{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will study the hyperparamter tuning of fully connected scatternet\n",
    "\n",
    "# first find optimal number of layers and neuron numbers\n",
    "# second optimize the batch size and number of epochs for the best learned architecture\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the dataset here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T18:17:43.677784Z",
     "start_time": "2018-09-02T18:17:43.467146Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset has been loaded\n",
      "x-train (44999, 8)\n",
      "x-test  (55001, 8)\n",
      "y-train (44999, 256)\n",
      "y-test  (55001, 256)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import h5py\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#now load this dataset \n",
    "h5f = h5py.File('./datasets/s8_sio2tio2_v2.h5','r')\n",
    "X = h5f['sizes'][:]\n",
    "Y = h5f['spectrum'][:]\n",
    "\n",
    "#get the ranges of the loaded data\n",
    "num_layers = X.shape[1]\n",
    "num_lpoints = Y.shape[1]\n",
    "size_max = np.amax(X)\n",
    "size_min = np.amin(X)\n",
    "size_av = 0.5*(size_max + size_min)\n",
    "\n",
    "#this information is not given in the dataset\n",
    "lam_min = 300\n",
    "lam_max = 1200\n",
    "lams = np.linspace(lam_min, lam_max, num_lpoints)\n",
    "\n",
    "#create a train - test split of the dataset\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.55, random_state=42)\n",
    "\n",
    "# normalize inputs \n",
    "x_train = (x_train - 50)/20 \n",
    "x_test = (x_test - 50)/20 \n",
    "\n",
    "print(\"Dataset has been loaded\")\n",
    "print(\"x-train\", x_train.shape)\n",
    "print(\"x-test \", x_test.shape)\n",
    "print(\"y-train\", y_train.shape)\n",
    "print(\"y-test \", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create models here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-04T08:36:29.520672Z",
     "start_time": "2018-09-04T08:36:29.498108Z"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'scnets' has no attribute 'convprel'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-7bcf81ac1584>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m#model = scn.fullycon(num_layers, num_lpoints, 4, 500, 2)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m model = scn.convprel(in_size=8, \n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0mout_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mc1_nf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'scnets' has no attribute 'convprel'"
     ]
    }
   ],
   "source": [
    "import scnets as scn\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "#define and visualize the model here\n",
    "#model = scn.fullycon(num_layers, num_lpoints, 4, 500, 2)\n",
    "\n",
    "model = scn.convprel(in_size=8, \n",
    "        out_size=256,\n",
    "        c1_nf=64,\n",
    "        clayers=3,\n",
    "        ker_size=3)\n",
    "model.summary()\n",
    "#SVG(model_to_dot(model).create(prog='dot', format='svg'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T18:14:24.301951Z",
     "start_time": "2018-09-02T18:14:05.497Z"
    }
   },
   "outputs": [],
   "source": [
    "x_t, x_v, y_t, y_v = train_test_split(x_train, y_train, test_size=0.2, random_state=42)\n",
    "history = model.fit(x_t, y_t,\n",
    "                    batch_size=64,\n",
    "                    epochs=250, \n",
    "                    verbose=0,\n",
    "                    validation_data=(x_v, y_v))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-02T17:56:26.451429Z",
     "start_time": "2018-09-02T17:56:26.130943Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scn.plot_training_history(history, 64*2.56)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2018-09-02T18:24:17.871Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 72 candidates, totalling 216 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  train_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:92: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md\n",
      "  test_symbol = func(*args, **kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/mxnet/module/bucketing_module.py:408: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.03125). Is this intended?\n",
      "  force_init=force_init)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "import scnets as scn\n",
    "#model = KerasClassifier(build_fn=scn.fullycon, in_size=8, out_size=250, N_gpus=1, epochs=500, verbose=0)\n",
    "\n",
    "model = KerasClassifier(build_fn=scn.conv1dmodel, \n",
    "                        in_size=8, \n",
    "                        out_size=256, \n",
    "                        c1_nf=64,\n",
    "                        clayers=3,\n",
    "                        ker_size=3,\n",
    "                        epochs=250, \n",
    "                        verbose=0)\n",
    "\n",
    "param_grid = dict(c1_nf=[32, 64, 96], \n",
    "                  clayers=[1,2,3,4], \n",
    "                  ker_size=[3,5,7], \n",
    "                  batch_size=[32,64])\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=1, verbose=1)\n",
    "grid_result = grid.fit(x_train, y_train)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-04T02:43:27.769684Z",
     "start_time": "2018-09-04T02:43:27.763319Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'batch_size': 64, 'c1_nf': 32, 'clayers': 2, 'ker_size': 5}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_result.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-03T23:19:09.386978Z",
     "start_time": "2018-09-03T23:19:09.366512Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/home/hegder/anaconda3/envs/deep/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 440.59602499,  481.73871398,  477.10135643,  504.1547633 ,\n",
       "         488.27373465,  564.76856009,  571.80046884,  569.00620119,\n",
       "         650.78964178,  740.00103672,  597.98015889,  659.03726633,\n",
       "         476.14166268,  508.75317804,  505.10563159,  556.87875859,\n",
       "         556.58007542,  542.48226706,  591.323385  ,  616.57070398,\n",
       "         658.64879243,  891.65342418, 1055.23733377,  993.75831501,\n",
       "         647.80959813,  629.8490928 ,  516.74331371,  787.65950712,\n",
       "         635.2311732 ,  545.72071338,  904.7078863 ,  685.55618747,\n",
       "         530.5135781 ,  815.14902997,  821.7310555 ,  685.34639025,\n",
       "         240.90511258,  246.83409182,  259.09067996,  262.85167106,\n",
       "         228.2099692 ,  392.25437156,  374.78024157,  452.21288816,\n",
       "         459.90044618,  495.74129097,  501.12204981,  368.91637723,\n",
       "         240.11106253,  237.93171946,  261.74841015,  265.67413298,\n",
       "         274.72749496,  378.51081745,  436.21683431,  241.44164371,\n",
       "         274.60629002,  330.47249166,  394.98302094,  318.71901139,\n",
       "         237.12692181,  248.02535923,  254.61501988,  243.65626915,\n",
       "         298.34629599,  273.75161084,  318.28928526,  312.33878072,\n",
       "         309.61013468,  335.33733678,  316.16733519,  284.3205506 ]),\n",
       " 'std_fit_time': array([ 16.87972466,   7.92785374,   5.77026847,  46.09935758,\n",
       "         54.51365785,   4.27852784,  94.58970885,  62.52370001,\n",
       "          8.4561614 ,   2.7468705 , 145.0622183 ,  99.96551438,\n",
       "          5.17816229,  12.67787649,  23.55063229,   9.66484519,\n",
       "         36.6158132 ,  34.19875912, 100.63498294,  97.25506357,\n",
       "          8.78125165,  87.44096196,  58.64011269,  34.89501886,\n",
       "         34.69794873,   6.55967573,  16.17142818,  23.55024969,\n",
       "         52.92218389,  50.06045253,  25.01777585,  18.80812352,\n",
       "         81.798268  ,   2.03161994,  42.98112108, 110.64334738,\n",
       "          9.66915548,   0.84179362,   6.43580261,  14.37030668,\n",
       "         10.02095687,  28.2914121 , 100.27866673,   4.12918697,\n",
       "          4.19608165,  48.65261655,  25.96345247,  61.99284042,\n",
       "          4.68735307,  10.21360139,   9.8859609 ,  20.70074481,\n",
       "         20.51026577,  56.21085735,  55.81395671,   8.02054713,\n",
       "         50.9381089 ,  50.45400177,  22.28014196,  67.88824331,\n",
       "          5.44755769,  11.84267318,   5.09502951,  13.30998334,\n",
       "         10.04504684,  34.3255134 ,   0.83824628,  33.02031315,\n",
       "         44.69969273,  41.65936289,  50.8080964 ,  36.12854555]),\n",
       " 'mean_score_time': array([1.48248084, 1.61645206, 1.52614307, 1.50255108, 1.5502789 ,\n",
       "        1.57044665, 1.6061248 , 1.58751472, 1.59509166, 1.68163586,\n",
       "        1.79815904, 1.7393082 , 1.47499736, 1.52648115, 1.5446895 ,\n",
       "        1.50675567, 1.54464761, 1.62923439, 1.55051088, 1.56298979,\n",
       "        1.7317764 , 1.75597771, 1.8009282 , 1.95601543, 1.52369038,\n",
       "        1.48506896, 1.56178371, 1.53837752, 1.58946848, 1.53822851,\n",
       "        1.67706863, 1.74836882, 1.67516692, 1.74954589, 1.6798114 ,\n",
       "        1.72637788, 0.76347407, 0.68936872, 0.79896879, 0.85983062,\n",
       "        0.81541355, 0.84865721, 0.83474739, 0.88329641, 0.89842264,\n",
       "        0.93576241, 0.91452034, 0.85306342, 0.77134498, 0.7801017 ,\n",
       "        0.85619156, 0.82537786, 0.82716012, 0.88619153, 0.92515985,\n",
       "        0.85173941, 0.8888003 , 0.89410488, 0.86849999, 0.88820569,\n",
       "        0.73052979, 0.86012634, 0.82792679, 0.85549124, 0.85315657,\n",
       "        0.82530626, 0.88788549, 0.90339359, 0.88132191, 0.8901968 ,\n",
       "        0.94266113, 0.90914003]),\n",
       " 'std_score_time': array([0.0809353 , 0.0434697 , 0.06246195, 0.05935619, 0.00983632,\n",
       "        0.10535178, 0.13783207, 0.08746206, 0.05477639, 0.03246781,\n",
       "        0.05349619, 0.13466161, 0.14158229, 0.07571865, 0.08168413,\n",
       "        0.05528071, 0.06024407, 0.02103915, 0.07350117, 0.1008799 ,\n",
       "        0.04944533, 0.08355515, 0.09599189, 0.11462328, 0.02945484,\n",
       "        0.01043705, 0.0536539 , 0.0657963 , 0.07744391, 0.04640753,\n",
       "        0.03043051, 0.07704773, 0.05899877, 0.07714815, 0.06984614,\n",
       "        0.08692043, 0.05506912, 0.07381311, 0.03992863, 0.05990567,\n",
       "        0.05875238, 0.04115549, 0.03138722, 0.02226701, 0.01140741,\n",
       "        0.01028036, 0.04148711, 0.01940906, 0.03398852, 0.04651999,\n",
       "        0.03599124, 0.06798827, 0.05388377, 0.02796698, 0.0052635 ,\n",
       "        0.0512727 , 0.0245259 , 0.06295023, 0.02127435, 0.05833234,\n",
       "        0.05479642, 0.0411167 , 0.0659268 , 0.04616794, 0.06244371,\n",
       "        0.02330616, 0.02381002, 0.03937768, 0.10743165, 0.04943378,\n",
       "        0.0353715 , 0.05471914]),\n",
       " 'param_batch_size': masked_array(data=[32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
       "                    32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
       "                    32, 32, 32, 32, 32, 32, 32, 32, 64, 64, 64, 64, 64, 64,\n",
       "                    64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64,\n",
       "                    64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64,\n",
       "                    64, 64],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_c1_nf': masked_array(data=[32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 64, 64,\n",
       "                    64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 96, 96, 96, 96,\n",
       "                    96, 96, 96, 96, 96, 96, 96, 96, 32, 32, 32, 32, 32, 32,\n",
       "                    32, 32, 32, 32, 32, 32, 64, 64, 64, 64, 64, 64, 64, 64,\n",
       "                    64, 64, 64, 64, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96,\n",
       "                    96, 96],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_clayers': masked_array(data=[1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2,\n",
       "                    3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4,\n",
       "                    1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2,\n",
       "                    3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_ker_size': masked_array(data=[3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7,\n",
       "                    3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7,\n",
       "                    3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7,\n",
       "                    3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'batch_size': 32, 'c1_nf': 32, 'clayers': 1, 'ker_size': 3},\n",
       "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 1, 'ker_size': 5},\n",
       "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 1, 'ker_size': 7},\n",
       "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 2, 'ker_size': 3},\n",
       "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 2, 'ker_size': 5},\n",
       "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 2, 'ker_size': 7},\n",
       "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 3, 'ker_size': 3},\n",
       "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 3, 'ker_size': 5},\n",
       "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 3, 'ker_size': 7},\n",
       "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 4, 'ker_size': 3},\n",
       "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 4, 'ker_size': 5},\n",
       "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 4, 'ker_size': 7},\n",
       "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 1, 'ker_size': 3},\n",
       "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 1, 'ker_size': 5},\n",
       "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 1, 'ker_size': 7},\n",
       "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 2, 'ker_size': 3},\n",
       "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 2, 'ker_size': 5},\n",
       "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 2, 'ker_size': 7},\n",
       "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 3, 'ker_size': 3},\n",
       "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 3, 'ker_size': 5},\n",
       "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 3, 'ker_size': 7},\n",
       "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 4, 'ker_size': 3},\n",
       "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 4, 'ker_size': 5},\n",
       "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 4, 'ker_size': 7},\n",
       "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 1, 'ker_size': 3},\n",
       "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 1, 'ker_size': 5},\n",
       "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 1, 'ker_size': 7},\n",
       "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 2, 'ker_size': 3},\n",
       "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 2, 'ker_size': 5},\n",
       "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 2, 'ker_size': 7},\n",
       "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 3, 'ker_size': 3},\n",
       "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 3, 'ker_size': 5},\n",
       "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 3, 'ker_size': 7},\n",
       "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 4, 'ker_size': 3},\n",
       "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 4, 'ker_size': 5},\n",
       "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 4, 'ker_size': 7},\n",
       "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 1, 'ker_size': 3},\n",
       "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 1, 'ker_size': 5},\n",
       "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 1, 'ker_size': 7},\n",
       "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 2, 'ker_size': 3},\n",
       "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 2, 'ker_size': 5},\n",
       "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 2, 'ker_size': 7},\n",
       "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 3, 'ker_size': 3},\n",
       "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 3, 'ker_size': 5},\n",
       "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 3, 'ker_size': 7},\n",
       "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 4, 'ker_size': 3},\n",
       "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 4, 'ker_size': 5},\n",
       "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 4, 'ker_size': 7},\n",
       "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 1, 'ker_size': 3},\n",
       "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 1, 'ker_size': 5},\n",
       "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 1, 'ker_size': 7},\n",
       "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 2, 'ker_size': 3},\n",
       "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 2, 'ker_size': 5},\n",
       "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 2, 'ker_size': 7},\n",
       "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 3, 'ker_size': 3},\n",
       "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 3, 'ker_size': 5},\n",
       "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 3, 'ker_size': 7},\n",
       "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 4, 'ker_size': 3},\n",
       "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 4, 'ker_size': 5},\n",
       "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 4, 'ker_size': 7},\n",
       "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 1, 'ker_size': 3},\n",
       "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 1, 'ker_size': 5},\n",
       "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 1, 'ker_size': 7},\n",
       "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 2, 'ker_size': 3},\n",
       "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 2, 'ker_size': 5},\n",
       "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 2, 'ker_size': 7},\n",
       "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 3, 'ker_size': 3},\n",
       "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 3, 'ker_size': 5},\n",
       "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 3, 'ker_size': 7},\n",
       "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 4, 'ker_size': 3},\n",
       "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 4, 'ker_size': 5},\n",
       "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 4, 'ker_size': 7}],\n",
       " 'split0_test_score': array([0.00166667, 0.0016    , 0.00193333, 0.00266667, 0.00186667,\n",
       "        0.00206667, 0.00246667, 0.00293333, 0.00273333, 0.00286667,\n",
       "        0.00213333, 0.00253333, 0.00173333, 0.00193333, 0.0028    ,\n",
       "        0.0026    , 0.00193333, 0.00193333, 0.00226667, 0.00233333,\n",
       "        0.00246667, 0.0024    , 0.00253333, 0.00233333, 0.0022    ,\n",
       "        0.00186667, 0.0022    , 0.002     , 0.00186667, 0.00226667,\n",
       "        0.002     , 0.00193333, 0.00213333, 0.0022    , 0.00213333,\n",
       "        0.0022    , 0.00173333, 0.002     , 0.0014    , 0.002     ,\n",
       "        0.00253333, 0.00233333, 0.00206667, 0.00246667, 0.002     ,\n",
       "        0.00193333, 0.00266667, 0.0024    , 0.0016    , 0.00213333,\n",
       "        0.00186667, 0.0024    , 0.00213333, 0.0024    , 0.00253333,\n",
       "        0.00226667, 0.0024    , 0.00186667, 0.0022    , 0.0024    ,\n",
       "        0.00153333, 0.002     , 0.0024    , 0.002     , 0.0016    ,\n",
       "        0.00186667, 0.00253333, 0.00246667, 0.0022    , 0.00233333,\n",
       "        0.00193333, 0.0016    ]),\n",
       " 'split1_test_score': array([0.0024    , 0.00193333, 0.0012    , 0.00213333, 0.00206667,\n",
       "        0.00246667, 0.00193333, 0.00253333, 0.00233333, 0.00173333,\n",
       "        0.00246667, 0.0018    , 0.0018    , 0.00166667, 0.002     ,\n",
       "        0.00186667, 0.00193333, 0.00193333, 0.0024    , 0.00173333,\n",
       "        0.00173333, 0.002     , 0.00186667, 0.0024    , 0.00126667,\n",
       "        0.0022    , 0.00206667, 0.00153333, 0.00206667, 0.00233333,\n",
       "        0.00213333, 0.002     , 0.00226667, 0.00173333, 0.0018    ,\n",
       "        0.00213333, 0.00153333, 0.0016    , 0.00186667, 0.001     ,\n",
       "        0.00233333, 0.0018    , 0.0024    , 0.00213333, 0.00206667,\n",
       "        0.0026    , 0.00226667, 0.00213333, 0.00193333, 0.00146667,\n",
       "        0.0018    , 0.00213333, 0.002     , 0.00193333, 0.0018    ,\n",
       "        0.00166667, 0.002     , 0.00186667, 0.00166667, 0.002     ,\n",
       "        0.00233333, 0.00226667, 0.00186667, 0.0018    , 0.0016    ,\n",
       "        0.00206667, 0.00193333, 0.00213333, 0.00186667, 0.00233333,\n",
       "        0.00246667, 0.00226667]),\n",
       " 'split2_test_score': array([0.0030002 , 0.00173345, 0.00260017, 0.00286686, 0.00293353,\n",
       "        0.00246683, 0.00280019, 0.00233349, 0.00280019, 0.0020668 ,\n",
       "        0.0020668 , 0.00273352, 0.00220015, 0.00286686, 0.00246683,\n",
       "        0.0020668 , 0.00266684, 0.00313354, 0.00226682, 0.00200013,\n",
       "        0.00200013, 0.00313354, 0.00273352, 0.00286686, 0.00246683,\n",
       "        0.00260017, 0.00233349, 0.00233349, 0.00233349, 0.00260017,\n",
       "        0.00246683, 0.00220015, 0.00226682, 0.00293353, 0.0030002 ,\n",
       "        0.00306687, 0.00273352, 0.00186679, 0.00166678, 0.00293353,\n",
       "        0.00306687, 0.00213348, 0.00220015, 0.00286686, 0.00246683,\n",
       "        0.00173345, 0.00266684, 0.00280019, 0.00260017, 0.0020668 ,\n",
       "        0.00226682, 0.00246683, 0.00280019, 0.00260017, 0.00266684,\n",
       "        0.0025335 , 0.0025335 , 0.00273352, 0.00273352, 0.00260017,\n",
       "        0.00260017, 0.00233349, 0.00326688, 0.00193346, 0.00273352,\n",
       "        0.0025335 , 0.00226682, 0.00233349, 0.00280019, 0.00193346,\n",
       "        0.00266684, 0.00246683]),\n",
       " 'mean_test_score': array([0.00235561, 0.00175559, 0.00191115, 0.00255561, 0.00228894,\n",
       "        0.00233339, 0.00240005, 0.00260006, 0.00262228, 0.00222227,\n",
       "        0.00222227, 0.00235561, 0.00191115, 0.0021556 , 0.00242228,\n",
       "        0.00217783, 0.00217783, 0.00233339, 0.00231116, 0.00202227,\n",
       "        0.00206671, 0.00251117, 0.00237783, 0.00253339, 0.00197782,\n",
       "        0.00222227, 0.00220005, 0.0019556 , 0.00208894, 0.00240005,\n",
       "        0.00220005, 0.00204449, 0.00222227, 0.00228894, 0.00231116,\n",
       "        0.00246672, 0.00200004, 0.00182226, 0.00164448, 0.00197782,\n",
       "        0.0026445 , 0.00208894, 0.00222227, 0.00248894, 0.00217783,\n",
       "        0.00208894, 0.00253339, 0.0024445 , 0.00204449, 0.00188893,\n",
       "        0.00197782, 0.00233339, 0.00231116, 0.00231116, 0.00233339,\n",
       "        0.0021556 , 0.00231116, 0.0021556 , 0.00220005, 0.00233339,\n",
       "        0.0021556 , 0.00220005, 0.00251117, 0.00191115, 0.00197782,\n",
       "        0.0021556 , 0.00224449, 0.00231116, 0.00228894, 0.00220005,\n",
       "        0.00235561, 0.00211116]),\n",
       " 'std_test_score': array([5.45313915e-04, 1.36982222e-04, 5.71830319e-04, 3.09583745e-04,\n",
       "        4.63034070e-04, 1.88601629e-04, 3.57010463e-04, 2.49388000e-04,\n",
       "        2.06134688e-04, 4.75566024e-04, 1.74937538e-04, 4.01290372e-04,\n",
       "        2.06146739e-04, 5.14564491e-04, 3.28118192e-04, 3.09506255e-04,\n",
       "        3.45776617e-04, 5.65777616e-04, 6.28186970e-05, 2.45451156e-04,\n",
       "        3.03064138e-04, 4.69393085e-04, 3.70577110e-04, 2.37355340e-04,\n",
       "        5.14534288e-04, 2.99865023e-04, 1.08929121e-04, 3.28165699e-04,\n",
       "        1.91227745e-04, 1.44095217e-04, 1.96333991e-04, 1.13377551e-04,\n",
       "        6.28899342e-05, 4.93994775e-04, 5.05855189e-04, 4.25227642e-04,\n",
       "        5.25013167e-04, 1.66308696e-04, 1.91169192e-04, 7.89511257e-04,\n",
       "        3.09609747e-04, 2.20000709e-04, 1.36980546e-04, 2.99872491e-04,\n",
       "        2.06155046e-04, 3.70481852e-04, 1.88604774e-04, 2.74052629e-04,\n",
       "        4.15813294e-04, 2.99823569e-04, 2.06148816e-04, 1.44067451e-04,\n",
       "        3.50039135e-04, 2.79388549e-04, 3.81084580e-04, 3.62491814e-04,\n",
       "        2.26678399e-04, 4.08631950e-04, 4.35536821e-04, 2.49505205e-04,\n",
       "        4.53303035e-04, 1.44064707e-04, 5.77012935e-04, 8.31603039e-05,\n",
       "        5.34338425e-04, 2.79402571e-04, 2.45459743e-04, 1.36996891e-04,\n",
       "        3.86260180e-04, 1.88498951e-04, 3.09579276e-04, 3.70571422e-04]),\n",
       " 'rank_test_score': array([16, 71, 66,  4, 30, 19, 13,  3,  2, 34, 34, 16, 66, 47, 12, 44, 44,\n",
       "        19, 24, 59, 56,  7, 15,  5, 61, 34, 39, 65, 53, 13, 39, 57, 34, 30,\n",
       "        24, 10, 60, 70, 72, 61,  1, 53, 34,  9, 44, 53,  5, 11, 57, 69, 61,\n",
       "        19, 24, 24, 19, 47, 24, 47, 39, 19, 47, 39,  7, 66, 61, 47, 33, 24,\n",
       "        30, 39, 16, 52], dtype=int32),\n",
       " 'split0_train_score': array([0.00173339, 0.00160005, 0.00190006, 0.00283343, 0.00240008,\n",
       "        0.00223341, 0.00243341, 0.00280009, 0.00240008, 0.00306677,\n",
       "        0.00183339, 0.00263342, 0.00190006, 0.00200007, 0.00243341,\n",
       "        0.00223341, 0.00220007, 0.00223341, 0.00210007, 0.00236675,\n",
       "        0.00236675, 0.00223341, 0.00226674, 0.00230008, 0.00200007,\n",
       "        0.00220007, 0.00226674, 0.00196673, 0.00293343, 0.00216674,\n",
       "        0.00233341, 0.00206674, 0.00233341, 0.00216674, 0.00220007,\n",
       "        0.00250008, 0.0019334 , 0.00186673, 0.00123337, 0.0020334 ,\n",
       "        0.00183339, 0.00170006, 0.00233341, 0.0020334 , 0.00196673,\n",
       "        0.00226674, 0.00243341, 0.00223341, 0.00163339, 0.00226674,\n",
       "        0.00183339, 0.00236675, 0.0021334 , 0.00240008, 0.00240008,\n",
       "        0.00220007, 0.00233341, 0.00233341, 0.00220007, 0.00240008,\n",
       "        0.00176673, 0.00200007, 0.00226674, 0.00236675, 0.00210007,\n",
       "        0.0020334 , 0.00233341, 0.00256675, 0.00210007, 0.00223341,\n",
       "        0.00246675, 0.00223341]),\n",
       " 'split1_train_score': array([0.00283343, 0.00220007, 0.00186673, 0.00186673, 0.00196673,\n",
       "        0.00246675, 0.00173339, 0.00220007, 0.00220007, 0.00226674,\n",
       "        0.00246675, 0.00250008, 0.00233341, 0.00230008, 0.00250008,\n",
       "        0.00256675, 0.00250008, 0.00253342, 0.00230008, 0.00250008,\n",
       "        0.00206674, 0.00243341, 0.00260009, 0.00246675, 0.0020334 ,\n",
       "        0.00196673, 0.00256675, 0.00220007, 0.00250008, 0.0021334 ,\n",
       "        0.00256675, 0.00223341, 0.00210007, 0.00226674, 0.00266676,\n",
       "        0.00250008, 0.00183339, 0.00206674, 0.00180006, 0.00216674,\n",
       "        0.00176673, 0.00220007, 0.00220007, 0.00233341, 0.00186673,\n",
       "        0.00333344, 0.00210007, 0.00260009, 0.00216674, 0.00220007,\n",
       "        0.00206674, 0.0030001 , 0.00243341, 0.00226674, 0.00216674,\n",
       "        0.0021334 , 0.00223341, 0.00250008, 0.00240008, 0.0031001 ,\n",
       "        0.00226674, 0.00220007, 0.00250008, 0.00280009, 0.00186673,\n",
       "        0.00210007, 0.00273342, 0.00260009, 0.00250008, 0.00253342,\n",
       "        0.00246675, 0.00243341]),\n",
       " 'split2_train_score': array([0.00236667, 0.00183333, 0.0018    , 0.00223333, 0.0023    ,\n",
       "        0.00183333, 0.0027    , 0.0021    , 0.00236667, 0.00223333,\n",
       "        0.0021    , 0.0021    , 0.0022    , 0.00183333, 0.00213333,\n",
       "        0.00136667, 0.00183333, 0.0024    , 0.002     , 0.00203333,\n",
       "        0.00203333, 0.00236667, 0.0023    , 0.00233333, 0.00206667,\n",
       "        0.0021    , 0.00213333, 0.00193333, 0.00233333, 0.0022    ,\n",
       "        0.00173333, 0.00223333, 0.00233333, 0.0023    , 0.00253333,\n",
       "        0.0022    , 0.00186667, 0.00156667, 0.00156667, 0.0025    ,\n",
       "        0.002     , 0.00223333, 0.00203333, 0.00246667, 0.00226667,\n",
       "        0.00213333, 0.00253333, 0.0022    , 0.0022    , 0.00183333,\n",
       "        0.00223333, 0.00206667, 0.00243333, 0.00223333, 0.00213333,\n",
       "        0.00233333, 0.0023    , 0.0022    , 0.00226667, 0.00196667,\n",
       "        0.00163333, 0.00213333, 0.00223333, 0.00193333, 0.00243333,\n",
       "        0.002     , 0.0024    , 0.002     , 0.0024    , 0.00216667,\n",
       "        0.0021    , 0.00256667]),\n",
       " 'mean_train_score': array([0.00231116, 0.00187782, 0.0018556 , 0.00231116, 0.00222227,\n",
       "        0.00217783, 0.00228894, 0.00236672, 0.00232227, 0.00252228,\n",
       "        0.00213338, 0.00241117, 0.00214449, 0.00204449, 0.00235561,\n",
       "        0.00205561, 0.00217783, 0.00238894, 0.00213338, 0.00230005,\n",
       "        0.0021556 , 0.0023445 , 0.00238894, 0.00236672, 0.00203338,\n",
       "        0.00208894, 0.00232228, 0.00203338, 0.00258895, 0.00216671,\n",
       "        0.00221117, 0.00217783, 0.0022556 , 0.00224449, 0.00246672,\n",
       "        0.00240006, 0.00187782, 0.00183338, 0.00153337, 0.00223338,\n",
       "        0.00186671, 0.00204449, 0.00218894, 0.00227783, 0.00203338,\n",
       "        0.00257784, 0.00235561, 0.0023445 , 0.00200004, 0.00210005,\n",
       "        0.00204449, 0.00247784, 0.00233338, 0.00230005, 0.00223338,\n",
       "        0.00222227, 0.00228894, 0.0023445 , 0.00228894, 0.00248895,\n",
       "        0.00188893, 0.00211116, 0.00233339, 0.00236672, 0.00213338,\n",
       "        0.00204449, 0.00248895, 0.00238895, 0.00233338, 0.00231116,\n",
       "        0.0023445 , 0.00241116]),\n",
       " 'std_train_score': array([4.50799849e-04, 2.46968686e-04, 4.16020849e-05, 3.98471929e-04,\n",
       "        1.85254694e-04, 2.61560043e-04, 4.07626363e-04, 3.09151051e-04,\n",
       "        8.74785407e-05, 3.85252248e-04, 2.59641018e-04, 2.26662677e-04,\n",
       "        1.81215288e-04, 1.93119234e-04, 1.59512793e-04, 5.05806729e-04,\n",
       "        2.72653585e-04, 1.22727916e-04, 1.24749829e-04, 1.96298593e-04,\n",
       "        1.49920505e-04, 8.31436934e-05, 1.49917239e-04, 7.20226404e-05,\n",
       "        2.71893401e-05, 9.55818711e-05, 1.81247488e-04, 1.18656268e-04,\n",
       "        2.52919225e-04, 2.71875262e-05, 3.51050561e-04, 7.85524977e-05,\n",
       "        1.09979726e-04, 5.66326068e-05, 1.96258337e-04, 1.41460641e-04,\n",
       "        4.15809102e-05, 2.05509908e-04, 2.32543586e-04, 1.96230151e-04,\n",
       "        9.81037528e-05, 2.43927789e-04, 1.22758962e-04, 1.81194212e-04,\n",
       "        1.69938407e-04, 5.37061786e-04, 1.85238410e-04, 1.81242266e-04,\n",
       "        2.59619191e-04, 1.90550733e-04, 1.64030487e-04, 3.89084822e-04,\n",
       "        1.41406956e-04, 7.20336100e-05, 1.18658142e-04, 8.31160684e-05,\n",
       "        4.15685417e-05, 1.22759097e-04, 8.31574522e-05, 4.66971147e-04,\n",
       "        2.72644860e-04, 8.31443848e-05, 1.18659079e-04, 3.53853290e-04,\n",
       "        2.32511198e-04, 4.15991192e-05, 1.74997336e-04, 2.75363049e-04,\n",
       "        1.69962527e-04, 1.59501570e-04, 1.72887086e-04, 1.36959161e-04])}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_result.cv_results_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'mean_fit_time': array([ 440.59602499,  481.73871398,  477.10135643,  504.1547633 ,\n",
    "         488.27373465,  564.76856009,  571.80046884,  569.00620119,\n",
    "         650.78964178,  740.00103672,  597.98015889,  659.03726633,\n",
    "         476.14166268,  508.75317804,  505.10563159,  556.87875859,\n",
    "         556.58007542,  542.48226706,  591.323385  ,  616.57070398,\n",
    "         658.64879243,  891.65342418, 1055.23733377,  993.75831501,\n",
    "         647.80959813,  629.8490928 ,  516.74331371,  787.65950712,\n",
    "         635.2311732 ,  545.72071338,  904.7078863 ,  685.55618747,\n",
    "         530.5135781 ,  815.14902997,  821.7310555 ,  685.34639025,\n",
    "         240.90511258,  246.83409182,  259.09067996,  262.85167106,\n",
    "         228.2099692 ,  392.25437156,  374.78024157,  452.21288816,\n",
    "         459.90044618,  495.74129097,  501.12204981,  368.91637723,\n",
    "         240.11106253,  237.93171946,  261.74841015,  265.67413298,\n",
    "         274.72749496,  378.51081745,  436.21683431,  241.44164371,\n",
    "         274.60629002,  330.47249166,  394.98302094,  318.71901139,\n",
    "         237.12692181,  248.02535923,  254.61501988,  243.65626915,\n",
    "         298.34629599,  273.75161084,  318.28928526,  312.33878072,\n",
    "         309.61013468,  335.33733678,  316.16733519,  284.3205506 ]),\n",
    " 'std_fit_time': array([ 16.87972466,   7.92785374,   5.77026847,  46.09935758,\n",
    "         54.51365785,   4.27852784,  94.58970885,  62.52370001,\n",
    "          8.4561614 ,   2.7468705 , 145.0622183 ,  99.96551438,\n",
    "          5.17816229,  12.67787649,  23.55063229,   9.66484519,\n",
    "         36.6158132 ,  34.19875912, 100.63498294,  97.25506357,\n",
    "          8.78125165,  87.44096196,  58.64011269,  34.89501886,\n",
    "         34.69794873,   6.55967573,  16.17142818,  23.55024969,\n",
    "         52.92218389,  50.06045253,  25.01777585,  18.80812352,\n",
    "         81.798268  ,   2.03161994,  42.98112108, 110.64334738,\n",
    "          9.66915548,   0.84179362,   6.43580261,  14.37030668,\n",
    "         10.02095687,  28.2914121 , 100.27866673,   4.12918697,\n",
    "          4.19608165,  48.65261655,  25.96345247,  61.99284042,\n",
    "          4.68735307,  10.21360139,   9.8859609 ,  20.70074481,\n",
    "         20.51026577,  56.21085735,  55.81395671,   8.02054713,\n",
    "         50.9381089 ,  50.45400177,  22.28014196,  67.88824331,\n",
    "          5.44755769,  11.84267318,   5.09502951,  13.30998334,\n",
    "         10.04504684,  34.3255134 ,   0.83824628,  33.02031315,\n",
    "         44.69969273,  41.65936289,  50.8080964 ,  36.12854555]),\n",
    " 'mean_score_time': array([1.48248084, 1.61645206, 1.52614307, 1.50255108, 1.5502789 ,\n",
    "        1.57044665, 1.6061248 , 1.58751472, 1.59509166, 1.68163586,\n",
    "        1.79815904, 1.7393082 , 1.47499736, 1.52648115, 1.5446895 ,\n",
    "        1.50675567, 1.54464761, 1.62923439, 1.55051088, 1.56298979,\n",
    "        1.7317764 , 1.75597771, 1.8009282 , 1.95601543, 1.52369038,\n",
    "        1.48506896, 1.56178371, 1.53837752, 1.58946848, 1.53822851,\n",
    "        1.67706863, 1.74836882, 1.67516692, 1.74954589, 1.6798114 ,\n",
    "        1.72637788, 0.76347407, 0.68936872, 0.79896879, 0.85983062,\n",
    "        0.81541355, 0.84865721, 0.83474739, 0.88329641, 0.89842264,\n",
    "        0.93576241, 0.91452034, 0.85306342, 0.77134498, 0.7801017 ,\n",
    "        0.85619156, 0.82537786, 0.82716012, 0.88619153, 0.92515985,\n",
    "        0.85173941, 0.8888003 , 0.89410488, 0.86849999, 0.88820569,\n",
    "        0.73052979, 0.86012634, 0.82792679, 0.85549124, 0.85315657,\n",
    "        0.82530626, 0.88788549, 0.90339359, 0.88132191, 0.8901968 ,\n",
    "        0.94266113, 0.90914003]),\n",
    " 'std_score_time': array([0.0809353 , 0.0434697 , 0.06246195, 0.05935619, 0.00983632,\n",
    "        0.10535178, 0.13783207, 0.08746206, 0.05477639, 0.03246781,\n",
    "        0.05349619, 0.13466161, 0.14158229, 0.07571865, 0.08168413,\n",
    "        0.05528071, 0.06024407, 0.02103915, 0.07350117, 0.1008799 ,\n",
    "        0.04944533, 0.08355515, 0.09599189, 0.11462328, 0.02945484,\n",
    "        0.01043705, 0.0536539 , 0.0657963 , 0.07744391, 0.04640753,\n",
    "        0.03043051, 0.07704773, 0.05899877, 0.07714815, 0.06984614,\n",
    "        0.08692043, 0.05506912, 0.07381311, 0.03992863, 0.05990567,\n",
    "        0.05875238, 0.04115549, 0.03138722, 0.02226701, 0.01140741,\n",
    "        0.01028036, 0.04148711, 0.01940906, 0.03398852, 0.04651999,\n",
    "        0.03599124, 0.06798827, 0.05388377, 0.02796698, 0.0052635 ,\n",
    "        0.0512727 , 0.0245259 , 0.06295023, 0.02127435, 0.05833234,\n",
    "        0.05479642, 0.0411167 , 0.0659268 , 0.04616794, 0.06244371,\n",
    "        0.02330616, 0.02381002, 0.03937768, 0.10743165, 0.04943378,\n",
    "        0.0353715 , 0.05471914]),\n",
    " 'param_batch_size': masked_array(data=[32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
    "                    32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
    "                    32, 32, 32, 32, 32, 32, 32, 32, 64, 64, 64, 64, 64, 64,\n",
    "                    64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64,\n",
    "                    64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64,\n",
    "                    64, 64],\n",
    "              mask=[False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False],\n",
    "        fill_value='?',\n",
    "             dtype=object),\n",
    " 'param_c1_nf': masked_array(data=[32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 64, 64,\n",
    "                    64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 96, 96, 96, 96,\n",
    "                    96, 96, 96, 96, 96, 96, 96, 96, 32, 32, 32, 32, 32, 32,\n",
    "                    32, 32, 32, 32, 32, 32, 64, 64, 64, 64, 64, 64, 64, 64,\n",
    "                    64, 64, 64, 64, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96,\n",
    "                    96, 96],\n",
    "              mask=[False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False],\n",
    "        fill_value='?',\n",
    "             dtype=object),\n",
    " 'param_clayers': masked_array(data=[1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2,\n",
    "                    3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4,\n",
    "                    1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2,\n",
    "                    3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4],\n",
    "              mask=[False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False],\n",
    "        fill_value='?',\n",
    "             dtype=object),\n",
    " 'param_ker_size': masked_array(data=[3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7,\n",
    "                    3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7,\n",
    "                    3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7,\n",
    "                    3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7, 3, 5, 7],\n",
    "              mask=[False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False, False],\n",
    "        fill_value='?',\n",
    "             dtype=object),\n",
    " 'params': [{'batch_size': 32, 'c1_nf': 32, 'clayers': 1, 'ker_size': 3},\n",
    "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 1, 'ker_size': 5},\n",
    "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 1, 'ker_size': 7},\n",
    "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 2, 'ker_size': 3},\n",
    "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 2, 'ker_size': 5},\n",
    "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 2, 'ker_size': 7},\n",
    "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 3, 'ker_size': 3},\n",
    "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 3, 'ker_size': 5},\n",
    "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 3, 'ker_size': 7},\n",
    "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 4, 'ker_size': 3},\n",
    "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 4, 'ker_size': 5},\n",
    "  {'batch_size': 32, 'c1_nf': 32, 'clayers': 4, 'ker_size': 7},\n",
    "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 1, 'ker_size': 3},\n",
    "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 1, 'ker_size': 5},\n",
    "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 1, 'ker_size': 7},\n",
    "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 2, 'ker_size': 3},\n",
    "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 2, 'ker_size': 5},\n",
    "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 2, 'ker_size': 7},\n",
    "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 3, 'ker_size': 3},\n",
    "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 3, 'ker_size': 5},\n",
    "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 3, 'ker_size': 7},\n",
    "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 4, 'ker_size': 3},\n",
    "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 4, 'ker_size': 5},\n",
    "  {'batch_size': 32, 'c1_nf': 64, 'clayers': 4, 'ker_size': 7},\n",
    "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 1, 'ker_size': 3},\n",
    "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 1, 'ker_size': 5},\n",
    "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 1, 'ker_size': 7},\n",
    "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 2, 'ker_size': 3},\n",
    "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 2, 'ker_size': 5},\n",
    "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 2, 'ker_size': 7},\n",
    "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 3, 'ker_size': 3},\n",
    "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 3, 'ker_size': 5},\n",
    "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 3, 'ker_size': 7},\n",
    "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 4, 'ker_size': 3},\n",
    "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 4, 'ker_size': 5},\n",
    "  {'batch_size': 32, 'c1_nf': 96, 'clayers': 4, 'ker_size': 7},\n",
    "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 1, 'ker_size': 3},\n",
    "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 1, 'ker_size': 5},\n",
    "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 1, 'ker_size': 7},\n",
    "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 2, 'ker_size': 3},\n",
    "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 2, 'ker_size': 5},\n",
    "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 2, 'ker_size': 7},\n",
    "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 3, 'ker_size': 3},\n",
    "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 3, 'ker_size': 5},\n",
    "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 3, 'ker_size': 7},\n",
    "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 4, 'ker_size': 3},\n",
    "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 4, 'ker_size': 5},\n",
    "  {'batch_size': 64, 'c1_nf': 32, 'clayers': 4, 'ker_size': 7},\n",
    "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 1, 'ker_size': 3},\n",
    "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 1, 'ker_size': 5},\n",
    "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 1, 'ker_size': 7},\n",
    "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 2, 'ker_size': 3},\n",
    "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 2, 'ker_size': 5},\n",
    "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 2, 'ker_size': 7},\n",
    "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 3, 'ker_size': 3},\n",
    "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 3, 'ker_size': 5},\n",
    "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 3, 'ker_size': 7},\n",
    "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 4, 'ker_size': 3},\n",
    "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 4, 'ker_size': 5},\n",
    "  {'batch_size': 64, 'c1_nf': 64, 'clayers': 4, 'ker_size': 7},\n",
    "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 1, 'ker_size': 3},\n",
    "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 1, 'ker_size': 5},\n",
    "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 1, 'ker_size': 7},\n",
    "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 2, 'ker_size': 3},\n",
    "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 2, 'ker_size': 5},\n",
    "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 2, 'ker_size': 7},\n",
    "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 3, 'ker_size': 3},\n",
    "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 3, 'ker_size': 5},\n",
    "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 3, 'ker_size': 7},\n",
    "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 4, 'ker_size': 3},\n",
    "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 4, 'ker_size': 5},\n",
    "  {'batch_size': 64, 'c1_nf': 96, 'clayers': 4, 'ker_size': 7}],\n",
    " 'split0_test_score': array([0.00166667, 0.0016    , 0.00193333, 0.00266667, 0.00186667,\n",
    "        0.00206667, 0.00246667, 0.00293333, 0.00273333, 0.00286667,\n",
    "        0.00213333, 0.00253333, 0.00173333, 0.00193333, 0.0028    ,\n",
    "        0.0026    , 0.00193333, 0.00193333, 0.00226667, 0.00233333,\n",
    "        0.00246667, 0.0024    , 0.00253333, 0.00233333, 0.0022    ,\n",
    "        0.00186667, 0.0022    , 0.002     , 0.00186667, 0.00226667,\n",
    "        0.002     , 0.00193333, 0.00213333, 0.0022    , 0.00213333,\n",
    "        0.0022    , 0.00173333, 0.002     , 0.0014    , 0.002     ,\n",
    "        0.00253333, 0.00233333, 0.00206667, 0.00246667, 0.002     ,\n",
    "        0.00193333, 0.00266667, 0.0024    , 0.0016    , 0.00213333,\n",
    "        0.00186667, 0.0024    , 0.00213333, 0.0024    , 0.00253333,\n",
    "        0.00226667, 0.0024    , 0.00186667, 0.0022    , 0.0024    ,\n",
    "        0.00153333, 0.002     , 0.0024    , 0.002     , 0.0016    ,\n",
    "        0.00186667, 0.00253333, 0.00246667, 0.0022    , 0.00233333,\n",
    "        0.00193333, 0.0016    ]),\n",
    " 'split1_test_score': array([0.0024    , 0.00193333, 0.0012    , 0.00213333, 0.00206667,\n",
    "        0.00246667, 0.00193333, 0.00253333, 0.00233333, 0.00173333,\n",
    "        0.00246667, 0.0018    , 0.0018    , 0.00166667, 0.002     ,\n",
    "        0.00186667, 0.00193333, 0.00193333, 0.0024    , 0.00173333,\n",
    "        0.00173333, 0.002     , 0.00186667, 0.0024    , 0.00126667,\n",
    "        0.0022    , 0.00206667, 0.00153333, 0.00206667, 0.00233333,\n",
    "        0.00213333, 0.002     , 0.00226667, 0.00173333, 0.0018    ,\n",
    "        0.00213333, 0.00153333, 0.0016    , 0.00186667, 0.001     ,\n",
    "        0.00233333, 0.0018    , 0.0024    , 0.00213333, 0.00206667,\n",
    "        0.0026    , 0.00226667, 0.00213333, 0.00193333, 0.00146667,\n",
    "        0.0018    , 0.00213333, 0.002     , 0.00193333, 0.0018    ,\n",
    "        0.00166667, 0.002     , 0.00186667, 0.00166667, 0.002     ,\n",
    "        0.00233333, 0.00226667, 0.00186667, 0.0018    , 0.0016    ,\n",
    "        0.00206667, 0.00193333, 0.00213333, 0.00186667, 0.00233333,\n",
    "        0.00246667, 0.00226667]),\n",
    " 'split2_test_score': array([0.0030002 , 0.00173345, 0.00260017, 0.00286686, 0.00293353,\n",
    "        0.00246683, 0.00280019, 0.00233349, 0.00280019, 0.0020668 ,\n",
    "        0.0020668 , 0.00273352, 0.00220015, 0.00286686, 0.00246683,\n",
    "        0.0020668 , 0.00266684, 0.00313354, 0.00226682, 0.00200013,\n",
    "        0.00200013, 0.00313354, 0.00273352, 0.00286686, 0.00246683,\n",
    "        0.00260017, 0.00233349, 0.00233349, 0.00233349, 0.00260017,\n",
    "        0.00246683, 0.00220015, 0.00226682, 0.00293353, 0.0030002 ,\n",
    "        0.00306687, 0.00273352, 0.00186679, 0.00166678, 0.00293353,\n",
    "        0.00306687, 0.00213348, 0.00220015, 0.00286686, 0.00246683,\n",
    "        0.00173345, 0.00266684, 0.00280019, 0.00260017, 0.0020668 ,\n",
    "        0.00226682, 0.00246683, 0.00280019, 0.00260017, 0.00266684,\n",
    "        0.0025335 , 0.0025335 , 0.00273352, 0.00273352, 0.00260017,\n",
    "        0.00260017, 0.00233349, 0.00326688, 0.00193346, 0.00273352,\n",
    "        0.0025335 , 0.00226682, 0.00233349, 0.00280019, 0.00193346,\n",
    "        0.00266684, 0.00246683]),\n",
    " 'mean_test_score': array([0.00235561, 0.00175559, 0.00191115, 0.00255561, 0.00228894,\n",
    "        0.00233339, 0.00240005, 0.00260006, 0.00262228, 0.00222227,\n",
    "        0.00222227, 0.00235561, 0.00191115, 0.0021556 , 0.00242228,\n",
    "        0.00217783, 0.00217783, 0.00233339, 0.00231116, 0.00202227,\n",
    "        0.00206671, 0.00251117, 0.00237783, 0.00253339, 0.00197782,\n",
    "        0.00222227, 0.00220005, 0.0019556 , 0.00208894, 0.00240005,\n",
    "        0.00220005, 0.00204449, 0.00222227, 0.00228894, 0.00231116,\n",
    "        0.00246672, 0.00200004, 0.00182226, 0.00164448, 0.00197782,\n",
    "        0.0026445 , 0.00208894, 0.00222227, 0.00248894, 0.00217783,\n",
    "        0.00208894, 0.00253339, 0.0024445 , 0.00204449, 0.00188893,\n",
    "        0.00197782, 0.00233339, 0.00231116, 0.00231116, 0.00233339,\n",
    "        0.0021556 , 0.00231116, 0.0021556 , 0.00220005, 0.00233339,\n",
    "        0.0021556 , 0.00220005, 0.00251117, 0.00191115, 0.00197782,\n",
    "        0.0021556 , 0.00224449, 0.00231116, 0.00228894, 0.00220005,\n",
    "        0.00235561, 0.00211116]),\n",
    " 'std_test_score': array([5.45313915e-04, 1.36982222e-04, 5.71830319e-04, 3.09583745e-04,\n",
    "        4.63034070e-04, 1.88601629e-04, 3.57010463e-04, 2.49388000e-04,\n",
    "        2.06134688e-04, 4.75566024e-04, 1.74937538e-04, 4.01290372e-04,\n",
    "        2.06146739e-04, 5.14564491e-04, 3.28118192e-04, 3.09506255e-04,\n",
    "        3.45776617e-04, 5.65777616e-04, 6.28186970e-05, 2.45451156e-04,\n",
    "        3.03064138e-04, 4.69393085e-04, 3.70577110e-04, 2.37355340e-04,\n",
    "        5.14534288e-04, 2.99865023e-04, 1.08929121e-04, 3.28165699e-04,\n",
    "        1.91227745e-04, 1.44095217e-04, 1.96333991e-04, 1.13377551e-04,\n",
    "        6.28899342e-05, 4.93994775e-04, 5.05855189e-04, 4.25227642e-04,\n",
    "        5.25013167e-04, 1.66308696e-04, 1.91169192e-04, 7.89511257e-04,\n",
    "        3.09609747e-04, 2.20000709e-04, 1.36980546e-04, 2.99872491e-04,\n",
    "        2.06155046e-04, 3.70481852e-04, 1.88604774e-04, 2.74052629e-04,\n",
    "        4.15813294e-04, 2.99823569e-04, 2.06148816e-04, 1.44067451e-04,\n",
    "        3.50039135e-04, 2.79388549e-04, 3.81084580e-04, 3.62491814e-04,\n",
    "        2.26678399e-04, 4.08631950e-04, 4.35536821e-04, 2.49505205e-04,\n",
    "        4.53303035e-04, 1.44064707e-04, 5.77012935e-04, 8.31603039e-05,\n",
    "        5.34338425e-04, 2.79402571e-04, 2.45459743e-04, 1.36996891e-04,\n",
    "        3.86260180e-04, 1.88498951e-04, 3.09579276e-04, 3.70571422e-04]),\n",
    " 'rank_test_score': array([16, 71, 66,  4, 30, 19, 13,  3,  2, 34, 34, 16, 66, 47, 12, 44, 44,\n",
    "        19, 24, 59, 56,  7, 15,  5, 61, 34, 39, 65, 53, 13, 39, 57, 34, 30,\n",
    "        24, 10, 60, 70, 72, 61,  1, 53, 34,  9, 44, 53,  5, 11, 57, 69, 61,\n",
    "        19, 24, 24, 19, 47, 24, 47, 39, 19, 47, 39,  7, 66, 61, 47, 33, 24,\n",
    "        30, 39, 16, 52], dtype=int32),\n",
    " 'split0_train_score': array([0.00173339, 0.00160005, 0.00190006, 0.00283343, 0.00240008,\n",
    "        0.00223341, 0.00243341, 0.00280009, 0.00240008, 0.00306677,\n",
    "        0.00183339, 0.00263342, 0.00190006, 0.00200007, 0.00243341,\n",
    "        0.00223341, 0.00220007, 0.00223341, 0.00210007, 0.00236675,\n",
    "        0.00236675, 0.00223341, 0.00226674, 0.00230008, 0.00200007,\n",
    "        0.00220007, 0.00226674, 0.00196673, 0.00293343, 0.00216674,\n",
    "        0.00233341, 0.00206674, 0.00233341, 0.00216674, 0.00220007,\n",
    "        0.00250008, 0.0019334 , 0.00186673, 0.00123337, 0.0020334 ,\n",
    "        0.00183339, 0.00170006, 0.00233341, 0.0020334 , 0.00196673,\n",
    "        0.00226674, 0.00243341, 0.00223341, 0.00163339, 0.00226674,\n",
    "        0.00183339, 0.00236675, 0.0021334 , 0.00240008, 0.00240008,\n",
    "        0.00220007, 0.00233341, 0.00233341, 0.00220007, 0.00240008,\n",
    "        0.00176673, 0.00200007, 0.00226674, 0.00236675, 0.00210007,\n",
    "        0.0020334 , 0.00233341, 0.00256675, 0.00210007, 0.00223341,\n",
    "        0.00246675, 0.00223341]),\n",
    " 'split1_train_score': array([0.00283343, 0.00220007, 0.00186673, 0.00186673, 0.00196673,\n",
    "        0.00246675, 0.00173339, 0.00220007, 0.00220007, 0.00226674,\n",
    "        0.00246675, 0.00250008, 0.00233341, 0.00230008, 0.00250008,\n",
    "        0.00256675, 0.00250008, 0.00253342, 0.00230008, 0.00250008,\n",
    "        0.00206674, 0.00243341, 0.00260009, 0.00246675, 0.0020334 ,\n",
    "        0.00196673, 0.00256675, 0.00220007, 0.00250008, 0.0021334 ,\n",
    "        0.00256675, 0.00223341, 0.00210007, 0.00226674, 0.00266676,\n",
    "        0.00250008, 0.00183339, 0.00206674, 0.00180006, 0.00216674,\n",
    "        0.00176673, 0.00220007, 0.00220007, 0.00233341, 0.00186673,\n",
    "        0.00333344, 0.00210007, 0.00260009, 0.00216674, 0.00220007,\n",
    "        0.00206674, 0.0030001 , 0.00243341, 0.00226674, 0.00216674,\n",
    "        0.0021334 , 0.00223341, 0.00250008, 0.00240008, 0.0031001 ,\n",
    "        0.00226674, 0.00220007, 0.00250008, 0.00280009, 0.00186673,\n",
    "        0.00210007, 0.00273342, 0.00260009, 0.00250008, 0.00253342,\n",
    "        0.00246675, 0.00243341]),\n",
    " 'split2_train_score': array([0.00236667, 0.00183333, 0.0018    , 0.00223333, 0.0023    ,\n",
    "        0.00183333, 0.0027    , 0.0021    , 0.00236667, 0.00223333,\n",
    "        0.0021    , 0.0021    , 0.0022    , 0.00183333, 0.00213333,\n",
    "        0.00136667, 0.00183333, 0.0024    , 0.002     , 0.00203333,\n",
    "        0.00203333, 0.00236667, 0.0023    , 0.00233333, 0.00206667,\n",
    "        0.0021    , 0.00213333, 0.00193333, 0.00233333, 0.0022    ,\n",
    "        0.00173333, 0.00223333, 0.00233333, 0.0023    , 0.00253333,\n",
    "        0.0022    , 0.00186667, 0.00156667, 0.00156667, 0.0025    ,\n",
    "        0.002     , 0.00223333, 0.00203333, 0.00246667, 0.00226667,\n",
    "        0.00213333, 0.00253333, 0.0022    , 0.0022    , 0.00183333,\n",
    "        0.00223333, 0.00206667, 0.00243333, 0.00223333, 0.00213333,\n",
    "        0.00233333, 0.0023    , 0.0022    , 0.00226667, 0.00196667,\n",
    "        0.00163333, 0.00213333, 0.00223333, 0.00193333, 0.00243333,\n",
    "        0.002     , 0.0024    , 0.002     , 0.0024    , 0.00216667,\n",
    "        0.0021    , 0.00256667]),\n",
    " 'mean_train_score': array([0.00231116, 0.00187782, 0.0018556 , 0.00231116, 0.00222227,\n",
    "        0.00217783, 0.00228894, 0.00236672, 0.00232227, 0.00252228,\n",
    "        0.00213338, 0.00241117, 0.00214449, 0.00204449, 0.00235561,\n",
    "        0.00205561, 0.00217783, 0.00238894, 0.00213338, 0.00230005,\n",
    "        0.0021556 , 0.0023445 , 0.00238894, 0.00236672, 0.00203338,\n",
    "        0.00208894, 0.00232228, 0.00203338, 0.00258895, 0.00216671,\n",
    "        0.00221117, 0.00217783, 0.0022556 , 0.00224449, 0.00246672,\n",
    "        0.00240006, 0.00187782, 0.00183338, 0.00153337, 0.00223338,\n",
    "        0.00186671, 0.00204449, 0.00218894, 0.00227783, 0.00203338,\n",
    "        0.00257784, 0.00235561, 0.0023445 , 0.00200004, 0.00210005,\n",
    "        0.00204449, 0.00247784, 0.00233338, 0.00230005, 0.00223338,\n",
    "        0.00222227, 0.00228894, 0.0023445 , 0.00228894, 0.00248895,\n",
    "        0.00188893, 0.00211116, 0.00233339, 0.00236672, 0.00213338,\n",
    "        0.00204449, 0.00248895, 0.00238895, 0.00233338, 0.00231116,\n",
    "        0.0023445 , 0.00241116]),\n",
    " 'std_train_score': array([4.50799849e-04, 2.46968686e-04, 4.16020849e-05, 3.98471929e-04,\n",
    "        1.85254694e-04, 2.61560043e-04, 4.07626363e-04, 3.09151051e-04,\n",
    "        8.74785407e-05, 3.85252248e-04, 2.59641018e-04, 2.26662677e-04,\n",
    "        1.81215288e-04, 1.93119234e-04, 1.59512793e-04, 5.05806729e-04,\n",
    "        2.72653585e-04, 1.22727916e-04, 1.24749829e-04, 1.96298593e-04,\n",
    "        1.49920505e-04, 8.31436934e-05, 1.49917239e-04, 7.20226404e-05,\n",
    "        2.71893401e-05, 9.55818711e-05, 1.81247488e-04, 1.18656268e-04,\n",
    "        2.52919225e-04, 2.71875262e-05, 3.51050561e-04, 7.85524977e-05,\n",
    "        1.09979726e-04, 5.66326068e-05, 1.96258337e-04, 1.41460641e-04,\n",
    "        4.15809102e-05, 2.05509908e-04, 2.32543586e-04, 1.96230151e-04,\n",
    "        9.81037528e-05, 2.43927789e-04, 1.22758962e-04, 1.81194212e-04,\n",
    "        1.69938407e-04, 5.37061786e-04, 1.85238410e-04, 1.81242266e-04,\n",
    "        2.59619191e-04, 1.90550733e-04, 1.64030487e-04, 3.89084822e-04,\n",
    "        1.41406956e-04, 7.20336100e-05, 1.18658142e-04, 8.31160684e-05,\n",
    "        4.15685417e-05, 1.22759097e-04, 8.31574522e-05, 4.66971147e-04,\n",
    "        2.72644860e-04, 8.31443848e-05, 1.18659079e-04, 3.53853290e-04,\n",
    "        2.32511198e-04, 4.15991192e-05, 1.74997336e-04, 2.75363049e-04,\n",
    "        1.69962527e-04, 1.59501570e-04, 1.72887086e-04, 1.36959161e-04])}\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "{'mean_fit_time': array([ 893.33654523,  898.53078222, 1119.14394736, 1130.1775128 ,\n",
    "         956.51246222,  964.45365715, 1209.3984166 , 1582.91039157,\n",
    "        1394.26560704, 1616.26630108, 1266.47200227, 1116.83488099,\n",
    "        1205.42738708, 1201.92515103, 1210.92550143]),\n",
    " 'std_fit_time': array([  3.08891285,   6.81113186, 212.20371026, 238.93357922,\n",
    "          6.97112622,  18.87349827, 223.00445851,  57.7875855 ,\n",
    "        100.70476936,  43.95356933, 134.83849082,  11.29690679,\n",
    "          5.42330543,   6.19267952,   4.92641743]),\n",
    " 'mean_score_time': array([1.63011034, 1.61093879, 1.66868186, 1.48953597, 1.43644238,\n",
    "        1.57432818, 1.69542178, 1.78519742, 1.65484571, 1.89959979,\n",
    "        1.76437203, 1.6481727 , 1.7160941 , 1.75274881, 1.71138732]),\n",
    " 'std_score_time': array([0.02974069, 0.08114865, 0.04441992, 0.06436249, 0.04163585,\n",
    "        0.03811946, 0.09529008, 0.0232277 , 0.0417081 , 0.07397102,\n",
    "        0.14329706, 0.06546494, 0.07330068, 0.05566151, 0.052498  ]),\n",
    " 'param_N_hidden': masked_array(data=[1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5],\n",
    "              mask=[False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False],\n",
    "        fill_value='?',\n",
    "             dtype=object),\n",
    " 'param_N_neurons': masked_array(data=[250, 500, 1000, 250, 500, 1000, 250, 500, 1000, 250,\n",
    "                    500, 1000, 250, 500, 1000],\n",
    "              mask=[False, False, False, False, False, False, False, False,\n",
    "                    False, False, False, False, False, False, False],\n",
    "        fill_value='?',\n",
    "             dtype=object),\n",
    " 'params': [{'N_hidden': 1, 'N_neurons': 250},\n",
    "  {'N_hidden': 1, 'N_neurons': 500},\n",
    "  {'N_hidden': 1, 'N_neurons': 1000},\n",
    "  {'N_hidden': 2, 'N_neurons': 250},\n",
    "  {'N_hidden': 2, 'N_neurons': 500},\n",
    "  {'N_hidden': 2, 'N_neurons': 1000},\n",
    "  {'N_hidden': 3, 'N_neurons': 250},\n",
    "  {'N_hidden': 3, 'N_neurons': 500},\n",
    "  {'N_hidden': 3, 'N_neurons': 1000},\n",
    "  {'N_hidden': 4, 'N_neurons': 250},\n",
    "  {'N_hidden': 4, 'N_neurons': 500},\n",
    "  {'N_hidden': 4, 'N_neurons': 1000},\n",
    "  {'N_hidden': 5, 'N_neurons': 250},\n",
    "  {'N_hidden': 5, 'N_neurons': 500},\n",
    "  {'N_hidden': 5, 'N_neurons': 1000}],\n",
    " 'split0_test_score': array([0.00235, 0.00225, 0.0025 , 0.0024 , 0.0026 , 0.0021 , 0.00165,\n",
    "        0.0023 , 0.00255, 0.00255, 0.0024 , 0.0027 , 0.0021 , 0.00225,\n",
    "        0.0022 ]),\n",
    " 'split1_test_score': array([0.0024 , 0.00225, 0.0022 , 0.0022 , 0.00235, 0.0022 , 0.0021 ,\n",
    "        0.00195, 0.00215, 0.0022 , 0.00185, 0.00195, 0.002  , 0.00195,\n",
    "        0.0021 ]),\n",
    " 'split2_test_score': array([0.00255, 0.00315, 0.00275, 0.00295, 0.00355, 0.0032 , 0.00275,\n",
    "        0.0031 , 0.0028 , 0.00305, 0.00305, 0.00355, 0.00275, 0.00285,\n",
    "        0.00305]),\n",
    " 'mean_test_score': array([0.00243333, 0.00255   , 0.00248333, 0.00251667, 0.00283333,\n",
    "        0.0025    , 0.00216667, 0.00245   , 0.0025    , 0.0026    ,\n",
    "        0.00243333, 0.00273333, 0.00228333, 0.00235   , 0.00245   ]),\n",
    " 'std_test_score': array([8.49836586e-05, 4.24264069e-04, 2.24845626e-04, 3.17104960e-04,\n",
    "        5.16935414e-04, 4.96655481e-04, 4.51540573e-04, 4.81317636e-04,\n",
    "        2.67706307e-04, 3.48807492e-04, 4.90464632e-04, 6.53622385e-04,\n",
    "        3.32498956e-04, 3.74165739e-04, 4.26223728e-04]),\n",
    " 'rank_test_score': array([11,  4,  8,  5,  1,  6, 15,  9,  6,  3, 11,  2, 14, 13,  9],\n",
    "       dtype=int32),\n",
    " 'split0_train_score': array([0.00255 , 0.0024  , 0.0023  , 0.00255 , 0.00255 , 0.002375,\n",
    "        0.002125, 0.002625, 0.0025  , 0.002775, 0.002625, 0.0027  ,\n",
    "        0.0023  , 0.00245 , 0.002275]),\n",
    " 'split1_train_score': array([0.002975, 0.0025  , 0.00255 , 0.0028  , 0.0029  , 0.002475,\n",
    "        0.002575, 0.0027  , 0.002575, 0.002375, 0.002475, 0.002475,\n",
    "        0.002325, 0.002425, 0.002375]),\n",
    " 'split2_train_score': array([0.00215 , 0.0022  , 0.00215 , 0.002375, 0.00205 , 0.002175,\n",
    "        0.00215 , 0.0022  , 0.002   , 0.00235 , 0.00235 , 0.002525,\n",
    "        0.00215 , 0.002175, 0.002175]),\n",
    " 'mean_train_score': array([0.00255833, 0.00236667, 0.00233333, 0.002575  , 0.0025    ,\n",
    "        0.00234167, 0.00228333, 0.00250833, 0.00235833, 0.0025    ,\n",
    "        0.00248333, 0.00256667, 0.00225833, 0.00235   , 0.002275  ]),\n",
    " 'std_train_score': array([3.36856382e-04, 1.24721913e-04, 1.64991582e-04, 1.74403746e-04,\n",
    "        3.48807492e-04, 1.24721913e-04, 2.06491862e-04, 2.20164080e-04,\n",
    "        2.55223214e-04, 1.94722024e-04, 1.12422813e-04, 9.64653075e-05,\n",
    "        7.72801541e-05, 1.24163870e-04, 8.16496581e-05])}\n",
    "        \n",
    " \n",
    " {'mean_fit_time': array([  685.01906315,  1809.28454868,   336.60541034,   878.23016135]),\n",
    " 'mean_score_time': array([ 1.38006322,  1.27389534,  0.6934317 ,  0.69225407]),\n",
    " 'mean_test_score': array([ 0.00241667,  0.00251667,  0.00243333,  0.00261667]),\n",
    " 'mean_train_score': array([ 0.00245833,  0.00236667,  0.00248333,  0.00253333]),\n",
    " 'param_batch_size': masked_array(data = [32 32 64 64],\n",
    "              mask = [False False False False],\n",
    "        fill_value = ?),\n",
    " 'param_epochs': masked_array(data = [200 500 200 500],\n",
    "              mask = [False False False False],\n",
    "        fill_value = ?),\n",
    " 'params': ({'batch_size': 32, 'epochs': 200},\n",
    "  {'batch_size': 32, 'epochs': 500},\n",
    "  {'batch_size': 64, 'epochs': 200},\n",
    "  {'batch_size': 64, 'epochs': 500}),\n",
    " 'rank_test_score': array([4, 2, 3, 1], dtype=int32),\n",
    " 'split0_test_score': array([ 0.0021 ,  0.00225,  0.00215,  0.00225]),\n",
    " 'split0_train_score': array([ 0.00235 ,  0.0023  ,  0.002625,  0.002575]),\n",
    " 'split1_test_score': array([ 0.00225,  0.00225,  0.00215,  0.00235]),\n",
    " 'split1_train_score': array([ 0.002675,  0.002725,  0.002675,  0.002825]),\n",
    " 'split2_test_score': array([ 0.0029 ,  0.00305,  0.003  ,  0.00325]),\n",
    " 'split2_train_score': array([ 0.00235 ,  0.002075,  0.00215 ,  0.0022  ]),\n",
    " 'std_fit_time': array([  27.85582158,  121.41697465,    1.58335506,   11.64839192]),\n",
    " 'std_score_time': array([ 0.01602076,  0.06291871,  0.03384719,  0.05541393]),\n",
    " 'std_test_score': array([ 0.00034721,  0.00037712,  0.00040069,  0.00044969]),\n",
    " 'std_train_score': array([ 0.00015321,  0.00026952,  0.00023658,  0.00025685])}\n",
    "        \n",
    "        \n",
    "        'mean_fit_time': array([1236.77363722, 1263.8373781 , 1283.07971772,  617.23694984,\n",
    "         644.64875857,  630.75466394]),\n",
    " 'std_fit_time': array([15.23634435,  1.04774932, 70.7173362 , 19.87266061,  3.13235316,\n",
    "        19.13357172]),\n",
    " 'mean_score_time': array([1.9509182 , 2.09144211, 2.07234033, 1.05850196, 1.09700545,\n",
    "        1.07024908]),\n",
    " 'std_score_time': array([0.09494565, 0.09207867, 0.09335411, 0.04954752, 0.04209056,\n",
    "        0.05320864]),\n",
    " 'param_batch_size': masked_array(data=[32, 32, 32, 64, 64, 64],\n",
    "              mask=[False, False, False, False, False, False],\n",
    "        fill_value='?',\n",
    "             dtype=object),\n",
    " 'param_ker_size': masked_array(data=[3, 5, 7, 3, 5, 7],\n",
    "              mask=[False, False, False, False, False, False],\n",
    "        fill_value='?',\n",
    "             dtype=object),\n",
    " 'params': [{'batch_size': 32, 'ker_size': 3},\n",
    "  {'batch_size': 32, 'ker_size': 5},\n",
    "  {'batch_size': 32, 'ker_size': 7},\n",
    "  {'batch_size': 64, 'ker_size': 3},\n",
    "  {'batch_size': 64, 'ker_size': 5},\n",
    "  {'batch_size': 64, 'ker_size': 7}],\n",
    " 'split0_test_score': array([0.00225, 0.0017 , 0.0027 , 0.00225, 0.00205, 0.0024 ]),\n",
    " 'split1_test_score': array([0.0021 , 0.0023 , 0.0023 , 0.0019 , 0.002  , 0.00165]),\n",
    " 'split2_test_score': array([0.00325, 0.00215, 0.0021 , 0.00245, 0.0024 , 0.0024 ]),\n",
    " 'mean_test_score': array([0.00253333, 0.00205   , 0.00236667, 0.0022    , 0.00215   ,\n",
    "        0.00215   ]),\n",
    " 'std_test_score': array([0.00051045, 0.00025495, 0.00024944, 0.0002273 , 0.00017795,\n",
    "        0.00035355]),\n",
    " 'rank_test_score': array([1, 6, 2, 3, 4, 4], dtype=int32),\n",
    " 'split0_train_score': array([0.001875, 0.001625, 0.002525, 0.002175, 0.0019  , 0.002275]),\n",
    " 'split1_train_score': array([0.0027  , 0.00275 , 0.00245 , 0.0022  , 0.002125, 0.002475]),\n",
    " 'split2_train_score': array([0.0024  , 0.00205 , 0.0019  , 0.0021  , 0.00225 , 0.001775]),\n",
    " 'mean_train_score': array([0.002325  , 0.00214167, 0.00229167, 0.00215833, 0.00209167,\n",
    "        0.002175  ]),\n",
    " 'std_train_score': array([3.40954542e-04, 4.63830668e-04, 2.78637558e-04, 4.24918293e-05,\n",
    "        1.44817893e-04, 2.94392029e-04])}\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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