Prediction.ipynb

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   "execution_count": 1,
   "id": "fitting-carrier",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import tensorflow.keras as keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "sharp-atmosphere",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.load_model(\"model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "moderate-presence",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(horizontal_flip=True,\n",
    "                                   vertical_flip=False,\n",
    "                                   rescale=1./255,\n",
    "                                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "smoking-watson",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1 images belonging to 1 classes.\n"
     ]
    }
   ],
   "source": [
    "test_path = \"Test\"\n",
    "test = train_datagen.flow_from_directory(test_path,\n",
    "                                          class_mode=\"binary\",\n",
    "                                          target_size=(96, 96),\n",
    "                                          batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dated-forestry",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "different-kentucky",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.000000e+00 4.380703e-11]]\n"
     ]
    }
   ],
   "source": [
    "print(predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "japanese-novelty",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = np.argmax(predict, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "auburn-group",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "pharmaceutical-chart",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived\n"
     ]
    }
   ],
   "source": [
    "if res == 0:\n",
    "    print('Survived')\n",
    "else:\n",
    "    print('Povtorka')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "indirect-italy",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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