PCA_autoencoder.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from utils import *\n",
    "from preprocessing import *\n",
    "from cnn_model import *\n",
    "from skimage import measure\n",
    "from skimage.segmentation import active_contour\n",
    "from skimage.filters import gaussian\n",
    "from skimage import feature\n",
    "SIZE = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of examples: 260\n",
      "\n",
      "Processing 260 images and labels ...\n",
      "\n",
      "Number of examples: 279\n",
      "\n",
      "Processing 279 images and labels ...\n",
      "\n",
      "Number of examples: 266\n",
      "\n",
      "Processing 266 images and labels ...\n",
      "\n",
      "(526, 256, 256, 1)\n",
      "(526, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "# original image, binary mask, contour(self-defined class)\n",
    "img_train, mask_train, contour_train = prepareDataset(TRAIN_CONTOUR_PATH, TRAIN_IMG_PATH)\n",
    "img_test, mask_test, contour_test = prepareDataset(TEST_CONTOUR_PATH, TEST_IMG_PATH)\n",
    "img_val, mask_val, contour_val = prepareDataset(VAL_CONTOUR_PATH, VAL_IMG_PATH)\n",
    "\n",
    "#Region of interest, aka bounding box computed from the contour\n",
    "ROI_train = get_ROI(contour_train)\n",
    "ROI_test = get_ROI(contour_test)\n",
    "ROI_val = get_ROI(contour_val)\n",
    "\n",
    "#Re-organize the data for CNN\n",
    "img_tv = np.concatenate((img_train, img_val), axis = 0)\n",
    "roi_tv = np.concatenate((ROI_train, ROI_val), axis = 0)\n",
    "print(img_tv.shape)\n",
    "print(roi_tv.shape)\n",
    "\n",
    "X, Y = reformDataXY(img_tv, roi_tv)\n",
    "X_test, Y_test = reformDataXY(img_test, ROI_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(279, 1, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "cnn_data = {'X_train': X, 'X_test': X_test, 'Y_train': Y, 'Y_test': Y_test}\n",
    "y_pred = run_cnn(cnn_data)\n",
    "cropped = get_cropped(img_test, y_pred, win_size = 100)\n",
    "print(y_pred.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/fromnumeric.py:2957: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/_methods.py:73: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ret, rcount, out=ret, casting='unsafe', subok=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(526, 100, 100, 1)\n",
      "(526, 100, 100, 1)\n",
      "(279, 100, 100, 1)\n",
      "(279, 100, 100, 1)\n"
     ]
    }
   ],
   "source": [
    "roi_tv_re = np.reshape(roi_tv, (roi_tv.shape[0], 1, roi_tv.shape[1], roi_tv.shape[2]))\n",
    "\n",
    "cropped_img_train = get_cropped(img_tv, roi_tv_re)\n",
    "cropped_mask_train = get_cropped(np.concatenate((mask_train, mask_val), axis = 0), roi_tv_re)\n",
    "\n",
    "cropped_img_test = cropped\n",
    "cropped_mask_test = get_cropped(mask_test, y_pred)\n",
    "\n",
    "print(cropped_img_train.shape)\n",
    "print(cropped_mask_train.shape)\n",
    "print(cropped_img_test.shape)\n",
    "print(cropped_mask_test.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(526, 100, 100, 1)\n",
      "(526, 100, 100, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x169f07290>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16a06b690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16a06bb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(cropped_img_train.shape)\n",
    "print(cropped_mask_train.shape)\n",
    "plt.figure()\n",
    "plt.imshow(cropped_img_train[10,:,:,0])\n",
    "plt.figure()\n",
    "plt.imshow(cropped_mask_train[10, :,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# down sampling the training image to (N, 64, 64) then reshape to (N, 4096)\n",
    "from scipy import ndimage\n",
    "def prepareDataSAE(X, Y, LEN = 64):\n",
    "    # input should be cropped image and binary mask, shape (N, 100, 100, 1)\n",
    "    # output the expected shape data for SAE model, which is (N, 4096)\n",
    "    # conduct down sampling and reshaping in this method\n",
    "    N = X.shape[0]\n",
    "    X_re = np.zeros((N, LEN * LEN))\n",
    "    Y_re = np.zeros((N, LEN * LEN))\n",
    "    for i in range(N):\n",
    "        img = X[i, :, :, 0]\n",
    "        mask = Y[i, :, :, 0]\n",
    "        img = cv2.resize(img, (LEN, LEN))\n",
    "        mask = cv2.resize(mask, (LEN, LEN), interpolation = cv2.INTERSECT_NONE)\n",
    "        X_re[i, :] = np.reshape(img, (-1, LEN * LEN))\n",
    "        Y_re[i, :] = np.reshape(mask, (-1, LEN * LEN))\n",
    "    return X_re, Y_re\n",
    "\n",
    "def augmentData(X, y, rot = True, flip = True):\n",
    "    # rotation in all 4 directions, flip in 2 direction, get total 6 times the size\n",
    "    N = X.shape[0]\n",
    "    w = X.shape[1]\n",
    "    if rot:\n",
    "        rX = np.zeros((N * 3, w, w, 1))\n",
    "        rY = np.zeros((N * 3, w, w, 1))\n",
    "        for i in range(N):\n",
    "            img = X[i, :, :, 0]\n",
    "            label = y[i, :, :, 0]\n",
    "            for j in range(3):\n",
    "                rx = ndimage.rotate(img, 90 * (j + 1))\n",
    "                ry = ndimage.rotate(label, 90 * (j + 1))\n",
    "                rX[i * 3 + j, :, :, 0] = rx\n",
    "                rY[i * 3 + j, :, :, 0] = ry\n",
    "    X = np.concatenate((X, rX), axis = 0)\n",
    "    y = np.concatenate((y, rY), axis = 0)\n",
    "    \n",
    "    if flip:\n",
    "        fX = np.zeros((X.shape[0], w, w, 1))\n",
    "        fY = np.zeros((X.shape[0], w, w, 1))\n",
    "        for i in range(X.shape[0]):\n",
    "            img = X[i, :, :, 0]\n",
    "            label = y[i, :, :, 0]\n",
    "            fX[i, :, :, 0] = np.flipud(img)\n",
    "            fY[i, :, :, 0] = np.flipud(label)\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_aug, y_aug = augmentData(cropped_img_train, cropped_mask_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_aug' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-20c1c79d2733>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_aug\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m900\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_aug\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'X_aug' is not defined"
     ]
    }
   ],
   "source": [
    "print(X_aug.shape)\n",
    "n = 900\n",
    "plt.figure()\n",
    "plt.imshow(X_aug[n,:,:,0])\n",
    "plt.figure()\n",
    "plt.imshow(y_aug[n, :, :, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_SAE, Y_train_SAE = prepareDataSAE(cropped_img_train, cropped_mask_train)\n",
    "X_test_SAE, Y_test_SAE = prepareDataSAE(cropped_img_test, cropped_mask_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(279, 4096)\n",
      "(526, 4096)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x184837d10>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x185467510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(X_test_SAE.shape)\n",
    "print(X_train_SAE.shape)\n",
    "plt.imshow(cropped_mask_test[10,:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import decomposition\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getEigen(X, k = 400):\n",
    "    avg = np.mean(X, axis = 0)\n",
    "    X = X - avg\n",
    "    cov = np.dot(X.T, X)\n",
    "    U, s, V = np.linalg.svd(cov, full_matrices=True)\n",
    "    return np.dot(U[0:k, :], X.T).T, U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "K = 400\n",
    "X_PCA_train, U = getEigen(X_train_SAE, K)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model1(X_train, get_history=False, verbose=1, param_reg=0.001):\n",
    "    autoencoder_1 = Sequential()\n",
    "    encoder_1 = Dense(input_dim=K, units=100, kernel_regularizer=regularizers.l2(param_reg))\n",
    "    decoder_1 = Dense(input_dim=100, units=K, kernel_regularizer=regularizers.l2(param_reg))\n",
    "    autoencoder_1.add(encoder_1)\n",
    "    autoencoder_1.add(decoder_1)\n",
    "    autoencoder_1.compile(loss= 'mse', optimizer='adam', metrics=['accuracy'])\n",
    "    h = autoencoder_1.fit(X_train, X_train, epochs=100, verbose=verbose)\n",
    "    \n",
    "    temp_0 = Sequential()\n",
    "    temp_0.add(encoder_1)\n",
    "    temp_0.compile(loss= 'mse', optimizer='adam', metrics=['accuracy'])\n",
    "    if get_history:\n",
    "        return h.history['loss'], encoder_1\n",
    "    else:\n",
    "        return encoder_1\n",
    "\n",
    "def model2(X_train, Y_train, encoder_1, init='zero',\n",
    "           get_history=False, verbose=1, param_reg=0.001):\n",
    "    model = Sequential()\n",
    "    model.add(encoder_1)\n",
    "    model.add(Dense(input_dim=100, units=4096, kernel_initializer=init, kernel_regularizer=regularizers.l2(param_reg)))\n",
    "    model.compile(optimizer = 'adam', loss = \"MSE\", metrics=['accuracy'])\n",
    "    h = model.fit(X_train, Y_train, epochs=200, verbose=verbose)\n",
    "    if get_history:\n",
    "        return h.history['loss'], model\n",
    "    else:\n",
    "        return model\n",
    "\n",
    "\n",
    "def SAE(X_train,Y_train,init='zero'):\n",
    "    encoder_1 = model1(X_train)\n",
    "    h, model = model2(X_train, Y_train, encoder_1,init, get_history=True)\n",
    "    return h,model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "526/526 [==============================] - 1s 1ms/step - loss: 10597.0641 - acc: 0.0114\n",
      "Epoch 2/100\n",
      "526/526 [==============================] - 0s 91us/step - loss: 6821.3250 - acc: 0.0570\n",
      "Epoch 3/100\n",
      "526/526 [==============================] - 0s 83us/step - loss: 5122.0178 - acc: 0.1122\n",
      "Epoch 4/100\n",
      "526/526 [==============================] - 0s 87us/step - loss: 4278.0238 - acc: 0.1616\n",
      "Epoch 5/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 3697.3950 - acc: 0.1996\n",
      "Epoch 6/100\n",
      "526/526 [==============================] - 0s 83us/step - loss: 3291.6047 - acc: 0.2319\n",
      "Epoch 7/100\n",
      "526/526 [==============================] - 0s 81us/step - loss: 2978.2050 - acc: 0.2719\n",
      "Epoch 8/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 2730.4937 - acc: 0.2776\n",
      "Epoch 9/100\n",
      "526/526 [==============================] - 0s 88us/step - loss: 2518.7130 - acc: 0.2833\n",
      "Epoch 10/100\n",
      "526/526 [==============================] - 0s 83us/step - loss: 2352.9218 - acc: 0.3099\n",
      "Epoch 11/100\n",
      "526/526 [==============================] - 0s 93us/step - loss: 2205.5351 - acc: 0.3232\n",
      "Epoch 12/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 2073.4843 - acc: 0.3422\n",
      "Epoch 13/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 1963.2074 - acc: 0.3593\n",
      "Epoch 14/100\n",
      "526/526 [==============================] - 0s 87us/step - loss: 1863.0488 - acc: 0.3726\n",
      "Epoch 15/100\n",
      "526/526 [==============================] - 0s 89us/step - loss: 1773.1469 - acc: 0.3840\n",
      "Epoch 16/100\n",
      "526/526 [==============================] - 0s 83us/step - loss: 1699.0181 - acc: 0.3954\n",
      "Epoch 17/100\n",
      "526/526 [==============================] - 0s 82us/step - loss: 1628.6699 - acc: 0.4011\n",
      "Epoch 18/100\n",
      "526/526 [==============================] - 0s 82us/step - loss: 1565.1425 - acc: 0.4087\n",
      "Epoch 19/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 1509.1681 - acc: 0.4259\n",
      "Epoch 20/100\n",
      "526/526 [==============================] - 0s 82us/step - loss: 1456.9550 - acc: 0.4392\n",
      "Epoch 21/100\n",
      "526/526 [==============================] - 0s 82us/step - loss: 1409.1228 - acc: 0.4392\n",
      "Epoch 22/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 1370.4967 - acc: 0.4354\n",
      "Epoch 23/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 1330.0734 - acc: 0.4525\n",
      "Epoch 24/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 1293.6005 - acc: 0.4487\n",
      "Epoch 25/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 1262.8778 - acc: 0.4468\n",
      "Epoch 26/100\n",
      "526/526 [==============================] - 0s 83us/step - loss: 1231.0485 - acc: 0.4430\n",
      "Epoch 27/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 1198.0215 - acc: 0.4677\n",
      "Epoch 28/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 1170.7399 - acc: 0.4734\n",
      "Epoch 29/100\n",
      "526/526 [==============================] - 0s 83us/step - loss: 1146.0636 - acc: 0.4449\n",
      "Epoch 30/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 1121.0032 - acc: 0.4772\n",
      "Epoch 31/100\n",
      "526/526 [==============================] - 0s 88us/step - loss: 1102.0751 - acc: 0.4734\n",
      "Epoch 32/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 1078.4906 - acc: 0.4791\n",
      "Epoch 33/100\n",
      "526/526 [==============================] - 0s 82us/step - loss: 1057.6251 - acc: 0.4715\n",
      "Epoch 34/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 1040.3983 - acc: 0.4905\n",
      "Epoch 35/100\n",
      "526/526 [==============================] - 0s 86us/step - loss: 1021.7931 - acc: 0.4867\n",
      "Epoch 36/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 1003.5627 - acc: 0.4943\n",
      "Epoch 37/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 986.3531 - acc: 0.4962\n",
      "Epoch 38/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 971.0426 - acc: 0.4867\n",
      "Epoch 39/100\n",
      "526/526 [==============================] - 0s 83us/step - loss: 957.8486 - acc: 0.4810\n",
      "Epoch 40/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 942.7543 - acc: 0.4886\n",
      "Epoch 41/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 929.8478 - acc: 0.4924\n",
      "Epoch 42/100\n",
      "526/526 [==============================] - 0s 88us/step - loss: 917.4353 - acc: 0.5076\n",
      "Epoch 43/100\n",
      "526/526 [==============================] - 0s 89us/step - loss: 903.5171 - acc: 0.5095\n",
      "Epoch 44/100\n",
      "526/526 [==============================] - 0s 82us/step - loss: 893.8891 - acc: 0.5076\n",
      "Epoch 45/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 883.6482 - acc: 0.5038\n",
      "Epoch 46/100\n",
      "526/526 [==============================] - 0s 86us/step - loss: 872.9025 - acc: 0.4962\n",
      "Epoch 47/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 861.8145 - acc: 0.5019\n",
      "Epoch 48/100\n",
      "526/526 [==============================] - 0s 83us/step - loss: 855.4726 - acc: 0.5152\n",
      "Epoch 49/100\n",
      "526/526 [==============================] - 0s 83us/step - loss: 845.1096 - acc: 0.5095\n",
      "Epoch 50/100\n",
      "526/526 [==============================] - 0s 89us/step - loss: 835.9371 - acc: 0.5019\n",
      "Epoch 51/100\n",
      "526/526 [==============================] - 0s 86us/step - loss: 826.6359 - acc: 0.5095\n",
      "Epoch 52/100\n",
      "526/526 [==============================] - 0s 86us/step - loss: 818.6632 - acc: 0.5133\n",
      "Epoch 53/100\n",
      "526/526 [==============================] - 0s 82us/step - loss: 812.7125 - acc: 0.5266\n",
      "Epoch 54/100\n",
      "526/526 [==============================] - 0s 83us/step - loss: 805.0981 - acc: 0.5114\n",
      "Epoch 55/100\n",
      "526/526 [==============================] - 0s 87us/step - loss: 797.2176 - acc: 0.5228\n",
      "Epoch 56/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 789.9099 - acc: 0.5152\n",
      "Epoch 57/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 783.6383 - acc: 0.5190\n",
      "Epoch 58/100\n",
      "526/526 [==============================] - 0s 82us/step - loss: 777.9411 - acc: 0.5228\n",
      "Epoch 59/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 772.2350 - acc: 0.5342\n",
      "Epoch 60/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 767.3903 - acc: 0.5361\n",
      "Epoch 61/100\n",
      "526/526 [==============================] - 0s 82us/step - loss: 760.0022 - acc: 0.5304\n",
      "Epoch 62/100\n",
      "526/526 [==============================] - 0s 83us/step - loss: 755.2799 - acc: 0.5323\n",
      "Epoch 63/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 750.3050 - acc: 0.5399\n",
      "Epoch 64/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 744.2335 - acc: 0.5304\n",
      "Epoch 65/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 737.8582 - acc: 0.5456\n",
      "Epoch 66/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 735.3358 - acc: 0.5342\n",
      "Epoch 67/100\n",
      "526/526 [==============================] - 0s 86us/step - loss: 727.9735 - acc: 0.5513\n",
      "Epoch 68/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 724.7005 - acc: 0.5399\n",
      "Epoch 69/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 721.3734 - acc: 0.5285\n",
      "Epoch 70/100\n",
      "526/526 [==============================] - 0s 81us/step - loss: 716.2022 - acc: 0.5494\n",
      "Epoch 71/100\n",
      "526/526 [==============================] - 0s 81us/step - loss: 711.8849 - acc: 0.5456\n",
      "Epoch 72/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 709.6336 - acc: 0.5532\n",
      "Epoch 73/100\n",
      "526/526 [==============================] - 0s 86us/step - loss: 703.9647 - acc: 0.5513\n",
      "Epoch 74/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 700.1287 - acc: 0.5570\n",
      "Epoch 75/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 699.0635 - acc: 0.5475\n",
      "Epoch 76/100\n",
      "526/526 [==============================] - 0s 86us/step - loss: 695.2172 - acc: 0.5532\n",
      "Epoch 77/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 691.7857 - acc: 0.5608\n",
      "Epoch 78/100\n",
      "526/526 [==============================] - 0s 85us/step - loss: 689.0991 - acc: 0.5551\n",
      "Epoch 79/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 685.1558 - acc: 0.5589\n",
      "Epoch 80/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 681.7219 - acc: 0.5513\n",
      "Epoch 81/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 678.6967 - acc: 0.5494\n",
      "Epoch 82/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "526/526 [==============================] - 0s 85us/step - loss: 675.6008 - acc: 0.5532\n",
      "Epoch 83/100\n",
      "526/526 [==============================] - 0s 83us/step - loss: 674.2659 - acc: 0.5418\n",
      "Epoch 84/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 670.8271 - acc: 0.5532\n",
      "Epoch 85/100\n",
      "526/526 [==============================] - 0s 82us/step - loss: 668.4172 - acc: 0.5665\n",
      "Epoch 86/100\n",
      "526/526 [==============================] - 0s 81us/step - loss: 665.6452 - acc: 0.5608\n",
      "Epoch 87/100\n",
      "526/526 [==============================] - 0s 80us/step - loss: 661.1412 - acc: 0.5608\n",
      "Epoch 88/100\n",
      "526/526 [==============================] - 0s 83us/step - loss: 659.9246 - acc: 0.5608\n",
      "Epoch 89/100\n",
      "526/526 [==============================] - 0s 81us/step - loss: 657.4854 - acc: 0.5589\n",
      "Epoch 90/100\n",
      "526/526 [==============================] - 0s 81us/step - loss: 656.4028 - acc: 0.5513\n",
      "Epoch 91/100\n",
      "526/526 [==============================] - 0s 83us/step - loss: 654.8454 - acc: 0.5627\n",
      "Epoch 92/100\n",
      "526/526 [==============================] - 0s 81us/step - loss: 652.5958 - acc: 0.5551\n",
      "Epoch 93/100\n",
      "526/526 [==============================] - 0s 81us/step - loss: 651.2896 - acc: 0.5532\n",
      "Epoch 94/100\n",
      "526/526 [==============================] - 0s 83us/step - loss: 649.3845 - acc: 0.5589\n",
      "Epoch 95/100\n",
      "526/526 [==============================] - 0s 81us/step - loss: 649.0604 - acc: 0.5665\n",
      "Epoch 96/100\n",
      "526/526 [==============================] - 0s 84us/step - loss: 645.5386 - acc: 0.5646\n",
      "Epoch 97/100\n",
      "526/526 [==============================] - 0s 81us/step - loss: 642.6458 - acc: 0.5551\n",
      "Epoch 98/100\n",
      "526/526 [==============================] - 0s 80us/step - loss: 641.3166 - acc: 0.5703\n",
      "Epoch 99/100\n",
      "526/526 [==============================] - 0s 80us/step - loss: 640.7433 - acc: 0.5608\n",
      "Epoch 100/100\n",
      "526/526 [==============================] - 0s 82us/step - loss: 637.3408 - acc: 0.5589\n",
      "Epoch 1/200\n",
      "526/526 [==============================] - 1s 1ms/step - loss: 1.2131 - acc: 0.0000e+00\n",
      "Epoch 2/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 1.0156 - acc: 0.0000e+00\n",
      "Epoch 3/200\n",
      "526/526 [==============================] - 0s 237us/step - loss: 0.8022 - acc: 0.0000e+00\n",
      "Epoch 4/200\n",
      "526/526 [==============================] - 0s 241us/step - loss: 0.6127 - acc: 0.0000e+00\n",
      "Epoch 5/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.4789 - acc: 0.0000e+00\n",
      "Epoch 6/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.3489 - acc: 0.0019\n",
      "Epoch 7/200\n",
      "526/526 [==============================] - 0s 253us/step - loss: 0.2985 - acc: 0.0038\n",
      "Epoch 8/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.2556 - acc: 0.0019\n",
      "Epoch 9/200\n",
      "526/526 [==============================] - 0s 242us/step - loss: 0.2359 - acc: 0.0019\n",
      "Epoch 10/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.2184 - acc: 0.0000e+00\n",
      "Epoch 11/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.2118 - acc: 0.0000e+00\n",
      "Epoch 12/200\n",
      "526/526 [==============================] - 0s 245us/step - loss: 0.2044 - acc: 0.0038\n",
      "Epoch 13/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.1994 - acc: 0.0000e+00\n",
      "Epoch 14/200\n",
      "526/526 [==============================] - 0s 247us/step - loss: 0.2002 - acc: 0.0000e+00\n",
      "Epoch 15/200\n",
      "526/526 [==============================] - 0s 246us/step - loss: 0.1830 - acc: 0.0019\n",
      "Epoch 16/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.1768 - acc: 0.0000e+00\n",
      "Epoch 17/200\n",
      "526/526 [==============================] - 0s 246us/step - loss: 0.1827 - acc: 0.0038\n",
      "Epoch 18/200\n",
      "526/526 [==============================] - 0s 240us/step - loss: 0.1671 - acc: 0.0000e+00\n",
      "Epoch 19/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.1679 - acc: 0.0019\n",
      "Epoch 20/200\n",
      "526/526 [==============================] - 0s 250us/step - loss: 0.1626 - acc: 0.0019\n",
      "Epoch 21/200\n",
      "526/526 [==============================] - 0s 241us/step - loss: 0.1624 - acc: 0.0000e+00\n",
      "Epoch 22/200\n",
      "526/526 [==============================] - 0s 247us/step - loss: 0.1535 - acc: 0.0019\n",
      "Epoch 23/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.1478 - acc: 0.0000e+00\n",
      "Epoch 24/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.1447 - acc: 0.0038\n",
      "Epoch 25/200\n",
      "526/526 [==============================] - 0s 247us/step - loss: 0.1406 - acc: 0.0019\n",
      "Epoch 26/200\n",
      "526/526 [==============================] - 0s 251us/step - loss: 0.1392 - acc: 0.0000e+00\n",
      "Epoch 27/200\n",
      "526/526 [==============================] - 0s 246us/step - loss: 0.1319 - acc: 0.0000e+00\n",
      "Epoch 28/200\n",
      "526/526 [==============================] - 0s 251us/step - loss: 0.1264 - acc: 0.0000e+00\n",
      "Epoch 29/200\n",
      "526/526 [==============================] - 0s 239us/step - loss: 0.1249 - acc: 0.0019\n",
      "Epoch 30/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.1212 - acc: 0.0000e+00\n",
      "Epoch 31/200\n",
      "526/526 [==============================] - 0s 246us/step - loss: 0.1169 - acc: 0.0019\n",
      "Epoch 32/200\n",
      "526/526 [==============================] - 0s 254us/step - loss: 0.1130 - acc: 0.0019\n",
      "Epoch 33/200\n",
      "526/526 [==============================] - 0s 246us/step - loss: 0.1113 - acc: 0.0019\n",
      "Epoch 34/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.1087 - acc: 0.0000e+00\n",
      "Epoch 35/200\n",
      "526/526 [==============================] - 0s 241us/step - loss: 0.1058 - acc: 0.0038\n",
      "Epoch 36/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.1021 - acc: 0.0000e+00\n",
      "Epoch 37/200\n",
      "526/526 [==============================] - 0s 250us/step - loss: 0.0971 - acc: 0.0019\n",
      "Epoch 38/200\n",
      "526/526 [==============================] - 0s 245us/step - loss: 0.0958 - acc: 0.0019\n",
      "Epoch 39/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.0942 - acc: 0.0000e+00\n",
      "Epoch 40/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0913 - acc: 0.0038\n",
      "Epoch 41/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0876 - acc: 0.0000e+00\n",
      "Epoch 42/200\n",
      "526/526 [==============================] - 0s 247us/step - loss: 0.0848 - acc: 0.0057\n",
      "Epoch 43/200\n",
      "526/526 [==============================] - 0s 241us/step - loss: 0.0826 - acc: 0.0000e+00\n",
      "Epoch 44/200\n",
      "526/526 [==============================] - 0s 245us/step - loss: 0.0806 - acc: 0.0019\n",
      "Epoch 45/200\n",
      "526/526 [==============================] - 0s 250us/step - loss: 0.0794 - acc: 0.0000e+00\n",
      "Epoch 46/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0770 - acc: 0.0019\n",
      "Epoch 47/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.0754 - acc: 0.0000e+00\n",
      "Epoch 48/200\n",
      "526/526 [==============================] - 0s 254us/step - loss: 0.0733 - acc: 0.0038\n",
      "Epoch 49/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.0712 - acc: 0.0038\n",
      "Epoch 50/200\n",
      "526/526 [==============================] - 0s 250us/step - loss: 0.0690 - acc: 0.0038\n",
      "Epoch 51/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.0669 - acc: 0.0019\n",
      "Epoch 52/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0655 - acc: 0.0019\n",
      "Epoch 53/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0639 - acc: 0.0038\n",
      "Epoch 54/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0625 - acc: 0.0019\n",
      "Epoch 55/200\n",
      "526/526 [==============================] - 0s 238us/step - loss: 0.0603 - acc: 0.0057\n",
      "Epoch 56/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0593 - acc: 0.0019\n",
      "Epoch 57/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0583 - acc: 0.0019\n",
      "Epoch 58/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0564 - acc: 0.0019\n",
      "Epoch 59/200\n",
      "526/526 [==============================] - 0s 267us/step - loss: 0.0552 - acc: 0.0019\n",
      "Epoch 60/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0539 - acc: 0.0000e+00\n",
      "Epoch 61/200\n",
      "526/526 [==============================] - 0s 239us/step - loss: 0.0529 - acc: 0.0000e+00\n",
      "Epoch 62/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0521 - acc: 0.0019\n",
      "Epoch 63/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "526/526 [==============================] - 0s 247us/step - loss: 0.0500 - acc: 0.0038\n",
      "Epoch 64/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0487 - acc: 0.0000e+00\n",
      "Epoch 65/200\n",
      "526/526 [==============================] - 0s 250us/step - loss: 0.0473 - acc: 0.0038\n",
      "Epoch 66/200\n",
      "526/526 [==============================] - 0s 234us/step - loss: 0.0465 - acc: 0.0000e+00\n",
      "Epoch 67/200\n",
      "526/526 [==============================] - 0s 240us/step - loss: 0.0454 - acc: 0.0057\n",
      "Epoch 68/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0449 - acc: 0.0038\n",
      "Epoch 69/200\n",
      "526/526 [==============================] - 0s 245us/step - loss: 0.0440 - acc: 0.0019\n",
      "Epoch 70/200\n",
      "526/526 [==============================] - 0s 255us/step - loss: 0.0431 - acc: 0.0057\n",
      "Epoch 71/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.0425 - acc: 0.0000e+00\n",
      "Epoch 72/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.0414 - acc: 0.0019\n",
      "Epoch 73/200\n",
      "526/526 [==============================] - 0s 247us/step - loss: 0.0402 - acc: 0.0019\n",
      "Epoch 74/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0394 - acc: 0.0019\n",
      "Epoch 75/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0381 - acc: 0.0019\n",
      "Epoch 76/200\n",
      "526/526 [==============================] - 0s 242us/step - loss: 0.0377 - acc: 0.0000e+00\n",
      "Epoch 77/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.0366 - acc: 0.0019\n",
      "Epoch 78/200\n",
      "526/526 [==============================] - 0s 247us/step - loss: 0.0360 - acc: 0.0019\n",
      "Epoch 79/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0355 - acc: 0.0019\n",
      "Epoch 80/200\n",
      "526/526 [==============================] - 0s 242us/step - loss: 0.0344 - acc: 0.0000e+00\n",
      "Epoch 81/200\n",
      "526/526 [==============================] - 0s 247us/step - loss: 0.0336 - acc: 0.0019\n",
      "Epoch 82/200\n",
      "526/526 [==============================] - 0s 246us/step - loss: 0.0331 - acc: 0.0038\n",
      "Epoch 83/200\n",
      "526/526 [==============================] - 0s 247us/step - loss: 0.0326 - acc: 0.0019\n",
      "Epoch 84/200\n",
      "526/526 [==============================] - 0s 235us/step - loss: 0.0323 - acc: 0.0000e+00\n",
      "Epoch 85/200\n",
      "526/526 [==============================] - 0s 239us/step - loss: 0.0308 - acc: 0.0019\n",
      "Epoch 86/200\n",
      "526/526 [==============================] - 0s 241us/step - loss: 0.0302 - acc: 0.0000e+00\n",
      "Epoch 87/200\n",
      "526/526 [==============================] - 0s 261us/step - loss: 0.0296 - acc: 0.0019\n",
      "Epoch 88/200\n",
      "526/526 [==============================] - 0s 245us/step - loss: 0.0288 - acc: 0.0000e+00\n",
      "Epoch 89/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0284 - acc: 0.0019\n",
      "Epoch 90/200\n",
      "526/526 [==============================] - 0s 239us/step - loss: 0.0280 - acc: 0.0019\n",
      "Epoch 91/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0275 - acc: 0.0019\n",
      "Epoch 92/200\n",
      "526/526 [==============================] - 0s 245us/step - loss: 0.0267 - acc: 0.0000e+00\n",
      "Epoch 93/200\n",
      "526/526 [==============================] - 0s 247us/step - loss: 0.0262 - acc: 0.0019\n",
      "Epoch 94/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.0258 - acc: 0.0019\n",
      "Epoch 95/200\n",
      "526/526 [==============================] - 0s 245us/step - loss: 0.0255 - acc: 0.0019\n",
      "Epoch 96/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0252 - acc: 0.0019\n",
      "Epoch 97/200\n",
      "526/526 [==============================] - 0s 241us/step - loss: 0.0245 - acc: 0.0019\n",
      "Epoch 98/200\n",
      "526/526 [==============================] - ETA: 0s - loss: 0.0239 - acc: 0.0021   - 0s 247us/step - loss: 0.0240 - acc: 0.0019\n",
      "Epoch 99/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.0233 - acc: 0.0019\n",
      "Epoch 100/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0228 - acc: 0.0019\n",
      "Epoch 101/200\n",
      "526/526 [==============================] - 0s 246us/step - loss: 0.0229 - acc: 0.0000e+00\n",
      "Epoch 102/200\n",
      "526/526 [==============================] - 0s 246us/step - loss: 0.0223 - acc: 0.0019\n",
      "Epoch 103/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.0219 - acc: 0.0019\n",
      "Epoch 104/200\n",
      "526/526 [==============================] - 0s 246us/step - loss: 0.0215 - acc: 0.0019\n",
      "Epoch 105/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0214 - acc: 0.0019\n",
      "Epoch 106/200\n",
      "526/526 [==============================] - 0s 340us/step - loss: 0.0222 - acc: 0.0019\n",
      "Epoch 107/200\n",
      "526/526 [==============================] - 0s 245us/step - loss: 0.0211 - acc: 0.0019\n",
      "Epoch 108/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0205 - acc: 0.0019\n",
      "Epoch 109/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0205 - acc: 0.0019\n",
      "Epoch 110/200\n",
      "526/526 [==============================] - 0s 240us/step - loss: 0.0204 - acc: 0.0019\n",
      "Epoch 111/200\n",
      "526/526 [==============================] - 0s 246us/step - loss: 0.0198 - acc: 0.0019\n",
      "Epoch 112/200\n",
      "526/526 [==============================] - 0s 253us/step - loss: 0.0192 - acc: 0.0038\n",
      "Epoch 113/200\n",
      "526/526 [==============================] - 0s 250us/step - loss: 0.0196 - acc: 0.0019\n",
      "Epoch 114/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0195 - acc: 0.0000e+00\n",
      "Epoch 115/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0187 - acc: 0.0019\n",
      "Epoch 116/200\n",
      "526/526 [==============================] - 0s 250us/step - loss: 0.0184 - acc: 0.0019\n",
      "Epoch 117/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.0183 - acc: 0.0000e+00\n",
      "Epoch 118/200\n",
      "526/526 [==============================] - 0s 239us/step - loss: 0.0180 - acc: 0.0019\n",
      "Epoch 119/200\n",
      "526/526 [==============================] - 0s 247us/step - loss: 0.0176 - acc: 0.0019\n",
      "Epoch 120/200\n",
      "526/526 [==============================] - 0s 247us/step - loss: 0.0174 - acc: 0.0000e+00\n",
      "Epoch 121/200\n",
      "526/526 [==============================] - 0s 252us/step - loss: 0.0173 - acc: 0.0038\n",
      "Epoch 122/200\n",
      "526/526 [==============================] - 0s 240us/step - loss: 0.0173 - acc: 0.0019\n",
      "Epoch 123/200\n",
      "526/526 [==============================] - 0s 263us/step - loss: 0.0172 - acc: 0.0019\n",
      "Epoch 124/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0171 - acc: 0.0019\n",
      "Epoch 125/200\n",
      "526/526 [==============================] - 0s 253us/step - loss: 0.0169 - acc: 0.0019\n",
      "Epoch 126/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.0167 - acc: 0.0019\n",
      "Epoch 127/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.0160 - acc: 0.0019\n",
      "Epoch 128/200\n",
      "526/526 [==============================] - 0s 251us/step - loss: 0.0162 - acc: 0.0019\n",
      "Epoch 129/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.0161 - acc: 0.0038\n",
      "Epoch 130/200\n",
      "526/526 [==============================] - 0s 251us/step - loss: 0.0159 - acc: 0.0019\n",
      "Epoch 131/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.0159 - acc: 0.0019\n",
      "Epoch 132/200\n",
      "526/526 [==============================] - 0s 255us/step - loss: 0.0166 - acc: 0.0019\n",
      "Epoch 133/200\n",
      "526/526 [==============================] - 0s 253us/step - loss: 0.0164 - acc: 0.0019\n",
      "Epoch 134/200\n",
      "526/526 [==============================] - 0s 250us/step - loss: 0.0160 - acc: 0.0019\n",
      "Epoch 135/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0158 - acc: 0.0019\n",
      "Epoch 136/200\n",
      "526/526 [==============================] - 0s 250us/step - loss: 0.0160 - acc: 0.0019\n",
      "Epoch 137/200\n",
      "526/526 [==============================] - 0s 256us/step - loss: 0.0157 - acc: 0.0019\n",
      "Epoch 138/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.0152 - acc: 0.0038\n",
      "Epoch 139/200\n",
      "526/526 [==============================] - 0s 258us/step - loss: 0.0153 - acc: 0.0019\n",
      "Epoch 140/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.0151 - acc: 0.0019\n",
      "Epoch 141/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0150 - acc: 0.0019\n",
      "Epoch 142/200\n",
      "526/526 [==============================] - 0s 242us/step - loss: 0.0155 - acc: 0.0019\n",
      "Epoch 143/200\n",
      "526/526 [==============================] - 0s 250us/step - loss: 0.0154 - acc: 0.0019\n",
      "Epoch 144/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "526/526 [==============================] - 0s 255us/step - loss: 0.0152 - acc: 0.0019\n",
      "Epoch 145/200\n",
      "526/526 [==============================] - 0s 238us/step - loss: 0.0147 - acc: 0.0019\n",
      "Epoch 146/200\n",
      "526/526 [==============================] - 0s 245us/step - loss: 0.0148 - acc: 0.0019\n",
      "Epoch 147/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0148 - acc: 0.0019\n",
      "Epoch 148/200\n",
      "526/526 [==============================] - 0s 240us/step - loss: 0.0146 - acc: 0.0019\n",
      "Epoch 149/200\n",
      "526/526 [==============================] - 0s 250us/step - loss: 0.0144 - acc: 0.0000e+00\n",
      "Epoch 150/200\n",
      "526/526 [==============================] - 0s 245us/step - loss: 0.0142 - acc: 0.0019\n",
      "Epoch 151/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.0148 - acc: 0.0019\n",
      "Epoch 152/200\n",
      "526/526 [==============================] - 0s 246us/step - loss: 0.0148 - acc: 0.0019\n",
      "Epoch 153/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.0145 - acc: 0.0038\n",
      "Epoch 154/200\n",
      "526/526 [==============================] - 0s 245us/step - loss: 0.0146 - acc: 0.0019\n",
      "Epoch 155/200\n",
      "526/526 [==============================] - 0s 242us/step - loss: 0.0139 - acc: 0.0019\n",
      "Epoch 156/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0139 - acc: 0.0019\n",
      "Epoch 157/200\n",
      "526/526 [==============================] - 0s 242us/step - loss: 0.0142 - acc: 0.0019\n",
      "Epoch 158/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.0152 - acc: 0.0019\n",
      "Epoch 159/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0147 - acc: 0.0019\n",
      "Epoch 160/200\n",
      "526/526 [==============================] - 0s 271us/step - loss: 0.0148 - acc: 0.0038\n",
      "Epoch 161/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.0144 - acc: 0.0019\n",
      "Epoch 162/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.0138 - acc: 0.0019\n",
      "Epoch 163/200\n",
      "526/526 [==============================] - 0s 252us/step - loss: 0.0139 - acc: 0.0019\n",
      "Epoch 164/200\n",
      "526/526 [==============================] - 0s 241us/step - loss: 0.0147 - acc: 0.0019\n",
      "Epoch 165/200\n",
      "526/526 [==============================] - 0s 245us/step - loss: 0.0144 - acc: 0.0019\n",
      "Epoch 166/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.0150 - acc: 0.0019\n",
      "Epoch 167/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.0145 - acc: 0.0019\n",
      "Epoch 168/200\n",
      "526/526 [==============================] - 0s 251us/step - loss: 0.0142 - acc: 0.0019\n",
      "Epoch 169/200\n",
      "526/526 [==============================] - 0s 245us/step - loss: 0.0141 - acc: 0.0019\n",
      "Epoch 170/200\n",
      "526/526 [==============================] - 0s 253us/step - loss: 0.0139 - acc: 0.0019\n",
      "Epoch 171/200\n",
      "526/526 [==============================] - 0s 251us/step - loss: 0.0141 - acc: 0.0019\n",
      "Epoch 172/200\n",
      "526/526 [==============================] - 0s 240us/step - loss: 0.0138 - acc: 0.0019\n",
      "Epoch 173/200\n",
      "526/526 [==============================] - 0s 241us/step - loss: 0.0135 - acc: 0.0019\n",
      "Epoch 174/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0136 - acc: 0.0019\n",
      "Epoch 175/200\n",
      "526/526 [==============================] - 0s 257us/step - loss: 0.0139 - acc: 0.0019\n",
      "Epoch 176/200\n",
      "526/526 [==============================] - 0s 240us/step - loss: 0.0143 - acc: 0.0019\n",
      "Epoch 177/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0140 - acc: 0.0019\n",
      "Epoch 178/200\n",
      "526/526 [==============================] - 0s 244us/step - loss: 0.0141 - acc: 0.0019\n",
      "Epoch 179/200\n",
      "526/526 [==============================] - 0s 246us/step - loss: 0.0139 - acc: 0.0019\n",
      "Epoch 180/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0140 - acc: 0.0019\n",
      "Epoch 181/200\n",
      "526/526 [==============================] - 0s 243us/step - loss: 0.0134 - acc: 0.0019\n",
      "Epoch 182/200\n",
      "526/526 [==============================] - 0s 241us/step - loss: 0.0137 - acc: 0.0000e+00\n",
      "Epoch 183/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0137 - acc: 0.0019\n",
      "Epoch 184/200\n",
      "526/526 [==============================] - 0s 251us/step - loss: 0.0139 - acc: 0.0019\n",
      "Epoch 185/200\n",
      "526/526 [==============================] - 0s 242us/step - loss: 0.0137 - acc: 0.0019\n",
      "Epoch 186/200\n",
      "526/526 [==============================] - 0s 246us/step - loss: 0.0137 - acc: 0.0019\n",
      "Epoch 187/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0137 - acc: 0.0019\n",
      "Epoch 188/200\n",
      "526/526 [==============================] - 0s 249us/step - loss: 0.0137 - acc: 0.0019\n",
      "Epoch 189/200\n",
      "526/526 [==============================] - 0s 247us/step - loss: 0.0139 - acc: 0.0019\n",
      "Epoch 190/200\n",
      "526/526 [==============================] - 0s 237us/step - loss: 0.0152 - acc: 0.0019\n",
      "Epoch 191/200\n",
      "526/526 [==============================] - 0s 246us/step - loss: 0.0151 - acc: 0.0019\n",
      "Epoch 192/200\n",
      "526/526 [==============================] - 0s 248us/step - loss: 0.0143 - acc: 0.0019\n",
      "Epoch 193/200\n",
      "526/526 [==============================] - 0s 268us/step - loss: 0.0141 - acc: 0.0019\n",
      "Epoch 194/200\n",
      "526/526 [==============================] - 0s 255us/step - loss: 0.0139 - acc: 0.0019\n",
      "Epoch 195/200\n",
      "526/526 [==============================] - 0s 262us/step - loss: 0.0136 - acc: 0.0019\n",
      "Epoch 196/200\n",
      "526/526 [==============================] - 0s 245us/step - loss: 0.0143 - acc: 0.0019\n",
      "Epoch 197/200\n",
      "526/526 [==============================] - 0s 258us/step - loss: 0.0141 - acc: 0.0000e+00\n",
      "Epoch 198/200\n",
      "526/526 [==============================] - 0s 251us/step - loss: 0.0141 - acc: 0.0019\n",
      "Epoch 199/200\n",
      "526/526 [==============================] - 0s 264us/step - loss: 0.0143 - acc: 0.0000e+00\n",
      "Epoch 200/200\n",
      "526/526 [==============================] - 0s 264us/step - loss: 0.0143 - acc: 0.0019\n"
     ]
    }
   ],
   "source": [
    "h, model = SAE(X_PCA_train, Y_train_SAE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'Loss Curve')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x171bb8790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(h)), h)\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.title('Loss Curve')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pydot\n",
    "import graphviz\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from keras.utils import plot_model\n",
    "\n",
    "plot_model(model, to_file='PCA_FC.png', show_shapes=True, show_layer_names=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17378ac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x173a63c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x173883310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x173900910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x173d80210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x176e5ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d088b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x193cccc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x194021f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1940082d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find 5 highest \n",
    "demo_order = np.flip(np.argsort(dm),axis=0)\n",
    "for j in range(10):\n",
    "    i = demo_order[j]\n",
    "    plt.figure()\n",
    "    plt.subplot(131)\n",
    "    plt.imshow(Y_test_SAE[i].reshape((64,64)))\n",
    "    plt.title('Ground Truth')\n",
    "    plt.subplot(132)\n",
    "    plt.imshow(pred_mask[i])\n",
    "    plt.title('Result from SAE')\n",
    "    plt.subplot(133)\n",
    "    plt.imshow(pred_img[i])\n",
    "    plt.title('ROI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('The mean dice metric is', 0.48632061774297347)\n",
      "('The mean conformity coefficient', -3.196249656836529)\n"
     ]
    }
   ],
   "source": [
    "def prediction_plot(Y_pred, idx):\n",
    "    contour = Y_pred[idx].reshape((64,64))\n",
    "    binary = cv2.threshold(contour, 0, 1, cv2.INTERSECT_NONE)\n",
    "    return binary[1], binary[1]*X[idx].reshape(64,64)\n",
    "def dice_metric(X, Y):\n",
    "    return np.sum(X[Y==1])*2.0 / (np.sum(X) + np.sum(Y))\n",
    "\n",
    "def conformity_coefficient(X, Y):\n",
    "    return (3*dice_metric(X,Y)-2)/dice_metric(X,Y)\n",
    "\n",
    "#X_test_PCA = getEigen(X_test_SAE, K)\n",
    "X_test_PCA = np.dot(U[0:K, :], X_test_SAE.T).T\n",
    "Y_pred_PCA = model.predict(X_test_PCA)\n",
    "\n",
    "n = img_test.shape[0]\n",
    "pred_mask,pred_img=[],[]\n",
    "for i in range(n):\n",
    "    bin_mask,img = prediction_plot(Y_pred_PCA, i)\n",
    "    pred_mask.append(bin_mask)\n",
    "    pred_img.append(img)\n",
    "pred_mask = np.array(pred_mask)\n",
    "pred_img = np.array(pred_img)\n",
    "\n",
    "dm = np.zeros(n)\n",
    "cc= np.zeros(n)\n",
    "for i in range(n):\n",
    "    Y_true = Y_test_SAE[i].reshape((64,64))\n",
    "    dm[i] = dice_coef(Y_true, pred_mask[i])\n",
    "    cc[i] = conformity_coefficient(pred_mask[i],Y_true)\n",
    "    \n",
    "print ('The mean dice metric is',dm.mean())\n",
    "print ('The mean conformity coefficient',cc.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400\n"
     ]
    }
   ],
   "source": [
    "print(K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}