land-cover-and-land-use-classification.ipynb

{
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    "tags": []
   },
   "source": [
    "# Declaring paths and variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "epochs = 1000\n",
    "channels = 3\n",
    "img_height = 256\n",
    "img_width = 256\n",
    "learning_rate = 0.0001\n",
    "train_dir = '../input/rgbeurosat/RBG/train'\n",
    "val_dir = '../input/rgbeurosat/RBG/val'\n",
    "test_dir = '../input/rgbeurosat/RBG/test'"
   ]
  },
  {
   "cell_type": "markdown",
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     "start_time": "2020-11-28T11:00:03.669679",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_data_generator = ImageDataGenerator(\n",
    "    rescale = 1./255,\n",
    "    width_shift_range = 0.15,\n",
    "    height_shift_range = 0.15,\n",
    "    horizontal_flip=True,\n",
    "    zoom_range=0.3\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "validation_data_generator = ImageDataGenerator(\n",
    "    rescale=1./255\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2020-11-28T11:00:13.113039Z",
     "shell.execute_reply": "2020-11-28T11:00:13.112080Z"
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     "duration": 3.620166,
     "end_time": "2020-11-28T11:00:13.113151",
     "exception": false,
     "start_time": "2020-11-28T11:00:09.492985",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 21600 images belonging to 10 classes.\n"
     ]
    }
   ],
   "source": [
    "train_data = train_data_generator.flow_from_directory(\n",
    "    batch_size=batch_size,\n",
    "    directory=train_dir,\n",
    "    shuffle=True,\n",
    "    target_size=(img_height, img_width),\n",
    "    class_mode='categorical'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-28T11:00:13.157338Z",
     "iopub.status.busy": "2020-11-28T11:00:13.156653Z",
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     "shell.execute_reply": "2020-11-28T11:00:13.382870Z"
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    "papermill": {
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     "end_time": "2020-11-28T11:00:13.383062",
     "exception": false,
     "start_time": "2020-11-28T11:00:13.132878",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2700 images belonging to 10 classes.\n"
     ]
    }
   ],
   "source": [
    "val_data = validation_data_generator.flow_from_directory(\n",
    "    batch_size=batch_size,\n",
    "    directory=val_dir,\n",
    "    shuffle=True,\n",
    "    target_size=(img_height, img_width),\n",
    "    class_mode='categorical'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.02076,
     "end_time": "2020-11-28T11:00:13.425668",
     "exception": false,
     "start_time": "2020-11-28T11:00:13.404908",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Building model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 256, 256, 8)       224       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 128, 128, 8)       0         \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 128, 128, 8)       32        \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 128, 128, 16)      1168      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 64, 64, 16)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 64, 64, 16)        64        \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 64, 64, 32)        4640      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 64, 64, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 64, 64, 16)        4624      \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 64, 64, 16)        64        \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 64, 64, 32)        4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 32, 32, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 32, 32, 64)        18496     \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 32, 32, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 32, 32, 32)        18464     \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 32, 32, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 32, 32, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 16, 16, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 16, 16, 128)       73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 16, 16, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        73792     \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 16384)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 100)               1638500   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 1,869,834\n",
      "Trainable params: 1,868,922\n",
      "Non-trainable params: 912\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# importing libraries\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, InputLayer, BatchNormalization, Dropout\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "# build a sequential model\n",
    "\n",
    "model = Sequential()\n",
    "model.add(InputLayer(input_shape=(img_height, img_width, channels)))\n",
    "\n",
    "# 1st conv block\n",
    "\n",
    "model.add(Conv2D(8, (3, 3), activation='relu', strides=(1, 1), padding='same'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "# 2nd conv block\n",
    "\n",
    "model.add(Conv2D(16, (3, 3), activation='relu', strides=(1, 1), padding='same'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "# 3rd conv block\n",
    "\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', strides=(1, 1), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "# 4th conv block\n",
    "\n",
    "model.add(Conv2D(16, (3, 3), activation='relu', strides=(1, 1), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "# 5th conv block\n",
    "\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', strides=(1, 1), padding='same'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "# 6th conv block\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), activation='relu', strides=(1, 1), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "# 7th conv block\n",
    "\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', strides=(1, 1), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "# 8th conv block\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), activation='relu', strides=(1, 1), padding='same'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "# 9th conv block\n",
    "\n",
    "model.add(Conv2D(128, (3, 3), activation='relu', strides=(1, 1), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "# 10th conv bock\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), activation='relu', strides=(1, 1), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "# ANN block\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(units=100, activation='relu'))\n",
    "model.add(Dense(units=100, activation='relu'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# output layer\n",
    "\n",
    "model.add(Dense(units=10, activation='softmax'))\n",
    "\n",
    "\n",
    "# compile model\n",
    "\n",
    "opt = Adam(learning_rate=learning_rate)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
    "\n",
    "# model summary\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2020-11-28T11:00:16.988878Z"
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     "exception": false,
     "start_time": "2020-11-28T11:00:16.958690",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "mcp_save = ModelCheckpoint('PKNet.h5', save_best_only=True, monitor='val_accuracy', mode='max')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "duration": 0.022147,
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     "start_time": "2020-11-28T11:00:17.010900",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Running Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "24/24 [==============================] - 14s 564ms/step - loss: 2.1162 - accuracy: 0.3411 - val_loss: 2.3067 - val_accuracy: 0.1375\n",
      "Epoch 2/1000\n",
      "24/24 [==============================] - 14s 581ms/step - loss: 1.7065 - accuracy: 0.4154 - val_loss: 2.4138 - val_accuracy: 0.1406\n",
      "Epoch 3/1000\n",
      "24/24 [==============================] - 14s 569ms/step - loss: 1.4121 - accuracy: 0.4948 - val_loss: 2.8118 - val_accuracy: 0.1125\n",
      "Epoch 4/1000\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 1.3752 - accuracy: 0.5156 - val_loss: 4.0541 - val_accuracy: 0.1031\n",
      "Epoch 5/1000\n",
      "24/24 [==============================] - 14s 569ms/step - loss: 1.3273 - accuracy: 0.5312 - val_loss: 5.0200 - val_accuracy: 0.1125\n",
      "Epoch 6/1000\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 1.2426 - accuracy: 0.5586 - val_loss: 5.1192 - val_accuracy: 0.1281\n",
      "Epoch 7/1000\n",
      "24/24 [==============================] - 14s 581ms/step - loss: 1.2910 - accuracy: 0.5547 - val_loss: 6.3292 - val_accuracy: 0.1312\n",
      "Epoch 8/1000\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 1.1262 - accuracy: 0.6276 - val_loss: 6.0176 - val_accuracy: 0.1094\n",
      "Epoch 9/1000\n",
      "24/24 [==============================] - 14s 579ms/step - loss: 1.1523 - accuracy: 0.6133 - val_loss: 7.4403 - val_accuracy: 0.0781\n",
      "Epoch 10/1000\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 1.0613 - accuracy: 0.6432 - val_loss: 9.3685 - val_accuracy: 0.1125\n",
      "Epoch 11/1000\n",
      "24/24 [==============================] - 14s 575ms/step - loss: 1.1352 - accuracy: 0.6133 - val_loss: 5.9168 - val_accuracy: 0.1063\n",
      "Epoch 12/1000\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 1.1452 - accuracy: 0.6081 - val_loss: 8.7708 - val_accuracy: 0.1219\n",
      "Epoch 13/1000\n",
      "24/24 [==============================] - 15s 626ms/step - loss: 1.1022 - accuracy: 0.6263 - val_loss: 6.1970 - val_accuracy: 0.1156\n",
      "Epoch 14/1000\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.9835 - accuracy: 0.6745 - val_loss: 5.8355 - val_accuracy: 0.1312\n",
      "Epoch 15/1000\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.9366 - accuracy: 0.6745 - val_loss: 5.8338 - val_accuracy: 0.1000\n",
      "Epoch 16/1000\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.9721 - accuracy: 0.6849 - val_loss: 4.0206 - val_accuracy: 0.1375\n",
      "Epoch 17/1000\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.9591 - accuracy: 0.6719 - val_loss: 4.1693 - val_accuracy: 0.1594\n",
      "Epoch 18/1000\n",
      "24/24 [==============================] - 13s 556ms/step - loss: 0.9624 - accuracy: 0.6667 - val_loss: 2.9626 - val_accuracy: 0.2750\n",
      "Epoch 19/1000\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 1.0137 - accuracy: 0.6419 - val_loss: 2.4461 - val_accuracy: 0.2937\n",
      "Epoch 20/1000\n",
      "24/24 [==============================] - 14s 580ms/step - loss: 0.9663 - accuracy: 0.6901 - val_loss: 2.5569 - val_accuracy: 0.3375\n",
      "Epoch 21/1000\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.9367 - accuracy: 0.6706 - val_loss: 1.8736 - val_accuracy: 0.3844\n",
      "Epoch 22/1000\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.8727 - accuracy: 0.7148 - val_loss: 2.2587 - val_accuracy: 0.4344\n",
      "Epoch 23/1000\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.9716 - accuracy: 0.6927 - val_loss: 1.3872 - val_accuracy: 0.5437\n",
      "Epoch 24/1000\n",
      "24/24 [==============================] - 13s 562ms/step - loss: 0.9197 - accuracy: 0.6979 - val_loss: 1.4325 - val_accuracy: 0.5031\n",
      "Epoch 25/1000\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.9197 - accuracy: 0.6927 - val_loss: 1.0656 - val_accuracy: 0.6687\n",
      "Epoch 26/1000\n",
      "24/24 [==============================] - 14s 572ms/step - loss: 0.8763 - accuracy: 0.6901 - val_loss: 0.8943 - val_accuracy: 0.6719\n",
      "Epoch 27/1000\n",
      "24/24 [==============================] - 14s 575ms/step - loss: 0.8888 - accuracy: 0.6992 - val_loss: 0.7129 - val_accuracy: 0.7563\n",
      "Epoch 28/1000\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.9350 - accuracy: 0.7057 - val_loss: 0.6229 - val_accuracy: 0.7750\n",
      "Epoch 29/1000\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.8832 - accuracy: 0.6771 - val_loss: 0.9063 - val_accuracy: 0.6719\n",
      "Epoch 30/1000\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.8561 - accuracy: 0.7005 - val_loss: 0.8239 - val_accuracy: 0.6938\n",
      "Epoch 31/1000\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.8937 - accuracy: 0.6810 - val_loss: 0.9387 - val_accuracy: 0.6656\n",
      "Epoch 32/1000\n",
      "24/24 [==============================] - 13s 552ms/step - loss: 0.8982 - accuracy: 0.6901 - val_loss: 1.0269 - val_accuracy: 0.6313\n",
      "Epoch 33/1000\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.7736 - accuracy: 0.7357 - val_loss: 0.8176 - val_accuracy: 0.7344\n",
      "Epoch 34/1000\n",
      "24/24 [==============================] - 13s 540ms/step - loss: 0.8156 - accuracy: 0.7331 - val_loss: 0.7386 - val_accuracy: 0.7437\n",
      "Epoch 35/1000\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.9672 - accuracy: 0.6641 - val_loss: 0.7665 - val_accuracy: 0.7312\n",
      "Epoch 36/1000\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.8280 - accuracy: 0.6914 - val_loss: 0.8876 - val_accuracy: 0.7156\n",
      "Epoch 37/1000\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.8763 - accuracy: 0.7018 - val_loss: 0.9202 - val_accuracy: 0.6500\n",
      "Epoch 38/1000\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.8651 - accuracy: 0.7005 - val_loss: 0.6564 - val_accuracy: 0.7750\n",
      "Epoch 39/1000\n",
      "24/24 [==============================] - 14s 571ms/step - loss: 0.7786 - accuracy: 0.7435 - val_loss: 0.7608 - val_accuracy: 0.7437\n",
      "Epoch 40/1000\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.8007 - accuracy: 0.7279 - val_loss: 0.7835 - val_accuracy: 0.7188\n",
      "Epoch 41/1000\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.8449 - accuracy: 0.7201 - val_loss: 0.7261 - val_accuracy: 0.7375\n",
      "Epoch 42/1000\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.8789 - accuracy: 0.7148 - val_loss: 0.7198 - val_accuracy: 0.7531\n",
      "Epoch 43/1000\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.7222 - accuracy: 0.7487 - val_loss: 0.8045 - val_accuracy: 0.7312\n",
      "Epoch 44/1000\n",
      "24/24 [==============================] - 14s 573ms/step - loss: 0.8155 - accuracy: 0.7253 - val_loss: 0.6752 - val_accuracy: 0.7906\n",
      "Epoch 45/1000\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.7999 - accuracy: 0.7279 - val_loss: 0.6509 - val_accuracy: 0.8031\n",
      "Epoch 46/1000\n",
      "24/24 [==============================] - 13s 549ms/step - loss: 0.7138 - accuracy: 0.7526 - val_loss: 0.7444 - val_accuracy: 0.7625\n",
      "Epoch 47/1000\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.8173 - accuracy: 0.7031 - val_loss: 0.7802 - val_accuracy: 0.7375\n",
      "Epoch 48/1000\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.7701 - accuracy: 0.7461 - val_loss: 0.6581 - val_accuracy: 0.7563\n",
      "Epoch 49/1000\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.8079 - accuracy: 0.7201 - val_loss: 0.7520 - val_accuracy: 0.7406\n",
      "Epoch 50/1000\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.7851 - accuracy: 0.7344 - val_loss: 1.0025 - val_accuracy: 0.7125\n",
      "Epoch 51/1000\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.6506 - accuracy: 0.7865 - val_loss: 1.0158 - val_accuracy: 0.6906\n",
      "Epoch 52/1000\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.8082 - accuracy: 0.7344 - val_loss: 0.8828 - val_accuracy: 0.7156\n",
      "Epoch 53/1000\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.6977 - accuracy: 0.7487 - val_loss: 0.8528 - val_accuracy: 0.7312\n",
      "Epoch 54/1000\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.7316 - accuracy: 0.7565 - val_loss: 0.7680 - val_accuracy: 0.7625\n",
      "Epoch 55/1000\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.6751 - accuracy: 0.7747 - val_loss: 0.6680 - val_accuracy: 0.7812\n",
      "Epoch 56/1000\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.7227 - accuracy: 0.7435 - val_loss: 0.7882 - val_accuracy: 0.7375\n",
      "Epoch 57/1000\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.6932 - accuracy: 0.7578 - val_loss: 0.8707 - val_accuracy: 0.7125\n",
      "Epoch 58/1000\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.6424 - accuracy: 0.7630 - val_loss: 0.9637 - val_accuracy: 0.6750\n",
      "Epoch 59/1000\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.7008 - accuracy: 0.7682 - val_loss: 0.6874 - val_accuracy: 0.7719\n",
      "Epoch 60/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.7056 - accuracy: 0.7552 - val_loss: 0.9187 - val_accuracy: 0.6781\n",
      "Epoch 61/1000\n",
      "24/24 [==============================] - 14s 564ms/step - loss: 0.7596 - accuracy: 0.7409 - val_loss: 0.7691 - val_accuracy: 0.7406\n",
      "Epoch 62/1000\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.7022 - accuracy: 0.7630 - val_loss: 0.5975 - val_accuracy: 0.7844\n",
      "Epoch 63/1000\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.7030 - accuracy: 0.7578 - val_loss: 0.5877 - val_accuracy: 0.7906\n",
      "Epoch 64/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.6599 - accuracy: 0.7591 - val_loss: 0.7073 - val_accuracy: 0.7656\n",
      "Epoch 65/1000\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.6778 - accuracy: 0.7747 - val_loss: 0.5815 - val_accuracy: 0.7844\n",
      "Epoch 66/1000\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.7216 - accuracy: 0.7617 - val_loss: 0.8727 - val_accuracy: 0.6781\n",
      "Epoch 67/1000\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.7781 - accuracy: 0.7240 - val_loss: 0.7681 - val_accuracy: 0.7563\n",
      "Epoch 68/1000\n",
      "24/24 [==============================] - 13s 555ms/step - loss: 0.7263 - accuracy: 0.7383 - val_loss: 0.8170 - val_accuracy: 0.7312\n",
      "Epoch 69/1000\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.7194 - accuracy: 0.7474 - val_loss: 0.8099 - val_accuracy: 0.7188\n",
      "Epoch 70/1000\n",
      "24/24 [==============================] - 14s 583ms/step - loss: 0.6958 - accuracy: 0.7773 - val_loss: 0.6877 - val_accuracy: 0.7531\n",
      "Epoch 71/1000\n",
      "24/24 [==============================] - 13s 561ms/step - loss: 0.6860 - accuracy: 0.7539 - val_loss: 0.5483 - val_accuracy: 0.8156\n",
      "Epoch 72/1000\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.6835 - accuracy: 0.7643 - val_loss: 0.7930 - val_accuracy: 0.7531\n",
      "Epoch 73/1000\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.6979 - accuracy: 0.7721 - val_loss: 0.8754 - val_accuracy: 0.7188\n",
      "Epoch 74/1000\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.6659 - accuracy: 0.7682 - val_loss: 0.6333 - val_accuracy: 0.7844\n",
      "Epoch 75/1000\n",
      "24/24 [==============================] - 13s 559ms/step - loss: 0.5666 - accuracy: 0.8099 - val_loss: 0.8526 - val_accuracy: 0.7437\n",
      "Epoch 76/1000\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.6657 - accuracy: 0.7721 - val_loss: 0.7912 - val_accuracy: 0.7250\n",
      "Epoch 77/1000\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.7210 - accuracy: 0.7552 - val_loss: 0.5376 - val_accuracy: 0.8188\n",
      "Epoch 78/1000\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.6957 - accuracy: 0.7695 - val_loss: 0.8322 - val_accuracy: 0.7031\n",
      "Epoch 79/1000\n",
      "24/24 [==============================] - 14s 577ms/step - loss: 0.6398 - accuracy: 0.7539 - val_loss: 0.4705 - val_accuracy: 0.8594\n",
      "Epoch 80/1000\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.6827 - accuracy: 0.7747 - val_loss: 0.7214 - val_accuracy: 0.7719\n",
      "Epoch 81/1000\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.6164 - accuracy: 0.7969 - val_loss: 0.5749 - val_accuracy: 0.7906\n",
      "Epoch 82/1000\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.7065 - accuracy: 0.7799 - val_loss: 0.6202 - val_accuracy: 0.8062\n",
      "Epoch 83/1000\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.6009 - accuracy: 0.8112 - val_loss: 0.6133 - val_accuracy: 0.7875\n",
      "Epoch 84/1000\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.5748 - accuracy: 0.8099 - val_loss: 0.7267 - val_accuracy: 0.7594\n",
      "Epoch 85/1000\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.6887 - accuracy: 0.7721 - val_loss: 0.6989 - val_accuracy: 0.7375\n",
      "Epoch 86/1000\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.6761 - accuracy: 0.7695 - val_loss: 0.6739 - val_accuracy: 0.7906\n",
      "Epoch 87/1000\n",
      "24/24 [==============================] - 13s 562ms/step - loss: 0.6953 - accuracy: 0.7474 - val_loss: 0.6321 - val_accuracy: 0.7812\n",
      "Epoch 88/1000\n",
      "24/24 [==============================] - 15s 640ms/step - loss: 0.6794 - accuracy: 0.7708 - val_loss: 0.6037 - val_accuracy: 0.8219\n",
      "Epoch 89/1000\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.6755 - accuracy: 0.7878 - val_loss: 0.5650 - val_accuracy: 0.8250\n",
      "Epoch 90/1000\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.6226 - accuracy: 0.7721 - val_loss: 0.5839 - val_accuracy: 0.7781\n",
      "Epoch 91/1000\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.6475 - accuracy: 0.7812 - val_loss: 0.6167 - val_accuracy: 0.8031\n",
      "Epoch 92/1000\n",
      "24/24 [==============================] - 17s 716ms/step - loss: 0.6863 - accuracy: 0.7513 - val_loss: 0.6592 - val_accuracy: 0.7969\n",
      "Epoch 93/1000\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.5872 - accuracy: 0.8086 - val_loss: 0.6957 - val_accuracy: 0.7875\n",
      "Epoch 94/1000\n",
      "24/24 [==============================] - 13s 541ms/step - loss: 0.5872 - accuracy: 0.7826 - val_loss: 0.7085 - val_accuracy: 0.8000\n",
      "Epoch 95/1000\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.6081 - accuracy: 0.7878 - val_loss: 0.6461 - val_accuracy: 0.8094\n",
      "Epoch 96/1000\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.6353 - accuracy: 0.7904 - val_loss: 0.6170 - val_accuracy: 0.8062\n",
      "Epoch 97/1000\n",
      "24/24 [==============================] - 13s 553ms/step - loss: 0.6041 - accuracy: 0.7852 - val_loss: 0.5821 - val_accuracy: 0.8250\n",
      "Epoch 98/1000\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.6326 - accuracy: 0.7865 - val_loss: 0.5377 - val_accuracy: 0.8094\n",
      "Epoch 99/1000\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.6698 - accuracy: 0.7695 - val_loss: 0.6441 - val_accuracy: 0.8062\n",
      "Epoch 100/1000\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.5878 - accuracy: 0.7917 - val_loss: 0.6905 - val_accuracy: 0.7656\n",
      "Epoch 101/1000\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.6640 - accuracy: 0.7982 - val_loss: 0.5573 - val_accuracy: 0.8125\n",
      "Epoch 102/1000\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.6494 - accuracy: 0.7826 - val_loss: 0.6365 - val_accuracy: 0.7656\n",
      "Epoch 103/1000\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.5693 - accuracy: 0.8112 - val_loss: 0.6540 - val_accuracy: 0.8062\n",
      "Epoch 104/1000\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.6134 - accuracy: 0.7904 - val_loss: 0.5930 - val_accuracy: 0.7906\n",
      "Epoch 105/1000\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.6205 - accuracy: 0.7760 - val_loss: 0.5483 - val_accuracy: 0.8094\n",
      "Epoch 106/1000\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.6961 - accuracy: 0.7721 - val_loss: 0.5791 - val_accuracy: 0.7937\n",
      "Epoch 107/1000\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.5694 - accuracy: 0.8099 - val_loss: 0.6436 - val_accuracy: 0.7969\n",
      "Epoch 108/1000\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.6199 - accuracy: 0.7852 - val_loss: 0.5031 - val_accuracy: 0.8344\n",
      "Epoch 109/1000\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.6024 - accuracy: 0.7839 - val_loss: 0.6041 - val_accuracy: 0.7750\n",
      "Epoch 110/1000\n",
      "24/24 [==============================] - 14s 570ms/step - loss: 0.5960 - accuracy: 0.7943 - val_loss: 0.5399 - val_accuracy: 0.8406\n",
      "Epoch 111/1000\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.6250 - accuracy: 0.7891 - val_loss: 0.6254 - val_accuracy: 0.8313\n",
      "Epoch 112/1000\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.6392 - accuracy: 0.7865 - val_loss: 0.5772 - val_accuracy: 0.7969\n",
      "Epoch 113/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.5599 - accuracy: 0.8203 - val_loss: 0.4800 - val_accuracy: 0.8469\n",
      "Epoch 114/1000\n",
      "24/24 [==============================] - 13s 562ms/step - loss: 0.6525 - accuracy: 0.7812 - val_loss: 0.6223 - val_accuracy: 0.7750\n",
      "Epoch 115/1000\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.5636 - accuracy: 0.8099 - val_loss: 0.5272 - val_accuracy: 0.8156\n",
      "Epoch 116/1000\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.6080 - accuracy: 0.7826 - val_loss: 0.5608 - val_accuracy: 0.8219\n",
      "Epoch 117/1000\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.6454 - accuracy: 0.7930 - val_loss: 0.5684 - val_accuracy: 0.8188\n",
      "Epoch 118/1000\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.5265 - accuracy: 0.8125 - val_loss: 0.6170 - val_accuracy: 0.8031\n",
      "Epoch 119/1000\n",
      "24/24 [==============================] - 14s 598ms/step - loss: 0.5453 - accuracy: 0.8060 - val_loss: 0.6385 - val_accuracy: 0.7969\n",
      "Epoch 120/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.5711 - accuracy: 0.8086 - val_loss: 0.6902 - val_accuracy: 0.7750\n",
      "Epoch 121/1000\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.5802 - accuracy: 0.8294 - val_loss: 0.5926 - val_accuracy: 0.7875\n",
      "Epoch 122/1000\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.5717 - accuracy: 0.7995 - val_loss: 0.5375 - val_accuracy: 0.8281\n",
      "Epoch 123/1000\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.5275 - accuracy: 0.8294 - val_loss: 0.6990 - val_accuracy: 0.7812\n",
      "Epoch 124/1000\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.6545 - accuracy: 0.7826 - val_loss: 0.5874 - val_accuracy: 0.8219\n",
      "Epoch 125/1000\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.5759 - accuracy: 0.7839 - val_loss: 0.6170 - val_accuracy: 0.7906\n",
      "Epoch 126/1000\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.6284 - accuracy: 0.7904 - val_loss: 0.5098 - val_accuracy: 0.8188\n",
      "Epoch 127/1000\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.5902 - accuracy: 0.8021 - val_loss: 0.5739 - val_accuracy: 0.8188\n",
      "Epoch 128/1000\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.5382 - accuracy: 0.8281 - val_loss: 0.5155 - val_accuracy: 0.8156\n",
      "Epoch 129/1000\n",
      "24/24 [==============================] - 14s 579ms/step - loss: 0.6005 - accuracy: 0.7930 - val_loss: 0.6676 - val_accuracy: 0.7812\n",
      "Epoch 130/1000\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.5537 - accuracy: 0.8099 - val_loss: 0.4180 - val_accuracy: 0.8375\n",
      "Epoch 131/1000\n",
      "24/24 [==============================] - 13s 562ms/step - loss: 0.5854 - accuracy: 0.8125 - val_loss: 0.6649 - val_accuracy: 0.8000\n",
      "Epoch 132/1000\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.5086 - accuracy: 0.8333 - val_loss: 0.6200 - val_accuracy: 0.8219\n",
      "Epoch 133/1000\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.4981 - accuracy: 0.8281 - val_loss: 0.6383 - val_accuracy: 0.8031\n",
      "Epoch 134/1000\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.5302 - accuracy: 0.8281 - val_loss: 0.5642 - val_accuracy: 0.8125\n",
      "Epoch 135/1000\n",
      "24/24 [==============================] - 12s 491ms/step - loss: 0.5110 - accuracy: 0.8346 - val_loss: 0.8407 - val_accuracy: 0.7531\n",
      "Epoch 136/1000\n",
      "24/24 [==============================] - 13s 561ms/step - loss: 0.5767 - accuracy: 0.8021 - val_loss: 0.5161 - val_accuracy: 0.8375\n",
      "Epoch 137/1000\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.5422 - accuracy: 0.8099 - val_loss: 0.4801 - val_accuracy: 0.8594\n",
      "Epoch 138/1000\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.4358 - accuracy: 0.8464 - val_loss: 0.6353 - val_accuracy: 0.8000\n",
      "Epoch 139/1000\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.5580 - accuracy: 0.8034 - val_loss: 0.4651 - val_accuracy: 0.8438\n",
      "Epoch 140/1000\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.4712 - accuracy: 0.8372 - val_loss: 0.6098 - val_accuracy: 0.8062\n",
      "Epoch 141/1000\n",
      "24/24 [==============================] - 14s 575ms/step - loss: 0.5529 - accuracy: 0.8086 - val_loss: 0.6689 - val_accuracy: 0.8031\n",
      "Epoch 142/1000\n",
      "24/24 [==============================] - 13s 548ms/step - loss: 0.4996 - accuracy: 0.8372 - val_loss: 0.4826 - val_accuracy: 0.8344\n",
      "Epoch 143/1000\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.5760 - accuracy: 0.8164 - val_loss: 0.6234 - val_accuracy: 0.8094\n",
      "Epoch 144/1000\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.5310 - accuracy: 0.8190 - val_loss: 0.5412 - val_accuracy: 0.8469\n",
      "Epoch 145/1000\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.4992 - accuracy: 0.8229 - val_loss: 0.4318 - val_accuracy: 0.8375\n",
      "Epoch 146/1000\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.6319 - accuracy: 0.8021 - val_loss: 0.5646 - val_accuracy: 0.8031\n",
      "Epoch 147/1000\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.5814 - accuracy: 0.8073 - val_loss: 0.4205 - val_accuracy: 0.8687\n",
      "Epoch 148/1000\n",
      "24/24 [==============================] - 14s 565ms/step - loss: 0.5114 - accuracy: 0.8073 - val_loss: 0.5094 - val_accuracy: 0.8156\n",
      "Epoch 149/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.5200 - accuracy: 0.8268 - val_loss: 0.4990 - val_accuracy: 0.8250\n",
      "Epoch 150/1000\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.4991 - accuracy: 0.8229 - val_loss: 0.5379 - val_accuracy: 0.8062\n",
      "Epoch 151/1000\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.6053 - accuracy: 0.7904 - val_loss: 0.4541 - val_accuracy: 0.8375\n",
      "Epoch 152/1000\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.5453 - accuracy: 0.8281 - val_loss: 0.7442 - val_accuracy: 0.7875\n",
      "Epoch 153/1000\n",
      "24/24 [==============================] - 14s 573ms/step - loss: 0.5529 - accuracy: 0.8086 - val_loss: 0.6508 - val_accuracy: 0.7906\n",
      "Epoch 154/1000\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.4974 - accuracy: 0.8346 - val_loss: 0.6064 - val_accuracy: 0.8094\n",
      "Epoch 155/1000\n",
      "24/24 [==============================] - 15s 618ms/step - loss: 0.5167 - accuracy: 0.8281 - val_loss: 0.4836 - val_accuracy: 0.8375\n",
      "Epoch 156/1000\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.4914 - accuracy: 0.8320 - val_loss: 0.4208 - val_accuracy: 0.8594\n",
      "Epoch 157/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.5194 - accuracy: 0.8203 - val_loss: 0.6763 - val_accuracy: 0.8031\n",
      "Epoch 158/1000\n",
      "24/24 [==============================] - 14s 577ms/step - loss: 0.4663 - accuracy: 0.8411 - val_loss: 0.6097 - val_accuracy: 0.8031\n",
      "Epoch 159/1000\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.5785 - accuracy: 0.8099 - val_loss: 0.5130 - val_accuracy: 0.8656\n",
      "Epoch 160/1000\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.4480 - accuracy: 0.8464 - val_loss: 0.5009 - val_accuracy: 0.8281\n",
      "Epoch 161/1000\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.4957 - accuracy: 0.8216 - val_loss: 0.4383 - val_accuracy: 0.8469\n",
      "Epoch 162/1000\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.4423 - accuracy: 0.8555 - val_loss: 0.4230 - val_accuracy: 0.8656\n",
      "Epoch 163/1000\n",
      "24/24 [==============================] - 14s 569ms/step - loss: 0.4738 - accuracy: 0.8568 - val_loss: 0.5041 - val_accuracy: 0.8562\n",
      "Epoch 164/1000\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.5553 - accuracy: 0.8125 - val_loss: 0.3727 - val_accuracy: 0.8844\n",
      "Epoch 165/1000\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.5682 - accuracy: 0.7969 - val_loss: 0.5418 - val_accuracy: 0.8094\n",
      "Epoch 166/1000\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.4605 - accuracy: 0.8464 - val_loss: 0.4920 - val_accuracy: 0.8406\n",
      "Epoch 167/1000\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.5021 - accuracy: 0.8438 - val_loss: 0.5755 - val_accuracy: 0.8281\n",
      "Epoch 168/1000\n",
      "24/24 [==============================] - 14s 568ms/step - loss: 0.5286 - accuracy: 0.8164 - val_loss: 0.5687 - val_accuracy: 0.7969\n",
      "Epoch 169/1000\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.4330 - accuracy: 0.8594 - val_loss: 0.5207 - val_accuracy: 0.8469\n",
      "Epoch 170/1000\n",
      "24/24 [==============================] - 13s 557ms/step - loss: 0.4477 - accuracy: 0.8451 - val_loss: 0.4142 - val_accuracy: 0.8656\n",
      "Epoch 171/1000\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.5315 - accuracy: 0.8034 - val_loss: 0.5690 - val_accuracy: 0.8406\n",
      "Epoch 172/1000\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.5062 - accuracy: 0.8490 - val_loss: 0.4520 - val_accuracy: 0.8469\n",
      "Epoch 173/1000\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.5003 - accuracy: 0.8398 - val_loss: 0.4717 - val_accuracy: 0.8469\n",
      "Epoch 174/1000\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.4587 - accuracy: 0.8424 - val_loss: 0.4061 - val_accuracy: 0.8406\n",
      "Epoch 175/1000\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.4713 - accuracy: 0.8438 - val_loss: 0.3812 - val_accuracy: 0.8625\n",
      "Epoch 176/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.5344 - accuracy: 0.8307 - val_loss: 0.6407 - val_accuracy: 0.8031\n",
      "Epoch 177/1000\n",
      "24/24 [==============================] - 13s 557ms/step - loss: 0.5253 - accuracy: 0.8307 - val_loss: 0.6278 - val_accuracy: 0.7937\n",
      "Epoch 178/1000\n",
      "24/24 [==============================] - 13s 548ms/step - loss: 0.4646 - accuracy: 0.8346 - val_loss: 0.4771 - val_accuracy: 0.8375\n",
      "Epoch 179/1000\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.4695 - accuracy: 0.8424 - val_loss: 0.5325 - val_accuracy: 0.8375\n",
      "Epoch 180/1000\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.4173 - accuracy: 0.8542 - val_loss: 0.4550 - val_accuracy: 0.8594\n",
      "Epoch 181/1000\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.4927 - accuracy: 0.8307 - val_loss: 0.5546 - val_accuracy: 0.8031\n",
      "Epoch 182/1000\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.5013 - accuracy: 0.8346 - val_loss: 0.6151 - val_accuracy: 0.7844\n",
      "Epoch 183/1000\n",
      "24/24 [==============================] - 12s 486ms/step - loss: 0.4982 - accuracy: 0.8424 - val_loss: 0.5085 - val_accuracy: 0.8094\n",
      "Epoch 184/1000\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.4463 - accuracy: 0.8372 - val_loss: 0.4616 - val_accuracy: 0.8500\n",
      "Epoch 185/1000\n",
      "24/24 [==============================] - 13s 541ms/step - loss: 0.4848 - accuracy: 0.8333 - val_loss: 0.3946 - val_accuracy: 0.8969\n",
      "Epoch 186/1000\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.5154 - accuracy: 0.8255 - val_loss: 0.3559 - val_accuracy: 0.8656\n",
      "Epoch 187/1000\n",
      "24/24 [==============================] - 14s 578ms/step - loss: 0.5409 - accuracy: 0.8385 - val_loss: 0.4443 - val_accuracy: 0.8687\n",
      "Epoch 188/1000\n",
      "24/24 [==============================] - 11s 474ms/step - loss: 0.4753 - accuracy: 0.8464 - val_loss: 0.5138 - val_accuracy: 0.8438\n",
      "Epoch 189/1000\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.5179 - accuracy: 0.8086 - val_loss: 0.3744 - val_accuracy: 0.8781\n",
      "Epoch 190/1000\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.4734 - accuracy: 0.8385 - val_loss: 0.4135 - val_accuracy: 0.8781\n",
      "Epoch 191/1000\n",
      "24/24 [==============================] - 14s 576ms/step - loss: 0.4204 - accuracy: 0.8464 - val_loss: 0.3655 - val_accuracy: 0.8656\n",
      "Epoch 192/1000\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.4815 - accuracy: 0.8424 - val_loss: 0.3639 - val_accuracy: 0.8844\n",
      "Epoch 193/1000\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.4801 - accuracy: 0.8372 - val_loss: 0.2941 - val_accuracy: 0.9094\n",
      "Epoch 194/1000\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.4136 - accuracy: 0.8555 - val_loss: 0.5076 - val_accuracy: 0.8125\n",
      "Epoch 195/1000\n",
      "24/24 [==============================] - 13s 561ms/step - loss: 0.4595 - accuracy: 0.8477 - val_loss: 0.6184 - val_accuracy: 0.8188\n",
      "Epoch 196/1000\n",
      "24/24 [==============================] - 13s 560ms/step - loss: 0.5191 - accuracy: 0.8398 - val_loss: 0.3865 - val_accuracy: 0.8781\n",
      "Epoch 197/1000\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.4863 - accuracy: 0.8346 - val_loss: 0.5133 - val_accuracy: 0.8250\n",
      "Epoch 198/1000\n",
      "24/24 [==============================] - 12s 481ms/step - loss: 0.5412 - accuracy: 0.8216 - val_loss: 0.5245 - val_accuracy: 0.8406\n",
      "Epoch 199/1000\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.4622 - accuracy: 0.8516 - val_loss: 0.3744 - val_accuracy: 0.8719\n",
      "Epoch 200/1000\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.4847 - accuracy: 0.8411 - val_loss: 0.3582 - val_accuracy: 0.8562\n",
      "Epoch 201/1000\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.5002 - accuracy: 0.8112 - val_loss: 0.4022 - val_accuracy: 0.8594\n",
      "Epoch 202/1000\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.4146 - accuracy: 0.8542 - val_loss: 0.5131 - val_accuracy: 0.8438\n",
      "Epoch 203/1000\n",
      "24/24 [==============================] - 11s 478ms/step - loss: 0.5609 - accuracy: 0.8164 - val_loss: 0.4847 - val_accuracy: 0.8562\n",
      "Epoch 204/1000\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.4704 - accuracy: 0.8320 - val_loss: 0.4081 - val_accuracy: 0.8719\n",
      "Epoch 205/1000\n",
      "24/24 [==============================] - 14s 582ms/step - loss: 0.4901 - accuracy: 0.8346 - val_loss: 0.3915 - val_accuracy: 0.8719\n",
      "Epoch 206/1000\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.4488 - accuracy: 0.8242 - val_loss: 0.3898 - val_accuracy: 0.8719\n",
      "Epoch 207/1000\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.4559 - accuracy: 0.8568 - val_loss: 0.3817 - val_accuracy: 0.8750\n",
      "Epoch 208/1000\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 0.4646 - accuracy: 0.8307 - val_loss: 0.4342 - val_accuracy: 0.8562\n",
      "Epoch 209/1000\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.4228 - accuracy: 0.8776 - val_loss: 0.4530 - val_accuracy: 0.8844\n",
      "Epoch 210/1000\n",
      "24/24 [==============================] - 14s 572ms/step - loss: 0.4475 - accuracy: 0.8490 - val_loss: 0.4041 - val_accuracy: 0.8594\n",
      "Epoch 211/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.4368 - accuracy: 0.8607 - val_loss: 0.5029 - val_accuracy: 0.8438\n",
      "Epoch 212/1000\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.3725 - accuracy: 0.8750 - val_loss: 0.4495 - val_accuracy: 0.8438\n",
      "Epoch 213/1000\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.4602 - accuracy: 0.8490 - val_loss: 0.4051 - val_accuracy: 0.8813\n",
      "Epoch 214/1000\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.4363 - accuracy: 0.8529 - val_loss: 0.5058 - val_accuracy: 0.8281\n",
      "Epoch 215/1000\n",
      "24/24 [==============================] - 14s 573ms/step - loss: 0.4318 - accuracy: 0.8646 - val_loss: 0.5234 - val_accuracy: 0.8250\n",
      "Epoch 216/1000\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.4144 - accuracy: 0.8490 - val_loss: 0.4756 - val_accuracy: 0.8406\n",
      "Epoch 217/1000\n",
      "24/24 [==============================] - 11s 472ms/step - loss: 0.4156 - accuracy: 0.8516 - val_loss: 0.4592 - val_accuracy: 0.8531\n",
      "Epoch 218/1000\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.4772 - accuracy: 0.8529 - val_loss: 0.3299 - val_accuracy: 0.9000\n",
      "Epoch 219/1000\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.4585 - accuracy: 0.8490 - val_loss: 0.4582 - val_accuracy: 0.8594\n",
      "Epoch 220/1000\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.3986 - accuracy: 0.8620 - val_loss: 0.4411 - val_accuracy: 0.8750\n",
      "Epoch 221/1000\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.4307 - accuracy: 0.8411 - val_loss: 0.5036 - val_accuracy: 0.8406\n",
      "Epoch 222/1000\n",
      "24/24 [==============================] - 11s 473ms/step - loss: 0.4586 - accuracy: 0.8438 - val_loss: 0.4176 - val_accuracy: 0.8500\n",
      "Epoch 223/1000\n",
      "24/24 [==============================] - 14s 563ms/step - loss: 0.3838 - accuracy: 0.8711 - val_loss: 0.5491 - val_accuracy: 0.8469\n",
      "Epoch 224/1000\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.3918 - accuracy: 0.8633 - val_loss: 0.4094 - val_accuracy: 0.8500\n",
      "Epoch 225/1000\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.4216 - accuracy: 0.8581 - val_loss: 0.5023 - val_accuracy: 0.8500\n",
      "Epoch 226/1000\n",
      "24/24 [==============================] - 12s 480ms/step - loss: 0.4484 - accuracy: 0.8542 - val_loss: 0.4092 - val_accuracy: 0.8687\n",
      "Epoch 227/1000\n",
      "24/24 [==============================] - 12s 491ms/step - loss: 0.4615 - accuracy: 0.8503 - val_loss: 0.3710 - val_accuracy: 0.8875\n",
      "Epoch 228/1000\n",
      "24/24 [==============================] - 14s 563ms/step - loss: 0.4444 - accuracy: 0.8685 - val_loss: 0.4482 - val_accuracy: 0.8687\n",
      "Epoch 229/1000\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.3918 - accuracy: 0.8620 - val_loss: 0.6099 - val_accuracy: 0.8219\n",
      "Epoch 230/1000\n",
      "24/24 [==============================] - 11s 456ms/step - loss: 0.3806 - accuracy: 0.8763 - val_loss: 0.4618 - val_accuracy: 0.8719\n",
      "Epoch 231/1000\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.4134 - accuracy: 0.8646 - val_loss: 0.4227 - val_accuracy: 0.8656\n",
      "Epoch 232/1000\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.4092 - accuracy: 0.8646 - val_loss: 0.2484 - val_accuracy: 0.9250\n",
      "Epoch 233/1000\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.4392 - accuracy: 0.8555 - val_loss: 0.5230 - val_accuracy: 0.8313\n",
      "Epoch 234/1000\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.3711 - accuracy: 0.8789 - val_loss: 0.5739 - val_accuracy: 0.8469\n",
      "Epoch 235/1000\n",
      "24/24 [==============================] - 11s 464ms/step - loss: 0.4284 - accuracy: 0.8607 - val_loss: 0.3709 - val_accuracy: 0.8813\n",
      "Epoch 236/1000\n",
      "24/24 [==============================] - 13s 540ms/step - loss: 0.4289 - accuracy: 0.8620 - val_loss: 0.4478 - val_accuracy: 0.8469\n",
      "Epoch 237/1000\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.4996 - accuracy: 0.8424 - val_loss: 0.4057 - val_accuracy: 0.8844\n",
      "Epoch 238/1000\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.3616 - accuracy: 0.8711 - val_loss: 0.4041 - val_accuracy: 0.8813\n",
      "Epoch 239/1000\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.3973 - accuracy: 0.8724 - val_loss: 0.3505 - val_accuracy: 0.8656\n",
      "Epoch 240/1000\n",
      "24/24 [==============================] - 11s 477ms/step - loss: 0.4542 - accuracy: 0.8594 - val_loss: 0.5872 - val_accuracy: 0.8344\n",
      "Epoch 241/1000\n",
      "24/24 [==============================] - 14s 569ms/step - loss: 0.5049 - accuracy: 0.8281 - val_loss: 0.3281 - val_accuracy: 0.8906\n",
      "Epoch 242/1000\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 0.4581 - accuracy: 0.8581 - val_loss: 0.2714 - val_accuracy: 0.9062\n",
      "Epoch 243/1000\n",
      "24/24 [==============================] - 11s 469ms/step - loss: 0.4500 - accuracy: 0.8424 - val_loss: 0.3979 - val_accuracy: 0.8625\n",
      "Epoch 244/1000\n",
      "24/24 [==============================] - 13s 555ms/step - loss: 0.4199 - accuracy: 0.8477 - val_loss: 0.4012 - val_accuracy: 0.8625\n",
      "Epoch 245/1000\n",
      "24/24 [==============================] - 11s 463ms/step - loss: 0.4183 - accuracy: 0.8490 - val_loss: 0.3323 - val_accuracy: 0.8969\n",
      "Epoch 246/1000\n",
      "24/24 [==============================] - 15s 620ms/step - loss: 0.4205 - accuracy: 0.8750 - val_loss: 0.6002 - val_accuracy: 0.8188\n",
      "Epoch 247/1000\n",
      "24/24 [==============================] - 11s 473ms/step - loss: 0.4066 - accuracy: 0.8503 - val_loss: 0.3916 - val_accuracy: 0.8687\n",
      "Epoch 248/1000\n",
      "24/24 [==============================] - 11s 464ms/step - loss: 0.3602 - accuracy: 0.8750 - val_loss: 0.4040 - val_accuracy: 0.8469\n",
      "Epoch 249/1000\n",
      "24/24 [==============================] - 14s 576ms/step - loss: 0.4122 - accuracy: 0.8581 - val_loss: 0.4322 - val_accuracy: 0.8594\n",
      "Epoch 250/1000\n",
      "24/24 [==============================] - 11s 464ms/step - loss: 0.4187 - accuracy: 0.8529 - val_loss: 0.3896 - val_accuracy: 0.8656\n",
      "Epoch 251/1000\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.3927 - accuracy: 0.8594 - val_loss: 0.5899 - val_accuracy: 0.8406\n",
      "Epoch 252/1000\n",
      "24/24 [==============================] - 14s 580ms/step - loss: 0.4130 - accuracy: 0.8503 - val_loss: 0.3732 - val_accuracy: 0.8781\n",
      "Epoch 253/1000\n",
      "24/24 [==============================] - 11s 470ms/step - loss: 0.3665 - accuracy: 0.8685 - val_loss: 0.4296 - val_accuracy: 0.8719\n",
      "Epoch 254/1000\n",
      "24/24 [==============================] - 13s 561ms/step - loss: 0.5022 - accuracy: 0.8281 - val_loss: 0.4899 - val_accuracy: 0.8625\n",
      "Epoch 255/1000\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.4078 - accuracy: 0.8555 - val_loss: 0.5001 - val_accuracy: 0.8375\n",
      "Epoch 256/1000\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.4615 - accuracy: 0.8542 - val_loss: 0.6194 - val_accuracy: 0.7969\n",
      "Epoch 257/1000\n",
      "24/24 [==============================] - 14s 575ms/step - loss: 0.4489 - accuracy: 0.8568 - val_loss: 0.4940 - val_accuracy: 0.8313\n",
      "Epoch 258/1000\n",
      "24/24 [==============================] - 11s 463ms/step - loss: 0.4291 - accuracy: 0.8776 - val_loss: 0.3544 - val_accuracy: 0.8875\n",
      "Epoch 259/1000\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.3861 - accuracy: 0.8763 - val_loss: 0.5919 - val_accuracy: 0.8438\n",
      "Epoch 260/1000\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.3908 - accuracy: 0.8659 - val_loss: 0.4570 - val_accuracy: 0.8594\n",
      "Epoch 261/1000\n",
      "24/24 [==============================] - 11s 471ms/step - loss: 0.4344 - accuracy: 0.8594 - val_loss: 0.3689 - val_accuracy: 0.9000\n",
      "Epoch 262/1000\n",
      "24/24 [==============================] - 13s 541ms/step - loss: 0.3850 - accuracy: 0.8711 - val_loss: 0.4238 - val_accuracy: 0.9031\n",
      "Epoch 263/1000\n",
      "24/24 [==============================] - 11s 464ms/step - loss: 0.4171 - accuracy: 0.8620 - val_loss: 0.2624 - val_accuracy: 0.9031\n",
      "Epoch 264/1000\n",
      "24/24 [==============================] - 11s 472ms/step - loss: 0.3955 - accuracy: 0.8698 - val_loss: 0.3458 - val_accuracy: 0.8813\n",
      "Epoch 265/1000\n",
      "24/24 [==============================] - 14s 574ms/step - loss: 0.3990 - accuracy: 0.8620 - val_loss: 0.6483 - val_accuracy: 0.8000\n",
      "Epoch 266/1000\n",
      "24/24 [==============================] - 11s 466ms/step - loss: 0.4492 - accuracy: 0.8438 - val_loss: 0.2882 - val_accuracy: 0.8906\n",
      "Epoch 267/1000\n",
      "24/24 [==============================] - 12s 482ms/step - loss: 0.3750 - accuracy: 0.8633 - val_loss: 0.3965 - val_accuracy: 0.8844\n",
      "Epoch 268/1000\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.4021 - accuracy: 0.8659 - val_loss: 0.3899 - val_accuracy: 0.8750\n",
      "Epoch 269/1000\n",
      "24/24 [==============================] - 11s 457ms/step - loss: 0.3969 - accuracy: 0.8698 - val_loss: 0.2678 - val_accuracy: 0.9219\n",
      "Epoch 270/1000\n",
      "24/24 [==============================] - 15s 613ms/step - loss: 0.3440 - accuracy: 0.8789 - val_loss: 0.3511 - val_accuracy: 0.8625\n",
      "Epoch 271/1000\n",
      "24/24 [==============================] - 11s 460ms/step - loss: 0.3741 - accuracy: 0.8633 - val_loss: 0.3588 - val_accuracy: 0.8875\n",
      "Epoch 272/1000\n",
      "24/24 [==============================] - 12s 487ms/step - loss: 0.3511 - accuracy: 0.8815 - val_loss: 0.3129 - val_accuracy: 0.9062\n",
      "Epoch 273/1000\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.3697 - accuracy: 0.8880 - val_loss: 0.3302 - val_accuracy: 0.9000\n",
      "Epoch 274/1000\n",
      "24/24 [==============================] - 11s 468ms/step - loss: 0.3658 - accuracy: 0.8763 - val_loss: 0.2731 - val_accuracy: 0.9125\n",
      "Epoch 275/1000\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3843 - accuracy: 0.8711 - val_loss: 0.3140 - val_accuracy: 0.8813\n",
      "Epoch 276/1000\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.3661 - accuracy: 0.8698 - val_loss: 0.7791 - val_accuracy: 0.7969\n",
      "Epoch 277/1000\n",
      "24/24 [==============================] - 11s 463ms/step - loss: 0.4330 - accuracy: 0.8542 - val_loss: 0.4954 - val_accuracy: 0.8469\n",
      "Epoch 278/1000\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.3566 - accuracy: 0.8841 - val_loss: 0.3314 - val_accuracy: 0.8875\n",
      "Epoch 279/1000\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.3421 - accuracy: 0.8750 - val_loss: 0.5787 - val_accuracy: 0.8188\n",
      "Epoch 280/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.3610 - accuracy: 0.8711 - val_loss: 0.5917 - val_accuracy: 0.8375\n",
      "Epoch 281/1000\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.3654 - accuracy: 0.8802 - val_loss: 0.6413 - val_accuracy: 0.8250\n",
      "Epoch 282/1000\n",
      "24/24 [==============================] - 11s 463ms/step - loss: 0.4088 - accuracy: 0.8620 - val_loss: 0.4221 - val_accuracy: 0.8656\n",
      "Epoch 283/1000\n",
      "24/24 [==============================] - 11s 479ms/step - loss: 0.3817 - accuracy: 0.8659 - val_loss: 0.5711 - val_accuracy: 0.8125\n",
      "Epoch 284/1000\n",
      "24/24 [==============================] - 14s 579ms/step - loss: 0.4169 - accuracy: 0.8659 - val_loss: 0.3030 - val_accuracy: 0.8906\n",
      "Epoch 285/1000\n",
      "24/24 [==============================] - 12s 483ms/step - loss: 0.3716 - accuracy: 0.8867 - val_loss: 0.3017 - val_accuracy: 0.9062\n",
      "Epoch 286/1000\n",
      "24/24 [==============================] - 11s 475ms/step - loss: 0.3583 - accuracy: 0.8802 - val_loss: 0.2314 - val_accuracy: 0.9094\n",
      "Epoch 287/1000\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.4096 - accuracy: 0.8763 - val_loss: 0.3714 - val_accuracy: 0.8750\n",
      "Epoch 288/1000\n",
      "24/24 [==============================] - 12s 483ms/step - loss: 0.3930 - accuracy: 0.8724 - val_loss: 0.4189 - val_accuracy: 0.8687\n",
      "Epoch 289/1000\n",
      "24/24 [==============================] - 13s 557ms/step - loss: 0.3624 - accuracy: 0.8750 - val_loss: 0.3098 - val_accuracy: 0.8875\n",
      "Epoch 290/1000\n",
      "24/24 [==============================] - 12s 483ms/step - loss: 0.3436 - accuracy: 0.8906 - val_loss: 0.4800 - val_accuracy: 0.8406\n",
      "Epoch 291/1000\n",
      "24/24 [==============================] - 11s 468ms/step - loss: 0.4089 - accuracy: 0.8581 - val_loss: 0.3786 - val_accuracy: 0.9062\n",
      "Epoch 292/1000\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.4013 - accuracy: 0.8789 - val_loss: 0.3739 - val_accuracy: 0.8906\n",
      "Epoch 293/1000\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.4192 - accuracy: 0.8685 - val_loss: 0.3133 - val_accuracy: 0.8906\n",
      "Epoch 294/1000\n",
      "24/24 [==============================] - 12s 486ms/step - loss: 0.3234 - accuracy: 0.8919 - val_loss: 0.3550 - val_accuracy: 0.8781\n",
      "Epoch 295/1000\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.3547 - accuracy: 0.8750 - val_loss: 0.3072 - val_accuracy: 0.8969\n",
      "Epoch 296/1000\n",
      "24/24 [==============================] - 11s 446ms/step - loss: 0.3732 - accuracy: 0.8802 - val_loss: 0.3804 - val_accuracy: 0.8719\n",
      "Epoch 297/1000\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3622 - accuracy: 0.8919 - val_loss: 0.4778 - val_accuracy: 0.8531\n",
      "Epoch 298/1000\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.3098 - accuracy: 0.8880 - val_loss: 0.3741 - val_accuracy: 0.8750\n",
      "Epoch 299/1000\n",
      "24/24 [==============================] - 12s 483ms/step - loss: 0.3074 - accuracy: 0.9089 - val_loss: 0.4408 - val_accuracy: 0.8625\n",
      "Epoch 300/1000\n",
      "24/24 [==============================] - 13s 542ms/step - loss: 0.3519 - accuracy: 0.8919 - val_loss: 0.2643 - val_accuracy: 0.9250\n",
      "Epoch 301/1000\n",
      "24/24 [==============================] - 11s 473ms/step - loss: 0.3450 - accuracy: 0.8750 - val_loss: 0.2877 - val_accuracy: 0.9187\n",
      "Epoch 302/1000\n",
      "24/24 [==============================] - 11s 442ms/step - loss: 0.3444 - accuracy: 0.8919 - val_loss: 0.2971 - val_accuracy: 0.8844\n",
      "Epoch 303/1000\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3552 - accuracy: 0.8750 - val_loss: 0.3649 - val_accuracy: 0.8813\n",
      "Epoch 304/1000\n",
      "24/24 [==============================] - 11s 463ms/step - loss: 0.4207 - accuracy: 0.8698 - val_loss: 0.4542 - val_accuracy: 0.8500\n",
      "Epoch 305/1000\n",
      "24/24 [==============================] - 11s 474ms/step - loss: 0.4265 - accuracy: 0.8633 - val_loss: 0.3563 - val_accuracy: 0.8844\n",
      "Epoch 306/1000\n",
      "24/24 [==============================] - 14s 574ms/step - loss: 0.4263 - accuracy: 0.8542 - val_loss: 0.3441 - val_accuracy: 0.8813\n",
      "Epoch 307/1000\n",
      "24/24 [==============================] - 11s 452ms/step - loss: 0.3403 - accuracy: 0.8750 - val_loss: 0.3292 - val_accuracy: 0.8906\n",
      "Epoch 308/1000\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.3519 - accuracy: 0.8854 - val_loss: 0.2791 - val_accuracy: 0.9062\n",
      "Epoch 309/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.3503 - accuracy: 0.8789 - val_loss: 0.4729 - val_accuracy: 0.8750\n",
      "Epoch 310/1000\n",
      "24/24 [==============================] - 11s 464ms/step - loss: 0.4231 - accuracy: 0.8555 - val_loss: 0.3517 - val_accuracy: 0.8719\n",
      "Epoch 311/1000\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.3018 - accuracy: 0.8971 - val_loss: 0.3580 - val_accuracy: 0.8938\n",
      "Epoch 312/1000\n",
      "24/24 [==============================] - 11s 456ms/step - loss: 0.3383 - accuracy: 0.8776 - val_loss: 0.3926 - val_accuracy: 0.8750\n",
      "Epoch 313/1000\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2819 - accuracy: 0.8984 - val_loss: 0.2526 - val_accuracy: 0.9219\n",
      "Epoch 314/1000\n",
      "24/24 [==============================] - 13s 560ms/step - loss: 0.3658 - accuracy: 0.8802 - val_loss: 0.4629 - val_accuracy: 0.8562\n",
      "Epoch 315/1000\n",
      "24/24 [==============================] - 11s 455ms/step - loss: 0.3321 - accuracy: 0.8854 - val_loss: 0.3436 - val_accuracy: 0.8938\n",
      "Epoch 316/1000\n",
      "24/24 [==============================] - 11s 476ms/step - loss: 0.3254 - accuracy: 0.8867 - val_loss: 0.5510 - val_accuracy: 0.8500\n",
      "Epoch 317/1000\n",
      "24/24 [==============================] - 13s 559ms/step - loss: 0.3229 - accuracy: 0.8971 - val_loss: 0.3066 - val_accuracy: 0.9062\n",
      "Epoch 318/1000\n",
      "24/24 [==============================] - 11s 476ms/step - loss: 0.3301 - accuracy: 0.8880 - val_loss: 0.3911 - val_accuracy: 0.8813\n",
      "Epoch 319/1000\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.3542 - accuracy: 0.8932 - val_loss: 0.3479 - val_accuracy: 0.8844\n",
      "Epoch 320/1000\n",
      "24/24 [==============================] - 11s 477ms/step - loss: 0.3500 - accuracy: 0.8867 - val_loss: 0.7894 - val_accuracy: 0.8219\n",
      "Epoch 321/1000\n",
      "24/24 [==============================] - 12s 486ms/step - loss: 0.3528 - accuracy: 0.8802 - val_loss: 0.3590 - val_accuracy: 0.8750\n",
      "Epoch 322/1000\n",
      "24/24 [==============================] - 14s 572ms/step - loss: 0.3176 - accuracy: 0.8893 - val_loss: 0.2453 - val_accuracy: 0.9250\n",
      "Epoch 323/1000\n",
      "24/24 [==============================] - 11s 470ms/step - loss: 0.3432 - accuracy: 0.8867 - val_loss: 0.3535 - val_accuracy: 0.8938\n",
      "Epoch 324/1000\n",
      "24/24 [==============================] - 12s 482ms/step - loss: 0.3221 - accuracy: 0.8945 - val_loss: 0.3396 - val_accuracy: 0.8750\n",
      "Epoch 325/1000\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.3534 - accuracy: 0.8802 - val_loss: 0.2289 - val_accuracy: 0.9125\n",
      "Epoch 326/1000\n",
      "24/24 [==============================] - 11s 478ms/step - loss: 0.2794 - accuracy: 0.9089 - val_loss: 0.2841 - val_accuracy: 0.9156\n",
      "Epoch 327/1000\n",
      "24/24 [==============================] - 12s 479ms/step - loss: 0.3958 - accuracy: 0.8828 - val_loss: 0.3276 - val_accuracy: 0.8813\n",
      "Epoch 328/1000\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.3382 - accuracy: 0.8789 - val_loss: 0.2224 - val_accuracy: 0.9125\n",
      "Epoch 329/1000\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.3095 - accuracy: 0.8984 - val_loss: 0.3817 - val_accuracy: 0.8719\n",
      "Epoch 330/1000\n",
      "24/24 [==============================] - 12s 479ms/step - loss: 0.3562 - accuracy: 0.8945 - val_loss: 0.4062 - val_accuracy: 0.8719\n",
      "Epoch 331/1000\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.3041 - accuracy: 0.8997 - val_loss: 0.5295 - val_accuracy: 0.8781\n",
      "Epoch 332/1000\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 0.3695 - accuracy: 0.8659 - val_loss: 0.4097 - val_accuracy: 0.8656\n",
      "Epoch 333/1000\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.3654 - accuracy: 0.8698 - val_loss: 0.3082 - val_accuracy: 0.9062\n",
      "Epoch 334/1000\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.3314 - accuracy: 0.8854 - val_loss: 0.3610 - val_accuracy: 0.8875\n",
      "Epoch 335/1000\n",
      "24/24 [==============================] - 11s 455ms/step - loss: 0.3232 - accuracy: 0.8945 - val_loss: 0.3685 - val_accuracy: 0.8813\n",
      "Epoch 336/1000\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.3711 - accuracy: 0.8724 - val_loss: 0.4140 - val_accuracy: 0.8750\n",
      "Epoch 337/1000\n",
      "24/24 [==============================] - 11s 477ms/step - loss: 0.3094 - accuracy: 0.8932 - val_loss: 0.3216 - val_accuracy: 0.8938\n",
      "Epoch 338/1000\n",
      "24/24 [==============================] - 11s 455ms/step - loss: 0.3688 - accuracy: 0.8763 - val_loss: 0.3660 - val_accuracy: 0.8781\n",
      "Epoch 339/1000\n",
      "24/24 [==============================] - 14s 563ms/step - loss: 0.3995 - accuracy: 0.8711 - val_loss: 0.4085 - val_accuracy: 0.8719\n",
      "Epoch 340/1000\n",
      "24/24 [==============================] - 11s 457ms/step - loss: 0.3207 - accuracy: 0.8802 - val_loss: 0.3656 - val_accuracy: 0.9000\n",
      "Epoch 341/1000\n",
      "24/24 [==============================] - 11s 464ms/step - loss: 0.3589 - accuracy: 0.8802 - val_loss: 0.5110 - val_accuracy: 0.8781\n",
      "Epoch 342/1000\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3906 - accuracy: 0.8607 - val_loss: 0.3182 - val_accuracy: 0.8844\n",
      "Epoch 343/1000\n",
      "24/24 [==============================] - 11s 454ms/step - loss: 0.3381 - accuracy: 0.8919 - val_loss: 0.3275 - val_accuracy: 0.9187\n",
      "Epoch 344/1000\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.3811 - accuracy: 0.8828 - val_loss: 0.2835 - val_accuracy: 0.8781\n",
      "Epoch 345/1000\n",
      "24/24 [==============================] - 11s 456ms/step - loss: 0.3687 - accuracy: 0.8867 - val_loss: 0.4434 - val_accuracy: 0.8625\n",
      "Epoch 346/1000\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.3494 - accuracy: 0.8880 - val_loss: 0.3084 - val_accuracy: 0.9344\n",
      "Epoch 347/1000\n",
      "24/24 [==============================] - 14s 575ms/step - loss: 0.3236 - accuracy: 0.8854 - val_loss: 0.4138 - val_accuracy: 0.8906\n",
      "Epoch 348/1000\n",
      "24/24 [==============================] - 11s 452ms/step - loss: 0.3041 - accuracy: 0.8958 - val_loss: 0.3280 - val_accuracy: 0.8813\n",
      "Epoch 349/1000\n",
      "24/24 [==============================] - 12s 486ms/step - loss: 0.3259 - accuracy: 0.8971 - val_loss: 0.3648 - val_accuracy: 0.8781\n",
      "Epoch 350/1000\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.3747 - accuracy: 0.8854 - val_loss: 0.3762 - val_accuracy: 0.8719\n",
      "Epoch 351/1000\n",
      "24/24 [==============================] - 12s 487ms/step - loss: 0.3266 - accuracy: 0.9036 - val_loss: 0.3213 - val_accuracy: 0.8875\n",
      "Epoch 352/1000\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.3674 - accuracy: 0.8750 - val_loss: 0.6080 - val_accuracy: 0.8094\n",
      "Epoch 353/1000\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.3397 - accuracy: 0.8958 - val_loss: 0.4428 - val_accuracy: 0.8813\n",
      "Epoch 354/1000\n",
      "24/24 [==============================] - 11s 461ms/step - loss: 0.3256 - accuracy: 0.8945 - val_loss: 0.2918 - val_accuracy: 0.9031\n",
      "Epoch 355/1000\n",
      "24/24 [==============================] - 14s 578ms/step - loss: 0.3037 - accuracy: 0.9010 - val_loss: 0.3804 - val_accuracy: 0.9031\n",
      "Epoch 356/1000\n",
      "24/24 [==============================] - 12s 482ms/step - loss: 0.3238 - accuracy: 0.9089 - val_loss: 0.2676 - val_accuracy: 0.9156\n",
      "Epoch 357/1000\n",
      "24/24 [==============================] - 12s 486ms/step - loss: 0.2679 - accuracy: 0.9023 - val_loss: 0.3189 - val_accuracy: 0.8875\n",
      "Epoch 358/1000\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.3237 - accuracy: 0.8802 - val_loss: 0.4054 - val_accuracy: 0.8719\n",
      "Epoch 359/1000\n",
      "24/24 [==============================] - 11s 453ms/step - loss: 0.2839 - accuracy: 0.9102 - val_loss: 0.4036 - val_accuracy: 0.8719\n",
      "Epoch 360/1000\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.2862 - accuracy: 0.9128 - val_loss: 0.2825 - val_accuracy: 0.8906\n",
      "Epoch 361/1000\n",
      "24/24 [==============================] - 14s 575ms/step - loss: 0.3670 - accuracy: 0.8763 - val_loss: 0.4868 - val_accuracy: 0.8687\n",
      "Epoch 362/1000\n",
      "24/24 [==============================] - 12s 480ms/step - loss: 0.3619 - accuracy: 0.8789 - val_loss: 0.2825 - val_accuracy: 0.9156\n",
      "Epoch 363/1000\n",
      "24/24 [==============================] - 11s 458ms/step - loss: 0.3857 - accuracy: 0.8841 - val_loss: 0.2740 - val_accuracy: 0.8969\n",
      "Epoch 364/1000\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.3655 - accuracy: 0.8750 - val_loss: 0.2676 - val_accuracy: 0.9031\n",
      "Epoch 365/1000\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.3156 - accuracy: 0.8893 - val_loss: 0.3558 - val_accuracy: 0.8938\n",
      "Epoch 366/1000\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.3319 - accuracy: 0.8802 - val_loss: 0.3851 - val_accuracy: 0.8938\n",
      "Epoch 367/1000\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.3494 - accuracy: 0.8971 - val_loss: 0.2787 - val_accuracy: 0.9000\n",
      "Epoch 368/1000\n",
      "24/24 [==============================] - 11s 460ms/step - loss: 0.3344 - accuracy: 0.8815 - val_loss: 0.3244 - val_accuracy: 0.8813\n",
      "Epoch 369/1000\n",
      "24/24 [==============================] - 13s 558ms/step - loss: 0.3359 - accuracy: 0.8958 - val_loss: 0.3980 - val_accuracy: 0.8781\n",
      "Epoch 370/1000\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.3552 - accuracy: 0.8815 - val_loss: 0.3187 - val_accuracy: 0.8844\n",
      "Epoch 371/1000\n",
      "24/24 [==============================] - 11s 454ms/step - loss: 0.3169 - accuracy: 0.8997 - val_loss: 0.2735 - val_accuracy: 0.9219\n",
      "Epoch 372/1000\n",
      "24/24 [==============================] - 14s 568ms/step - loss: 0.3240 - accuracy: 0.8945 - val_loss: 0.2883 - val_accuracy: 0.9000\n",
      "Epoch 373/1000\n",
      "24/24 [==============================] - 11s 452ms/step - loss: 0.3103 - accuracy: 0.9128 - val_loss: 0.4791 - val_accuracy: 0.8687\n",
      "Epoch 374/1000\n",
      "24/24 [==============================] - 11s 458ms/step - loss: 0.2871 - accuracy: 0.9010 - val_loss: 0.3552 - val_accuracy: 0.8844\n",
      "Epoch 375/1000\n",
      "24/24 [==============================] - 15s 619ms/step - loss: 0.2738 - accuracy: 0.9102 - val_loss: 0.4399 - val_accuracy: 0.8781\n",
      "Epoch 376/1000\n",
      "24/24 [==============================] - 11s 453ms/step - loss: 0.3290 - accuracy: 0.8854 - val_loss: 0.3488 - val_accuracy: 0.8625\n",
      "Epoch 377/1000\n",
      "24/24 [==============================] - 11s 473ms/step - loss: 0.3283 - accuracy: 0.8919 - val_loss: 0.2292 - val_accuracy: 0.9312\n",
      "Epoch 378/1000\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.3199 - accuracy: 0.8841 - val_loss: 0.2229 - val_accuracy: 0.9156\n",
      "Epoch 379/1000\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.2939 - accuracy: 0.9010 - val_loss: 0.3049 - val_accuracy: 0.9156\n",
      "Epoch 380/1000\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.2820 - accuracy: 0.9154 - val_loss: 0.3227 - val_accuracy: 0.9031\n",
      "Epoch 381/1000\n",
      "24/24 [==============================] - 12s 482ms/step - loss: 0.2805 - accuracy: 0.9089 - val_loss: 0.3460 - val_accuracy: 0.8969\n",
      "Epoch 382/1000\n",
      "24/24 [==============================] - 11s 451ms/step - loss: 0.3379 - accuracy: 0.8867 - val_loss: 0.3515 - val_accuracy: 0.8687\n",
      "Epoch 383/1000\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2946 - accuracy: 0.9076 - val_loss: 0.2754 - val_accuracy: 0.9187\n",
      "Epoch 384/1000\n",
      "24/24 [==============================] - 12s 486ms/step - loss: 0.3086 - accuracy: 0.9089 - val_loss: 0.2033 - val_accuracy: 0.9250\n",
      "Epoch 385/1000\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.2896 - accuracy: 0.8945 - val_loss: 0.2566 - val_accuracy: 0.9156\n",
      "Epoch 386/1000\n",
      "24/24 [==============================] - 13s 548ms/step - loss: 0.3851 - accuracy: 0.8815 - val_loss: 0.4449 - val_accuracy: 0.8656\n",
      "Epoch 387/1000\n",
      "24/24 [==============================] - 11s 447ms/step - loss: 0.2642 - accuracy: 0.9154 - val_loss: 0.3821 - val_accuracy: 0.8438\n",
      "Epoch 388/1000\n",
      "24/24 [==============================] - 11s 472ms/step - loss: 0.2781 - accuracy: 0.9089 - val_loss: 0.3971 - val_accuracy: 0.8813\n",
      "Epoch 389/1000\n",
      "24/24 [==============================] - 14s 598ms/step - loss: 0.3140 - accuracy: 0.9036 - val_loss: 0.2220 - val_accuracy: 0.9156\n",
      "Epoch 390/1000\n",
      "24/24 [==============================] - 12s 480ms/step - loss: 0.3223 - accuracy: 0.9010 - val_loss: 0.2803 - val_accuracy: 0.9156\n",
      "Epoch 391/1000\n",
      "24/24 [==============================] - 11s 441ms/step - loss: 0.3091 - accuracy: 0.8919 - val_loss: 0.2419 - val_accuracy: 0.9094\n",
      "Epoch 392/1000\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.2781 - accuracy: 0.8984 - val_loss: 0.2687 - val_accuracy: 0.9156\n",
      "Epoch 393/1000\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 0.2951 - accuracy: 0.8932 - val_loss: 0.5552 - val_accuracy: 0.8500\n",
      "Epoch 394/1000\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.3110 - accuracy: 0.8906 - val_loss: 0.3992 - val_accuracy: 0.8750\n",
      "Epoch 395/1000\n",
      "24/24 [==============================] - 14s 566ms/step - loss: 0.3154 - accuracy: 0.9023 - val_loss: 0.2631 - val_accuracy: 0.9156\n",
      "Epoch 396/1000\n",
      "24/24 [==============================] - 11s 455ms/step - loss: 0.3037 - accuracy: 0.9089 - val_loss: 0.3456 - val_accuracy: 0.8813\n",
      "Epoch 397/1000\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.3153 - accuracy: 0.9036 - val_loss: 0.2878 - val_accuracy: 0.9219\n",
      "Epoch 398/1000\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2920 - accuracy: 0.9049 - val_loss: 0.3418 - val_accuracy: 0.9000\n",
      "Epoch 399/1000\n",
      "24/24 [==============================] - 12s 480ms/step - loss: 0.3257 - accuracy: 0.8893 - val_loss: 0.4028 - val_accuracy: 0.8906\n",
      "Epoch 400/1000\n",
      "24/24 [==============================] - 14s 573ms/step - loss: 0.3022 - accuracy: 0.8958 - val_loss: 0.4252 - val_accuracy: 0.8531\n",
      "Epoch 401/1000\n",
      "24/24 [==============================] - 11s 454ms/step - loss: 0.2801 - accuracy: 0.9128 - val_loss: 0.2338 - val_accuracy: 0.9250\n",
      "Epoch 402/1000\n",
      "24/24 [==============================] - 11s 451ms/step - loss: 0.3347 - accuracy: 0.8789 - val_loss: 0.3644 - val_accuracy: 0.8969\n",
      "Epoch 403/1000\n",
      "24/24 [==============================] - 15s 637ms/step - loss: 0.3159 - accuracy: 0.8984 - val_loss: 0.3082 - val_accuracy: 0.8875\n",
      "Epoch 404/1000\n",
      "24/24 [==============================] - 11s 466ms/step - loss: 0.3065 - accuracy: 0.8945 - val_loss: 0.2392 - val_accuracy: 0.9094\n",
      "Epoch 405/1000\n",
      "24/24 [==============================] - 11s 450ms/step - loss: 0.3172 - accuracy: 0.8945 - val_loss: 0.2457 - val_accuracy: 0.9219\n",
      "Epoch 406/1000\n",
      "24/24 [==============================] - 14s 583ms/step - loss: 0.2696 - accuracy: 0.9128 - val_loss: 0.2289 - val_accuracy: 0.9156\n",
      "Epoch 407/1000\n",
      "24/24 [==============================] - 11s 452ms/step - loss: 0.3325 - accuracy: 0.8893 - val_loss: 0.2979 - val_accuracy: 0.9125\n",
      "Epoch 408/1000\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2917 - accuracy: 0.9036 - val_loss: 0.3809 - val_accuracy: 0.8781\n",
      "Epoch 409/1000\n",
      "24/24 [==============================] - 13s 562ms/step - loss: 0.3034 - accuracy: 0.9062 - val_loss: 0.3407 - val_accuracy: 0.8750\n",
      "Epoch 410/1000\n",
      "24/24 [==============================] - 11s 454ms/step - loss: 0.3002 - accuracy: 0.8932 - val_loss: 0.3018 - val_accuracy: 0.8938\n",
      "Epoch 411/1000\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2353 - accuracy: 0.9154 - val_loss: 0.2371 - val_accuracy: 0.9125\n",
      "Epoch 412/1000\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2579 - accuracy: 0.9141 - val_loss: 0.2823 - val_accuracy: 0.9219\n",
      "Epoch 413/1000\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.3228 - accuracy: 0.8906 - val_loss: 0.2491 - val_accuracy: 0.9125\n",
      "Epoch 414/1000\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.3127 - accuracy: 0.9076 - val_loss: 0.3081 - val_accuracy: 0.9031\n",
      "Epoch 415/1000\n",
      "24/24 [==============================] - 11s 450ms/step - loss: 0.3493 - accuracy: 0.8919 - val_loss: 0.3222 - val_accuracy: 0.8938\n",
      "Epoch 416/1000\n",
      "24/24 [==============================] - 12s 480ms/step - loss: 0.3099 - accuracy: 0.8984 - val_loss: 0.2525 - val_accuracy: 0.8969\n",
      "Epoch 417/1000\n",
      "24/24 [==============================] - 14s 572ms/step - loss: 0.2977 - accuracy: 0.8984 - val_loss: 0.5531 - val_accuracy: 0.8250\n",
      "Epoch 418/1000\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2972 - accuracy: 0.9154 - val_loss: 0.3088 - val_accuracy: 0.8938\n",
      "Epoch 419/1000\n",
      "24/24 [==============================] - 11s 457ms/step - loss: 0.3158 - accuracy: 0.9049 - val_loss: 0.2366 - val_accuracy: 0.9250\n",
      "Epoch 420/1000\n",
      "24/24 [==============================] - 14s 563ms/step - loss: 0.2732 - accuracy: 0.9128 - val_loss: 0.3580 - val_accuracy: 0.8906\n",
      "Epoch 421/1000\n",
      "24/24 [==============================] - 11s 473ms/step - loss: 0.2299 - accuracy: 0.9271 - val_loss: 0.2229 - val_accuracy: 0.9094\n",
      "Epoch 422/1000\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.3108 - accuracy: 0.9010 - val_loss: 0.2372 - val_accuracy: 0.9250\n",
      "Epoch 423/1000\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2904 - accuracy: 0.8971 - val_loss: 0.2930 - val_accuracy: 0.8969\n",
      "Epoch 424/1000\n",
      "24/24 [==============================] - 11s 464ms/step - loss: 0.2970 - accuracy: 0.8906 - val_loss: 0.1951 - val_accuracy: 0.9312\n",
      "Epoch 425/1000\n",
      "24/24 [==============================] - 11s 456ms/step - loss: 0.2944 - accuracy: 0.8945 - val_loss: 0.3673 - val_accuracy: 0.8844\n",
      "Epoch 426/1000\n",
      "24/24 [==============================] - 14s 571ms/step - loss: 0.2721 - accuracy: 0.9036 - val_loss: 0.2394 - val_accuracy: 0.9281\n",
      "Epoch 427/1000\n",
      "24/24 [==============================] - 12s 491ms/step - loss: 0.2826 - accuracy: 0.9128 - val_loss: 0.1489 - val_accuracy: 0.9375\n",
      "Epoch 428/1000\n",
      "24/24 [==============================] - 14s 581ms/step - loss: 0.3263 - accuracy: 0.8893 - val_loss: 0.2432 - val_accuracy: 0.9312\n",
      "Epoch 429/1000\n",
      "24/24 [==============================] - 11s 473ms/step - loss: 0.2857 - accuracy: 0.9115 - val_loss: 0.2689 - val_accuracy: 0.9156\n",
      "Epoch 430/1000\n",
      "24/24 [==============================] - 11s 454ms/step - loss: 0.2697 - accuracy: 0.9089 - val_loss: 0.3045 - val_accuracy: 0.9062\n",
      "Epoch 431/1000\n",
      "24/24 [==============================] - 15s 612ms/step - loss: 0.3270 - accuracy: 0.8971 - val_loss: 0.3314 - val_accuracy: 0.8813\n",
      "Epoch 432/1000\n",
      "24/24 [==============================] - 12s 479ms/step - loss: 0.3089 - accuracy: 0.8906 - val_loss: 0.2988 - val_accuracy: 0.9031\n",
      "Epoch 433/1000\n",
      "24/24 [==============================] - 12s 491ms/step - loss: 0.3267 - accuracy: 0.8919 - val_loss: 0.3197 - val_accuracy: 0.9062\n",
      "Epoch 434/1000\n",
      "24/24 [==============================] - 14s 582ms/step - loss: 0.2430 - accuracy: 0.9206 - val_loss: 0.3800 - val_accuracy: 0.8719\n",
      "Epoch 435/1000\n",
      "24/24 [==============================] - 11s 451ms/step - loss: 0.2518 - accuracy: 0.9141 - val_loss: 0.3630 - val_accuracy: 0.8875\n",
      "Epoch 436/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3020 - accuracy: 0.9023 - val_loss: 0.2407 - val_accuracy: 0.9094\n",
      "Epoch 437/1000\n",
      "24/24 [==============================] - 14s 578ms/step - loss: 0.2882 - accuracy: 0.8971 - val_loss: 0.3512 - val_accuracy: 0.8875\n",
      "Epoch 438/1000\n",
      "24/24 [==============================] - 12s 482ms/step - loss: 0.3025 - accuracy: 0.8880 - val_loss: 0.4895 - val_accuracy: 0.8344\n",
      "Epoch 439/1000\n",
      "24/24 [==============================] - 11s 455ms/step - loss: 0.2668 - accuracy: 0.9141 - val_loss: 0.2668 - val_accuracy: 0.9187\n",
      "Epoch 440/1000\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.3288 - accuracy: 0.8828 - val_loss: 0.1796 - val_accuracy: 0.9375\n",
      "Epoch 441/1000\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2500 - accuracy: 0.9049 - val_loss: 0.1982 - val_accuracy: 0.9344\n",
      "Epoch 442/1000\n",
      "24/24 [==============================] - 12s 483ms/step - loss: 0.2830 - accuracy: 0.8958 - val_loss: 0.2386 - val_accuracy: 0.9250\n",
      "Epoch 443/1000\n",
      "24/24 [==============================] - 13s 559ms/step - loss: 0.2904 - accuracy: 0.8997 - val_loss: 0.3515 - val_accuracy: 0.9062\n",
      "Epoch 444/1000\n",
      "24/24 [==============================] - 11s 461ms/step - loss: 0.2680 - accuracy: 0.9167 - val_loss: 0.1870 - val_accuracy: 0.9281\n",
      "Epoch 445/1000\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.2755 - accuracy: 0.9076 - val_loss: 0.3967 - val_accuracy: 0.8844\n",
      "Epoch 446/1000\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2531 - accuracy: 0.9102 - val_loss: 0.2325 - val_accuracy: 0.9187\n",
      "Epoch 447/1000\n",
      "24/24 [==============================] - 11s 458ms/step - loss: 0.2893 - accuracy: 0.9154 - val_loss: 0.2465 - val_accuracy: 0.9219\n",
      "Epoch 448/1000\n",
      "24/24 [==============================] - 14s 597ms/step - loss: 0.3243 - accuracy: 0.8919 - val_loss: 0.2258 - val_accuracy: 0.9031\n",
      "Epoch 449/1000\n",
      "24/24 [==============================] - 11s 455ms/step - loss: 0.3206 - accuracy: 0.8854 - val_loss: 0.2531 - val_accuracy: 0.9187\n",
      "Epoch 450/1000\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.2875 - accuracy: 0.9036 - val_loss: 0.3799 - val_accuracy: 0.8969\n",
      "Epoch 451/1000\n",
      "24/24 [==============================] - 15s 613ms/step - loss: 0.3365 - accuracy: 0.8880 - val_loss: 0.7413 - val_accuracy: 0.8313\n",
      "Epoch 452/1000\n",
      "24/24 [==============================] - 11s 472ms/step - loss: 0.2583 - accuracy: 0.9076 - val_loss: 0.3099 - val_accuracy: 0.8813\n",
      "Epoch 453/1000\n",
      "24/24 [==============================] - 12s 483ms/step - loss: 0.3290 - accuracy: 0.8867 - val_loss: 0.3095 - val_accuracy: 0.8969\n",
      "Epoch 454/1000\n",
      "24/24 [==============================] - 13s 560ms/step - loss: 0.2896 - accuracy: 0.9128 - val_loss: 0.2296 - val_accuracy: 0.9406\n",
      "Epoch 455/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2441 - accuracy: 0.9206 - val_loss: 0.4228 - val_accuracy: 0.8875\n",
      "Epoch 456/1000\n",
      "24/24 [==============================] - 12s 487ms/step - loss: 0.3019 - accuracy: 0.8854 - val_loss: 0.3144 - val_accuracy: 0.9156\n",
      "Epoch 457/1000\n",
      "24/24 [==============================] - 14s 576ms/step - loss: 0.2840 - accuracy: 0.9049 - val_loss: 0.4067 - val_accuracy: 0.8875\n",
      "Epoch 458/1000\n",
      "24/24 [==============================] - 12s 481ms/step - loss: 0.2618 - accuracy: 0.9193 - val_loss: 0.2054 - val_accuracy: 0.9250\n",
      "Epoch 459/1000\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2790 - accuracy: 0.9062 - val_loss: 0.2827 - val_accuracy: 0.8969\n",
      "Epoch 460/1000\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2710 - accuracy: 0.9089 - val_loss: 0.2564 - val_accuracy: 0.9031\n",
      "Epoch 461/1000\n",
      "24/24 [==============================] - 12s 485ms/step - loss: 0.3221 - accuracy: 0.8893 - val_loss: 0.2253 - val_accuracy: 0.9281\n",
      "Epoch 462/1000\n",
      "24/24 [==============================] - 13s 558ms/step - loss: 0.2867 - accuracy: 0.9076 - val_loss: 0.4004 - val_accuracy: 0.8531\n",
      "Epoch 463/1000\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.2650 - accuracy: 0.9062 - val_loss: 0.2833 - val_accuracy: 0.8906\n",
      "Epoch 464/1000\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.3022 - accuracy: 0.9036 - val_loss: 0.2807 - val_accuracy: 0.9125\n",
      "Epoch 465/1000\n",
      "24/24 [==============================] - 14s 573ms/step - loss: 0.3179 - accuracy: 0.8815 - val_loss: 0.1764 - val_accuracy: 0.9375\n",
      "Epoch 466/1000\n",
      "24/24 [==============================] - 12s 479ms/step - loss: 0.2867 - accuracy: 0.9036 - val_loss: 0.2517 - val_accuracy: 0.9156\n",
      "Epoch 467/1000\n",
      "24/24 [==============================] - 11s 456ms/step - loss: 0.3258 - accuracy: 0.8971 - val_loss: 0.2859 - val_accuracy: 0.9156\n",
      "Epoch 468/1000\n",
      "24/24 [==============================] - 15s 620ms/step - loss: 0.3333 - accuracy: 0.8828 - val_loss: 0.1989 - val_accuracy: 0.9312\n",
      "Epoch 469/1000\n",
      "24/24 [==============================] - 12s 486ms/step - loss: 0.2780 - accuracy: 0.8997 - val_loss: 0.2577 - val_accuracy: 0.9062\n",
      "Epoch 470/1000\n",
      "24/24 [==============================] - 11s 443ms/step - loss: 0.3240 - accuracy: 0.8945 - val_loss: 0.2335 - val_accuracy: 0.9156\n",
      "Epoch 471/1000\n",
      "24/24 [==============================] - 14s 580ms/step - loss: 0.3178 - accuracy: 0.9023 - val_loss: 0.4807 - val_accuracy: 0.8500\n",
      "Epoch 472/1000\n",
      "24/24 [==============================] - 11s 447ms/step - loss: 0.2343 - accuracy: 0.9128 - val_loss: 0.3804 - val_accuracy: 0.8906\n",
      "Epoch 473/1000\n",
      "24/24 [==============================] - 12s 491ms/step - loss: 0.2975 - accuracy: 0.9102 - val_loss: 0.2297 - val_accuracy: 0.9156\n",
      "Epoch 474/1000\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2879 - accuracy: 0.8971 - val_loss: 0.4122 - val_accuracy: 0.9031\n",
      "Epoch 475/1000\n",
      "24/24 [==============================] - 11s 463ms/step - loss: 0.2181 - accuracy: 0.9232 - val_loss: 0.2810 - val_accuracy: 0.9219\n",
      "Epoch 476/1000\n",
      "24/24 [==============================] - 12s 491ms/step - loss: 0.2893 - accuracy: 0.9102 - val_loss: 0.3894 - val_accuracy: 0.8813\n",
      "Epoch 477/1000\n",
      "24/24 [==============================] - 13s 562ms/step - loss: 0.2520 - accuracy: 0.9128 - val_loss: 0.3283 - val_accuracy: 0.8938\n",
      "Epoch 478/1000\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2765 - accuracy: 0.9036 - val_loss: 0.2088 - val_accuracy: 0.9281\n",
      "Epoch 479/1000\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2602 - accuracy: 0.9023 - val_loss: 0.2949 - val_accuracy: 0.8969\n",
      "Epoch 480/1000\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2716 - accuracy: 0.9010 - val_loss: 0.3967 - val_accuracy: 0.8875\n",
      "Epoch 481/1000\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.2767 - accuracy: 0.9036 - val_loss: 0.2095 - val_accuracy: 0.9281\n",
      "Epoch 482/1000\n",
      "24/24 [==============================] - 13s 542ms/step - loss: 0.2336 - accuracy: 0.9206 - val_loss: 0.1894 - val_accuracy: 0.9344\n",
      "Epoch 483/1000\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.2840 - accuracy: 0.9076 - val_loss: 0.2256 - val_accuracy: 0.9125\n",
      "Epoch 484/1000\n",
      "24/24 [==============================] - 11s 454ms/step - loss: 0.2708 - accuracy: 0.9010 - val_loss: 0.3540 - val_accuracy: 0.8813\n",
      "Epoch 485/1000\n",
      "24/24 [==============================] - 14s 569ms/step - loss: 0.2411 - accuracy: 0.9180 - val_loss: 0.6091 - val_accuracy: 0.8188\n",
      "Epoch 486/1000\n",
      "24/24 [==============================] - 11s 478ms/step - loss: 0.2551 - accuracy: 0.9141 - val_loss: 0.3397 - val_accuracy: 0.9031\n",
      "Epoch 487/1000\n",
      "24/24 [==============================] - 11s 474ms/step - loss: 0.2653 - accuracy: 0.9167 - val_loss: 0.3446 - val_accuracy: 0.8969\n",
      "Epoch 488/1000\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2181 - accuracy: 0.9193 - val_loss: 0.2486 - val_accuracy: 0.9250\n",
      "Epoch 489/1000\n",
      "24/24 [==============================] - 11s 457ms/step - loss: 0.2359 - accuracy: 0.9180 - val_loss: 0.3691 - val_accuracy: 0.9000\n",
      "Epoch 490/1000\n",
      "24/24 [==============================] - 11s 471ms/step - loss: 0.2678 - accuracy: 0.9102 - val_loss: 0.4632 - val_accuracy: 0.8750\n",
      "Epoch 491/1000\n",
      "24/24 [==============================] - 14s 596ms/step - loss: 0.3130 - accuracy: 0.8945 - val_loss: 0.2963 - val_accuracy: 0.9094\n",
      "Epoch 492/1000\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2870 - accuracy: 0.8880 - val_loss: 0.2516 - val_accuracy: 0.8969\n",
      "Epoch 493/1000\n",
      "24/24 [==============================] - 12s 483ms/step - loss: 0.2808 - accuracy: 0.9036 - val_loss: 0.2060 - val_accuracy: 0.9281\n",
      "Epoch 494/1000\n",
      "24/24 [==============================] - 14s 582ms/step - loss: 0.2786 - accuracy: 0.9089 - val_loss: 0.2271 - val_accuracy: 0.9219\n",
      "Epoch 495/1000\n",
      "24/24 [==============================] - 11s 457ms/step - loss: 0.3040 - accuracy: 0.9023 - val_loss: 0.2839 - val_accuracy: 0.9000\n",
      "Epoch 496/1000\n",
      "24/24 [==============================] - 11s 475ms/step - loss: 0.2884 - accuracy: 0.8984 - val_loss: 0.5551 - val_accuracy: 0.8562\n",
      "Epoch 497/1000\n",
      "24/24 [==============================] - 15s 614ms/step - loss: 0.2901 - accuracy: 0.9206 - val_loss: 0.3823 - val_accuracy: 0.8813\n",
      "Epoch 498/1000\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2597 - accuracy: 0.9180 - val_loss: 0.1846 - val_accuracy: 0.9219\n",
      "Epoch 499/1000\n",
      "24/24 [==============================] - 11s 456ms/step - loss: 0.2114 - accuracy: 0.9349 - val_loss: 0.2157 - val_accuracy: 0.9094\n",
      "Epoch 500/1000\n",
      "24/24 [==============================] - 14s 576ms/step - loss: 0.2411 - accuracy: 0.9154 - val_loss: 0.2482 - val_accuracy: 0.9219\n",
      "Epoch 501/1000\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2595 - accuracy: 0.9076 - val_loss: 0.2706 - val_accuracy: 0.9187\n",
      "Epoch 502/1000\n",
      "24/24 [==============================] - 13s 559ms/step - loss: 0.2795 - accuracy: 0.9010 - val_loss: 0.2262 - val_accuracy: 0.9250\n",
      "Epoch 503/1000\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2891 - accuracy: 0.9089 - val_loss: 0.2682 - val_accuracy: 0.9031\n",
      "Epoch 504/1000\n",
      "24/24 [==============================] - 11s 454ms/step - loss: 0.2388 - accuracy: 0.9062 - val_loss: 0.2227 - val_accuracy: 0.9406\n",
      "Epoch 505/1000\n",
      "24/24 [==============================] - 13s 562ms/step - loss: 0.2360 - accuracy: 0.9219 - val_loss: 0.2639 - val_accuracy: 0.9031\n",
      "Epoch 506/1000\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2409 - accuracy: 0.9245 - val_loss: 0.2037 - val_accuracy: 0.9031\n",
      "Epoch 507/1000\n",
      "24/24 [==============================] - 11s 464ms/step - loss: 0.2447 - accuracy: 0.9245 - val_loss: 0.3280 - val_accuracy: 0.8938\n",
      "Epoch 508/1000\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.2469 - accuracy: 0.9089 - val_loss: 0.2034 - val_accuracy: 0.9312\n",
      "Epoch 509/1000\n",
      "24/24 [==============================] - 11s 457ms/step - loss: 0.2067 - accuracy: 0.9271 - val_loss: 0.3316 - val_accuracy: 0.8969\n",
      "Epoch 510/1000\n",
      "24/24 [==============================] - 11s 473ms/step - loss: 0.2877 - accuracy: 0.9062 - val_loss: 0.2253 - val_accuracy: 0.9094\n",
      "Epoch 511/1000\n",
      "24/24 [==============================] - 15s 637ms/step - loss: 0.2607 - accuracy: 0.9089 - val_loss: 0.2644 - val_accuracy: 0.9125\n",
      "Epoch 512/1000\n",
      "24/24 [==============================] - 11s 456ms/step - loss: 0.2658 - accuracy: 0.9010 - val_loss: 0.2415 - val_accuracy: 0.9187\n",
      "Epoch 513/1000\n",
      "24/24 [==============================] - 11s 467ms/step - loss: 0.2798 - accuracy: 0.9062 - val_loss: 0.1877 - val_accuracy: 0.9375\n",
      "Epoch 514/1000\n",
      "24/24 [==============================] - 11s 475ms/step - loss: 0.2941 - accuracy: 0.9049 - val_loss: 0.3216 - val_accuracy: 0.9031\n",
      "Epoch 515/1000\n",
      "24/24 [==============================] - 11s 472ms/step - loss: 0.3060 - accuracy: 0.8958 - val_loss: 0.2592 - val_accuracy: 0.9187\n",
      "Epoch 516/1000\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2322 - accuracy: 0.9128 - val_loss: 0.2851 - val_accuracy: 0.9094\n",
      "Epoch 517/1000\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2392 - accuracy: 0.9154 - val_loss: 0.1883 - val_accuracy: 0.9281\n",
      "Epoch 518/1000\n",
      "24/24 [==============================] - 11s 453ms/step - loss: 0.2616 - accuracy: 0.9089 - val_loss: 0.2258 - val_accuracy: 0.9250\n",
      "Epoch 519/1000\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.2622 - accuracy: 0.9180 - val_loss: 0.3006 - val_accuracy: 0.9000\n",
      "Epoch 520/1000\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.2370 - accuracy: 0.9271 - val_loss: 0.1575 - val_accuracy: 0.9438\n",
      "Epoch 521/1000\n",
      "24/24 [==============================] - 11s 469ms/step - loss: 0.2012 - accuracy: 0.9323 - val_loss: 0.2668 - val_accuracy: 0.8969\n",
      "Epoch 522/1000\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.2764 - accuracy: 0.9219 - val_loss: 0.3771 - val_accuracy: 0.8844\n",
      "Epoch 523/1000\n",
      "24/24 [==============================] - 11s 457ms/step - loss: 0.2345 - accuracy: 0.9245 - val_loss: 0.3700 - val_accuracy: 0.8844\n",
      "Epoch 524/1000\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2704 - accuracy: 0.9023 - val_loss: 0.2054 - val_accuracy: 0.9406\n",
      "Epoch 525/1000\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2552 - accuracy: 0.9115 - val_loss: 0.2267 - val_accuracy: 0.9187\n",
      "Epoch 526/1000\n",
      "24/24 [==============================] - 11s 470ms/step - loss: 0.2783 - accuracy: 0.9154 - val_loss: 0.2678 - val_accuracy: 0.9469\n",
      "Epoch 527/1000\n",
      "24/24 [==============================] - 15s 612ms/step - loss: 0.2179 - accuracy: 0.9297 - val_loss: 0.2854 - val_accuracy: 0.9000\n",
      "Epoch 528/1000\n",
      "24/24 [==============================] - 11s 460ms/step - loss: 0.2155 - accuracy: 0.9284 - val_loss: 0.1866 - val_accuracy: 0.9187\n",
      "Epoch 529/1000\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2381 - accuracy: 0.9219 - val_loss: 0.2924 - val_accuracy: 0.9125\n",
      "Epoch 530/1000\n",
      "24/24 [==============================] - 11s 468ms/step - loss: 0.2462 - accuracy: 0.9193 - val_loss: 0.3271 - val_accuracy: 0.9000\n",
      "Epoch 531/1000\n",
      "24/24 [==============================] - 11s 465ms/step - loss: 0.2253 - accuracy: 0.9206 - val_loss: 0.2547 - val_accuracy: 0.9219\n",
      "Epoch 532/1000\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2995 - accuracy: 0.8997 - val_loss: 0.3081 - val_accuracy: 0.9031\n",
      "Epoch 533/1000\n",
      "24/24 [==============================] - 13s 549ms/step - loss: 0.3151 - accuracy: 0.8958 - val_loss: 0.2318 - val_accuracy: 0.9125\n",
      "Epoch 534/1000\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2488 - accuracy: 0.9167 - val_loss: 0.2178 - val_accuracy: 0.9125\n",
      "Epoch 535/1000\n",
      "24/24 [==============================] - 11s 474ms/step - loss: 0.2522 - accuracy: 0.9128 - val_loss: 0.2606 - val_accuracy: 0.9062\n",
      "Epoch 536/1000\n",
      "24/24 [==============================] - 11s 461ms/step - loss: 0.2219 - accuracy: 0.9180 - val_loss: 0.2435 - val_accuracy: 0.9125\n",
      "Epoch 537/1000\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2304 - accuracy: 0.9297 - val_loss: 0.2696 - val_accuracy: 0.9031\n",
      "Epoch 538/1000\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.1775 - accuracy: 0.9388 - val_loss: 0.3187 - val_accuracy: 0.8969\n",
      "Epoch 539/1000\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.3079 - accuracy: 0.9102 - val_loss: 0.3133 - val_accuracy: 0.8906\n",
      "Epoch 540/1000\n",
      "24/24 [==============================] - 11s 469ms/step - loss: 0.2396 - accuracy: 0.9206 - val_loss: 0.3521 - val_accuracy: 0.9000\n",
      "Epoch 541/1000\n",
      "24/24 [==============================] - 11s 458ms/step - loss: 0.2493 - accuracy: 0.9154 - val_loss: 0.1790 - val_accuracy: 0.9281\n",
      "Epoch 542/1000\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.2942 - accuracy: 0.8880 - val_loss: 0.2281 - val_accuracy: 0.9281\n",
      "Epoch 543/1000\n",
      "24/24 [==============================] - 14s 598ms/step - loss: 0.2186 - accuracy: 0.9115 - val_loss: 0.3583 - val_accuracy: 0.9000\n",
      "Epoch 544/1000\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.2502 - accuracy: 0.9154 - val_loss: 0.2642 - val_accuracy: 0.9125\n",
      "Epoch 545/1000\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2609 - accuracy: 0.9154 - val_loss: 0.2824 - val_accuracy: 0.9094\n",
      "Epoch 546/1000\n",
      "24/24 [==============================] - 91s 4s/step - loss: 0.2800 - accuracy: 0.9036 - val_loss: 0.2498 - val_accuracy: 0.9156\n",
      "Epoch 547/1000\n",
      "24/24 [==============================] - 146s 6s/step - loss: 0.2791 - accuracy: 0.9062 - val_loss: 0.1757 - val_accuracy: 0.9375\n",
      "Epoch 548/1000\n",
      "24/24 [==============================] - 15s 624ms/step - loss: 0.2190 - accuracy: 0.9258 - val_loss: 0.2181 - val_accuracy: 0.9344\n",
      "Epoch 549/1000\n",
      "24/24 [==============================] - 12s 483ms/step - loss: 0.2366 - accuracy: 0.9310 - val_loss: 0.2045 - val_accuracy: 0.9312\n",
      "Epoch 550/1000\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2477 - accuracy: 0.9271 - val_loss: 0.1760 - val_accuracy: 0.9344\n",
      "Epoch 551/1000\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2570 - accuracy: 0.9154 - val_loss: 0.1774 - val_accuracy: 0.9469\n",
      "Epoch 552/1000\n",
      "24/24 [==============================] - 12s 479ms/step - loss: 0.2557 - accuracy: 0.9180 - val_loss: 0.2974 - val_accuracy: 0.8781\n",
      "Epoch 553/1000\n",
      "24/24 [==============================] - 16s 668ms/step - loss: 0.2382 - accuracy: 0.9193 - val_loss: 0.4227 - val_accuracy: 0.8938\n",
      "Epoch 554/1000\n",
      "24/24 [==============================] - 12s 481ms/step - loss: 0.2433 - accuracy: 0.9219 - val_loss: 0.3287 - val_accuracy: 0.9031\n",
      "Epoch 555/1000\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2323 - accuracy: 0.9310 - val_loss: 0.2569 - val_accuracy: 0.8969\n",
      "Epoch 556/1000\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.2601 - accuracy: 0.9219 - val_loss: 0.2114 - val_accuracy: 0.9312\n",
      "Epoch 557/1000\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2013 - accuracy: 0.9206 - val_loss: 0.3562 - val_accuracy: 0.8813\n",
      "Epoch 558/1000\n",
      "24/24 [==============================] - 16s 665ms/step - loss: 0.2249 - accuracy: 0.9206 - val_loss: 0.2148 - val_accuracy: 0.9312\n",
      "Epoch 559/1000\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2376 - accuracy: 0.9167 - val_loss: 0.2454 - val_accuracy: 0.9281\n",
      "Epoch 560/1000\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2376 - accuracy: 0.9271 - val_loss: 0.1713 - val_accuracy: 0.9344\n",
      "Epoch 561/1000\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1874 - accuracy: 0.9375 - val_loss: 0.1998 - val_accuracy: 0.9281\n",
      "Epoch 562/1000\n",
      "24/24 [==============================] - 12s 481ms/step - loss: 0.1960 - accuracy: 0.9271 - val_loss: 0.2817 - val_accuracy: 0.9156\n",
      "Epoch 563/1000\n",
      "24/24 [==============================] - 16s 667ms/step - loss: 0.2233 - accuracy: 0.9232 - val_loss: 0.2513 - val_accuracy: 0.9156\n",
      "Epoch 564/1000\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2167 - accuracy: 0.9245 - val_loss: 0.2547 - val_accuracy: 0.9312\n",
      "Epoch 565/1000\n",
      "24/24 [==============================] - 12s 491ms/step - loss: 0.2204 - accuracy: 0.9193 - val_loss: 0.2703 - val_accuracy: 0.9156\n",
      "Epoch 566/1000\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2473 - accuracy: 0.9115 - val_loss: 0.2708 - val_accuracy: 0.9125\n",
      "Epoch 567/1000\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 0.2514 - accuracy: 0.9245 - val_loss: 0.1801 - val_accuracy: 0.9469\n",
      "Epoch 568/1000\n",
      "24/24 [==============================] - 17s 704ms/step - loss: 0.2091 - accuracy: 0.9271 - val_loss: 0.1773 - val_accuracy: 0.9312\n",
      "Epoch 569/1000\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2093 - accuracy: 0.9375 - val_loss: 0.2015 - val_accuracy: 0.9344\n",
      "Epoch 570/1000\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2758 - accuracy: 0.9102 - val_loss: 0.2317 - val_accuracy: 0.9375\n",
      "Epoch 571/1000\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.2294 - accuracy: 0.9245 - val_loss: 0.2668 - val_accuracy: 0.9125\n",
      "Epoch 572/1000\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2600 - accuracy: 0.9076 - val_loss: 0.3725 - val_accuracy: 0.8656\n",
      "Epoch 573/1000\n",
      "24/24 [==============================] - 16s 683ms/step - loss: 0.2588 - accuracy: 0.9154 - val_loss: 0.2958 - val_accuracy: 0.9062\n",
      "Epoch 574/1000\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2355 - accuracy: 0.9128 - val_loss: 0.2350 - val_accuracy: 0.9219\n",
      "Epoch 575/1000\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2017 - accuracy: 0.9310 - val_loss: 0.1695 - val_accuracy: 0.9531\n",
      "Epoch 576/1000\n",
      "24/24 [==============================] - 12s 481ms/step - loss: 0.1839 - accuracy: 0.9362 - val_loss: 0.1402 - val_accuracy: 0.9563\n",
      "Epoch 577/1000\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.2036 - accuracy: 0.9323 - val_loss: 0.1493 - val_accuracy: 0.9531\n",
      "Epoch 578/1000\n",
      "24/24 [==============================] - 14s 596ms/step - loss: 0.2651 - accuracy: 0.9128 - val_loss: 0.2036 - val_accuracy: 0.9344\n",
      "Epoch 579/1000\n",
      "24/24 [==============================] - 14s 575ms/step - loss: 0.2461 - accuracy: 0.9232 - val_loss: 0.1399 - val_accuracy: 0.9531\n",
      "Epoch 580/1000\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2261 - accuracy: 0.9323 - val_loss: 0.2063 - val_accuracy: 0.9344\n",
      "Epoch 581/1000\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.1934 - accuracy: 0.9427 - val_loss: 0.2724 - val_accuracy: 0.9125\n",
      "Epoch 582/1000\n",
      "24/24 [==============================] - 13s 549ms/step - loss: 0.2293 - accuracy: 0.9297 - val_loss: 0.2126 - val_accuracy: 0.9125\n",
      "Epoch 583/1000\n",
      "24/24 [==============================] - 14s 572ms/step - loss: 0.2296 - accuracy: 0.9115 - val_loss: 0.2368 - val_accuracy: 0.9125\n",
      "Epoch 584/1000\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.1758 - accuracy: 0.9362 - val_loss: 0.2080 - val_accuracy: 0.9187\n",
      "Epoch 585/1000\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2501 - accuracy: 0.9180 - val_loss: 0.1714 - val_accuracy: 0.9375\n",
      "Epoch 586/1000\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2517 - accuracy: 0.9219 - val_loss: 0.1562 - val_accuracy: 0.9438\n",
      "Epoch 587/1000\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2828 - accuracy: 0.9076 - val_loss: 0.2803 - val_accuracy: 0.9125\n",
      "Epoch 588/1000\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2815 - accuracy: 0.9076 - val_loss: 0.3102 - val_accuracy: 0.9094\n",
      "Epoch 589/1000\n",
      "24/24 [==============================] - 15s 620ms/step - loss: 0.2204 - accuracy: 0.9193 - val_loss: 0.1976 - val_accuracy: 0.9469\n",
      "Epoch 590/1000\n",
      "24/24 [==============================] - 13s 549ms/step - loss: 0.2234 - accuracy: 0.9336 - val_loss: 0.2664 - val_accuracy: 0.9219\n",
      "Epoch 591/1000\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2292 - accuracy: 0.9193 - val_loss: 0.2844 - val_accuracy: 0.9156\n",
      "Epoch 592/1000\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2892 - accuracy: 0.9102 - val_loss: 0.3018 - val_accuracy: 0.9156\n",
      "Epoch 593/1000\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.2000 - accuracy: 0.9284 - val_loss: 0.2579 - val_accuracy: 0.9156\n",
      "Epoch 594/1000\n",
      "24/24 [==============================] - 16s 687ms/step - loss: 0.2354 - accuracy: 0.9219 - val_loss: 0.4068 - val_accuracy: 0.8906\n",
      "Epoch 595/1000\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.2501 - accuracy: 0.9115 - val_loss: 0.1932 - val_accuracy: 0.9281\n",
      "Epoch 596/1000\n",
      "24/24 [==============================] - 12s 481ms/step - loss: 0.2414 - accuracy: 0.9206 - val_loss: 0.1297 - val_accuracy: 0.9531\n",
      "Epoch 597/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1982 - accuracy: 0.9336 - val_loss: 0.2366 - val_accuracy: 0.9250\n",
      "Epoch 598/1000\n",
      "24/24 [==============================] - 12s 483ms/step - loss: 0.2147 - accuracy: 0.9323 - val_loss: 0.2191 - val_accuracy: 0.9062\n",
      "Epoch 599/1000\n",
      "24/24 [==============================] - 17s 712ms/step - loss: 0.2855 - accuracy: 0.9193 - val_loss: 0.1782 - val_accuracy: 0.9344\n",
      "Epoch 600/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2183 - accuracy: 0.9258 - val_loss: 0.1882 - val_accuracy: 0.9187\n",
      "Epoch 601/1000\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.2546 - accuracy: 0.9062 - val_loss: 0.2046 - val_accuracy: 0.9375\n",
      "Epoch 602/1000\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2282 - accuracy: 0.9193 - val_loss: 0.1938 - val_accuracy: 0.9250\n",
      "Epoch 603/1000\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 0.1703 - accuracy: 0.9401 - val_loss: 0.3409 - val_accuracy: 0.9062\n",
      "Epoch 604/1000\n",
      "24/24 [==============================] - 18s 750ms/step - loss: 0.2842 - accuracy: 0.9089 - val_loss: 0.2506 - val_accuracy: 0.9000\n",
      "Epoch 605/1000\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.2095 - accuracy: 0.9284 - val_loss: 0.2456 - val_accuracy: 0.9187\n",
      "Epoch 606/1000\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.2044 - accuracy: 0.9323 - val_loss: 0.4291 - val_accuracy: 0.9062\n",
      "Epoch 607/1000\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2423 - accuracy: 0.9193 - val_loss: 0.2155 - val_accuracy: 0.9187\n",
      "Epoch 608/1000\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2464 - accuracy: 0.9245 - val_loss: 0.2524 - val_accuracy: 0.9094\n",
      "Epoch 609/1000\n",
      "24/24 [==============================] - 16s 676ms/step - loss: 0.2376 - accuracy: 0.9180 - val_loss: 0.1548 - val_accuracy: 0.9469\n",
      "Epoch 610/1000\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1685 - accuracy: 0.9414 - val_loss: 0.1744 - val_accuracy: 0.9375\n",
      "Epoch 611/1000\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2162 - accuracy: 0.9245 - val_loss: 0.1107 - val_accuracy: 0.9531\n",
      "Epoch 612/1000\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2272 - accuracy: 0.9154 - val_loss: 0.2803 - val_accuracy: 0.9250\n",
      "Epoch 613/1000\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2037 - accuracy: 0.9284 - val_loss: 0.2172 - val_accuracy: 0.9187\n",
      "Epoch 614/1000\n",
      "24/24 [==============================] - 16s 669ms/step - loss: 0.2322 - accuracy: 0.9193 - val_loss: 0.1449 - val_accuracy: 0.9406\n",
      "Epoch 615/1000\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 0.2064 - accuracy: 0.9362 - val_loss: 0.2010 - val_accuracy: 0.9250\n",
      "Epoch 616/1000\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2165 - accuracy: 0.9141 - val_loss: 0.2006 - val_accuracy: 0.9031\n",
      "Epoch 617/1000\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2135 - accuracy: 0.9167 - val_loss: 0.2712 - val_accuracy: 0.9187\n",
      "Epoch 618/1000\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2553 - accuracy: 0.9154 - val_loss: 0.2386 - val_accuracy: 0.9156\n",
      "Epoch 619/1000\n",
      "24/24 [==============================] - 17s 706ms/step - loss: 0.2014 - accuracy: 0.9284 - val_loss: 0.2390 - val_accuracy: 0.9250\n",
      "Epoch 620/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2657 - accuracy: 0.9154 - val_loss: 0.3203 - val_accuracy: 0.8813\n",
      "Epoch 621/1000\n",
      "24/24 [==============================] - 13s 549ms/step - loss: 0.2239 - accuracy: 0.9271 - val_loss: 0.1857 - val_accuracy: 0.9312\n",
      "Epoch 622/1000\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.2242 - accuracy: 0.9258 - val_loss: 0.3029 - val_accuracy: 0.8938\n",
      "Epoch 623/1000\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2233 - accuracy: 0.9193 - val_loss: 0.1554 - val_accuracy: 0.9406\n",
      "Epoch 624/1000\n",
      "24/24 [==============================] - 16s 672ms/step - loss: 0.2099 - accuracy: 0.9245 - val_loss: 0.1995 - val_accuracy: 0.9344\n",
      "Epoch 625/1000\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2118 - accuracy: 0.9258 - val_loss: 0.1819 - val_accuracy: 0.9344\n",
      "Epoch 626/1000\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2186 - accuracy: 0.9245 - val_loss: 0.2540 - val_accuracy: 0.9094\n",
      "Epoch 627/1000\n",
      "24/24 [==============================] - 11s 477ms/step - loss: 0.2148 - accuracy: 0.9258 - val_loss: 0.2035 - val_accuracy: 0.9250\n",
      "Epoch 628/1000\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2317 - accuracy: 0.9154 - val_loss: 0.2456 - val_accuracy: 0.9406\n",
      "Epoch 629/1000\n",
      "24/24 [==============================] - 15s 643ms/step - loss: 0.2089 - accuracy: 0.9297 - val_loss: 0.1497 - val_accuracy: 0.9625\n",
      "Epoch 630/1000\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.2540 - accuracy: 0.9258 - val_loss: 0.3008 - val_accuracy: 0.9281\n",
      "Epoch 631/1000\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 0.2386 - accuracy: 0.9154 - val_loss: 0.3095 - val_accuracy: 0.9094\n",
      "Epoch 632/1000\n",
      "24/24 [==============================] - 12s 482ms/step - loss: 0.2319 - accuracy: 0.9141 - val_loss: 0.1820 - val_accuracy: 0.9281\n",
      "Epoch 633/1000\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2183 - accuracy: 0.9258 - val_loss: 0.1996 - val_accuracy: 0.9312\n",
      "Epoch 634/1000\n",
      "24/24 [==============================] - 14s 572ms/step - loss: 0.2319 - accuracy: 0.9036 - val_loss: 0.1115 - val_accuracy: 0.9469\n",
      "Epoch 635/1000\n",
      "24/24 [==============================] - 16s 651ms/step - loss: 0.1651 - accuracy: 0.9479 - val_loss: 0.1959 - val_accuracy: 0.9250\n",
      "Epoch 636/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2435 - accuracy: 0.9115 - val_loss: 0.2311 - val_accuracy: 0.9125\n",
      "Epoch 637/1000\n",
      "24/24 [==============================] - 11s 463ms/step - loss: 0.2015 - accuracy: 0.9297 - val_loss: 0.1776 - val_accuracy: 0.9531\n",
      "Epoch 638/1000\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2272 - accuracy: 0.9245 - val_loss: 0.1413 - val_accuracy: 0.9469\n",
      "Epoch 639/1000\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.2185 - accuracy: 0.9271 - val_loss: 0.3577 - val_accuracy: 0.8750\n",
      "Epoch 640/1000\n",
      "24/24 [==============================] - 16s 683ms/step - loss: 0.1699 - accuracy: 0.9414 - val_loss: 0.2440 - val_accuracy: 0.9219\n",
      "Epoch 641/1000\n",
      "24/24 [==============================] - 12s 486ms/step - loss: 0.2507 - accuracy: 0.9219 - val_loss: 0.2135 - val_accuracy: 0.9406\n",
      "Epoch 642/1000\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1819 - accuracy: 0.9362 - val_loss: 0.1679 - val_accuracy: 0.9500\n",
      "Epoch 643/1000\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2225 - accuracy: 0.9206 - val_loss: 0.2336 - val_accuracy: 0.9281\n",
      "Epoch 644/1000\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1716 - accuracy: 0.9401 - val_loss: 0.2949 - val_accuracy: 0.8938\n",
      "Epoch 645/1000\n",
      "24/24 [==============================] - 17s 702ms/step - loss: 0.1863 - accuracy: 0.9336 - val_loss: 0.4076 - val_accuracy: 0.8687\n",
      "Epoch 646/1000\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.2260 - accuracy: 0.9232 - val_loss: 0.2473 - val_accuracy: 0.9187\n",
      "Epoch 647/1000\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2131 - accuracy: 0.9232 - val_loss: 0.2093 - val_accuracy: 0.9406\n",
      "Epoch 648/1000\n",
      "24/24 [==============================] - 13s 551ms/step - loss: 0.2051 - accuracy: 0.9375 - val_loss: 0.1934 - val_accuracy: 0.9344\n",
      "Epoch 649/1000\n",
      "24/24 [==============================] - 12s 482ms/step - loss: 0.2704 - accuracy: 0.9245 - val_loss: 0.2875 - val_accuracy: 0.9094\n",
      "Epoch 650/1000\n",
      "24/24 [==============================] - 16s 684ms/step - loss: 0.2257 - accuracy: 0.9115 - val_loss: 0.2780 - val_accuracy: 0.9062\n",
      "Epoch 651/1000\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.1661 - accuracy: 0.9414 - val_loss: 0.2392 - val_accuracy: 0.9000\n",
      "Epoch 652/1000\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2093 - accuracy: 0.9336 - val_loss: 0.2853 - val_accuracy: 0.9031\n",
      "Epoch 653/1000\n",
      "24/24 [==============================] - 12s 481ms/step - loss: 0.2299 - accuracy: 0.9245 - val_loss: 0.2161 - val_accuracy: 0.9281\n",
      "Epoch 654/1000\n",
      "24/24 [==============================] - 12s 487ms/step - loss: 0.1885 - accuracy: 0.9323 - val_loss: 0.2014 - val_accuracy: 0.9281\n",
      "Epoch 655/1000\n",
      "24/24 [==============================] - 16s 652ms/step - loss: 0.1932 - accuracy: 0.9362 - val_loss: 0.1898 - val_accuracy: 0.9344\n",
      "Epoch 656/1000\n",
      "24/24 [==============================] - 12s 485ms/step - loss: 0.2081 - accuracy: 0.9336 - val_loss: 0.2145 - val_accuracy: 0.9250\n",
      "Epoch 657/1000\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2119 - accuracy: 0.9206 - val_loss: 0.2134 - val_accuracy: 0.9312\n",
      "Epoch 658/1000\n",
      "24/24 [==============================] - 11s 469ms/step - loss: 0.2117 - accuracy: 0.9284 - val_loss: 0.2083 - val_accuracy: 0.9281\n",
      "Epoch 659/1000\n",
      "24/24 [==============================] - 11s 463ms/step - loss: 0.2366 - accuracy: 0.9180 - val_loss: 0.2820 - val_accuracy: 0.9094\n",
      "Epoch 660/1000\n",
      "24/24 [==============================] - 14s 563ms/step - loss: 0.2067 - accuracy: 0.9154 - val_loss: 0.1529 - val_accuracy: 0.9500\n",
      "Epoch 661/1000\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2233 - accuracy: 0.9271 - val_loss: 0.2482 - val_accuracy: 0.9250\n",
      "Epoch 662/1000\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2196 - accuracy: 0.9141 - val_loss: 0.2331 - val_accuracy: 0.9156\n",
      "Epoch 663/1000\n",
      "24/24 [==============================] - 11s 461ms/step - loss: 0.1817 - accuracy: 0.9414 - val_loss: 0.1624 - val_accuracy: 0.9125\n",
      "Epoch 664/1000\n",
      "24/24 [==============================] - 11s 461ms/step - loss: 0.2088 - accuracy: 0.9232 - val_loss: 0.2220 - val_accuracy: 0.9375\n",
      "Epoch 665/1000\n",
      "24/24 [==============================] - 11s 469ms/step - loss: 0.1908 - accuracy: 0.9271 - val_loss: 0.1908 - val_accuracy: 0.9250\n",
      "Epoch 666/1000\n",
      "24/24 [==============================] - 16s 681ms/step - loss: 0.2216 - accuracy: 0.9271 - val_loss: 0.1875 - val_accuracy: 0.9469\n",
      "Epoch 667/1000\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1906 - accuracy: 0.9375 - val_loss: 0.2409 - val_accuracy: 0.9125\n",
      "Epoch 668/1000\n",
      "24/24 [==============================] - 11s 453ms/step - loss: 0.2673 - accuracy: 0.9076 - val_loss: 0.2294 - val_accuracy: 0.9375\n",
      "Epoch 669/1000\n",
      "24/24 [==============================] - 11s 450ms/step - loss: 0.2259 - accuracy: 0.9232 - val_loss: 0.1626 - val_accuracy: 0.9406\n",
      "Epoch 670/1000\n",
      "24/24 [==============================] - 11s 472ms/step - loss: 0.2103 - accuracy: 0.9284 - val_loss: 0.1831 - val_accuracy: 0.9500\n",
      "Epoch 671/1000\n",
      "24/24 [==============================] - 13s 558ms/step - loss: 0.2132 - accuracy: 0.9271 - val_loss: 0.1988 - val_accuracy: 0.9344\n",
      "Epoch 672/1000\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2238 - accuracy: 0.9154 - val_loss: 0.1665 - val_accuracy: 0.9438\n",
      "Epoch 673/1000\n",
      "24/24 [==============================] - 11s 453ms/step - loss: 0.2925 - accuracy: 0.9089 - val_loss: 0.1123 - val_accuracy: 0.9563\n",
      "Epoch 674/1000\n",
      "24/24 [==============================] - 11s 457ms/step - loss: 0.1729 - accuracy: 0.9388 - val_loss: 0.2084 - val_accuracy: 0.9250\n",
      "Epoch 675/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1822 - accuracy: 0.9453 - val_loss: 0.2588 - val_accuracy: 0.9000\n",
      "Epoch 676/1000\n",
      "24/24 [==============================] - 12s 486ms/step - loss: 0.2300 - accuracy: 0.9193 - val_loss: 0.3219 - val_accuracy: 0.8938\n",
      "Epoch 677/1000\n",
      "24/24 [==============================] - 15s 629ms/step - loss: 0.2308 - accuracy: 0.9206 - val_loss: 0.2265 - val_accuracy: 0.9438\n",
      "Epoch 678/1000\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2556 - accuracy: 0.9180 - val_loss: 0.4122 - val_accuracy: 0.8813\n",
      "Epoch 679/1000\n",
      "24/24 [==============================] - 11s 463ms/step - loss: 0.2209 - accuracy: 0.9258 - val_loss: 0.4429 - val_accuracy: 0.8469\n",
      "Epoch 680/1000\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2042 - accuracy: 0.9245 - val_loss: 0.5215 - val_accuracy: 0.8750\n",
      "Epoch 681/1000\n",
      "24/24 [==============================] - 11s 476ms/step - loss: 0.1776 - accuracy: 0.9466 - val_loss: 0.2178 - val_accuracy: 0.9219\n",
      "Epoch 682/1000\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.1967 - accuracy: 0.9284 - val_loss: 0.2010 - val_accuracy: 0.9250\n",
      "Epoch 683/1000\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2095 - accuracy: 0.9219 - val_loss: 0.1401 - val_accuracy: 0.9563\n",
      "Epoch 684/1000\n",
      "24/24 [==============================] - 11s 448ms/step - loss: 0.2201 - accuracy: 0.9232 - val_loss: 0.1669 - val_accuracy: 0.9594\n",
      "Epoch 685/1000\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.2166 - accuracy: 0.9219 - val_loss: 0.2710 - val_accuracy: 0.9250\n",
      "Epoch 686/1000\n",
      "24/24 [==============================] - 11s 460ms/step - loss: 0.1973 - accuracy: 0.9310 - val_loss: 0.1262 - val_accuracy: 0.9500\n",
      "Epoch 687/1000\n",
      "24/24 [==============================] - 11s 470ms/step - loss: 0.2075 - accuracy: 0.9245 - val_loss: 0.1888 - val_accuracy: 0.9438\n",
      "Epoch 688/1000\n",
      "24/24 [==============================] - 16s 658ms/step - loss: 0.1233 - accuracy: 0.9544 - val_loss: 0.1782 - val_accuracy: 0.9375\n",
      "Epoch 689/1000\n",
      "24/24 [==============================] - 11s 477ms/step - loss: 0.1679 - accuracy: 0.9401 - val_loss: 0.1576 - val_accuracy: 0.9375\n",
      "Epoch 690/1000\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1887 - accuracy: 0.9466 - val_loss: 0.1603 - val_accuracy: 0.9531\n",
      "Epoch 691/1000\n",
      "24/24 [==============================] - 11s 469ms/step - loss: 0.2363 - accuracy: 0.9219 - val_loss: 0.2026 - val_accuracy: 0.9438\n",
      "Epoch 692/1000\n",
      "24/24 [==============================] - 11s 465ms/step - loss: 0.2046 - accuracy: 0.9232 - val_loss: 0.2411 - val_accuracy: 0.9219\n",
      "Epoch 693/1000\n",
      "24/24 [==============================] - 16s 670ms/step - loss: 0.2695 - accuracy: 0.9102 - val_loss: 0.1813 - val_accuracy: 0.9344\n",
      "Epoch 694/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2319 - accuracy: 0.9232 - val_loss: 0.1620 - val_accuracy: 0.9469\n",
      "Epoch 695/1000\n",
      "24/24 [==============================] - 11s 470ms/step - loss: 0.1416 - accuracy: 0.9479 - val_loss: 0.1431 - val_accuracy: 0.9563\n",
      "Epoch 696/1000\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2222 - accuracy: 0.9206 - val_loss: 0.2021 - val_accuracy: 0.9312\n",
      "Epoch 697/1000\n",
      "24/24 [==============================] - 11s 458ms/step - loss: 0.2602 - accuracy: 0.9115 - val_loss: 0.1943 - val_accuracy: 0.9187\n",
      "Epoch 698/1000\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2376 - accuracy: 0.9232 - val_loss: 0.1396 - val_accuracy: 0.9563\n",
      "Epoch 699/1000\n",
      "24/24 [==============================] - 16s 668ms/step - loss: 0.1643 - accuracy: 0.9388 - val_loss: 0.1237 - val_accuracy: 0.9531\n",
      "Epoch 700/1000\n",
      "24/24 [==============================] - 11s 456ms/step - loss: 0.2332 - accuracy: 0.9115 - val_loss: 0.1660 - val_accuracy: 0.9375\n",
      "Epoch 701/1000\n",
      "24/24 [==============================] - 12s 482ms/step - loss: 0.2316 - accuracy: 0.9206 - val_loss: 0.1678 - val_accuracy: 0.9406\n",
      "Epoch 702/1000\n",
      "24/24 [==============================] - 11s 454ms/step - loss: 0.2231 - accuracy: 0.9245 - val_loss: 0.2216 - val_accuracy: 0.9469\n",
      "Epoch 703/1000\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.2129 - accuracy: 0.9362 - val_loss: 0.1551 - val_accuracy: 0.9625\n",
      "Epoch 704/1000\n",
      "24/24 [==============================] - 16s 686ms/step - loss: 0.1982 - accuracy: 0.9284 - val_loss: 0.2905 - val_accuracy: 0.9125\n",
      "Epoch 705/1000\n",
      "24/24 [==============================] - 11s 468ms/step - loss: 0.2207 - accuracy: 0.9245 - val_loss: 0.1511 - val_accuracy: 0.9406\n",
      "Epoch 706/1000\n",
      "24/24 [==============================] - 12s 482ms/step - loss: 0.2549 - accuracy: 0.9219 - val_loss: 0.1951 - val_accuracy: 0.9250\n",
      "Epoch 707/1000\n",
      "24/24 [==============================] - 11s 459ms/step - loss: 0.1968 - accuracy: 0.9349 - val_loss: 0.2528 - val_accuracy: 0.9062\n",
      "Epoch 708/1000\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2525 - accuracy: 0.9271 - val_loss: 0.1718 - val_accuracy: 0.9438\n",
      "Epoch 709/1000\n",
      "24/24 [==============================] - 12s 491ms/step - loss: 0.1820 - accuracy: 0.9479 - val_loss: 0.1535 - val_accuracy: 0.9500\n",
      "Epoch 710/1000\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.1951 - accuracy: 0.9258 - val_loss: 0.1500 - val_accuracy: 0.9469\n",
      "Epoch 711/1000\n",
      "24/24 [==============================] - 12s 482ms/step - loss: 0.1913 - accuracy: 0.9297 - val_loss: 0.1619 - val_accuracy: 0.9469\n",
      "Epoch 712/1000\n",
      "24/24 [==============================] - 11s 459ms/step - loss: 0.2274 - accuracy: 0.9258 - val_loss: 0.1503 - val_accuracy: 0.9625\n",
      "Epoch 713/1000\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2285 - accuracy: 0.9271 - val_loss: 0.2156 - val_accuracy: 0.9344\n",
      "Epoch 714/1000\n",
      "24/24 [==============================] - 11s 462ms/step - loss: 0.2246 - accuracy: 0.9206 - val_loss: 0.1961 - val_accuracy: 0.9375\n",
      "Epoch 715/1000\n",
      "24/24 [==============================] - 16s 658ms/step - loss: 0.2123 - accuracy: 0.9245 - val_loss: 0.2359 - val_accuracy: 0.9406\n",
      "Epoch 716/1000\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.2255 - accuracy: 0.9154 - val_loss: 0.1373 - val_accuracy: 0.9594\n",
      "Epoch 717/1000\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.1933 - accuracy: 0.9414 - val_loss: 0.2373 - val_accuracy: 0.9375\n",
      "Epoch 718/1000\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2341 - accuracy: 0.9206 - val_loss: 0.2011 - val_accuracy: 0.9281\n",
      "Epoch 719/1000\n",
      "24/24 [==============================] - 11s 472ms/step - loss: 0.2435 - accuracy: 0.9258 - val_loss: 0.2604 - val_accuracy: 0.9187\n",
      "Epoch 720/1000\n",
      "24/24 [==============================] - 14s 580ms/step - loss: 0.1868 - accuracy: 0.9388 - val_loss: 0.1342 - val_accuracy: 0.9563\n",
      "Epoch 721/1000\n",
      "24/24 [==============================] - 14s 571ms/step - loss: 0.2128 - accuracy: 0.9310 - val_loss: 0.2152 - val_accuracy: 0.9375\n",
      "Epoch 722/1000\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2723 - accuracy: 0.9128 - val_loss: 0.1504 - val_accuracy: 0.9531\n",
      "Epoch 723/1000\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2347 - accuracy: 0.9258 - val_loss: 0.1375 - val_accuracy: 0.9656\n",
      "Epoch 724/1000\n",
      "24/24 [==============================] - 11s 466ms/step - loss: 0.1985 - accuracy: 0.9349 - val_loss: 0.1462 - val_accuracy: 0.9469\n",
      "Epoch 725/1000\n",
      "24/24 [==============================] - 11s 462ms/step - loss: 0.1958 - accuracy: 0.9401 - val_loss: 0.2071 - val_accuracy: 0.9375\n",
      "Epoch 726/1000\n",
      "24/24 [==============================] - 17s 715ms/step - loss: 0.2310 - accuracy: 0.9323 - val_loss: 0.1419 - val_accuracy: 0.9500\n",
      "Epoch 727/1000\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 0.2094 - accuracy: 0.9349 - val_loss: 0.3179 - val_accuracy: 0.9187\n",
      "Epoch 728/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1508 - accuracy: 0.9479 - val_loss: 0.4982 - val_accuracy: 0.8687\n",
      "Epoch 729/1000\n",
      "24/24 [==============================] - 11s 476ms/step - loss: 0.2162 - accuracy: 0.9336 - val_loss: 0.1647 - val_accuracy: 0.9438\n",
      "Epoch 730/1000\n",
      "24/24 [==============================] - 11s 469ms/step - loss: 0.1753 - accuracy: 0.9401 - val_loss: 0.2231 - val_accuracy: 0.9156\n",
      "Epoch 731/1000\n",
      "24/24 [==============================] - 18s 744ms/step - loss: 0.1730 - accuracy: 0.9271 - val_loss: 0.1978 - val_accuracy: 0.9344\n",
      "Epoch 732/1000\n",
      "24/24 [==============================] - 11s 471ms/step - loss: 0.1805 - accuracy: 0.9388 - val_loss: 0.1796 - val_accuracy: 0.9469\n",
      "Epoch 733/1000\n",
      "24/24 [==============================] - 11s 476ms/step - loss: 0.1792 - accuracy: 0.9362 - val_loss: 0.1823 - val_accuracy: 0.9531\n",
      "Epoch 734/1000\n",
      "24/24 [==============================] - 11s 466ms/step - loss: 0.1812 - accuracy: 0.9336 - val_loss: 0.1773 - val_accuracy: 0.9406\n",
      "Epoch 735/1000\n",
      "24/24 [==============================] - 11s 473ms/step - loss: 0.2298 - accuracy: 0.9284 - val_loss: 0.1563 - val_accuracy: 0.9563\n",
      "Epoch 736/1000\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2032 - accuracy: 0.9258 - val_loss: 0.2793 - val_accuracy: 0.9031\n",
      "Epoch 737/1000\n",
      "24/24 [==============================] - 16s 652ms/step - loss: 0.2080 - accuracy: 0.9284 - val_loss: 0.1258 - val_accuracy: 0.9531\n",
      "Epoch 738/1000\n",
      "24/24 [==============================] - 12s 491ms/step - loss: 0.2279 - accuracy: 0.9336 - val_loss: 0.1772 - val_accuracy: 0.9344\n",
      "Epoch 739/1000\n",
      "24/24 [==============================] - 11s 459ms/step - loss: 0.2562 - accuracy: 0.9245 - val_loss: 0.1523 - val_accuracy: 0.9438\n",
      "Epoch 740/1000\n",
      "24/24 [==============================] - 11s 470ms/step - loss: 0.1799 - accuracy: 0.9375 - val_loss: 0.1579 - val_accuracy: 0.9344\n",
      "Epoch 741/1000\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1983 - accuracy: 0.9401 - val_loss: 0.2462 - val_accuracy: 0.9156\n",
      "Epoch 742/1000\n",
      "24/24 [==============================] - 16s 681ms/step - loss: 0.2309 - accuracy: 0.9232 - val_loss: 0.2193 - val_accuracy: 0.9281\n",
      "Epoch 743/1000\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1701 - accuracy: 0.9505 - val_loss: 0.1437 - val_accuracy: 0.9469\n",
      "Epoch 744/1000\n",
      "24/24 [==============================] - 11s 477ms/step - loss: 0.1864 - accuracy: 0.9349 - val_loss: 0.1963 - val_accuracy: 0.9312\n",
      "Epoch 745/1000\n",
      "24/24 [==============================] - 12s 486ms/step - loss: 0.1550 - accuracy: 0.9466 - val_loss: 0.1964 - val_accuracy: 0.9250\n",
      "Epoch 746/1000\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1664 - accuracy: 0.9375 - val_loss: 0.2113 - val_accuracy: 0.9344\n",
      "Epoch 747/1000\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.1962 - accuracy: 0.9349 - val_loss: 0.1493 - val_accuracy: 0.9438\n",
      "Epoch 748/1000\n",
      "24/24 [==============================] - 15s 614ms/step - loss: 0.1988 - accuracy: 0.9323 - val_loss: 0.1319 - val_accuracy: 0.9500\n",
      "Epoch 749/1000\n",
      "24/24 [==============================] - 12s 487ms/step - loss: 0.1951 - accuracy: 0.9362 - val_loss: 0.1220 - val_accuracy: 0.9750\n",
      "Epoch 750/1000\n",
      "24/24 [==============================] - 12s 486ms/step - loss: 0.1853 - accuracy: 0.9375 - val_loss: 0.2007 - val_accuracy: 0.9344\n",
      "Epoch 751/1000\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1860 - accuracy: 0.9271 - val_loss: 0.2014 - val_accuracy: 0.9375\n",
      "Epoch 752/1000\n",
      "24/24 [==============================] - 11s 471ms/step - loss: 0.2105 - accuracy: 0.9193 - val_loss: 0.1730 - val_accuracy: 0.9375\n",
      "Epoch 753/1000\n",
      "24/24 [==============================] - 17s 720ms/step - loss: 0.1828 - accuracy: 0.9427 - val_loss: 0.1700 - val_accuracy: 0.9563\n",
      "Epoch 754/1000\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.1777 - accuracy: 0.9453 - val_loss: 0.1687 - val_accuracy: 0.9406\n",
      "Epoch 755/1000\n",
      "24/24 [==============================] - 11s 474ms/step - loss: 0.1609 - accuracy: 0.9362 - val_loss: 0.1451 - val_accuracy: 0.9563\n",
      "Epoch 756/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1685 - accuracy: 0.9466 - val_loss: 0.1132 - val_accuracy: 0.9656\n",
      "Epoch 757/1000\n",
      "24/24 [==============================] - 11s 470ms/step - loss: 0.1784 - accuracy: 0.9427 - val_loss: 0.2092 - val_accuracy: 0.9438\n",
      "Epoch 758/1000\n",
      "24/24 [==============================] - 15s 620ms/step - loss: 0.2549 - accuracy: 0.9193 - val_loss: 0.1012 - val_accuracy: 0.9656\n",
      "Epoch 759/1000\n",
      "24/24 [==============================] - 14s 573ms/step - loss: 0.1821 - accuracy: 0.9401 - val_loss: 0.1932 - val_accuracy: 0.9469\n",
      "Epoch 760/1000\n",
      "24/24 [==============================] - 11s 465ms/step - loss: 0.2036 - accuracy: 0.9349 - val_loss: 0.1951 - val_accuracy: 0.9375\n",
      "Epoch 761/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1498 - accuracy: 0.9583 - val_loss: 0.1524 - val_accuracy: 0.9531\n",
      "Epoch 762/1000\n",
      "24/24 [==============================] - 11s 475ms/step - loss: 0.2060 - accuracy: 0.9375 - val_loss: 0.1460 - val_accuracy: 0.9594\n",
      "Epoch 763/1000\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.1528 - accuracy: 0.9609 - val_loss: 0.1580 - val_accuracy: 0.9500\n",
      "Epoch 764/1000\n",
      "24/24 [==============================] - 17s 695ms/step - loss: 0.1649 - accuracy: 0.9492 - val_loss: 0.1970 - val_accuracy: 0.9438\n",
      "Epoch 765/1000\n",
      "24/24 [==============================] - 11s 467ms/step - loss: 0.1985 - accuracy: 0.9323 - val_loss: 0.1790 - val_accuracy: 0.9344\n",
      "Epoch 766/1000\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1947 - accuracy: 0.9362 - val_loss: 0.2699 - val_accuracy: 0.9000\n",
      "Epoch 767/1000\n",
      "24/24 [==============================] - 11s 472ms/step - loss: 0.1535 - accuracy: 0.9440 - val_loss: 0.3250 - val_accuracy: 0.8969\n",
      "Epoch 768/1000\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.1664 - accuracy: 0.9505 - val_loss: 0.1525 - val_accuracy: 0.9469\n",
      "Epoch 769/1000\n",
      "24/24 [==============================] - 16s 686ms/step - loss: 0.2505 - accuracy: 0.9180 - val_loss: 0.1502 - val_accuracy: 0.9469\n",
      "Epoch 770/1000\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 0.2118 - accuracy: 0.9245 - val_loss: 0.1581 - val_accuracy: 0.9406\n",
      "Epoch 771/1000\n",
      "24/24 [==============================] - 11s 478ms/step - loss: 0.2037 - accuracy: 0.9336 - val_loss: 0.3021 - val_accuracy: 0.9156\n",
      "Epoch 772/1000\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2365 - accuracy: 0.9206 - val_loss: 0.1830 - val_accuracy: 0.9344\n",
      "Epoch 773/1000\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2048 - accuracy: 0.9362 - val_loss: 0.2628 - val_accuracy: 0.9281\n",
      "Epoch 774/1000\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.1650 - accuracy: 0.9453 - val_loss: 0.1878 - val_accuracy: 0.9438\n",
      "Epoch 775/1000\n",
      "24/24 [==============================] - 14s 600ms/step - loss: 0.1550 - accuracy: 0.9427 - val_loss: 0.2202 - val_accuracy: 0.9375\n",
      "Epoch 776/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1992 - accuracy: 0.9245 - val_loss: 0.1960 - val_accuracy: 0.9250\n",
      "Epoch 777/1000\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.2141 - accuracy: 0.9349 - val_loss: 0.1711 - val_accuracy: 0.9625\n",
      "Epoch 778/1000\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1563 - accuracy: 0.9453 - val_loss: 0.2036 - val_accuracy: 0.9469\n",
      "Epoch 779/1000\n",
      "24/24 [==============================] - 11s 469ms/step - loss: 0.2208 - accuracy: 0.9258 - val_loss: 0.2261 - val_accuracy: 0.9344\n",
      "Epoch 780/1000\n",
      "24/24 [==============================] - 17s 726ms/step - loss: 0.2130 - accuracy: 0.9258 - val_loss: 0.1922 - val_accuracy: 0.9344\n",
      "Epoch 781/1000\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.2182 - accuracy: 0.9310 - val_loss: 0.2882 - val_accuracy: 0.8906\n",
      "Epoch 782/1000\n",
      "24/24 [==============================] - 11s 473ms/step - loss: 0.2150 - accuracy: 0.9258 - val_loss: 0.2590 - val_accuracy: 0.9187\n",
      "Epoch 783/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1978 - accuracy: 0.9310 - val_loss: 0.1523 - val_accuracy: 0.9281\n",
      "Epoch 784/1000\n",
      "24/24 [==============================] - 11s 472ms/step - loss: 0.1723 - accuracy: 0.9401 - val_loss: 0.3512 - val_accuracy: 0.9156\n",
      "Epoch 785/1000\n",
      "24/24 [==============================] - 18s 745ms/step - loss: 0.1613 - accuracy: 0.9323 - val_loss: 0.2121 - val_accuracy: 0.9156\n",
      "Epoch 786/1000\n",
      "24/24 [==============================] - 11s 477ms/step - loss: 0.1801 - accuracy: 0.9479 - val_loss: 0.2333 - val_accuracy: 0.9281\n",
      "Epoch 787/1000\n",
      "24/24 [==============================] - 11s 472ms/step - loss: 0.2011 - accuracy: 0.9323 - val_loss: 0.2506 - val_accuracy: 0.9156\n",
      "Epoch 788/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1933 - accuracy: 0.9362 - val_loss: 0.1696 - val_accuracy: 0.9594\n",
      "Epoch 789/1000\n",
      "24/24 [==============================] - 11s 470ms/step - loss: 0.1810 - accuracy: 0.9388 - val_loss: 0.1386 - val_accuracy: 0.9500\n",
      "Epoch 790/1000\n",
      "24/24 [==============================] - 13s 540ms/step - loss: 0.2037 - accuracy: 0.9323 - val_loss: 0.2852 - val_accuracy: 0.9156\n",
      "Epoch 791/1000\n",
      "24/24 [==============================] - 16s 680ms/step - loss: 0.1283 - accuracy: 0.9570 - val_loss: 0.2144 - val_accuracy: 0.9281\n",
      "Epoch 792/1000\n",
      "24/24 [==============================] - 11s 469ms/step - loss: 0.1463 - accuracy: 0.9466 - val_loss: 0.3075 - val_accuracy: 0.9094\n",
      "Epoch 793/1000\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1732 - accuracy: 0.9505 - val_loss: 0.2321 - val_accuracy: 0.9375\n",
      "Epoch 794/1000\n",
      "24/24 [==============================] - 11s 468ms/step - loss: 0.2298 - accuracy: 0.9323 - val_loss: 0.2670 - val_accuracy: 0.9187\n",
      "Epoch 795/1000\n",
      "24/24 [==============================] - 13s 557ms/step - loss: 0.1825 - accuracy: 0.9453 - val_loss: 0.2049 - val_accuracy: 0.9312\n",
      "Epoch 796/1000\n",
      "24/24 [==============================] - 17s 693ms/step - loss: 0.1952 - accuracy: 0.9258 - val_loss: 0.2256 - val_accuracy: 0.9156\n",
      "Epoch 797/1000\n",
      "24/24 [==============================] - 11s 467ms/step - loss: 0.2609 - accuracy: 0.9258 - val_loss: 0.1782 - val_accuracy: 0.9312\n",
      "Epoch 798/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2146 - accuracy: 0.9258 - val_loss: 0.1706 - val_accuracy: 0.9531\n",
      "Epoch 799/1000\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.1848 - accuracy: 0.9375 - val_loss: 0.1717 - val_accuracy: 0.9406\n",
      "Epoch 800/1000\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2019 - accuracy: 0.9271 - val_loss: 0.1653 - val_accuracy: 0.9219\n",
      "Epoch 801/1000\n",
      "24/24 [==============================] - 13s 541ms/step - loss: 0.1653 - accuracy: 0.9427 - val_loss: 0.3363 - val_accuracy: 0.9156\n",
      "Epoch 802/1000\n",
      "24/24 [==============================] - 14s 600ms/step - loss: 0.1861 - accuracy: 0.9297 - val_loss: 0.1660 - val_accuracy: 0.9438\n",
      "Epoch 803/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2385 - accuracy: 0.9323 - val_loss: 0.2707 - val_accuracy: 0.9250\n",
      "Epoch 804/1000\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1901 - accuracy: 0.9336 - val_loss: 0.1993 - val_accuracy: 0.9344\n",
      "Epoch 805/1000\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.1929 - accuracy: 0.9401 - val_loss: 0.1995 - val_accuracy: 0.9219\n",
      "Epoch 806/1000\n",
      "24/24 [==============================] - 11s 468ms/step - loss: 0.1600 - accuracy: 0.9427 - val_loss: 0.2954 - val_accuracy: 0.9187\n",
      "Epoch 807/1000\n",
      "24/24 [==============================] - 17s 701ms/step - loss: 0.1650 - accuracy: 0.9453 - val_loss: 0.1963 - val_accuracy: 0.9281\n",
      "Epoch 808/1000\n",
      "24/24 [==============================] - 13s 549ms/step - loss: 0.1588 - accuracy: 0.9453 - val_loss: 0.1649 - val_accuracy: 0.9281\n",
      "Epoch 809/1000\n",
      "24/24 [==============================] - 12s 487ms/step - loss: 0.1445 - accuracy: 0.9479 - val_loss: 0.1337 - val_accuracy: 0.9500\n",
      "Epoch 810/1000\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1707 - accuracy: 0.9427 - val_loss: 0.1209 - val_accuracy: 0.9625\n",
      "Epoch 811/1000\n",
      "24/24 [==============================] - 11s 474ms/step - loss: 0.1736 - accuracy: 0.9401 - val_loss: 0.2203 - val_accuracy: 0.9281\n",
      "Epoch 812/1000\n",
      "24/24 [==============================] - 15s 631ms/step - loss: 0.1854 - accuracy: 0.9336 - val_loss: 0.1048 - val_accuracy: 0.9594\n",
      "Epoch 813/1000\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1982 - accuracy: 0.9323 - val_loss: 0.1962 - val_accuracy: 0.9500\n",
      "Epoch 814/1000\n",
      "24/24 [==============================] - 12s 491ms/step - loss: 0.2082 - accuracy: 0.9336 - val_loss: 0.2947 - val_accuracy: 0.9094\n",
      "Epoch 815/1000\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1428 - accuracy: 0.9466 - val_loss: 0.1405 - val_accuracy: 0.9625\n",
      "Epoch 816/1000\n",
      "24/24 [==============================] - 11s 475ms/step - loss: 0.2061 - accuracy: 0.9388 - val_loss: 0.1442 - val_accuracy: 0.9406\n",
      "Epoch 817/1000\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1697 - accuracy: 0.9375 - val_loss: 0.1328 - val_accuracy: 0.9469\n",
      "Epoch 818/1000\n",
      "24/24 [==============================] - 18s 757ms/step - loss: 0.1497 - accuracy: 0.9492 - val_loss: 0.1904 - val_accuracy: 0.9438\n",
      "Epoch 819/1000\n",
      "24/24 [==============================] - 11s 473ms/step - loss: 0.1833 - accuracy: 0.9401 - val_loss: 0.2185 - val_accuracy: 0.9250\n",
      "Epoch 820/1000\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.2117 - accuracy: 0.9245 - val_loss: 0.1191 - val_accuracy: 0.9531\n",
      "Epoch 821/1000\n",
      "24/24 [==============================] - 12s 481ms/step - loss: 0.1781 - accuracy: 0.9362 - val_loss: 0.1226 - val_accuracy: 0.9656\n",
      "Epoch 822/1000\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1803 - accuracy: 0.9336 - val_loss: 0.1146 - val_accuracy: 0.9656\n",
      "Epoch 823/1000\n",
      "24/24 [==============================] - 17s 713ms/step - loss: 0.1869 - accuracy: 0.9297 - val_loss: 0.1423 - val_accuracy: 0.9594\n",
      "Epoch 824/1000\n",
      "24/24 [==============================] - 11s 473ms/step - loss: 0.1528 - accuracy: 0.9453 - val_loss: 0.1632 - val_accuracy: 0.9438\n",
      "Epoch 825/1000\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1549 - accuracy: 0.9427 - val_loss: 0.1328 - val_accuracy: 0.9625\n",
      "Epoch 826/1000\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1875 - accuracy: 0.9388 - val_loss: 0.2414 - val_accuracy: 0.9250\n",
      "Epoch 827/1000\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1389 - accuracy: 0.9570 - val_loss: 0.1850 - val_accuracy: 0.9344\n",
      "Epoch 828/1000\n",
      "24/24 [==============================] - 14s 565ms/step - loss: 0.1849 - accuracy: 0.9401 - val_loss: 0.2162 - val_accuracy: 0.9219\n",
      "Epoch 829/1000\n",
      "24/24 [==============================] - 15s 611ms/step - loss: 0.1543 - accuracy: 0.9427 - val_loss: 0.2017 - val_accuracy: 0.9406\n",
      "Epoch 830/1000\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.2221 - accuracy: 0.9206 - val_loss: 0.2511 - val_accuracy: 0.9344\n",
      "Epoch 831/1000\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1611 - accuracy: 0.9427 - val_loss: 0.2160 - val_accuracy: 0.9062\n",
      "Epoch 832/1000\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2186 - accuracy: 0.9245 - val_loss: 0.0850 - val_accuracy: 0.9688\n",
      "Epoch 833/1000\n",
      "24/24 [==============================] - 11s 462ms/step - loss: 0.1636 - accuracy: 0.9492 - val_loss: 0.2170 - val_accuracy: 0.9187\n",
      "Epoch 834/1000\n",
      "24/24 [==============================] - 18s 732ms/step - loss: 0.1596 - accuracy: 0.9492 - val_loss: 0.1749 - val_accuracy: 0.9344\n",
      "Epoch 835/1000\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1985 - accuracy: 0.9323 - val_loss: 0.2176 - val_accuracy: 0.9250\n",
      "Epoch 836/1000\n",
      "24/24 [==============================] - 11s 477ms/step - loss: 0.1769 - accuracy: 0.9310 - val_loss: 0.2720 - val_accuracy: 0.9125\n",
      "Epoch 837/1000\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1442 - accuracy: 0.9479 - val_loss: 0.1323 - val_accuracy: 0.9656\n",
      "Epoch 838/1000\n",
      "24/24 [==============================] - 12s 479ms/step - loss: 0.1825 - accuracy: 0.9466 - val_loss: 0.1919 - val_accuracy: 0.9438\n",
      "Epoch 839/1000\n",
      "24/24 [==============================] - 17s 699ms/step - loss: 0.1864 - accuracy: 0.9427 - val_loss: 0.1469 - val_accuracy: 0.9500\n",
      "Epoch 840/1000\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2312 - accuracy: 0.9284 - val_loss: 0.2561 - val_accuracy: 0.9125\n",
      "Epoch 841/1000\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.1634 - accuracy: 0.9440 - val_loss: 0.2475 - val_accuracy: 0.9156\n",
      "Epoch 842/1000\n",
      "24/24 [==============================] - 11s 473ms/step - loss: 0.1699 - accuracy: 0.9440 - val_loss: 0.1486 - val_accuracy: 0.9406\n",
      "Epoch 843/1000\n",
      "24/24 [==============================] - 11s 471ms/step - loss: 0.1585 - accuracy: 0.9466 - val_loss: 0.1566 - val_accuracy: 0.9563\n",
      "Epoch 844/1000\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1904 - accuracy: 0.9362 - val_loss: 0.2238 - val_accuracy: 0.9094\n",
      "Epoch 845/1000\n",
      "24/24 [==============================] - 17s 723ms/step - loss: 0.2002 - accuracy: 0.9388 - val_loss: 0.1558 - val_accuracy: 0.9406\n",
      "Epoch 846/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1525 - accuracy: 0.9492 - val_loss: 0.1883 - val_accuracy: 0.9406\n",
      "Epoch 847/1000\n",
      "24/24 [==============================] - 11s 467ms/step - loss: 0.1412 - accuracy: 0.9609 - val_loss: 0.1713 - val_accuracy: 0.9375\n",
      "Epoch 848/1000\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1609 - accuracy: 0.9440 - val_loss: 0.2190 - val_accuracy: 0.9219\n",
      "Epoch 849/1000\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1389 - accuracy: 0.9453 - val_loss: 0.1489 - val_accuracy: 0.9500\n",
      "Epoch 850/1000\n",
      "24/24 [==============================] - 17s 717ms/step - loss: 0.1549 - accuracy: 0.9505 - val_loss: 0.1442 - val_accuracy: 0.9625\n",
      "Epoch 851/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2315 - accuracy: 0.9258 - val_loss: 0.1532 - val_accuracy: 0.9281\n",
      "Epoch 852/1000\n",
      "24/24 [==============================] - 11s 466ms/step - loss: 0.1735 - accuracy: 0.9388 - val_loss: 0.2066 - val_accuracy: 0.9187\n",
      "Epoch 853/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2114 - accuracy: 0.9401 - val_loss: 0.2689 - val_accuracy: 0.9000\n",
      "Epoch 854/1000\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1867 - accuracy: 0.9323 - val_loss: 0.1724 - val_accuracy: 0.9469\n",
      "Epoch 855/1000\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.2538 - accuracy: 0.9258 - val_loss: 0.1428 - val_accuracy: 0.9406\n",
      "Epoch 856/1000\n",
      "24/24 [==============================] - 17s 699ms/step - loss: 0.1609 - accuracy: 0.9453 - val_loss: 0.1835 - val_accuracy: 0.9438\n",
      "Epoch 857/1000\n",
      "24/24 [==============================] - 12s 487ms/step - loss: 0.1874 - accuracy: 0.9349 - val_loss: 0.2616 - val_accuracy: 0.9094\n",
      "Epoch 858/1000\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2199 - accuracy: 0.9258 - val_loss: 0.2412 - val_accuracy: 0.9438\n",
      "Epoch 859/1000\n",
      "24/24 [==============================] - 11s 471ms/step - loss: 0.1458 - accuracy: 0.9414 - val_loss: 0.1976 - val_accuracy: 0.9438\n",
      "Epoch 860/1000\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.1740 - accuracy: 0.9505 - val_loss: 0.1518 - val_accuracy: 0.9500\n",
      "Epoch 861/1000\n",
      "24/24 [==============================] - 18s 742ms/step - loss: 0.1964 - accuracy: 0.9362 - val_loss: 0.1794 - val_accuracy: 0.9406\n",
      "Epoch 862/1000\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2227 - accuracy: 0.9323 - val_loss: 0.1629 - val_accuracy: 0.9406\n",
      "Epoch 863/1000\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1296 - accuracy: 0.9583 - val_loss: 0.1440 - val_accuracy: 0.9531\n",
      "Epoch 864/1000\n",
      "24/24 [==============================] - 11s 470ms/step - loss: 0.1524 - accuracy: 0.9466 - val_loss: 0.2363 - val_accuracy: 0.9250\n",
      "Epoch 865/1000\n",
      "24/24 [==============================] - 11s 468ms/step - loss: 0.1883 - accuracy: 0.9310 - val_loss: 0.2183 - val_accuracy: 0.9250\n",
      "Epoch 866/1000\n",
      "24/24 [==============================] - 13s 542ms/step - loss: 0.1772 - accuracy: 0.9414 - val_loss: 0.2943 - val_accuracy: 0.9031\n",
      "Epoch 867/1000\n",
      "24/24 [==============================] - 16s 679ms/step - loss: 0.1800 - accuracy: 0.9271 - val_loss: 0.2239 - val_accuracy: 0.9500\n",
      "Epoch 868/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2047 - accuracy: 0.9349 - val_loss: 0.1583 - val_accuracy: 0.9406\n",
      "Epoch 869/1000\n",
      "24/24 [==============================] - 11s 465ms/step - loss: 0.1558 - accuracy: 0.9479 - val_loss: 0.3177 - val_accuracy: 0.9094\n",
      "Epoch 870/1000\n",
      "24/24 [==============================] - 11s 464ms/step - loss: 0.2224 - accuracy: 0.9310 - val_loss: 0.2205 - val_accuracy: 0.9125\n",
      "Epoch 871/1000\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1577 - accuracy: 0.9505 - val_loss: 0.1829 - val_accuracy: 0.9344\n",
      "Epoch 872/1000\n",
      "24/24 [==============================] - 17s 694ms/step - loss: 0.1719 - accuracy: 0.9440 - val_loss: 0.1945 - val_accuracy: 0.9312\n",
      "Epoch 873/1000\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1819 - accuracy: 0.9375 - val_loss: 0.1301 - val_accuracy: 0.9719\n",
      "Epoch 874/1000\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2020 - accuracy: 0.9388 - val_loss: 0.2111 - val_accuracy: 0.9375\n",
      "Epoch 875/1000\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.1837 - accuracy: 0.9401 - val_loss: 0.2427 - val_accuracy: 0.9281\n",
      "Epoch 876/1000\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1534 - accuracy: 0.9466 - val_loss: 0.1940 - val_accuracy: 0.9469\n",
      "Epoch 877/1000\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.1961 - accuracy: 0.9388 - val_loss: 0.1695 - val_accuracy: 0.9438\n",
      "Epoch 878/1000\n",
      "24/24 [==============================] - 16s 683ms/step - loss: 0.1930 - accuracy: 0.9284 - val_loss: 0.2687 - val_accuracy: 0.9250\n",
      "Epoch 879/1000\n",
      "24/24 [==============================] - 11s 477ms/step - loss: 0.1750 - accuracy: 0.9297 - val_loss: 0.2406 - val_accuracy: 0.9219\n",
      "Epoch 880/1000\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.1417 - accuracy: 0.9479 - val_loss: 0.1349 - val_accuracy: 0.9469\n",
      "Epoch 881/1000\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.1766 - accuracy: 0.9440 - val_loss: 0.0902 - val_accuracy: 0.9625\n",
      "Epoch 882/1000\n",
      "24/24 [==============================] - 11s 473ms/step - loss: 0.1721 - accuracy: 0.9271 - val_loss: 0.1462 - val_accuracy: 0.9500\n",
      "Epoch 883/1000\n",
      "24/24 [==============================] - 18s 738ms/step - loss: 0.1321 - accuracy: 0.9466 - val_loss: 0.1509 - val_accuracy: 0.9406\n",
      "Epoch 884/1000\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1802 - accuracy: 0.9401 - val_loss: 0.1487 - val_accuracy: 0.9344\n",
      "Epoch 885/1000\n",
      "24/24 [==============================] - 12s 479ms/step - loss: 0.1700 - accuracy: 0.9414 - val_loss: 0.1405 - val_accuracy: 0.9469\n",
      "Epoch 886/1000\n",
      "24/24 [==============================] - 11s 477ms/step - loss: 0.1603 - accuracy: 0.9427 - val_loss: 0.2050 - val_accuracy: 0.9312\n",
      "Epoch 887/1000\n",
      "24/24 [==============================] - 11s 468ms/step - loss: 0.1531 - accuracy: 0.9466 - val_loss: 0.1978 - val_accuracy: 0.9344\n",
      "Epoch 888/1000\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.1764 - accuracy: 0.9388 - val_loss: 0.1137 - val_accuracy: 0.9656\n",
      "Epoch 889/1000\n",
      "24/24 [==============================] - 16s 658ms/step - loss: 0.1720 - accuracy: 0.9362 - val_loss: 0.1767 - val_accuracy: 0.9375\n",
      "Epoch 890/1000\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1818 - accuracy: 0.9297 - val_loss: 0.2041 - val_accuracy: 0.9281\n",
      "Epoch 891/1000\n",
      "24/24 [==============================] - 11s 463ms/step - loss: 0.1663 - accuracy: 0.9440 - val_loss: 0.1151 - val_accuracy: 0.9688\n",
      "Epoch 892/1000\n",
      "24/24 [==============================] - 45s 2s/step - loss: 0.1748 - accuracy: 0.9375 - val_loss: 0.2904 - val_accuracy: 0.9250\n",
      "Epoch 893/1000\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2251 - accuracy: 0.9206 - val_loss: 0.2348 - val_accuracy: 0.9406\n",
      "Epoch 894/1000\n",
      "24/24 [==============================] - 11s 471ms/step - loss: 0.1729 - accuracy: 0.9362 - val_loss: 0.1241 - val_accuracy: 0.9344\n",
      "Epoch 895/1000\n",
      "24/24 [==============================] - 11s 458ms/step - loss: 0.2051 - accuracy: 0.9362 - val_loss: 0.0844 - val_accuracy: 0.9750\n",
      "Epoch 896/1000\n",
      "24/24 [==============================] - 13s 540ms/step - loss: 0.1524 - accuracy: 0.9518 - val_loss: 0.1552 - val_accuracy: 0.9469\n",
      "Epoch 897/1000\n",
      "24/24 [==============================] - 11s 469ms/step - loss: 0.1816 - accuracy: 0.9414 - val_loss: 0.1508 - val_accuracy: 0.9531\n",
      "Epoch 898/1000\n",
      "24/24 [==============================] - 17s 721ms/step - loss: 0.2042 - accuracy: 0.9232 - val_loss: 0.1358 - val_accuracy: 0.9500\n",
      "Epoch 899/1000\n",
      "24/24 [==============================] - 11s 470ms/step - loss: 0.1629 - accuracy: 0.9453 - val_loss: 0.1858 - val_accuracy: 0.9187\n",
      "Epoch 900/1000\n",
      "24/24 [==============================] - 12s 487ms/step - loss: 0.1682 - accuracy: 0.9349 - val_loss: 0.2240 - val_accuracy: 0.9250\n",
      "Epoch 901/1000\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1570 - accuracy: 0.9414 - val_loss: 0.0937 - val_accuracy: 0.9688\n",
      "Epoch 902/1000\n",
      "24/24 [==============================] - 11s 479ms/step - loss: 0.1563 - accuracy: 0.9518 - val_loss: 0.1549 - val_accuracy: 0.9438\n",
      "Epoch 903/1000\n",
      "24/24 [==============================] - 17s 716ms/step - loss: 0.2247 - accuracy: 0.9336 - val_loss: 0.1026 - val_accuracy: 0.9688\n",
      "Epoch 904/1000\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.1665 - accuracy: 0.9466 - val_loss: 0.1634 - val_accuracy: 0.9438\n",
      "Epoch 905/1000\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2175 - accuracy: 0.9271 - val_loss: 0.1592 - val_accuracy: 0.9312\n",
      "Epoch 906/1000\n",
      "24/24 [==============================] - 11s 465ms/step - loss: 0.1530 - accuracy: 0.9479 - val_loss: 0.1403 - val_accuracy: 0.9469\n",
      "Epoch 907/1000\n",
      "24/24 [==============================] - 11s 463ms/step - loss: 0.1555 - accuracy: 0.9453 - val_loss: 0.1608 - val_accuracy: 0.9281\n",
      "Epoch 908/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1419 - accuracy: 0.9492 - val_loss: 0.1943 - val_accuracy: 0.9344\n",
      "Epoch 909/1000\n",
      "24/24 [==============================] - 18s 744ms/step - loss: 0.1782 - accuracy: 0.9362 - val_loss: 0.1062 - val_accuracy: 0.9594\n",
      "Epoch 910/1000\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1374 - accuracy: 0.9531 - val_loss: 0.1540 - val_accuracy: 0.9469\n",
      "Epoch 911/1000\n",
      "24/24 [==============================] - 11s 463ms/step - loss: 0.1600 - accuracy: 0.9453 - val_loss: 0.1565 - val_accuracy: 0.9469\n",
      "Epoch 912/1000\n",
      "24/24 [==============================] - 11s 463ms/step - loss: 0.1457 - accuracy: 0.9453 - val_loss: 0.1058 - val_accuracy: 0.9500\n",
      "Epoch 913/1000\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1637 - accuracy: 0.9492 - val_loss: 0.2480 - val_accuracy: 0.9187\n",
      "Epoch 914/1000\n",
      "24/24 [==============================] - 17s 723ms/step - loss: 0.1637 - accuracy: 0.9531 - val_loss: 0.2327 - val_accuracy: 0.9250\n",
      "Epoch 915/1000\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1642 - accuracy: 0.9466 - val_loss: 0.1257 - val_accuracy: 0.9656\n",
      "Epoch 916/1000\n",
      "24/24 [==============================] - 11s 463ms/step - loss: 0.1797 - accuracy: 0.9479 - val_loss: 0.1539 - val_accuracy: 0.9531\n",
      "Epoch 917/1000\n",
      "24/24 [==============================] - 11s 460ms/step - loss: 0.1945 - accuracy: 0.9323 - val_loss: 0.1808 - val_accuracy: 0.9344\n",
      "Epoch 918/1000\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.1227 - accuracy: 0.9570 - val_loss: 0.2948 - val_accuracy: 0.9031\n",
      "Epoch 919/1000\n",
      "24/24 [==============================] - 12s 485ms/step - loss: 0.1596 - accuracy: 0.9401 - val_loss: 0.1148 - val_accuracy: 0.9563\n",
      "Epoch 920/1000\n",
      "24/24 [==============================] - 18s 739ms/step - loss: 0.1735 - accuracy: 0.9440 - val_loss: 0.1503 - val_accuracy: 0.9500\n",
      "Epoch 921/1000\n",
      "24/24 [==============================] - 11s 468ms/step - loss: 0.1378 - accuracy: 0.9609 - val_loss: 0.1776 - val_accuracy: 0.9344\n",
      "Epoch 922/1000\n",
      "24/24 [==============================] - 11s 469ms/step - loss: 0.2009 - accuracy: 0.9362 - val_loss: 0.2001 - val_accuracy: 0.9312\n",
      "Epoch 923/1000\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1869 - accuracy: 0.9401 - val_loss: 0.1115 - val_accuracy: 0.9750\n",
      "Epoch 924/1000\n",
      "24/24 [==============================] - 11s 469ms/step - loss: 0.1354 - accuracy: 0.9544 - val_loss: 0.1933 - val_accuracy: 0.9406\n",
      "Epoch 925/1000\n",
      "24/24 [==============================] - 16s 677ms/step - loss: 0.1573 - accuracy: 0.9479 - val_loss: 0.1608 - val_accuracy: 0.9531\n",
      "Epoch 926/1000\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.1630 - accuracy: 0.9466 - val_loss: 0.2126 - val_accuracy: 0.9375\n",
      "Epoch 927/1000\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1598 - accuracy: 0.9453 - val_loss: 0.2019 - val_accuracy: 0.9375\n",
      "Epoch 928/1000\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1978 - accuracy: 0.9362 - val_loss: 0.1705 - val_accuracy: 0.9406\n",
      "Epoch 929/1000\n",
      "24/24 [==============================] - 11s 467ms/step - loss: 0.1914 - accuracy: 0.9375 - val_loss: 0.1266 - val_accuracy: 0.9500\n",
      "Epoch 930/1000\n",
      "24/24 [==============================] - 11s 467ms/step - loss: 0.1950 - accuracy: 0.9388 - val_loss: 0.1616 - val_accuracy: 0.9563\n",
      "Epoch 931/1000\n",
      "24/24 [==============================] - 18s 740ms/step - loss: 0.1316 - accuracy: 0.9479 - val_loss: 0.1633 - val_accuracy: 0.9438\n",
      "Epoch 932/1000\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1540 - accuracy: 0.9518 - val_loss: 0.1306 - val_accuracy: 0.9469\n",
      "Epoch 933/1000\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1772 - accuracy: 0.9323 - val_loss: 0.1699 - val_accuracy: 0.9500\n",
      "Epoch 934/1000\n",
      "24/24 [==============================] - 11s 473ms/step - loss: 0.1481 - accuracy: 0.9466 - val_loss: 0.1934 - val_accuracy: 0.9469\n",
      "Epoch 935/1000\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1555 - accuracy: 0.9388 - val_loss: 0.1580 - val_accuracy: 0.9500\n",
      "Epoch 936/1000\n",
      "24/24 [==============================] - 16s 654ms/step - loss: 0.1901 - accuracy: 0.9310 - val_loss: 0.1835 - val_accuracy: 0.9250\n",
      "Epoch 937/1000\n",
      "24/24 [==============================] - 15s 626ms/step - loss: 0.1158 - accuracy: 0.9609 - val_loss: 0.1534 - val_accuracy: 0.9531\n",
      "Epoch 938/1000\n",
      "24/24 [==============================] - 11s 472ms/step - loss: 0.1698 - accuracy: 0.9466 - val_loss: 0.2670 - val_accuracy: 0.9187\n",
      "Epoch 939/1000\n",
      "24/24 [==============================] - 11s 470ms/step - loss: 0.1820 - accuracy: 0.9401 - val_loss: 0.2211 - val_accuracy: 0.9094\n",
      "Epoch 940/1000\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 0.1438 - accuracy: 0.9453 - val_loss: 0.2737 - val_accuracy: 0.9219\n",
      "Epoch 941/1000\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.1335 - accuracy: 0.9492 - val_loss: 0.3345 - val_accuracy: 0.8938\n",
      "Epoch 942/1000\n",
      "24/24 [==============================] - 18s 756ms/step - loss: 0.1857 - accuracy: 0.9375 - val_loss: 0.2188 - val_accuracy: 0.9406\n",
      "Epoch 943/1000\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.1532 - accuracy: 0.9414 - val_loss: 0.1251 - val_accuracy: 0.9469\n",
      "Epoch 944/1000\n",
      "24/24 [==============================] - 11s 476ms/step - loss: 0.1557 - accuracy: 0.9401 - val_loss: 0.1324 - val_accuracy: 0.9531\n",
      "Epoch 945/1000\n",
      "24/24 [==============================] - 13s 554ms/step - loss: 0.1673 - accuracy: 0.9570 - val_loss: 0.1728 - val_accuracy: 0.9438\n",
      "Epoch 946/1000\n",
      "24/24 [==============================] - 11s 476ms/step - loss: 0.1616 - accuracy: 0.9401 - val_loss: 0.2408 - val_accuracy: 0.9219\n",
      "Epoch 947/1000\n",
      "24/24 [==============================] - 16s 687ms/step - loss: 0.2068 - accuracy: 0.9323 - val_loss: 0.1468 - val_accuracy: 0.9531\n",
      "Epoch 948/1000\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2032 - accuracy: 0.9284 - val_loss: 0.1558 - val_accuracy: 0.9375\n",
      "Epoch 949/1000\n",
      "24/24 [==============================] - 12s 480ms/step - loss: 0.1562 - accuracy: 0.9466 - val_loss: 0.1644 - val_accuracy: 0.9156\n",
      "Epoch 950/1000\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1525 - accuracy: 0.9440 - val_loss: 0.2199 - val_accuracy: 0.9250\n",
      "Epoch 951/1000\n",
      "24/24 [==============================] - 11s 474ms/step - loss: 0.1758 - accuracy: 0.9401 - val_loss: 0.2433 - val_accuracy: 0.9312\n",
      "Epoch 952/1000\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1882 - accuracy: 0.9323 - val_loss: 0.2147 - val_accuracy: 0.9500\n",
      "Epoch 953/1000\n",
      "24/24 [==============================] - 18s 734ms/step - loss: 0.1586 - accuracy: 0.9414 - val_loss: 0.1797 - val_accuracy: 0.9406\n",
      "Epoch 954/1000\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1093 - accuracy: 0.9544 - val_loss: 0.0861 - val_accuracy: 0.9719\n",
      "Epoch 955/1000\n",
      "24/24 [==============================] - 11s 478ms/step - loss: 0.1928 - accuracy: 0.9427 - val_loss: 0.1323 - val_accuracy: 0.9563\n",
      "Epoch 956/1000\n",
      "24/24 [==============================] - 11s 473ms/step - loss: 0.1425 - accuracy: 0.9544 - val_loss: 0.0760 - val_accuracy: 0.9688\n",
      "Epoch 957/1000\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.1709 - accuracy: 0.9440 - val_loss: 0.1214 - val_accuracy: 0.9594\n",
      "Epoch 958/1000\n",
      "24/24 [==============================] - 17s 715ms/step - loss: 0.1811 - accuracy: 0.9310 - val_loss: 0.1468 - val_accuracy: 0.9469\n",
      "Epoch 959/1000\n",
      "24/24 [==============================] - 14s 563ms/step - loss: 0.1566 - accuracy: 0.9362 - val_loss: 0.1433 - val_accuracy: 0.9563\n",
      "Epoch 960/1000\n",
      "24/24 [==============================] - 11s 475ms/step - loss: 0.1439 - accuracy: 0.9557 - val_loss: 0.1614 - val_accuracy: 0.9406\n",
      "Epoch 961/1000\n",
      "24/24 [==============================] - 11s 475ms/step - loss: 0.1598 - accuracy: 0.9440 - val_loss: 0.1473 - val_accuracy: 0.9500\n",
      "Epoch 962/1000\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1325 - accuracy: 0.9466 - val_loss: 0.2272 - val_accuracy: 0.9281\n",
      "Epoch 963/1000\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1647 - accuracy: 0.9453 - val_loss: 0.1595 - val_accuracy: 0.9500\n",
      "Epoch 964/1000\n",
      "24/24 [==============================] - 18s 757ms/step - loss: 0.1581 - accuracy: 0.9479 - val_loss: 0.2105 - val_accuracy: 0.9187\n",
      "Epoch 965/1000\n",
      "24/24 [==============================] - 11s 467ms/step - loss: 0.1563 - accuracy: 0.9440 - val_loss: 0.1627 - val_accuracy: 0.9531\n",
      "Epoch 966/1000\n",
      "24/24 [==============================] - 11s 478ms/step - loss: 0.1400 - accuracy: 0.9453 - val_loss: 0.1523 - val_accuracy: 0.9594\n",
      "Epoch 967/1000\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.1842 - accuracy: 0.9336 - val_loss: 0.1727 - val_accuracy: 0.9406\n",
      "Epoch 968/1000\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.1506 - accuracy: 0.9479 - val_loss: 0.1881 - val_accuracy: 0.9281\n",
      "Epoch 969/1000\n",
      "24/24 [==============================] - 17s 728ms/step - loss: 0.1515 - accuracy: 0.9388 - val_loss: 0.1169 - val_accuracy: 0.9469\n",
      "Epoch 970/1000\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1589 - accuracy: 0.9479 - val_loss: 0.1369 - val_accuracy: 0.9563\n",
      "Epoch 971/1000\n",
      "24/24 [==============================] - 11s 475ms/step - loss: 0.1555 - accuracy: 0.9492 - val_loss: 0.1428 - val_accuracy: 0.9500\n",
      "Epoch 972/1000\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.1385 - accuracy: 0.9505 - val_loss: 0.1721 - val_accuracy: 0.9438\n",
      "Epoch 973/1000\n",
      "24/24 [==============================] - 11s 468ms/step - loss: 0.2157 - accuracy: 0.9245 - val_loss: 0.4633 - val_accuracy: 0.8875\n",
      "Epoch 974/1000\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1996 - accuracy: 0.9336 - val_loss: 0.1959 - val_accuracy: 0.9375\n",
      "Epoch 975/1000\n",
      "24/24 [==============================] - 18s 739ms/step - loss: 0.1215 - accuracy: 0.9570 - val_loss: 0.0863 - val_accuracy: 0.9688\n",
      "Epoch 976/1000\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.1844 - accuracy: 0.9336 - val_loss: 0.0813 - val_accuracy: 0.9750\n",
      "Epoch 977/1000\n",
      "24/24 [==============================] - 11s 475ms/step - loss: 0.1478 - accuracy: 0.9505 - val_loss: 0.1960 - val_accuracy: 0.9312\n",
      "Epoch 978/1000\n",
      "24/24 [==============================] - 11s 479ms/step - loss: 0.1192 - accuracy: 0.9583 - val_loss: 0.2218 - val_accuracy: 0.9219\n",
      "Epoch 979/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1537 - accuracy: 0.9492 - val_loss: 0.1253 - val_accuracy: 0.9656\n",
      "Epoch 980/1000\n",
      "24/24 [==============================] - 18s 770ms/step - loss: 0.1545 - accuracy: 0.9427 - val_loss: 0.1523 - val_accuracy: 0.9469\n",
      "Epoch 981/1000\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1630 - accuracy: 0.9466 - val_loss: 0.0828 - val_accuracy: 0.9719\n",
      "Epoch 982/1000\n",
      "24/24 [==============================] - 11s 471ms/step - loss: 0.1777 - accuracy: 0.9479 - val_loss: 0.2619 - val_accuracy: 0.9094\n",
      "Epoch 983/1000\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 0.1851 - accuracy: 0.9310 - val_loss: 0.1652 - val_accuracy: 0.9344\n",
      "Epoch 984/1000\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1630 - accuracy: 0.9375 - val_loss: 0.1339 - val_accuracy: 0.9469\n",
      "Epoch 985/1000\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1370 - accuracy: 0.9557 - val_loss: 0.1624 - val_accuracy: 0.9375\n",
      "Epoch 986/1000\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1503 - accuracy: 0.9466 - val_loss: 0.1389 - val_accuracy: 0.9438\n",
      "Epoch 987/1000\n",
      "24/24 [==============================] - 12s 479ms/step - loss: 0.2120 - accuracy: 0.9245 - val_loss: 0.1762 - val_accuracy: 0.9438\n",
      "Epoch 988/1000\n",
      "24/24 [==============================] - 11s 471ms/step - loss: 0.1285 - accuracy: 0.9635 - val_loss: 0.1113 - val_accuracy: 0.9594\n",
      "Epoch 989/1000\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2001 - accuracy: 0.9375 - val_loss: 0.2032 - val_accuracy: 0.9438\n",
      "Epoch 990/1000\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.1749 - accuracy: 0.9362 - val_loss: 0.1372 - val_accuracy: 0.9563\n",
      "Epoch 991/1000\n",
      "24/24 [==============================] - 16s 669ms/step - loss: 0.1453 - accuracy: 0.9466 - val_loss: 0.1021 - val_accuracy: 0.9594\n",
      "Epoch 992/1000\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.1485 - accuracy: 0.9466 - val_loss: 0.1716 - val_accuracy: 0.9438\n",
      "Epoch 993/1000\n",
      "24/24 [==============================] - 12s 481ms/step - loss: 0.1509 - accuracy: 0.9440 - val_loss: 0.1694 - val_accuracy: 0.9375\n",
      "Epoch 994/1000\n",
      "24/24 [==============================] - 13s 556ms/step - loss: 0.1435 - accuracy: 0.9479 - val_loss: 0.0837 - val_accuracy: 0.9781\n",
      "Epoch 995/1000\n",
      "24/24 [==============================] - 12s 486ms/step - loss: 0.1449 - accuracy: 0.9505 - val_loss: 0.2088 - val_accuracy: 0.9469\n",
      "Epoch 996/1000\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1372 - accuracy: 0.9544 - val_loss: 0.2026 - val_accuracy: 0.9281\n",
      "Epoch 997/1000\n",
      "24/24 [==============================] - 11s 474ms/step - loss: 0.1734 - accuracy: 0.9388 - val_loss: 0.1198 - val_accuracy: 0.9531\n",
      "Epoch 998/1000\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1652 - accuracy: 0.9414 - val_loss: 0.1654 - val_accuracy: 0.9406\n",
      "Epoch 999/1000\n",
      "24/24 [==============================] - 13s 550ms/step - loss: 0.1594 - accuracy: 0.9427 - val_loss: 0.3171 - val_accuracy: 0.9125\n",
      "Epoch 1000/1000\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.1769 - accuracy: 0.9414 - val_loss: 0.3396 - val_accuracy: 0.9031\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "    train_data,\n",
    "    epochs=epochs,\n",
    "    validation_data=val_data,\n",
    "    steps_per_epoch=24,\n",
    "    validation_steps=10,\n",
    "    callbacks=[mcp_save]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 9.248389,
     "end_time": "2020-11-28T14:44:05.995751",
     "exception": false,
     "start_time": "2020-11-28T14:43:56.747362",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Plotting Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-28T14:44:25.786468Z",
     "iopub.status.busy": "2020-11-28T14:44:25.785027Z",
     "iopub.status.idle": "2020-11-28T14:44:25.787116Z",
     "shell.execute_reply": "2020-11-28T14:44:25.787585Z"
    },
    "papermill": {
     "duration": 10.343152,
     "end_time": "2020-11-28T14:44:25.787709",
     "exception": false,
     "start_time": "2020-11-28T14:44:15.444557",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-28T14:44:44.673844Z",
     "iopub.status.busy": "2020-11-28T14:44:44.672813Z",
     "iopub.status.idle": "2020-11-28T14:44:44.907623Z",
     "shell.execute_reply": "2020-11-28T14:44:44.908113Z"
    },
    "papermill": {
     "duration": 9.530772,
     "end_time": "2020-11-28T14:44:44.908248",
     "exception": false,
     "start_time": "2020-11-28T14:44:35.377476",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,10))\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'vaidation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-28T14:45:03.351159Z",
     "iopub.status.busy": "2020-11-28T14:45:03.349905Z",
     "iopub.status.idle": "2020-11-28T14:45:03.539098Z",
     "shell.execute_reply": "2020-11-28T14:45:03.539580Z"
    },
    "papermill": {
     "duration": 9.793821,
     "end_time": "2020-11-28T14:45:03.539712",
     "exception": false,
     "start_time": "2020-11-28T14:44:53.745891",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,10))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'vaidation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 9.880629,
     "end_time": "2020-11-28T14:45:22.735757",
     "exception": false,
     "start_time": "2020-11-28T14:45:12.855128",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Loading test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-28T14:45:41.974072Z",
     "iopub.status.busy": "2020-11-28T14:45:41.972406Z",
     "iopub.status.idle": "2020-11-28T14:45:41.974740Z",
     "shell.execute_reply": "2020-11-28T14:45:41.975253Z"
    },
    "papermill": {
     "duration": 9.212921,
     "end_time": "2020-11-28T14:45:41.975377",
     "exception": false,
     "start_time": "2020-11-28T14:45:32.762456",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_data_generator = ImageDataGenerator(\n",
    "    rescale=1./255\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-28T14:46:00.576304Z",
     "iopub.status.busy": "2020-11-28T14:46:00.575593Z",
     "iopub.status.idle": "2020-11-28T14:46:00.909584Z",
     "shell.execute_reply": "2020-11-28T14:46:00.910643Z"
    },
    "papermill": {
     "duration": 9.541116,
     "end_time": "2020-11-28T14:46:00.911047",
     "exception": false,
     "start_time": "2020-11-28T14:45:51.369931",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2700 images belonging to 10 classes.\n"
     ]
    }
   ],
   "source": [
    "test_data = test_data_generator.flow_from_directory(\n",
    "    batch_size=1,\n",
    "    directory=test_dir,\n",
    "    shuffle=False,\n",
    "    target_size=(img_height, img_width),\n",
    "    class_mode='categorical'\n",
    ")\n",
    "idx2label_dict = {test_data.class_indices[k]: k for k in test_data.class_indices}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 9.755556,
     "end_time": "2020-11-28T14:46:20.116907",
     "exception": false,
     "start_time": "2020-11-28T14:46:10.361351",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Predicting values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-28T14:46:39.369573Z",
     "iopub.status.busy": "2020-11-28T14:46:39.367993Z",
     "iopub.status.idle": "2020-11-28T14:47:15.214574Z",
     "shell.execute_reply": "2020-11-28T14:47:15.215228Z"
    },
    "papermill": {
     "duration": 45.662633,
     "end_time": "2020-11-28T14:47:15.215419",
     "exception": false,
     "start_time": "2020-11-28T14:46:29.552786",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average inference time: 7.16 seconds\n"
     ]
    }
   ],
   "source": [
    "import time \n",
    "\n",
    "model.load_weights('PKNet.h5')\n",
    "inference_times = []\n",
    "for i in range(5):\n",
    "    start_time = time.time()\n",
    "    y_pred = model.predict_generator(test_data, steps=2700)\n",
    "    inference_time = time.time() - start_time\n",
    "    inference_times.append(inference_time)\n",
    "print('Average inference time: %.2f seconds' % (sum(inference_times)/len(inference_times)))\n",
    "y_true = test_data.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-28T14:47:34.725315Z",
     "iopub.status.busy": "2020-11-28T14:47:34.724318Z",
     "iopub.status.idle": "2020-11-28T14:47:34.726381Z",
     "shell.execute_reply": "2020-11-28T14:47:34.726884Z"
    },
    "papermill": {
     "duration": 9.220263,
     "end_time": "2020-11-28T14:47:34.727036",
     "exception": false,
     "start_time": "2020-11-28T14:47:25.506773",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "y_pred = np.argmax(y_pred, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-28T14:47:53.666923Z",
     "iopub.status.busy": "2020-11-28T14:47:53.666260Z",
     "iopub.status.idle": "2020-11-28T14:47:54.453506Z",
     "shell.execute_reply": "2020-11-28T14:47:54.452598Z"
    },
    "papermill": {
     "duration": 10.064827,
     "end_time": "2020-11-28T14:47:54.453648",
     "exception": false,
     "start_time": "2020-11-28T14:47:44.388821",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sn\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-28T14:48:13.693812Z",
     "iopub.status.busy": "2020-11-28T14:48:13.692903Z",
     "iopub.status.idle": "2020-11-28T14:48:13.704025Z",
     "shell.execute_reply": "2020-11-28T14:48:13.704647Z"
    },
    "papermill": {
     "duration": 9.747477,
     "end_time": "2020-11-28T14:48:13.704827",
     "exception": false,
     "start_time": "2020-11-28T14:48:03.957350",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_key(mydict,val): \n",
    "    for key, value in mydict.items(): \n",
    "         if val == value: \n",
    "             return key "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-28T14:48:32.905817Z",
     "iopub.status.busy": "2020-11-28T14:48:32.904049Z",
     "iopub.status.idle": "2020-11-28T14:48:32.906496Z",
     "shell.execute_reply": "2020-11-28T14:48:32.906954Z"
    },
    "papermill": {
     "duration": 9.390265,
     "end_time": "2020-11-28T14:48:32.907096",
     "exception": false,
     "start_time": "2020-11-28T14:48:23.516831",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def find_metrics(y_true, y_pred, idx2label_dict, class_name):\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    out1 = np.sum(cm, axis = 1)\n",
    "    out2 = np.sum(cm, axis = 0)\n",
    "    id = get_key(idx2label_dict, class_name)\n",
    "    r1 = cm[id][id]/out1[id]\n",
    "    r2 = cm[id][id]/out2[id]\n",
    "    s = cm[id][id]\n",
    "    return (r1, r2, s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 9.621584,
     "end_time": "2020-11-28T14:48:52.014297",
     "exception": false,
     "start_time": "2020-11-28T14:48:42.392713",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Precision and Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-28T14:49:10.490136Z",
     "iopub.status.busy": "2020-11-28T14:49:10.484997Z",
     "iopub.status.idle": "2020-11-28T14:49:10.634650Z",
     "shell.execute_reply": "2020-11-28T14:49:10.635154Z"
    },
    "papermill": {
     "duration": 9.335637,
     "end_time": "2020-11-28T14:49:10.635299",
     "exception": false,
     "start_time": "2020-11-28T14:49:01.299662",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------------------+--------+-----------+----------+----------+\n",
      "|        Class         | Recall | Precision | Accuracy | F1 Score |\n",
      "+----------------------+--------+-----------+----------+----------+\n",
      "|      AnnualCrop      |  0.95  |    0.91   |   0.98   |   0.93   |\n",
      "|        Forest        |  1.0   |    0.98   |   1.0    |   0.99   |\n",
      "| HerbaceousVegetation |  0.96  |    0.91   |   0.98   |   0.93   |\n",
      "|       Highway        |  0.96  |    0.96   |   0.99   |   0.96   |\n",
      "|      Industrial      |  0.98  |    0.96   |   0.99   |   0.97   |\n",
      "|       Pasture        |  0.96  |    0.95   |   0.99   |   0.95   |\n",
      "|    PermanentCrop     |  0.86  |    0.94   |   0.98   |   0.9    |\n",
      "|     Residential      |  0.99  |    0.99   |   1.0    |   0.99   |\n",
      "|        River         |  0.93  |    0.96   |   0.99   |   0.95   |\n",
      "|       SeaLake        |  0.96  |    0.99   |   0.99   |   0.97   |\n",
      "+----------------------+--------+-----------+----------+----------+\n",
      "Accuracy: 95.52\n"
     ]
    }
   ],
   "source": [
    "import prettytable\n",
    "\n",
    "table = prettytable.PrettyTable(['Class', 'Recall', 'Precision', 'Accuracy', 'F1 Score'])\n",
    "class_names = ['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake']\n",
    "sum = 0\n",
    "\n",
    "cm = confusion_matrix(y_true, y_pred)\n",
    "cm_sum = np.sum(cm)\n",
    "col_sum = np.sum(cm, axis = 0)\n",
    "row_sum = np.sum(cm, axis = 1)\n",
    "\n",
    "class_acc = []\n",
    "\n",
    "row = len(cm)\n",
    "\n",
    "for x in range(0,row):\n",
    "    tp = cm[x][x] \n",
    "    fp = row_sum[x] - cm[x][x]\n",
    "    fn = col_sum[x] - cm[x][x]\n",
    "    tn = cm_sum - row_sum[x]- col_sum[x] + cm[x][x]\n",
    "\n",
    "    temp = (tp+tn)/(tp+fn+fp+tn)\n",
    "    class_acc.append(temp)\n",
    "\n",
    "temp = 0    \n",
    "for _class in class_names:\n",
    "    result1, result2, s = find_metrics(y_true, y_pred, idx2label_dict, _class)\n",
    "    sum += s\n",
    "    f1 = (2*(result1* result2))/ (result1 + result2)\n",
    "    table.add_row([_class, round(result1, 2), round(result2, 2), round(class_acc[temp], 2), round(f1, 2)])\n",
    "    temp +=1\n",
    "print(table)\n",
    "print(\"Accuracy: %.2f\" % (sum/2700*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 9.299592,
     "end_time": "2020-11-28T14:49:30.658418",
     "exception": false,
     "start_time": "2020-11-28T14:49:21.358826",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-28T14:49:48.844773Z",
     "iopub.status.busy": "2020-11-28T14:49:48.843884Z",
     "iopub.status.idle": "2020-11-28T14:49:49.463437Z",
     "shell.execute_reply": "2020-11-28T14:49:49.462469Z"
    },
    "papermill": {
     "duration": 9.895425,
     "end_time": "2020-11-28T14:49:49.463550",
     "exception": false,
     "start_time": "2020-11-28T14:49:39.568125",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f866636bc90>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_true, y_pred)\n",
    "df_cm = pd.DataFrame(cm, index = [idx2label_dict[int(i)] for i in \"0123456789\"], columns = [idx2label_dict[int(i)] for i in \"0123456789\"])\n",
    "plt.figure(figsize = (20,10))\n",
    "sn.heatmap(df_cm, annot=True, linewidths=.5, fmt=\"d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 9.2068,
     "end_time": "2020-11-28T14:50:08.683990",
     "exception": false,
     "start_time": "2020-11-28T14:49:59.477190",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "papermill": {
   "duration": 13820.442624,
   "end_time": "2020-11-28T14:50:19.795695",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2020-11-28T10:59:59.353071",
   "version": "2.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}