train_model.ipynb

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   "source": [
    "# train_model"
   ]
  },
  {
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   "source": [
    "import keras\n",
    "import tensorflow as tf\n",
    "import data_utils,model_utils\n",
    "\n",
    "config =  tf.compat.v1.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "session = tf.compat.v1.Session(config=config)\n"
   ]
  },
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   "outputs": [],
   "source": [
    "# 64ms, 128ms, 256ms\n",
    "choonse_time_bin=\"64ms\"\n",
    "# plain-CNN, ResNet, ResNet-CBAM\n",
    "choose_model=\"plain-CNN\"\n",
    "\n",
    "data_set_dir=r\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "pycharm": {
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   "outputs": [],
   "source": [
    "# load and pre-process data (train and validate)\n",
    "(train_x, train_y, train_info), (val_x, val_y, val_info)=data_utils.get_train_val_data(data_set_dir,choonse_time_bin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "\n",
    "model_func_dic={\n",
    "    \"plain-CNN-64ms\": model_utils.plain_cnn_64ms,\n",
    "    \"plain-CNN-128ms\": model_utils.plain_cnn_128ms,\n",
    "    \"plain-CNN-256ms\": model_utils.plain_cnn_256ms,\n",
    "\n",
    "    \"ResNet-64ms\": model_utils.resnet_64ms,\n",
    "    \"ResNet-128ms\": model_utils.resnet_128ms,\n",
    "    \"ResNet-256ms\": model_utils.resnet_256ms,\n",
    "\n",
    "    \"ResNet-CBAM-64ms\": model_utils.resnet_CBAM_64ms,\n",
    "    \"ResNet-CBAM-128ms\": model_utils.resnet_CBAM_128ms,\n",
    "    \"ResNet-CBAM-256ms\": model_utils.resnet_CBAM_256ms,\n",
    "}\n",
    "model_func=model_func_dic.get(choose_model+\"-\"+choonse_time_bin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "K.clear_session()\n",
    "\n",
    "input_shape, nb_classes = (train_x.shape[1:]), 2\n",
    "input_layer = keras.layers.Input(shape=input_shape, name='input')\n",
    "\n",
    "model = model_func(input_layer, nb_classes)\n",
    "model_name = choose_model+choonse_time_bin\n",
    "\n",
    "adam = keras.optimizers.Adam(lr=0.0001, beta_1=0.95, beta_2=0.999, epsilon=1e-08)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "trainEpochs=1000\n",
    "trainBatchSize=32\n",
    "\n",
    "model_utils.train_model(model, train_x, train_y, val_x, val_y, trainEpochs, trainBatchSize,modelName=model_name,outDir=\"input/out_gpu/\", binSize=choonse_time_bin)\n",
    "print(\"done\")"
   ]
  }
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