LSTM+Attention-3Classes-0%overlap-Gro.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[:,:,3:6]#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: 1.1311 - acc: 0.3993WARNING: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 0.40362, saving model to M2-weights-improvement-01-0.40.hdf5\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 1.1311 - acc: 0.3993 - val_loss: 1.1097 - val_acc: 0.4036\n",
      "Epoch 2/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 1.0680 - acc: 0.4347\n",
      "Epoch 00002: val_acc improved from 0.40362 to 0.44373, saving model to M2-weights-improvement-02-0.44.hdf5\n",
      "13910/13910 [==============================] - 82s 6ms/sample - loss: 1.0680 - acc: 0.4347 - val_loss: 1.0941 - val_acc: 0.4437\n",
      "Epoch 3/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 1.0273 - acc: 0.4674\n",
      "Epoch 00003: val_acc improved from 0.44373 to 0.46507, saving model to M2-weights-improvement-03-0.47.hdf5\n",
      "13910/13910 [==============================] - 97s 7ms/sample - loss: 1.0273 - acc: 0.4674 - val_loss: 1.0231 - val_acc: 0.4651\n",
      "Epoch 4/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.9958 - acc: 0.4899\n",
      "Epoch 00004: val_acc improved from 0.46507 to 0.52458, saving model to M2-weights-improvement-04-0.52.hdf5\n",
      "13910/13910 [==============================] - 92s 7ms/sample - loss: 0.9958 - acc: 0.4899 - val_loss: 0.9392 - val_acc: 0.5246\n",
      "Epoch 5/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.9616 - acc: 0.5095\n",
      "Epoch 00005: val_acc improved from 0.52458 to 0.54657, saving model to M2-weights-improvement-05-0.55.hdf5\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.9616 - acc: 0.5095 - val_loss: 0.9203 - val_acc: 0.5466\n",
      "Epoch 6/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.9315 - acc: 0.5325\n",
      "Epoch 00006: val_acc did not improve from 0.54657\n",
      "13910/13910 [==============================] - 80s 6ms/sample - loss: 0.9315 - acc: 0.5325 - val_loss: 0.9867 - val_acc: 0.4942\n",
      "Epoch 7/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.9048 - acc: 0.5569\n",
      "Epoch 00007: val_acc improved from 0.54657 to 0.56598, saving model to M2-weights-improvement-07-0.57.hdf5\n",
      "13910/13910 [==============================] - 80s 6ms/sample - loss: 0.9048 - acc: 0.5569 - val_loss: 0.8619 - val_acc: 0.5660\n",
      "Epoch 8/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.8767 - acc: 0.5725\n",
      "Epoch 00008: val_acc did not improve from 0.56598\n",
      "13910/13910 [==============================] - 80s 6ms/sample - loss: 0.8767 - acc: 0.5725 - val_loss: 0.8737 - val_acc: 0.5569\n",
      "Epoch 9/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.8444 - acc: 0.6006\n",
      "Epoch 00009: val_acc improved from 0.56598 to 0.62484, saving model to M2-weights-improvement-09-0.62.hdf5\n",
      "13910/13910 [==============================] - 82s 6ms/sample - loss: 0.8444 - acc: 0.6006 - val_loss: 0.8118 - val_acc: 0.6248\n",
      "Epoch 10/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.8251 - acc: 0.6117\n",
      "Epoch 00010: val_acc did not improve from 0.62484\n",
      "13910/13910 [==============================] - 83s 6ms/sample - loss: 0.8251 - acc: 0.6117 - val_loss: 0.8393 - val_acc: 0.5906\n",
      "Epoch 11/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.7984 - acc: 0.6322\n",
      "Epoch 00011: val_acc improved from 0.62484 to 0.64230, saving model to M2-weights-improvement-11-0.64.hdf5\n",
      "13910/13910 [==============================] - 79s 6ms/sample - loss: 0.7984 - acc: 0.6322 - val_loss: 0.8287 - val_acc: 0.6423\n",
      "Epoch 12/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.7763 - acc: 0.6423\n",
      "Epoch 00012: val_acc improved from 0.64230 to 0.65201, saving model to M2-weights-improvement-12-0.65.hdf5\n",
      "13910/13910 [==============================] - 80s 6ms/sample - loss: 0.7763 - acc: 0.6423 - val_loss: 0.7652 - val_acc: 0.6520\n",
      "Epoch 13/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.7434 - acc: 0.6619\n",
      "Epoch 00013: val_acc did not improve from 0.65201\n",
      "13910/13910 [==============================] - 80s 6ms/sample - loss: 0.7434 - acc: 0.6619 - val_loss: 0.7852 - val_acc: 0.6494\n",
      "Epoch 14/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.7228 - acc: 0.6769\n",
      "Epoch 00014: val_acc improved from 0.65201 to 0.67206, saving model to M2-weights-improvement-14-0.67.hdf5\n",
      "13910/13910 [==============================] - 80s 6ms/sample - loss: 0.7228 - acc: 0.6769 - val_loss: 0.7464 - val_acc: 0.6721\n",
      "Epoch 15/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.7057 - acc: 0.6919\n",
      "Epoch 00015: val_acc improved from 0.67206 to 0.68305, saving model to M2-weights-improvement-15-0.68.hdf5\n",
      "13910/13910 [==============================] - 80s 6ms/sample - loss: 0.7057 - acc: 0.6919 - val_loss: 0.7366 - val_acc: 0.6831\n",
      "Epoch 16/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.6846 - acc: 0.7052\n",
      "Epoch 00016: val_acc improved from 0.68305 to 0.71604, saving model to M2-weights-improvement-16-0.72.hdf5\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.6846 - acc: 0.7052 - val_loss: 0.6885 - val_acc: 0.7160\n",
      "Epoch 17/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.6578 - acc: 0.7229\n",
      "Epoch 00017: val_acc did not improve from 0.71604\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.6578 - acc: 0.7229 - val_loss: 0.6975 - val_acc: 0.7057\n",
      "Epoch 18/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.6449 - acc: 0.7270\n",
      "Epoch 00018: val_acc did not improve from 0.71604\n",
      "13910/13910 [==============================] - 79s 6ms/sample - loss: 0.6449 - acc: 0.7270 - val_loss: 0.7845 - val_acc: 0.6785\n",
      "Epoch 19/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.6205 - acc: 0.7387\n",
      "Epoch 00019: val_acc did not improve from 0.71604\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.6205 - acc: 0.7387 - val_loss: 0.8558 - val_acc: 0.6656\n",
      "Epoch 20/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.6042 - acc: 0.7508\n",
      "Epoch 00020: val_acc improved from 0.71604 to 0.74515, saving model to M2-weights-improvement-20-0.75.hdf5\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.6042 - acc: 0.7508 - val_loss: 0.6582 - val_acc: 0.7451\n",
      "Epoch 21/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.6005 - acc: 0.7504\n",
      "Epoch 00021: val_acc did not improve from 0.74515\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.6005 - acc: 0.7504 - val_loss: 0.6861 - val_acc: 0.7096\n",
      "Epoch 22/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5809 - acc: 0.7579\n",
      "Epoch 00022: val_acc did not improve from 0.74515\n",
      "13910/13910 [==============================] - 79s 6ms/sample - loss: 0.5809 - acc: 0.7579 - val_loss: 0.6997 - val_acc: 0.7070\n",
      "Epoch 23/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5624 - acc: 0.7668\n",
      "Epoch 00023: val_acc did not improve from 0.74515\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.5624 - acc: 0.7668 - val_loss: 0.6985 - val_acc: 0.7160\n",
      "Epoch 24/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5527 - acc: 0.7742\n",
      "Epoch 00024: val_acc did not improve from 0.74515\n",
      "13910/13910 [==============================] - 79s 6ms/sample - loss: 0.5527 - acc: 0.7742 - val_loss: 0.7346 - val_acc: 0.7025\n",
      "Epoch 25/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5413 - acc: 0.7820\n",
      "Epoch 00025: val_acc improved from 0.74515 to 0.76585, saving model to M2-weights-improvement-25-0.77.hdf5\n",
      "13910/13910 [==============================] - 79s 6ms/sample - loss: 0.5413 - acc: 0.7820 - val_loss: 0.5751 - val_acc: 0.7658\n",
      "Epoch 26/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5227 - acc: 0.7895\n",
      "Epoch 00026: val_acc did not improve from 0.76585\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.5227 - acc: 0.7895 - val_loss: 0.6145 - val_acc: 0.7406\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5195 - acc: 0.7949\n",
      "Epoch 00027: val_acc did not improve from 0.76585\n",
      "13910/13910 [==============================] - 79s 6ms/sample - loss: 0.5195 - acc: 0.7949 - val_loss: 0.5694 - val_acc: 0.7607\n",
      "Epoch 28/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5086 - acc: 0.7948\n",
      "Epoch 00028: val_acc improved from 0.76585 to 0.76649, saving model to M2-weights-improvement-28-0.77.hdf5\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.5086 - acc: 0.7948 - val_loss: 0.5636 - val_acc: 0.7665\n",
      "Epoch 29/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5046 - acc: 0.7953\n",
      "Epoch 00029: val_acc did not improve from 0.76649\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.5046 - acc: 0.7953 - val_loss: 0.5899 - val_acc: 0.7652\n",
      "Epoch 30/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4849 - acc: 0.8070\n",
      "Epoch 00030: val_acc improved from 0.76649 to 0.79819, saving model to M2-weights-improvement-30-0.80.hdf5\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.4849 - acc: 0.8070 - val_loss: 0.5451 - val_acc: 0.7982\n",
      "Epoch 31/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4731 - acc: 0.8114\n",
      "Epoch 00031: val_acc did not improve from 0.79819\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.4731 - acc: 0.8114 - val_loss: 0.5835 - val_acc: 0.7749\n",
      "Epoch 32/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4727 - acc: 0.8138\n",
      "Epoch 00032: val_acc did not improve from 0.79819\n",
      "13910/13910 [==============================] - 85s 6ms/sample - loss: 0.4727 - acc: 0.8138 - val_loss: 0.5829 - val_acc: 0.7671\n",
      "Epoch 33/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4533 - acc: 0.8178\n",
      "Epoch 00033: val_acc did not improve from 0.79819\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.4533 - acc: 0.8178 - val_loss: 0.5935 - val_acc: 0.7587\n",
      "Epoch 34/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4517 - acc: 0.8196\n",
      "Epoch 00034: val_acc did not improve from 0.79819\n",
      "13910/13910 [==============================] - 79s 6ms/sample - loss: 0.4517 - acc: 0.8196 - val_loss: 0.5578 - val_acc: 0.7762\n",
      "Epoch 35/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4466 - acc: 0.8239\n",
      "Epoch 00035: val_acc did not improve from 0.79819\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.4466 - acc: 0.8239 - val_loss: 0.5617 - val_acc: 0.7827\n",
      "Epoch 36/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4429 - acc: 0.8253\n",
      "Epoch 00036: val_acc did not improve from 0.79819\n",
      "13910/13910 [==============================] - 79s 6ms/sample - loss: 0.4429 - acc: 0.8253 - val_loss: 0.5561 - val_acc: 0.7723\n",
      "Epoch 37/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4318 - acc: 0.8291\n",
      "Epoch 00037: val_acc did not improve from 0.79819\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.4318 - acc: 0.8291 - val_loss: 0.5846 - val_acc: 0.7762\n",
      "Epoch 38/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4163 - acc: 0.8365\n",
      "Epoch 00038: val_acc did not improve from 0.79819\n",
      "\n",
      "Epoch 00038: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.4163 - acc: 0.8365 - val_loss: 0.5690 - val_acc: 0.7853\n",
      "Epoch 39/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3649 - acc: 0.8584\n",
      "Epoch 00039: val_acc improved from 0.79819 to 0.82018, saving model to M2-weights-improvement-39-0.82.hdf5\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.3649 - acc: 0.8584 - val_loss: 0.4862 - val_acc: 0.8202\n",
      "Epoch 40/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3258 - acc: 0.8763\n",
      "Epoch 00040: val_acc improved from 0.82018 to 0.82277, saving model to M2-weights-improvement-40-0.82.hdf5\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.3258 - acc: 0.8763 - val_loss: 0.4635 - val_acc: 0.8228\n",
      "Epoch 41/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3214 - acc: 0.8789\n",
      "Epoch 00041: val_acc improved from 0.82277 to 0.83182, saving model to M2-weights-improvement-41-0.83.hdf5\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.3214 - acc: 0.8789 - val_loss: 0.4644 - val_acc: 0.8318\n",
      "Epoch 42/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3091 - acc: 0.8831\n",
      "Epoch 00042: val_acc did not improve from 0.83182\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.3091 - acc: 0.8831 - val_loss: 0.4593 - val_acc: 0.8305\n",
      "Epoch 43/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3022 - acc: 0.8858\n",
      "Epoch 00043: val_acc improved from 0.83182 to 0.83829, saving model to M2-weights-improvement-43-0.84.hdf5\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.3022 - acc: 0.8858 - val_loss: 0.4424 - val_acc: 0.8383\n",
      "Epoch 44/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3009 - acc: 0.8871\n",
      "Epoch 00044: val_acc did not improve from 0.83829\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.3009 - acc: 0.8871 - val_loss: 0.4458 - val_acc: 0.8376\n",
      "Epoch 45/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2992 - acc: 0.8851\n",
      "Epoch 00045: val_acc improved from 0.83829 to 0.84153, saving model to M2-weights-improvement-45-0.84.hdf5\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.2992 - acc: 0.8851 - val_loss: 0.4567 - val_acc: 0.8415\n",
      "Epoch 46/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2885 - acc: 0.8927\n",
      "Epoch 00046: val_acc improved from 0.84153 to 0.84282, saving model to M2-weights-improvement-46-0.84.hdf5\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.2885 - acc: 0.8927 - val_loss: 0.4506 - val_acc: 0.8428\n",
      "Epoch 47/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2837 - acc: 0.8922\n",
      "Epoch 00047: val_acc did not improve from 0.84282\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.2837 - acc: 0.8922 - val_loss: 0.4576 - val_acc: 0.8338\n",
      "Epoch 48/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2822 - acc: 0.8950\n",
      "Epoch 00048: val_acc did not improve from 0.84282\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.2822 - acc: 0.8950 - val_loss: 0.4427 - val_acc: 0.8344\n",
      "Epoch 49/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2773 - acc: 0.8975\n",
      "Epoch 00049: val_acc did not improve from 0.84282\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.2773 - acc: 0.8975 - val_loss: 0.4592 - val_acc: 0.8344\n",
      "Epoch 50/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2759 - acc: 0.8984\n",
      "Epoch 00050: val_acc did not improve from 0.84282\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.2759 - acc: 0.8984 - val_loss: 0.4743 - val_acc: 0.8396\n",
      "Epoch 51/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2676 - acc: 0.8999\n",
      "Epoch 00051: val_acc did not improve from 0.84282\n",
      "\n",
      "Epoch 00051: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.2676 - acc: 0.8999 - val_loss: 0.4574 - val_acc: 0.8376\n",
      "Epoch 52/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2509 - acc: 0.9083\n",
      "Epoch 00052: val_acc improved from 0.84282 to 0.85058, saving model to M2-weights-improvement-52-0.85.hdf5\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.2509 - acc: 0.9083 - val_loss: 0.4359 - val_acc: 0.8506\n",
      "Epoch 53/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2437 - acc: 0.9129\n",
      "Epoch 00053: val_acc improved from 0.85058 to 0.85252, saving model to M2-weights-improvement-53-0.85.hdf5\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.2437 - acc: 0.9129 - val_loss: 0.4428 - val_acc: 0.8525\n",
      "Epoch 54/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2451 - acc: 0.9112\n",
      "Epoch 00054: val_acc improved from 0.85252 to 0.85511, saving model to M2-weights-improvement-54-0.86.hdf5\n",
      "13910/13910 [==============================] - 80s 6ms/sample - loss: 0.2451 - acc: 0.9112 - val_loss: 0.4406 - val_acc: 0.8551\n",
      "Epoch 55/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2380 - acc: 0.9143\n",
      "Epoch 00055: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 83s 6ms/sample - loss: 0.2380 - acc: 0.9143 - val_loss: 0.4509 - val_acc: 0.8519\n",
      "Epoch 56/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2377 - acc: 0.9124\n",
      "Epoch 00056: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.2377 - acc: 0.9124 - val_loss: 0.4427 - val_acc: 0.8512\n",
      "Epoch 57/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2332 - acc: 0.9177\n",
      "Epoch 00057: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.2332 - acc: 0.9177 - val_loss: 0.4432 - val_acc: 0.8506\n",
      "Epoch 58/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2379 - acc: 0.9159\n",
      "Epoch 00058: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.2379 - acc: 0.9159 - val_loss: 0.4451 - val_acc: 0.8493\n",
      "Epoch 59/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2372 - acc: 0.9150\n",
      "Epoch 00059: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.2372 - acc: 0.9150 - val_loss: 0.4429 - val_acc: 0.8512\n",
      "Epoch 60/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2319 - acc: 0.9168\n",
      "Epoch 00060: val_acc did not improve from 0.85511\n",
      "\n",
      "Epoch 00060: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.2319 - acc: 0.9168 - val_loss: 0.4399 - val_acc: 0.8519\n",
      "Epoch 61/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2252 - acc: 0.9207\n",
      "Epoch 00061: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.2252 - acc: 0.9207 - val_loss: 0.4420 - val_acc: 0.8519\n",
      "Epoch 62/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2244 - acc: 0.9196\n",
      "Epoch 00062: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.2244 - acc: 0.9196 - val_loss: 0.4447 - val_acc: 0.8525\n",
      "Epoch 63/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2239 - acc: 0.9219\n",
      "Epoch 00063: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.2239 - acc: 0.9219 - val_loss: 0.4475 - val_acc: 0.8551\n",
      "Epoch 64/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2247 - acc: 0.9224\n",
      "Epoch 00064: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 78s 6ms/sample - loss: 0.2247 - acc: 0.9224 - val_loss: 0.4433 - val_acc: 0.8525\n",
      "Epoch 65/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2276 - acc: 0.9185\n",
      "Epoch 00065: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 77s 6ms/sample - loss: 0.2276 - acc: 0.9185 - val_loss: 0.4447 - val_acc: 0.8538\n",
      "Epoch 66/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2201 - acc: 0.9221\n",
      "Epoch 00066: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 79s 6ms/sample - loss: 0.2201 - acc: 0.9221 - val_loss: 0.4440 - val_acc: 0.8545\n",
      "Epoch 00066: 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: 85.10%\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",
      " [[624  60   5]\n",
      " [ 65 543  45]\n",
      " [  7  74 295]]\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       0.90      0.91      0.90       689\n",
      "           1       0.80      0.83      0.82       653\n",
      "           2       0.86      0.78      0.82       376\n",
      "\n",
      "    accuracy                           0.85      1718\n",
      "   macro avg       0.85      0.84      0.85      1718\n",
      "weighted avg       0.85      0.85      0.85      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
}