LSTM+Attention-3Classes-0%overlap-Acc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM-3Classes-Acc"
   ]
  },
  {
   "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": [
      "(17174, 200, 3)\n",
      "(17174, 1)\n"
     ]
    }
   ],
   "source": [
    "#只取前三列Acc数据\n",
    "X_train=X_train[:,:,0:3]#只取前三列Acc数据\n",
    "print(X_train.shape)\n",
    "print(Y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15456, 200, 3)\n",
      "(15456, 1)\n",
      "(1718, 200, 3)\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": 5,
   "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": 6,
   "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": 7,
   "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": 8,
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 200, 128)          67584     \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: 117,123\n",
      "Trainable params: 117,123\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": 10,
   "metadata": {},
   "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: 1.1331 - acc: 0.4125WARNING: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 0.44761, saving model to M2-weights-improvement-01-0.45.hdf5\n",
      "13910/13910 [==============================] - 60s 4ms/sample - loss: 1.1331 - acc: 0.4125 - val_loss: 1.0612 - val_acc: 0.4476\n",
      "Epoch 2/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 1.0589 - acc: 0.4569\n",
      "Epoch 00002: val_acc improved from 0.44761 to 0.49871, saving model to M2-weights-improvement-02-0.50.hdf5\n",
      "13910/13910 [==============================] - 63s 5ms/sample - loss: 1.0589 - acc: 0.4569 - val_loss: 1.0135 - val_acc: 0.4987\n",
      "Epoch 3/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 1.0089 - acc: 0.4886\n",
      "Epoch 00003: val_acc did not improve from 0.49871\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 1.0089 - acc: 0.4886 - val_loss: 1.0466 - val_acc: 0.4741\n",
      "Epoch 4/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.9584 - acc: 0.5263\n",
      "Epoch 00004: val_acc improved from 0.49871 to 0.56339, saving model to M2-weights-improvement-04-0.56.hdf5\n",
      "13910/13910 [==============================] - 63s 5ms/sample - loss: 0.9584 - acc: 0.5263 - val_loss: 0.9038 - val_acc: 0.5634\n",
      "Epoch 5/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.9067 - acc: 0.5586\n",
      "Epoch 00005: val_acc improved from 0.56339 to 0.57050, saving model to M2-weights-improvement-05-0.57.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.9067 - acc: 0.5586 - val_loss: 0.8650 - val_acc: 0.5705\n",
      "Epoch 6/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.8703 - acc: 0.5776\n",
      "Epoch 00006: val_acc improved from 0.57050 to 0.66106, saving model to M2-weights-improvement-06-0.66.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.8703 - acc: 0.5776 - val_loss: 0.7713 - val_acc: 0.6611\n",
      "Epoch 7/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.8293 - acc: 0.6096\n",
      "Epoch 00007: val_acc improved from 0.66106 to 0.67270, saving model to M2-weights-improvement-07-0.67.hdf5\n",
      "13910/13910 [==============================] - 69s 5ms/sample - loss: 0.8293 - acc: 0.6096 - val_loss: 0.7413 - val_acc: 0.6727\n",
      "Epoch 8/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.7833 - acc: 0.6432\n",
      "Epoch 00008: val_acc did not improve from 0.67270\n",
      "13910/13910 [==============================] - 73s 5ms/sample - loss: 0.7833 - acc: 0.6432 - val_loss: 1.0014 - val_acc: 0.5136\n",
      "Epoch 9/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.7673 - acc: 0.6439\n",
      "Epoch 00009: val_acc did not improve from 0.67270\n",
      "13910/13910 [==============================] - 65s 5ms/sample - loss: 0.7673 - acc: 0.6439 - val_loss: 0.8191 - val_acc: 0.6080\n",
      "Epoch 10/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.7296 - acc: 0.6673\n",
      "Epoch 00010: val_acc improved from 0.67270 to 0.70763, saving model to M2-weights-improvement-10-0.71.hdf5\n",
      "13910/13910 [==============================] - 65s 5ms/sample - loss: 0.7296 - acc: 0.6673 - val_loss: 0.6654 - val_acc: 0.7076\n",
      "Epoch 11/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.7004 - acc: 0.6840\n",
      "Epoch 00011: val_acc improved from 0.70763 to 0.70893, saving model to M2-weights-improvement-11-0.71.hdf5\n",
      "13910/13910 [==============================] - 67s 5ms/sample - loss: 0.7004 - acc: 0.6840 - val_loss: 0.6686 - val_acc: 0.7089\n",
      "Epoch 12/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.6871 - acc: 0.6907\n",
      "Epoch 00012: val_acc improved from 0.70893 to 0.71345, saving model to M2-weights-improvement-12-0.71.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.6871 - acc: 0.6907 - val_loss: 0.6550 - val_acc: 0.7135\n",
      "Epoch 13/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.6531 - acc: 0.7094\n",
      "Epoch 00013: val_acc did not improve from 0.71345\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.6531 - acc: 0.7094 - val_loss: 0.6551 - val_acc: 0.6960\n",
      "Epoch 14/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.6436 - acc: 0.7124\n",
      "Epoch 00014: val_acc improved from 0.71345 to 0.74191, saving model to M2-weights-improvement-14-0.74.hdf5\n",
      "13910/13910 [==============================] - 66s 5ms/sample - loss: 0.6436 - acc: 0.7124 - val_loss: 0.6103 - val_acc: 0.7419\n",
      "Epoch 15/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.6115 - acc: 0.7354\n",
      "Epoch 00015: val_acc did not improve from 0.74191\n",
      "13910/13910 [==============================] - 66s 5ms/sample - loss: 0.6115 - acc: 0.7354 - val_loss: 0.6562 - val_acc: 0.7005\n",
      "Epoch 16/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5996 - acc: 0.7413\n",
      "Epoch 00016: val_acc did not improve from 0.74191\n",
      "13910/13910 [==============================] - 66s 5ms/sample - loss: 0.5996 - acc: 0.7413 - val_loss: 0.6356 - val_acc: 0.7270\n",
      "Epoch 17/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5745 - acc: 0.7536\n",
      "Epoch 00017: val_acc did not improve from 0.74191\n",
      "13910/13910 [==============================] - 67s 5ms/sample - loss: 0.5745 - acc: 0.7536 - val_loss: 0.6749 - val_acc: 0.6973\n",
      "Epoch 18/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5552 - acc: 0.7645\n",
      "Epoch 00018: val_acc improved from 0.74191 to 0.77555, saving model to M2-weights-improvement-18-0.78.hdf5\n",
      "13910/13910 [==============================] - 66s 5ms/sample - loss: 0.5552 - acc: 0.7645 - val_loss: 0.5457 - val_acc: 0.7755\n",
      "Epoch 19/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5396 - acc: 0.7702\n",
      "Epoch 00019: val_acc did not improve from 0.77555\n",
      "13910/13910 [==============================] - 66s 5ms/sample - loss: 0.5396 - acc: 0.7702 - val_loss: 0.5364 - val_acc: 0.7710\n",
      "Epoch 20/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5148 - acc: 0.7838\n",
      "Epoch 00020: val_acc improved from 0.77555 to 0.78331, saving model to M2-weights-improvement-20-0.78.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.5148 - acc: 0.7838 - val_loss: 0.5241 - val_acc: 0.7833\n",
      "Epoch 21/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5124 - acc: 0.7871\n",
      "Epoch 00021: val_acc improved from 0.78331 to 0.81436, saving model to M2-weights-improvement-21-0.81.hdf5\n",
      "13910/13910 [==============================] - 66s 5ms/sample - loss: 0.5124 - acc: 0.7871 - val_loss: 0.4860 - val_acc: 0.8144\n",
      "Epoch 22/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4885 - acc: 0.8042\n",
      "Epoch 00022: val_acc did not improve from 0.81436\n",
      "13910/13910 [==============================] - 66s 5ms/sample - loss: 0.4885 - acc: 0.8042 - val_loss: 0.5360 - val_acc: 0.7840\n",
      "Epoch 23/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4699 - acc: 0.8102\n",
      "Epoch 00023: val_acc did not improve from 0.81436\n",
      "13910/13910 [==============================] - 67s 5ms/sample - loss: 0.4699 - acc: 0.8102 - val_loss: 0.5002 - val_acc: 0.8027\n",
      "Epoch 24/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4614 - acc: 0.8147\n",
      "Epoch 00024: val_acc did not improve from 0.81436\n",
      "13910/13910 [==============================] - 70s 5ms/sample - loss: 0.4614 - acc: 0.8147 - val_loss: 0.7504 - val_acc: 0.7005\n",
      "Epoch 25/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4559 - acc: 0.8160\n",
      "Epoch 00025: val_acc did not improve from 0.81436\n",
      "13910/13910 [==============================] - 72s 5ms/sample - loss: 0.4559 - acc: 0.8160 - val_loss: 0.5269 - val_acc: 0.7898\n",
      "Epoch 26/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4355 - acc: 0.8252\n",
      "Epoch 00026: val_acc did not improve from 0.81436\n",
      "13910/13910 [==============================] - 65s 5ms/sample - loss: 0.4355 - acc: 0.8252 - val_loss: 0.5047 - val_acc: 0.8053\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4224 - acc: 0.8309\n",
      "Epoch 00027: val_acc improved from 0.81436 to 0.82212, saving model to M2-weights-improvement-27-0.82.hdf5\n",
      "13910/13910 [==============================] - 65s 5ms/sample - loss: 0.4224 - acc: 0.8309 - val_loss: 0.4506 - val_acc: 0.8221\n",
      "Epoch 28/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4091 - acc: 0.8420\n",
      "Epoch 00028: val_acc did not improve from 0.82212\n",
      "13910/13910 [==============================] - 71s 5ms/sample - loss: 0.4091 - acc: 0.8420 - val_loss: 0.4715 - val_acc: 0.8131\n",
      "Epoch 29/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3934 - acc: 0.8470\n",
      "Epoch 00029: val_acc did not improve from 0.82212\n",
      "13910/13910 [==============================] - 76s 5ms/sample - loss: 0.3934 - acc: 0.8470 - val_loss: 0.5054 - val_acc: 0.8014\n",
      "Epoch 30/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3879 - acc: 0.8531\n",
      "Epoch 00030: val_acc improved from 0.82212 to 0.83829, saving model to M2-weights-improvement-30-0.84.hdf5\n",
      "13910/13910 [==============================] - 68s 5ms/sample - loss: 0.3879 - acc: 0.8531 - val_loss: 0.4140 - val_acc: 0.8383\n",
      "Epoch 31/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3838 - acc: 0.8495\n",
      "Epoch 00031: val_acc did not improve from 0.83829\n",
      "13910/13910 [==============================] - 67s 5ms/sample - loss: 0.3838 - acc: 0.8495 - val_loss: 0.5346 - val_acc: 0.7775\n",
      "Epoch 32/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3727 - acc: 0.8566\n",
      "Epoch 00032: val_acc improved from 0.83829 to 0.84411, saving model to M2-weights-improvement-32-0.84.hdf5\n",
      "13910/13910 [==============================] - 67s 5ms/sample - loss: 0.3727 - acc: 0.8566 - val_loss: 0.4299 - val_acc: 0.8441\n",
      "Epoch 33/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3616 - acc: 0.8648\n",
      "Epoch 00033: val_acc did not improve from 0.84411\n",
      "13910/13910 [==============================] - 66s 5ms/sample - loss: 0.3616 - acc: 0.8648 - val_loss: 0.5209 - val_acc: 0.8092\n",
      "Epoch 34/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3536 - acc: 0.8664\n",
      "Epoch 00034: val_acc did not improve from 0.84411\n",
      "13910/13910 [==============================] - 70s 5ms/sample - loss: 0.3536 - acc: 0.8664 - val_loss: 0.4957 - val_acc: 0.7995\n",
      "Epoch 35/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3477 - acc: 0.8662\n",
      "Epoch 00035: val_acc did not improve from 0.84411\n",
      "13910/13910 [==============================] - 68s 5ms/sample - loss: 0.3477 - acc: 0.8662 - val_loss: 0.7474 - val_acc: 0.7477\n",
      "Epoch 36/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3457 - acc: 0.8693\n",
      "Epoch 00036: val_acc did not improve from 0.84411\n",
      "13910/13910 [==============================] - 67s 5ms/sample - loss: 0.3457 - acc: 0.8693 - val_loss: 0.4374 - val_acc: 0.8279\n",
      "Epoch 37/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3340 - acc: 0.8760\n",
      "Epoch 00037: val_acc improved from 0.84411 to 0.84476, saving model to M2-weights-improvement-37-0.84.hdf5\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.3340 - acc: 0.8760 - val_loss: 0.4054 - val_acc: 0.8448\n",
      "Epoch 38/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3144 - acc: 0.8822\n",
      "Epoch 00038: val_acc did not improve from 0.84476\n",
      "13910/13910 [==============================] - 68s 5ms/sample - loss: 0.3144 - acc: 0.8822 - val_loss: 0.7353 - val_acc: 0.7173\n",
      "Epoch 39/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3203 - acc: 0.8799\n",
      "Epoch 00039: val_acc did not improve from 0.84476\n",
      "13910/13910 [==============================] - 68s 5ms/sample - loss: 0.3203 - acc: 0.8799 - val_loss: 0.4576 - val_acc: 0.8364\n",
      "Epoch 40/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3009 - acc: 0.8902\n",
      "Epoch 00040: val_acc did not improve from 0.84476\n",
      "13910/13910 [==============================] - 67s 5ms/sample - loss: 0.3009 - acc: 0.8902 - val_loss: 0.4278 - val_acc: 0.8338\n",
      "Epoch 41/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3006 - acc: 0.8892\n",
      "Epoch 00041: val_acc did not improve from 0.84476\n",
      "13910/13910 [==============================] - 64s 5ms/sample - loss: 0.3006 - acc: 0.8892 - val_loss: 0.5051 - val_acc: 0.8182\n",
      "Epoch 42/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2958 - acc: 0.8925\n",
      "Epoch 00042: val_acc did not improve from 0.84476\n",
      "13910/13910 [==============================] - 63s 5ms/sample - loss: 0.2958 - acc: 0.8925 - val_loss: 0.5589 - val_acc: 0.8131\n",
      "Epoch 43/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2957 - acc: 0.8940\n",
      "Epoch 00043: val_acc improved from 0.84476 to 0.85770, saving model to M2-weights-improvement-43-0.86.hdf5\n",
      "13910/13910 [==============================] - 70s 5ms/sample - loss: 0.2957 - acc: 0.8940 - val_loss: 0.3818 - val_acc: 0.8577\n",
      "Epoch 44/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2792 - acc: 0.9006\n",
      "Epoch 00044: val_acc did not improve from 0.85770\n",
      "13910/13910 [==============================] - 75s 5ms/sample - loss: 0.2792 - acc: 0.9006 - val_loss: 0.4733 - val_acc: 0.8195\n",
      "Epoch 45/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2777 - acc: 0.9014\n",
      "Epoch 00045: val_acc did not improve from 0.85770\n",
      "13910/13910 [==============================] - 70s 5ms/sample - loss: 0.2777 - acc: 0.9014 - val_loss: 0.3860 - val_acc: 0.8571\n",
      "Epoch 46/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2701 - acc: 0.9024\n",
      "Epoch 00046: val_acc did not improve from 0.85770\n",
      "13910/13910 [==============================] - 68s 5ms/sample - loss: 0.2701 - acc: 0.9024 - val_loss: 0.4646 - val_acc: 0.8292\n",
      "Epoch 47/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2686 - acc: 0.9048\n",
      "Epoch 00047: val_acc improved from 0.85770 to 0.87128, saving model to M2-weights-improvement-47-0.87.hdf5\n",
      "13910/13910 [==============================] - 67s 5ms/sample - loss: 0.2686 - acc: 0.9048 - val_loss: 0.3641 - val_acc: 0.8713\n",
      "Epoch 48/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2559 - acc: 0.9101\n",
      "Epoch 00048: val_acc did not improve from 0.87128\n",
      "13910/13910 [==============================] - 68s 5ms/sample - loss: 0.2559 - acc: 0.9101 - val_loss: 0.5601 - val_acc: 0.8008\n",
      "Epoch 49/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2517 - acc: 0.9106\n",
      "Epoch 00049: val_acc did not improve from 0.87128\n",
      "13910/13910 [==============================] - 69s 5ms/sample - loss: 0.2517 - acc: 0.9106 - val_loss: 0.4390 - val_acc: 0.8448\n",
      "Epoch 50/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2439 - acc: 0.9156\n",
      "Epoch 00050: val_acc did not improve from 0.87128\n",
      "13910/13910 [==============================] - 80s 6ms/sample - loss: 0.2439 - acc: 0.9156 - val_loss: 0.4104 - val_acc: 0.8622\n",
      "Epoch 51/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2334 - acc: 0.9183\n",
      "Epoch 00051: val_acc did not improve from 0.87128\n",
      "13910/13910 [==============================] - 71s 5ms/sample - loss: 0.2334 - acc: 0.9183 - val_loss: 0.5172 - val_acc: 0.8182\n",
      "Epoch 52/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2314 - acc: 0.9213\n",
      "Epoch 00052: val_acc did not improve from 0.87128\n",
      "13910/13910 [==============================] - 72s 5ms/sample - loss: 0.2314 - acc: 0.9213 - val_loss: 0.3880 - val_acc: 0.8668\n",
      "Epoch 53/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2324 - acc: 0.9168\n",
      "Epoch 00053: val_acc did not improve from 0.87128\n",
      "13910/13910 [==============================] - 70s 5ms/sample - loss: 0.2324 - acc: 0.9168 - val_loss: 0.4234 - val_acc: 0.8609\n",
      "Epoch 54/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2224 - acc: 0.9212\n",
      "Epoch 00054: val_acc did not improve from 0.87128\n",
      "13910/13910 [==============================] - 69s 5ms/sample - loss: 0.2224 - acc: 0.9212 - val_loss: 0.5473 - val_acc: 0.8234\n",
      "Epoch 55/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2223 - acc: 0.9234\n",
      "Epoch 00055: val_acc did not improve from 0.87128\n",
      "\n",
      "Epoch 00055: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "13910/13910 [==============================] - 68s 5ms/sample - loss: 0.2223 - acc: 0.9234 - val_loss: 0.4384 - val_acc: 0.8571\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 56/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1552 - acc: 0.9520\n",
      "Epoch 00056: val_acc improved from 0.87128 to 0.87840, saving model to M2-weights-improvement-56-0.88.hdf5\n",
      "13910/13910 [==============================] - 70s 5ms/sample - loss: 0.1552 - acc: 0.9520 - val_loss: 0.3918 - val_acc: 0.8784\n",
      "Epoch 57/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1445 - acc: 0.9554\n",
      "Epoch 00057: val_acc did not improve from 0.87840\n",
      "13910/13910 [==============================] - 68s 5ms/sample - loss: 0.1445 - acc: 0.9554 - val_loss: 0.4204 - val_acc: 0.8648\n",
      "Epoch 58/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1383 - acc: 0.9592\n",
      "Epoch 00058: val_acc improved from 0.87840 to 0.87969, saving model to M2-weights-improvement-58-0.88.hdf5\n",
      "13910/13910 [==============================] - 70s 5ms/sample - loss: 0.1383 - acc: 0.9592 - val_loss: 0.3874 - val_acc: 0.8797\n",
      "Epoch 59/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1273 - acc: 0.9624\n",
      "Epoch 00059: val_acc did not improve from 0.87969\n",
      "13910/13910 [==============================] - 71s 5ms/sample - loss: 0.1273 - acc: 0.9624 - val_loss: 0.4116 - val_acc: 0.8784\n",
      "Epoch 60/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1288 - acc: 0.9625\n",
      "Epoch 00060: val_acc improved from 0.87969 to 0.88292, saving model to M2-weights-improvement-60-0.88.hdf5\n",
      "13910/13910 [==============================] - 68s 5ms/sample - loss: 0.1288 - acc: 0.9625 - val_loss: 0.4019 - val_acc: 0.8829\n",
      "Epoch 61/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1208 - acc: 0.9636\n",
      "Epoch 00061: val_acc did not improve from 0.88292\n",
      "13910/13910 [==============================] - 68s 5ms/sample - loss: 0.1208 - acc: 0.9636 - val_loss: 0.4065 - val_acc: 0.8816\n",
      "Epoch 00061: 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": 11,
   "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": 12,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 88.53%\n",
      "WARNING:tensorflow:From <ipython-input-13-2256ef933b4f>: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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Confusion Matrix |\n",
      "--------------------\n",
      "\n",
      " [[633  54   2]\n",
      " [ 81 550  22]\n",
      " [  1  37 338]]\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       0.89      0.92      0.90       689\n",
      "           1       0.86      0.84      0.85       653\n",
      "           2       0.93      0.90      0.92       376\n",
      "\n",
      "    accuracy                           0.89      1718\n",
      "   macro avg       0.89      0.89      0.89      1718\n",
      "weighted avg       0.89      0.89      0.89      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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "K.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}