DDUL_2Dsyn_patch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\anaconda_intiall352\\lib\\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"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.layers import Input, Dense, Dropout, concatenate, LeakyReLU,BatchNormalization\n",
    "from tensorflow.keras.models import Model\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "import numpy as np\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from tensorflow.keras.models import load_model\n",
    "import scipy.io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def yc_patch(A,l1,l2,o1,o2):\n",
    "\n",
    "    n1,n2=np.shape(A);\n",
    "    tmp=np.mod(n1-l1,o1)\n",
    "    if tmp!=0:\n",
    "        print(np.shape(A), o1-tmp, n2)\n",
    "        A=np.concatenate([A,np.zeros((o1-tmp,n2))],axis=0)\n",
    "\n",
    "    tmp=np.mod(n2-l2,o2);\n",
    "    if tmp!=0:\n",
    "        A=np.concatenate([A,np.zeros((A.shape[0],o2-tmp))],axis=-1);\n",
    "\n",
    "\n",
    "    N1,N2 = np.shape(A)\n",
    "    X=[]\n",
    "    for i1 in range (0,N1-l1+1, o1):\n",
    "        for i2 in range (0,N2-l2+1,o2):\n",
    "            tmp=np.reshape(A[i1:i1+l1,i2:i2+l2],(l1*l2,1));\n",
    "            X.append(tmp);\n",
    "    X = np.array(X)\n",
    "    return X[:,:,0]\n",
    "\n",
    "\n",
    "def yc_patch_inv(X1, n1, n2, l1, l2, o1, o2):\n",
    "    tmp1 = np.mod(n1 - l1, o1)\n",
    "    tmp2 = np.mod(n2 - l2, o2)\n",
    "    if (tmp1 != 0) and (tmp2 != 0):\n",
    "        A = np.zeros((n1 + o1 - tmp1, n2 + o2 - tmp2))\n",
    "        mask = np.zeros((n1 + o1 - tmp1, n2 + o2 - tmp2))\n",
    "\n",
    "    if (tmp1 != 0) and (tmp2 == 0):\n",
    "        A = np.zeros((n1 + o1 - tmp1, n2))\n",
    "        mask = np.zeros((n1 + o1 - tmp1, n2))\n",
    "\n",
    "    if (tmp1 == 0) and (tmp2 != 0):\n",
    "        A = np.zeros((n1, n2 + o2 - tmp2))\n",
    "        mask = np.zeros((n1, n2 + o2 - tmp2))\n",
    "\n",
    "    if (tmp1 == 0) and (tmp2 == 0):\n",
    "        A = np.zeros((n1, n2))\n",
    "        mask = np.zeros((n1, n2))\n",
    "\n",
    "    N1, N2 = np.shape(A)\n",
    "    ids = 0\n",
    "    for i1 in range(0, N1 - l1 + 1, o1):\n",
    "        for i2 in range(0, N2 - l2 + 1, o2):\n",
    "            # print(i1,i2)\n",
    "            #       [i1,i2,ids]\n",
    "            A[i1:i1 + l1, i2:i2 + l2] = A[i1:i1 + l1, i2:i2 + l2] + np.reshape(X1[:, ids], (l1, l2))\n",
    "            mask[i1:i1 + l1, i2:i2 + l2] = mask[i1:i1 + l1, i2:i2 + l2] + np.ones((l1, l2))\n",
    "            ids = ids + 1\n",
    "\n",
    "    A = A / mask;\n",
    "    A = A[0:n1, 0:n2]\n",
    "\n",
    "    return A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = scipy.io.loadmat(r'.\\Datasets\\2Dsyn_data.mat')\n",
    "#data = scipy.io.loadmat(r'test.mat')\n",
    "dataNoisy = data['DataNoisy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16066, 2304)\n"
     ]
    }
   ],
   "source": [
    "w1=48\n",
    "w2=48\n",
    "z1=1\n",
    "z2=1\n",
    "\n",
    "dataNoisy = np.array(dataNoisy)\n",
    "#dataNoisy=dataNoisy/np.abs(dataNoisy).max()\n",
    "\n",
    "dataNoisy = yc_patch(dataNoisy, w1, w2, z1, z2)\n",
    "print(dataNoisy.shape)\n",
    "\n",
    "ind = np.random.permutation(len(dataNoisy))\n",
    "dataNoisy = np.array(dataNoisy)\n",
    "dataNoisy = dataNoisy[ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 2304)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 128)          295040      input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 128)          512         dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu (LeakyReLU)         (None, 128)          0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 128)          16512       leaky_re_lu[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 128)          512         dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)       (None, 128)          0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 64)           8256        leaky_re_lu_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 64)           256         dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)       (None, 64)           0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 64)           4160        leaky_re_lu_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 64)           256         dense_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)       (None, 64)           0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dense_6 (Dense)                 (None, 32)           2080        leaky_re_lu_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 32)           128         dense_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)       (None, 32)           0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dense_7 (Dense)                 (None, 32)           1056        leaky_re_lu_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 32)           128         dense_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_7 (LeakyReLU)       (None, 32)           0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dense_9 (Dense)                 (None, 16)           528         leaky_re_lu_7[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 16)           64          dense_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_9 (LeakyReLU)       (None, 16)           0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dense_10 (Dense)                (None, 16)           272         leaky_re_lu_9[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 16)           64          dense_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_10 (LeakyReLU)      (None, 16)           0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_12 (Dense)                (None, 8)            136         leaky_re_lu_10[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 8)            32          dense_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_12 (LeakyReLU)      (None, 8)            0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_13 (Dense)                (None, 8)            72          leaky_re_lu_12[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 8)            32          dense_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_13 (LeakyReLU)      (None, 8)            0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_15 (Dense)                (None, 4)            36          leaky_re_lu_13[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 4)            16          dense_15[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_15 (LeakyReLU)      (None, 4)            0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_16 (Dense)                (None, 4)            20          leaky_re_lu_15[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 4)            16          dense_16[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_16 (LeakyReLU)      (None, 4)            0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_17 (Dense)                (None, 4)            20          leaky_re_lu_16[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 4)            16          dense_17[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_17 (LeakyReLU)      (None, 4)            0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_18 (Dense)                (None, 4)            20          leaky_re_lu_17[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 4)            16          dense_18[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_18 (LeakyReLU)      (None, 4)            0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_19 (Dense)                (None, 4)            20          leaky_re_lu_18[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 4)            16          dense_19[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_19 (LeakyReLU)      (None, 4)            0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 8)            0           leaky_re_lu_19[0][0]             \n",
      "                                                                 leaky_re_lu_17[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_20 (Dense)                (None, 8)            72          concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 8)            32          dense_20[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_20 (LeakyReLU)      (None, 8)            0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_21 (Dense)                (None, 8)            72          leaky_re_lu_20[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_14 (Dense)                (None, 8)            72          leaky_re_lu_13[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 8)            32          dense_21[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 8)            32          dense_14[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_21 (LeakyReLU)      (None, 8)            0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_14 (LeakyReLU)      (None, 8)            0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 16)           0           leaky_re_lu_21[0][0]             \n",
      "                                                                 leaky_re_lu_14[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_22 (Dense)                (None, 16)           272         concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 16)           64          dense_22[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_22 (LeakyReLU)      (None, 16)           0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_23 (Dense)                (None, 16)           272         leaky_re_lu_22[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_11 (Dense)                (None, 16)           272         leaky_re_lu_10[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 16)           64          dense_23[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 16)           64          dense_11[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_23 (LeakyReLU)      (None, 16)           0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_11 (LeakyReLU)      (None, 16)           0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 32)           0           leaky_re_lu_23[0][0]             \n",
      "                                                                 leaky_re_lu_11[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_24 (Dense)                (None, 32)           1056        concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 32)           128         dense_24[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_24 (LeakyReLU)      (None, 32)           0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_25 (Dense)                (None, 32)           1056        leaky_re_lu_24[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_8 (Dense)                 (None, 32)           1056        leaky_re_lu_7[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 32)           128         dense_25[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 32)           128         dense_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_25 (LeakyReLU)      (None, 32)           0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_8 (LeakyReLU)       (None, 32)           0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 64)           0           leaky_re_lu_25[0][0]             \n",
      "                                                                 leaky_re_lu_8[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_26 (Dense)                (None, 64)           4160        concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 64)           256         dense_26[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_26 (LeakyReLU)      (None, 64)           0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_27 (Dense)                (None, 64)           4160        leaky_re_lu_26[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 64)           4160        leaky_re_lu_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 64)           256         dense_27[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 64)           256         dense_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_27 (LeakyReLU)      (None, 64)           0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)       (None, 64)           0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 128)          0           leaky_re_lu_27[0][0]             \n",
      "                                                                 leaky_re_lu_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_28 (Dense)                (None, 128)          16512       concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 128)          512         dense_28[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_28 (LeakyReLU)      (None, 128)          0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_29 (Dense)                (None, 128)          16512       leaky_re_lu_28[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 128)          16512       leaky_re_lu_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 128)          512         dense_29[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 128)          512         dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_29 (LeakyReLU)      (None, 128)          0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)       (None, 128)          0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, 256)          0           leaky_re_lu_29[0][0]             \n",
      "                                                                 leaky_re_lu_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_30 (Dense)                (None, 2304)         592128      concatenate_5[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 991,612\n",
      "Trainable params: 989,092\n",
      "Non-trainable params: 2,520\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "INPUT_SIZE1 = dataNoisy.shape[0]\n",
    "INPUT_SIZE2 = dataNoisy.shape[1]\n",
    "# ENCODING_SIZE = 16\n",
    "\n",
    "input_img = Input(shape=(INPUT_SIZE2,))\n",
    "\n",
    "\n",
    "\n",
    "encoded1 = Dense(128)(input_img)\n",
    "encoded1 = BatchNormalization()(encoded1)\n",
    "encoded1 = LeakyReLU(0.2)(encoded1)\n",
    "encoded1 = Dense(128)(encoded1)\n",
    "encoded1 = BatchNormalization()(encoded1)\n",
    "encoded1 = LeakyReLU(0.2)(encoded1)\n",
    "\n",
    "skip1 = Dense(128)(encoded1)\n",
    "skip1 = BatchNormalization()(skip1)\n",
    "skip1 = LeakyReLU(0.2)(skip1)\n",
    "\n",
    "encoded2 = Dense(64)(encoded1)\n",
    "encoded2 = BatchNormalization()(encoded2)\n",
    "encoded2 = LeakyReLU(0.2)(encoded2)\n",
    "encoded2 = Dense(64)(encoded2)\n",
    "encoded2 = BatchNormalization()(encoded2)\n",
    "encoded2 = LeakyReLU(0.2)(encoded2)\n",
    "\n",
    "skip2 = Dense(64)(encoded2)\n",
    "skip2 = BatchNormalization()(skip2)\n",
    "skip2 = LeakyReLU(0.2)(skip2)\n",
    "\n",
    "encoded3 = Dense(32)(encoded2)\n",
    "encoded3 = BatchNormalization()(encoded3)\n",
    "encoded3 = LeakyReLU(0.2)(encoded3)\n",
    "encoded3 = Dense(32)(encoded3)\n",
    "encoded3 = BatchNormalization()(encoded3)\n",
    "encoded3 = LeakyReLU(0.2)(encoded3)\n",
    "\n",
    "skip3 = Dense(32)(encoded3)\n",
    "skip3 = BatchNormalization()(skip3)\n",
    "skip3 = LeakyReLU(0.2)(skip3)\n",
    "\n",
    "encoded4 = Dense(16)(encoded3)\n",
    "encoded4 = BatchNormalization()(encoded4)\n",
    "encoded4 = LeakyReLU(0.2)(encoded4)\n",
    "encoded4 = Dense(16)(encoded4)\n",
    "encoded4 = BatchNormalization()(encoded4)\n",
    "encoded4 = LeakyReLU(0.2)(encoded4)\n",
    "\n",
    "skip4 = Dense(16)(encoded4)\n",
    "skip4 = BatchNormalization()(skip4)\n",
    "skip4 = LeakyReLU(0.2)(skip4)\n",
    "\n",
    "encoded5 = Dense(8)(encoded4)\n",
    "encoded5 = BatchNormalization()(encoded5)\n",
    "encoded5 = LeakyReLU(0.2)(encoded5)\n",
    "encoded5 = Dense(8)(encoded5)\n",
    "encoded5 = BatchNormalization()(encoded5)\n",
    "encoded5 = LeakyReLU(0.2)(encoded5)\n",
    "\n",
    "skip5 = Dense(8)(encoded5)\n",
    "skip5 = BatchNormalization()(skip5)\n",
    "skip5 = LeakyReLU(0.2)(skip5)\n",
    "\n",
    "encoded6 = Dense(4)(encoded5)\n",
    "encoded6 = BatchNormalization()(encoded6)\n",
    "encoded6 = LeakyReLU(0.2)(encoded6)\n",
    "encoded6 = Dense(4)(encoded6)\n",
    "encoded6 = BatchNormalization()(encoded6)\n",
    "encoded6 = LeakyReLU(0.2)(encoded6)\n",
    "\n",
    "skip6 = Dense(4)(encoded6)\n",
    "skip6 = BatchNormalization()(skip6)\n",
    "skip6 = LeakyReLU(0.2)(skip6)\n",
    "# Decoder\n",
    "\n",
    "decoded6 = Dense(4)(skip6)\n",
    "decoded6 = BatchNormalization()(decoded6)\n",
    "decoded6 = LeakyReLU(0.2)(decoded6)\n",
    "decoded6 = Dense(4)(decoded6)\n",
    "decoded6 = BatchNormalization()(decoded6)\n",
    "decoded6 = LeakyReLU(0.2)(decoded6)\n",
    "decoded6 = concatenate([decoded6, skip6])\n",
    "\n",
    "\n",
    "decoded5 = Dense(8)(decoded6)\n",
    "decoded5 = BatchNormalization()(decoded5)\n",
    "decoded5 = LeakyReLU(0.2)(decoded5)\n",
    "decoded5 = Dense(8)(decoded5)\n",
    "decoded5 = BatchNormalization()(decoded5)\n",
    "decoded5 = LeakyReLU(0.2)(decoded5)\n",
    "decoded5 = concatenate([decoded5, skip5])\n",
    "\n",
    "decoded4 = Dense(16)(decoded5)\n",
    "decoded4 = BatchNormalization()(decoded4)\n",
    "decoded4 = LeakyReLU(0.2)(decoded4)\n",
    "decoded4 = Dense(16)(decoded4)\n",
    "decoded4 = BatchNormalization()(decoded4)\n",
    "decoded4 = LeakyReLU(0.2)(decoded4)\n",
    "decoded4 = concatenate([decoded4, skip4])\n",
    "\n",
    "decoded3 = Dense(32)(decoded4)\n",
    "decoded3 = BatchNormalization()(decoded3)\n",
    "decoded3 = LeakyReLU(0.2)(decoded3)\n",
    "decoded3 = Dense(32)(decoded3)\n",
    "decoded3 = BatchNormalization()(decoded3)\n",
    "decoded3 = LeakyReLU(0.2)(decoded3)\n",
    "decoded3 = concatenate([decoded3, skip3])\n",
    "\n",
    "decoded2 = Dense(64)(decoded3)\n",
    "decoded2 = BatchNormalization()(decoded2)\n",
    "decoded2 = LeakyReLU(0.2)(decoded2)\n",
    "decoded2 = Dense(64)(decoded2)\n",
    "decoded2 = BatchNormalization()(decoded2)\n",
    "decoded2 = LeakyReLU(0.2)(decoded2)\n",
    "decoded2 = concatenate([decoded2, skip2])\n",
    "\n",
    "decoded1 = Dense(128)(decoded2)\n",
    "decoded1 = BatchNormalization()(decoded1)\n",
    "decoded1 = LeakyReLU(0.2)(decoded1)\n",
    "decoded1 = Dense(128)(decoded1)\n",
    "decoded1 = BatchNormalization()(decoded1)\n",
    "decoded1 = LeakyReLU(0.2)(decoded1)\n",
    "decoded1 = concatenate([decoded1, skip1])\n",
    "\n",
    "\n",
    "\n",
    "decoded = Dense(INPUT_SIZE2, activation='linear')(decoded1)\n",
    "\n",
    "autoencoder = Model(input_img, decoded)\n",
    "\n",
    "autoencoder.compile(optimizer='adam', loss='mse')\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 12852 samples, validate on 3214 samples\n",
      "Epoch 1/100\n",
      " - 13s - loss: 0.0788 - val_loss: 0.0631\n",
      "Epoch 2/100\n",
      " - 6s - loss: 0.0599 - val_loss: 0.0575\n",
      "Epoch 3/100\n",
      " - 5s - loss: 0.0557 - val_loss: 0.0546\n",
      "Epoch 4/100\n",
      " - 5s - loss: 0.0531 - val_loss: 0.0525\n",
      "Epoch 5/100\n",
      " - 5s - loss: 0.0513 - val_loss: 0.0510\n",
      "Epoch 6/100\n",
      " - 5s - loss: 0.0500 - val_loss: 0.0500\n",
      "Epoch 7/100\n",
      " - 5s - loss: 0.0490 - val_loss: 0.0492\n",
      "Epoch 8/100\n",
      " - 5s - loss: 0.0482 - val_loss: 0.0485\n",
      "Epoch 9/100\n",
      " - 5s - loss: 0.0475 - val_loss: 0.0481\n",
      "Epoch 10/100\n",
      " - 5s - loss: 0.0469 - val_loss: 0.0476\n",
      "Epoch 11/100\n",
      " - 5s - loss: 0.0465 - val_loss: 0.0472\n",
      "Epoch 12/100\n",
      " - 5s - loss: 0.0461 - val_loss: 0.0469\n",
      "Epoch 13/100\n",
      " - 5s - loss: 0.0457 - val_loss: 0.0465\n",
      "Epoch 14/100\n",
      " - 5s - loss: 0.0453 - val_loss: 0.0464\n",
      "Epoch 15/100\n",
      " - 5s - loss: 0.0451 - val_loss: 0.0461\n",
      "Epoch 16/100\n",
      " - 5s - loss: 0.0448 - val_loss: 0.0460\n",
      "Epoch 17/100\n",
      " - 5s - loss: 0.0446 - val_loss: 0.0458\n",
      "Epoch 18/100\n",
      " - 5s - loss: 0.0443 - val_loss: 0.0456\n",
      "Epoch 19/100\n",
      " - 5s - loss: 0.0441 - val_loss: 0.0454\n",
      "Epoch 20/100\n",
      " - 5s - loss: 0.0439 - val_loss: 0.0454\n",
      "Epoch 21/100\n",
      " - 5s - loss: 0.0437 - val_loss: 0.0452\n",
      "Epoch 22/100\n",
      " - 5s - loss: 0.0435 - val_loss: 0.0451\n",
      "Epoch 23/100\n",
      " - 5s - loss: 0.0434 - val_loss: 0.0450\n",
      "Epoch 24/100\n",
      " - 5s - loss: 0.0432 - val_loss: 0.0449\n",
      "Epoch 25/100\n",
      " - 5s - loss: 0.0431 - val_loss: 0.0448\n",
      "Epoch 26/100\n",
      " - 5s - loss: 0.0430 - val_loss: 0.0447\n",
      "Epoch 27/100\n",
      " - 5s - loss: 0.0428 - val_loss: 0.0446\n",
      "Epoch 28/100\n",
      " - 5s - loss: 0.0427 - val_loss: 0.0445\n",
      "Epoch 29/100\n",
      " - 5s - loss: 0.0426 - val_loss: 0.0445\n",
      "Epoch 30/100\n",
      " - 5s - loss: 0.0425 - val_loss: 0.0444\n",
      "Epoch 31/100\n",
      " - 5s - loss: 0.0424 - val_loss: 0.0444\n",
      "Epoch 32/100\n",
      " - 5s - loss: 0.0423 - val_loss: 0.0443\n",
      "Epoch 33/100\n",
      " - 5s - loss: 0.0422 - val_loss: 0.0443\n",
      "Epoch 34/100\n",
      " - 5s - loss: 0.0421 - val_loss: 0.0442\n",
      "Epoch 35/100\n",
      " - 5s - loss: 0.0420 - val_loss: 0.0441\n",
      "Epoch 36/100\n",
      " - 5s - loss: 0.0420 - val_loss: 0.0441\n",
      "Epoch 37/100\n",
      " - 5s - loss: 0.0419 - val_loss: 0.0441\n",
      "Epoch 38/100\n",
      " - 5s - loss: 0.0418 - val_loss: 0.0441\n",
      "Epoch 39/100\n",
      " - 5s - loss: 0.0418 - val_loss: 0.0440\n",
      "Epoch 40/100\n",
      " - 5s - loss: 0.0417 - val_loss: 0.0440\n",
      "Epoch 41/100\n",
      " - 5s - loss: 0.0417 - val_loss: 0.0439\n",
      "Epoch 42/100\n",
      " - 5s - loss: 0.0416 - val_loss: 0.0439\n",
      "Epoch 43/100\n",
      " - 5s - loss: 0.0415 - val_loss: 0.0439\n",
      "Epoch 44/100\n",
      " - 5s - loss: 0.0415 - val_loss: 0.0439\n",
      "Epoch 45/100\n",
      " - 5s - loss: 0.0414 - val_loss: 0.0438\n",
      "Epoch 46/100\n",
      " - 5s - loss: 0.0414 - val_loss: 0.0438\n",
      "Epoch 47/100\n",
      " - 5s - loss: 0.0413 - val_loss: 0.0438\n",
      "Epoch 48/100\n",
      " - 5s - loss: 0.0413 - val_loss: 0.0437\n",
      "Epoch 49/100\n",
      " - 5s - loss: 0.0413 - val_loss: 0.0437\n",
      "Epoch 50/100\n",
      " - 5s - loss: 0.0412 - val_loss: 0.0437\n",
      "Epoch 51/100\n",
      " - 5s - loss: 0.0412 - val_loss: 0.0437\n",
      "Epoch 52/100\n",
      " - 5s - loss: 0.0411 - val_loss: 0.0437\n",
      "Epoch 53/100\n",
      " - 5s - loss: 0.0411 - val_loss: 0.0437\n",
      "Epoch 54/100\n",
      " - 5s - loss: 0.0411 - val_loss: 0.0436\n",
      "Epoch 55/100\n",
      " - 5s - loss: 0.0410 - val_loss: 0.0436\n",
      "Epoch 56/100\n",
      " - 5s - loss: 0.0410 - val_loss: 0.0436\n",
      "Epoch 57/100\n",
      " - 5s - loss: 0.0410 - val_loss: 0.0435\n",
      "Epoch 58/100\n",
      " - 5s - loss: 0.0409 - val_loss: 0.0435\n",
      "Epoch 59/100\n",
      " - 5s - loss: 0.0409 - val_loss: 0.0435\n",
      "Epoch 60/100\n",
      " - 5s - loss: 0.0409 - val_loss: 0.0435\n",
      "Epoch 61/100\n",
      " - 5s - loss: 0.0408 - val_loss: 0.0435\n",
      "Epoch 62/100\n",
      " - 5s - loss: 0.0408 - val_loss: 0.0435\n",
      "Epoch 63/100\n",
      " - 5s - loss: 0.0408 - val_loss: 0.0435\n",
      "Epoch 64/100\n",
      " - 5s - loss: 0.0408 - val_loss: 0.0435\n",
      "Epoch 65/100\n",
      " - 5s - loss: 0.0407 - val_loss: 0.0434\n",
      "Epoch 66/100\n",
      " - 5s - loss: 0.0407 - val_loss: 0.0434\n",
      "Epoch 67/100\n",
      " - 5s - loss: 0.0407 - val_loss: 0.0434\n",
      "Epoch 68/100\n",
      " - 5s - loss: 0.0407 - val_loss: 0.0434\n",
      "Epoch 69/100\n",
      " - 5s - loss: 0.0406 - val_loss: 0.0434\n",
      "Epoch 70/100\n",
      " - 5s - loss: 0.0406 - val_loss: 0.0434\n",
      "Epoch 71/100\n",
      " - 5s - loss: 0.0406 - val_loss: 0.0434\n",
      "Epoch 72/100\n",
      " - 5s - loss: 0.0406 - val_loss: 0.0434\n",
      "Epoch 73/100\n",
      " - 5s - loss: 0.0405 - val_loss: 0.0434\n",
      "Epoch 74/100\n",
      " - 5s - loss: 0.0405 - val_loss: 0.0433\n",
      "Epoch 75/100\n",
      " - 5s - loss: 0.0405 - val_loss: 0.0433\n",
      "Epoch 76/100\n",
      " - 5s - loss: 0.0405 - val_loss: 0.0433\n",
      "Epoch 77/100\n",
      " - 5s - loss: 0.0405 - val_loss: 0.0434\n",
      "Epoch 78/100\n",
      " - 5s - loss: 0.0404 - val_loss: 0.0433\n",
      "Epoch 79/100\n",
      " - 5s - loss: 0.0404 - val_loss: 0.0433\n",
      "Epoch 80/100\n",
      " - 5s - loss: 0.0404 - val_loss: 0.0433\n",
      "Epoch 81/100\n",
      " - 5s - loss: 0.0404 - val_loss: 0.0433\n",
      "Epoch 82/100\n",
      " - 5s - loss: 0.0404 - val_loss: 0.0433\n",
      "Epoch 83/100\n",
      " - 5s - loss: 0.0403 - val_loss: 0.0433\n",
      "Epoch 84/100\n",
      " - 5s - loss: 0.0403 - val_loss: 0.0433\n",
      "Epoch 85/100\n",
      " - 5s - loss: 0.0403 - val_loss: 0.0432\n",
      "Epoch 86/100\n",
      " - 5s - loss: 0.0403 - val_loss: 0.0432\n",
      "Epoch 87/100\n",
      " - 5s - loss: 0.0403 - val_loss: 0.0433\n",
      "Epoch 88/100\n",
      " - 5s - loss: 0.0403 - val_loss: 0.0432\n",
      "Epoch 89/100\n",
      " - 5s - loss: 0.0403 - val_loss: 0.0432\n",
      "Epoch 90/100\n",
      " - 5s - loss: 0.0402 - val_loss: 0.0432\n",
      "Epoch 91/100\n",
      " - 5s - loss: 0.0402 - val_loss: 0.0433\n",
      "Epoch 92/100\n",
      " - 5s - loss: 0.0402 - val_loss: 0.0432\n",
      "Epoch 93/100\n",
      " - 5s - loss: 0.0402 - val_loss: 0.0433\n",
      "Epoch 00093: early stopping\n"
     ]
    }
   ],
   "source": [
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=5)\n",
    "mc = ModelCheckpoint('best_model_2dsyn_patch.h5', monitor='val_loss', mode='min', save_best_only=True)\n",
    "\n",
    "batch = 128\n",
    "\n",
    "history = autoencoder.fit(dataNoisy, dataNoisy, verbose=2,epochs=100, batch_size=batch, shuffle=\"batch\", callbacks=[es, mc],\n",
    "                          validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16066, 2304)\n",
      "(16066, 2304)\n"
     ]
    }
   ],
   "source": [
    "w1=48\n",
    "w2=48\n",
    "z1=1\n",
    "z2=1\n",
    "\n",
    "model = load_model('best_model_2dsyn_patch.h5')\n",
    "\n",
    "datan = scipy.io.loadmat(r'.\\Datasets\\2Dsyn_data.mat')\n",
    "dataNoisyy = datan['DataNoisy']\n",
    "\n",
    "n1,n2 = np.shape(dataNoisyy)\n",
    "dataNoisy = yc_patch(dataNoisyy, w1, w2, z1, z2)\n",
    "dataNoisy = np.array(dataNoisy)\n",
    "print(dataNoisy.shape)\n",
    "\n",
    "out = model.predict(dataNoisy)\n",
    "print(out.shape)\n",
    "\n",
    "#predicted=np.reshape(out,(out.shape[0],w1*w2))\n",
    "#print(predicted.shape)\n",
    "\n",
    "predicted = np.transpose(out)\n",
    "predicted = yc_patch_inv(predicted,n1,n2,w1,w2,z1,z2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def cseis():\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    import numpy as np\n",
    "    seis=np.concatenate(\n",
    "(np.concatenate((0.5*np.ones([1,40]),np.expand_dims(np.linspace(0.5,1,88),axis=1).transpose(),np.expand_dims(np.linspace(1,0,88),axis=1).transpose(),np.zeros([1,40])),axis=1).transpose(),\n",
    "np.concatenate((0.25*np.ones([1,40]),np.expand_dims(np.linspace(0.25,1,88),axis=1).transpose(),np.expand_dims(np.linspace(1,0,88),axis=1).transpose(),np.zeros([1,40])),axis=1).transpose(),\n",
    "np.concatenate((np.zeros([1,40]),np.expand_dims(np.linspace(0,1,88),axis=1).transpose(),np.expand_dims(np.linspace(1,0,88),axis=1).transpose(),np.zeros([1,40])),axis=1).transpose()),axis=1)\n",
    "    return ListedColormap(seis)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming cseis() is a function returning a colormap\n",
    "# and dataNoisyy, predicted are your image data arrays\n",
    "\n",
    "fig = plt.figure(figsize=(8, 10))\n",
    "\n",
    "# Subplot 1: Noisy data\n",
    "ax1 = fig.add_subplot(1, 3, 1)\n",
    "im1 = ax1.imshow(dataNoisyy, cmap=cseis(), clim=(-1, 1), aspect=0.2)\n",
    "ax1.set_xticks([])\n",
    "ax1.set_yticks([])\n",
    "ax1.set_title('Noisy data')\n",
    "\n",
    "# Add color bar for subplot 1\n",
    "cbar1 = fig.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04)\n",
    "\n",
    "# Subplot 2: Denoised data\n",
    "ax2 = fig.add_subplot(1, 3, 2)\n",
    "im2 = ax2.imshow(predicted, cmap=cseis(), clim=(-1, 1), aspect=0.2)\n",
    "ax2.set_xticks([])\n",
    "ax2.set_yticks([])\n",
    "ax2.set_title('Denoised data')\n",
    "\n",
    "# Add color bar for subplot 2\n",
    "cbar2 = fig.colorbar(im2, ax=ax2, fraction=0.046, pad=0.04)\n",
    "\n",
    "# Subplot 3: Removed noise\n",
    "ax3 = fig.add_subplot(1, 3, 3)\n",
    "im3 = ax3.imshow(dataNoisyy - predicted, cmap=cseis(), clim=(-1, 1), aspect=0.2)\n",
    "ax3.set_xticks([])\n",
    "ax3.set_yticks([])\n",
    "ax3.set_title('Removed noise')\n",
    "\n",
    "# Add color bar for subplot 3\n",
    "cbar3 = fig.colorbar(im3, ax=ax3, fraction=0.046, pad=0.04)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNR of initial denoising is -0.298058\n",
      "SNR of denoised data using DDUL is 14.2103\n"
     ]
    }
   ],
   "source": [
    "import pyortho as lo\n",
    "\n",
    "datan = scipy.io.loadmat(r'.\\Datasets\\2Dsyn_data.mat')\n",
    "dataCleann = datan['DataClean']\n",
    "## compare SNR\n",
    "print('SNR of initial denoising is %g'%lo.snr(dataCleann,dataNoisyy));\n",
    "print('SNR of denoised data using DDUL is %g'%lo.snr(dataCleann,predicted));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration: 1, res: 1 !\n",
      "iteration: 2, res: 0.994221 !\n",
      "iteration: 3, res: 0.991711 !\n",
      "iteration: 4, res: 0.989888 !\n",
      "iteration: 5, res: 0.987051 !\n",
      "iteration: 6, res: 0.98437 !\n",
      "iteration: 7, res: 0.982194 !\n",
      "iteration: 8, res: 0.979079 !\n",
      "iteration: 9, res: 0.977053 !\n",
      "iteration: 10, res: 0.974146 !\n",
      "iteration: 11, res: 0.972067 !\n",
      "iteration: 12, res: 0.969262 !\n",
      "iteration: 13, res: 0.967035 !\n",
      "iteration: 14, res: 0.965315 !\n",
      "iteration: 15, res: 0.96362 !\n",
      "iteration: 16, res: 0.962255 !\n",
      "iteration: 17, res: 0.961362 !\n",
      "iteration: 18, res: 0.960508 !\n",
      "iteration: 19, res: 0.959805 !\n",
      "iteration: 20, res: 0.959328 !\n",
      "iteration: 1, res: 1 !\n",
      "iteration: 2, res: 0.964834 !\n",
      "iteration: 3, res: 0.949612 !\n",
      "iteration: 4, res: 0.94121 !\n",
      "iteration: 5, res: 0.937547 !\n",
      "iteration: 6, res: 0.935846 !\n",
      "iteration: 7, res: 0.934925 !\n",
      "iteration: 8, res: 0.934489 !\n",
      "iteration: 9, res: 0.934338 !\n",
      "iteration: 10, res: 0.934284 !\n",
      "iteration: 11, res: 0.934265 !\n",
      "iteration: 12, res: 0.934259 !\n",
      "iteration: 13, res: 0.934257 !\n"
     ]
    }
   ],
   "source": [
    "## calculate local orthogonalization\n",
    "\n",
    "d1=predicted;\n",
    "noi1=dataNoisyy-d1;\n",
    "rect=[20,20,1];\n",
    "eps=0;\n",
    "niter=20;\n",
    "verb=1;\n",
    "\n",
    "simi1=lo.localsimi(d1,noi1,[5,5,1],niter,eps,verb);\n",
    "simi1 = np.squeeze(simi1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x27a4a3dd470>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.imshow(simi1,cmap='jet',clim=(0,1),aspect=0.2)\n",
    "plt.title('Local similarity: Initial denoising');\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}