPRuKFfOhSBGRCBKTO5U/XfspDT9oSFzeOObfPT/DMPj8cxg40AuFBg18KFJEJMLEVCA453jrm7e4bcxt1Chdg4VdF1KjdI0/jduxA+6+G2rUgBdf9KFQEZEIFDNLRkfSjtBjcg/+t+R/tL6sNcNuHUbhuMJ/Gucc3H8/7NoFU6ZAgQI+FCsiEoFiZobw/c7vGbZ8GD2v68mY1mMyDAOAUaPg44/hX/+CmjVzuUgRkQgWluZ2oRZsc7tNv22i4jkVM70/KclbJqpeHebMgbx5Mx0qIhL1stvcLmZmCECWYZCeDp07w9GjMGyYwkBE5GQxsw/hVN57D6ZP91pTXHyx39WIiESemJohZOb77+GJJ6BZM+jWze9qREQiU8wHwtGj0LEjFCoEQ4aocZ2ISGZifsnolVdg0SIYMwbKl/e7GhGRyBXTM4QlS+CFF+COO6BNG7+rERGJbDEbCIcOQYcOULo09OvndzUiIpEvZpeMevWCNWtg6lTvwjciIpK1mJwhzJwJb78N3btDkyZ+VyMiEh1iLhD27IFOnaBKFe/CNyIiEpyYWzJ6+GGvRcW8eVCkiN/ViIhEj5iaIUyYAB98AE89BXXr+l2NiEh0iZlA+PVX7yzkWrXguef8rkZEJPrETCAcPQrXXAPDh0P+/H5XIyISfWJmH0L58t5lMUVEJGdiZoYgIiKnR4EgIiKAAkFERAIUCCIiAigQREQkQIEgIiKAAkFERAIUCCIiAoA55/yu4ZTMLBnYFOTwUsCOMJYTLqo790Vr7ao790Vr7VWdc8WCHRwVZyo7584NdqyZJTrn4sNZTzio7twXrbWr7twXrbWbWWJ2xmvJSEREAAWCiIgExGIgDPS7gBxS3bkvWmtX3bkvWmvPVt1RsVNZRETCLxZnCCIikgMKBBERAWIsEMxsiJnNN7Nn/K4lWGZ2tplNNrMvzewTM4uq672ZWRkz+9bvOnLCzN4zsxZ+1xEsMytuZl+Y2VwzG+B3PcEI/P8xN3A7zswmBX5Hu/hd26mcVPsFZjbLzL4ys4FmZn7Xl5kT6z5hWw0zm3aqx8ZMIJhZSyCvc64eUN7MqvhdU5DaAW855xoD24BmPteTXW8ChfwuIrvMrAFQ1jn3md+1ZEMH4EPnXAOgmJlF9HHxZlYcGAYUCWzqASQGfkebm1nQJ0zltgxqvxe43zl3A3A+cLlftWUlg7oJhNdbwCm/bMZMIAAJwNjA7a+A+v6VEjzn3HvOuS8DP54L/OpnPdlhZjcAB/CCLGqYWRwwCNhoZrf4XU827ASqmtk5eB9KP/tcz6mkAX8H9gZ+TuD47+h8IJID7Q+1O+d6OefWBO4rSeSetXzy3zlAZ2BmMA+OpUAoAmwO3N4LlPGxlmwzs2uB4s65BX7XEozA0tZzQE+/a8mBjsB3wOtAbTPr4XM9wfoaqAL8A1gL7Pa3nKw55/Y65/acsClqfkczqB0AM/s7sNo5t8WHsk7p5LrNrCTQHm8mf0qxFAj7Ob50UZQoem9mVgLoC0T8uuoJegLvOud+87uQHKgFDHTObQM+BK73uZ5g9QHuc869gBcInX2uJ7ui9ncUwMwuBB4DHva7lmx4FXjKOXckmMFR9Q9yCks4vkx0JbDRv1KCF/imPRbvHy3YBn6R4C/AA2Y2C6hpZoN9ric71gMXBm7HE3zjRL8VBi43s7xAHSDaTiKKyt9ROLY2PxroktHMIYI1Al474ff0pawGx8yJaWZ2FjAXmAHcBNSNhn84M7sf75vf8sCm/s65MT6WlG1mNss5l+B3HcEK7Mx8H2/JIg5o7ZzbnPWj/GdmtYGhQEXgG+A259x+f6s6td///zCzisAXwHSgHt7vaJq/1WXthNpfw1tq/D5w1/POudk+lpaljH4ng/k9jZlAgGMp3hiYE1gOEJEIYmbl8WYJU6PhC9uZJqYCQUREci6W9iGIiMhpUCCIiAigQBARkQAFgghgZr3NbE2gX80sM6t5ms+VEMLyRHJFVFxTWSSXvOyc+9DvIkT8okAQyYCZfQCchXeuwrfOuQfNrADwAVAeSMI7UzhPYFsF4DegTeApGpvZv4Cz8RoW7gH+L/CcO4A2zrmjufR2RIKiJSOR43r9vmQE5AU+ds5dB1Q2s6uBe4BVzrlGwDq8ViPdgOXOufrAOKBG4LkuDowbBdwAXAakO+ca4l3WsGguvi+RoCgQRI572TmXEDibMw2v1QLACqAS3of6wsC2hcClQDVgUWDbB8DiwO3hgT9/xWs7vBRYFehJ3wI4GK43IZJTCgSRzNUO/FkTr//RaqBuYFvdwM9rgWsC254GugZuHzjpua4E5jnnmgDFgQZhqlkkxxQIIseduGT0d7yLuMwD1jrnlgODgepmNgevDfUHeMs/VwUecxUwIpPn3gj8w8zmA2WBxDC+D5EcUesKkQwEdir3ds5t9LkUkVyjQBAREUBLRiIiEqBAEBERQIEgIiIBCgQREQEUCCIiEvD/AYjQm6jrzaMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('acc')\n",
    "plt.title('VGG-16 acc Trend')\n",
    "plt.plot(acc, 'blue', label='Training acc')\n",
    "plt.plot(val_acc, 'green', label='Validation acc')\n",
    "plt.xticks(range(0, epochs)[0::2])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(test_data, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am 100.00% sure this is a Dog\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am 99.46% sure this is a Dog\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am 99.21% sure this is a Cat\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am 100.00% sure this is a Cat\n"
     ]
    },
    {
     "data": {
      "image/png": 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7+yT92Ze+SG32N+6l2oZXf0+1ohKeHbZvrz9kl45I8np1Iz/XgOPXuGpiH9W6u/zZebvf55l5vT08vHbFpz5NtY0bXqJaRyc/Zhz+TLp+/isjF03hdIcXIiDk8EIEhBxeiICQwwsREHJ4IQJCDi9EQOQ9LBeLx1FcWe3VZp81j9oVmb9I5LS53GbbG5uotvxzPAS4+plnqfbZzyzxjl/z59dRm1u+87dUe+gXd1Lt6ZcaqLarifdZW/vsE97xn915K5/HP/6Kan19vDdesnoq1Q40+rP2yksrqU1tHQ+JdnXzDMfmNp492N3qL7py8CDPRjz//POp9u+P/yvVert5MdN4gsfY+tP+3oRnnTmb2uQC3eGFCAg5vBABIYcXIiDk8EIEhBxeiIDI+y59IpbAlEp/myeL8d3gVpJ4cHgXL6F37nm88tbRiF913VZeh7O82j/3OVP8rbAA4Pt/9XWq3XvPP1NtzUsvU620+EyqnTXry97xn9z9c2oTL+atpsrLKqhW3cPbRh0u8ifxoIinfby99TWq1Z1RR7V923jbqM6WVu94vJivgZfXr6daUVkp1YznEuGyyz5LtReeXe0d/9qXVvBzWURmTYboDi9EQMjhhQgIObwQASGHFyIg5PBCBIQcXoiAyHtYrigxDZPH/9SrvbmGJ1XESLhjUtVV1Kajs5lqAzt4IsbVn6ESdm33H/P51S9Qm0TiEqol49dQbWr1lVQrTvIadL1dR73j7Yd4WGvOXP7Sd7XyxJRkNX/Nihr994+iBD9XdY0/7AkADbt2U62khIcOS0v8raGOptu940D0nS+e4kX5nOMhxy1bNlDti1/wh98u/th8PpE0T9TJlGHv8GZWZWZPmdmzZvaYmRWb2UNmts7MvjfiGQghCkYmH+m/AuAe59wyAE0Avgwg7pxbDGCqmZ3SLloIMTYZ9iO9c+7+476tBXAtgL8b+v53AJYA2JH7qQkhck3Gm3ZmtghADYD3ARwr8t4O4JQewWa20sw2mdmmnh5eT10IUVgycngzGw/g5wCuB9AJ4NjDxUnfMZxzq5xz9c65+pKSCbmaqxBihGSyaVcM4NcAbnfO7QGwGYMf4wFgPoCGvM1OCJFTMgnL3QBgAYA7zOwOAL8A8FUzmwpgOYCFkdYWRzpW7pWSSZ5q5EhNu1SK25RV8jBUZQV/b9v6+kSq9R70Z/RNq7ue2rz/Hg8n1cz31/cDgPQRHjbqbOG/d6J4knd88ozLqc2BRl7TrrKWz7G0yN8iCQCmTvPXY9vTwK/H2bP8LcUAwCJS0Rr3v0+1WMwfKksYXwPxUp4Rd8klPMz68gYeepsz6wKq7WjY5h3/zrxvU5uuri6qZUomm3YPAHjg+DEzewLAMgB3Oed40FYIMabI6sEb51wLBj/mCyE+QujRWiECQg4vREDI4YUICDm8EAGR/1ZTZigtKfZq3V3+QpUAEIv534vYsQDgaDcvsNjfx7OaplTysFxfjT8IYd28CGRvihe4/KCdZ71VJ/lDSgO9PDvs8CF/iKf1CC/q2dbSRLVW0qoJACqT/kw0AJg92x+W62zrpDZHO/uoNrmWt7U60sbbRvX0+ddVvChJbfoH/K2fAODNrW9T7eyZ51DtjTc2Um3yBP+aO2MmDy03N/Ns0EzRHV6IgJDDCxEQcnghAkIOL0RAyOGFCAg5vBABkfewnMWA0jL/+0o84ux9/f6iky0dvJCfS/BQWayIFyI81MKzkKrq/KG+7iM8RLK3yd/bDAA+aPiAah//OA95DbgyqiUSZ3jHk+N4CLDVeAgTxbxQ5cHWRqq9+f/8feLS4K/ZRfM+QbW2Lt57cHxEyO5A0yHveHEpD+m6Tn8hUADo6uBZjDUVp9R/+QPl5f4sUQBIx/x94uIx0p8PQPNBHorMFN3hhQgIObwQASGHFyIg5PBCBIQcXoiAKMAuvYOV+HeEXYq/31RU+Xfcj6b5LubuHXwX84wZ/rpvAPD2lvVU+/TkRd7xrohkiykTeKLOGRN4gky8jB/TOiPq3aX2eMff++CH1GbBBR+n2nNrVlMtEdE2ysH/OheTRCgAaNi/k2rnzr6Yanv28vLn4yv8EY3eHh6ZSEe0cRpXwuv47T/I6/UlIn7vnh5/FKooxSNG3d18fWSK7vBCBIQcXoiAkMMLERByeCECQg4vREDI4YUIiGHDcmZWBeBXQz/bCeAaADsB7Br6kZudc28ye+cMqQH/+0pZGU9oiY3zJxe0N/CEkIsu5vXAps/m56qb4Q+9AUBZ0p9UUT2BJ7PseNsfcgGAqiQPK3a18zm++DQPo91y64+942+8zrt4v/zKW1SLClGlUqcf2uqJsCnp49fqne2vUm3mdN6iqq3DH77qOHqE2kT9zm6Avy7meAg26pj9/f5afp193KaqqopqmZLJHf4rAO5xzi0D0ATguwAedc4tHfpHnV0IMbbIpLfc/cd9W4vB/vArzOxSAHsAXOeci8i1FEKMFTL+G97MFgGoAfAsgMudc0sAtAK4Ok9zE0LkmIwerTWz8QB+DuBPATQ5545VJtgOYI7n51cCWAkAFZUzcjNTIcSIGfYOb2bFGOwUe7tzbg+Ah81svpnFAawA8MbJNs65Vc65eudcfWlZbc4nLYTIjkw+0t8AYAGAO8xsDYCtAB4G8DqA9c655/I3PSFELslk0+4BAA+cNPyjTE9gsTQSJf4MoL4UD3c07/HXM5t9lr9+GwCk0rxtUSsvJYfx1XzPcT/ryBTn7ZPKxkeF175IteIYfzn6Y7we24/uvN07buYPbQJAfz9v8+UiQk0DESEq1h7M4jwU2d7OswCjMvN6jvLMsT4SBhxwfH2kBrjWF5HVOZDidgZ+/Vl4Myrs2bj/ANUyRQ/eCBEQcnghAkIOL0RAyOGFCAg5vBABIYcXIiDyX8TSYigqrvBqe9/lLZlmnuF/YKckycNCA6Q9FQD0jCul2vRpvCDi08/8N+/49ldfoTZtnR1UKx7H22H19/HfLQ4eKmPhsKhsrUQJn0c8Quvp4OHIiopq73hqgIeaOlt4BluijL9m3RHhwa52fzHTgV4eyotH3Pt6unlhSXbtgegQJpw/ZOd6eXut1vYWfrwM0R1eiICQwwsREHJ4IQJCDi9EQMjhhQgIObwQAZH3sJzrTyN1wB+m6jzwK2o3+7PTvOP7tm6hNjVlU6l2621fp9r+A/x971s3+0v2uTQPkxXHi6hmA9xu6mTe/665hYcwi4r84SuX5hlxE2ecUrfkeEuqdEdkt/U7fxiqbtJkatPT1ka18y+4kGq9/f7iogCwfZt/HmXdPLOt+Ugj1aKyB6O0KM67YJ53/PARfj0ONLHUzczRHV6IgJDDCxEQcnghAkIOL0RAyOGFCAg5vBABkfewXNrtR2//D73a2Rclqd22Df6qk59bsYLaXHTuTKr19/OMrbYOHuIpKvNnjiXKeW+54h6eLdcbkQ3V3sGz/f7kE5dTzcX8RSJv/OY3qM0Vl15Mtajil909fP4PPHi/d3yARwcji0d+9tNXUq2zk2ftrd200Tv+H08/T23SCZ5ZePgAD9lFZSRGXcelVyzxjvcd5WvgSCsPzWaK7vBCBIQcXoiAkMMLERByeCECQg4vRECcTvfYBQB+75xrPp0TTJxYi7/4+o1erSTBdzHj5f7WSq2th6jNQIzvIDtXTrWqSn8tNgC4/Qc/8Y7fc9dd1KZ+7gKq7f+A97xKV/Cd///zb/9EtRJSHy2qbVEUUbvLlUl+HW+95Vve8ebDvBbbT+/6GdWqpvBkosZX9lPtHx64zzseESCBQ0TrrYid+Gw5Y/p073hD4z5qk4hoRZYpmXSPrQPwWwCXAHjBzGrN7CEzW2dm3xvxDIQQBSOTj/RzAdzinLsTwDMArgAQd84tBjDVzKLyLIUQY4hMusc+BwBm9kkM3uXHY7BfPAD8DsASADvyNUEhRO7IaNPOBv+ouwZAPwADcOzRo3YAp1Q3MLOVZrbJzDa1RNQdF0IUlowc3g3y3wGsA7AQwLESK0nfMZxzq5xz9c65+pqa8TmbrBBiZGSyaXebmX1t6NtqAD/F4Md4AJgPoCE/UxNC5BobriaXmdVg8G/2cQDeAnA7gBcBPA9gOYCFzjlaiGvBggVu3YYNXq2hYQ897zjSkqmqqora9PfzRIw0eKgpWcbDYc2t/vZExTEexokKa0UR1bYoSmPny3Ye2cLOF7XGUhGZNS9GJLv81+uupRpbO1Gtn6KSYLJNkIli1apV3vHWZh527ujh8//xD+7Y7JyrH+68mWzatQBYdvyYmS0dGrsrytmFEGOLrCL5Q28Cvx72B4UQYwo9WitEQMjhhQgIObwQASGHFyIghg3LjfgEZocAHB9/mwjgtDLu8oTmcSKax4l81OYx0zlXO9wP5d3hTzmh2aZM4oWah+aheeR+HvpIL0RAyOGFCIjRcHj/M4WFR/M4Ec3jRP4o51Hwv+GFEKOHPtILERBBObyZJcxsr5mtGfo3b7TnNFqY2WQzWzv09TQz23fcdRk2vPPHhJlVmdlTZvasmT1mZsWjtU7MbLyZLTOzifk4fkEdfgwUv7wIwKPOuaVD/94cjUmc5GxFZvbk0HW5vkDnrwHwSwDHStB+AsCdx10XnqOZ23n4HG001shXANzjnFsGoAnAdzEK66QQBWML5vBm9iWMfvHLhQBWmNlLZvaImeW9mebJeJztZgCbhq7L582sogDTGMBgybL2oe8XArjJzNab2b0FOP8xTna0L2MU1ohz7n7n3LND39YCSGF01kneC8YW8g6/FKcWvyw0rwK43Dm3BEArgKtHYQ4nO9tSfHhd1gHI+8Mezrn2k+oYPAVgsXNuEYBzzOyifM9haB4nO9q1GMU1YmaLANQAeBajsE6cc8855zYcVzD2KuT4ehTyDleOE4tfzi7guY+xxTl3rB3BdgAF/5ThnGsHTqiUcvJ1OaUoaAFY57kuWwp18uMcrQGjtEaGmq38HMCfAmgarXUyTMHYEV+PQt7hOzFM8csC8LCZzTezOIAVAN4YhTmczFi4Ls+YWZ2ZlWHwrvJWoU58nKNdj1G6FmZWjME76e3OuT0YxXVyugVjT5dCLq7NGP3ilz8G8DCA1wGsP1Zzf5QZC9flRwBeALABwIPOuXcKcVKPo43WtbgBg63U7jCzNQC2YhTWSSEKxhbswRszqwSwFhkWv/xjx8zWOOeWmtlMAKsBPAdgMQavC69W+EeEmd0I4Cf48A76CwDfQqBrZKQFYzM6RyGftBv6hZYBeNE511SwE49xzGwqBt/JnwlpgfvQGjmRXF8PPVorREAE9aSdEKEjhxciIOTwQgSEHF6IgJDDCxEQ/x9e5dUepE+PoAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am 100.00% sure this is a Dog\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am 99.93% sure this is a Dog\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am 100.00% sure this is a Cat\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am 100.00% sure this is a Dog\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPwAAAD6CAYAAACF8ip6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHaZJREFUeJztnXuQnFd55p+3v+6enrtmRncJS8axjYNtQVCIrTixcnESvGylTP4wWROWgl3VQoo/NpuqwOJNAlmoVFJlUvEuprRhKcchJK6EAMna5bLBCmZlgqX1JTb4qvvVkmc0l55bX979Y2awPDrPUas13SNznl+VSjPf2+f7Tp/vvHO6z/O972vuDiFEGuSWuwNCiPYhhxciIeTwQiSEHF6IhJDDC5EQcnghEkIOL0RCyOGFSAg5vBAJkW/1BczMcznyd6WZp/zMmupHoVCgtjqaOWekTXNdbBojY5LLZbRNdWaa2hz8vphF1ghyP1vxLKdFzsossdtSj8zFy9+ykdomp/k4WsavuKK3P3j8lX37aJuVQ4PUdvT4ydPuvoq+YJ6mHd7MvgTgGgAPuPt/Z6/L5XLoKpWCtnq9Hjs/PV8zrF23ntomrYPa2KPHsX5Yxm3NPsrMxgMA8vnwbezs7aNthl95ntoq9Rq1FYpd1Farhdux4+clMla5HJ879J5FXH6mXqG2u/7gv1HbEy/wcexcUaS2X//FXwsef9/t/462+dAHfpPa/usf/elBajyLprzHzN4HIHP3bQDWm9mVzZxHCNFemv0Ovx3A/fM/fxvATUvSGyFES2nW4bsBHJ3/eQzAmrONZrbDzPaY2R5F4wlx6dDsd/gJAJ3zP/dg0R8Od98JYCcAZFkmjxfiEqHZFX4vXv8YvwXAgSXpjRCipVgzH7nNrA/AYwC+BeA9AG5w99HQa7NczjvJLn1s95btPNciu9VrOsPXAYDa0FpqQ45/0GFKQmyXPrqD71wqi6kWWRaR2MiQZHnej8kTh6htthLZHS9xRaNAmsXmWOw9u0dUnFxEOlzir5H1mQlq2/Gh/0htAz1cCl41OBA8/s8/+CFtM17hc//v7713r7tvpS+Yp6kV3t3HMLdx9z0Av8CcXQhxadG0Du/uI3h9p14I8SZAj9YKkRByeCESQg4vRELI4YVIiJZHy8GsqYAXFizSGYnWqq9eR22dRS7ZVWqRyCsi8cRkspgM5TUurcTOGQueYaaY5NXVxYNgMMUDSeqRe+k1fj1GfG7w91z3Km/VRERlrB/Ts7PU1t8fjnoDgNHRKWpbMxTu43Qk+m6mevHrs1Z4IRJCDi9EQsjhhUgIObwQCSGHFyIhWr5LnzNDieSTq1T4juTQ0Mrg8RrJBQYAnV3d1OYe2x3ntg6yU1yt8sAfy/HzVcB3lyuRPnZEzlkkKbW8wG9vZTKSMqsQsWU8IITNplqNv+dYAFXsnuUiak2NqBP52O59ZJe+IxowxM85emqcX+7K8PyOhf3MVLha0Cha4YVICDm8EAkhhxciIeTwQiSEHF6IhJDDC5EQLZflioUMl60Ll8gZWH0ZbTdG8plVIznhYkEr+Uipqc5iJ7U9eM//DB5/70c/SttMV3nQRLE2Q22VQi+19ZZ4H2tEsjv+2ghtk+/mVVEcfKyGhoaojZXzGh4+TdtUqs1JTdUql/qYLVYlq1jk47Fx9QZqq1R5oFG2ggcozU6H50gPyf8IACdHX6O2RtEKL0RCyOGFSAg5vBAJIYcXIiHk8EIkhBxeiIRouSxnWQfy/ZuCtv/yMS5tfeZ//0XwuM/wvGn5SA40j0RDZZEItmpn+HqFUqT0U5n3o1TkkVezEVmxPM3lvPHxcCmkGeeSUXchEgGWj1xrikeAVcbCkW8dEcmrO5JrMBbcFovAq9bC77tW55F5sfP9/h/+AbXt+tYuajt4eB+1bV51ffD48WE+vlkTuSEXc8FnMLO8mR0ys13z/6676F4IIdpCMyv89QC+6u6/t9SdEUK0lmY+I9wA4DYz+66ZfcXMWp/qWgixJDTj8E8AuNndbwJwBsCti19gZjvMbI+Z7aksQZYOIcTS0Mzq/Iy7L+zqPA/gysUvcPedAHYCQE9P39IW6xZCNE0zK/x9ZrbFzDIAtwF4eon7JIRoEc2s8J8B8NeYqwP0TXd/JPrqnCHrDktAf/6Xf02b1WvhrnV0cK1mJhK5VIlIMjMV/nfv777+cPB4PsdlrZkaj5abrPL+Txr/MDSQ47LRl//0j4LHs04ueU1HkkdWq1yW687zSLqZaljC/OB/ikQW8luGLOPT00k0JQBk+bBkGiu9FYu+O3zoELXl6ryP79x0FbV95RsPBY8Xh9bTNpa78FJei7lgh3f3ZzG3Uy+EeJOhJ+2ESAg5vBAJIYcXIiHk8EIkhBxeiIRo+WOx+SyHgb5wzbfJWV5bbmpyMnw8cq1CVyQCLOPRbZbjti997RvB45MVLl1ZRLpidc8A4Hd/8zZq27x5M7WBRKMVI5F5FumHd/KEmahz+aojH36q8i/+107aZqTMn8S862uPU5t18anbP/Jy8Pje7zzGzxe5Z5lxCbN3RQ+1rennUYLZ98PXqxFpEwDymWrLCSEuADm8EAkhhxciIeTwQiSEHF6IhGj5Ln29XsfkZHhvPRcJBsg6wruYRsoqAUCJBE0AgEfy1r0e7Xsu7HK9kZJAH/zQb1HbxBiPFlm/MVySCwCyiMrA9pCrdX4tAw/Uqc1GApQieeZA8g1OTHFtZXqsTG2nX9xLbVdv5kEmJ8jtvCNSHuxvv3A3tXVE5unN1/F71pnn7Y68Gi6/NXTVAG1TqF58pLlWeCESQg4vRELI4YVICDm8EAkhhxciIeTwQiRE62U5ByZJGZ/uSM61zlpYWykUeEBC3XhwQa3CJY18gQdOrFmxLmxYx/OV/Y+/fYDa/u22n6a26vp+apuJpPvutnAAx7HT4RJUANDV1UVtp8bHqK2nxINFSqS0lVW4PFjMuHT11hXhoCsAOPSDZ6jtwP7DwePjEzxY63fv/ENqs6kD1NZHAoYAcL0UQGU23K588ghtM1Pm97NRtMILkRByeCESQg4vRELI4YVICDm8EAkhhxciIVouy+VyOXR39wZt05M8Si2fC3etm0TRAcDoFJd/6s7/tt3+3tupbdeTTwWPl198krYZ6uTS1aYhni/uzMQ4ta0c4FFZ+06EI6/ykX7MjPFrjc1wPen48DFqe+sgeW8R2XPqzDC15Z1HOB7Zf5Ta1q5aGTzeO3qStvmzv/s6td358++ktn88NEptiJQ3u7wQfm+z/Wtpm45BLi0feYnfl7NpaIU3szVm9tj8zwUz+ycz221mH27oKkKIS4LzOryZDQC4F8DCUxAfB7DH3bcBeK+ZhZdvIcQlRyMrfA3A7QAWHr/aDuD++Z93A9i69N0SQrSC836Hd/cxADD7UaqTbgALX6DGAKxZ3MbMdgDYAQAdsRznQoi20swu/QSABS/uCZ3D3Xe6+1Z33xorhiCEaC/NOPxeADfN/7wFwIEl640QoqU0I8vdC+ABM/s5AD8J4F9iL67X6yhPhKN83LnMUJ4JS3avkRJUANAT+fpQiSRtPDTCpZVjr7wQPH74xf20TdbNI9Emf+6nqK13oI/anj14nNqefOFQ8Pgvv/s62qac8U9enQWeMPP4wXAkGgA891RYYluzkr+vazatprafvPZqatt3nMt5V1z5luDxpx7npaY6VvPkkSfr11CbRSRHgGf8dAtLyLkcX4NzsfC7Bml4hXf37fP/HwRwC4D/C+CX3f3ieyGEaAtNPXjj7sfw+k69EOJNgh6tFSIh5PBCJIQcXoiEkMMLkRAtj5Zzd9RqZCM/UieuuzecwLCzxGWQ2TpPiDhT5pFXjzz6XWqrV8PyyabLr6Btjp/kUVlHSGQbAEzUuRz2+c99htqGBjYGj79tTfg4ALz12rB0BQAZz/WIF57lKuzD3wxHnBWKXC698/c/QW2e8fXo6rfzJKJPfj/cx3qen2/qNJf5amUehTkxyhNL8rsJVAvh+R2rnRhRlhtGK7wQCSGHFyIh5PBCJIQcXoiEkMMLkRByeCESouWynJkhnw9fphiJDKqSSLrxaS5bZCQCCYhHIQ1083OOeFgGLBW56LL92m3UdnyYy3LlGpcVrc6j247uD0fuHZvmUYC/NPh2asPMFDUNDG3gtny4j6VIAs7OSITjiR++TG1PP7GX2qwSjrRk8xAAchERrRKpjVcq8fqI9Ui7fD58vbrxeVqv8vnRKFrhhUgIObwQCSGHFyIh5PBCJIQcXoiEaPkuPYzvkFciwS5G2hTzvE1WWkFttYkytZ0ZG6O2TrILO1Xlmb1efuUlauvrC5dBAoB3b9lCbZdtuZbaSlk4Z9z7t/HzZRHVAh185/mXtvEyBKv7wjvunQNDtE1XkU/B/QcOUFuW40FUHSvC/ejq4UrHWDlSMqo6S03jZV4ubUUvr9FS7QrbvM7vi1V5AFijaIUXIiHk8EIkhBxeiISQwwuREHJ4IRJCDi9EQrQ+eAaGnIWDU7IOLpMYaVPMeJBDtVCktjp47jFEcqexfHx10j8A+IV3voPaXj59KnItHrSy6W287NLanlXB47kSH49jr3IZanCAB7QMDfRT27oN4cCaU6M8YOj0a/y+eCRWxMElqiwLS3aFSE64onFXGBnjY2XGJcyxGZ4csK8UHuN8RG6ciMz9RmlohTezNWb22PzPG8zsiJntmv8Xnm1CiEuO867wZjaAuQKSC2k2fwbAZ939nlZ2TAix9DSywtcA3A5g4XG0GwB8zMweN7PPt6xnQogl57wO7+5j7n72l5gHAWxz9xsBXGVm1y9uY2Y7zGyPme2pzPLHEoUQ7aWZXfrd7j4+//PzAK5c/AJ33+nuW919a6HIN46EEO2lGYd/yMzWmVkXgF8F8OwS90kI0SKakeU+DeBRALMAvujuL8Re7ACYumLgOb+6LSxbTLOyVQC6clxOmi1wSWNyhkc8zdbCWs7VG9bRNhvespbaJiKRVy/tO0ptv/LTPPKtXAm/txnn0tX0LLfVKly/iqSFw+VXbA4eXzfFc9q99NzT1Hbk+Anej0juN5CosnokEq0eidwc7OFRb/39vGTXqZFXqK2K8FytFPj8xvjFfz1u2OHdffv8/48CeNtFX1kI0Xb0pJ0QCSGHFyIh5PBCJIQcXoiEkMMLkRAtj5bLmdFEkCwSDQDGamGpLHMepbaih0tvhSJPHlk4/Rq1jUyFI57WDvHz9USSF65evZrant53iNqGR4apra9/IHi8PDZJ27w6wiPzisbln/Ure6ht39HDweOjwzxC8OSpEWorT3PZtqeDR5XNEpm1oxRJEBmJfjxzhsu2d//VH1PbHbfeTG0FUlbsc5/4bdrm7x/8FrV971/2UNvZaIUXIiHk8EIkhBxeiISQwwuREHJ4IRJCDi9EQrRclnM4qjR6iUcoVYhkV3MeyVWrcBlnsDdcfw0AVnR1U9sgkexKJT50HZHknH3dPOnhql7ej6lpnhDx6ivCtdtOHOZyY3mS5yk4VOOyXCmS8HNVfzgC7Mj+8eBxAJia5XJY3SNRjBUu6WYIv7fZGf6ePSL3zuS53PuXO/+M2saOcTny/7381eDx3/ncF2mb00f3UVujaIUXIiHk8EIkhBxeiISQwwuREHJ4IRKi9bv07qiQ3fNY8Ex3Z3jHd3SyTNvsP8Zzwv3U1bxUU0cvz4XX3RUun9RZjJSniigJHqmf9JZIYM3BV49TW74e3mH+xjcfom2u2ngZtb1y+Dlqq/3av6G2634irISMz3KF4R8feoTacpEUbsZjZ1C1sCrE5iEAVDySS67G233tvgeoLV/knaxUwm8un/H5UchxJaFRtMILkRByeCESQg4vRELI4YVICDm8EAkhhxciIVouywHG84VFAjGcSFsD3Tyn2lSVyyeP7nmS2q6+jJeNKhTCgTDXXnZO0dwfMTzM88+NR8pa5SNaUyzn2sHjYTnywAvP0DZPfv+fqa2zxINFrrmOl7w6NbQxeLwQCT6pznDtzSPBVbWYRGXhdtORACTP+Pl6IvVQTzg/Zykiy/WRvIcT/FIwcLm3Uc67wptZv5k9aGYPm9k/mFnRzL5kZrvN7M6L7oEQom008pH+DgB3ufstAE4AeD+AzN23AVhvZueUixZCXJqc9yO9u3/hrF9XAfgAgIUg4G8DuAnAS0vfNSHEUtPwpp2Z3QhgAMBhAAtfGscArAm8doeZ7TGzPdXZiy9xK4RYGhpyeDMbBHA3gA9jbl9h4eHzntA53H2nu2919635YmTHQwjRVhrZtCsCuB/AJ939IIC9mPsYDwBbABxoWe+EEEtKI7LcRwC8C8CnzOxTAL4M4LfMbD2A9wC4Id7cUScSWzUiu0zNhAWKwRWraJvpOo94ikkaz73Mc4V15sPSyq/87FbaZpJEQgHA6pXnfAP6EXuefJraenu5HImZcATh4Eo+Vsde5e95fS+XKUdP8XJYYyNhqaleCUevAfE8hLmIFMnyJAIALLyOxdpkGZfQRiYjOfkmeDmvUi+XI70atlmRv+didvEqeiObdvcAuOcNnTL7JoBbAPyJu49edC+EEG2hqT8Z7j6CuY/5Qog3EXq0VoiEkMMLkRByeCESQg4vREK0IYklUCGJG2OlpjKEZYvZyTHaZrbCz5evxxJL8r97s9NhKScWvcYi/QCgPM4lnt4unkyzPMbjqF4jDzd19XJZLsvzclizHf3UtnqQJ7984cWw1Ne1gpfXQizhJ28VTYBquXDLWJuic1mug5SuAoCpWR79OAhe3qxSCbfLwMuNGfj8bhSt8EIkhBxeiISQwwuREHJ4IRJCDi9EQsjhhUiIlstyBiBP9JVINS9M18JyWL7Ku1yPJERERNIwiwhAGYn0i+RQjATtYTQiK+bzkfcWkZRe3H8geLxvA5f5bt3069TWVeKSXbnGE3T29IVlqKkKrwcYkzCbJSa/MWaqvE05IvdORhKnduS5HFkhyUxzkfHoyHj0XaNohRciIeTwQiSEHF6IhJDDC5EQcnghEqL1u/RmdPe5Vudb3axNLOAmFtCSy/G/bR0dfFea5VWL5UeL7bZnNb7TOjrKs4WVSnzHt4f0JSvxgJAsx0sk9ZR4//MR29hYODConvGxysV2nqM7+Be+ux+bO4jYZiMBMrF5Va/xc06Tcw5E5uJMrP8NohVeiISQwwuREHJ4IRJCDi9EQsjhhUgIObwQCXFeWc7M+gH8zfxrJwDcDuBlAAsJzD7u7v/K2rvXUakRCSgiM9RJdMp0k9VoY8EuRefSUDEftuWMD13fCl4WavIEl3hi8mDsfZdK4Zxr5QmePy+LyGHlOr9Wf4EH5Hg9fJ+LRd4GJN/hHM3lDTRSair2nuuR4KrpGX6tXBaR0Wo8sGZqNjxWgxG5t6vr4guzNrLC3wHgLne/BcAJAJ8A8FV33z7/jzq7EOLSopHacl8469dVmKsPf5uZ/SyAgwD+vbtHKvsJIS4VGv4Ob2Y3AhgA8DCAm939JgBnANzaor4JIZaYhh6tNbNBAHcD+A0AJ9x94Yvo8wCuDLx+B4AdAFCMPBIqhGgv513hzayIuUqxn3T3gwDuM7MtZpYBuA3AOUXN3X2nu291962FAn+eWwjRXhr5SP8RAO8C8Ckz2wXgOQD3AXgKwOPu/kjruieEWEoa2bS7B8A9iw5/utELuAE1EnFWi8hhs7WwNGREJgPOEy0XyXdXrnGpbHo6nOusUuGSS8zW39NLbWMjPFpuxnkfy9NE9qxxOake2Wed+/AWZnic5+Tr7g6XSRo5w+VBJxIaEF+NYn20XNhWjVzLI3LY4Bp+z8rTfDw8Mh/HJ8LtRp495wPzj8gN8n40ih68ESIh5PBCJIQcXoiEkMMLkRByeCESQg4vREK0PIllznLoKoajfGJSU64QljQqM80lsUQk2WBXxttlWTjSK5a8cGaaS4Axya6jg0dDnTw1RW35QliGqpHjADBLSh0BQKmri9rqEfnKEL7e+NgkbYNcpCxUZIyziMRWJUXMSnl+n+tVLmGeGDtDbZ19PDKyI8cfOuvrDI9x6bKraJvqayeorVG0wguREHJ4IRJCDi9EQsjhhUgIObwQCSGHFyIhWi7L1es1lMvloC0mUbEkhbVIMq1YYsOYLSaxdXWFZZfh4WF+rUhexti1Yn286iou1xw5ejh4PDK8sMi1ikRGBeJ182aI1Ldr1y7ej4gkGo1+jCSkLObJGDc5P96xfg21TeS5hLl392PUNjg4GDw+HZFLezojyUAbRCu8EAkhhxciIeTwQiSEHF6IhJDDC5EQcnghEqLlslw+l2GIJDfMRaLDyuNhKa87Esllzt9ODTyCra/UR22jM+EEkWfO8AiqUgeXT/r6+LViteViEubGdeuDx0+Pcumwu8DHfnqaR7dZRFYcYbXsCvx9RQMcI9FtsQSRBVLvrV7n0tvqgbBMBgBH9x2ltum+fmo7fOAAtV2zeUPweDmSbLV7gEfmNYpWeCESQg4vRELI4YVICDm8EAkhhxciIS6keuy7ADzp7qcv5ALFYhGbN14WtJWneJ62vo7wbnxHke/Sz85GyklVeH6xaqR8VUc9XP02iwRvxApoep3vth84fJLaNq5ZS20TREno7+WKwPAIVxn2HwkH4wDA29aHFQEAmCiH+xELgsmySN66SGBNDHa9XOR873jnddRWO8FzyU1F1JNcjqs1pVJYSbhiKKxoAUAeF1+YtZHqsesA/B8A7wbwqJmtMrMvmdluM7vzonsghGgbjXykfzuA/+zunwXwEIBfBJC5+zYA683snPrwQohLk0aqxz4CAGb285hb5QcxVy8eAL4N4CYAL7Wqg0KIpaOhTTub+1J0O4AKAAOw8OjRGIBzsgOY2Q4z22Nme6Yj39OFEO2lIYf3OX4bwG4ANwBY2I3oCZ3D3Xe6+1Z331pagiwdQoiloZFNu98zsw/O/7oCwB9j7mM8AGwBcKA1XRNCLDWNyHI7AdxvZv8BwLMAvg7gO2a2HsB7MLfiU9wdlXo4yVshkh+NBZlMlnnOr3xEDuuOyD8ekdhAyietW8tlstFREkSCeL67w8e4LPfy8ePUtpHkRxt9jfdjmEh5ANBXigQ1Rcpo/fAHLwaPWy1STsoipcPqfH4UI/eaUa/w4Jm+wQFqe3rvE9TWtelyaqtFbrbnw2P8/D4uAV65eTO1NUojm3YjAG45+5iZbZ8/9ifuPnrRvRBCtIWmwmPn/wjcf94XCiEuKfRorRAJIYcXIiHk8EIkhBxeiISwWImdJbmA2SkAB886tBLABUXctQj1442oH2/kzdaPTe6+6nwvarnDn3NBsz3uvrWtF1U/1A/1A4A+0guRFHJ4IRJiORx+5zJcM4T68UbUjzfyY9mPtn+HF0IsH/pIL0RCJOXwZpY3s0Nmtmv+H89c+GOOma0xs8fmf95gZkfOGpfzyjs/TphZv5k9aGYPm9k/mFlxueaJmQ2a2S1mtrIV52+rw18CyS+vB/BVd98+/+9fl6MTi5ytYGb/ND8uH27T9QcA3AtgIUXqzwD47FnjcqpN/Qg52nLMkTsA3OXutwA4AeATWIZ50o6EsW1zeDN7H5Y/+eUNAG4zs++a2VfMrOXFNBcTcLaPA9gzPy7vNbPeNnSjhrmUZWPzv98A4GNm9riZfb4N119gsaO9H8swR9z9C+7+8PyvqwBUsTzzpOUJY9u5wm/Huckv280TAG5295sAnAFw6zL0YbGzbcfr47IbQMsf9nD3sUV5DB4EsM3dbwRwlZld3+o+zPdjsaN9AMs4R8zsRgADAB7GMswTd3/E3b93VsLYX8USj0c7V7huvDH55U+08doLPOPuCylzngfQ9k8Z7j4GvKFYwuJxOScpaBvYHRiXZ9p18bMc7QCWaY7MF1u5G8BvADixXPPkPAljL3o82rnCT+A8yS/bwH1mtsXMMgC3AXh6GfqwmEthXB4ys3Vm1oW5VeXZdl34LEf7MJZpLMysiLmV9JPufhDLOE8uNGHshdLOybUXy5/88jMA7gPwFIDHF3LuLzOXwrh8GsCjAL4H4Ivu/kI7LhpwtOUai49grpTap8xsF4DnsAzzpB0JY9v24I2Z9QF4DMC3MJ/8MuV8eGa2y923m9kmAA8AeATANsyNSyTr448PZvZRAJ/D6yvolwH8DhKdI/MbuvcD6MDcp6xPAvgOlnA82vqk3fwbugXAd9ydp+dMjPkMwDcBeCilCR5Cc+SNLPV46NFaIRIiqSfthEgdObwQCSGHFyIh5PBCJIQcXoiE+P+nXPis4+XYgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am 99.74% sure this is a Dog\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am 96.80% sure this is a Cat\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,10):\n",
    "    p = predictions[i]\n",
    "    \n",
    "    if round(p[1]) == 1: \n",
    "        print('I am {:.2%} sure this is a Dog'.format(p[1]))\n",
    "    else: \n",
    "        print('I am {:.2%} sure this is a Cat'.format(1 - p[1]))\n",
    "        \n",
    "    plt.imshow(test_data[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练得到的猫狗识别模型的准确度是：0.793067\n"
     ]
    }
   ],
   "source": [
    "predictions_test_array = []\n",
    "test_label_array = []\n",
    "\n",
    "# 将onehot编码的数据进行格式转换\n",
    "for p in predictions:\n",
    "    if round(p[1]) == 1:\n",
    "        predictions_test_array.append(1)\n",
    "    else:\n",
    "        predictions_test_array.append(0)\n",
    "        \n",
    "for t in test_label:\n",
    "    if int(t[1]) == 1:\n",
    "        test_label_array.append(1)\n",
    "    else:\n",
    "        test_label_array.append(0)\n",
    "        \n",
    "acc = accuracy_score(test_label_array, predictions_test_array)\n",
    "\n",
    "print('训练得到的猫狗识别模型的准确度是：%f' % acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "减小图片输入像素尺寸，也会不利于识别。"
   ]
  }
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