DouLSTM-3Classes-50%overlap-Acc-Gro-Pos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM Fatigue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.compat.v1  as tf\n",
    "import numpy as np\n",
    "import os,sys\n",
    "tf.compat.v1.disable_eager_execution()\n",
    "import scipy.io as scio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34334, 200, 9)\n",
      "(34334, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train_mat = scio.loadmat('X_train-3Classes-50%overlap.mat')\n",
    "key_name = list(X_train_mat.keys())[-1]\n",
    "X_train=X_train_mat[key_name]\n",
    "print(X_train.shape)\n",
    "\n",
    "Y_train_mat = scio.loadmat('Y_train-3Classes-50%overlap.mat')\n",
    "key_name = list(Y_train_mat.keys())[-1]\n",
    "Y_train=Y_train_mat[key_name]\n",
    "print(Y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30900, 200, 9)\n",
      "(30900, 1)\n",
      "(3434, 200, 9)\n",
      "(3434, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X_train, Y_train, test_size = 0.1, random_state = 0)\n",
    "#X_test, X_valid, Y_test, Y_valid = train_test_split(X_test, Y_test, test_size = 0.5, random_state = 0)\n",
    "print(X_train.shape)\n",
    "print(Y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\Anaconda3\\envs\\tensorflow1\\lib\\site-packages\\tensorflow\\python\\compat\\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()\n",
    "tf.set_random_seed(42)\n",
    "# Configuring a session\n",
    "session_conf = tf.ConfigProto(\n",
    "    intra_op_parallelism_threads=1,\n",
    "    inter_op_parallelism_threads=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import Keras\n",
    "from __future__ import print_function\n",
    "import keras\n",
    "from keras import regularizers\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout,Flatten, Reshape, Softmax\n",
    "from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPooling1D, TimeDistributed,LSTM,Bidirectional,BatchNormalization\n",
    "#from keras import backend as K\n",
    "from tensorflow.compat.v1.keras import backend as K\n",
    "from attention import Attention\n",
    "sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)\n",
    "K.set_session(sess)\n",
    "\n",
    "# callbacks:\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.callbacks import TensorBoard\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.callbacks import ReduceLROnPlateau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utility function to count the number of classes\n",
    "def _count_classes(y):\n",
    "    return len(set([tuple(category) for category in y]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initializing parameters\n",
    "epochs = 200\n",
    "batch_size = 256\n",
    "timesteps = 200\n",
    "input_dim = len(X_train[0][0])\n",
    "n_classes = _count_classes(Y_train)\n",
    "#input_shape = (timesteps, input_dim)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**LSTM Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 200, 128)          70656     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 200, 128)          0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 64)                49408     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 3)                 195       \n",
      "=================================================================\n",
      "Total params: 120,259\n",
      "Trainable params: 120,259\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Initiliazing the sequential model\n",
    "model2 = Sequential()\n",
    "# Configuring the parameters\n",
    "model2.add(LSTM(128, return_sequences=True, input_shape=(timesteps, input_dim), kernel_initializer='glorot_normal'))\n",
    "# Adding a dropout layer\n",
    "model2.add(Dropout(0.5))\n",
    "#model2.add(Attention(64))\n",
    "# #model2.add(BatchNormalization())\n",
    "model2.add(LSTM(64))\n",
    "# # Adding a dropout layer\n",
    "model2.add(Dropout(0.5))\n",
    "# # Adding a dense output layer with sigmoid activation\n",
    "model2.add(Dense(n_classes, activation='softmax',kernel_regularizer=regularizers.l2(0.01),\n",
    "                activity_regularizer=regularizers.l1(0.01)))\n",
    "\n",
    "model2.summary()\n",
    "#-------------------\n",
    "# Compiling the model\n",
    "model2.compile(loss='sparse_categorical_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])#\n",
    "\n",
    "filepath=\"M2-weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5\"\n",
    "\n",
    "mc = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n",
    "\n",
    "tb = TensorBoard(log_dir='./logs',  # log 目录\n",
    "                 histogram_freq=1,  # 按照何等频率（epoch）来计算直方图，0为不计算\n",
    "                 batch_size=64,     # 用多大量的数据计算直方图\n",
    "                 write_graph=True,  # 是否存储网络结构图\n",
    "                 write_grads=True, # 是否可视化梯度直方图\n",
    "                 write_images=True,# 是否可视化参数\n",
    "                 embeddings_freq=0,\n",
    "                 embeddings_layer_names=None,\n",
    "                 embeddings_metadata=None)\n",
    "\n",
    "es=EarlyStopping(monitor='val_loss', patience=14, verbose=2)\n",
    "rp=ReduceLROnPlateau(\n",
    "    monitor='val_loss',\n",
    "    factor=0.25,\n",
    "    patience=8,#\n",
    "    verbose=2,\n",
    "    mode='auto',\n",
    "    min_delta=0.0001,\n",
    "    cooldown=0,\n",
    "    min_lr=0\n",
    ")\n",
    "\n",
    "# callbacks_list = [mc,tb,es,rp]\n",
    "callbacks_list =[mc,es,rp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 27810 samples, validate on 3090 samples\n",
      "Epoch 1/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.8354 - acc: 0.6490WARNING:tensorflow:From D:\\Anaconda3\\envs\\tensorflow1\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_v1.py:2048: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.81327, saving model to M2-weights-improvement-01-0.81.hdf5\n",
      "27810/27810 [==============================] - 219s 8ms/sample - loss: 0.8354 - acc: 0.6490 - val_loss: 0.5448 - val_acc: 0.8133\n",
      "Epoch 2/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.5137 - acc: 0.8172\n",
      "Epoch 00002: val_acc improved from 0.81327 to 0.82589, saving model to M2-weights-improvement-02-0.83.hdf5\n",
      "27810/27810 [==============================] - 237s 9ms/sample - loss: 0.5137 - acc: 0.8172 - val_loss: 0.4843 - val_acc: 0.8259\n",
      "Epoch 3/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.4177 - acc: 0.8578\n",
      "Epoch 00003: val_acc improved from 0.82589 to 0.88350, saving model to M2-weights-improvement-03-0.88.hdf5\n",
      "27810/27810 [==============================] - 231s 8ms/sample - loss: 0.4177 - acc: 0.8578 - val_loss: 0.3584 - val_acc: 0.8835\n",
      "Epoch 4/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.3646 - acc: 0.8817\n",
      "Epoch 00004: val_acc improved from 0.88350 to 0.89288, saving model to M2-weights-improvement-04-0.89.hdf5\n",
      "27810/27810 [==============================] - 247s 9ms/sample - loss: 0.3646 - acc: 0.8817 - val_loss: 0.3311 - val_acc: 0.8929\n",
      "Epoch 5/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.3106 - acc: 0.9027\n",
      "Epoch 00005: val_acc improved from 0.89288 to 0.91262, saving model to M2-weights-improvement-05-0.91.hdf5\n",
      "27810/27810 [==============================] - 251s 9ms/sample - loss: 0.3106 - acc: 0.9027 - val_loss: 0.2712 - val_acc: 0.9126\n",
      "Epoch 6/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.2719 - acc: 0.9170\n",
      "Epoch 00006: val_acc improved from 0.91262 to 0.92362, saving model to M2-weights-improvement-06-0.92.hdf5\n",
      "27810/27810 [==============================] - 264s 9ms/sample - loss: 0.2719 - acc: 0.9170 - val_loss: 0.2559 - val_acc: 0.9236\n",
      "Epoch 7/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.2546 - acc: 0.9243\n",
      "Epoch 00007: val_acc improved from 0.92362 to 0.93463, saving model to M2-weights-improvement-07-0.93.hdf5\n",
      "27810/27810 [==============================] - 263s 9ms/sample - loss: 0.2546 - acc: 0.9243 - val_loss: 0.2235 - val_acc: 0.9346\n",
      "Epoch 8/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.2231 - acc: 0.9341\n",
      "Epoch 00008: val_acc did not improve from 0.93463\n",
      "27810/27810 [==============================] - 261s 9ms/sample - loss: 0.2231 - acc: 0.9341 - val_loss: 0.3293 - val_acc: 0.8916\n",
      "Epoch 9/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.2082 - acc: 0.9401\n",
      "Epoch 00009: val_acc improved from 0.93463 to 0.94434, saving model to M2-weights-improvement-09-0.94.hdf5\n",
      "27810/27810 [==============================] - 258s 9ms/sample - loss: 0.2082 - acc: 0.9401 - val_loss: 0.1975 - val_acc: 0.9443\n",
      "Epoch 10/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.1884 - acc: 0.9490\n",
      "Epoch 00010: val_acc improved from 0.94434 to 0.94498, saving model to M2-weights-improvement-10-0.94.hdf5\n",
      "27810/27810 [==============================] - 264s 9ms/sample - loss: 0.1884 - acc: 0.9490 - val_loss: 0.1846 - val_acc: 0.9450\n",
      "Epoch 11/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.1705 - acc: 0.9535\n",
      "Epoch 00011: val_acc did not improve from 0.94498\n",
      "27810/27810 [==============================] - 257s 9ms/sample - loss: 0.1705 - acc: 0.9535 - val_loss: 0.2892 - val_acc: 0.9201\n",
      "Epoch 12/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.1683 - acc: 0.9557\n",
      "Epoch 00012: val_acc improved from 0.94498 to 0.94984, saving model to M2-weights-improvement-12-0.95.hdf5\n",
      "27810/27810 [==============================] - 242s 9ms/sample - loss: 0.1683 - acc: 0.9557 - val_loss: 0.1848 - val_acc: 0.9498\n",
      "Epoch 13/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.1454 - acc: 0.9617\n",
      "Epoch 00013: val_acc improved from 0.94984 to 0.95955, saving model to M2-weights-improvement-13-0.96.hdf5\n",
      "27810/27810 [==============================] - 241s 9ms/sample - loss: 0.1454 - acc: 0.9617 - val_loss: 0.1601 - val_acc: 0.9595\n",
      "Epoch 14/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.1448 - acc: 0.9618\n",
      "Epoch 00014: val_acc did not improve from 0.95955\n",
      "27810/27810 [==============================] - 243s 9ms/sample - loss: 0.1448 - acc: 0.9618 - val_loss: 0.1899 - val_acc: 0.9421\n",
      "Epoch 15/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.1320 - acc: 0.9657\n",
      "Epoch 00015: val_acc did not improve from 0.95955\n",
      "27810/27810 [==============================] - 247s 9ms/sample - loss: 0.1320 - acc: 0.9657 - val_loss: 0.1748 - val_acc: 0.9521\n",
      "Epoch 16/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.1255 - acc: 0.9686\n",
      "Epoch 00016: val_acc did not improve from 0.95955\n",
      "27810/27810 [==============================] - 244s 9ms/sample - loss: 0.1255 - acc: 0.9686 - val_loss: 0.1708 - val_acc: 0.9563\n",
      "Epoch 17/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.1181 - acc: 0.9702\n",
      "Epoch 00017: val_acc improved from 0.95955 to 0.96634, saving model to M2-weights-improvement-17-0.97.hdf5\n",
      "27810/27810 [==============================] - 243s 9ms/sample - loss: 0.1181 - acc: 0.9702 - val_loss: 0.1302 - val_acc: 0.9663\n",
      "Epoch 18/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.1206 - acc: 0.9716\n",
      "Epoch 00018: val_acc improved from 0.96634 to 0.96796, saving model to M2-weights-improvement-18-0.97.hdf5\n",
      "27810/27810 [==============================] - 253s 9ms/sample - loss: 0.1206 - acc: 0.9716 - val_loss: 0.1228 - val_acc: 0.9680\n",
      "Epoch 19/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.1066 - acc: 0.9757\n",
      "Epoch 00019: val_acc did not improve from 0.96796\n",
      "27810/27810 [==============================] - 241s 9ms/sample - loss: 0.1066 - acc: 0.9757 - val_loss: 0.1439 - val_acc: 0.9647\n",
      "Epoch 20/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.1070 - acc: 0.9749\n",
      "Epoch 00020: val_acc did not improve from 0.96796\n",
      "27810/27810 [==============================] - 246s 9ms/sample - loss: 0.1070 - acc: 0.9749 - val_loss: 0.1503 - val_acc: 0.9608\n",
      "Epoch 21/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0994 - acc: 0.9774\n",
      "Epoch 00021: val_acc improved from 0.96796 to 0.97379, saving model to M2-weights-improvement-21-0.97.hdf5\n",
      "27810/27810 [==============================] - 246s 9ms/sample - loss: 0.0994 - acc: 0.9774 - val_loss: 0.1134 - val_acc: 0.9738\n",
      "Epoch 22/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0931 - acc: 0.9787\n",
      "Epoch 00022: val_acc improved from 0.97379 to 0.97508, saving model to M2-weights-improvement-22-0.98.hdf5\n",
      "27810/27810 [==============================] - 250s 9ms/sample - loss: 0.0931 - acc: 0.9787 - val_loss: 0.1004 - val_acc: 0.9751\n",
      "Epoch 23/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0859 - acc: 0.9819\n",
      "Epoch 00023: val_acc did not improve from 0.97508\n",
      "27810/27810 [==============================] - 248s 9ms/sample - loss: 0.0859 - acc: 0.9819 - val_loss: 0.1506 - val_acc: 0.9592\n",
      "Epoch 24/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0878 - acc: 0.9811\n",
      "Epoch 00024: val_acc did not improve from 0.97508\n",
      "27810/27810 [==============================] - 250s 9ms/sample - loss: 0.0878 - acc: 0.9811 - val_loss: 0.1216 - val_acc: 0.9673\n",
      "Epoch 25/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0854 - acc: 0.9813\n",
      "Epoch 00025: val_acc did not improve from 0.97508\n",
      "27810/27810 [==============================] - 259s 9ms/sample - loss: 0.0854 - acc: 0.9813 - val_loss: 0.1037 - val_acc: 0.9735\n",
      "Epoch 26/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0825 - acc: 0.9833\n",
      "Epoch 00026: val_acc improved from 0.97508 to 0.97702, saving model to M2-weights-improvement-26-0.98.hdf5\n",
      "27810/27810 [==============================] - 246s 9ms/sample - loss: 0.0825 - acc: 0.9833 - val_loss: 0.0930 - val_acc: 0.9770\n",
      "Epoch 27/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0796 - acc: 0.9832\n",
      "Epoch 00027: val_acc improved from 0.97702 to 0.98058, saving model to M2-weights-improvement-27-0.98.hdf5\n",
      "27810/27810 [==============================] - 258s 9ms/sample - loss: 0.0796 - acc: 0.9832 - val_loss: 0.0883 - val_acc: 0.9806\n",
      "Epoch 28/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0743 - acc: 0.9855\n",
      "Epoch 00028: val_acc did not improve from 0.98058\n",
      "27810/27810 [==============================] - 272s 10ms/sample - loss: 0.0743 - acc: 0.9855 - val_loss: 0.1123 - val_acc: 0.9686\n",
      "Epoch 29/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0742 - acc: 0.9851\n",
      "Epoch 00029: val_acc did not improve from 0.98058\n",
      "27810/27810 [==============================] - 269s 10ms/sample - loss: 0.0742 - acc: 0.9851 - val_loss: 0.1079 - val_acc: 0.9715\n",
      "Epoch 30/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0705 - acc: 0.9859\n",
      "Epoch 00030: val_acc did not improve from 0.98058\n",
      "27810/27810 [==============================] - 274s 10ms/sample - loss: 0.0705 - acc: 0.9859 - val_loss: 0.0970 - val_acc: 0.9773\n",
      "Epoch 31/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0724 - acc: 0.9857\n",
      "Epoch 00031: val_acc did not improve from 0.98058\n",
      "27810/27810 [==============================] - 266s 10ms/sample - loss: 0.0724 - acc: 0.9857 - val_loss: 0.1099 - val_acc: 0.9735\n",
      "Epoch 32/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0728 - acc: 0.9852\n",
      "Epoch 00032: val_acc did not improve from 0.98058\n",
      "27810/27810 [==============================] - 290s 10ms/sample - loss: 0.0728 - acc: 0.9852 - val_loss: 0.0839 - val_acc: 0.9803\n",
      "Epoch 33/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0683 - acc: 0.9881\n",
      "Epoch 00033: val_acc improved from 0.98058 to 0.98252, saving model to M2-weights-improvement-33-0.98.hdf5\n",
      "27810/27810 [==============================] - 282s 10ms/sample - loss: 0.0683 - acc: 0.9881 - val_loss: 0.0844 - val_acc: 0.9825\n",
      "Epoch 34/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0634 - acc: 0.9895\n",
      "Epoch 00034: val_acc did not improve from 0.98252\n",
      "27810/27810 [==============================] - 256s 9ms/sample - loss: 0.0634 - acc: 0.9895 - val_loss: 0.1262 - val_acc: 0.9722\n",
      "Epoch 35/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0641 - acc: 0.9889\n",
      "Epoch 00035: val_acc did not improve from 0.98252\n",
      "27810/27810 [==============================] - 253s 9ms/sample - loss: 0.0641 - acc: 0.9889 - val_loss: 0.0847 - val_acc: 0.9825\n",
      "Epoch 36/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0658 - acc: 0.9878\n",
      "Epoch 00036: val_acc improved from 0.98252 to 0.98350, saving model to M2-weights-improvement-36-0.98.hdf5\n",
      "27810/27810 [==============================] - 252s 9ms/sample - loss: 0.0658 - acc: 0.9878 - val_loss: 0.0814 - val_acc: 0.9835\n",
      "Epoch 37/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0604 - acc: 0.9892\n",
      "Epoch 00037: val_acc did not improve from 0.98350\n",
      "27810/27810 [==============================] - 252s 9ms/sample - loss: 0.0604 - acc: 0.9892 - val_loss: 0.1030 - val_acc: 0.9773\n",
      "Epoch 38/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0622 - acc: 0.9888\n",
      "Epoch 00038: val_acc did not improve from 0.98350\n",
      "27810/27810 [==============================] - 250s 9ms/sample - loss: 0.0622 - acc: 0.9888 - val_loss: 0.1063 - val_acc: 0.9770\n",
      "Epoch 39/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0614 - acc: 0.9891\n",
      "Epoch 00039: val_acc did not improve from 0.98350\n",
      "27810/27810 [==============================] - 252s 9ms/sample - loss: 0.0614 - acc: 0.9891 - val_loss: 0.0951 - val_acc: 0.9770\n",
      "Epoch 40/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0594 - acc: 0.9897\n",
      "Epoch 00040: val_acc did not improve from 0.98350\n",
      "27810/27810 [==============================] - 249s 9ms/sample - loss: 0.0594 - acc: 0.9897 - val_loss: 0.0990 - val_acc: 0.9812\n",
      "Epoch 41/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0573 - acc: 0.9907\n",
      "Epoch 00041: val_acc did not improve from 0.98350\n",
      "27810/27810 [==============================] - 252s 9ms/sample - loss: 0.0573 - acc: 0.9907 - val_loss: 0.0838 - val_acc: 0.9809\n",
      "Epoch 42/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0562 - acc: 0.9905\n",
      "Epoch 00042: val_acc did not improve from 0.98350\n",
      "27810/27810 [==============================] - 255s 9ms/sample - loss: 0.0562 - acc: 0.9905 - val_loss: 0.0820 - val_acc: 0.9828\n",
      "Epoch 43/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0566 - acc: 0.9905\n",
      "Epoch 00043: val_acc did not improve from 0.98350\n",
      "27810/27810 [==============================] - 274s 10ms/sample - loss: 0.0566 - acc: 0.9905 - val_loss: 0.2250 - val_acc: 0.9498\n",
      "Epoch 44/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0549 - acc: 0.9908\n",
      "Epoch 00044: val_acc did not improve from 0.98350\n",
      "\n",
      "Epoch 00044: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "27810/27810 [==============================] - 282s 10ms/sample - loss: 0.0549 - acc: 0.9908 - val_loss: 0.1393 - val_acc: 0.9689\n",
      "Epoch 45/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0416 - acc: 0.9953\n",
      "Epoch 00045: val_acc improved from 0.98350 to 0.98641, saving model to M2-weights-improvement-45-0.99.hdf5\n",
      "27810/27810 [==============================] - 295s 11ms/sample - loss: 0.0416 - acc: 0.9953 - val_loss: 0.0756 - val_acc: 0.9864\n",
      "Epoch 46/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0361 - acc: 0.9973\n",
      "Epoch 00046: val_acc did not improve from 0.98641\n",
      "27810/27810 [==============================] - 274s 10ms/sample - loss: 0.0361 - acc: 0.9973 - val_loss: 0.0841 - val_acc: 0.9854\n",
      "Epoch 47/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0346 - acc: 0.9973\n",
      "Epoch 00047: val_acc did not improve from 0.98641\n",
      "27810/27810 [==============================] - 261s 9ms/sample - loss: 0.0346 - acc: 0.9973 - val_loss: 0.0839 - val_acc: 0.9858\n",
      "Epoch 48/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0342 - acc: 0.9974\n",
      "Epoch 00048: val_acc did not improve from 0.98641\n",
      "27810/27810 [==============================] - 278s 10ms/sample - loss: 0.0342 - acc: 0.9974 - val_loss: 0.0957 - val_acc: 0.9812\n",
      "Epoch 49/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0340 - acc: 0.9974\n",
      "Epoch 00049: val_acc improved from 0.98641 to 0.98803, saving model to M2-weights-improvement-49-0.99.hdf5\n",
      "27810/27810 [==============================] - 263s 9ms/sample - loss: 0.0340 - acc: 0.9974 - val_loss: 0.0706 - val_acc: 0.9880\n",
      "Epoch 50/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0327 - acc: 0.9978\n",
      "Epoch 00050: val_acc did not improve from 0.98803\n",
      "27810/27810 [==============================] - 262s 9ms/sample - loss: 0.0327 - acc: 0.9978 - val_loss: 0.0738 - val_acc: 0.9871\n",
      "Epoch 51/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0331 - acc: 0.9980\n",
      "Epoch 00051: val_acc did not improve from 0.98803\n",
      "27810/27810 [==============================] - 249s 9ms/sample - loss: 0.0331 - acc: 0.9980 - val_loss: 0.0790 - val_acc: 0.9854\n",
      "Epoch 52/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0330 - acc: 0.9979\n",
      "Epoch 00052: val_acc did not improve from 0.98803\n",
      "27810/27810 [==============================] - 257s 9ms/sample - loss: 0.0330 - acc: 0.9979 - val_loss: 0.0870 - val_acc: 0.9854\n",
      "Epoch 53/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0325 - acc: 0.9981\n",
      "Epoch 00053: val_acc did not improve from 0.98803\n",
      "27810/27810 [==============================] - 260s 9ms/sample - loss: 0.0325 - acc: 0.9981 - val_loss: 0.0813 - val_acc: 0.9851\n",
      "Epoch 54/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0320 - acc: 0.9980\n",
      "Epoch 00054: val_acc did not improve from 0.98803\n",
      "27810/27810 [==============================] - 276s 10ms/sample - loss: 0.0320 - acc: 0.9980 - val_loss: 0.0838 - val_acc: 0.9861\n",
      "Epoch 55/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0318 - acc: 0.9980\n",
      "Epoch 00055: val_acc did not improve from 0.98803\n",
      "27810/27810 [==============================] - 264s 9ms/sample - loss: 0.0318 - acc: 0.9980 - val_loss: 0.0786 - val_acc: 0.9874\n",
      "Epoch 56/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0313 - acc: 0.9984\n",
      "Epoch 00056: val_acc did not improve from 0.98803\n",
      "27810/27810 [==============================] - 274s 10ms/sample - loss: 0.0313 - acc: 0.9984 - val_loss: 0.0792 - val_acc: 0.9871\n",
      "Epoch 57/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0309 - acc: 0.9984\n",
      "Epoch 00057: val_acc did not improve from 0.98803\n",
      "\n",
      "Epoch 00057: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "27810/27810 [==============================] - 275s 10ms/sample - loss: 0.0309 - acc: 0.9984 - val_loss: 0.0803 - val_acc: 0.9871\n",
      "Epoch 58/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0298 - acc: 0.9986\n",
      "Epoch 00058: val_acc improved from 0.98803 to 0.98835, saving model to M2-weights-improvement-58-0.99.hdf5\n",
      "27810/27810 [==============================] - 265s 10ms/sample - loss: 0.0298 - acc: 0.9986 - val_loss: 0.0763 - val_acc: 0.9883\n",
      "Epoch 59/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0295 - acc: 0.9989\n",
      "Epoch 00059: val_acc did not improve from 0.98835\n",
      "27810/27810 [==============================] - 266s 10ms/sample - loss: 0.0295 - acc: 0.9989 - val_loss: 0.0814 - val_acc: 0.9861\n",
      "Epoch 60/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0300 - acc: 0.9987\n",
      "Epoch 00060: val_acc did not improve from 0.98835\n",
      "27810/27810 [==============================] - 253s 9ms/sample - loss: 0.0300 - acc: 0.9987 - val_loss: 0.0778 - val_acc: 0.9877\n",
      "Epoch 61/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0295 - acc: 0.9986\n",
      "Epoch 00061: val_acc did not improve from 0.98835\n",
      "27810/27810 [==============================] - 259s 9ms/sample - loss: 0.0295 - acc: 0.9986 - val_loss: 0.0763 - val_acc: 0.9877\n",
      "Epoch 62/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0297 - acc: 0.9988\n",
      "Epoch 00062: val_acc did not improve from 0.98835\n",
      "27810/27810 [==============================] - 278s 10ms/sample - loss: 0.0297 - acc: 0.9988 - val_loss: 0.0749 - val_acc: 0.9877\n",
      "Epoch 63/200\n",
      "27810/27810 [==============================] - ETA: 0s - loss: 0.0296 - acc: 0.9988\n",
      "Epoch 00063: val_acc improved from 0.98835 to 0.98867, saving model to M2-weights-improvement-63-0.99.hdf5\n",
      "27810/27810 [==============================] - 267s 10ms/sample - loss: 0.0296 - acc: 0.9988 - val_loss: 0.0775 - val_acc: 0.9887\n",
      "Epoch 00063: early stopping\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "#model2.load_weights(\"M2-weights-improvement-23-0.70.hdf5\")\n",
    "history=model2.fit(X_train,\n",
    "          Y_train,\n",
    "          batch_size=batch_size,\n",
    "          validation_split=0.1,\n",
    "          validation_data=None,\n",
    "          shuffle=True,\n",
    "          epochs=epochs,\n",
    "          callbacks=callbacks_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**绘制acc 和loss**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Bitstream Vera Sans'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Bitstream Vera Sans'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Bitstream Vera Sans'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "########画图\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics\n",
    "acc = history.history['acc']     #获取训练集准确性数据\n",
    "val_acc = history.history['val_acc']    #获取验证集准确性数据\n",
    "loss = history.history['loss']          #获取训练集错误值数据\n",
    "val_loss = history.history['val_loss']  #获取验证集错误值数据\n",
    "font = {\n",
    "    'family' : 'Bitstream Vera Sans',\n",
    "    'weight' : 'bold',\n",
    "    'size'   : 18\n",
    "}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "width = 12\n",
    "height = 8\n",
    "plt.figure(figsize=(width, height))\n",
    "epochs = range(1,len(acc)+1)\n",
    "plt.plot(epochs,acc,'r',label='Trainning acc',linewidth=2)     #以epochs为横坐标，以训练集准确性为纵坐标\n",
    "plt.plot(epochs,val_acc,'b--',label='Vaildation acc',linewidth=2) #以epochs为横坐标，以验证集准确性为纵坐标\n",
    "plt.legend(loc='best', shadow=True)   #绘制图例，即标明图中的线段代表何种含义\n",
    "plt.title(\"Training session's progress over epochs\")\n",
    "plt.ylabel('Training Progress (Accuracy values)')\n",
    "plt.xlabel('Training epochs')\n",
    "\n",
    "plt.figure(figsize=(width, height))\n",
    "plt.plot(epochs,loss,'r',label='Trainning loss',linewidth=2)\n",
    "plt.plot(epochs,val_loss,'b--',label='Vaildation loss',linewidth=2)\n",
    "plt.legend(loc='best', shadow=True)   #绘制图例，即标明图中的线段代表何种含义\n",
    "plt.title(\"Training session's progress over epochs\")\n",
    "plt.ylabel('Training Progress (Loss values)')\n",
    "plt.xlabel('Training epochs') \n",
    "plt.show()    #显示所有图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "plt.rcParams[\"font.family\"] = 'DejaVu Sans'\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=90)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 99.13%\n",
      "WARNING:tensorflow:From <ipython-input-12-2256ef933b4f>:9: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
      "Instructions for updating:\n",
      "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "(3434,)\n"
     ]
    }
   ],
   "source": [
    "#model2.load_weights(\"M2-weights-improvement-45-0.97.hdf5\")\n",
    "model2.compile(loss='sparse_categorical_crossentropy',\n",
    "              optimizer='Adam',\n",
    "              metrics=['accuracy'])\n",
    "scores = model2.evaluate(X_test, Y_test, verbose=0)\n",
    "print(\"%s: %.2f%%\" % (model2.metrics_names[1], scores[1]*100))\n",
    "\n",
    "# confusion matrix\n",
    "Y_pred=model2.predict_classes(X_test)\n",
    "print(Y_pred.shape)\n",
    "# Y_pred=np.transpose([Y_pred])\n",
    "# print(Y_pred.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Confusion Matrix |\n",
      "--------------------\n",
      "\n",
      " [[1356    2    5]\n",
      " [   3 1316    7]\n",
      " [   6    7  732]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Classifiction Report |\n",
      "-------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1363\n",
      "           1       0.99      0.99      0.99      1326\n",
      "           2       0.98      0.98      0.98       745\n",
      "\n",
      "    accuracy                           0.99      3434\n",
      "   macro avg       0.99      0.99      0.99      3434\n",
      "weighted avg       0.99      0.99      0.99      3434\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm_cmap=plt.cm.Greens\n",
    "cm = confusion_matrix(Y_test,Y_pred)\n",
    "\n",
    "\n",
    "print('| Confusion Matrix |')\n",
    "print('--------------------')\n",
    "print('\\n {}'.format(cm))\n",
    "class_labels=['Pre','Mid','Post']\n",
    "# plot confusin matrix\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.grid(b=False)\n",
    "plot_confusion_matrix(cm, classes=class_labels, normalize=True, title='Normalized confusion matrix', cmap = cm_cmap)\n",
    "plt.show()\n",
    "class_labels=[0,1,2]\n",
    "# get classification report\n",
    "print('| Classifiction Report |')\n",
    "print('-------------------------')\n",
    "classification_report = metrics.classification_report(Y_test,Y_pred,labels =class_labels)\n",
    "# store report in results\n",
    "\n",
    "\n",
    "print(classification_report)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}