train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'cv2'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-6847526cd588>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mskimage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'cv2'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting opencv-python\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/34/a3/403dbaef909fee9f9f6a8eaff51d44085a14e5bb1a1ff7257117d744986a/opencv_python-4.2.0.32-cp37-cp37m-manylinux1_x86_64.whl (28.2MB)\n",
      "\u001b[K     |████████████████████████████████| 28.2MB 173kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.14.5 in /home/ojas/anaconda3/lib/python3.7/site-packages (from opencv-python) (1.17.2)\n",
      "Installing collected packages: opencv-python\n",
      "Successfully installed opencv-python-4.2.0.32\n"
     ]
    }
   ],
   "source": [
    "!pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f4/28/96efba1a516cdacc2e2d6d081f699c001d414cc8ca3250e6d59ae657eb2b/tensorflow-1.14.0-cp37-cp37m-manylinux1_x86_64.whl (109.3MB)\n",
      "\u001b[K    100% |████████████████████████████████| 109.3MB 11kB/s eta 0:00:0111% |▍                               | 1.4MB 1.7MB/s eta 0:01:05    5% |█▊                              | 6.0MB 2.7MB/s eta 0:00:39    7% |██▌                             | 8.4MB 2.8MB/s eta 0:00:36    28% |█████████                       | 30.6MB 1.3MB/s eta 0:00:59    28% |█████████▏                      | 31.3MB 2.2MB/s eta 0:00:36    44% |██████████████▍                 | 49.0MB 1.9MB/s eta 0:00:32    59% |███████████████████▏            | 65.5MB 2.2MB/s eta 0:00:21\n",
      "\u001b[?25hCollecting grpcio>=1.8.6 (from tensorflow)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/79/a8/de2806fa43a0f4a4293ca268739e3c10478aa3cada1eb516b8d0fbd1d102/grpcio-1.27.2-cp37-cp37m-manylinux1_x86_64.whl (2.7MB)\n",
      "\u001b[K    100% |████████████████████████████████| 2.7MB 344kB/s ta 0:00:011\n",
      "\u001b[?25hRequirement already satisfied: wheel>=0.26 in /usr/lib/python3/dist-packages (from tensorflow) (0.32.3)\n",
      "Collecting google-pasta>=0.1.6 (from tensorflow)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c3/fd/1e86bc4837cc9a3a5faf3db9b1854aa04ad35b5f381f9648fbe81a6f94e4/google_pasta-0.1.8-py3-none-any.whl (57kB)\n",
      "\u001b[K    100% |████████████████████████████████| 61kB 1.5MB/s ta 0:00:011\n",
      "\u001b[?25hRequirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.0.8)\n",
      "Requirement already satisfied: protobuf>=3.6.1 in /usr/lib/python3/dist-packages (from tensorflow) (3.6.1)\n",
      "Collecting tensorboard<1.15.0,>=1.14.0 (from tensorflow)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/91/2d/2ed263449a078cd9c8a9ba50ebd50123adf1f8cfbea1492f9084169b89d9/tensorboard-1.14.0-py3-none-any.whl (3.1MB)\n",
      "\u001b[K    100% |████████████████████████████████| 3.2MB 319kB/s ta 0:00:011\n",
      "\u001b[?25hCollecting astor>=0.6.0 (from tensorflow)\n",
      "  Downloading https://files.pythonhosted.org/packages/c3/88/97eef84f48fa04fbd6750e62dcceafba6c63c81b7ac1420856c8dcc0a3f9/astor-0.8.1-py2.py3-none-any.whl\n",
      "Collecting absl-py>=0.7.0 (from tensorflow)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1a/53/9243c600e047bd4c3df9e69cfabc1e8004a82cac2e0c484580a78a94ba2a/absl-py-0.9.0.tar.gz (104kB)\n",
      "\u001b[K    100% |████████████████████████████████| 112kB 1.6MB/s ta 0:00:01\n",
      "\u001b[?25hCollecting wrapt>=1.11.1 (from tensorflow)\n",
      "  Downloading https://files.pythonhosted.org/packages/ee/bc/7993faa8084b5a5dbabb07a197ae1b7590da4752dc80455d878573553e2f/wrapt-1.12.0.tar.gz\n",
      "Collecting gast>=0.2.0 (from tensorflow)\n",
      "  Downloading https://files.pythonhosted.org/packages/d6/84/759f5dd23fec8ba71952d97bcc7e2c9d7d63bdc582421f3cd4be845f0c98/gast-0.3.3-py2.py3-none-any.whl\n",
      "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/lib/python3/dist-packages (from tensorflow) (1.16.2)\n",
      "Collecting termcolor>=1.1.0 (from tensorflow)\n",
      "  Downloading https://files.pythonhosted.org/packages/8a/48/a76be51647d0eb9f10e2a4511bf3ffb8cc1e6b14e9e4fab46173aa79f981/termcolor-1.1.0.tar.gz\n",
      "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.1.0)\n",
      "Requirement already satisfied: six>=1.10.0 in /usr/lib/python3/dist-packages (from tensorflow) (1.12.0)\n",
      "Collecting tensorflow-estimator<1.15.0rc0,>=1.14.0rc0 (from tensorflow)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3c/d5/21860a5b11caf0678fbc8319341b0ae21a07156911132e0e71bffed0510d/tensorflow_estimator-1.14.0-py2.py3-none-any.whl (488kB)\n",
      "\u001b[K    100% |████████████████████████████████| 491kB 610kB/s ta 0:00:01    94% |██████████████████████████████▏ | 460kB 1.8MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from keras-applications>=1.0.6->tensorflow) (2.10.0)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /usr/lib/python3/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow) (41.1.0)\n",
      "Collecting markdown>=2.6.8 (from tensorboard<1.15.0,>=1.14.0->tensorflow)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ab/c4/ba46d44855e6eb1770a12edace5a165a0c6de13349f592b9036257f3c3d3/Markdown-3.2.1-py2.py3-none-any.whl (88kB)\n",
      "\u001b[K    100% |████████████████████████████████| 92kB 2.1MB/s ta 0:00:01\n",
      "\u001b[?25hCollecting werkzeug>=0.11.15 (from tensorboard<1.15.0,>=1.14.0->tensorflow)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ba/a5/d6f8a6e71f15364d35678a4ec8a0186f980b3bd2545f40ad51dd26a87fb1/Werkzeug-1.0.0-py2.py3-none-any.whl (298kB)\n",
      "\u001b[K    100% |████████████████████████████████| 307kB 1.9MB/s ta 0:00:01\n",
      "\u001b[?25hBuilding wheels for collected packages: absl-py, wrapt, termcolor\n",
      "  Running setup.py bdist_wheel for absl-py ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/ojas/.cache/pip/wheels/8e/28/49/fad4e7f0b9a1227708cbbee4487ac8558a7334849cb81c813d\n",
      "  Running setup.py bdist_wheel for wrapt ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/ojas/.cache/pip/wheels/54/f9/95/099544e9f879f719b14cf567fabb5aa7984263df0f025f3eef\n",
      "  Running setup.py bdist_wheel for termcolor ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/ojas/.cache/pip/wheels/7c/06/54/bc84598ba1daf8f970247f550b175aaaee85f68b4b0c5ab2c6\n",
      "Successfully built absl-py wrapt termcolor\n",
      "Installing collected packages: grpcio, google-pasta, absl-py, markdown, werkzeug, tensorboard, astor, wrapt, gast, termcolor, tensorflow-estimator, tensorflow\n",
      "\u001b[33m  The script markdown_py is installed in '/home/ojas/.local/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
      "\u001b[33m  The script tensorboard is installed in '/home/ojas/.local/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
      "\u001b[33m  The scripts freeze_graph, saved_model_cli, tensorboard, tf_upgrade_v2, tflite_convert, toco and toco_from_protos are installed in '/home/ojas/.local/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
      "Successfully installed absl-py-0.9.0 astor-0.8.1 gast-0.3.3 google-pasta-0.1.8 grpcio-1.27.2 markdown-3.2.1 tensorboard-1.14.0 tensorflow-1.14.0 tensorflow-estimator-1.14.0 termcolor-1.1.0 werkzeug-1.0.0 wrapt-1.12.0\n"
     ]
    }
   ],
   "source": [
    "!pip3 install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: keras in /usr/local/lib/python3.7/dist-packages (2.3.1)\n",
      "Requirement already satisfied: six>=1.9.0 in /usr/lib/python3/dist-packages (from keras) (1.12.0)\n",
      "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.7/dist-packages (from keras) (1.0.8)\n",
      "Requirement already satisfied: pyyaml in /usr/lib/python3/dist-packages (from keras) (5.1.2)\n",
      "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from keras) (2.10.0)\n",
      "Requirement already satisfied: numpy>=1.9.1 in /usr/lib/python3/dist-packages (from keras) (1.16.2)\n",
      "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.7/dist-packages (from keras) (1.4.1)\n",
      "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.7/dist-packages (from keras) (1.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip3 install keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import cv2\n",
    "import os\n",
    "import skimage\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from keras.datasets import mnist\n",
    "from keras.utils import np_utils\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "<class 'numpy.ndarray'>\n",
      "dsgdb9nsd_010300.jpeg 10000\n"
     ]
    }
   ],
   "source": [
    "# Load data\n",
    "\n",
    "nums = re.compile(r'(\\d+)')\n",
    "def key(value):\n",
    "    parts = nums.split(value)\n",
    "    parts[1::2] = map(int, parts[1::2])\n",
    "    return parts\n",
    "\n",
    "data1 = pd.read_csv(\"/home/ojas/sop_qm9/potential_energy.csv\")\n",
    "# data2 = pd.read_csv(\"/home/nahush/Desktop/nnfl-assignment-i/test_data_file_id.csv\")\n",
    "# t = cv2.imread('./data/final_train/682.jpg')\n",
    "\n",
    "trainingImages1 = []\n",
    "\n",
    "# for i in trainingImages:\n",
    "#     j = skimage.transform.rescale(i, 0.25, anti_aliasing = True)\n",
    "#     trainingImages1.append(j.astype('float32') / 255.)\n",
    "\n",
    "#test_path = './data/final_test'\n",
    "\n",
    "counter = 0\n",
    "\n",
    "for i in sorted(os.listdir('/home/ojas/sop_qm9/images/'),key=key):\n",
    "    imgFile='/home/ojas/sop_qm9/images/'+i\n",
    "    img = cv2.imread(imgFile)\n",
    "    img = cv2.resize(img,(64,64))\n",
    "    print(type(img))\n",
    "    img = img.astype('float32')\n",
    "    trainingImages1.append(img/255.)\n",
    "    counter = counter + 1\n",
    "    print(i + \" \" +str(counter))\n",
    "\n",
    "y_train = data1['potential_energy'].to_numpy() \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -40.5236795  -56.5602457  -76.4260771 ... -385.0537791 -401.1508097\n",
      " -313.2483627]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000, 64, 64, 3), (10000,))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load data part2\n",
    "\n",
    "x_train = np.asarray(trainingImages1)\n",
    "\n",
    "x_train.shape, y_train.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample image\n",
    "\n",
    "\n",
    "\n",
    "# x_samp, y_samp = x_train[0], y_train[0]\n",
    "\n",
    "# print (\"Shape of x_samp =\", x_samp.shape)\n",
    "# plt.imshow(x_samp)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 10\n",
    "# NUM_CLASSES = 2 # (0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000, 64, 64, 3), (10000,))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# One hot encoding\n",
    "# y_train = np_utils.to_categorical(y_train)\n",
    "\n",
    "x_train.shape, y_train.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9500, 64, 64, 3) (9500,)\n",
      "(500, 64, 64, 3) (500,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x, x_test, y, y_test = train_test_split(x_train, y_train, test_size=0.05, random_state=42)\n",
    "print(x.shape, y.shape)\n",
    "print(x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ojas/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3, 3), input_shape=(64,64,3), activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Conv2D(64, (3, 3),activation='relu'))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Conv2D(128, (5, 5), activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dense(1, activation='linear'))\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import optimizers\n",
    "opt = optimizers.Adam(lr = 1e-3, decay = 1e-3 / 200)\n",
    "model.compile(loss=\"mean_absolute_percentage_error\", optimizer=opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 62, 62, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 29, 29, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 27, 27, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 13, 13, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 13, 13, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 9, 9, 128)         204928    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               524544    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 786,049\n",
      "Trainable params: 786,049\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 9500 samples, validate on 500 samples\n",
      "Epoch 1/10\n",
      "9500/9500 [==============================] - 63s 7ms/step - loss: 11.6384 - val_loss: 19.7382\n",
      "Epoch 2/10\n",
      "9500/9500 [==============================] - 64s 7ms/step - loss: 11.8763 - val_loss: 18.7724\n",
      "Epoch 3/10\n",
      "9500/9500 [==============================] - 63s 7ms/step - loss: 11.5878 - val_loss: 13.7496\n",
      "Epoch 4/10\n",
      "9500/9500 [==============================] - 63s 7ms/step - loss: 11.6255 - val_loss: 15.5905\n",
      "Epoch 5/10\n",
      "9500/9500 [==============================] - 64s 7ms/step - loss: 11.7071 - val_loss: 14.9457\n",
      "Epoch 6/10\n",
      "9500/9500 [==============================] - 63s 7ms/step - loss: 11.6645 - val_loss: 14.3131\n",
      "Epoch 7/10\n",
      "9500/9500 [==============================] - 65s 7ms/step - loss: 11.5465 - val_loss: 18.6461\n",
      "Epoch 8/10\n",
      "9500/9500 [==============================] - 63s 7ms/step - loss: 11.3958 - val_loss: 15.3878\n",
      "Epoch 9/10\n",
      "9500/9500 [==============================] - 63s 7ms/step - loss: 11.5520 - val_loss: 23.4466\n",
      "Epoch 10/10\n",
      "9500/9500 [==============================] - 65s 7ms/step - loss: 11.3360 - val_loss: 14.0712\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7fd830280350>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x, y, validation_split = 0.1, validation_data = (x_test, y_test), epochs=EPOCHS, batch_size=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 9000 samples, validate on 1000 samples\n",
      "Epoch 1/5\n",
      "9000/9000 [==============================] - 61s 7ms/step - loss: 11.8068 - val_loss: 17.8147\n",
      "Epoch 2/5\n",
      "9000/9000 [==============================] - 62s 7ms/step - loss: 11.7469 - val_loss: 17.1766\n",
      "Epoch 3/5\n",
      "9000/9000 [==============================] - 61s 7ms/step - loss: 11.7353 - val_loss: 17.1942\n",
      "Epoch 4/5\n",
      "9000/9000 [==============================] - 60s 7ms/step - loss: 11.6349 - val_loss: 15.6118\n",
      "Epoch 5/5\n",
      "9000/9000 [==============================] - 62s 7ms/step - loss: 11.4700 - val_loss: 15.9824\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7fd828304610>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, validation_split = 0.1, validation_data = None, epochs=5, batch_size=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model.save('model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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