correct_CNN_GRU.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 解压文件\n",
    "# 运行先 pip install mne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-31T13:40:26.479920Z",
     "iopub.status.busy": "2021-10-31T13:40:26.479534Z",
     "iopub.status.idle": "2021-10-31T13:40:26.483138Z",
     "shell.execute_reply": "2021-10-31T13:40:26.482344Z",
     "shell.execute_reply.started": "2021-10-31T13:40:26.479850Z"
    }
   },
   "outputs": [],
   "source": [
    "# !unzip \"data/data112870/BCICIV_2a_mat.zip\" -d data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-31T13:40:26.484620Z",
     "iopub.status.busy": "2021-10-31T13:40:26.484256Z",
     "iopub.status.idle": "2021-10-31T13:40:27.261856Z",
     "shell.execute_reply": "2021-10-31T13:40:27.261104Z",
     "shell.execute_reply.started": "2021-10-31T13:40:26.484517Z"
    }
   },
   "outputs": [],
   "source": [
    "import mne\n",
    "import scipy.io as scio\n",
    "import numpy as np\n",
    "\n",
    "def read_data(data_path,i):\n",
    "    data_path = data_path\n",
    "    DATA = scio.loadmat(data_path)\n",
    "    DATA = DATA[\"data\"][0]\n",
    "    DATA = DATA[i] #主程序倒叙读6个\n",
    "    EEG_DATA = DATA[\"X\"][0][0].transpose(1,0)\n",
    "    EEG_label = DATA['y'][0][0][:,0]\n",
    "\n",
    "    ch_names = ['Fz','Fp1','Fp2','AF3','AF4','AF7','AF8','C3','POz','Cz','PO3','C4','PO4','PO5','PO6','PO7','PO8','Oz','O1','Pz','P6','P7','EOG-left','EOG-central','EOG-right'] #Fz是对应，其余随便写的不影响\n",
    "    ch_types = ['eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg',\n",
    "                'eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg',\n",
    "                'eeg','eeg','eog','eog','eog']\n",
    "\n",
    "    info = mne.create_info(ch_names = ch_names,\n",
    "                            ch_types=ch_types,\n",
    "                            sfreq=250)\n",
    "    info.set_montage('standard_1020')\n",
    "    raw = mne.io.RawArray(EEG_DATA,info)\n",
    "\n",
    "    n_times = DATA[\"trial\"][0][0][:,0] #时间戳\n",
    "    event = np.zeros((4,12),int)\n",
    "    v,b,n,m=0,0,0,0\n",
    "    for i in range (0,n_times.shape[0]):\n",
    "        if EEG_label[i]==1:\n",
    "            event[0,v]=n_times[i]\n",
    "            v+=1\n",
    "        if EEG_label[i]==2:\n",
    "            event[1,b]=n_times[i]\n",
    "            b+=1\n",
    "        if EEG_label[i]==3:\n",
    "            event[2,n]=n_times[i]\n",
    "            n+=1\n",
    "        if EEG_label[i]==4:\n",
    "            event[3,m]=n_times[i]\n",
    "            m+=1\n",
    "\n",
    "\n",
    "    events = np.zeros((48,3),int)\n",
    "    j=0\n",
    "    for i in range(events.shape[0]):\n",
    "        if i<12:\n",
    "            events[i][0]=event[0][j]\n",
    "            events[i][2]=1\n",
    "        elif i<24:\n",
    "            events[i][0]=event[1][j]\n",
    "            events[i][2]=2\n",
    "        elif i<36:\n",
    "            events[i][0]=event[2][j]\n",
    "            events[i][2]=3\n",
    "        elif i<48:\n",
    "            events[i][0]=event[3][j]\n",
    "            events[i][2]=4\n",
    "        j+=1\n",
    "        if j>=12:\n",
    "            j=0\n",
    "    events = sorted(events, key = lambda events: events[0])\n",
    "    event_id = dict(lefthand=1,righthand=2,feet=3,tongue=4)\n",
    "    picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False, ecg=False,\n",
    "                    exclude='bads')\n",
    "    epochs = mne.Epochs(raw, events, event_id, tmin=3, tmax=6, proj=True,baseline=(None, None), picks=picks,preload=True)\n",
    "\n",
    "\n",
    "    data = epochs.get_data()\n",
    "    # data =np.array(data).reshape((48*22,751))\n",
    "    label = EEG_label.repeat(22)\n",
    "    label=label.reshape(48,22)\n",
    "    return data,label\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trian Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-31T13:40:27.262965Z",
     "iopub.status.busy": "2021-10-31T13:40:27.262749Z",
     "iopub.status.idle": "2021-10-31T13:40:31.787255Z",
     "shell.execute_reply": "2021-10-31T13:40:31.786531Z",
     "shell.execute_reply.started": "2021-10-31T13:40:27.262928Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating RawArray with float64 data, n_channels=25, n_times=96735\n",
      "    Range : 0 ... 96734 =      0.000 ...   386.936 secs\n",
      "Ready.\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "48 matching events found\n",
      "Setting baseline interval to [3.0, 6.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 48 events and 751 original time points ...\n",
      "0 bad epochs dropped\n",
      "Creating RawArray with float64 data, n_channels=25, n_times=96735\n",
      "    Range : 0 ... 96734 =      0.000 ...   386.936 secs\n",
      "Ready.\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "48 matching events found\n",
      "Setting baseline interval to [3.0, 6.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 48 events and 751 original time points ...\n",
      "0 bad epochs dropped\n",
      "Creating RawArray with float64 data, n_channels=25, n_times=96735\n",
      "    Range : 0 ... 96734 =      0.000 ...   386.936 secs\n",
      "Ready.\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "48 matching events found\n",
      "Setting baseline interval to [3.0, 6.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 48 events and 751 original time points ...\n",
      "0 bad epochs dropped\n",
      "Creating RawArray with float64 data, n_channels=25, n_times=96735\n",
      "    Range : 0 ... 96734 =      0.000 ...   386.936 secs\n",
      "Ready.\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "48 matching events found\n",
      "Setting baseline interval to [3.0, 6.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 48 events and 751 original time points ...\n",
      "0 bad epochs dropped\n",
      "Creating RawArray with float64 data, n_channels=25, n_times=96735\n",
      "    Range : 0 ... 96734 =      0.000 ...   386.936 secs\n",
      "Ready.\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "48 matching events found\n",
      "Setting baseline interval to [3.0, 6.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 48 events and 751 original time points ...\n",
      "0 bad epochs dropped\n",
      "Creating RawArray with float64 data, n_channels=25, n_times=96735\n",
      "    Range : 0 ... 96734 =      0.000 ...   386.936 secs\n",
      "Ready.\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "48 matching events found\n",
      "Setting baseline interval to [3.0, 6.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 48 events and 751 original time points ...\n",
      "0 bad epochs dropped\n"
     ]
    }
   ],
   "source": [
    "train_path = r\"data/A01T.mat\"\n",
    "train_data=np.zeros((6,48,22,751),dtype=np.float32)\n",
    "train_label=np.zeros((6,48,22),dtype=np.int64)\n",
    "for i in range(6):\n",
    "    data,label = read_data(train_path,-(i+1))\n",
    "    train_data[i]=data\n",
    "    label = np.array(label[:])\n",
    "    train_label[i]=label\n",
    "train_data=train_data.reshape((288,22,751))\n",
    "train_label=train_label.reshape((288,22))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-31T13:40:31.788415Z",
     "iopub.status.busy": "2021-10-31T13:40:31.788185Z",
     "iopub.status.idle": "2021-10-31T13:40:36.315715Z",
     "shell.execute_reply": "2021-10-31T13:40:36.314988Z",
     "shell.execute_reply.started": "2021-10-31T13:40:31.788373Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating RawArray with float64 data, n_channels=25, n_times=96735\n",
      "    Range : 0 ... 96734 =      0.000 ...   386.936 secs\n",
      "Ready.\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "48 matching events found\n",
      "Setting baseline interval to [3.0, 6.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 48 events and 751 original time points ...\n",
      "0 bad epochs dropped\n",
      "Creating RawArray with float64 data, n_channels=25, n_times=96735\n",
      "    Range : 0 ... 96734 =      0.000 ...   386.936 secs\n",
      "Ready.\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "48 matching events found\n",
      "Setting baseline interval to [3.0, 6.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 48 events and 751 original time points ...\n",
      "0 bad epochs dropped\n",
      "Creating RawArray with float64 data, n_channels=25, n_times=96735\n",
      "    Range : 0 ... 96734 =      0.000 ...   386.936 secs\n",
      "Ready.\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "48 matching events found\n",
      "Setting baseline interval to [3.0, 6.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 48 events and 751 original time points ...\n",
      "0 bad epochs dropped\n",
      "Creating RawArray with float64 data, n_channels=25, n_times=96735\n",
      "    Range : 0 ... 96734 =      0.000 ...   386.936 secs\n",
      "Ready.\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "48 matching events found\n",
      "Setting baseline interval to [3.0, 6.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 48 events and 751 original time points ...\n",
      "0 bad epochs dropped\n",
      "Creating RawArray with float64 data, n_channels=25, n_times=96735\n",
      "    Range : 0 ... 96734 =      0.000 ...   386.936 secs\n",
      "Ready.\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "48 matching events found\n",
      "Setting baseline interval to [3.0, 6.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 48 events and 751 original time points ...\n",
      "0 bad epochs dropped\n",
      "Creating RawArray with float64 data, n_channels=25, n_times=96735\n",
      "    Range : 0 ... 96734 =      0.000 ...   386.936 secs\n",
      "Ready.\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "48 matching events found\n",
      "Setting baseline interval to [3.0, 6.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Loading data for 48 events and 751 original time points ...\n",
      "0 bad epochs dropped\n"
     ]
    }
   ],
   "source": [
    "test_path = r\"data/A01E.mat\"\n",
    "test_data=np.zeros((6,48,22,751),dtype=np.float32)\n",
    "test_label=np.zeros((6,48,22),dtype=np.int64)\n",
    "for i in range(6):\n",
    "    data,label = read_data(test_path,-(i+1))\n",
    "    test_data[i]=data\n",
    "    label = np.array(label[:])\n",
    "    test_label[i]=label\n",
    "test_data=test_data.reshape((288,22,751))\n",
    "test_label=test_label.reshape((288,22))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-31T13:40:36.316726Z",
     "iopub.status.busy": "2021-10-31T13:40:36.316526Z",
     "iopub.status.idle": "2021-10-31T13:40:37.506724Z",
     "shell.execute_reply": "2021-10-31T13:40:37.505797Z",
     "shell.execute_reply.started": "2021-10-31T13:40:36.316688Z"
    }
   },
   "outputs": [],
   "source": [
    "import paddle\n",
    "import paddle.nn.functional as F\n",
    "import paddle.nn as nn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-31T13:40:37.509529Z",
     "iopub.status.busy": "2021-10-31T13:40:37.509287Z",
     "iopub.status.idle": "2021-10-31T13:40:37.520149Z",
     "shell.execute_reply": "2021-10-31T13:40:37.519484Z",
     "shell.execute_reply.started": "2021-10-31T13:40:37.509483Z"
    }
   },
   "outputs": [],
   "source": [
    "class CNN_GRU(nn.Layer):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv1D(1,32,9,1,padding='same',data_format='NCL')\n",
    "        self.padding1 = nn.MaxPool1D(kernel_size=2,stride=2,padding='valid')\n",
    "        self.conv2 = nn.Conv1D(32,32,9,1,padding='same',data_format='NCL')\n",
    "        self.padding2 = nn.MaxPool1D(kernel_size=2,stride=2,padding='valid')\n",
    "        self.conv3 = nn.Conv1D(32,32,9,1,padding='same',\n",
    "        data_format='NCL')\n",
    "        self.padding3 = nn.MaxPool1D(kernel_size=2,stride=2,padding='valid')\n",
    "        self.flatten1 = nn.Flatten()\n",
    "        self.flatten2 = nn.Flatten()\n",
    "        # self.gru = nn.GRU(1,64,1)\n",
    "        self.gru = nn.GRU(2976,64,1)\n",
    "        self.dense1 = nn.Linear(64,64)\n",
    "        self.dense2 = nn.Linear(64,4)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.relu2 = nn.ReLU()\n",
    "        self.relu3 = nn.ReLU()\n",
    "        self.relu4 = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(p=0.5)\n",
    "    def forward(self,x):\n",
    "        x=self.conv1(x)\n",
    "        x=self.relu1(x)\n",
    "        x=self.padding1(x)\n",
    "        x=self.conv2(x)\n",
    "        x=self.relu2(x)\n",
    "        x=self.padding2(x)\n",
    "        x=self.conv3(x)\n",
    "        x=self.relu3(x)\n",
    "        x=self.padding3(x)\n",
    "        x=self.flatten1(x)\n",
    "        # x = x.unsqueeze(-1)\n",
    "        x = x.unsqueeze(1)\n",
    "        x,h=self.gru(x)\n",
    "        x=self.dense1(x)\n",
    "        x=self.relu4(x)\n",
    "        x=self.dropout(x)\n",
    "        x=self.dense2(x)\n",
    "        x=self.flatten2(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-31T13:40:37.521519Z",
     "iopub.status.busy": "2021-10-31T13:40:37.521316Z",
     "iopub.status.idle": "2021-10-31T13:40:40.385538Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      " Layer (type)       Input Shape            Output Shape           Param #    \n",
      "===============================================================================\n",
      "   Conv1D-1        [[1, 1, 751]]           [1, 32, 751]             320      \n",
      "    ReLU-1         [[1, 32, 751]]          [1, 32, 751]              0       \n",
      "  MaxPool1D-1      [[1, 32, 751]]          [1, 32, 375]              0       \n",
      "   Conv1D-2        [[1, 32, 375]]          [1, 32, 375]            9,248     \n",
      "    ReLU-2         [[1, 32, 375]]          [1, 32, 375]              0       \n",
      "  MaxPool1D-2      [[1, 32, 375]]          [1, 32, 187]              0       \n",
      "   Conv1D-3        [[1, 32, 187]]          [1, 32, 187]            9,248     \n",
      "    ReLU-3         [[1, 32, 187]]          [1, 32, 187]              0       \n",
      "  MaxPool1D-3      [[1, 32, 187]]          [1, 32, 93]               0       \n",
      "   Flatten-1       [[1, 32, 93]]            [1, 2976]                0       \n",
      "     GRU-1         [[1, 1, 2976]]    [[1, 1, 64], [1, 1, 64]]     584,064    \n",
      "   Linear-1         [[1, 1, 64]]            [1, 1, 64]             4,160     \n",
      "    ReLU-4          [[1, 1, 64]]            [1, 1, 64]               0       \n",
      "   Dropout-1        [[1, 1, 64]]            [1, 1, 64]               0       \n",
      "   Linear-2         [[1, 1, 64]]            [1, 1, 4]               260      \n",
      "   Flatten-2        [[1, 1, 4]]               [1, 4]                 0       \n",
      "===============================================================================\n",
      "Total params: 607,300\n",
      "Trainable params: 607,300\n",
      "Non-trainable params: 0\n",
      "-------------------------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.83\n",
      "Params size (MB): 2.32\n",
      "Estimated Total Size (MB): 3.15\n",
      "-------------------------------------------------------------------------------\n",
      "\n",
      "[1, 4]\n"
     ]
    }
   ],
   "source": [
    "input_spec = paddle.static.InputSpec(\n",
    "    shape=(-1,1,751),\n",
    "    dtype='float32',\n",
    "    name='x'\n",
    ")\n",
    "model = CNN_GRU()\n",
    "\n",
    "paddle.summary(model,input_spec)\n",
    "out = model(paddle.randn((1,1,750)))\n",
    "print(out.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-31T13:40:40.386705Z",
     "iopub.status.busy": "2021-10-31T13:40:40.386425Z",
     "iopub.status.idle": "2021-10-31T13:40:40.390461Z"
    }
   },
   "outputs": [],
   "source": [
    "loss_function = paddle.nn.CrossEntropyLoss()\n",
    "# lr_scheduler = paddle.optimizer.lr.CosineAnnealingDecay(\n",
    "#     learning_rate=LEARNING_RATE,\n",
    "#     T_max=NUM_EPOCHS\n",
    "# )\n",
    "optimizer = paddle.optimizer.Adam(\n",
    "    learning_rate=1e-4,\n",
    "    parameters=model.parameters()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8:2 T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-31T13:40:40.391554Z",
     "iopub.status.busy": "2021-10-31T13:40:40.391337Z",
     "iopub.status.idle": "2021-10-31T13:40:40.395338Z"
    }
   },
   "outputs": [],
   "source": [
    "# data = train_data\n",
    "# label = train_label-1\n",
    "# # print(data.shape,label.shape)\n",
    "\n",
    "# from sklearn.preprocessing import StandardScaler #标准化\n",
    "# from sklearn.preprocessing import MinMaxScaler\n",
    "# sk = StandardScaler()\n",
    "# # data = sk.fit_transform(data)\n",
    "\n",
    "# from sklearn.model_selection import train_test_split\n",
    "# # data=np.expand_dims(data,1)\n",
    "# # label=np.expand_dims(label,1)\n",
    "# train_data,test_data,train_label,test_label = train_test_split(data,label,train_size=0.8)\n",
    "# print(train_data.shape,train_label.shape,test_data.shape,test_label.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-31T13:40:40.396313Z",
     "iopub.status.busy": "2021-10-31T13:40:40.396108Z",
     "iopub.status.idle": "2021-10-31T13:40:40.399308Z"
    }
   },
   "outputs": [],
   "source": [
    "# train_data=train_data.reshape((5060,751))\n",
    "# test_data=test_data.reshape((1276,751))\n",
    "# train_label=train_label.reshape((5060))\n",
    "# test_label=test_label.reshape((1276))\n",
    "# from sklearn.preprocessing import StandardScaler #标准化\n",
    "# from sklearn.preprocessing import MinMaxScaler\n",
    "# # sk = StandardScaler()\n",
    "# sk = MinMaxScaler()\n",
    "# train_data = sk.fit_transform(train_data)\n",
    "# test_data = sk.fit_transform(test_data)\n",
    "# train_data=np.expand_dims(train_data,1)\n",
    "# test_data=np.expand_dims(test_data,1)\n",
    "# train_label=np.expand_dims(train_label,1)\n",
    "# test_label=np.expand_dims(test_label,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8:2 E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-31T13:40:40.400221Z",
     "iopub.status.busy": "2021-10-31T13:40:40.400000Z",
     "iopub.status.idle": "2021-10-31T13:40:40.770716Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(288, 22, 751) (288, 22)\n",
      "(230, 22, 751) (230, 22) (58, 22, 751) (58, 22)\n"
     ]
    }
   ],
   "source": [
    "data = test_data\n",
    "label = test_label-1\n",
    "print(data.shape,label.shape)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler #标准化\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sk = StandardScaler()\n",
    "# data = sk.fit_transform(data)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "# data=np.expand_dims(data,1)\n",
    "# label=np.expand_dims(label,1)\n",
    "train_data,test_data,train_label,test_label = train_test_split(data,label,train_size=0.8)\n",
    "print(train_data.shape,train_label.shape,test_data.shape,test_label.shape)\n",
    "train_data=train_data.reshape((5060,751))\n",
    "test_data=test_data.reshape((1276,751))\n",
    "train_label=train_label.reshape((5060))\n",
    "test_label=test_label.reshape((1276))\n",
    "from sklearn.preprocessing import StandardScaler #标准化\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# sk = StandardScaler()\n",
    "sk = MinMaxScaler()\n",
    "train_data = sk.fit_transform(train_data)\n",
    "test_data = sk.fit_transform(test_data)\n",
    "train_data=np.expand_dims(train_data,1)\n",
    "test_data=np.expand_dims(test_data,1)\n",
    "train_label=np.expand_dims(train_label,1)\n",
    "test_label=np.expand_dims(test_label,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-31T13:40:40.771963Z",
     "iopub.status.busy": "2021-10-31T13:40:40.771701Z",
     "iopub.status.idle": "2021-10-31T13:40:40.774979Z"
    }
   },
   "outputs": [],
   "source": [
    "NUM_EPOCHS=100\n",
    "TRAIN_BATCH_SIZE=28"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 五折交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-31T13:40:40.776017Z",
     "iopub.status.busy": "2021-10-31T13:40:40.775781Z",
     "iopub.status.idle": "2021-10-31T13:40:40.780996Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from paddle.io import Dataset\n",
    "\n",
    "\n",
    "class MIDataset(Dataset):\n",
    "    def __init__(self, data,label):\n",
    "        self.data = data\n",
    "        self.label = label\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        data = self.data[idx]\n",
    "        label = self.label[idx]\n",
    "        return data, label\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-31T13:40:40.781962Z",
     "iopub.status.busy": "2021-10-31T13:40:40.781756Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n",
      "Epoch 1/100\n",
      "step  20/180 [==>...........................] - loss: 1.3685 - acc: 0.2196 - ETA: 2s - 15ms/step"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  return (isinstance(seq, collections.Sequence) and\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 180/180 [==============================] - loss: 1.3689 - acc: 0.2867 - 8ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 1.3724 - acc: 0.2508 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 2/100\n",
      "step 180/180 [==============================] - loss: 1.2532 - acc: 0.4012 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 1.4486 - acc: 0.2202 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 3/100\n",
      "step 180/180 [==============================] - loss: 1.1388 - acc: 0.5438 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 1.3594 - acc: 0.3245 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 4/100\n",
      "step 180/180 [==============================] - loss: 0.9651 - acc: 0.6917 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 1.8689 - acc: 0.2939 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 5/100\n",
      "step 180/180 [==============================] - loss: 0.5273 - acc: 0.7786 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 1.9893 - acc: 0.2861 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 6/100\n",
      "step 180/180 [==============================] - loss: 0.4574 - acc: 0.8623 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 2.2041 - acc: 0.2672 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 7/100\n",
      "step 180/180 [==============================] - loss: 0.2863 - acc: 0.9022 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 2.3970 - acc: 0.2461 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 8/100\n",
      "step 180/180 [==============================] - loss: 0.4334 - acc: 0.9313 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 2.9350 - acc: 0.2320 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 9/100\n",
      "step 180/180 [==============================] - loss: 0.1537 - acc: 0.9571 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 2.5006 - acc: 0.2414 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 10/100\n",
      "step 180/180 [==============================] - loss: 0.1345 - acc: 0.9667 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 3.3403 - acc: 0.2500 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 11/100\n",
      "step 180/180 [==============================] - loss: 0.0554 - acc: 0.9772 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 2.6429 - acc: 0.2500 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 12/100\n",
      "step 180/180 [==============================] - loss: 0.1005 - acc: 0.9796 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 3.2488 - acc: 0.2531 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 13/100\n",
      "step 180/180 [==============================] - loss: 0.0705 - acc: 0.9879 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 4.0855 - acc: 0.2524 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 14/100\n",
      "step 180/180 [==============================] - loss: 0.0748 - acc: 0.9901 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 4.3394 - acc: 0.2610 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 15/100\n",
      "step 180/180 [==============================] - loss: 0.0311 - acc: 0.9917 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 4.1119 - acc: 0.2539 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 16/100\n",
      "step 180/180 [==============================] - loss: 0.0730 - acc: 0.9940 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 3.7751 - acc: 0.2524 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 17/100\n",
      "step 180/180 [==============================] - loss: 0.0077 - acc: 0.9952 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 4.8625 - acc: 0.2633 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 18/100\n",
      "step 180/180 [==============================] - loss: 0.0357 - acc: 0.9948 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 5.4577 - acc: 0.2571 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 19/100\n",
      "step 180/180 [==============================] - loss: 0.0235 - acc: 0.9970 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 3.3805 - acc: 0.2516 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 20/100\n",
      "step 180/180 [==============================] - loss: 0.0179 - acc: 0.9960 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 6.6039 - acc: 0.2476 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 21/100\n",
      "step 180/180 [==============================] - loss: 0.0194 - acc: 0.9972 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 5.7909 - acc: 0.2484 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 22/100\n",
      "step 180/180 [==============================] - loss: 0.0086 - acc: 0.9976 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 4.0409 - acc: 0.2563 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 23/100\n",
      "step 180/180 [==============================] - loss: 0.0059 - acc: 0.9976 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 3.6320 - acc: 0.2578 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 24/100\n",
      "step 180/180 [==============================] - loss: 0.0043 - acc: 0.9988 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 4.2748 - acc: 0.2610 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 25/100\n",
      "step 180/180 [==============================] - loss: 0.0013 - acc: 0.9986 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 5.3310 - acc: 0.2578 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 26/100\n",
      "step 180/180 [==============================] - loss: 0.0154 - acc: 0.9990 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 6.0418 - acc: 0.2571 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 27/100\n",
      "step 180/180 [==============================] - loss: 0.0032 - acc: 0.9990 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 7.3031 - acc: 0.2665 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 28/100\n",
      "step 180/180 [==============================] - loss: 0.0125 - acc: 0.9988 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 4.9294 - acc: 0.2445 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 29/100\n",
      "step 180/180 [==============================] - loss: 0.0201 - acc: 0.9986 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 5.8028 - acc: 0.2594 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 30/100\n",
      "step 180/180 [==============================] - loss: 0.0025 - acc: 0.9992 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 3.8357 - acc: 0.2602 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 31/100\n",
      "step 180/180 [==============================] - loss: 0.0039 - acc: 0.9980 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 5.4144 - acc: 0.2500 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 32/100\n",
      "step 180/180 [==============================] - loss: 0.0035 - acc: 0.9996 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 6.5052 - acc: 0.2500 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 33/100\n",
      "step 180/180 [==============================] - loss: 0.0017 - acc: 0.9984 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 4.8183 - acc: 0.2508 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 34/100\n",
      "step 180/180 [==============================] - loss: 7.3235e-04 - acc: 0.9994 - 7ms/step       \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 4.7631 - acc: 0.2563 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 35/100\n",
      "step 180/180 [==============================] - loss: 0.0121 - acc: 0.9994 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 5.1270 - acc: 0.2547 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 36/100\n",
      "step 180/180 [==============================] - loss: 7.2671e-04 - acc: 0.9984 - 7ms/step         \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 7.6140 - acc: 0.2484 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 37/100\n",
      "step 180/180 [==============================] - loss: 0.0114 - acc: 0.9998 - 7ms/step           \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 7.3074 - acc: 0.2610 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 38/100\n",
      "step 180/180 [==============================] - loss: 0.0034 - acc: 0.9996 - 7ms/step             \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 9.1694 - acc: 0.2555 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 39/100\n",
      "step 180/180 [==============================] - loss: 0.0017 - acc: 0.9996 - 8ms/step              \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 5.5135 - acc: 0.2657 - 6ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 40/100\n",
      "step 180/180 [==============================] - loss: 0.0106 - acc: 0.9998 - 8ms/step              \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 5.0482 - acc: 0.2649 - 5ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 41/100\n",
      "step 180/180 [==============================] - loss: 0.0183 - acc: 0.9996 - 7ms/step             \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 5.5344 - acc: 0.2516 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 42/100\n",
      "step 180/180 [==============================] - loss: 0.0055 - acc: 0.9980 - 7ms/step             \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 7.9336 - acc: 0.2578 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 43/100\n",
      "step 180/180 [==============================] - loss: 0.0024 - acc: 0.9998 - 7ms/step              \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 5.1236 - acc: 0.2571 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 44/100\n",
      "step 180/180 [==============================] - loss: 0.0020 - acc: 0.9992 - 7ms/step              \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 6.4246 - acc: 0.2633 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 45/100\n",
      "step 180/180 [==============================] - loss: 4.0084e-04 - acc: 0.9994 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 7.8445 - acc: 0.2586 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 46/100\n",
      "step 180/180 [==============================] - loss: 9.7069e-04 - acc: 0.9996 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 6.2878 - acc: 0.2633 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 47/100\n",
      "step 180/180 [==============================] - loss: 2.9551e-04 - acc: 0.9998 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 8.7915 - acc: 0.2539 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 48/100\n",
      "step 180/180 [==============================] - loss: 2.7383e-04 - acc: 0.9998 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 7.0248 - acc: 0.2719 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 49/100\n",
      "step 180/180 [==============================] - loss: 0.0045 - acc: 1.0000 - 7ms/step             \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 6.7661 - acc: 0.2657 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 50/100\n",
      "step 180/180 [==============================] - loss: 1.6638e-04 - acc: 1.0000 - 7ms/step         \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 8.4058 - acc: 0.2618 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 51/100\n",
      "step 180/180 [==============================] - loss: 2.6548e-04 - acc: 1.0000 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 7.8900 - acc: 0.2563 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 52/100\n",
      "step 180/180 [==============================] - loss: 0.0011 - acc: 0.9990 - 7ms/step            \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 7.7754 - acc: 0.2727 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 53/100\n",
      "step 180/180 [==============================] - loss: 2.5093e-04 - acc: 1.0000 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 6.7492 - acc: 0.2602 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 54/100\n",
      "step 180/180 [==============================] - loss: 5.5084e-04 - acc: 1.0000 - 7ms/step         \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 8.5714 - acc: 0.2531 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 55/100\n",
      "step 180/180 [==============================] - loss: 0.0224 - acc: 0.9968 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 5.9686 - acc: 0.2868 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 56/100\n",
      "step 180/180 [==============================] - loss: 0.0046 - acc: 0.9982 - 7ms/step              \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 8.2194 - acc: 0.2516 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 57/100\n",
      "step 180/180 [==============================] - loss: 8.8604e-04 - acc: 0.9998 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 7.5505 - acc: 0.2625 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 58/100\n",
      "step 180/180 [==============================] - loss: 0.0011 - acc: 1.0000 - 7ms/step              \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 10.1895 - acc: 0.2633 - 4ms/step         \n",
      "Eval samples: 1276\n",
      "Epoch 59/100\n",
      "step 180/180 [==============================] - loss: 0.0021 - acc: 0.9996 - 7ms/step              \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 7.0318 - acc: 0.2602 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 60/100\n",
      "step 180/180 [==============================] - loss: 2.6387e-04 - acc: 1.0000 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 8.7751 - acc: 0.2539 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 61/100\n",
      "step 180/180 [==============================] - loss: 6.4277e-05 - acc: 1.0000 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 5.8332 - acc: 0.2704 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 62/100\n",
      "step 180/180 [==============================] - loss: 9.8329e-05 - acc: 1.0000 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 7.5896 - acc: 0.2719 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 63/100\n",
      "step 180/180 [==============================] - loss: 1.3659e-04 - acc: 1.0000 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 9.1747 - acc: 0.2696 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 64/100\n",
      "step 180/180 [==============================] - loss: 3.7494e-04 - acc: 0.9998 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 9.4773 - acc: 0.2641 - 4ms/step           \n",
      "Eval samples: 1276\n",
      "Epoch 65/100\n",
      "step 180/180 [==============================] - loss: 6.0358e-04 - acc: 1.0000 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 5.6718 - acc: 0.2680 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 66/100\n",
      "step 180/180 [==============================] - loss: 0.0068 - acc: 0.9964 - 7ms/step             \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 8.4776 - acc: 0.2884 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 67/100\n",
      "step 180/180 [==============================] - loss: 2.9123e-05 - acc: 0.9998 - 7ms/step         \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 5.0846 - acc: 0.2743 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 68/100\n",
      "step 180/180 [==============================] - loss: 1.1887e-04 - acc: 1.0000 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 7.3466 - acc: 0.2696 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 69/100\n",
      "step 180/180 [==============================] - loss: 4.5624e-04 - acc: 0.9998 - 7ms/step         \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 6.5277 - acc: 0.2751 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 70/100\n",
      "step 180/180 [==============================] - loss: 0.0017 - acc: 0.9994 - 7ms/step              \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 10.1823 - acc: 0.2727 - 4ms/step         \n",
      "Eval samples: 1276\n",
      "Epoch 71/100\n",
      "step 180/180 [==============================] - loss: 0.0030 - acc: 0.9986 - 7ms/step              \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 7.4184 - acc: 0.2719 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 72/100\n",
      "step 180/180 [==============================] - loss: 4.5291e-04 - acc: 1.0000 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 6.8726 - acc: 0.2735 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 73/100\n",
      "step 180/180 [==============================] - loss: 2.2295e-05 - acc: 1.0000 - 7ms/step          \n",
      "Eval begin...\n",
      "step 46/46 [==============================] - loss: 4.9751 - acc: 0.2712 - 4ms/step          \n",
      "Eval samples: 1276\n",
      "Epoch 74/100\n",
      "step  50/180 [=======>......................] - loss: 6.0246e-05 - acc: 1.0000 - ETA: 0s - 7ms/step"
     ]
    }
   ],
   "source": [
    "train_dataset = MIDataset(train_data,train_label)\n",
    "test_dataset = MIDataset(test_data,test_label)\n",
    "train_loader = paddle.io.DataLoader(\n",
    "    train_dataset, \n",
    "    batch_size=28, \n",
    "    shuffle=True,\n",
    "    drop_last=True)\n",
    "test_loader = paddle.io.DataLoader(\n",
    "    test_dataset, \n",
    "    batch_size=28,\n",
    "    shuffle=True\n",
    ")\n",
    "model = CNN_GRU()\n",
    "loss_function = paddle.nn.CrossEntropyLoss()\n",
    "optimizer = paddle.optimizer.Adam(\n",
    "learning_rate=1e-4,\n",
    "parameters=model.parameters())\n",
    "model_handler = paddle.Model(model)\n",
    "model_handler.prepare(optimizer=optimizer,\n",
    "            loss=loss_function,\n",
    "            metrics=paddle.metric.Accuracy())\n",
    "model_handler.fit(train_loader,\n",
    "        test_loader,\n",
    "        epochs=100,\n",
    "        batch_size=28,\n",
    "        verbose=1)\n",
    "result=(model_handler.evaluate(test_dataset, batch_size=test_data.shape[0]))\n",
    "result"
   ]
  }
 ],
 "metadata": {
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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