resnet_1_0.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, Input, BatchNormalization, Activation, Conv2D, MaxPooling2D, AveragePooling2D, Dropout\n",
    "from keras.layers import Add, Reshape, Flatten\n",
    "from keras import backend as K\n",
    "from keras.utils import plot_model\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from IPython.display import display\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ImageUtils import ImageUtils\n",
    "import pickle\n",
    "from keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#基本常量\n",
    "sourceFolder = 'D:/jupyter/car-management/recognitionalgorithm/dataset/rimages'\n",
    "targetFolder = 'D:/jupyter/car-management/recognitionalgorithm/dataset/rimagesResize320'\n",
    "root = 'D:/jupyter/car-management/recognitionalgorithm/dataset'\n",
    "Xfilename = 'licenseplateL.npy'\n",
    "Yfilename = 'label.npy'\n",
    "filename = '字典.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#加载图片数据\n",
    "utils = ImageUtils()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1222, 320, 320, 3)\n",
      "(1222, 7)\n",
      "云D86999\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2d914499be0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d915098b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#检查图片数据\n",
    "X_train, Y_train = utils.load(root, Xfilename, Yfilename)\n",
    "print(X_train.shape)\n",
    "print(Y_train.shape)\n",
    "print(utils.getLabel(root, filename, Y_train[10]))\n",
    "plt.imshow(X_train[10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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', kernel_regularizer = regularizers.l2(0.001))(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', kernel_regularizer = regularizers.l2(0.001))(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', kernel_regularizer = regularizers.l2(0.001))(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": 3,
   "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', kernel_regularizer = regularizers.l2(0.001))(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', kernel_regularizer = regularizers.l2(0.001))(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', kernel_regularizer = regularizers.l2(0.001))(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', kernel_regularizer = regularizers.l2(0.001))(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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''build full network'''\n",
    "def resnet(input_shape, full_conncet_nums = (512, 7)):\n",
    "    '''inputs'''\n",
    "    inputs = Input(shape = input_shape)\n",
    "    \n",
    "    \n",
    "    '''128 filters, identify name: 1  256 filters, bottleneck name: 1'''\n",
    "    bottleneck_block_1 = bottleneck_block(inputs, filters = (128, 128, 128, 128), name = 1, strides = 2)\n",
    "    identify_block_1 = identify_block(bottleneck_block_1, filters = (256, 256, 256), name = 1, strides = 1)\n",
    "    \n",
    "    \n",
    "    '''max pooling'''\n",
    "    mp2d = MaxPooling2D(pool_size = (2, 2))(identify_block_1)\n",
    "    \n",
    "    '''relu activation'''\n",
    "    an_block_1 = Activation('relu', name = 'model_an_1')(mp2d)\n",
    "    \n",
    "    '''dropout'''\n",
    "    d_bottleneck_block_1 = Dropout(0.25)(an_block_1)\n",
    "    \n",
    "    '''256 filters, identify name: 2  512 filters, bottleneck name: 2'''\n",
    "    bottleneck_block_2 = bottleneck_block(d_bottleneck_block_1, filters = (512, 512, 512, 512), name = 2, strides = 1)\n",
    "    identify_block_2 = identify_block(bottleneck_block_2, filters = (1024, 1024, 1024), name = 2, strides = 1)\n",
    "    \n",
    "    '''relu activation'''\n",
    "    an_block_2 = Activation('relu', name = 'model_an_2')(identify_block_2)\n",
    "    \n",
    "    '''dropout'''\n",
    "    d_bottleneck_block_2 = Dropout(0.25)(an_block_2)\n",
    "    \n",
    "    '''1024 filters, identify name: 2  2048 filters, bottleneck name: 2'''\n",
    "#     bottleneck_block_3 = bottleneck_block(d_bottleneck_block_2, filters = (1024, 1024, 1024, 1024), name = 3, strides = 2)\n",
    "#     identify_block_3 = identify_block(d_bottleneck_block_2, filters = (2048, 2048, 2048), name = 3, strides = 2)\n",
    "    \n",
    "    '''average pooling'''\n",
    "    ap2d = AveragePooling2D(pool_size = (1, 1))(d_bottleneck_block_2)\n",
    "    \n",
    "    '''dropout 0.05'''\n",
    "    d_bottleneck_block_3 = Dropout(0.5)(ap2d)\n",
    "    \n",
    "    \n",
    "    '''flatten'''\n",
    "    flatten = Flatten()(d_bottleneck_block_3)\n",
    "    \n",
    "    '''full connect params'''\n",
    "    full_connect_num_1, full_connect_num_2 = full_conncet_nums\n",
    "    \n",
    "    '''full connect layer 1'''\n",
    "    full_connect1 = Dense(full_connect_num_1, activation = 'relu', kernel_initializer = 'glorot_normal', kernel_regularizer = regularizers.l2(0.1))(flatten)\n",
    "    \n",
    "  \n",
    "    '''full conncet layer 2'''\n",
    "    end_full_connect = Dense(full_connect_num_2, activation = 'softmax', kernel_initializer = 'glorot_normal', kernel_regularizer = regularizers.l2(0.1))(full_connect1)\n",
    "\n",
    "    '''build model'''\n",
    "    model = Model(inputs = inputs, outputs = end_full_connect)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 320, 320, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "bk_conv11_1 (Conv2D)            (None, 160, 160, 128 512         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bk_bn11_1 (BatchNormalization)  (None, 160, 160, 128 512         bk_conv11_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_an11_1 (Activation)          (None, 160, 160, 128 0           bk_bn11_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_conv12_1 (Conv2D)            (None, 80, 80, 128)  147584      bk_an11_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_bn12_1 (BatchNormalization)  (None, 80, 80, 128)  512         bk_conv12_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_an12_1 (Activation)          (None, 80, 80, 128)  0           bk_bn12_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_conv13_1 (Conv2D)            (None, 40, 40, 128)  16512       bk_an12_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_sk1c_1_1 (Conv2D)            (None, 40, 40, 128)  3584        input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bk_bn13_1 (BatchNormalization)  (None, 40, 40, 128)  512         bk_conv13_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_sk1_1_1 (BatchNormalization) (None, 40, 40, 128)  512         bk_sk1c_1_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_an13_1 (Activation)          (None, 40, 40, 128)  0           bk_bn13_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_sk1an_1_1 (Activation)       (None, 40, 40, 128)  0           bk_sk1_1_1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "bk_add1_1_1 (Add)               (None, 40, 40, 128)  0           bk_an13_1[0][0]                  \n",
      "                                                                 bk_sk1an_1_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "identify_conv1_1_1 (Conv2D)     (None, 40, 40, 256)  295168      bk_add1_1_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "identify_bn_name1_1_1 (BatchNor (None, 40, 40, 256)  1024        identify_conv1_1_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "identify_activation_name1_1_1 ( (None, 40, 40, 256)  0           identify_bn_name1_1_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "identify_conv1_2_1 (Conv2D)     (None, 40, 40, 256)  590080      identify_activation_name1_1_1[0][\n",
      "__________________________________________________________________________________________________\n",
      "identify_bn_name1_2_1 (BatchNor (None, 40, 40, 256)  1024        identify_conv1_2_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "identify_activation_name1_2_1 ( (None, 40, 40, 256)  0           identify_bn_name1_2_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "identify_skip1_1_1 (Conv2D)     (None, 40, 40, 256)  295168      bk_add1_1_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "identify_add1_1_1 (Add)         (None, 40, 40, 256)  0           identify_activation_name1_2_1[0][\n",
      "                                                                 identify_skip1_1_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 20, 20, 256)  0           identify_add1_1_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "model_an_1 (Activation)         (None, 20, 20, 256)  0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 20, 20, 256)  0           model_an_1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "bk_conv21_1 (Conv2D)            (None, 20, 20, 512)  131584      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_bn21_1 (BatchNormalization)  (None, 20, 20, 512)  2048        bk_conv21_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_an21_1 (Activation)          (None, 20, 20, 512)  0           bk_bn21_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_conv22_1 (Conv2D)            (None, 20, 20, 512)  2359808     bk_an21_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_bn22_1 (BatchNormalization)  (None, 20, 20, 512)  2048        bk_conv22_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_an22_1 (Activation)          (None, 20, 20, 512)  0           bk_bn22_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_conv23_1 (Conv2D)            (None, 20, 20, 512)  262656      bk_an22_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_sk2c_1_1 (Conv2D)            (None, 20, 20, 512)  1180160     dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_bn23_1 (BatchNormalization)  (None, 20, 20, 512)  2048        bk_conv23_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_sk2_1_1 (BatchNormalization) (None, 20, 20, 512)  2048        bk_sk2c_1_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bk_an23_1 (Activation)          (None, 20, 20, 512)  0           bk_bn23_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bk_sk2an_1_1 (Activation)       (None, 20, 20, 512)  0           bk_sk2_1_1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "bk_add2_1_1 (Add)               (None, 20, 20, 512)  0           bk_an23_1[0][0]                  \n",
      "                                                                 bk_sk2an_1_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "identify_conv2_1_1 (Conv2D)     (None, 20, 20, 1024) 4719616     bk_add2_1_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "identify_bn_name2_1_1 (BatchNor (None, 20, 20, 1024) 4096        identify_conv2_1_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "identify_activation_name2_1_1 ( (None, 20, 20, 1024) 0           identify_bn_name2_1_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "identify_conv2_2_1 (Conv2D)     (None, 20, 20, 1024) 9438208     identify_activation_name2_1_1[0][\n",
      "__________________________________________________________________________________________________\n",
      "identify_bn_name2_2_1 (BatchNor (None, 20, 20, 1024) 4096        identify_conv2_2_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "identify_activation_name2_2_1 ( (None, 20, 20, 1024) 0           identify_bn_name2_2_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "identify_skip2_1_1 (Conv2D)     (None, 20, 20, 1024) 4719616     bk_add2_1_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "identify_add2_1_1 (Add)         (None, 20, 20, 1024) 0           identify_activation_name2_2_1[0][\n",
      "                                                                 identify_skip2_1_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "model_an_2 (Activation)         (None, 20, 20, 1024) 0           identify_add2_1_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 20, 20, 1024) 0           model_an_2[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 20, 20, 1024) 0           dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 20, 20, 1024) 0           average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 409600)       0           dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 512)          209715712   flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 7)            3591        dense_1[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 233,900,039\n",
      "Trainable params: 233,889,799\n",
      "Non-trainable params: 10,240\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "'''build model obejct'''\n",
    "model = resnet(input_shape = (320, 320, 3))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(model, to_file='resnet_model1.png', show_shapes=True, show_layer_names=True, rankdir='TB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.95166075e-02, 3.14072636e-03, 1.71656338e-05, 2.55471357e-04,\n",
       "        1.78040689e-04, 9.66890752e-01, 1.31486320e-06],\n",
       "       [1.60860568e-01, 2.48796795e-03, 1.73997923e-05, 1.50774707e-04,\n",
       "        1.02524878e-04, 8.36380124e-01, 7.19404227e-07],\n",
       "       [4.66179401e-02, 6.06348913e-04, 3.78257646e-05, 9.25019631e-05,\n",
       "        9.01411404e-05, 9.52554464e-01, 7.52346921e-07],\n",
       "       [4.41746749e-02, 6.77105680e-04, 2.04901353e-05, 9.73446586e-05,\n",
       "        4.91224455e-05, 9.54980969e-01, 3.53320019e-07],\n",
       "       [1.07831895e-01, 1.28591142e-03, 8.76783270e-06, 6.12076692e-05,\n",
       "        5.92523029e-05, 8.90752494e-01, 5.28443763e-07]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''test model'''\n",
    "inputs = np.random.rand(5, 320, 320, 3)\n",
    "model.predict(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''compile model'''\n",
    "model.compile(loss = 'mean_squared_error', optimizer = 'adadelta', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n"
     ]
    }
   ],
   "source": [
    "'''train model'''\n",
    "model.fit(X_train, Y_train, batch_size = 8, epochs = 5)"
   ]
  },
  {
   "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)     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 320, 320, 1)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 157, 157, 64) 3200        reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 157, 157, 64) 36928       conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 157, 157, 64) 0           conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 157, 157, 64) 0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 157, 157, 64) 36928       conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 157, 157, 64) 36928       dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 157, 157, 64) 0           conv2d_4[0][0]                   \n",
      "                                                                 conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 157, 157, 64) 0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 157, 157, 64) 0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 157, 157, 64) 36928       dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 157, 157, 64) 0           conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 157, 157, 64) 0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 157, 157, 64) 36928       dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 157, 157, 64) 36928       dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 157, 157, 64) 0           conv2d_7[0][0]                   \n",
      "                                                                 conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 157, 157, 64) 0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 157, 157, 64) 0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 157, 157, 64) 36928       dropout_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 157, 157, 64) 0           conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_5 (Dropout)             (None, 157, 157, 64) 0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 157, 157, 64) 36928       dropout_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 157, 157, 64) 36928       dropout_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 157, 157, 64) 0           conv2d_10[0][0]                  \n",
      "                                                                 conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 157, 157, 64) 0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_6 (Dropout)             (None, 157, 157, 64) 0           activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 157, 157, 64) 36928       dropout_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 157, 157, 64) 0           conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_7 (Dropout)             (None, 157, 157, 64) 0           activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 157, 157, 64) 36928       dropout_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 157, 157, 64) 36928       dropout_7[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 157, 157, 64) 0           conv2d_13[0][0]                  \n",
      "                                                                 conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 157, 157, 64) 0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_8 (Dropout)             (None, 157, 157, 64) 0           activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 157, 157, 64) 36928       dropout_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 157, 157, 64) 0           conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_9 (Dropout)             (None, 157, 157, 64) 0           activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 157, 157, 64) 36928       dropout_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 157, 157, 64) 36928       dropout_9[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 157, 157, 64) 0           conv2d_16[0][0]                  \n",
      "                                                                 conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 157, 157, 64) 0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_10 (Dropout)            (None, 157, 157, 64) 0           activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 157, 157, 64) 4160        dropout_10[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 157, 157, 64) 0           conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 157, 157, 64) 36928       activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 157, 157, 64) 0           conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 157, 157, 256 147712      activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 157, 157, 256 16640       dropout_10[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 157, 157, 256 0           conv2d_20[0][0]                  \n",
      "                                                                 conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 157, 157, 256 0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_11 (Dropout)            (None, 157, 157, 256 0           activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 157, 157, 64) 16448       dropout_11[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 157, 157, 64) 0           conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 157, 157, 64) 36928       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 157, 157, 64) 0           conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 157, 157, 256 147712      activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 157, 157, 256 65792       dropout_11[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 157, 157, 256 0           conv2d_24[0][0]                  \n",
      "                                                                 conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 157, 157, 256 0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_12 (Dropout)            (None, 157, 157, 256 0           activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 157, 157, 64) 16448       dropout_12[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 157, 157, 64) 0           conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 157, 157, 64) 36928       activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 157, 157, 64) 0           conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 157, 157, 256 147712      activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 157, 157, 256 65792       dropout_12[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 157, 157, 256 0           conv2d_28[0][0]                  \n",
      "                                                                 conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 157, 157, 256 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_13 (Dropout)            (None, 157, 157, 256 0           activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 157, 157, 64) 16448       dropout_13[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 157, 157, 64) 0           conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 157, 157, 64) 36928       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 157, 157, 64) 0           conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 157, 157, 256 147712      activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 157, 157, 256 65792       dropout_13[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 157, 157, 256 0           conv2d_32[0][0]                  \n",
      "                                                                 conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 157, 157, 256 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_14 (Dropout)            (None, 157, 157, 256 0           activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 157, 157, 64) 16448       dropout_14[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 157, 157, 64) 0           conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 157, 157, 64) 36928       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 157, 157, 64) 0           conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 157, 157, 256 147712      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 157, 157, 256 65792       dropout_14[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 157, 157, 256 0           conv2d_36[0][0]                  \n",
      "                                                                 conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 157, 157, 256 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_15 (Dropout)            (None, 157, 157, 256 0           activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 156, 156, 256 0           dropout_15[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 156, 156, 256 0           average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 6230016)      0           activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 7)            43610119    flatten_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 45,440,199\n",
      "Trainable params: 45,440,199\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_file = 'config_file.pickle'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = model.get_config()\n",
    "with open(config_file, 'wb') as f:\n",
    "    pickle.dump({'config': config}, f)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights_resnet_1 = 'weights_resnet_1.h5'\n",
    "model.save_weights(weights_resnet_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "云D86999\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x258662dfba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# X = X_train[10]\n",
    "# y = model.predict(X.reshape((1, 320, 320)))\n",
    "plt.imshow(X_train[10])\n",
    "print(utils.getLabel(root, filename, Y_train[10]))"
   ]
  },
  {
   "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
}