CNN-LSTM-0%overlap-Acc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN-LSTM自己"
   ]
  },
  {
   "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": [
    "#只取前列数据\n",
    "X_train=X_train[:,:,0:3]#只取前\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,Input\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout,Flatten, Reshape, Softmax,concatenate\n",
    "from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPooling1D, TimeDistributed,LSTM,Bidirectional,BatchNormalization\n",
    "from keras.models import Model\n",
    "#from keras import backend as K\n",
    "from tensorflow.compat.v1.keras import backend as K\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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 3 15456\n",
      "3\n"
     ]
    }
   ],
   "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",
    "\n",
    "print(timesteps,input_dim,len(X_train))\n",
    "print(n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**LSTM Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model():\n",
    "    inp=Input(shape=(200,3))\n",
    "\n",
    "    #CNN model\n",
    "    reshape=Reshape((200,3,1))(inp)\n",
    "    conv1=Conv2D(32,(18,1),activation='relu',strides=(2,1),padding='SAME',input_shape=(200,3,1))(reshape)\n",
    "    maxp1=MaxPooling2D(pool_size=(2, 1))(conv1)\n",
    "    conv2=Conv2D(64, (9, 1), activation='relu',padding='SAME')(maxp1)\n",
    "    conv3=Conv2D(128, (3, 1), activation='relu',padding='SAME')(conv2)\n",
    "    maxp3=MaxPooling2D(pool_size=(2, 1))(conv3)\n",
    "    conv4=Conv2D(128, (1, 3), activation='relu', padding='VALID')(maxp3)\n",
    "    cnn_out=Flatten()(conv4)\n",
    "\n",
    "\n",
    "    #LSTM model\n",
    "    lstm1=LSTM(64, return_sequences=True, input_shape=(timesteps, input_dim), kernel_initializer='glorot_normal')(inp)\n",
    "    # Adding a dropout layer\n",
    "    #model2.add(Dropout(0.25))\n",
    "    batch1=BatchNormalization()(lstm1)\n",
    "    lstm2=LSTM(64)(batch1)\n",
    "    # Adding a dropout layer\n",
    "    lstm_out=Dropout(0.5)(lstm2)\n",
    "\n",
    "    m2=concatenate([cnn_out,lstm_out],axis=1)\n",
    "\n",
    "    m2_out=Dense(n_classes, activation='softmax',kernel_regularizer=regularizers.l2(0.01),\n",
    "                    activity_regularizer=regularizers.l1(0.01))(m2)\n",
    "    model=Model(inputs=inp,outputs=m2_out)\n",
    "    model.summary()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 200, 3)]     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, 200, 3, 1)    0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 100, 3, 32)   608         reshape[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 50, 3, 32)    0           conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 50, 3, 64)    18496       max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 50, 3, 128)   24704       conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lstm (LSTM)                     (None, 200, 64)      17408       input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 25, 3, 128)   0           conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 200, 64)      256         lstm[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 25, 1, 128)   49280       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "lstm_1 (LSTM)                   (None, 64)           33024       batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 3200)         0           conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 64)           0           lstm_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 3264)         0           flatten[0][0]                    \n",
      "                                                                 dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 3)            9795        concatenate[0][0]                \n",
      "==================================================================================================\n",
      "Total params: 153,571\n",
      "Trainable params: 153,443\n",
      "Non-trainable params: 128\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#-------------------\n",
    "# Compiling the model\n",
    "model=get_model()\n",
    "model.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_acc', patience=16, verbose=2)\n",
    "rp=ReduceLROnPlateau(\n",
    "    monitor='val_acc',\n",
    "    factor=0.25,\n",
    "    patience=10,#多少epoch，如果monitor内容没有效果就earlystop\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": 11,
   "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: 3.9580 - acc: 0.4145WARNING: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 0.43273, saving model to M2-weights-improvement-01-0.43.hdf5\n",
      "13910/13910 [==============================] - 101s 7ms/sample - loss: 3.9580 - acc: 0.4145 - val_loss: 1.0707 - val_acc: 0.4327\n",
      "Epoch 2/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 1.0201 - acc: 0.4940\n",
      "Epoch 00002: val_acc improved from 0.43273 to 0.51940, saving model to M2-weights-improvement-02-0.52.hdf5\n",
      "13910/13910 [==============================] - 102s 7ms/sample - loss: 1.0201 - acc: 0.4940 - val_loss: 0.9634 - val_acc: 0.5194\n",
      "Epoch 3/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.8942 - acc: 0.5771\n",
      "Epoch 00003: val_acc did not improve from 0.51940\n",
      "13910/13910 [==============================] - 99s 7ms/sample - loss: 0.8942 - acc: 0.5771 - val_loss: 1.0230 - val_acc: 0.4832\n",
      "Epoch 4/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.7915 - acc: 0.6332\n",
      "Epoch 00004: val_acc improved from 0.51940 to 0.68305, saving model to M2-weights-improvement-04-0.68.hdf5\n",
      "13910/13910 [==============================] - 110s 8ms/sample - loss: 0.7915 - acc: 0.6332 - val_loss: 0.7111 - val_acc: 0.6831\n",
      "Epoch 5/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.6978 - acc: 0.6961\n",
      "Epoch 00005: val_acc did not improve from 0.68305\n",
      "13910/13910 [==============================] - 99s 7ms/sample - loss: 0.6978 - acc: 0.6961 - val_loss: 0.7761 - val_acc: 0.6527\n",
      "Epoch 6/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.6353 - acc: 0.7231\n",
      "Epoch 00006: val_acc improved from 0.68305 to 0.75420, saving model to M2-weights-improvement-06-0.75.hdf5\n",
      "13910/13910 [==============================] - 97s 7ms/sample - loss: 0.6353 - acc: 0.7231 - val_loss: 0.6107 - val_acc: 0.7542\n",
      "Epoch 7/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5811 - acc: 0.7473\n",
      "Epoch 00007: val_acc improved from 0.75420 to 0.77878, saving model to M2-weights-improvement-07-0.78.hdf5\n",
      "13910/13910 [==============================] - 98s 7ms/sample - loss: 0.5811 - acc: 0.7473 - val_loss: 0.5572 - val_acc: 0.7788\n",
      "Epoch 8/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.5333 - acc: 0.7782\n",
      "Epoch 00008: val_acc did not improve from 0.77878\n",
      "13910/13910 [==============================] - 107s 8ms/sample - loss: 0.5333 - acc: 0.7782 - val_loss: 0.6585 - val_acc: 0.7290\n",
      "Epoch 9/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4887 - acc: 0.7950\n",
      "Epoch 00009: val_acc did not improve from 0.77878\n",
      "13910/13910 [==============================] - 106s 8ms/sample - loss: 0.4887 - acc: 0.7950 - val_loss: 0.6926 - val_acc: 0.7238\n",
      "Epoch 10/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4570 - acc: 0.8164\n",
      "Epoch 00010: val_acc improved from 0.77878 to 0.78590, saving model to M2-weights-improvement-10-0.79.hdf5\n",
      "13910/13910 [==============================] - 101s 7ms/sample - loss: 0.4570 - acc: 0.8164 - val_loss: 0.5554 - val_acc: 0.7859\n",
      "Epoch 11/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.4133 - acc: 0.8410\n",
      "Epoch 00011: val_acc improved from 0.78590 to 0.82018, saving model to M2-weights-improvement-11-0.82.hdf5\n",
      "13910/13910 [==============================] - 102s 7ms/sample - loss: 0.4133 - acc: 0.8410 - val_loss: 0.4703 - val_acc: 0.8202\n",
      "Epoch 12/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3914 - acc: 0.8555\n",
      "Epoch 00012: val_acc did not improve from 0.82018\n",
      "13910/13910 [==============================] - 101s 7ms/sample - loss: 0.3914 - acc: 0.8555 - val_loss: 0.4989 - val_acc: 0.8092\n",
      "Epoch 13/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3533 - acc: 0.8740\n",
      "Epoch 00013: val_acc did not improve from 0.82018\n",
      "13910/13910 [==============================] - 103s 7ms/sample - loss: 0.3533 - acc: 0.8740 - val_loss: 0.5525 - val_acc: 0.7917\n",
      "Epoch 14/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3445 - acc: 0.8753\n",
      "Epoch 00014: val_acc improved from 0.82018 to 0.83959, saving model to M2-weights-improvement-14-0.84.hdf5\n",
      "13910/13910 [==============================] - 106s 8ms/sample - loss: 0.3445 - acc: 0.8753 - val_loss: 0.4432 - val_acc: 0.8396\n",
      "Epoch 15/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.3208 - acc: 0.8868\n",
      "Epoch 00015: val_acc did not improve from 0.83959\n",
      "13910/13910 [==============================] - 108s 8ms/sample - loss: 0.3208 - acc: 0.8868 - val_loss: 0.4680 - val_acc: 0.8176\n",
      "Epoch 16/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2788 - acc: 0.9095\n",
      "Epoch 00016: val_acc did not improve from 0.83959\n",
      "13910/13910 [==============================] - 108s 8ms/sample - loss: 0.2788 - acc: 0.9095 - val_loss: 0.6153 - val_acc: 0.7904\n",
      "Epoch 17/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2749 - acc: 0.9105\n",
      "Epoch 00017: val_acc did not improve from 0.83959\n",
      "13910/13910 [==============================] - 111s 8ms/sample - loss: 0.2749 - acc: 0.9105 - val_loss: 0.6004 - val_acc: 0.7885\n",
      "Epoch 18/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2578 - acc: 0.9177\n",
      "Epoch 00018: val_acc did not improve from 0.83959\n",
      "13910/13910 [==============================] - 109s 8ms/sample - loss: 0.2578 - acc: 0.9177 - val_loss: 0.7478 - val_acc: 0.7264\n",
      "Epoch 19/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2449 - acc: 0.9244\n",
      "Epoch 00019: val_acc improved from 0.83959 to 0.84153, saving model to M2-weights-improvement-19-0.84.hdf5\n",
      "13910/13910 [==============================] - 112s 8ms/sample - loss: 0.2449 - acc: 0.9244 - val_loss: 0.4650 - val_acc: 0.8415\n",
      "Epoch 20/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2314 - acc: 0.9311\n",
      "Epoch 00020: val_acc did not improve from 0.84153\n",
      "13910/13910 [==============================] - 110s 8ms/sample - loss: 0.2314 - acc: 0.9311 - val_loss: 0.4599 - val_acc: 0.8266\n",
      "Epoch 21/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2018 - acc: 0.9458\n",
      "Epoch 00021: val_acc did not improve from 0.84153\n",
      "13910/13910 [==============================] - 113s 8ms/sample - loss: 0.2018 - acc: 0.9458 - val_loss: 0.5567 - val_acc: 0.8085\n",
      "Epoch 22/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1948 - acc: 0.9487\n",
      "Epoch 00022: val_acc did not improve from 0.84153\n",
      "13910/13910 [==============================] - 110s 8ms/sample - loss: 0.1948 - acc: 0.9487 - val_loss: 0.7955 - val_acc: 0.7555\n",
      "Epoch 23/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.2021 - acc: 0.9440\n",
      "Epoch 00023: val_acc did not improve from 0.84153\n",
      "13910/13910 [==============================] - 113s 8ms/sample - loss: 0.2021 - acc: 0.9440 - val_loss: 0.4644 - val_acc: 0.8357\n",
      "Epoch 24/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1683 - acc: 0.9644\n",
      "Epoch 00024: val_acc did not improve from 0.84153\n",
      "13910/13910 [==============================] - 112s 8ms/sample - loss: 0.1683 - acc: 0.9644 - val_loss: 0.5162 - val_acc: 0.8266\n",
      "Epoch 25/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1640 - acc: 0.9617\n",
      "Epoch 00025: val_acc did not improve from 0.84153\n",
      "13910/13910 [==============================] - 117s 8ms/sample - loss: 0.1640 - acc: 0.9617 - val_loss: 0.6452 - val_acc: 0.8118\n",
      "Epoch 26/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1719 - acc: 0.9561\n",
      "Epoch 00026: val_acc did not improve from 0.84153\n",
      "13910/13910 [==============================] - 110s 8ms/sample - loss: 0.1719 - acc: 0.9561 - val_loss: 0.5009 - val_acc: 0.8305\n",
      "Epoch 27/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1535 - acc: 0.9653\n",
      "Epoch 00027: val_acc did not improve from 0.84153\n",
      "13910/13910 [==============================] - 110s 8ms/sample - loss: 0.1535 - acc: 0.9653 - val_loss: 0.6440 - val_acc: 0.7904\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1386 - acc: 0.9717\n",
      "Epoch 00028: val_acc improved from 0.84153 to 0.85252, saving model to M2-weights-improvement-28-0.85.hdf5\n",
      "13910/13910 [==============================] - 104s 7ms/sample - loss: 0.1386 - acc: 0.9717 - val_loss: 0.4976 - val_acc: 0.8525\n",
      "Epoch 29/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1484 - acc: 0.9678\n",
      "Epoch 00029: val_acc did not improve from 0.85252\n",
      "13910/13910 [==============================] - 82s 6ms/sample - loss: 0.1484 - acc: 0.9678 - val_loss: 0.5232 - val_acc: 0.8454\n",
      "Epoch 30/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1575 - acc: 0.9666\n",
      "Epoch 00030: val_acc did not improve from 0.85252\n",
      "13910/13910 [==============================] - 84s 6ms/sample - loss: 0.1575 - acc: 0.9666 - val_loss: 0.4674 - val_acc: 0.8409\n",
      "Epoch 31/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1200 - acc: 0.9764\n",
      "Epoch 00031: val_acc did not improve from 0.85252\n",
      "13910/13910 [==============================] - 83s 6ms/sample - loss: 0.1200 - acc: 0.9764 - val_loss: 0.5377 - val_acc: 0.8428\n",
      "Epoch 32/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1422 - acc: 0.9721\n",
      "Epoch 00032: val_acc did not improve from 0.85252\n",
      "13910/13910 [==============================] - 84s 6ms/sample - loss: 0.1422 - acc: 0.9721 - val_loss: 0.5403 - val_acc: 0.8467\n",
      "Epoch 33/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1361 - acc: 0.9728\n",
      "Epoch 00033: val_acc did not improve from 0.85252\n",
      "13910/13910 [==============================] - 83s 6ms/sample - loss: 0.1361 - acc: 0.9728 - val_loss: 0.5505 - val_acc: 0.8053\n",
      "Epoch 34/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1138 - acc: 0.9771\n",
      "Epoch 00034: val_acc did not improve from 0.85252\n",
      "13910/13910 [==============================] - 83s 6ms/sample - loss: 0.1138 - acc: 0.9771 - val_loss: 0.5189 - val_acc: 0.8467\n",
      "Epoch 35/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1151 - acc: 0.9771\n",
      "Epoch 00035: val_acc did not improve from 0.85252\n",
      "13910/13910 [==============================] - 87s 6ms/sample - loss: 0.1151 - acc: 0.9771 - val_loss: 1.0458 - val_acc: 0.7005\n",
      "Epoch 36/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1307 - acc: 0.9764\n",
      "Epoch 00036: val_acc did not improve from 0.85252\n",
      "13910/13910 [==============================] - 88s 6ms/sample - loss: 0.1307 - acc: 0.9764 - val_loss: 0.4908 - val_acc: 0.8519\n",
      "Epoch 37/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1304 - acc: 0.9770\n",
      "Epoch 00037: val_acc did not improve from 0.85252\n",
      "13910/13910 [==============================] - 84s 6ms/sample - loss: 0.1304 - acc: 0.9770 - val_loss: 0.5102 - val_acc: 0.8448\n",
      "Epoch 38/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.1051 - acc: 0.9829\n",
      "Epoch 00038: val_acc did not improve from 0.85252\n",
      "\n",
      "Epoch 00038: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "13910/13910 [==============================] - 83s 6ms/sample - loss: 0.1051 - acc: 0.9829 - val_loss: 0.5322 - val_acc: 0.8525\n",
      "Epoch 39/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0476 - acc: 0.9993\n",
      "Epoch 00039: val_acc did not improve from 0.85252\n",
      "13910/13910 [==============================] - 84s 6ms/sample - loss: 0.0476 - acc: 0.9993 - val_loss: 0.5450 - val_acc: 0.8499\n",
      "Epoch 40/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0429 - acc: 0.9994\n",
      "Epoch 00040: val_acc improved from 0.85252 to 0.85382, saving model to M2-weights-improvement-40-0.85.hdf5\n",
      "13910/13910 [==============================] - 87s 6ms/sample - loss: 0.0429 - acc: 0.9994 - val_loss: 0.5555 - val_acc: 0.8538\n",
      "Epoch 41/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0399 - acc: 0.9991\n",
      "Epoch 00041: val_acc did not improve from 0.85382\n",
      "13910/13910 [==============================] - 85s 6ms/sample - loss: 0.0399 - acc: 0.9991 - val_loss: 0.5467 - val_acc: 0.8461\n",
      "Epoch 42/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0364 - acc: 0.9996\n",
      "Epoch 00042: val_acc improved from 0.85382 to 0.85511, saving model to M2-weights-improvement-42-0.86.hdf5\n",
      "13910/13910 [==============================] - 116s 8ms/sample - loss: 0.0364 - acc: 0.9996 - val_loss: 0.5418 - val_acc: 0.8551\n",
      "Epoch 43/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0353 - acc: 0.9996\n",
      "Epoch 00043: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 113s 8ms/sample - loss: 0.0353 - acc: 0.9996 - val_loss: 0.5491 - val_acc: 0.8532\n",
      "Epoch 44/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0333 - acc: 0.9995\n",
      "Epoch 00044: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 117s 8ms/sample - loss: 0.0333 - acc: 0.9995 - val_loss: 0.5537 - val_acc: 0.8538\n",
      "Epoch 45/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0326 - acc: 0.9995\n",
      "Epoch 00045: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 111s 8ms/sample - loss: 0.0326 - acc: 0.9995 - val_loss: 0.5400 - val_acc: 0.8545\n",
      "Epoch 46/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0309 - acc: 0.9993\n",
      "Epoch 00046: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 116s 8ms/sample - loss: 0.0309 - acc: 0.9993 - val_loss: 0.5778 - val_acc: 0.8538\n",
      "Epoch 47/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0308 - acc: 0.9994\n",
      "Epoch 00047: val_acc did not improve from 0.85511\n",
      "13910/13910 [==============================] - 121s 9ms/sample - loss: 0.0308 - acc: 0.9994 - val_loss: 0.5828 - val_acc: 0.8480\n",
      "Epoch 48/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0298 - acc: 0.9997\n",
      "Epoch 00048: val_acc improved from 0.85511 to 0.86287, saving model to M2-weights-improvement-48-0.86.hdf5\n",
      "13910/13910 [==============================] - 118s 8ms/sample - loss: 0.0298 - acc: 0.9997 - val_loss: 0.5284 - val_acc: 0.8629\n",
      "Epoch 49/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0291 - acc: 0.9993\n",
      "Epoch 00049: val_acc did not improve from 0.86287\n",
      "13910/13910 [==============================] - 117s 8ms/sample - loss: 0.0291 - acc: 0.9993 - val_loss: 0.5291 - val_acc: 0.8571\n",
      "Epoch 50/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0286 - acc: 0.9995\n",
      "Epoch 00050: val_acc did not improve from 0.86287\n",
      "13910/13910 [==============================] - 113s 8ms/sample - loss: 0.0286 - acc: 0.9995 - val_loss: 0.5451 - val_acc: 0.8571\n",
      "Epoch 51/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0279 - acc: 0.9994\n",
      "Epoch 00051: val_acc did not improve from 0.86287\n",
      "13910/13910 [==============================] - 115s 8ms/sample - loss: 0.0279 - acc: 0.9994 - val_loss: 0.5515 - val_acc: 0.8538\n",
      "Epoch 52/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0273 - acc: 0.9992\n",
      "Epoch 00052: val_acc did not improve from 0.86287\n",
      "13910/13910 [==============================] - 116s 8ms/sample - loss: 0.0273 - acc: 0.9992 - val_loss: 0.5838 - val_acc: 0.8506\n",
      "Epoch 53/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0284 - acc: 0.9985\n",
      "Epoch 00053: val_acc did not improve from 0.86287\n",
      "13910/13910 [==============================] - 112s 8ms/sample - loss: 0.0284 - acc: 0.9985 - val_loss: 0.5345 - val_acc: 0.8616\n",
      "Epoch 54/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0266 - acc: 0.9988\n",
      "Epoch 00054: val_acc did not improve from 0.86287\n",
      "13910/13910 [==============================] - 109s 8ms/sample - loss: 0.0266 - acc: 0.9988 - val_loss: 0.5298 - val_acc: 0.8629\n",
      "Epoch 55/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0274 - acc: 0.9988\n",
      "Epoch 00055: val_acc improved from 0.86287 to 0.86417, saving model to M2-weights-improvement-55-0.86.hdf5\n",
      "13910/13910 [==============================] - 109s 8ms/sample - loss: 0.0274 - acc: 0.9988 - val_loss: 0.5264 - val_acc: 0.8642\n",
      "Epoch 56/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0255 - acc: 0.9996\n",
      "Epoch 00056: val_acc did not improve from 0.86417\n",
      "13910/13910 [==============================] - 125s 9ms/sample - loss: 0.0255 - acc: 0.9996 - val_loss: 0.5324 - val_acc: 0.8571\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 57/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0258 - acc: 0.9991\n",
      "Epoch 00057: val_acc did not improve from 0.86417\n",
      "13910/13910 [==============================] - 122s 9ms/sample - loss: 0.0258 - acc: 0.9991 - val_loss: 0.6714 - val_acc: 0.8364\n",
      "Epoch 58/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0264 - acc: 0.9986\n",
      "Epoch 00058: val_acc did not improve from 0.86417\n",
      "13910/13910 [==============================] - 115s 8ms/sample - loss: 0.0264 - acc: 0.9986 - val_loss: 0.5376 - val_acc: 0.8603\n",
      "Epoch 59/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0262 - acc: 0.9987\n",
      "Epoch 00059: val_acc did not improve from 0.86417\n",
      "13910/13910 [==============================] - 120s 9ms/sample - loss: 0.0262 - acc: 0.9987 - val_loss: 0.5092 - val_acc: 0.8577\n",
      "Epoch 60/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0253 - acc: 0.9991\n",
      "Epoch 00060: val_acc improved from 0.86417 to 0.86546, saving model to M2-weights-improvement-60-0.87.hdf5\n",
      "13910/13910 [==============================] - 109s 8ms/sample - loss: 0.0253 - acc: 0.9991 - val_loss: 0.5185 - val_acc: 0.8655\n",
      "Epoch 61/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0238 - acc: 0.9991\n",
      "Epoch 00061: val_acc did not improve from 0.86546\n",
      "13910/13910 [==============================] - 102s 7ms/sample - loss: 0.0238 - acc: 0.9991 - val_loss: 0.5264 - val_acc: 0.8622\n",
      "Epoch 62/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0234 - acc: 0.9994\n",
      "Epoch 00062: val_acc did not improve from 0.86546\n",
      "13910/13910 [==============================] - 103s 7ms/sample - loss: 0.0234 - acc: 0.9994 - val_loss: 0.5700 - val_acc: 0.8648\n",
      "Epoch 63/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0253 - acc: 0.9983\n",
      "Epoch 00063: val_acc did not improve from 0.86546\n",
      "13910/13910 [==============================] - 113s 8ms/sample - loss: 0.0253 - acc: 0.9983 - val_loss: 0.5558 - val_acc: 0.8616\n",
      "Epoch 64/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0261 - acc: 0.9978\n",
      "Epoch 00064: val_acc improved from 0.86546 to 0.86675, saving model to M2-weights-improvement-64-0.87.hdf5\n",
      "13910/13910 [==============================] - 110s 8ms/sample - loss: 0.0261 - acc: 0.9978 - val_loss: 0.5168 - val_acc: 0.8668\n",
      "Epoch 65/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0246 - acc: 0.9984\n",
      "Epoch 00065: val_acc did not improve from 0.86675\n",
      "13910/13910 [==============================] - 102s 7ms/sample - loss: 0.0246 - acc: 0.9984 - val_loss: 0.5250 - val_acc: 0.8642\n",
      "Epoch 66/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0248 - acc: 0.9986\n",
      "Epoch 00066: val_acc did not improve from 0.86675\n",
      "13910/13910 [==============================] - 112s 8ms/sample - loss: 0.0248 - acc: 0.9986 - val_loss: 0.5177 - val_acc: 0.8648\n",
      "Epoch 67/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0247 - acc: 0.9980\n",
      "Epoch 00067: val_acc did not improve from 0.86675\n",
      "13910/13910 [==============================] - 115s 8ms/sample - loss: 0.0247 - acc: 0.9980 - val_loss: 0.5317 - val_acc: 0.8629\n",
      "Epoch 68/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0238 - acc: 0.9984\n",
      "Epoch 00068: val_acc did not improve from 0.86675\n",
      "13910/13910 [==============================] - 110s 8ms/sample - loss: 0.0238 - acc: 0.9984 - val_loss: 0.5379 - val_acc: 0.8596\n",
      "Epoch 69/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0239 - acc: 0.9987\n",
      "Epoch 00069: val_acc did not improve from 0.86675\n",
      "13910/13910 [==============================] - 109s 8ms/sample - loss: 0.0239 - acc: 0.9987 - val_loss: 0.5690 - val_acc: 0.8564\n",
      "Epoch 70/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0247 - acc: 0.9981\n",
      "Epoch 00070: val_acc did not improve from 0.86675\n",
      "13910/13910 [==============================] - 104s 7ms/sample - loss: 0.0247 - acc: 0.9981 - val_loss: 0.5225 - val_acc: 0.8655\n",
      "Epoch 71/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0231 - acc: 0.9987\n",
      "Epoch 00071: val_acc did not improve from 0.86675\n",
      "13910/13910 [==============================] - 104s 7ms/sample - loss: 0.0231 - acc: 0.9987 - val_loss: 0.5297 - val_acc: 0.8622\n",
      "Epoch 72/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0239 - acc: 0.9980\n",
      "Epoch 00072: val_acc did not improve from 0.86675\n",
      "13910/13910 [==============================] - 100s 7ms/sample - loss: 0.0239 - acc: 0.9980 - val_loss: 0.5339 - val_acc: 0.8661\n",
      "Epoch 73/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0223 - acc: 0.9983\n",
      "Epoch 00073: val_acc did not improve from 0.86675\n",
      "13910/13910 [==============================] - 100s 7ms/sample - loss: 0.0223 - acc: 0.9983 - val_loss: 0.5940 - val_acc: 0.8655\n",
      "Epoch 74/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0245 - acc: 0.9976\n",
      "Epoch 00074: val_acc did not improve from 0.86675\n",
      "\n",
      "Epoch 00074: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "13910/13910 [==============================] - 100s 7ms/sample - loss: 0.0245 - acc: 0.9976 - val_loss: 0.5253 - val_acc: 0.8642\n",
      "Epoch 75/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0194 - acc: 0.9999\n",
      "Epoch 00075: val_acc improved from 0.86675 to 0.86934, saving model to M2-weights-improvement-75-0.87.hdf5\n",
      "13910/13910 [==============================] - 106s 8ms/sample - loss: 0.0194 - acc: 0.9999 - val_loss: 0.5486 - val_acc: 0.8693\n",
      "Epoch 76/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0188 - acc: 0.9999\n",
      "Epoch 00076: val_acc did not improve from 0.86934\n",
      "13910/13910 [==============================] - 108s 8ms/sample - loss: 0.0188 - acc: 0.9999 - val_loss: 0.5651 - val_acc: 0.8616\n",
      "Epoch 77/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0185 - acc: 0.9999\n",
      "Epoch 00077: val_acc did not improve from 0.86934\n",
      "13910/13910 [==============================] - 102s 7ms/sample - loss: 0.0185 - acc: 0.9999 - val_loss: 0.5582 - val_acc: 0.8648\n",
      "Epoch 78/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0182 - acc: 0.9999\n",
      "Epoch 00078: val_acc did not improve from 0.86934\n",
      "13910/13910 [==============================] - 105s 8ms/sample - loss: 0.0182 - acc: 0.9999 - val_loss: 0.5543 - val_acc: 0.8635\n",
      "Epoch 79/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0179 - acc: 0.9999\n",
      "Epoch 00079: val_acc did not improve from 0.86934\n",
      "13910/13910 [==============================] - 100s 7ms/sample - loss: 0.0179 - acc: 0.9999 - val_loss: 0.5535 - val_acc: 0.8687\n",
      "Epoch 80/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0177 - acc: 0.9999\n",
      "Epoch 00080: val_acc did not improve from 0.86934\n",
      "13910/13910 [==============================] - 100s 7ms/sample - loss: 0.0177 - acc: 0.9999 - val_loss: 0.5627 - val_acc: 0.8622\n",
      "Epoch 81/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0176 - acc: 0.9999\n",
      "Epoch 00081: val_acc did not improve from 0.86934\n",
      "13910/13910 [==============================] - 100s 7ms/sample - loss: 0.0176 - acc: 0.9999 - val_loss: 0.5616 - val_acc: 0.8609\n",
      "Epoch 82/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0174 - acc: 0.9998\n",
      "Epoch 00082: val_acc did not improve from 0.86934\n",
      "13910/13910 [==============================] - 100s 7ms/sample - loss: 0.0174 - acc: 0.9998 - val_loss: 0.5719 - val_acc: 0.8680\n",
      "Epoch 83/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0173 - acc: 0.9998\n",
      "Epoch 00083: val_acc did not improve from 0.86934\n",
      "13910/13910 [==============================] - 100s 7ms/sample - loss: 0.0173 - acc: 0.9998 - val_loss: 0.5613 - val_acc: 0.8616\n",
      "Epoch 84/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0170 - acc: 0.9999\n",
      "Epoch 00084: val_acc did not improve from 0.86934\n",
      "13910/13910 [==============================] - 102s 7ms/sample - loss: 0.0170 - acc: 0.9999 - val_loss: 0.5679 - val_acc: 0.8616\n",
      "Epoch 85/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0169 - acc: 0.9999\n",
      "Epoch 00085: val_acc did not improve from 0.86934\n",
      "\n",
      "Epoch 00085: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
      "13910/13910 [==============================] - 100s 7ms/sample - loss: 0.0169 - acc: 0.9999 - val_loss: 0.5717 - val_acc: 0.8577\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 86/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0166 - acc: 0.9999\n",
      "Epoch 00086: val_acc did not improve from 0.86934\n",
      "13910/13910 [==============================] - 108s 8ms/sample - loss: 0.0166 - acc: 0.9999 - val_loss: 0.5694 - val_acc: 0.8609\n",
      "Epoch 87/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0166 - acc: 0.9999\n",
      "Epoch 00087: val_acc did not improve from 0.86934\n",
      "13910/13910 [==============================] - 108s 8ms/sample - loss: 0.0166 - acc: 0.9999 - val_loss: 0.5686 - val_acc: 0.8616\n",
      "Epoch 88/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0165 - acc: 0.9999\n",
      "Epoch 00088: val_acc did not improve from 0.86934\n",
      "13910/13910 [==============================] - 111s 8ms/sample - loss: 0.0165 - acc: 0.9999 - val_loss: 0.5686 - val_acc: 0.8622\n",
      "Epoch 89/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0164 - acc: 0.9999\n",
      "Epoch 00089: val_acc did not improve from 0.86934\n",
      "13910/13910 [==============================] - 108s 8ms/sample - loss: 0.0164 - acc: 0.9999 - val_loss: 0.5646 - val_acc: 0.8622\n",
      "Epoch 90/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0164 - acc: 0.9998\n",
      "Epoch 00090: val_acc did not improve from 0.86934\n",
      "13910/13910 [==============================] - 108s 8ms/sample - loss: 0.0164 - acc: 0.9998 - val_loss: 0.5643 - val_acc: 0.8616\n",
      "Epoch 91/200\n",
      "13910/13910 [==============================] - ETA: 0s - loss: 0.0163 - acc: 0.9999\n",
      "Epoch 00091: val_acc did not improve from 0.86934\n",
      "13910/13910 [==============================] - 109s 8ms/sample - loss: 0.0163 - acc: 0.9999 - val_loss: 0.5656 - val_acc: 0.8622\n",
      "Epoch 00091: early stopping\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "#model.load_weights(\"M-weights-improvement-19-0.93.hdf5\")\n",
    "history=model.fit(X_train,\n",
    "          Y_train,\n",
    "          batch_size=batch_size,\n",
    "          validation_split=0.1,\n",
    "          epochs=epochs,\n",
    "          callbacks=callbacks_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
    "plt.ylim([0.6,1])\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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 88.18%\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'Functional' object has no attribute 'predict_classes'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-cc54c5cf9ee2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# confusion matrix\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mY_pred\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_classes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY_pred\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Functional' object has no attribute 'predict_classes'"
     ]
    }
   ],
   "source": [
    "#model2.load_weights(\"M2-weights-improvement-45-0.97.hdf5\")\n",
    "model.compile(loss='sparse_categorical_crossentropy',\n",
    "              optimizer='Adam',\n",
    "              metrics=['accuracy'])\n",
    "scores = model.evaluate(X_test, Y_test, verbose=0)\n",
    "print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
    "\n",
    "# confusion matrix\n",
    "Y_pred=model.predict_classes(X_test)\n",
    "print(Y_pred.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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