LSTM+Attention-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",
      "last_hidden_state (Lambda)   (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "attention_score_vec (Dense)  (None, 200, 128)          16384     \n",
      "_________________________________________________________________\n",
      "attention_score (Dot)        (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "attention_weight (Activation (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "context_vector (Dot)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "attention_output (Concatenat (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "attention_vector (Dense)     (None, 128)               32768     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 3)                 387       \n",
      "=================================================================\n",
      "Total params: 120,195\n",
      "Trainable params: 120,195\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: 1.0384 - acc: 0.5226WARNING: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 0.67982, saving model to M2-weights-improvement-01-0.68.hdf5\n",
      "13910/13910 [==============================] - 60s 4ms/sample - loss: 1.0384 - acc: 0.5226 - val_loss: 0.8349 - val_acc: 0.6798\n",
      "Epoch 2/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.7883 - acc: 0.6877\n",
      "Epoch 00002: val_acc improved from 0.67982 to 0.75032, saving model to M2-weights-improvement-02-0.75.hdf5\n",
      "13910/13910 [==============================] - 61s 4ms/sample - loss: 0.7883 - acc: 0.6877 - val_loss: 0.6819 - val_acc: 0.7503\n",
      "Epoch 3/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.6388 - acc: 0.7553\n",
      "Epoch 00003: val_acc improved from 0.75032 to 0.81436, saving model to M2-weights-improvement-03-0.81.hdf5\n",
      "13910/13910 [==============================] - 62s 4ms/sample - loss: 0.6388 - acc: 0.7553 - val_loss: 0.5241 - val_acc: 0.8144\n",
      "Epoch 4/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5509 - acc: 0.7958\n",
      "Epoch 00004: val_acc improved from 0.81436 to 0.83959, saving model to M2-weights-improvement-04-0.84.hdf5\n",
      "13910/13910 [==============================] - 62s 4ms/sample - loss: 0.5509 - acc: 0.7958 - val_loss: 0.4472 - val_acc: 0.8396\n",
      "Epoch 5/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4747 - acc: 0.8245\n",
      "Epoch 00005: val_acc improved from 0.83959 to 0.85317, saving model to M2-weights-improvement-05-0.85.hdf5\n",
      "13910/13910 [==============================] - 63s 5ms/sample - loss: 0.4747 - acc: 0.8245 - val_loss: 0.3986 - val_acc: 0.8532\n",
      "Epoch 6/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4315 - acc: 0.8439\n",
      "Epoch 00006: val_acc improved from 0.85317 to 0.86223, saving model to M2-weights-improvement-06-0.86.hdf5\n",
      "13910/13910 [==============================] - 63s 5ms/sample - loss: 0.4315 - acc: 0.8439 - val_loss: 0.3822 - val_acc: 0.8622\n",
      "Epoch 7/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3825 - acc: 0.8587\n",
      "Epoch 00007: val_acc improved from 0.86223 to 0.87128, saving model to M2-weights-improvement-07-0.87.hdf5\n",
      "13910/13910 [==============================] - 63s 5ms/sample - loss: 0.3825 - acc: 0.8587 - val_loss: 0.3789 - val_acc: 0.8713\n",
      "Epoch 8/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3531 - acc: 0.8726\n",
      "Epoch 00008: val_acc improved from 0.87128 to 0.88939, saving model to M2-weights-improvement-08-0.89.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.3531 - acc: 0.8726 - val_loss: 0.3075 - val_acc: 0.8894\n",
      "Epoch 9/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3307 - acc: 0.8821\n",
      "Epoch 00009: val_acc did not improve from 0.88939\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.3307 - acc: 0.8821 - val_loss: 0.3479 - val_acc: 0.8777\n",
      "Epoch 10/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3083 - acc: 0.8891\n",
      "Epoch 00010: val_acc improved from 0.88939 to 0.89651, saving model to M2-weights-improvement-10-0.90.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.3083 - acc: 0.8891 - val_loss: 0.2776 - val_acc: 0.8965\n",
      "Epoch 11/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2857 - acc: 0.8961\n",
      "Epoch 00011: val_acc did not improve from 0.89651\n",
      "13910/13910 [==============================] - 65s 5ms/sample - loss: 0.2857 - acc: 0.8961 - val_loss: 0.2929 - val_acc: 0.8913\n",
      "Epoch 12/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2651 - acc: 0.9059\n",
      "Epoch 00012: val_acc improved from 0.89651 to 0.90944, saving model to M2-weights-improvement-12-0.91.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.2651 - acc: 0.9059 - val_loss: 0.2626 - val_acc: 0.9094\n",
      "Epoch 13/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2483 - acc: 0.9141\n",
      "Epoch 00013: val_acc improved from 0.90944 to 0.91138, saving model to M2-weights-improvement-13-0.91.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.2483 - acc: 0.9141 - val_loss: 0.2551 - val_acc: 0.9114\n",
      "Epoch 14/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2387 - acc: 0.9171\n",
      "Epoch 00014: val_acc improved from 0.91138 to 0.91785, saving model to M2-weights-improvement-14-0.92.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.2387 - acc: 0.9171 - val_loss: 0.2300 - val_acc: 0.9179\n",
      "Epoch 15/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2275 - acc: 0.9208\n",
      "Epoch 00015: val_acc improved from 0.91785 to 0.92820, saving model to M2-weights-improvement-15-0.93.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.2275 - acc: 0.9208 - val_loss: 0.2090 - val_acc: 0.9282\n",
      "Epoch 16/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2009 - acc: 0.9322\n",
      "Epoch 00016: val_acc improved from 0.92820 to 0.92885, saving model to M2-weights-improvement-16-0.93.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.2009 - acc: 0.9322 - val_loss: 0.2067 - val_acc: 0.9288\n",
      "Epoch 17/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1956 - acc: 0.9353\n",
      "Epoch 00017: val_acc did not improve from 0.92885\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.1956 - acc: 0.9353 - val_loss: 0.2120 - val_acc: 0.9230\n",
      "Epoch 18/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1853 - acc: 0.9387\n",
      "Epoch 00018: val_acc improved from 0.92885 to 0.94049, saving model to M2-weights-improvement-18-0.94.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.1853 - acc: 0.9387 - val_loss: 0.1804 - val_acc: 0.9405\n",
      "Epoch 19/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1727 - acc: 0.9424\n",
      "Epoch 00019: val_acc did not improve from 0.94049\n",
      "13910/13910 [==============================] - 71s 5ms/sample - loss: 0.1727 - acc: 0.9424 - val_loss: 0.2345 - val_acc: 0.9263\n",
      "Epoch 20/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1607 - acc: 0.9487\n",
      "Epoch 00020: val_acc improved from 0.94049 to 0.94955, saving model to M2-weights-improvement-20-0.95.hdf5\n",
      "13910/13910 [==============================] - 66s 5ms/sample - loss: 0.1607 - acc: 0.9487 - val_loss: 0.1603 - val_acc: 0.9495\n",
      "Epoch 21/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1560 - acc: 0.9508\n",
      "Epoch 00021: val_acc did not improve from 0.94955\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.1560 - acc: 0.9508 - val_loss: 0.1744 - val_acc: 0.9411\n",
      "Epoch 22/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1511 - acc: 0.9520\n",
      "Epoch 00022: val_acc did not improve from 0.94955\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.1511 - acc: 0.9520 - val_loss: 0.1800 - val_acc: 0.9398\n",
      "Epoch 23/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1427 - acc: 0.9551\n",
      "Epoch 00023: val_acc did not improve from 0.94955\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.1427 - acc: 0.9551 - val_loss: 0.1676 - val_acc: 0.9444\n",
      "Epoch 24/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1391 - acc: 0.9564\n",
      "Epoch 00024: val_acc did not improve from 0.94955\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.1391 - acc: 0.9564 - val_loss: 0.1821 - val_acc: 0.9424\n",
      "Epoch 25/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1416 - acc: 0.9554\n",
      "Epoch 00025: val_acc did not improve from 0.94955\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.1416 - acc: 0.9554 - val_loss: 0.1865 - val_acc: 0.9386\n",
      "Epoch 26/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1310 - acc: 0.9597\n",
      "Epoch 00026: val_acc improved from 0.94955 to 0.95408, saving model to M2-weights-improvement-26-0.95.hdf5\n",
      "13910/13910 [==============================] - 66s 5ms/sample - loss: 0.1310 - acc: 0.9597 - val_loss: 0.1393 - val_acc: 0.9541\n",
      "Epoch 27/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1233 - acc: 0.9634\n",
      "Epoch 00027: val_acc improved from 0.95408 to 0.95537, saving model to M2-weights-improvement-27-0.96.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.1233 - acc: 0.9634 - val_loss: 0.1437 - val_acc: 0.9554\n",
      "Epoch 28/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1221 - acc: 0.9624\n",
      "Epoch 00028: val_acc did not improve from 0.95537\n",
      "13910/13910 [==============================] - 63s 5ms/sample - loss: 0.1221 - acc: 0.9624 - val_loss: 0.1829 - val_acc: 0.9347\n",
      "Epoch 29/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1207 - acc: 0.9647\n",
      "Epoch 00029: val_acc improved from 0.95537 to 0.96248, saving model to M2-weights-improvement-29-0.96.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.1207 - acc: 0.9647 - val_loss: 0.1349 - val_acc: 0.9625\n",
      "Epoch 30/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1147 - acc: 0.9649\n",
      "Epoch 00030: val_acc did not improve from 0.96248\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.1147 - acc: 0.9649 - val_loss: 0.1456 - val_acc: 0.9605\n",
      "Epoch 31/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1071 - acc: 0.9678\n",
      "Epoch 00031: val_acc did not improve from 0.96248\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.1071 - acc: 0.9678 - val_loss: 0.1640 - val_acc: 0.9528\n",
      "Epoch 32/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1138 - acc: 0.9664\n",
      "Epoch 00032: val_acc did not improve from 0.96248\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.1138 - acc: 0.9664 - val_loss: 0.1298 - val_acc: 0.9580\n",
      "Epoch 33/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1013 - acc: 0.9719\n",
      "Epoch 00033: val_acc did not improve from 0.96248\n",
      "13910/13910 [==============================] - 65s 5ms/sample - loss: 0.1013 - acc: 0.9719 - val_loss: 0.1785 - val_acc: 0.9463\n",
      "Epoch 34/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1050 - acc: 0.9691\n",
      "Epoch 00034: val_acc did not improve from 0.96248\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.1050 - acc: 0.9691 - val_loss: 0.1669 - val_acc: 0.9515\n",
      "Epoch 35/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0990 - acc: 0.9712\n",
      "Epoch 00035: val_acc improved from 0.96248 to 0.96313, saving model to M2-weights-improvement-35-0.96.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0990 - acc: 0.9712 - val_loss: 0.1264 - val_acc: 0.9631\n",
      "Epoch 36/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0933 - acc: 0.9729\n",
      "Epoch 00036: val_acc did not improve from 0.96313\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0933 - acc: 0.9729 - val_loss: 0.1415 - val_acc: 0.9618\n",
      "Epoch 37/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0966 - acc: 0.9724\n",
      "Epoch 00037: val_acc did not improve from 0.96313\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0966 - acc: 0.9724 - val_loss: 0.1285 - val_acc: 0.9592\n",
      "Epoch 38/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0950 - acc: 0.9721\n",
      "Epoch 00038: val_acc did not improve from 0.96313\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0950 - acc: 0.9721 - val_loss: 0.1555 - val_acc: 0.9528\n",
      "Epoch 39/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0881 - acc: 0.9760\n",
      "Epoch 00039: val_acc did not improve from 0.96313\n",
      "13910/13910 [==============================] - 63s 5ms/sample - loss: 0.0881 - acc: 0.9760 - val_loss: 0.1332 - val_acc: 0.9612\n",
      "Epoch 40/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0902 - acc: 0.9753\n",
      "Epoch 00040: val_acc improved from 0.96313 to 0.96895, saving model to M2-weights-improvement-40-0.97.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0902 - acc: 0.9753 - val_loss: 0.1200 - val_acc: 0.9690\n",
      "Epoch 41/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0850 - acc: 0.9768\n",
      "Epoch 00041: val_acc did not improve from 0.96895\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0850 - acc: 0.9768 - val_loss: 0.1341 - val_acc: 0.9599\n",
      "Epoch 42/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0853 - acc: 0.9766\n",
      "Epoch 00042: val_acc did not improve from 0.96895\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0853 - acc: 0.9766 - val_loss: 0.1203 - val_acc: 0.9690\n",
      "Epoch 43/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0787 - acc: 0.9782\n",
      "Epoch 00043: val_acc did not improve from 0.96895\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0787 - acc: 0.9782 - val_loss: 0.1424 - val_acc: 0.9567\n",
      "Epoch 44/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0843 - acc: 0.9763\n",
      "Epoch 00044: val_acc improved from 0.96895 to 0.96960, saving model to M2-weights-improvement-44-0.97.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0843 - acc: 0.9763 - val_loss: 0.1071 - val_acc: 0.9696\n",
      "Epoch 45/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0827 - acc: 0.9776\n",
      "Epoch 00045: val_acc did not improve from 0.96960\n",
      "13910/13910 [==============================] - 65s 5ms/sample - loss: 0.0827 - acc: 0.9776 - val_loss: 0.1254 - val_acc: 0.9586\n",
      "Epoch 46/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0804 - acc: 0.9789\n",
      "Epoch 00046: val_acc improved from 0.96960 to 0.97025, saving model to M2-weights-improvement-46-0.97.hdf5\n",
      "13910/13910 [==============================] - 66s 5ms/sample - loss: 0.0804 - acc: 0.9789 - val_loss: 0.1037 - val_acc: 0.9702\n",
      "Epoch 47/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0727 - acc: 0.9799\n",
      "Epoch 00047: val_acc did not improve from 0.97025\n",
      "13910/13910 [==============================] - 65s 5ms/sample - loss: 0.0727 - acc: 0.9799 - val_loss: 0.1101 - val_acc: 0.9644\n",
      "Epoch 48/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0783 - acc: 0.9786\n",
      "Epoch 00048: val_acc did not improve from 0.97025\n",
      "13910/13910 [==============================] - 71s 5ms/sample - loss: 0.0783 - acc: 0.9786 - val_loss: 0.1683 - val_acc: 0.9515\n",
      "Epoch 49/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0738 - acc: 0.9807\n",
      "Epoch 00049: val_acc improved from 0.97025 to 0.97154, saving model to M2-weights-improvement-49-0.97.hdf5\n",
      "13910/13910 [==============================] - 63s 5ms/sample - loss: 0.0738 - acc: 0.9807 - val_loss: 0.1052 - val_acc: 0.9715\n",
      "Epoch 50/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0742 - acc: 0.9809\n",
      "Epoch 00050: val_acc did not improve from 0.97154\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0742 - acc: 0.9809 - val_loss: 0.1078 - val_acc: 0.9690\n",
      "Epoch 51/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0692 - acc: 0.9820\n",
      "Epoch 00051: val_acc did not improve from 0.97154\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0692 - acc: 0.9820 - val_loss: 0.1055 - val_acc: 0.9709\n",
      "Epoch 52/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0704 - acc: 0.9819\n",
      "Epoch 00052: val_acc did not improve from 0.97154\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0704 - acc: 0.9819 - val_loss: 0.1098 - val_acc: 0.9696\n",
      "Epoch 53/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0699 - acc: 0.9825\n",
      "Epoch 00053: val_acc did not improve from 0.97154\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0699 - acc: 0.9825 - val_loss: 0.1414 - val_acc: 0.9618\n",
      "Epoch 54/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0680 - acc: 0.9819\n",
      "Epoch 00054: val_acc did not improve from 0.97154\n",
      "\n",
      "Epoch 00054: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0680 - acc: 0.9819 - val_loss: 0.1205 - val_acc: 0.9638\n",
      "Epoch 55/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0460 - acc: 0.9908\n",
      "Epoch 00055: val_acc improved from 0.97154 to 0.97801, saving model to M2-weights-improvement-55-0.98.hdf5\n",
      "13910/13910 [==============================] - 66s 5ms/sample - loss: 0.0460 - acc: 0.9908 - val_loss: 0.0930 - val_acc: 0.9780\n",
      "Epoch 56/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0421 - acc: 0.9918\n",
      "Epoch 00056: val_acc improved from 0.97801 to 0.97930, saving model to M2-weights-improvement-56-0.98.hdf5\n",
      "13910/13910 [==============================] - 63s 5ms/sample - loss: 0.0421 - acc: 0.9918 - val_loss: 0.0887 - val_acc: 0.9793\n",
      "Epoch 57/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0389 - acc: 0.9929\n",
      "Epoch 00057: val_acc did not improve from 0.97930\n",
      "13910/13910 [==============================] - 63s 5ms/sample - loss: 0.0389 - acc: 0.9929 - val_loss: 0.0970 - val_acc: 0.9748\n",
      "Epoch 58/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0374 - acc: 0.9939\n",
      "Epoch 00058: val_acc did not improve from 0.97930\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0374 - acc: 0.9939 - val_loss: 0.0914 - val_acc: 0.9767\n",
      "Epoch 59/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0360 - acc: 0.9942\n",
      "Epoch 00059: val_acc did not improve from 0.97930\n",
      "13910/13910 [==============================] - 63s 5ms/sample - loss: 0.0360 - acc: 0.9942 - val_loss: 0.0980 - val_acc: 0.9741\n",
      "Epoch 60/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0373 - acc: 0.9933\n",
      "Epoch 00060: val_acc did not improve from 0.97930\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0373 - acc: 0.9933 - val_loss: 0.1050 - val_acc: 0.9735\n",
      "Epoch 61/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0370 - acc: 0.9936\n",
      "Epoch 00061: val_acc did not improve from 0.97930\n",
      "13910/13910 [==============================] - 65s 5ms/sample - loss: 0.0370 - acc: 0.9936 - val_loss: 0.0916 - val_acc: 0.9754\n",
      "Epoch 62/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0351 - acc: 0.9949\n",
      "Epoch 00062: val_acc did not improve from 0.97930\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0351 - acc: 0.9949 - val_loss: 0.0973 - val_acc: 0.9780\n",
      "Epoch 63/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0337 - acc: 0.9947\n",
      "Epoch 00063: val_acc did not improve from 0.97930\n",
      "13910/13910 [==============================] - 65s 5ms/sample - loss: 0.0337 - acc: 0.9947 - val_loss: 0.0946 - val_acc: 0.9787\n",
      "Epoch 64/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0329 - acc: 0.9956\n",
      "Epoch 00064: val_acc did not improve from 0.97930\n",
      "\n",
      "Epoch 00064: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0329 - acc: 0.9956 - val_loss: 0.0932 - val_acc: 0.9767\n",
      "Epoch 65/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0301 - acc: 0.9960\n",
      "Epoch 00065: val_acc improved from 0.97930 to 0.98060, saving model to M2-weights-improvement-65-0.98.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0301 - acc: 0.9960 - val_loss: 0.0920 - val_acc: 0.9806\n",
      "Epoch 66/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0316 - acc: 0.9948\n",
      "Epoch 00066: val_acc did not improve from 0.98060\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0316 - acc: 0.9948 - val_loss: 0.0973 - val_acc: 0.9735\n",
      "Epoch 67/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0295 - acc: 0.9967\n",
      "Epoch 00067: val_acc did not improve from 0.98060\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0295 - acc: 0.9967 - val_loss: 0.0944 - val_acc: 0.9761\n",
      "Epoch 68/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0301 - acc: 0.9961\n",
      "Epoch 00068: val_acc did not improve from 0.98060\n",
      "13910/13910 [==============================] - 63s 5ms/sample - loss: 0.0301 - acc: 0.9961 - val_loss: 0.0956 - val_acc: 0.9754\n",
      "Epoch 69/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0283 - acc: 0.9965\n",
      "Epoch 00069: val_acc did not improve from 0.98060\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0283 - acc: 0.9965 - val_loss: 0.0980 - val_acc: 0.9767\n",
      "Epoch 70/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0277 - acc: 0.9973\n",
      "Epoch 00070: val_acc did not improve from 0.98060\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.0277 - acc: 0.9973 - val_loss: 0.0933 - val_acc: 0.9774\n",
      "Epoch 00070: 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: 97.15%\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   2   3]\n",
      " [  2 638  13]\n",
      " [  9  20 347]]\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       0.98      0.99      0.99       689\n",
      "           1       0.97      0.98      0.97       653\n",
      "           2       0.96      0.92      0.94       376\n",
      "\n",
      "    accuracy                           0.97      1718\n",
      "   macro avg       0.97      0.96      0.97      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",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}