DouLSTM-3Classes-0%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": [
      "(17174, 200, 9)\n",
      "(17174, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train_mat = scio.loadmat('X_train-3Classes-0%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-0%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": [
      "(15456, 200, 9)\n",
      "(15456, 1)\n",
      "(1718, 200, 9)\n",
      "(1718, 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 13910 samples, validate on 1546 samples\n",
      "Epoch 1/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.9744 - acc: 0.5625WARNING: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 0.70440, saving model to M2-weights-improvement-01-0.70.hdf5\n",
      "13910/13910 [==============================] - 124s 9ms/sample - loss: 0.9744 - acc: 0.5625 - val_loss: 0.7700 - val_acc: 0.7044\n",
      "Epoch 2/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.6633 - acc: 0.7551\n",
      "Epoch 00002: val_acc improved from 0.70440 to 0.75162, saving model to M2-weights-improvement-02-0.75.hdf5\n",
      "13910/13910 [==============================] - 157s 11ms/sample - loss: 0.6633 - acc: 0.7551 - val_loss: 0.6554 - val_acc: 0.7516\n",
      "Epoch 3/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5378 - acc: 0.8060\n",
      "Epoch 00003: val_acc improved from 0.75162 to 0.82859, saving model to M2-weights-improvement-03-0.83.hdf5\n",
      "13910/13910 [==============================] - 148s 11ms/sample - loss: 0.5378 - acc: 0.8060 - val_loss: 0.4908 - val_acc: 0.8286\n",
      "Epoch 4/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4759 - acc: 0.8332\n",
      "Epoch 00004: val_acc improved from 0.82859 to 0.85640, saving model to M2-weights-improvement-04-0.86.hdf5\n",
      "13910/13910 [==============================] - 148s 11ms/sample - loss: 0.4759 - acc: 0.8332 - val_loss: 0.4158 - val_acc: 0.8564\n",
      "Epoch 5/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4184 - acc: 0.8585\n",
      "Epoch 00005: val_acc did not improve from 0.85640\n",
      "13910/13910 [==============================] - 145s 10ms/sample - loss: 0.4184 - acc: 0.8585 - val_loss: 0.4226 - val_acc: 0.8558\n",
      "Epoch 6/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3745 - acc: 0.8771\n",
      "Epoch 00006: val_acc improved from 0.85640 to 0.88357, saving model to M2-weights-improvement-06-0.88.hdf5\n",
      "13910/13910 [==============================] - 150s 11ms/sample - loss: 0.3745 - acc: 0.8771 - val_loss: 0.3774 - val_acc: 0.8836\n",
      "Epoch 7/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3542 - acc: 0.8840\n",
      "Epoch 00007: val_acc did not improve from 0.88357\n",
      "13910/13910 [==============================] - 150s 11ms/sample - loss: 0.3542 - acc: 0.8840 - val_loss: 0.4109 - val_acc: 0.8745\n",
      "Epoch 8/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3243 - acc: 0.8974\n",
      "Epoch 00008: val_acc improved from 0.88357 to 0.89004, saving model to M2-weights-improvement-08-0.89.hdf5\n",
      "13910/13910 [==============================] - 149s 11ms/sample - loss: 0.3243 - acc: 0.8974 - val_loss: 0.3360 - val_acc: 0.8900\n",
      "Epoch 9/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3115 - acc: 0.9037\n",
      "Epoch 00009: val_acc improved from 0.89004 to 0.89845, saving model to M2-weights-improvement-09-0.90.hdf5\n",
      "13910/13910 [==============================] - 147s 11ms/sample - loss: 0.3115 - acc: 0.9037 - val_loss: 0.3431 - val_acc: 0.8984\n",
      "Epoch 10/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2833 - acc: 0.9129\n",
      "Epoch 00010: val_acc did not improve from 0.89845\n",
      "13910/13910 [==============================] - 149s 11ms/sample - loss: 0.2833 - acc: 0.9129 - val_loss: 0.3513 - val_acc: 0.8803\n",
      "Epoch 11/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2692 - acc: 0.9195\n",
      "Epoch 00011: val_acc did not improve from 0.89845\n",
      "13910/13910 [==============================] - 149s 11ms/sample - loss: 0.2692 - acc: 0.9195 - val_loss: 0.3546 - val_acc: 0.8797\n",
      "Epoch 12/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2484 - acc: 0.9257\n",
      "Epoch 00012: val_acc did not improve from 0.89845\n",
      "13910/13910 [==============================] - 146s 10ms/sample - loss: 0.2484 - acc: 0.9257 - val_loss: 0.3329 - val_acc: 0.8946\n",
      "Epoch 13/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2357 - acc: 0.9299\n",
      "Epoch 00013: val_acc improved from 0.89845 to 0.91915, saving model to M2-weights-improvement-13-0.92.hdf5\n",
      "13910/13910 [==============================] - 146s 10ms/sample - loss: 0.2357 - acc: 0.9299 - val_loss: 0.2546 - val_acc: 0.9191\n",
      "Epoch 14/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2281 - acc: 0.9321\n",
      "Epoch 00014: val_acc improved from 0.91915 to 0.92691, saving model to M2-weights-improvement-14-0.93.hdf5\n",
      "13910/13910 [==============================] - 147s 11ms/sample - loss: 0.2281 - acc: 0.9321 - val_loss: 0.2553 - val_acc: 0.9269\n",
      "Epoch 15/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2089 - acc: 0.9410\n",
      "Epoch 00015: val_acc did not improve from 0.92691\n",
      "13910/13910 [==============================] - 147s 11ms/sample - loss: 0.2089 - acc: 0.9410 - val_loss: 0.2606 - val_acc: 0.9237\n",
      "Epoch 16/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2004 - acc: 0.9418\n",
      "Epoch 00016: val_acc did not improve from 0.92691\n",
      "13910/13910 [==============================] - 130s 9ms/sample - loss: 0.2004 - acc: 0.9418 - val_loss: 0.2324 - val_acc: 0.9237\n",
      "Epoch 17/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1903 - acc: 0.9463\n",
      "Epoch 00017: val_acc improved from 0.92691 to 0.94308, saving model to M2-weights-improvement-17-0.94.hdf5\n",
      "13910/13910 [==============================] - 112s 8ms/sample - loss: 0.1903 - acc: 0.9463 - val_loss: 0.2064 - val_acc: 0.9431\n",
      "Epoch 18/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1794 - acc: 0.9493\n",
      "Epoch 00018: val_acc did not improve from 0.94308\n",
      "13910/13910 [==============================] - 113s 8ms/sample - loss: 0.1794 - acc: 0.9493 - val_loss: 0.2404 - val_acc: 0.9269\n",
      "Epoch 19/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1737 - acc: 0.9520\n",
      "Epoch 00019: val_acc did not improve from 0.94308\n",
      "13910/13910 [==============================] - 118s 8ms/sample - loss: 0.1737 - acc: 0.9520 - val_loss: 0.2356 - val_acc: 0.9373\n",
      "Epoch 20/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1629 - acc: 0.9561\n",
      "Epoch 00020: val_acc did not improve from 0.94308\n",
      "13910/13910 [==============================] - 112s 8ms/sample - loss: 0.1629 - acc: 0.9561 - val_loss: 0.2538 - val_acc: 0.9263\n",
      "Epoch 21/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1562 - acc: 0.9582\n",
      "Epoch 00021: val_acc did not improve from 0.94308\n",
      "13910/13910 [==============================] - 112s 8ms/sample - loss: 0.1562 - acc: 0.9582 - val_loss: 0.2713 - val_acc: 0.9250\n",
      "Epoch 22/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1526 - acc: 0.9602\n",
      "Epoch 00022: val_acc did not improve from 0.94308\n",
      "13910/13910 [==============================] - 114s 8ms/sample - loss: 0.1526 - acc: 0.9602 - val_loss: 0.2808 - val_acc: 0.9224\n",
      "Epoch 23/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1537 - acc: 0.9589\n",
      "Epoch 00023: val_acc improved from 0.94308 to 0.94631, saving model to M2-weights-improvement-23-0.95.hdf5\n",
      "13910/13910 [==============================] - 114s 8ms/sample - loss: 0.1537 - acc: 0.9589 - val_loss: 0.1874 - val_acc: 0.9463\n",
      "Epoch 24/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1428 - acc: 0.9623\n",
      "Epoch 00024: val_acc did not improve from 0.94631\n",
      "13910/13910 [==============================] - 113s 8ms/sample - loss: 0.1428 - acc: 0.9623 - val_loss: 0.2406 - val_acc: 0.9334\n",
      "Epoch 25/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1351 - acc: 0.9648\n",
      "Epoch 00025: val_acc improved from 0.94631 to 0.95149, saving model to M2-weights-improvement-25-0.95.hdf5\n",
      "13910/13910 [==============================] - 114s 8ms/sample - loss: 0.1351 - acc: 0.9648 - val_loss: 0.1912 - val_acc: 0.9515\n",
      "Epoch 26/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1333 - acc: 0.9667\n",
      "Epoch 00026: val_acc did not improve from 0.95149\n",
      "13910/13910 [==============================] - 114s 8ms/sample - loss: 0.1333 - acc: 0.9667 - val_loss: 0.2200 - val_acc: 0.9405\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1326 - acc: 0.9666\n",
      "Epoch 00027: val_acc improved from 0.95149 to 0.96313, saving model to M2-weights-improvement-27-0.96.hdf5\n",
      "13910/13910 [==============================] - 113s 8ms/sample - loss: 0.1326 - acc: 0.9666 - val_loss: 0.1444 - val_acc: 0.9631\n",
      "Epoch 28/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1251 - acc: 0.9692\n",
      "Epoch 00028: val_acc did not improve from 0.96313\n",
      "13910/13910 [==============================] - 114s 8ms/sample - loss: 0.1251 - acc: 0.9692 - val_loss: 0.2692 - val_acc: 0.9204\n",
      "Epoch 29/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1227 - acc: 0.9691\n",
      "Epoch 00029: val_acc did not improve from 0.96313\n",
      "13910/13910 [==============================] - 114s 8ms/sample - loss: 0.1227 - acc: 0.9691 - val_loss: 0.1933 - val_acc: 0.9534\n",
      "Epoch 30/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1211 - acc: 0.9721\n",
      "Epoch 00030: val_acc did not improve from 0.96313\n",
      "13910/13910 [==============================] - 113s 8ms/sample - loss: 0.1211 - acc: 0.9721 - val_loss: 0.1649 - val_acc: 0.9573\n",
      "Epoch 31/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1229 - acc: 0.9702\n",
      "Epoch 00031: val_acc did not improve from 0.96313\n",
      "13910/13910 [==============================] - 114s 8ms/sample - loss: 0.1229 - acc: 0.9702 - val_loss: 0.1500 - val_acc: 0.9618\n",
      "Epoch 32/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1132 - acc: 0.9735\n",
      "Epoch 00032: val_acc did not improve from 0.96313\n",
      "13910/13910 [==============================] - 114s 8ms/sample - loss: 0.1132 - acc: 0.9735 - val_loss: 0.1793 - val_acc: 0.9495\n",
      "Epoch 33/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1144 - acc: 0.9728\n",
      "Epoch 00033: val_acc did not improve from 0.96313\n",
      "13910/13910 [==============================] - 114s 8ms/sample - loss: 0.1144 - acc: 0.9728 - val_loss: 0.1658 - val_acc: 0.9612\n",
      "Epoch 34/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1088 - acc: 0.9748\n",
      "Epoch 00034: val_acc did not improve from 0.96313\n",
      "13910/13910 [==============================] - 121s 9ms/sample - loss: 0.1088 - acc: 0.9748 - val_loss: 0.2822 - val_acc: 0.9276\n",
      "Epoch 35/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1019 - acc: 0.9775\n",
      "Epoch 00035: val_acc did not improve from 0.96313\n",
      "\n",
      "Epoch 00035: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "13910/13910 [==============================] - 113s 8ms/sample - loss: 0.1019 - acc: 0.9775 - val_loss: 0.2848 - val_acc: 0.9321\n",
      "Epoch 36/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0760 - acc: 0.9859\n",
      "Epoch 00036: val_acc improved from 0.96313 to 0.97413, saving model to M2-weights-improvement-36-0.97.hdf5\n",
      "13910/13910 [==============================] - 115s 8ms/sample - loss: 0.0760 - acc: 0.9859 - val_loss: 0.1286 - val_acc: 0.9741\n",
      "Epoch 37/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0619 - acc: 0.9909\n",
      "Epoch 00037: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 116s 8ms/sample - loss: 0.0619 - acc: 0.9909 - val_loss: 0.1405 - val_acc: 0.9690\n",
      "Epoch 38/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0589 - acc: 0.9919\n",
      "Epoch 00038: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 115s 8ms/sample - loss: 0.0589 - acc: 0.9919 - val_loss: 0.1550 - val_acc: 0.9664\n",
      "Epoch 39/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0589 - acc: 0.9922\n",
      "Epoch 00039: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 116s 8ms/sample - loss: 0.0589 - acc: 0.9922 - val_loss: 0.1241 - val_acc: 0.9722\n",
      "Epoch 40/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0562 - acc: 0.9932\n",
      "Epoch 00040: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 116s 8ms/sample - loss: 0.0562 - acc: 0.9932 - val_loss: 0.1404 - val_acc: 0.9696\n",
      "Epoch 41/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0530 - acc: 0.9937\n",
      "Epoch 00041: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 115s 8ms/sample - loss: 0.0530 - acc: 0.9937 - val_loss: 0.1585 - val_acc: 0.9657\n",
      "Epoch 42/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0518 - acc: 0.9941\n",
      "Epoch 00042: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 116s 8ms/sample - loss: 0.0518 - acc: 0.9941 - val_loss: 0.1511 - val_acc: 0.9702\n",
      "Epoch 43/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0527 - acc: 0.9944\n",
      "Epoch 00043: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 115s 8ms/sample - loss: 0.0527 - acc: 0.9944 - val_loss: 0.1450 - val_acc: 0.9690\n",
      "Epoch 44/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0499 - acc: 0.9953\n",
      "Epoch 00044: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 117s 8ms/sample - loss: 0.0499 - acc: 0.9953 - val_loss: 0.1619 - val_acc: 0.9664\n",
      "Epoch 45/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0511 - acc: 0.9948\n",
      "Epoch 00045: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 116s 8ms/sample - loss: 0.0511 - acc: 0.9948 - val_loss: 0.1456 - val_acc: 0.9702\n",
      "Epoch 46/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0475 - acc: 0.9955\n",
      "Epoch 00046: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 118s 8ms/sample - loss: 0.0475 - acc: 0.9955 - val_loss: 0.1382 - val_acc: 0.9709\n",
      "Epoch 47/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0505 - acc: 0.9948\n",
      "Epoch 00047: val_acc did not improve from 0.97413\n",
      "\n",
      "Epoch 00047: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "13910/13910 [==============================] - 115s 8ms/sample - loss: 0.0505 - acc: 0.9948 - val_loss: 0.1617 - val_acc: 0.9670\n",
      "Epoch 48/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0449 - acc: 0.9967\n",
      "Epoch 00048: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 117s 8ms/sample - loss: 0.0449 - acc: 0.9967 - val_loss: 0.1657 - val_acc: 0.9670\n",
      "Epoch 49/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0429 - acc: 0.9972\n",
      "Epoch 00049: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 121s 9ms/sample - loss: 0.0429 - acc: 0.9972 - val_loss: 0.1593 - val_acc: 0.9690\n",
      "Epoch 50/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0435 - acc: 0.9976\n",
      "Epoch 00050: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 118s 8ms/sample - loss: 0.0435 - acc: 0.9976 - val_loss: 0.1529 - val_acc: 0.9696\n",
      "Epoch 51/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0419 - acc: 0.9970\n",
      "Epoch 00051: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 116s 8ms/sample - loss: 0.0419 - acc: 0.9970 - val_loss: 0.1550 - val_acc: 0.9709\n",
      "Epoch 52/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0423 - acc: 0.9976\n",
      "Epoch 00052: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 117s 8ms/sample - loss: 0.0423 - acc: 0.9976 - val_loss: 0.1618 - val_acc: 0.9677\n",
      "Epoch 53/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0420 - acc: 0.9976\n",
      "Epoch 00053: val_acc did not improve from 0.97413\n",
      "13910/13910 [==============================] - 115s 8ms/sample - loss: 0.0420 - acc: 0.9976 - val_loss: 0.1720 - val_acc: 0.9677\n",
      "Epoch 00053: 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: 96.68%\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",
      "(1718,)\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",
      " [[684   0   5]\n",
      " [  1 635  17]\n",
      " [  8  26 342]]\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       689\n",
      "           1       0.96      0.97      0.97       653\n",
      "           2       0.94      0.91      0.92       376\n",
      "\n",
      "    accuracy                           0.97      1718\n",
      "   macro avg       0.96      0.96      0.96      1718\n",
      "weighted avg       0.97      0.97      0.97      1718\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",
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