Convolutional neural network(Cats vs Dogs) Kaggem competition.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "collapsed": true
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   "outputs": [],
   "source": [
    "import os, cv2, random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm import tqdm    #Helps in vis\n",
    "from random import shuffle \n",
    "\n",
    "%matplotlib inline \n",
    "\n",
    "\n",
    "TRAIN_DIR = '../input/train'\n",
    "TEST_DIR = '../input/test'\n",
    "IMG_SIZE = 224\n",
    "\n",
    "\n",
    "LR = 1e-3\n",
    "\n",
    "MODEL_NAME = 'dogsvscats-{}-{}.model'.format(LR, '2conv-basic') # just so we remember which saved model is which, sizes must match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "collapsed": true
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   "source": [
    "### converting the images and labels to array information that we can pass through our network.\n",
    "### To do this, we'll need a helper function to convert the image name to an array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "1ee555f946ea9f10138aeccd7696d3f0171643e7",
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   "outputs": [],
   "source": [
    "#  converting the images and labels to array\n",
    "def label_img(img):\n",
    "    word_label = img.split('.')[-3]\n",
    "    # conversion to one-hot array [cat,dog]\n",
    "    #                            [much cat, no dog]\n",
    "    if word_label == 'cat': return [1,0]\n",
    "    #                             [no cat, very doggo]\n",
    "    elif word_label == 'dog': return [0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "03ee325b4f73215e82951775fd6bdb3e19032593",
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   "outputs": [],
   "source": [
    "#function to fully process the training images and their labels into arrays:\n",
    "\n",
    "def create_train_data():\n",
    "    training_data = []\n",
    "    for img in tqdm(os.listdir(TRAIN_DIR)):\n",
    "        # tqdm is used here only for the visualization of process ongoing\n",
    "        # i.e  to measure where you are in a process, rather than printing things out at intervals...etc,\n",
    "        # so it gives a progress bar. Super neat.\n",
    "        \n",
    "        label = label_img(img)\n",
    "        path = os.path.join(TRAIN_DIR,img)\n",
    "        img = cv2.imread(path,cv2.IMREAD_GRAYSCALE)\n",
    "        img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))\n",
    "        training_data.append([np.array(img),np.array(label)])\n",
    "        \n",
    "    shuffle(training_data)\n",
    "    np.save('train_data.npy', training_data)\n",
    "    return training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "7ecc4bfa00209371327d409d1e7d3b3fd4ac88d2",
    "collapsed": true
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   "outputs": [],
   "source": [
    "#function to process the testing data\n",
    "\n",
    "def process_test_data():\n",
    "    testing_data = []\n",
    "    for img in tqdm(os.listdir(TEST_DIR)):\n",
    "        path = os.path.join(TEST_DIR,img)\n",
    "        img_num = img.split('.')[0]\n",
    "        img = cv2.imread(path,cv2.IMREAD_GRAYSCALE)\n",
    "        img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))\n",
    "        testing_data.append([np.array(img), img_num])\n",
    "        \n",
    "    shuffle(testing_data)\n",
    "    np.save('test_data.npy', testing_data)\n",
    "    return testing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "7043715863f316a91a425b6002f06c313368a75e",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25000/25000 [00:44<00:00, 566.32it/s]\n"
     ]
    }
   ],
   "source": [
    "train_data = create_train_data()\n",
    "\n",
    "# If already created the dataset:\n",
    "#train_data = np.load('train_data.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d31e67b84c935d7f564c3acbc3a649670014b5f7"
   },
   "source": [
    "## Convolutional Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "f34884035ad8d6ac990f2807540254990741dd9f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# tflearn codes for building models\n",
    "import tflearn\n",
    "from tflearn.layers.conv import conv_2d, max_pool_2d\n",
    "from tflearn.layers.core import input_data, dropout, fully_connected\n",
    "from tflearn.layers.estimator import regression\n",
    "\n",
    "convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')\n",
    "\n",
    "convnet = conv_2d(convnet, 32, 5, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 5)\n",
    "\n",
    "convnet = conv_2d(convnet, 64, 5, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 5)\n",
    "\n",
    "convnet = fully_connected(convnet, 1024, activation='relu')\n",
    "convnet = dropout(convnet, 0.8)\n",
    "\n",
    "convnet = fully_connected(convnet, 2, activation='softmax')\n",
    "convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')\n",
    "\n",
    "model = tflearn.DNN(convnet, tensorboard_dir='log')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "3199ad1e776360e3c9b927b9c853bce84c608bc3",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#We want to be saving our model after every session, and reloading it if we have a saved version, \n",
    "# so add this:\n",
    "\n",
    "if os.path.exists('{}.meta'.format(MODEL_NAME)):\n",
    "    \n",
    "    model.load(MODEL_NAME)\n",
    "    print('model loaded!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "6234ee90991584cc6824bd283b545d5bf882e7d1",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#split out training and testing data:\n",
    "\n",
    "train = train_data[:-500]\n",
    "test = train_data[-500:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "5b54c28b41590316e6f79b075e017849a27922f7",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Now creating our data arrays. For some reason, typical numpy logic like:\n",
    "\n",
    "\n",
    "\n",
    "X = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE,IMG_SIZE,1)\n",
    "Y = [i[1] for i in train]\n",
    "\n",
    "test_x = np.array([i[0] for i in test]).reshape(-1,IMG_SIZE,IMG_SIZE,1)\n",
    "test_y = [i[1] for i in test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "c24bc108fa2a537e955a08488840ecbf21e31dde"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------\n",
      "Run id: dogsvscats-0.001-2conv-basic.model\n",
      "Log directory: log/\n",
      "---------------------------------\n",
      "Training samples: 98000\n",
      "Validation samples: 2000\n",
      "--\n"
     ]
    },
    {
     "ename": "InvalidArgumentError",
     "evalue": "You must feed a value for placeholder tensor 'input_6/X' with dtype float and shape [?,224,224,1]\n\t [[node input_6/X (defined at /opt/conda/lib/python3.6/site-packages/tflearn/layers/core.py:81)  = Placeholder[dtype=DT_FLOAT, shape=[?,224,224,1], _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n\nCaused by op 'input_6/X', defined at:\n  File \"/opt/conda/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/opt/conda/lib/python3.6/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 497, in start\n    self.io_loop.start()\n  File \"/opt/conda/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 132, in start\n    self.asyncio_loop.run_forever()\n  File \"/opt/conda/lib/python3.6/asyncio/base_events.py\", line 422, in run_forever\n    self._run_once()\n  File \"/opt/conda/lib/python3.6/asyncio/base_events.py\", line 1434, in _run_once\n    handle._run()\n  File \"/opt/conda/lib/python3.6/asyncio/events.py\", line 145, in _run\n    self._callback(*self._args)\n  File \"/opt/conda/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 122, in _handle_events\n    handler_func(fileobj, events)\n  File \"/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 450, in _handle_events\n    self._handle_recv()\n  File \"/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 480, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 432, in _run_callback\n    callback(*args, **kwargs)\n  File \"/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"/opt/conda/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/opt/conda/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2662, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2785, in _run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2901, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2961, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-15-cf27c8232561>\", line 7, in <module>\n    convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')\n  File \"/opt/conda/lib/python3.6/site-packages/tflearn/layers/core.py\", line 81, in input_data\n    placeholder = tf.placeholder(shape=shape, dtype=dtype, name=\"X\")\n  File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py\", line 1745, in placeholder\n    return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)\n  File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 5020, in placeholder\n    \"Placeholder\", dtype=dtype, shape=shape, name=name)\n  File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\", line 488, in new_func\n    return func(*args, **kwargs)\n  File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\", line 3274, in create_op\n    op_def=op_def)\n  File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\", line 1770, in __init__\n    self._traceback = tf_stack.extract_stack()\n\nInvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input_6/X' with dtype float and shape [?,224,224,1]\n\t [[node input_6/X (defined at /opt/conda/lib/python3.6/site-packages/tflearn/layers/core.py:81)  = Placeholder[dtype=DT_FLOAT, shape=[?,224,224,1], _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1291\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1292\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1293\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1276\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m-> 1277\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1364\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1365\u001b[0;31m         run_metadata)\n\u001b[0m\u001b[1;32m   1366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'input_6/X' with dtype float and shape [?,224,224,1]\n\t [[{{node input_6/X}} = Placeholder[dtype=DT_FLOAT, shape=[?,224,224,1], _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-a00d5acd156c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m model.fit({'input': X}, {'targets': Y}, n_epoch=2, validation_set=({'input': test_x}, {'targets': test_y}), \n\u001b[0;32m----> 4\u001b[0;31m     snapshot_step=50000, show_metric=True, run_id=MODEL_NAME)\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tflearn/models/dnn.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X_inputs, Y_targets, n_epoch, validation_set, show_metric, batch_size, shuffle, snapshot_epoch, snapshot_step, excl_trainops, validation_batch_size, run_id, callbacks)\u001b[0m\n\u001b[1;32m    204\u001b[0m                          \u001b[0mexcl_trainops\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexcl_trainops\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m                          \u001b[0mrun_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m                          callbacks=callbacks)\n\u001b[0m\u001b[1;32m    207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mretrieve_data_preprocessing_and_augmentation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tflearn/helpers/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, feed_dicts, n_epoch, val_feed_dicts, show_metric, snapshot_step, snapshot_epoch, shuffle_all, dprep_dict, daug_dict, excl_trainops, run_id, callbacks)\u001b[0m\n\u001b[1;32m    336\u001b[0m                                                        \u001b[0;34m(\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_checkpoint_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0msnapshot_epoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m                                                        \u001b[0msnapshot_step\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 338\u001b[0;31m                                                        show_metric)\n\u001b[0m\u001b[1;32m    339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m                             \u001b[0;31m# Update training state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tflearn/helpers/trainer.py\u001b[0m in \u001b[0;36m_train\u001b[0;34m(self, training_step, snapshot_epoch, snapshot_step, show_metric)\u001b[0m\n\u001b[1;32m    815\u001b[0m         \u001b[0mtflearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_training\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    816\u001b[0m         _, train_summ_str = self.session.run([self.train, self.summ_op],\n\u001b[0;32m--> 817\u001b[0;31m                                              feed_batch)\n\u001b[0m\u001b[1;32m    818\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    819\u001b[0m         \u001b[0;31m# Retrieve loss value from summary string\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    885\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    886\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 887\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    888\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    889\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1108\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1109\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1110\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1111\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1112\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1284\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1285\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1286\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1287\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1288\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1304\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1305\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0merror_interpolation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1306\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'input_6/X' with dtype float and shape [?,224,224,1]\n\t [[node input_6/X (defined at /opt/conda/lib/python3.6/site-packages/tflearn/layers/core.py:81)  = Placeholder[dtype=DT_FLOAT, shape=[?,224,224,1], _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n\nCaused by op 'input_6/X', defined at:\n  File \"/opt/conda/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/opt/conda/lib/python3.6/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 497, in start\n    self.io_loop.start()\n  File \"/opt/conda/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 132, in start\n    self.asyncio_loop.run_forever()\n  File \"/opt/conda/lib/python3.6/asyncio/base_events.py\", line 422, in run_forever\n    self._run_once()\n  File \"/opt/conda/lib/python3.6/asyncio/base_events.py\", line 1434, in _run_once\n    handle._run()\n  File \"/opt/conda/lib/python3.6/asyncio/events.py\", line 145, in _run\n    self._callback(*self._args)\n  File \"/opt/conda/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 122, in _handle_events\n    handler_func(fileobj, events)\n  File \"/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 450, in _handle_events\n    self._handle_recv()\n  File \"/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 480, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 432, in _run_callback\n    callback(*args, **kwargs)\n  File \"/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"/opt/conda/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/opt/conda/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2662, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2785, in _run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2901, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2961, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-15-cf27c8232561>\", line 7, in <module>\n    convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')\n  File \"/opt/conda/lib/python3.6/site-packages/tflearn/layers/core.py\", line 81, in input_data\n    placeholder = tf.placeholder(shape=shape, dtype=dtype, name=\"X\")\n  File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py\", line 1745, in placeholder\n    return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)\n  File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 5020, in placeholder\n    \"Placeholder\", dtype=dtype, shape=shape, name=name)\n  File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\", line 488, in new_func\n    return func(*args, **kwargs)\n  File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\", line 3274, in create_op\n    op_def=op_def)\n  File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\", line 1770, in __init__\n    self._traceback = tf_stack.extract_stack()\n\nInvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input_6/X' with dtype float and shape [?,224,224,1]\n\t [[node input_6/X (defined at /opt/conda/lib/python3.6/site-packages/tflearn/layers/core.py:81)  = Placeholder[dtype=DT_FLOAT, shape=[?,224,224,1], _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n"
     ]
    }
   ],
   "source": [
    "#fit for 2 epochs:\n",
    "\n",
    "model.fit({'input': X}, {'targets': Y}, n_epoch=2, validation_set=({'input': test_x}, {'targets': test_y}), \n",
    "    snapshot_step=50000, show_metric=True, run_id=MODEL_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "cf79c5de5b9e203bd35ffd5fe7e376fb68a21b49",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Size Matters\n",
    "#We're gonna need a bigger network\n",
    "\n",
    "#First, we need to reset the graph instance, since we're doing this in a continuous environment:\n",
    "\n",
    "import tensorflow as tf\n",
    "tf.reset_default_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "a143c51e4c2f7af50a51706c78c5d9c8d6eaadaf",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')\n",
    "\n",
    "convnet = conv_2d(convnet, 32, 5, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 5)\n",
    "\n",
    "convnet = conv_2d(convnet, 64, 5, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 5)\n",
    "\n",
    "convnet = conv_2d(convnet, 32, 5, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 5)\n",
    "\n",
    "convnet = conv_2d(convnet, 64, 5, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 5)\n",
    "\n",
    "convnet = conv_2d(convnet, 32, 5, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 5)\n",
    "\n",
    "convnet = conv_2d(convnet, 64, 5, activation='relu')\n",
    "convnet = max_pool_2d(convnet, 5)\n",
    "\n",
    "convnet = fully_connected(convnet, 1024, activation='relu')\n",
    "convnet = dropout(convnet, 0.8)\n",
    "\n",
    "convnet = fully_connected(convnet, 2, activation='softmax')\n",
    "convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')\n",
    "\n",
    "model = tflearn.DNN(convnet, tensorboard_dir='log')\n",
    "\n",
    "\n",
    "\n",
    "if os.path.exists('{}.meta'.format(MODEL_NAME)):\n",
    "    model.load(MODEL_NAME)\n",
    "    print('model loaded!')\n",
    "\n",
    "train = train_data[:-500]\n",
    "test = train_data[-500:]\n",
    "\n",
    "X = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE,IMG_SIZE,1)\n",
    "Y = [i[1] for i in train]\n",
    "\n",
    "test_x = np.array([i[0] for i in test]).reshape(-1,IMG_SIZE,IMG_SIZE,1)\n",
    "test_y = [i[1] for i in test]\n",
    "\n",
    "model.fit({'input': X}, {'targets': Y}, n_epoch=4, validation_set=({'input': test_x}, {'targets': test_y}), \n",
    "    snapshot_step=500, show_metric=True, run_id=MODEL_NAME)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "a086558595b67ece959f10420a4a05042dfe1867",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# visualizing our work\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# if you need to create the data:\n",
    "test_data = process_test_data()\n",
    "# if you already have some saved:\n",
    "#test_data = np.load('test_data.npy')\n",
    "\n",
    "fig=plt.figure()\n",
    "\n",
    "for num,data in enumerate(test_data[:12]):\n",
    "    # cat: [1,0]\n",
    "    # dog: [0,1]\n",
    "    \n",
    "    img_num = data[1]\n",
    "    img_data = data[0]\n",
    "    \n",
    "    y = fig.add_subplot(3,4,num+1)\n",
    "    orig = img_data\n",
    "    data = img_data.reshape(IMG_SIZE,IMG_SIZE,1)\n",
    "    #model_out = model.predict([data])[0]\n",
    "    model_out = model.predict([data])[0]\n",
    "    \n",
    "    if np.argmax(model_out) == 1: str_label='Dog'\n",
    "    else: str_label='Cat'\n",
    "        \n",
    "    y.imshow(orig,cmap='gray')\n",
    "    plt.title(str_label)\n",
    "    y.axes.get_xaxis().set_visible(False)\n",
    "    y.axes.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Saving the result\n",
    "with open('submission_file.csv','w') as f:\n",
    "    f.write('id,label\\n')\n",
    "            \n",
    "with open('submission_file.csv','a') as f:\n",
    "    for data in tqdm(test_data):\n",
    "        img_num = data[1]\n",
    "        img_data = data[0]\n",
    "        orig = img_data\n",
    "        data = img_data.reshape(IMG_SIZE,IMG_SIZE,1)\n",
    "        model_out = model.predict([data])[0]\n",
    "        f.write('{},{}\\n'.format(img_num,model_out[1]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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