resnet_1.1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\sf\\conda\\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",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Model\n",
    "from keras.layers import Dense, Conv2D, MaxPooling2D, AveragePooling2D, BatchNormalization\n",
    "from keras.layers import Concatenate, Add, Dropout, Input, Activation, Reshape, Flatten\n",
    "from keras.callbacks import TensorBoard\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ImageUtils import ImageUtils\n",
    "import pickle\n",
    "from keras.utils import plot_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#基本常量\n",
    "sourceFolder = 'D:/jupyter/car-management/recognitionalgorithm/dataset/rimages'\n",
    "targetFolder = 'D:/jupyter/car-management/recognitionalgorithm/dataset/rimagesResize320L'\n",
    "root = 'D:/jupyter/car-management/recognitionalgorithm/dataset'\n",
    "Xfilename = 'licenseplateL_1_1.npy'\n",
    "Yfilename = 'labe_l_1.npy'\n",
    "filename = '字典.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''数据处理工具类'''\n",
    "utils = ImageUtils()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "utils.processImage(sourceFolder, targetFolder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''处理数据'''\n",
    "X = utils.readXData(targetFolder)\n",
    "Y = utils.readYData(root, targetFolder, filename)\n",
    "utils.save(root, Xfilename, Yfilename, X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x's shape: (1222, 320, 320)\n",
      "y's shape: (1222, 7)\n",
      "冀A22222\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x113ae969cc0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113ae46feb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''加载处理的数据'''\n",
    "X_train, Y_train = utils.load(root, Xfilename, Yfilename)\n",
    "print(\"x's shape: \" + str(X_train.shape))\n",
    "print(\"y's shape: \" + str(Y_train.shape))\n",
    "print(utils.getLabel(root, filename, Y_train[100]))\n",
    "plt.imshow(X_train[100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inception_block(X, name, filters, strides):\n",
    "    '''first layers'''\n",
    "    f_skip_1, f_1_1, f_1_2, f_2_1, f_2_2, f_2_3 = filters \n",
    "    base_name = 'inception' + str(name)\n",
    "    \n",
    "    conv_1_1 = Conv2D(f_1_1, (1, 1), padding = 'same', strides = strides, name = base_name + '_c_1_1', activation = 'relu', kernel_initializer = 'truncated_normal')(X)\n",
    "    conv_1_2 = Conv2D(f_2_1, (1, 1), padding = 'same', strides = strides, name = base_name + '_c_1_2', activation = 'relu', kernel_initializer = 'truncated_normal')(X)\n",
    "    pool_1_1 = MaxPooling2D((3, 3), strides = strides, padding = 'same', name = base_name + '_p_1_1')(X)\n",
    "    \n",
    "    conv_skip_1 = Conv2D(f_skip_1, (1, 1), strides = 2 * strides, padding = 'same', name = base_name + '_skip_1', activation = 'relu', kernel_initializer = 'truncated_normal')(X)\n",
    "    \n",
    "    conv_2_1 = Conv2D(f_2_1, (3, 3), padding = 'same', strides = strides, name = base_name + '_c_2_1', activation = 'relu', kernel_initializer = 'truncated_normal')(conv_1_1)\n",
    "    conv_2_2 = Conv2D(f_2_2, (5, 5), padding = 'same', strides = strides, name = base_name + '_c_2_2', activation = 'relu', kernel_initializer = 'truncated_normal')(conv_1_2)\n",
    "    conv_2_3 = Conv2D(f_2_3, (1, 1), padding = 'same', strides =  strides, name = base_name + '_c_2_3', activation = 'relu', kernel_initializer = 'truncated_normal')(pool_1_1)\n",
    "    \n",
    "    # 合并\n",
    "    inception = Concatenate(axis = -1, name = base_name + '_ct_1_1')([conv_skip_1, conv_2_1, conv_2_2, conv_2_3])\n",
    "    \n",
    "    return inception"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 320, 320, 1)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "inception1_c_1_1 (Conv2D)       (None, 160, 160, 6)  12          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "inception1_c_1_2 (Conv2D)       (None, 160, 160, 6)  12          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "inception1_p_1_1 (MaxPooling2D) (None, 160, 160, 1)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "inception1_skip_1 (Conv2D)      (None, 80, 80, 6)    12          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "inception1_c_2_1 (Conv2D)       (None, 80, 80, 6)    330         inception1_c_1_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "inception1_c_2_2 (Conv2D)       (None, 80, 80, 6)    906         inception1_c_1_2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "inception1_c_2_3 (Conv2D)       (None, 80, 80, 6)    12          inception1_p_1_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "inception1_ct_1_1 (Concatenate) (None, 80, 80, 24)   0           inception1_skip_1[0][0]          \n",
      "                                                                 inception1_c_2_1[0][0]           \n",
      "                                                                 inception1_c_2_2[0][0]           \n",
      "                                                                 inception1_c_2_3[0][0]           \n",
      "==================================================================================================\n",
      "Total params: 1,284\n",
      "Trainable params: 1,284\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "(1, 80, 80, 24)\n"
     ]
    }
   ],
   "source": [
    "'''测试'''\n",
    "inputs_x = np.random.rand(1, 320, 320, 1)\n",
    "inputs = Input(shape = (320, 320, 1))\n",
    "outputs = inception_block(inputs, 1, (6, 6, 6, 6, 6, 6), strides = 2)\n",
    "inception_model = Model(inputs = inputs, outputs = outputs)\n",
    "inception_model.summary()\n",
    "# y = inception_model.predict_on_batch(inputs_x)\n",
    "y = inception_model.predict(inputs_x, batch_size = 1)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def identify_block(X, filters, name, strides):\n",
    "    conv_name = 'identify_conv' + str(name)\n",
    "    bn_name = 'identify_bn_name' + str(name)\n",
    "    activation_name = 'identify_activation_name' + str(name)\n",
    "    add_name = 'identify_add' + str(name)\n",
    "    skip_name = 'identify_skip' + str(name)\n",
    "    \n",
    "    f_1_1, f_1_2, skip_1_1 = filters\n",
    "    \n",
    "    '''first unit'''\n",
    "    conv_1_1 = Conv2D(f_1_1, (3, 3), strides = strides, padding = 'same', name = conv_name + '_1_1', kernel_initializer = 'truncated_normal')(X)\n",
    "    bn_1_1 = BatchNormalization(name = bn_name + '_1_1')(conv_1_1)\n",
    "    activation_1_1 = Activation('relu', name = activation_name + '_1_1')(bn_1_1)\n",
    "    \n",
    "    '''second unit'''\n",
    "    conv_2_1 = Conv2D(f_1_2, (3, 3), strides = strides, padding = 'same', name = conv_name + '_2_1', kernel_initializer = 'truncated_normal')(activation_1_1)\n",
    "    bn_2_1 = BatchNormalization(name = bn_name + '_2_1')(conv_2_1)\n",
    "    activation_2_1 = Activation('relu', name = activation_name + '_2_1')(bn_2_1)\n",
    "    \n",
    "    '''skip unit'''\n",
    "    skip_strides = 2 * strides\n",
    "    if strides == 1:\n",
    "        skip_strides = 1\n",
    "        \n",
    "    conv_skip_1_1 = Conv2D(skip_1_1, (3, 3), strides = skip_strides, padding = 'same', name = skip_name + '_1_1', kernel_initializer = 'truncated_normal')(X)\n",
    "    \n",
    "    identify = Add(name = add_name + '_1_1')([activation_2_1, conv_skip_1_1])\n",
    "    \n",
    "    return identify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 80, 80, 3)\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_6 (InputLayer)            (None, 320, 320, 1)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "identify_conv1_1_1 (Conv2D)     (None, 160, 160, 3)  30          input_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "identify_bn_name1_1_1 (BatchNor (None, 160, 160, 3)  12          identify_conv1_1_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "identify_activation_name1_1_1 ( (None, 160, 160, 3)  0           identify_bn_name1_1_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "identify_conv1_2_1 (Conv2D)     (None, 80, 80, 3)    84          identify_activation_name1_1_1[0][\n",
      "__________________________________________________________________________________________________\n",
      "identify_bn_name1_2_1 (BatchNor (None, 80, 80, 3)    12          identify_conv1_2_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "identify_activation_name1_2_1 ( (None, 80, 80, 3)    0           identify_bn_name1_2_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "identify_skip1_1_1 (Conv2D)     (None, 80, 80, 3)    30          input_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "identify_add1_1_1 (Add)         (None, 80, 80, 3)    0           identify_activation_name1_2_1[0][\n",
      "                                                                 identify_skip1_1_1[0][0]         \n",
      "==================================================================================================\n",
      "Total params: 168\n",
      "Trainable params: 156\n",
      "Non-trainable params: 12\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inputs_x = np.random.rand(1, 320, 320, 1)\n",
    "inputs = Input(shape = (320, 320, 1))\n",
    "outputs = identify_block(inputs, filters = (3, 3, 3), name = '1', strides = 2)\n",
    "identify_model = Model(inputs = inputs, outputs = outputs)\n",
    "identify_y = identify_model.predict(inputs_x)\n",
    "print(identify_y.shape)\n",
    "identify_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''build convlution block'''\n",
    "def bottleneck_block(X, filters, name, strides):\n",
    "    conv_name = 'bk_conv' + str(name)\n",
    "    bn_name = 'bk_bn' + str(name)\n",
    "    activation_name = 'bk_an' + str(name)\n",
    "    add_name = 'bk_add' + str(name)\n",
    "    skip_name = 'bk_sk' + str(name)\n",
    "    \n",
    "    f_1_1, f_1_2, f_1_3, skip_1_1 = filters\n",
    "    \n",
    "    '''first unit'''\n",
    "    conv_1_1 = Conv2D(f_1_1, (1, 1), strides = strides, padding = 'same', name = conv_name + '1_1', kernel_initializer = 'truncated_normal')(X)\n",
    "    bn_1_1 = BatchNormalization(name = bn_name + '1_1')(conv_1_1)\n",
    "    activation_1_1 = Activation('relu', name = activation_name + '1_1')(bn_1_1)\n",
    "    \n",
    "    '''second unit'''\n",
    "    conv_2_1 = Conv2D(f_1_2, (3, 3), strides = strides, padding = 'same', name = conv_name + '2_1', kernel_initializer = 'truncated_normal')(activation_1_1)\n",
    "    bn_2_1 = BatchNormalization(name = bn_name + '2_1')(conv_2_1)\n",
    "    activation_2_1 = Activation('relu', name = activation_name + '2_1')(bn_2_1)\n",
    "    \n",
    "    '''skip unit'''\n",
    "    conv_3_1 = Conv2D(f_1_3, (1, 1), strides = strides, padding = 'same', name = conv_name + '3_1', kernel_initializer = 'truncated_normal')(activation_2_1)\n",
    "    bn_3_1 = BatchNormalization(name = bn_name + '3_1')(conv_3_1)\n",
    "    activation_3_1 = Activation('relu', name = activation_name + '3_1')(bn_3_1)\n",
    "    \n",
    "    '''skip unit'''\n",
    "    skip_strides = int(pow(strides, 3))\n",
    "    if strides == 1:\n",
    "        skip_strides = 1\n",
    "    conv_skip_1_1 = Conv2D(skip_1_1, (3, 3), strides = skip_strides, name = skip_name + 'c_1_1', padding = 'same', kernel_initializer = 'truncated_normal')(X)\n",
    "    bn_skip_1_1 = BatchNormalization(name = skip_name + '_1_1')(conv_skip_1_1)\n",
    "    activation_skip_1_1 = Activation('relu', name = skip_name + 'an_1_1')(bn_skip_1_1)\n",
    "    \n",
    "    \n",
    "    bottleneck = Add(name = add_name + '_1_1')([activation_3_1, activation_skip_1_1])\n",
    "    \n",
    "    return bottleneck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 12, 12, 3)\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_7 (InputLayer)            (None, 320, 320, 1)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "bk_conv11_1 (Conv2D)            (None, 107, 107, 3)  6           input_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bk_bn11_1 (BatchNormalization)  (None, 107, 107, 3)  12          bk_conv11_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_an11_1 (Activation)          (None, 107, 107, 3)  0           bk_bn11_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_conv12_1 (Conv2D)            (None, 36, 36, 3)    84          bk_an11_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_bn12_1 (BatchNormalization)  (None, 36, 36, 3)    12          bk_conv12_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_an12_1 (Activation)          (None, 36, 36, 3)    0           bk_bn12_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_conv13_1 (Conv2D)            (None, 12, 12, 3)    12          bk_an12_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_sk1c_1_1 (Conv2D)            (None, 12, 12, 3)    30          input_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bk_bn13_1 (BatchNormalization)  (None, 12, 12, 3)    12          bk_conv13_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_sk1_1_1 (BatchNormalization) (None, 12, 12, 3)    12          bk_sk1c_1_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_an13_1 (Activation)          (None, 12, 12, 3)    0           bk_bn13_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_sk1an_1_1 (Activation)       (None, 12, 12, 3)    0           bk_sk1_1_1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "bk_add1_1_1 (Add)               (None, 12, 12, 3)    0           bk_an13_1[0][0]                  \n",
      "                                                                 bk_sk1an_1_1[0][0]               \n",
      "==================================================================================================\n",
      "Total params: 180\n",
      "Trainable params: 156\n",
      "Non-trainable params: 24\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inputs_x = np.random.rand(1, 320, 320, 1)\n",
    "inputs = Input(shape = (320, 320, 1))\n",
    "outputs = bottleneck_block(inputs, filters = (3, 3, 3, 3), name = '1', strides = 3)\n",
    "bottleneck_model = Model(inputs = inputs, outputs = outputs)\n",
    "bottleneck_y = bottleneck_model.predict(inputs_x)\n",
    "print(bottleneck_y.shape)\n",
    "bottleneck_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''build full network'''\n",
    "def resnet_1_1(input_shape, reshape, full_conncet_nums = (7, 32, 7)):\n",
    "    \n",
    "    inputs = Input(shape = input_shape)\n",
    "    \n",
    "    '''reshape (n * n) to  (n * n * 1)'''\n",
    "    rs = Reshape(reshape)(inputs)\n",
    "    \n",
    "    '''inception'''\n",
    "    inception = inception_block(rs, 1, (32, 32, 32, 32, 32, 32), strides = 2)\n",
    "    \n",
    "    '''dropout 0.5'''\n",
    "    d_inception = Dropout(0.5)(inception)\n",
    "    \n",
    "    '''128 filters, identify name: 1  256 filters, bottleneck name: 1'''\n",
    "    identify_block_1 = identify_block(d_inception, filters = (128, 128, 128), name = 1, strides = 2)\n",
    "    bottleneck_block_1 = bottleneck_block(identify_block_1, filters = (256, 256, 256, 256), name = 1, strides = 1)\n",
    "    \n",
    "    '''dropout'''\n",
    "    d_bottleneck_block_1 = Dropout(0.5)(bottleneck_block_1)\n",
    "    \n",
    "    '''256 filters, identify name: 2  512 filters, bottleneck name: 2'''\n",
    "    identify_block_2 = identify_block(d_bottleneck_block_1, filters = (512, 512, 512), name = 2, strides = 2)\n",
    "    bottleneck_block_2 = bottleneck_block(identify_block_2, filters = (1024, 1024, 1024, 1024), name = 2, strides = 1)\n",
    "    \n",
    "    ''''''\n",
    "    d_bottleneck_block_2 = Dropout(0.5)(bottleneck_block_2)\n",
    "    \n",
    "    '''1024 filters, identify name: 2  2048 filters, bottleneck name: 2'''\n",
    "#     identify_block_3 = identify_block(bottleneck_block_2, filters = (1024, 1024, 1024), name = 3, strides = 1)\n",
    "#     bottleneck_block_3 = bottleneck_block(identify_block_3, filters = (2048, 2048, 2048, 2048), name = 3, strides = 1)\n",
    "    \n",
    "    '''flatten'''\n",
    "    flatten = Flatten()(d_bottleneck_block_2)\n",
    "    \n",
    "    '''full connect params'''\n",
    "    full_connect_num_1, full_connect_num_2, full_connect_num_3 = full_conncet_nums\n",
    "    \n",
    "    '''full connect layer 1'''\n",
    "    full_connect1 = Dense(full_connect_num_1)(flatten)\n",
    "    \n",
    "    '''full conncet layer 2'''\n",
    "    full_connect2 = Dense(full_connect_num_2)(full_connect1)\n",
    "    \n",
    "    '''full connect layer 3'''\n",
    "    full_connect3 = Dense(full_connect_num_3)(full_connect2)\n",
    "    '''build model'''\n",
    "    model = Model(inputs = inputs, outputs = full_connect3)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 320, 320)     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 320, 320, 1)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "inception1_c_1_1 (Conv2D)       (None, 160, 160, 32) 64          reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "inception1_c_1_2 (Conv2D)       (None, 160, 160, 32) 64          reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "inception1_p_1_1 (MaxPooling2D) (None, 160, 160, 1)  0           reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "inception1_skip_1 (Conv2D)      (None, 80, 80, 32)   64          reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "inception1_c_2_1 (Conv2D)       (None, 80, 80, 32)   9248        inception1_c_1_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "inception1_c_2_2 (Conv2D)       (None, 80, 80, 32)   25632       inception1_c_1_2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "inception1_c_2_3 (Conv2D)       (None, 80, 80, 32)   64          inception1_p_1_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "inception1_ct_1_1 (Concatenate) (None, 80, 80, 128)  0           inception1_skip_1[0][0]          \n",
      "                                                                 inception1_c_2_1[0][0]           \n",
      "                                                                 inception1_c_2_2[0][0]           \n",
      "                                                                 inception1_c_2_3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 80, 80, 128)  0           inception1_ct_1_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "identify_conv1_1_1 (Conv2D)     (None, 40, 40, 128)  147584      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "identify_bn_name1_1_1 (BatchNor (None, 40, 40, 128)  512         identify_conv1_1_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "identify_activation_name1_1_1 ( (None, 40, 40, 128)  0           identify_bn_name1_1_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "identify_conv1_2_1 (Conv2D)     (None, 20, 20, 128)  147584      identify_activation_name1_1_1[0][\n",
      "__________________________________________________________________________________________________\n",
      "identify_bn_name1_2_1 (BatchNor (None, 20, 20, 128)  512         identify_conv1_2_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "identify_activation_name1_2_1 ( (None, 20, 20, 128)  0           identify_bn_name1_2_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "identify_skip1_1_1 (Conv2D)     (None, 20, 20, 128)  147584      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "identify_add1_1_1 (Add)         (None, 20, 20, 128)  0           identify_activation_name1_2_1[0][\n",
      "                                                                 identify_skip1_1_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "bk_conv11_1 (Conv2D)            (None, 20, 20, 256)  33024       identify_add1_1_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "bk_bn11_1 (BatchNormalization)  (None, 20, 20, 256)  1024        bk_conv11_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_an11_1 (Activation)          (None, 20, 20, 256)  0           bk_bn11_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_conv12_1 (Conv2D)            (None, 20, 20, 256)  590080      bk_an11_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_bn12_1 (BatchNormalization)  (None, 20, 20, 256)  1024        bk_conv12_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_an12_1 (Activation)          (None, 20, 20, 256)  0           bk_bn12_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_conv13_1 (Conv2D)            (None, 20, 20, 256)  65792       bk_an12_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_sk1c_1_1 (Conv2D)            (None, 20, 20, 256)  295168      identify_add1_1_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "bk_bn13_1 (BatchNormalization)  (None, 20, 20, 256)  1024        bk_conv13_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_sk1_1_1 (BatchNormalization) (None, 20, 20, 256)  1024        bk_sk1c_1_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_an13_1 (Activation)          (None, 20, 20, 256)  0           bk_bn13_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_sk1an_1_1 (Activation)       (None, 20, 20, 256)  0           bk_sk1_1_1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "bk_add1_1_1 (Add)               (None, 20, 20, 256)  0           bk_an13_1[0][0]                  \n",
      "                                                                 bk_sk1an_1_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 20, 20, 256)  0           bk_add1_1_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "identify_conv2_1_1 (Conv2D)     (None, 10, 10, 512)  1180160     dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "identify_bn_name2_1_1 (BatchNor (None, 10, 10, 512)  2048        identify_conv2_1_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "identify_activation_name2_1_1 ( (None, 10, 10, 512)  0           identify_bn_name2_1_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "identify_conv2_2_1 (Conv2D)     (None, 5, 5, 512)    2359808     identify_activation_name2_1_1[0][\n",
      "__________________________________________________________________________________________________\n",
      "identify_bn_name2_2_1 (BatchNor (None, 5, 5, 512)    2048        identify_conv2_2_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "identify_activation_name2_2_1 ( (None, 5, 5, 512)    0           identify_bn_name2_2_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "identify_skip2_1_1 (Conv2D)     (None, 5, 5, 512)    1180160     dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "identify_add2_1_1 (Add)         (None, 5, 5, 512)    0           identify_activation_name2_2_1[0][\n",
      "                                                                 identify_skip2_1_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "bk_conv21_1 (Conv2D)            (None, 5, 5, 1024)   525312      identify_add2_1_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "bk_bn21_1 (BatchNormalization)  (None, 5, 5, 1024)   4096        bk_conv21_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_an21_1 (Activation)          (None, 5, 5, 1024)   0           bk_bn21_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_conv22_1 (Conv2D)            (None, 5, 5, 1024)   9438208     bk_an21_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_bn22_1 (BatchNormalization)  (None, 5, 5, 1024)   4096        bk_conv22_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_an22_1 (Activation)          (None, 5, 5, 1024)   0           bk_bn22_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_conv23_1 (Conv2D)            (None, 5, 5, 1024)   1049600     bk_an22_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_sk2c_1_1 (Conv2D)            (None, 5, 5, 1024)   4719616     identify_add2_1_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "bk_bn23_1 (BatchNormalization)  (None, 5, 5, 1024)   4096        bk_conv23_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_sk2_1_1 (BatchNormalization) (None, 5, 5, 1024)   4096        bk_sk2c_1_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_an23_1 (Activation)          (None, 5, 5, 1024)   0           bk_bn23_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_sk2an_1_1 (Activation)       (None, 5, 5, 1024)   0           bk_sk2_1_1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "bk_add2_1_1 (Add)               (None, 5, 5, 1024)   0           bk_an23_1[0][0]                  \n",
      "                                                                 bk_sk2an_1_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 5, 5, 1024)   0           bk_add2_1_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 25600)        0           dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 7)            179207      flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 32)           256         dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 7)            231         dense_2[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 22,120,110\n",
      "Trainable params: 22,107,310\n",
      "Non-trainable params: 12,800\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "'''get network model object'''\n",
    "model = resnet_1_1(input_shape = (320, 320), reshape = (320, 320, 1))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''callback to record log'''\n",
    "logdir = './logs'\n",
    "tbcallback = TensorBoard(log_dir=logdir, histogram_freq=0, batch_size=32, write_graph=True, write_grads=False, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plot_model(model, to_file='resnet_model1.png', show_shapes=True, show_layer_names=True, rankdir='TB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''compile model'''\n",
    "model.compile(loss = 'mean_squared_logarithmic_error', optimizer = 'adam', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "  48/1222 [>.............................] - ETA: 13:39 - loss: 5.4445 - acc: 0.0208"
     ]
    }
   ],
   "source": [
    "'''train model'''\n",
    "model.fit(X_train, Y_train, batch_size = 16, epochs = 100, shuffle= True, callbacks = [tbcallback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''visual'''\n",
    "# run command tensorboard --logdir . in terminal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "resnet_1_1_config = 'resnet_1_1_config.pickle'\n",
    "resnet_1_1_weights = 'resnet_1_1_config_weights.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'save model config'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''save model config'''\n",
    "config = model.get_config()\n",
    "with open(resnet_1_1_config, 'wb') as f:\n",
    "    pickle.dump({'config': config}, f)\n",
    "\n",
    "'''save weights'''\n",
    "model.save_weights(resnet_1_1_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''build model weights'''\n",
    "from keras.models import model_from_config\n",
    "import json\n",
    "with open(resnet_1_1_config, 'rb') as f:\n",
    "    build_config =  pickle.load(f)\n",
    "    build_config = build_config['config']\n",
    "build_model = Model.from_config(build_config)\n",
    "build_model.load_weights(resnet_1_1_weights)\n",
    "# print(type(build_config))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\sf\\conda\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use the retry module or similar alternatives.\n"
     ]
    }
   ],
   "source": [
    "'''convert keras model to tensorflowjs model'''\n",
    "import tensorflowjs as tfjs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = './tfjs_target_model.h5'\n",
    "tfjs.converters.save_keras_model(build_model, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2818177ae48>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x281818ad5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label:[18 41 31 31 35 36 32],predict_label:[13 42 34 29 30 34 33]\n",
      "label:[18 41 37 31 36 48 64],predict_label:[19 49 41 33 39 45 55]\n",
      "label:[18 44 36 36 34 36 36],predict_label:[14 46 40 36 34 36 38]\n",
      "label:[18 46 46 43 33 33 34],predict_label:[18 45 44 38 35 32 32]\n",
      "label:[ 9 41 31 31 31 31 32],predict_label:[ 8 41 33 29 28 32 31]\n",
      "label:[ 9 41 47 37 32 31 35],predict_label:[13 44 44 34 30 28 34]\n",
      "label:[ 9 47 51 63 38 36 32],predict_label:[14 54 54 57 36 35 37]\n",
      "label:[ 9 54 65 47 40 31 40],predict_label:[ 9 49 57 43 38 33 37]\n",
      "label:[ 0 41 39 39 39 39 39],predict_label:[ 0 47 41 38 39 39 38]\n",
      "label:[ 0 45 63 55 40 32 32],predict_label:[ 1 44 58 44 39 28 31]\n",
      "label:[ 6 42 39 39 39 39 39],predict_label:[ 9 42 38 36 34 34 37]\n",
      "label:[17 41 51 53 31 37 36],predict_label:[18 45 51 42 32 38 40]\n",
      "label:[17 46 33 42 40 35 36],predict_label:[18 48 33 39 37 34 36]\n",
      "label:[ 5 42 38 31 39 39 32],predict_label:[ 8 45 36 32 35 34 33]\n",
      "label:[28 41 45 39 31 31 31],predict_label:[23 40 42 33 30 26 30]\n",
      "label:[30 41 40 40 40 40 40],predict_label:[25 41 42 42 36 39 39]\n"
     ]
    }
   ],
   "source": [
    "index = 0\n",
    "for i in range(20):\n",
    "    index = i * 3 + index\n",
    "    y = Y_train[index]\n",
    "    x = np.reshape(X_train[index], (1, 320, 320))\n",
    "    y_predict = build_model.predict(x)\n",
    "    y_predict = y_predict.reshape((7,))\n",
    "#     y_label = utils.getLabel(root, filename, y)\n",
    "#     y_predict_label = utils.getLabel(root, filename, np.absolute(y_predict))\n",
    "#     print('label:' + str(y_label) + ',' + 'predict_label:' + str(y_predict_label))\n",
    "#     print(y_predict)\n",
    "    print('label:' + str(y) + ',' + 'predict_label:' + str(np.absolute(y_predict).astype('int')))"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}