{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "from IPython import display\n",
    "import numpy as np\n",
    "\n",
    "def display_image(arr):\n",
    "    b = BytesIO()\n",
    "    Image.fromarray(np.uint8(arr)).save(b, format='png')\n",
    "    data = b.getvalue()\n",
    "    display.display(display.Image(data=data, format='png', embed=True))\n",
    "\n",
    "IMG_ROWS = 64\n",
    "IMG_COLS = 64\n",
    "\n",
    "data = np.load(\"../data/etl8.npz\")['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import sklearn.model_selection\n",
    "import keras\n",
    "from scipy import ndimage\n",
    "\n",
    "NUM_CLASSES = 956\n",
    "\n",
    "# 150 ~ 159 for hyper parameters\n",
    "alldata = data[0:150]\n",
    "dataset_len = alldata.shape[0]\n",
    "\n",
    "# flatten\n",
    "x_all = alldata.reshape((dataset_len * NUM_CLASSES, IMG_ROWS, IMG_COLS, 1))\n",
    "x_all = x_all.astype(np.float32)\n",
    "x_all /= 255\n",
    "y_all = np.tile(np.arange(NUM_CLASSES), dataset_len)\n",
    "y_all = keras.utils.to_categorical(y_all, NUM_CLASSES)\n",
    "\n",
    "# # distortion (gaussian noise)\n",
    "# hard_len = int(dataset_len / 10)\n",
    "# soft_len = int(dataset_len / 2)\n",
    "# keep_len = dataset_len - hard_len - soft_len\n",
    "# hard_seed = 0.2 * np.random.randn(hard_len * NUM_CLASSES, IMG_ROWS, IMG_COLS, 1)\n",
    "# soft_seed = 0.05 * np.random.randn(soft_len * NUM_CLASSES, IMG_ROWS, IMG_COLS, 1)\n",
    "# keep_seed = np.zeros((keep_len * NUM_CLASSES, IMG_ROWS, IMG_COLS, 1))\n",
    "# seed = np.absolute(np.r_[hard_seed, soft_seed])\n",
    "# seed = ndimage.gaussian_filter(seed, 1)\n",
    "# seed = np.r_[seed, keep_seed]\n",
    "# np.random.shuffle(seed)\n",
    "# x_all += seed\n",
    "# x_all /= (seed + 1)\n",
    "\n",
    "# #  distortion (inverse)\n",
    "# choice = np.random.rand(dataset_len * NUM_CLASSES)\n",
    "# x_all[np.where(choice > 0.5)] *= -1\n",
    "# x_all[np.where(choice > 0.5)] += 1\n",
    "\n",
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x_all, y_all, test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 62, 62, 32)        320       \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 62, 62, 32)        128       \n",
      "_________________________________________________________________\n",
      "p_re_lu_1 (PReLU)            (None, 62, 62, 32)        123008    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 29, 29, 64)        18496     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 29, 29, 64)        256       \n",
      "_________________________________________________________________\n",
      "p_re_lu_2 (PReLU)            (None, 29, 29, 64)        53824     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 12, 12, 128)       73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 12, 12, 128)       512       \n",
      "_________________________________________________________________\n",
      "p_re_lu_3 (PReLU)            (None, 12, 12, 128)       18432     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1024)              4719616   \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 1024)              4096      \n",
      "_________________________________________________________________\n",
      "p_re_lu_4 (PReLU)            (None, 1024)              1024      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 1024)              4096      \n",
      "_________________________________________________________________\n",
      "p_re_lu_5 (PReLU)            (None, 1024)              1024      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 956)               979900    \n",
      "=================================================================\n",
      "Total params: 7,048,188\n",
      "Trainable params: 7,043,644\n",
      "Non-trainable params: 4,544\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Conv2D, MaxPooling2D, Input\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers.merge import Concatenate\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.advanced_activations import PReLU\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "input_shape = (IMG_ROWS, IMG_COLS, 1)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3, 3), input_shape=input_shape))\n",
    "model.add(BatchNormalization())\n",
    "model.add(PReLU())\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(64, kernel_size=(3, 3), input_shape=input_shape))\n",
    "model.add(BatchNormalization())\n",
    "model.add(PReLU())\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(128, kernel_size=(3, 3), input_shape=input_shape))\n",
    "model.add(BatchNormalization())\n",
    "model.add(PReLU())\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1024))\n",
    "model.add(BatchNormalization())\n",
    "model.add(PReLU())\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Dense(1024))\n",
    "model.add(BatchNormalization())\n",
    "model.add(PReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(NUM_CLASSES, activation='softmax'))\n",
    "\n",
    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "        optimizer=keras.optimizers.Adadelta(),\n",
    "        metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "1008/1008 [==============================] - 120s - loss: 5.5930 - acc: 0.0578 - val_loss: 3.7261 - val_acc: 0.2089\n",
      "Epoch 2/2\n",
      "1008/1008 [==============================] - 118s - loss: 2.2687 - acc: 0.4729 - val_loss: 1.2346 - val_acc: 0.6761\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7eff792589b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import Callback, EarlyStopping\n",
    "datagen = ImageDataGenerator(\n",
    "    rotation_range=12,\n",
    "    zoom_range=[0.9, 1.5],\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    fill_mode=\"constant\",\n",
    "    cval=0.0\n",
    ")\n",
    "BATCH_SIZE = 128\n",
    "steps = int(x_train.shape[0] / BATCH_SIZE)\n",
    "model.fit_generator(datagen.flow(x_train, y_train, batch_size=BATCH_SIZE),\n",
    "        steps_per_epoch=steps, epochs=2, verbose=True,\n",
    "        validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAAAAACPAi4CAAAIEklEQVR4nFVXa3McRxU9p6d3Zx/S\nrlYvW7Js4lhRcEKcIJxHVSBxkaLygcoHfi0UlUDFECdAbCoGSsSObTmSrKdlSfuQdh59+NDdI6Ot\n0uzOTPe9fe+5557LJhJbUoWB6pSgAkhMUhJZScOkoEUhMjFQ4ZxoKAEQkNQ1FpsQIQCgvxIAKUGC\nkQASkL8n/wwABJISLAUCIiTEPwESCUgyACgCEIG4iYI9whCMy5wAv4+8A6hcAv1/hO8ESYCElb8Z\nLhTPvQD9lQIpSN6mf9EfVbTxlr8qnBeE/CbxSXXEagcJgmx4K94mIIXdgRAoQP5utAVQlOBICwJU\nFenKbEgM6QSFqKN6TEWbltEdn4oqyedR4LmL4YQgKQqEZAMIQuQo0Dtf3SAkv0CAGEFj/Ellxcrx\naE7nYfaQ0EtWKrgEQFnjE0chHIuCTxsIQYCcT3KAnABKJEQBNCTgXJX1ap/qhhQTw+APUMUJxjh5\n3IVYhq8SMPdmp4Jp9YKrYilJIIyqEJOGhJw3Kc19tNoOpw1bgyRIBpx5FyJ+BNAk4RgggPWnK8tJ\nWB8MeqSQITp+A8Vnfu/G5bb/Zkb/1I1FkmTS7TKGI6ImBBnGB07eFSlZ+WQpFBpf7C683wPIzqe3\n2oBJ7UsII2kMhaoWvAku3iwPQ9g4vtOav1LQoL2YTQ049f72WgFCHvkwIOQs4TxoCDjMfzr/+xcQ\nwLTe7PRk3327WRxvqCjJicWFw2e+SCUEHMB6fAr11qBQevPSdw8cVLvQujoz1antbWVvldvPD4dp\ngxyOLixul0LFgSIg6wJgpt794VH+yvLD22eUfeNWNjrZv55+vtlaLD/frx/OpeRwffZiYxBhFovW\nRtAd7r/dOvlw+M2JlLx6a/rLe5nSlTyr9THIrUwK5zbf6vYGniLESI82FB3y++k7rdoXG6Vw8Tdz\n974dIG0iIdRo9pk2WAIHzy4tbBexloycABqPQwFnX381OCuc0P3V4r/+NCBNKynJWrsOd1o2DTh+\nWl6ZDlzqaZWkhQhCovLvn6/eSB+OV1/buf3CEDTDEZDnQnlaTNUyauOVuj3ndI9YGwgbArD/56vv\nLB2sHn+xI0kqB2PAZKXKEUoacXCHx54i/HoCsC8xBzH+Hp/dxBcPnW9PTGQ6OaAynazlEI70cgJI\nCJa+Z3kGIQtj7twtKZLtJhZr8/X6SjdfqE+/OTzNRsfFSymkt9L0JEZKILqfLd//4yCZSeoTvYXX\nNBqmLeey4bRRv1Tr+V82fI+suiVo6YvL82Lz/eUHf+02eavravnhXQ37Tn1bHy+vPLs7TmeGByEC\nhIuO2EAFAoja6gf7/zi6tXjvSzsuy1EBBxGGMFf6O2M8VcWzqGJpQ0QoMfn5e89vPy7+01l6OPRv\nBpSgdDUDMHV5aICRTwQTuAoAXvkAX63l2D+cuRACE8KtUdZIid5vP0z5UpsmQVjf9glh/pPmNxsT\n9Y5Rc3GzOO9QAAajpH2My9daNquMB41hEaFor04OOx9NmKbZLWbTAmybrF5rmLNhJuX1puzM2Ubu\ntcu5nPHtnZDSj9/oqDE4eXCi0+uz809w5dd4Ya3Na2eP14rj+a5pTGebJUGGZgaRpKV8fbrT/nBr\nu7e0vgs1X72+NZ7o7T1/1neauXntYNs1GrbX2DwIPd7nniBkCUBOGn/9GHv58vWZHWDvaG5qJxv9\ncK8ENLjUYnFSzqQXWj8MXdQ3MQkw8uvJfGsr08F4NiGP1vILBv3TQiTKM6Q4OJ2cuzTezVylniIN\nW9+mGNtho21Kugenh2i3rUiQpgOcDHs35g92vKBjxKM8EmMDNHBo6HkJx+F/QWucD3On6ZifLLyK\nRwUJXwnefwWd4FueIaAkO40d2JpmM63BgTDIt509Ws/jytCHSciKIigagEnRyPMgF1ikv5iDG526\nqUYNOjib2N5SAHFoQvBZ8B2OhHt96et2mvFy+qQU0HR7u1m71pu9eFY3rn+aD3Mx6kkFPUpfjSLg\ngN4bTyeRmZWl4wOI5vj+AwfahV/2DDVt85n2EQMPKQg2AoZRtIj14Yi1In+EJQJArXCSyzb3LJ29\n1hlOLiSEmW0GCRCUgonNXbLNk37TjbG1eXVKSLrWkYZUzhovrpz8WF+egHn9dzdMqCVDY0hTFadM\n7cClwzHGzzqzgG0PhgAI9ftFazW9f3dz/pq9+nFyUEYGIABaj20BnGxtZYYAD4vppDT1szNfz6OT\nxutX1p8c359Z7S3l3/6oc6aIncnL4qVkt1Qh4Hh4eQKs9RqJJSRMXvrZ6LsT7j+evjm+s1awSoQE\nhuYKcOrSk13AGGK8/vb8cbfbeO+FzkZwP52uD57sAvlat/O3Dcf/78+WCvnI/r2Xp41MVPn06k/W\nix95irzW68xcxv7f9zIAR7dt3xnQoUqEShuGCGG4LtjayEl48aBjDv7gcohIe5eTRztekIx8W4/m\nfWfyEVQEw6kT4NZYaOiDlQ2esaChi2sqRvfy17qotL2aHRcCUZ5PYEQZqyeUkgInUQCMdYw1TqDe\nKBQUutddwWAQmYgbhd7rAD90RZZXOejLREEZHIBAyFVM6mcXVRNZI45pBpKZ6Y8jY8WhSVFPBHUU\n+YuAE8hmJdwhCSbI4EpPUyJo4MKP6EKEgqWi0IMEOC+tw6eaX6Km8NEOqwl6IFXDZnVsKkogOM8/\njHqAAsVwGhDWnI+3cY7ylOZNnPvhf/E8OgLgYAPJhtnOxOEEBs5VHVA4fydMbr5FOvwPoaru8Mdl\nkeoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAAAAACPAi4CAAAHSElEQVR4nFVX2XIcxxHM7O3dxeIm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\nEIz3KU9Nia7WrGOpiRR820CA/B+gIslNTrxtewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAAAAACPAi4CAAAGi0lEQVR4nGVXa3MbxxHsXiwBkBRf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\n/gcJyjV+u5ZdkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAAAAACPAi4CAAAD4ElEQVR4nO2W21PbRhTGP92RLfmG\nwNixjbmTMpNkBpo+dCZPfeyfnWYybdJS2sSEm/EFMLYMyJYtWZdVHywYsBbqvvSJ87Te3fPzOWe/\nsyvg2Z7t2f5PYySFe2RlGndWzpUzfxwT2hr/tCufMYdiOl/MKeLoqjs1gEEQjpLvOtWlgspWe1uZ\n1FVA2UsD8EvDjjMeckp2TbabF0coCnHOmxKg7rAfquPhkGiDi92qFSgQJVqwVEBaVtlwSHzh5uOp\nD8BzrGmLKBaDrg6wBABs46DuA+As75oKYKNTaS2uW4ivJwBI7vEXFwAUwRxRU4gChM2kVXOhLL8U\ngJjbHQAA5BlrCIBbXEs/dImmkF5MHNQDKBmbAZOIdfxxXiwHgCn/rNZ+Ob2fSyQCfjPn7Q8hvBAt\nAmFuZjA+O4ElLATttSJpBeHJCNJ55696gFhCr3rg464VqieRmkt/l0/1To6PnKcAbFm9/OaBKcSP\nuoCAy1C/Bvf2jekPPjU6E6c5CZhdUWtdIFZwmy6gsPpwPH/+YSvTrJ+3nYn9kwB2aV6vuGBK8xc3\nACMJ/XEN4fx+oA6siHsEkCwYvzUBOW+2fSCwa9e3K0G/H/WOALgNrXIcANpst+EBaHXccIURCK2V\nJgFywTh0gJmSoBsAQEjorc5nhxVqCA8B/NVNF2BX8pZxr9bx4mKcTTjsZxdRewgw3hMPkApivXU3\nJxTfZPqmE2Mt/98jCBwATLHs6/at+4uNvH3RauXyZmfqdl5T3FLNBQAmuzof6KeNXuytVKVeiTSA\nluxCm20BwOa2d93QdSJspgYtigioAHkdPc6XAYCdyx/92SbA7Ip/3aZdqRQAs5gz29kZDgDIrpN0\nCCBuzF1dmlT/aDtLOevr4cgeF6xXK77TgIViYBzSdRQB8GXppOGS24KbvYVtTV5n61/oQo42U6nQ\nOBmJAsIL0Pz4Os/35wd/1+kViACE1FnVhjJz23dBM9gp+nafrgEKwNkjBFAZK7wGQJreT1mhThNx\nGPPDn4FHAC7J2XclC3x3QMrLjzzu1HcBgmLqd38prfMne+KPO/J0KQAAYgpv3ubMrZb631rtV6+C\n3WmVCMR58yYcMgubfuXMPRnurFkH1KeJkgKr8lZYAibzkvlac0Eu3p+v/aDRPmcoAH4udu2F/lvy\n4eEIQHD168Hs9zkKgQIQEp4xlo28zlcq4c0w2P/kbm8kIilTaqDGPRMA2GwsOD67O0+v2VtdXukc\nGcEDUVEAdntkAQAI2e/d2xwYe1oxWdLdm/vVpGTFSMF4B8NMCpjh46Itjh5rrKmMATPVt+WzPdt/\nsn8AP1OZ92v6/OAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_len = x_train.shape[0]\n",
    "\n",
    "# distortion (gaussian noise)\n",
    "hard_len = int(train_len / 10)\n",
    "soft_len = int(train_len / 2)\n",
    "keep_len = train_len - hard_len - soft_len\n",
    "hard_seed = 0.2 * np.random.randn(hard_len, IMG_ROWS, IMG_COLS, 1)\n",
    "soft_seed = 0.05 * np.random.randn(soft_len, IMG_ROWS, IMG_COLS, 1)\n",
    "keep_seed = np.zeros((keep_len, IMG_ROWS, IMG_COLS, 1))\n",
    "seed = np.absolute(np.r_[hard_seed, soft_seed])\n",
    "seed = ndimage.gaussian_filter(seed, 1)\n",
    "seed = np.r_[seed, keep_seed]\n",
    "np.random.shuffle(seed)\n",
    "x_train += seed\n",
    "x_train /= (seed + 1)\n",
    "\n",
    "#  distortion (inverse)\n",
    "choice = np.random.rand(train_len)\n",
    "x_train[np.where(choice > 0.5)] *= -1\n",
    "x_train[np.where(choice > 0.5)] += 1\n",
    "\n",
    "for i in range(10):\n",
    "    display_image(x_train[i, :, :, 0] * 255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "1008/1008 [==============================] - 118s - loss: 2.3413 - acc: 0.4795 - val_loss: 0.3342 - val_acc: 0.9356\n",
      "Epoch 2/50\n",
      "1008/1008 [==============================] - 118s - loss: 1.1052 - acc: 0.7154 - val_loss: 0.1943 - val_acc: 0.9613\n",
      "Epoch 3/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.8139 - acc: 0.7859 - val_loss: 0.1714 - val_acc: 0.9636\n",
      "Epoch 4/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.6479 - acc: 0.8268 - val_loss: 0.1502 - val_acc: 0.9625\n",
      "Epoch 5/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.5441 - acc: 0.8535 - val_loss: 0.0932 - val_acc: 0.9781\n",
      "Epoch 6/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.4752 - acc: 0.8725 - val_loss: 0.0945 - val_acc: 0.9759\n",
      "Epoch 7/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.4277 - acc: 0.8844 - val_loss: 0.0959 - val_acc: 0.9738\n",
      "Epoch 8/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.3934 - acc: 0.8931 - val_loss: 0.0778 - val_acc: 0.9794\n",
      "Epoch 9/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.3644 - acc: 0.9024 - val_loss: 0.0750 - val_acc: 0.9801\n",
      "Epoch 10/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.3429 - acc: 0.9082 - val_loss: 0.0603 - val_acc: 0.9853\n",
      "Epoch 11/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.3210 - acc: 0.9142 - val_loss: 0.0535 - val_acc: 0.9861\n",
      "Epoch 12/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.3067 - acc: 0.9174 - val_loss: 0.0545 - val_acc: 0.9867\n",
      "Epoch 13/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2951 - acc: 0.9217 - val_loss: 0.0544 - val_acc: 0.9862\n",
      "Epoch 14/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2841 - acc: 0.9247 - val_loss: 0.0482 - val_acc: 0.9880\n",
      "Epoch 15/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2715 - acc: 0.9281 - val_loss: 0.0502 - val_acc: 0.9872\n",
      "Epoch 16/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2664 - acc: 0.9295 - val_loss: 0.0468 - val_acc: 0.9877\n",
      "Epoch 17/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2546 - acc: 0.9334 - val_loss: 0.0418 - val_acc: 0.9895\n",
      "Epoch 18/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2496 - acc: 0.9351 - val_loss: 0.0450 - val_acc: 0.9884\n",
      "Epoch 19/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2484 - acc: 0.9354 - val_loss: 0.0399 - val_acc: 0.9904\n",
      "Epoch 20/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2441 - acc: 0.9360 - val_loss: 0.0456 - val_acc: 0.9873\n",
      "Epoch 21/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2402 - acc: 0.9385 - val_loss: 0.0387 - val_acc: 0.9904\n",
      "Epoch 22/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2334 - acc: 0.9395 - val_loss: 0.0370 - val_acc: 0.9911\n",
      "Epoch 23/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2284 - acc: 0.9415 - val_loss: 0.0439 - val_acc: 0.9895\n",
      "Epoch 24/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2288 - acc: 0.9425 - val_loss: 0.0374 - val_acc: 0.9907\n",
      "Epoch 25/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2226 - acc: 0.9440 - val_loss: 0.0440 - val_acc: 0.9895\n",
      "Epoch 26/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2232 - acc: 0.9443 - val_loss: 0.0365 - val_acc: 0.9904\n",
      "Epoch 27/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2206 - acc: 0.9445 - val_loss: 0.0386 - val_acc: 0.9902\n",
      "Epoch 28/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2138 - acc: 0.9471 - val_loss: 0.0359 - val_acc: 0.9925\n",
      "Epoch 29/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2160 - acc: 0.9469 - val_loss: 0.0405 - val_acc: 0.9904\n",
      "Epoch 30/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2166 - acc: 0.9467 - val_loss: 0.0418 - val_acc: 0.9904\n",
      "Epoch 31/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2115 - acc: 0.9484 - val_loss: 0.0390 - val_acc: 0.9911\n",
      "Epoch 32/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2092 - acc: 0.9496 - val_loss: 0.0381 - val_acc: 0.9909\n",
      "Epoch 33/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2119 - acc: 0.9496 - val_loss: 0.0419 - val_acc: 0.9894\n",
      "Epoch 34/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2104 - acc: 0.9504 - val_loss: 0.0373 - val_acc: 0.9918\n",
      "Epoch 35/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2055 - acc: 0.9515 - val_loss: 0.0403 - val_acc: 0.9909\n",
      "Epoch 36/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2056 - acc: 0.9522 - val_loss: 0.0437 - val_acc: 0.9898\n",
      "Epoch 37/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2031 - acc: 0.9539 - val_loss: 0.0403 - val_acc: 0.9898\n",
      "Epoch 38/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.2035 - acc: 0.9542 - val_loss: 0.0397 - val_acc: 0.9911\n",
      "Epoch 39/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.1984 - acc: 0.9560 - val_loss: 0.0385 - val_acc: 0.9904\n",
      "Epoch 40/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.1981 - acc: 0.9569 - val_loss: 0.0368 - val_acc: 0.9918\n",
      "Epoch 41/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.1971 - acc: 0.9572 - val_loss: 0.0375 - val_acc: 0.9920\n",
      "Epoch 42/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.1970 - acc: 0.9571 - val_loss: 0.0483 - val_acc: 0.9900\n",
      "Epoch 43/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.1911 - acc: 0.9590 - val_loss: 0.0383 - val_acc: 0.9916\n",
      "Epoch 44/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.1918 - acc: 0.9587 - val_loss: 0.0411 - val_acc: 0.9911\n",
      "Epoch 45/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.1885 - acc: 0.9592 - val_loss: 0.0523 - val_acc: 0.9903\n",
      "Epoch 46/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.1881 - acc: 0.9590 - val_loss: 0.0382 - val_acc: 0.9919\n",
      "Epoch 47/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.1870 - acc: 0.9601 - val_loss: 0.0385 - val_acc: 0.9926\n",
      "Epoch 48/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.1820 - acc: 0.9615 - val_loss: 0.0373 - val_acc: 0.9934\n",
      "Epoch 49/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.1786 - acc: 0.9618 - val_loss: 0.0373 - val_acc: 0.9918\n",
      "Epoch 50/50\n",
      "1008/1008 [==============================] - 118s - loss: 0.1774 - acc: 0.9626 - val_loss: 0.0382 - val_acc: 0.9922\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f02983d84e0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BATCH_SIZE = 128\n",
    "steps = int(x_train.shape[0] / BATCH_SIZE)\n",
    "model.fit_generator(datagen.flow(x_train, y_train, batch_size=BATCH_SIZE),\n",
    "        steps_per_epoch=steps, epochs=50, verbose=True,\n",
    "        validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save(\"../etl8_noise_smoose.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9504/9560 [============================>.] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.021832448980774903, 0.99529288702928875]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaldata = data[150:]\n",
    "dataset_len = evaldata.shape[0]\n",
    "\n",
    "# flatten\n",
    "x_eval = evaldata.reshape((dataset_len * NUM_CLASSES, IMG_ROWS, IMG_COLS, 1))\n",
    "x_eval = x_eval.astype(np.float32)\n",
    "x_eval /= 255\n",
    "y_eval = np.tile(np.arange(NUM_CLASSES), dataset_len)\n",
    "y_eval = keras.utils.to_categorical(y_eval, NUM_CLASSES)\n",
    "\n",
    "model.evaluate(x_eval, y_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "score = model.predict(x_eval)\n",
    "# predicted = np.array([np.argmax(r) for r in score])\n",
    "predicted = np.argmax(score, axis=1)\n",
    "\n",
    "idx, val = np.where(y_eval == 1)\n",
    "ans = val[idx]\n",
    "\n",
    "correct = np.where(predicted == ans)[0]\n",
    "errors = np.where(predicted != ans)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45\n",
      "9515\n"
     ]
    },
    {
     "data": {
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     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAAAAACPAi4CAAACbUlEQVR4nO2V21PaQBTGT7Ih3IYI\nmBigYpEUSy+jI72MD/3r60M7dZTRse20FoRiSYHQhEtwIMmmDwhIiJvp5LF8byff2d+e3T3ZBVhr\nrbX+B9EM5ZlDykAvtqvNgU0GMKQC+Ke565MumUCskZOkpPJe+TcAhTBe1LD5OntWtuaeSzHOJVDp\nXblhzCKsnEaE4AgAgGYjMfZW052MlT0IiNvs93kNtnK+iQGA4cUtjpsY3S8tvJy/sgQkHIQ+/V7M\nw6CJjfjiTr8zGtIZoXOu2WQAUNl9o9xZZFEo+STZV38NTQxsrti8mHgAAOVLxrFyR2CTvBjp1+Xx\nNAweRT+oS9kufWBdBw5KxyMAgIBYFAd/rlrjmTdp7PPLa3BrJLMSyYs1mwrFpax1KuvmwrKHFEdb\n95NdO3Fc5QuDcTaP9M8Nx56NjQjyBkBPLh4hq13TJk6HoYbW8geX4VR463E8IZ8o1qolcl6AQCyZ\nETasTswYuYyP7fZbjpLuBzSKCPkMrd2UVXyYyl/OCQw3xcXehM8GDwOCL/loSP8ht0cWUF8Tz7T6\nzEm9o+sVM4CkaPmn42e430hMbsNs98fTU6MLh9qxfueE9t4Kmk6F+qcV57qWOpGm0bxlIPwqc/Ft\nNl1Aypm3mGo2VvaFcKEIJfOjPo9oGwBc7gP6YUC3Fk4v+Ni2bbfLjQDATTWdeNj2BoB+E3vO+gFg\nuZXKer0MJABMqj0p6gcAvavQHvIDwE15J+4HAEbdyJMeL0+ArSqPMn4AYFQMKegHAGqNS5OO0hNg\nyWYh7AcAvdqGQLDJWwwAYNWw6plElneVa621ln/9Bdml6dfv/UFDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAAAAACPAi4CAAABCUlEQVR4nO2TQWvCMBTHX9patWVO\n6+Y2WAcORTz5/T/DLjtYdhkiSEM3RyfDrtZo4kHYqM2j1fP7HZN/fnkv5AEQBEEQBEHkYWWBmmXI\nTF4oYI57P3RYGs8+kwsEptd/6Npit9rfQPQ+350paLbHQ8Xj32i9Ma7uHkXwpe9DK7AbXtd3zeVy\nHisFAADe6Hu21Qqs4lJr4Lk1JjlfpOqvHUcglWoEe8vMQp4mQv2n/NtEFZOIIHlhoPLx3gA48oiG\nZk1JmT9f7zvhB1KBTlDIPPvRa4ZtVhC0R2K6xjY1b3DK9SQLONJAlQrcMQtCfBhKK7Cftm8/6P3l\n08g61kr/BavCSgeeIAjiyAHSeFSBLzMumQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAAAAACPAi4CAAABOElEQVR4nO2XXUvDMBSGzzlpU9Mq\nWx2bZahj///XeCuC4GDMWVhb+23WeDlcM5a6C73Ic/m270ObhIQADAMBAQAPAQ3sIyIA4vk3LRaL\nxWKx/BVnDmrXURI6VN2vBHw0DXjTSUZ5nH0NFZAfjUJW1p/EWQey2aXtAAHnk7EgxevVrgUXFHkB\n894zQwFdhxPPc7JtUdXy4JzyTWUkwOhBtIms8lz+yJnwd5qBcPrRXpSbtGnVcdwunLWJQMVPsjhu\nAwB0DVF/OjVf0CWaNgAA3jj9mdAITuK5uiE37/ui2V8kEJTIfmouCOZOoYmNBexunF4ioNl9+ab5\nA1MB3i5plWvNZv1wcRV/aDcFk3XA3NmjG7/qdwQDAUZLn9Yvpf6pgUDlAp7X9Sn9eQHQfL/VrMEB\n2DuS5f/zDZhWYLz82W1NAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAAAAACPAi4CAAABJElEQVR4nO2UUWvCMBCAL22qtIpO\n1mYbMsocjOHYH/D//4GxBxnCEEHGdG1FS9rFttmTMOoF4/qwl3yPyeXLXcgdgMFgMBgMBoOhDjkV\nYLktAmWRl38R2J3u4Np3rDwt4/dYnimwOyxkcpcnQnwXvtd+W6FJKAWDB0Z49JmICkACcViwnWMG\nqjjvjMPpnItD2lIs4alYIFWoBMV0E21+x8uP3u2aq/LFIPXqvMkQCbOUAlnPN4u6SLRacGzc991G\nAqC0mcBxd1kjQdvL8iYCMnTTqomAjTjWDtqC/iOfCWRd9RNrkOAZXmNsR0/QCu/h5Qvt55MDBQBs\nf3SznCXnzoMD1sXdZbpYYfVrCZyroFpHAr9eQ0B7dLtXzUMdiKXzSoZ/5gfgNlxAJ2JkeQAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAAAAACPAi4CAAABN0lEQVR4nO2Ty1KDMBSGQyDcUsRx\nLKMsXDm+/xN1HMa6QBjoDQohiYtuLObU42XhIt8OEr6cc/5AiMVisVgslv+Fc3ExZJ4WQkr9E4Eb\nJWniSncrdbsdQAUgYOHdInDedt1ElRckrGy/JQiWSxk29XGQp2c/i156vMDLcn9fdOJD1f5t+Ho0\nC7x57Wnq+9VmP501LTruAhWcC9hjvqif28N8ZHpUvFcIgRrFetUYJj7Rq3ZECOSqUNK0TTvMwwiI\nFsZdxCGj+XtCza/nRAy6SfMUANTOPEJsBTTWG+NssAIWTx3QA0pAOel+1QK/HqECUAL3JmiAeFEC\nmoT9AK4iYozSQwVEgBE4SSZK4BZiBC7PVQ0O4EuBEz94svr0d+MF/CkpCzDB0xkXBf79VMEBYKCX\nD7BY/op390pwln34aiUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(len(errors))\n",
    "print(len(correct))\n",
    "for e in errors:\n",
    "    display_image((x_eval[e] * 255).reshape(IMG_ROWS, IMG_COLS))\n",
    "    display_image(data[0][predicted[e]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}