code/train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "actions = [\n",
    "    'zero',\n",
    "    'one',\n",
    "    'two',\n",
    "    'three',\n",
    "    'four',\n",
    "    'five'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numpy 배열 합치기\n",
    "data = np.concatenate([\n",
    "    np.load('dataset/seq_zero_1660296101.npy'),\n",
    "    np.load('dataset/seq_one_1660296101.npy'),\n",
    "    np.load('dataset/seq_two_1660296101.npy'),\n",
    "    np.load('dataset/seq_three_1660296101.npy'),\n",
    "    np.load('dataset/seq_four_1660296101.npy'),\n",
    "    np.load('dataset/seq_five_1660296101.npy')\n",
    "], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5145, 30, 100)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data의 마지막 값이 라벨이므로 x_data와 labels로 나누기\n",
    "x_data = data[:, :, :-1]\n",
    "labels = data[:, 0, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5145, 30, 99)\n",
      "(5145,)\n"
     ]
    }
   ],
   "source": [
    "print(x_data.shape)\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5145, 6)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One-hot encoding 진행\n",
    "y_data = to_categorical(labels, num_classes=len(actions))\n",
    "y_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = x_data.astype(np.float32)\n",
    "y_data = y_data.astype(np.float32)\n",
    "\n",
    "x_train, x_val, y_train, y_val = train_test_split(x_data, y_data, test_size=0.1, random_state=2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4630, 30, 99) (4630, 6)\n",
      "(515, 30, 99) (515, 6)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape, y_train.shape)\n",
    "print(x_val.shape, y_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    LSTM(64, activation='relu', input_shape=x_train.shape[1:3]), # input_shape = [30, 99], 30->윈도우의 크기, 99->랜드마크, visibility, 각도\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(len(actions), activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 64)                41984     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 6)                 198       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 44,262\n",
      "Trainable params: 44,262\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss = 'categorical_crossentropy', metrics=['acc'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 44.2676 - acc: 0.5162\n",
      "Epoch 1: val_acc improved from -inf to 0.60000, saving model to models\\model.h5\n",
      "145/145 [==============================] - 3s 12ms/step - loss: 43.9470 - acc: 0.5171 - val_loss: 21.3827 - val_acc: 0.6000 - lr: 0.0010\n",
      "Epoch 2/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 7.2883 - acc: 0.7953\n",
      "Epoch 2: val_acc improved from 0.60000 to 0.70291, saving model to models\\model.h5\n",
      "145/145 [==============================] - 2s 13ms/step - loss: 7.2527 - acc: 0.7948 - val_loss: 6.5958 - val_acc: 0.7029 - lr: 0.0010\n",
      "Epoch 3/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 4.1551 - acc: 0.8220\n",
      "Epoch 3: val_acc improved from 0.70291 to 0.87573, saving model to models\\model.h5\n",
      "145/145 [==============================] - 2s 11ms/step - loss: 4.1366 - acc: 0.8225 - val_loss: 2.4770 - val_acc: 0.8757 - lr: 0.0010\n",
      "Epoch 4/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 13.3643 - acc: 0.7968\n",
      "Epoch 4: val_acc did not improve from 0.87573\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 13.3643 - acc: 0.7968 - val_loss: 29.7994 - val_acc: 0.7534 - lr: 0.0010\n",
      "Epoch 5/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 258.7551 - acc: 0.3125\n",
      "Epoch 5: val_acc did not improve from 0.87573\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 258.7551 - acc: 0.3125 - val_loss: 140.2874 - val_acc: 0.3165 - lr: 0.0010\n",
      "Epoch 6/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 39.4280 - acc: 0.4749\n",
      "Epoch 6: val_acc did not improve from 0.87573\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 39.1274 - acc: 0.4773 - val_loss: 29.3028 - val_acc: 0.6602 - lr: 0.0010\n",
      "Epoch 7/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 14.7886 - acc: 0.6562\n",
      "Epoch 7: val_acc did not improve from 0.87573\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 15.1089 - acc: 0.6564 - val_loss: 2.5205 - val_acc: 0.8078 - lr: 0.0010\n",
      "Epoch 8/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 7.8777 - acc: 0.7143\n",
      "Epoch 8: val_acc did not improve from 0.87573\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 8.0170 - acc: 0.7121 - val_loss: 13.3557 - val_acc: 0.6505 - lr: 0.0010\n",
      "Epoch 9/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 11.7035 - acc: 0.6973\n",
      "Epoch 9: val_acc did not improve from 0.87573\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 11.6793 - acc: 0.6968 - val_loss: 4.7387 - val_acc: 0.6175 - lr: 0.0010\n",
      "Epoch 10/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 7.6432 - acc: 0.7212\n",
      "Epoch 10: val_acc did not improve from 0.87573\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 7.5068 - acc: 0.7240 - val_loss: 5.3863 - val_acc: 0.7087 - lr: 0.0010\n",
      "Epoch 11/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 4.0115 - acc: 0.8173\n",
      "Epoch 11: val_acc did not improve from 0.87573\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 4.0115 - acc: 0.8173 - val_loss: 11.4383 - val_acc: 0.6738 - lr: 0.0010\n",
      "Epoch 12/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 7.0981 - acc: 0.7464\n",
      "Epoch 12: val_acc improved from 0.87573 to 0.88350, saving model to models\\model.h5\n",
      "145/145 [==============================] - 3s 24ms/step - loss: 7.0981 - acc: 0.7464 - val_loss: 1.7185 - val_acc: 0.8835 - lr: 0.0010\n",
      "Epoch 13/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 4.0637 - acc: 0.8003\n",
      "Epoch 13: val_acc improved from 0.88350 to 0.96311, saving model to models\\model.h5\n",
      "145/145 [==============================] - 5s 31ms/step - loss: 4.0419 - acc: 0.8013 - val_loss: 0.8718 - val_acc: 0.9631 - lr: 0.0010\n",
      "Epoch 14/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 6.0580 - acc: 0.7980\n",
      "Epoch 14: val_acc did not improve from 0.96311\n",
      "145/145 [==============================] - 4s 29ms/step - loss: 6.1153 - acc: 0.7976 - val_loss: 9.8659 - val_acc: 0.8039 - lr: 0.0010\n",
      "Epoch 15/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 4.0620 - acc: 0.8375\n",
      "Epoch 15: val_acc did not improve from 0.96311\n",
      "145/145 [==============================] - 4s 30ms/step - loss: 4.0470 - acc: 0.8378 - val_loss: 0.8860 - val_acc: 0.9437 - lr: 0.0010\n",
      "Epoch 16/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 5.0762 - acc: 0.8188\n",
      "Epoch 16: val_acc did not improve from 0.96311\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 5.0799 - acc: 0.8181 - val_loss: 4.6726 - val_acc: 0.7748 - lr: 0.0010\n",
      "Epoch 17/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 3.9445 - acc: 0.8328\n",
      "Epoch 17: val_acc did not improve from 0.96311\n",
      "145/145 [==============================] - 2s 11ms/step - loss: 3.8641 - acc: 0.8339 - val_loss: 0.9422 - val_acc: 0.9243 - lr: 0.0010\n",
      "Epoch 18/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 2.0566 - acc: 0.9038\n",
      "Epoch 18: val_acc did not improve from 0.96311\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 2.2438 - acc: 0.9017 - val_loss: 3.6944 - val_acc: 0.7534 - lr: 0.0010\n",
      "Epoch 19/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 2.7697 - acc: 0.8698\n",
      "Epoch 19: val_acc did not improve from 0.96311\n",
      "145/145 [==============================] - 2s 11ms/step - loss: 2.8339 - acc: 0.8680 - val_loss: 1.1036 - val_acc: 0.9437 - lr: 0.0010\n",
      "Epoch 20/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 2.4894 - acc: 0.8983\n",
      "Epoch 20: val_acc did not improve from 0.96311\n",
      "145/145 [==============================] - 2s 14ms/step - loss: 2.4894 - acc: 0.8983 - val_loss: 0.4659 - val_acc: 0.9437 - lr: 0.0010\n",
      "Epoch 21/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 1.1634 - acc: 0.9302\n",
      "Epoch 21: val_acc did not improve from 0.96311\n",
      "145/145 [==============================] - 2s 11ms/step - loss: 1.1539 - acc: 0.9302 - val_loss: 0.8487 - val_acc: 0.8951 - lr: 0.0010\n",
      "Epoch 22/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 3.0635 - acc: 0.8763\n",
      "Epoch 22: val_acc improved from 0.96311 to 0.97476, saving model to models\\model.h5\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 3.0572 - acc: 0.8765 - val_loss: 0.3003 - val_acc: 0.9748 - lr: 0.0010\n",
      "Epoch 23/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 2.6407 - acc: 0.8790\n",
      "Epoch 23: val_acc improved from 0.97476 to 0.97864, saving model to models\\model.h5\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.6083 - acc: 0.8803 - val_loss: 0.5103 - val_acc: 0.9786 - lr: 0.0010\n",
      "Epoch 24/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 2.9542 - acc: 0.9021\n",
      "Epoch 24: val_acc did not improve from 0.97864\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 2.9422 - acc: 0.9022 - val_loss: 2.9582 - val_acc: 0.8350 - lr: 0.0010\n",
      "Epoch 25/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 1.0654 - acc: 0.9367\n",
      "Epoch 25: val_acc did not improve from 0.97864\n",
      "145/145 [==============================] - 2s 11ms/step - loss: 1.0654 - acc: 0.9367 - val_loss: 0.4016 - val_acc: 0.9748 - lr: 0.0010\n",
      "Epoch 26/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 2.2625 - acc: 0.9245\n",
      "Epoch 26: val_acc did not improve from 0.97864\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 2.2246 - acc: 0.9251 - val_loss: 1.6964 - val_acc: 0.8893 - lr: 0.0010\n",
      "Epoch 27/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 1.8586 - acc: 0.9259\n",
      "Epoch 27: val_acc did not improve from 0.97864\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 1.8586 - acc: 0.9259 - val_loss: 11.4684 - val_acc: 0.7204 - lr: 0.0010\n",
      "Epoch 28/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 7.5071 - acc: 0.8225\n",
      "Epoch 28: val_acc did not improve from 0.97864\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 7.4714 - acc: 0.8233 - val_loss: 1.7956 - val_acc: 0.9359 - lr: 0.0010\n",
      "Epoch 29/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 0.7440 - acc: 0.9607\n",
      "Epoch 29: val_acc did not improve from 0.97864\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 0.8248 - acc: 0.9587 - val_loss: 4.5986 - val_acc: 0.8427 - lr: 0.0010\n",
      "Epoch 30/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 2.5897 - acc: 0.9098\n",
      "Epoch 30: val_acc improved from 0.97864 to 0.98447, saving model to models\\model.h5\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 2.5302 - acc: 0.9117 - val_loss: 0.1749 - val_acc: 0.9845 - lr: 0.0010\n",
      "Epoch 31/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 1.1943 - acc: 0.9469\n",
      "Epoch 31: val_acc did not improve from 0.98447\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.1625 - acc: 0.9475 - val_loss: 3.7845 - val_acc: 0.8660 - lr: 0.0010\n",
      "Epoch 32/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 2.9796 - acc: 0.9153\n",
      "Epoch 32: val_acc did not improve from 0.98447\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 2.9253 - acc: 0.9166 - val_loss: 1.0743 - val_acc: 0.8932 - lr: 0.0010\n",
      "Epoch 33/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 2.6834 - acc: 0.9263\n",
      "Epoch 33: val_acc did not improve from 0.98447\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 2.7041 - acc: 0.9248 - val_loss: 2.8991 - val_acc: 0.8583 - lr: 0.0010\n",
      "Epoch 34/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 1.7702 - acc: 0.9293\n",
      "Epoch 34: val_acc improved from 0.98447 to 0.99223, saving model to models\\model.h5\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 1.7618 - acc: 0.9296 - val_loss: 0.0256 - val_acc: 0.9922 - lr: 0.0010\n",
      "Epoch 35/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 0.4216 - acc: 0.9766\n",
      "Epoch 35: val_acc improved from 0.99223 to 1.00000, saving model to models\\model.h5\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 0.4181 - acc: 0.9767 - val_loss: 4.3352e-04 - val_acc: 1.0000 - lr: 0.0010\n",
      "Epoch 36/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 0.5330 - acc: 0.9733\n",
      "Epoch 36: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 0.5306 - acc: 0.9734 - val_loss: 0.3932 - val_acc: 0.9864 - lr: 0.0010\n",
      "Epoch 37/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 0.4758 - acc: 0.9709\n",
      "Epoch 37: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 0.4799 - acc: 0.9708 - val_loss: 0.4208 - val_acc: 0.9806 - lr: 0.0010\n",
      "Epoch 38/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 1.5747 - acc: 0.9365\n",
      "Epoch 38: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 1.5747 - acc: 0.9365 - val_loss: 1.1164 - val_acc: 0.9379 - lr: 0.0010\n",
      "Epoch 39/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 1.3886 - acc: 0.9478\n",
      "Epoch 39: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 1.3740 - acc: 0.9486 - val_loss: 0.0472 - val_acc: 0.9961 - lr: 0.0010\n",
      "Epoch 40/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 2.6733 - acc: 0.9156\n",
      "Epoch 40: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.6561 - acc: 0.9164 - val_loss: 0.3286 - val_acc: 0.9864 - lr: 0.0010\n",
      "Epoch 41/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 4.8494 - acc: 0.8856\n",
      "Epoch 41: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 3s 20ms/step - loss: 4.8540 - acc: 0.8851 - val_loss: 7.6048 - val_acc: 0.6816 - lr: 0.0010\n",
      "Epoch 42/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.8323 - acc: 0.9646\n",
      "Epoch 42: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 4s 30ms/step - loss: 0.8323 - acc: 0.9646 - val_loss: 0.0600 - val_acc: 0.9961 - lr: 0.0010\n",
      "Epoch 43/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.1972 - acc: 0.9886\n",
      "Epoch 43: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 31ms/step - loss: 0.1972 - acc: 0.9886 - val_loss: 0.2360 - val_acc: 0.9748 - lr: 0.0010\n",
      "Epoch 44/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.3677 - acc: 0.9778\n",
      "Epoch 44: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 31ms/step - loss: 0.3677 - acc: 0.9778 - val_loss: 0.3971 - val_acc: 0.9883 - lr: 0.0010\n",
      "Epoch 45/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 4.8008 - acc: 0.9197\n",
      "Epoch 45: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 31ms/step - loss: 4.8008 - acc: 0.9197 - val_loss: 0.9521 - val_acc: 0.9262 - lr: 0.0010\n",
      "Epoch 46/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 1.4726 - acc: 0.9525\n",
      "Epoch 46: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 31ms/step - loss: 1.4726 - acc: 0.9525 - val_loss: 6.9798 - val_acc: 0.8466 - lr: 0.0010\n",
      "Epoch 47/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 1.1543 - acc: 0.9667\n",
      "Epoch 47: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 4s 30ms/step - loss: 1.1543 - acc: 0.9667 - val_loss: 0.1946 - val_acc: 0.9922 - lr: 0.0010\n",
      "Epoch 48/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.6269 - acc: 0.9829\n",
      "Epoch 48: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 4s 31ms/step - loss: 0.6269 - acc: 0.9829 - val_loss: 0.4161 - val_acc: 0.9748 - lr: 0.0010\n",
      "Epoch 49/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.1924 - acc: 0.9920\n",
      "Epoch 49: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 33ms/step - loss: 0.1924 - acc: 0.9920 - val_loss: 0.0129 - val_acc: 0.9981 - lr: 0.0010\n",
      "Epoch 50/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.6227 - acc: 0.9745\n",
      "Epoch 50: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 32ms/step - loss: 0.6227 - acc: 0.9745 - val_loss: 0.5554 - val_acc: 0.9864 - lr: 0.0010\n",
      "Epoch 51/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 0.3061 - acc: 0.9824\n",
      "Epoch 51: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 32ms/step - loss: 0.3046 - acc: 0.9825 - val_loss: 0.0532 - val_acc: 0.9961 - lr: 0.0010\n",
      "Epoch 52/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 0.2851 - acc: 0.9874\n",
      "Epoch 52: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 33ms/step - loss: 0.2873 - acc: 0.9873 - val_loss: 0.0747 - val_acc: 0.9903 - lr: 0.0010\n",
      "Epoch 53/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 0.2849 - acc: 0.9835\n",
      "Epoch 53: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 31ms/step - loss: 0.2835 - acc: 0.9836 - val_loss: 0.8437 - val_acc: 0.8990 - lr: 0.0010\n",
      "Epoch 54/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 1.1476 - acc: 0.9635\n",
      "Epoch 54: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 4s 31ms/step - loss: 1.1476 - acc: 0.9635 - val_loss: 0.1680 - val_acc: 0.9961 - lr: 0.0010\n",
      "Epoch 55/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 0.6645 - acc: 0.9759\n",
      "Epoch 55: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 31ms/step - loss: 0.6614 - acc: 0.9760 - val_loss: 0.1160 - val_acc: 0.9981 - lr: 0.0010\n",
      "Epoch 56/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 0.1494 - acc: 0.9924\n",
      "Epoch 56: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 33ms/step - loss: 0.1560 - acc: 0.9920 - val_loss: 0.1835 - val_acc: 0.9961 - lr: 0.0010\n",
      "Epoch 57/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.1069 - acc: 0.9963\n",
      "Epoch 57: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 4s 31ms/step - loss: 0.1069 - acc: 0.9963 - val_loss: 4.8026e-05 - val_acc: 1.0000 - lr: 0.0010\n",
      "Epoch 58/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 3.6202 - acc: 0.9298\n",
      "Epoch 58: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 33ms/step - loss: 3.6202 - acc: 0.9298 - val_loss: 0.0945 - val_acc: 0.9883 - lr: 0.0010\n",
      "Epoch 59/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.4147 - acc: 0.9795\n",
      "Epoch 59: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 33ms/step - loss: 0.4147 - acc: 0.9795 - val_loss: 0.1656 - val_acc: 0.9922 - lr: 0.0010\n",
      "Epoch 60/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.1062 - acc: 0.9950\n",
      "Epoch 60: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 32ms/step - loss: 0.1062 - acc: 0.9950 - val_loss: 0.0793 - val_acc: 0.9942 - lr: 0.0010\n",
      "Epoch 61/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.4898 - acc: 0.9862\n",
      "Epoch 61: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 33ms/step - loss: 0.4898 - acc: 0.9862 - val_loss: 1.3284 - val_acc: 0.9495 - lr: 0.0010\n",
      "Epoch 62/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.7677 - acc: 0.9788\n",
      "Epoch 62: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 33ms/step - loss: 0.7677 - acc: 0.9788 - val_loss: 0.1848 - val_acc: 0.9961 - lr: 0.0010\n",
      "Epoch 63/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.2578 - acc: 0.9933\n",
      "Epoch 63: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 33ms/step - loss: 0.2578 - acc: 0.9933 - val_loss: 0.0497 - val_acc: 0.9961 - lr: 0.0010\n",
      "Epoch 64/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.0909 - acc: 0.9961\n",
      "Epoch 64: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 33ms/step - loss: 0.0909 - acc: 0.9961 - val_loss: 0.0750 - val_acc: 0.9942 - lr: 0.0010\n",
      "Epoch 65/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.3539 - acc: 0.9868\n",
      "Epoch 65: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 33ms/step - loss: 0.3539 - acc: 0.9868 - val_loss: 1.0091 - val_acc: 0.9767 - lr: 0.0010\n",
      "Epoch 66/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 0.8968 - acc: 0.9757\n",
      "Epoch 66: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 32ms/step - loss: 0.9153 - acc: 0.9754 - val_loss: 18.5211 - val_acc: 0.7961 - lr: 0.0010\n",
      "Epoch 67/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 2.0917 - acc: 0.9557\n",
      "Epoch 67: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 3s 22ms/step - loss: 2.0917 - acc: 0.9557 - val_loss: 5.5750 - val_acc: 0.8427 - lr: 0.0010\n",
      "Epoch 68/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 1.1507 - acc: 0.9726\n",
      "Epoch 68: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 32ms/step - loss: 1.1507 - acc: 0.9726 - val_loss: 0.1663 - val_acc: 0.9942 - lr: 0.0010\n",
      "Epoch 69/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.1192 - acc: 0.9946\n",
      "Epoch 69: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 33ms/step - loss: 0.1192 - acc: 0.9946 - val_loss: 0.2028 - val_acc: 0.9961 - lr: 0.0010\n",
      "Epoch 70/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 0.6877 - acc: 0.9789\n",
      "Epoch 70: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 5s 33ms/step - loss: 0.6944 - acc: 0.9784 - val_loss: 0.2885 - val_acc: 0.9961 - lr: 0.0010\n",
      "Epoch 71/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 3.5468 - acc: 0.9421\n",
      "Epoch 71: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 4s 31ms/step - loss: 3.5299 - acc: 0.9423 - val_loss: 0.2279 - val_acc: 0.9903 - lr: 0.0010\n",
      "Epoch 72/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 0.6099 - acc: 0.9878\n",
      "Epoch 72: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 2s 13ms/step - loss: 0.5943 - acc: 0.9881 - val_loss: 2.3389e-05 - val_acc: 1.0000 - lr: 0.0010\n",
      "Epoch 73/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 3.3513 - acc: 0.9395\n",
      "Epoch 73: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 2s 11ms/step - loss: 3.2515 - acc: 0.9410 - val_loss: 0.2117 - val_acc: 0.9942 - lr: 0.0010\n",
      "Epoch 74/200\n",
      "139/145 [===========================>..] - ETA: 0s - loss: 0.0415 - acc: 0.9975\n",
      "Epoch 74: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 0.0399 - acc: 0.9976 - val_loss: 1.7929e-06 - val_acc: 1.0000 - lr: 0.0010\n",
      "Epoch 75/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 0.1055 - acc: 0.9980\n",
      "Epoch 75: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 0.1070 - acc: 0.9978 - val_loss: 0.2402 - val_acc: 0.9942 - lr: 0.0010\n",
      "Epoch 76/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 0.1095 - acc: 0.9969\n",
      "Epoch 76: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 0.1093 - acc: 0.9965 - val_loss: 0.0652 - val_acc: 0.9903 - lr: 0.0010\n",
      "Epoch 77/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 0.2835 - acc: 0.9931\n",
      "Epoch 77: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 0.2787 - acc: 0.9931 - val_loss: 0.7172 - val_acc: 0.9786 - lr: 0.0010\n",
      "Epoch 78/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 0.4438 - acc: 0.9831\n",
      "Epoch 78: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 0.4417 - acc: 0.9832 - val_loss: 1.1256e-05 - val_acc: 1.0000 - lr: 0.0010\n",
      "Epoch 79/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 0.0944 - acc: 0.9958\n",
      "Epoch 79: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 0.0920 - acc: 0.9959 - val_loss: 0.0322 - val_acc: 0.9922 - lr: 0.0010\n",
      "Epoch 80/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 0.4675 - acc: 0.9885\n",
      "Epoch 80: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 0.4653 - acc: 0.9886 - val_loss: 0.0424 - val_acc: 0.9981 - lr: 0.0010\n",
      "Epoch 81/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 3.6342 - acc: 0.9157\n",
      "Epoch 81: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 3.5667 - acc: 0.9173 - val_loss: 7.3329e-05 - val_acc: 1.0000 - lr: 0.0010\n",
      "Epoch 82/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 0.0731 - acc: 0.9978\n",
      "Epoch 82: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 0.0728 - acc: 0.9978 - val_loss: 0.1619 - val_acc: 0.9981 - lr: 0.0010\n",
      "Epoch 83/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.0689 - acc: 0.9965\n",
      "Epoch 83: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 0.0689 - acc: 0.9965 - val_loss: 7.5549e-05 - val_acc: 1.0000 - lr: 0.0010\n",
      "Epoch 84/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 0.0021 - acc: 0.9996\n",
      "Epoch 84: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 0.0021 - acc: 0.9996 - val_loss: 1.1521e-05 - val_acc: 1.0000 - lr: 0.0010\n",
      "Epoch 85/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 0.0107 - acc: 0.9991\n",
      "Epoch 85: val_acc did not improve from 1.00000\n",
      "\n",
      "Epoch 85: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 0.0104 - acc: 0.9991 - val_loss: 0.0991 - val_acc: 0.9981 - lr: 0.0010\n",
      "Epoch 86/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 0.0042 - acc: 0.9996\n",
      "Epoch 86: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 0.0042 - acc: 0.9996 - val_loss: 6.7224e-05 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 87/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 3.4782e-05 - acc: 1.0000\n",
      "Epoch 87: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 3.4782e-05 - acc: 1.0000 - val_loss: 1.2672e-04 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 88/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 2.0917e-05 - acc: 1.0000\n",
      "Epoch 88: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.0270e-05 - acc: 1.0000 - val_loss: 1.8858e-04 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 89/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 1.4629e-05 - acc: 1.0000\n",
      "Epoch 89: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.4198e-05 - acc: 1.0000 - val_loss: 2.5086e-04 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 90/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 1.0172e-05 - acc: 1.0000\n",
      "Epoch 90: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 1.0957e-05 - acc: 1.0000 - val_loss: 3.2494e-04 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 91/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 8.9735e-06 - acc: 1.0000\n",
      "Epoch 91: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 8.9310e-06 - acc: 1.0000 - val_loss: 4.0710e-04 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 92/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 7.2606e-06 - acc: 1.0000\n",
      "Epoch 92: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 7.0818e-06 - acc: 1.0000 - val_loss: 4.8289e-04 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 93/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 6.1152e-06 - acc: 1.0000\n",
      "Epoch 93: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 2s 11ms/step - loss: 6.0027e-06 - acc: 1.0000 - val_loss: 5.6204e-04 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 94/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 5.1924e-06 - acc: 1.0000\n",
      "Epoch 94: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 5.1679e-06 - acc: 1.0000 - val_loss: 6.4233e-04 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 95/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 4.5312e-06 - acc: 1.0000\n",
      "Epoch 95: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 4.4489e-06 - acc: 1.0000 - val_loss: 7.3274e-04 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 96/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 3.9765e-06 - acc: 1.0000\n",
      "Epoch 96: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 3.9066e-06 - acc: 1.0000 - val_loss: 8.2114e-04 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 97/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 3.4415e-06 - acc: 1.0000\n",
      "Epoch 97: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 3.4415e-06 - acc: 1.0000 - val_loss: 8.9682e-04 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 98/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 3.0951e-06 - acc: 1.0000\n",
      "Epoch 98: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 3.0823e-06 - acc: 1.0000 - val_loss: 0.0010 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 99/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 2.8205e-06 - acc: 1.0000\n",
      "Epoch 99: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.7518e-06 - acc: 1.0000 - val_loss: 0.0011 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 100/200\n",
      "139/145 [===========================>..] - ETA: 0s - loss: 2.5553e-06 - acc: 1.0000\n",
      "Epoch 100: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 2.4610e-06 - acc: 1.0000 - val_loss: 0.0012 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 101/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 2.2464e-06 - acc: 1.0000\n",
      "Epoch 101: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 2.2218e-06 - acc: 1.0000 - val_loss: 0.0013 - val_acc: 1.0000 - lr: 5.0000e-04\n",
      "Epoch 102/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 2.0194e-06 - acc: 1.0000\n",
      "Epoch 102: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.0149e-06 - acc: 1.0000 - val_loss: 0.0014 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 103/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 1.8871e-06 - acc: 1.0000\n",
      "Epoch 103: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.8274e-06 - acc: 1.0000 - val_loss: 0.0015 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 104/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 1.6967e-06 - acc: 1.0000\n",
      "Epoch 104: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.6775e-06 - acc: 1.0000 - val_loss: 0.0016 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 105/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 1.5723e-06 - acc: 1.0000\n",
      "Epoch 105: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.5279e-06 - acc: 1.0000 - val_loss: 0.0017 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 106/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 1.4273e-06 - acc: 1.0000\n",
      "Epoch 106: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.3954e-06 - acc: 1.0000 - val_loss: 0.0018 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 107/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 1.2814e-06 - acc: 1.0000\n",
      "Epoch 107: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.2814e-06 - acc: 1.0000 - val_loss: 0.0020 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 108/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 1.1980e-06 - acc: 1.0000\n",
      "Epoch 108: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.1780e-06 - acc: 1.0000 - val_loss: 0.0021 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 109/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 1.1192e-06 - acc: 1.0000\n",
      "Epoch 109: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.0854e-06 - acc: 1.0000 - val_loss: 0.0022 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 110/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 1.0054e-06 - acc: 1.0000\n",
      "Epoch 110: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 1.0007e-06 - acc: 1.0000 - val_loss: 0.0023 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 111/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 9.2375e-07 - acc: 1.0000\n",
      "Epoch 111: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 9.2389e-07 - acc: 1.0000 - val_loss: 0.0024 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 112/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 8.6298e-07 - acc: 1.0000\n",
      "Epoch 112: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 8.5459e-07 - acc: 1.0000 - val_loss: 0.0026 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 113/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 8.0049e-07 - acc: 1.0000\n",
      "Epoch 113: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 7.9208e-07 - acc: 1.0000 - val_loss: 0.0027 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 114/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 7.5160e-07 - acc: 1.0000\n",
      "Epoch 114: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 7.3466e-07 - acc: 1.0000 - val_loss: 0.0028 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 115/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 7.0111e-07 - acc: 1.0000\n",
      "Epoch 115: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 6.8164e-07 - acc: 1.0000 - val_loss: 0.0029 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 116/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 5.5069e-07 - acc: 1.0000\n",
      "Epoch 116: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 6.3812e-07 - acc: 1.0000 - val_loss: 0.0030 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 117/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 6.1003e-07 - acc: 1.0000\n",
      "Epoch 117: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 5.9197e-07 - acc: 1.0000 - val_loss: 0.0032 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 118/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 5.4850e-07 - acc: 1.0000\n",
      "Epoch 118: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 5.4850e-07 - acc: 1.0000 - val_loss: 0.0033 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 119/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 5.1603e-07 - acc: 1.0000\n",
      "Epoch 119: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 5.1076e-07 - acc: 1.0000 - val_loss: 0.0034 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 120/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 4.8005e-07 - acc: 1.0000\n",
      "Epoch 120: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 4.7780e-07 - acc: 1.0000 - val_loss: 0.0036 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 121/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 4.5470e-07 - acc: 1.0000\n",
      "Epoch 121: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 4.4360e-07 - acc: 1.0000 - val_loss: 0.0037 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 122/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 4.2247e-07 - acc: 1.0000\n",
      "Epoch 122: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 4.1490e-07 - acc: 1.0000 - val_loss: 0.0038 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 123/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 3.8979e-07 - acc: 1.0000\n",
      "Epoch 123: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 3.8801e-07 - acc: 1.0000 - val_loss: 0.0039 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 124/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 3.7315e-07 - acc: 1.0000\n",
      "Epoch 124: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 3.6398e-07 - acc: 1.0000 - val_loss: 0.0040 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 125/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 3.5025e-07 - acc: 1.0000\n",
      "Epoch 125: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 3.4169e-07 - acc: 1.0000 - val_loss: 0.0041 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 126/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 3.2186e-07 - acc: 1.0000\n",
      "Epoch 126: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 3.2002e-07 - acc: 1.0000 - val_loss: 0.0043 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 127/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 3.0691e-07 - acc: 1.0000\n",
      "Epoch 127: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 3.0015e-07 - acc: 1.0000 - val_loss: 0.0044 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 128/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 2.8345e-07 - acc: 1.0000\n",
      "Epoch 128: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 2.8213e-07 - acc: 1.0000 - val_loss: 0.0045 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 129/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 2.6578e-07 - acc: 1.0000\n",
      "Epoch 129: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.6578e-07 - acc: 1.0000 - val_loss: 0.0045 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 130/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 2.5609e-07 - acc: 1.0000\n",
      "Epoch 130: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.4890e-07 - acc: 1.0000 - val_loss: 0.0047 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 131/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 2.4207e-07 - acc: 1.0000\n",
      "Epoch 131: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.3438e-07 - acc: 1.0000 - val_loss: 0.0048 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 132/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 2.2502e-07 - acc: 1.0000\n",
      "Epoch 132: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.2117e-07 - acc: 1.0000 - val_loss: 0.0048 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 133/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 2.1196e-07 - acc: 1.0000\n",
      "Epoch 133: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.0843e-07 - acc: 1.0000 - val_loss: 0.0049 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 134/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 1.9836e-07 - acc: 1.0000\n",
      "Epoch 134: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 1.9741e-07 - acc: 1.0000 - val_loss: 0.0049 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 135/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 1.6937e-07 - acc: 1.0000\n",
      "Epoch 135: val_acc did not improve from 1.00000\n",
      "\n",
      "Epoch 135: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.8637e-07 - acc: 1.0000 - val_loss: 0.0050 - val_acc: 0.9981 - lr: 5.0000e-04\n",
      "Epoch 136/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 1.7722e-07 - acc: 1.0000\n",
      "Epoch 136: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.7785e-07 - acc: 1.0000 - val_loss: 0.0050 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 137/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 1.7734e-07 - acc: 1.0000\n",
      "Epoch 137: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 1.7255e-07 - acc: 1.0000 - val_loss: 0.0050 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 138/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 1.6975e-07 - acc: 1.0000\n",
      "Epoch 138: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.6773e-07 - acc: 1.0000 - val_loss: 0.0051 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 139/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 1.6481e-07 - acc: 1.0000\n",
      "Epoch 139: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.6318e-07 - acc: 1.0000 - val_loss: 0.0051 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 140/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 1.6220e-07 - acc: 1.0000\n",
      "Epoch 140: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.5785e-07 - acc: 1.0000 - val_loss: 0.0051 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 141/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 1.5554e-07 - acc: 1.0000\n",
      "Epoch 141: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.5298e-07 - acc: 1.0000 - val_loss: 0.0051 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 142/200\n",
      "139/145 [===========================>..] - ETA: 0s - loss: 1.3997e-07 - acc: 1.0000\n",
      "Epoch 142: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.4801e-07 - acc: 1.0000 - val_loss: 0.0051 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 143/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 1.2620e-07 - acc: 1.0000\n",
      "Epoch 143: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.4286e-07 - acc: 1.0000 - val_loss: 0.0051 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 144/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 1.3988e-07 - acc: 1.0000\n",
      "Epoch 144: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.3779e-07 - acc: 1.0000 - val_loss: 0.0051 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 145/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 1.3667e-07 - acc: 1.0000\n",
      "Epoch 145: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.3324e-07 - acc: 1.0000 - val_loss: 0.0051 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 146/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 1.3101e-07 - acc: 1.0000\n",
      "Epoch 146: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 1.2860e-07 - acc: 1.0000 - val_loss: 0.0051 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 147/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 1.2628e-07 - acc: 1.0000\n",
      "Epoch 147: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.2368e-07 - acc: 1.0000 - val_loss: 0.0050 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 148/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 1.1926e-07 - acc: 1.0000\n",
      "Epoch 148: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.1926e-07 - acc: 1.0000 - val_loss: 0.0050 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 149/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 1.1640e-07 - acc: 1.0000\n",
      "Epoch 149: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.1447e-07 - acc: 1.0000 - val_loss: 0.0050 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 150/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 1.1277e-07 - acc: 1.0000\n",
      "Epoch 150: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.1017e-07 - acc: 1.0000 - val_loss: 0.0049 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 151/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 1.0667e-07 - acc: 1.0000\n",
      "Epoch 151: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.0595e-07 - acc: 1.0000 - val_loss: 0.0049 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 152/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 1.0245e-07 - acc: 1.0000\n",
      "Epoch 152: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.0118e-07 - acc: 1.0000 - val_loss: 0.0049 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 153/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 9.8183e-08 - acc: 1.0000\n",
      "Epoch 153: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 9.7269e-08 - acc: 1.0000 - val_loss: 0.0048 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 154/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 8.5230e-08 - acc: 1.0000\n",
      "Epoch 154: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 9.2841e-08 - acc: 1.0000 - val_loss: 0.0047 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 155/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 8.9404e-08 - acc: 1.0000\n",
      "Epoch 155: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 8.8645e-08 - acc: 1.0000 - val_loss: 0.0047 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 156/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 8.5246e-08 - acc: 1.0000\n",
      "Epoch 156: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 8.5246e-08 - acc: 1.0000 - val_loss: 0.0046 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 157/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 8.1592e-08 - acc: 1.0000\n",
      "Epoch 157: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 8.1204e-08 - acc: 1.0000 - val_loss: 0.0046 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 158/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 7.9973e-08 - acc: 1.0000\n",
      "Epoch 158: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 7.8037e-08 - acc: 1.0000 - val_loss: 0.0045 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 159/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 7.5259e-08 - acc: 1.0000\n",
      "Epoch 159: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 7.4459e-08 - acc: 1.0000 - val_loss: 0.0045 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 160/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 7.3147e-08 - acc: 1.0000\n",
      "Epoch 160: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 7.1369e-08 - acc: 1.0000 - val_loss: 0.0043 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 161/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 6.9336e-08 - acc: 1.0000\n",
      "Epoch 161: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 6.8228e-08 - acc: 1.0000 - val_loss: 0.0043 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 162/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 6.6367e-08 - acc: 1.0000\n",
      "Epoch 162: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 6.4830e-08 - acc: 1.0000 - val_loss: 0.0042 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 163/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 6.3485e-08 - acc: 1.0000\n",
      "Epoch 163: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 6.2744e-08 - acc: 1.0000 - val_loss: 0.0040 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 164/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 6.1282e-08 - acc: 1.0000\n",
      "Epoch 164: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 6.0221e-08 - acc: 1.0000 - val_loss: 0.0039 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 165/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 5.8912e-08 - acc: 1.0000\n",
      "Epoch 165: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 5.7518e-08 - acc: 1.0000 - val_loss: 0.0038 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 166/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 5.6455e-08 - acc: 1.0000\n",
      "Epoch 166: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 5.5510e-08 - acc: 1.0000 - val_loss: 0.0037 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 167/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 5.5053e-08 - acc: 1.0000\n",
      "Epoch 167: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 5.3527e-08 - acc: 1.0000 - val_loss: 0.0036 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 168/200\n",
      "139/145 [===========================>..] - ETA: 0s - loss: 5.3225e-08 - acc: 1.0000\n",
      "Epoch 168: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 5.1622e-08 - acc: 1.0000 - val_loss: 0.0035 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 169/200\n",
      "139/145 [===========================>..] - ETA: 0s - loss: 5.2180e-08 - acc: 1.0000\n",
      "Epoch 169: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 5.0232e-08 - acc: 1.0000 - val_loss: 0.0033 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 170/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 4.9775e-08 - acc: 1.0000\n",
      "Epoch 170: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 4.8532e-08 - acc: 1.0000 - val_loss: 0.0032 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 171/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 4.6936e-08 - acc: 1.0000\n",
      "Epoch 171: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 4.6936e-08 - acc: 1.0000 - val_loss: 0.0031 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 172/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 4.6895e-08 - acc: 1.0000\n",
      "Epoch 172: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 4.5855e-08 - acc: 1.0000 - val_loss: 0.0029 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 173/200\n",
      "139/145 [===========================>..] - ETA: 0s - loss: 4.5828e-08 - acc: 1.0000\n",
      "Epoch 173: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 4.4181e-08 - acc: 1.0000 - val_loss: 0.0028 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 174/200\n",
      "139/145 [===========================>..] - ETA: 0s - loss: 4.4193e-08 - acc: 1.0000\n",
      "Epoch 174: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 4.2662e-08 - acc: 1.0000 - val_loss: 0.0027 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 175/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 4.2201e-08 - acc: 1.0000\n",
      "Epoch 175: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 4.1323e-08 - acc: 1.0000 - val_loss: 0.0026 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 176/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 4.0113e-08 - acc: 1.0000\n",
      "Epoch 176: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 4.0113e-08 - acc: 1.0000 - val_loss: 0.0025 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 177/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 3.9656e-08 - acc: 1.0000\n",
      "Epoch 177: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 3.8878e-08 - acc: 1.0000 - val_loss: 0.0024 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 178/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 3.8905e-08 - acc: 1.0000\n",
      "Epoch 178: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 3.8234e-08 - acc: 1.0000 - val_loss: 0.0022 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 179/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 3.7678e-08 - acc: 1.0000\n",
      "Epoch 179: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 3.6483e-08 - acc: 1.0000 - val_loss: 0.0021 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 180/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 2.5252e-08 - acc: 1.0000\n",
      "Epoch 180: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 3.5299e-08 - acc: 1.0000 - val_loss: 0.0019 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 181/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 3.4576e-08 - acc: 1.0000\n",
      "Epoch 181: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 3.4114e-08 - acc: 1.0000 - val_loss: 0.0019 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 182/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 3.3471e-08 - acc: 1.0000\n",
      "Epoch 182: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 3.3471e-08 - acc: 1.0000 - val_loss: 0.0018 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 183/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 3.2516e-08 - acc: 1.0000\n",
      "Epoch 183: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 3.1463e-08 - acc: 1.0000 - val_loss: 0.0018 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 184/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 3.0733e-08 - acc: 1.0000\n",
      "Epoch 184: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 3.0613e-08 - acc: 1.0000 - val_loss: 0.0017 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 185/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 2.8871e-08 - acc: 1.0000\n",
      "Epoch 185: val_acc did not improve from 1.00000\n",
      "\n",
      "Epoch 185: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.8759e-08 - acc: 1.0000 - val_loss: 0.0015 - val_acc: 0.9981 - lr: 2.5000e-04\n",
      "Epoch 186/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 2.7345e-08 - acc: 1.0000\n",
      "Epoch 186: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.7395e-08 - acc: 1.0000 - val_loss: 0.0015 - val_acc: 0.9981 - lr: 1.2500e-04\n",
      "Epoch 187/200\n",
      "141/145 [============================>.] - ETA: 0s - loss: 2.7715e-08 - acc: 1.0000\n",
      "Epoch 187: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.7163e-08 - acc: 1.0000 - val_loss: 0.0014 - val_acc: 0.9981 - lr: 1.2500e-04\n",
      "Epoch 188/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 2.6465e-08 - acc: 1.0000\n",
      "Epoch 188: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.6339e-08 - acc: 1.0000 - val_loss: 0.0014 - val_acc: 0.9981 - lr: 1.2500e-04\n",
      "Epoch 189/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 2.6654e-08 - acc: 1.0000\n",
      "Epoch 189: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.6159e-08 - acc: 1.0000 - val_loss: 0.0014 - val_acc: 0.9981 - lr: 1.2500e-04\n",
      "Epoch 190/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 2.5762e-08 - acc: 1.0000\n",
      "Epoch 190: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 10ms/step - loss: 2.5309e-08 - acc: 1.0000 - val_loss: 0.0013 - val_acc: 1.0000 - lr: 1.2500e-04\n",
      "Epoch 191/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 2.4975e-08 - acc: 1.0000\n",
      "Epoch 191: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.4588e-08 - acc: 1.0000 - val_loss: 0.0012 - val_acc: 1.0000 - lr: 1.2500e-04\n",
      "Epoch 192/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 2.4293e-08 - acc: 1.0000\n",
      "Epoch 192: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.3893e-08 - acc: 1.0000 - val_loss: 0.0012 - val_acc: 1.0000 - lr: 1.2500e-04\n",
      "Epoch 193/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 2.3309e-08 - acc: 1.0000\n",
      "Epoch 193: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.3198e-08 - acc: 1.0000 - val_loss: 0.0012 - val_acc: 1.0000 - lr: 1.2500e-04\n",
      "Epoch 194/200\n",
      "142/145 [============================>.] - ETA: 0s - loss: 2.3217e-08 - acc: 1.0000\n",
      "Epoch 194: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.2786e-08 - acc: 1.0000 - val_loss: 0.0011 - val_acc: 1.0000 - lr: 1.2500e-04\n",
      "Epoch 195/200\n",
      "145/145 [==============================] - ETA: 0s - loss: 2.2631e-08 - acc: 1.0000\n",
      "Epoch 195: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.2631e-08 - acc: 1.0000 - val_loss: 0.0012 - val_acc: 1.0000 - lr: 1.2500e-04\n",
      "Epoch 196/200\n",
      "139/145 [===========================>..] - ETA: 0s - loss: 2.2405e-08 - acc: 1.0000\n",
      "Epoch 196: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.1524e-08 - acc: 1.0000 - val_loss: 9.1998e-04 - val_acc: 1.0000 - lr: 1.2500e-04\n",
      "Epoch 197/200\n",
      "144/145 [============================>.] - ETA: 0s - loss: 2.1317e-08 - acc: 1.0000\n",
      "Epoch 197: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.1215e-08 - acc: 1.0000 - val_loss: 9.2730e-04 - val_acc: 1.0000 - lr: 1.2500e-04\n",
      "Epoch 198/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 2.1553e-08 - acc: 1.0000\n",
      "Epoch 198: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.0906e-08 - acc: 1.0000 - val_loss: 8.8898e-04 - val_acc: 1.0000 - lr: 1.2500e-04\n",
      "Epoch 199/200\n",
      "140/145 [===========================>..] - ETA: 0s - loss: 2.0782e-08 - acc: 1.0000\n",
      "Epoch 199: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 2.0108e-08 - acc: 1.0000 - val_loss: 8.7698e-04 - val_acc: 1.0000 - lr: 1.2500e-04\n",
      "Epoch 200/200\n",
      "143/145 [============================>.] - ETA: 0s - loss: 1.9486e-08 - acc: 1.0000\n",
      "Epoch 200: val_acc did not improve from 1.00000\n",
      "145/145 [==============================] - 1s 9ms/step - loss: 1.9259e-08 - acc: 1.0000 - val_loss: 8.3277e-04 - val_acc: 1.0000 - lr: 1.2500e-04\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    x_train,\n",
    "    y_train,\n",
    "    validation_data=(x_val, y_val),\n",
    "    epochs=200,\n",
    "    callbacks=[\n",
    "        ModelCheckpoint('models/model.h5', monitor='val_acc', verbose=1, save_best_only=True, mode='auto'),\n",
    "        ReduceLROnPlateau(monitor='val_acc', factor=0.5, patience=50, verbose=1, mode='auto')\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, loss_ax = plt.subplots(figsize=(16, 10))\n",
    "acc_ax = loss_ax.twinx()\n",
    "\n",
    "loss_ax.plot(history.history['loss'], 'y', label='train loss')\n",
    "loss_ax.plot(history.history['val_loss'], 'r', label='val loss')\n",
    "loss_ax.set_xlabel('epoch')\n",
    "loss_ax.set_ylabel('loss')\n",
    "loss_ax.legend(loc='upper left')\n",
    "\n",
    "acc_ax.plot(history.history['acc'], 'b', label='train acc')\n",
    "acc_ax.plot(history.history['val_acc'], 'g', label='val acc')\n",
    "acc_ax.set_ylabel('accuracy')\n",
    "acc_ax.legend(loc='upper left')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import multilabel_confusion_matrix\n",
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 0s 6ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[426,   0],\n",
       "        [  0,  89]],\n",
       "\n",
       "       [[433,   0],\n",
       "        [  0,  82]],\n",
       "\n",
       "       [[434,   0],\n",
       "        [  0,  81]],\n",
       "\n",
       "       [[418,   0],\n",
       "        [  0,  97]],\n",
       "\n",
       "       [[441,   0],\n",
       "        [  0,  74]],\n",
       "\n",
       "       [[423,   0],\n",
       "        [  0,  92]]], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = load_model('models/model.h5')\n",
    "\n",
    "y_pred = model.predict(x_val)\n",
    "\n",
    "multilabel_confusion_matrix(np.argmax(y_val, axis=1), np.argmax(y_pred, axis=1))"
   ]
  }
 ],
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