office31.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "URa4W8sGk_mm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CJKtjPz7kR9s"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "import keras\n",
        "import numpy as np\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, Softmax, Input, Concatenate, BatchNormalization, LSTM, Reshape\n",
        "from keras.optimizers import Adam, SGD\n",
        "from keras import regularizers\n",
        "import matplotlib.pyplot as pyplot\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "from keras.callbacks import ModelCheckpoint,EarlyStopping,ReduceLROnPlateau\n",
        "import random\n",
        "import pickle as pk\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/gdrive/MyDrive/Colab Notebooks/office31/trainx.pkl', 'rb') as file:\n",
        "    trainx = pk.load(file)\n",
        "with open('/content/gdrive/MyDrive/Colab Notebooks/office31/trainy.pkl', 'rb') as file:\n",
        "    trainy = pk.load(file)\n",
        "with open('/content/gdrive/MyDrive/Colab Notebooks/office31/testx.pkl', 'rb') as file:\n",
        "    testx = pk.load(file)\n",
        "with open('/content/gdrive/MyDrive/Colab Notebooks/office31/testy.pkl', 'rb') as file:\n",
        "    testy = pk.load(file)"
      ],
      "metadata": {
        "id": "oWsEW4zslU5_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "URSgey78mX2a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print((trainx.shape), (testx.shape))\n",
        "\n",
        "print((trainy.shape), (testy.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQCmlai7l_cg",
        "outputId": "d78b93f2-9ebd-407f-9fd7-0d07f4e76449"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2265, 256, 256, 1) (552, 256, 256, 1)\n",
            "(2265, 31) (552, 31)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_model(inputShape, loss):\n",
        "    dl_model = Sequential()\n",
        "\n",
        "    dl_model.add(Conv2D(3, 3, activation='relu', dilation_rate=2, input_shape=inputShape))\n",
        "    dl_model.add(Conv2D(3, 3, activation='relu'))\n",
        "    dl_model.add(Conv2D(3, 3, activation='relu'))\n",
        "    dl_model.add(Conv2D(3, 3, activation='relu'))\n",
        "    dl_model.add(BatchNormalization())\n",
        "    dl_model.add(MaxPool2D((2, 2)))\n",
        "    dl_model.add(Dropout(0.2))\n",
        "\n",
        "    dl_model.add(Conv2D(3, 3, activation='relu'))\n",
        "    dl_model.add(Conv2D(3, 3, activation='relu'))\n",
        "    dl_model.add(Conv2D(3, 3, activation='relu'))\n",
        "    dl_model.add(Conv2D(3, 3, activation='relu'))\n",
        "    dl_model.add(BatchNormalization())\n",
        "    dl_model.add(MaxPool2D((2, 2)))\n",
        "    dl_model.add(Dropout(0.2))\n",
        "\n",
        "    dl_model.add(Conv2D(3, 3, activation='relu'))\n",
        "    dl_model.add(Conv2D(3, 3, activation='relu'))\n",
        "    dl_model.add(Conv2D(3, 3, activation='relu'))\n",
        "    dl_model.add(Conv2D(3, 3, activation='relu'))\n",
        "    dl_model.add(BatchNormalization())\n",
        "    dl_model.add(MaxPool2D((2, 2)))\n",
        "    dl_model.add(Dropout(0.2))\n",
        "\n",
        "    dl_model.add(Reshape((12, 144)))\n",
        "\n",
        "    dl_model.add(LSTM(128, activation=\"tanh\", return_sequences=True))\n",
        "    dl_model.add(LSTM(64, activation=\"tanh\"))\n",
        "    dl_model.add(Dropout(rate=0.1))\n",
        "\n",
        "    dl_model.add(Flatten())\n",
        "\n",
        "    dl_model.add(Dense(31, activation='softmax', kernel_regularizer=regularizers.l2(0.01), bias_regularizer=regularizers.l2(0.001)))\n",
        "\n",
        "    opt = SGD(lr=0.01, momentum=0.9)\n",
        "\n",
        "    dl_model.compile(loss=loss, optimizer=opt, metrics=['accuracy'])\n",
        "    dl_model.summary()\n",
        "\n",
        "    return dl_model\n"
      ],
      "metadata": {
        "id": "IzvulxPZnzg_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputShape = (trainx.shape[1], trainx.shape[2], trainx.shape[3])\n",
        "model = build_model(inputShape, 'categorical_crossentropy')\n",
        "\n",
        "earlystopper = EarlyStopping(patience=20, verbose=1)\n",
        "\n",
        "checkpointer = ModelCheckpoint(filepath = '/content/weight/model.{epoch:02d}-{loss:.6f}.hdf5', verbose=1, save_best_only=True, save_weights_only = True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,patience=5, min_lr=0.000001, verbose=1, cooldown=1)\n",
        "\n",
        "filepath= '/content/weight/model.{epoch:02d}-{loss:.6f}.hdf5'\n",
        "\n",
        "history = model.fit(trainx, trainy, batch_size=16, epochs=200, validation_data=(testx, testy), verbose=1,callbacks=[earlystopper, checkpointer, reduce_lr])\n",
        "\n",
        "_, train_acc = model.evaluate(trainx, trainy, verbose=0)\n",
        "_, test_acc = model.evaluate(testx, testy, verbose=0)\n",
        "print('Train: %.3f, Test: %.3f' % (train_acc, test_acc))\n",
        "# plot loss during training\n",
        "pyplot.subplot(211)\n",
        "pyplot.title('Loss')\n",
        "pyplot.plot(history.history['loss'], label='train')\n",
        "pyplot.plot(history.history['val_loss'], label='test')\n",
        "pyplot.legend()\n",
        "# plot accuracy during training\n",
        "pyplot.subplot(212)\n",
        "pyplot.title('Accuracy')\n",
        "pyplot.plot(history.history['accuracy'], label='train')\n",
        "pyplot.plot(history.history['val_accuracy'], label='test')\n",
        "pyplot.legend()\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2nG532-Xk2Nv",
        "outputId": "16e865bd-1eaa-42dd-b077-1df6fb1f073a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.SGD.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_13\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_138 (Conv2D)         (None, 252, 252, 3)       30        \n",
            "                                                                 \n",
            " conv2d_139 (Conv2D)         (None, 250, 250, 3)       84        \n",
            "                                                                 \n",
            " conv2d_140 (Conv2D)         (None, 248, 248, 3)       84        \n",
            "                                                                 \n",
            " conv2d_141 (Conv2D)         (None, 246, 246, 3)       84        \n",
            "                                                                 \n",
            " batch_normalization_39 (Ba  (None, 246, 246, 3)       12        \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " max_pooling2d_39 (MaxPooli  (None, 123, 123, 3)       0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_43 (Dropout)        (None, 123, 123, 3)       0         \n",
            "                                                                 \n",
            " conv2d_142 (Conv2D)         (None, 121, 121, 3)       84        \n",
            "                                                                 \n",
            " conv2d_143 (Conv2D)         (None, 119, 119, 3)       84        \n",
            "                                                                 \n",
            " conv2d_144 (Conv2D)         (None, 117, 117, 3)       84        \n",
            "                                                                 \n",
            " conv2d_145 (Conv2D)         (None, 115, 115, 3)       84        \n",
            "                                                                 \n",
            " batch_normalization_40 (Ba  (None, 115, 115, 3)       12        \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " max_pooling2d_40 (MaxPooli  (None, 57, 57, 3)         0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_44 (Dropout)        (None, 57, 57, 3)         0         \n",
            "                                                                 \n",
            " conv2d_146 (Conv2D)         (None, 55, 55, 3)         84        \n",
            "                                                                 \n",
            " conv2d_147 (Conv2D)         (None, 53, 53, 3)         84        \n",
            "                                                                 \n",
            " conv2d_148 (Conv2D)         (None, 51, 51, 3)         84        \n",
            "                                                                 \n",
            " conv2d_149 (Conv2D)         (None, 49, 49, 3)         84        \n",
            "                                                                 \n",
            " batch_normalization_41 (Ba  (None, 49, 49, 3)         12        \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " max_pooling2d_41 (MaxPooli  (None, 24, 24, 3)         0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_45 (Dropout)        (None, 24, 24, 3)         0         \n",
            "                                                                 \n",
            " reshape_4 (Reshape)         (None, 12, 144)           0         \n",
            "                                                                 \n",
            " lstm_11 (LSTM)              (None, 12, 128)           139776    \n",
            "                                                                 \n",
            " lstm_12 (LSTM)              (None, 64)                49408     \n",
            "                                                                 \n",
            " dropout_46 (Dropout)        (None, 64)                0         \n",
            "                                                                 \n",
            " flatten_9 (Flatten)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 31)                2015      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 192189 (750.74 KB)\n",
            "Trainable params: 192171 (750.67 KB)\n",
            "Non-trainable params: 18 (72.00 Byte)\n",
            "_________________________________________________________________\n",
            "Epoch 1/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 3.5923 - accuracy: 0.1020\n",
            "Epoch 1: val_loss improved from inf to 3.71806, saving model to /content/weight/model.01-3.591354.hdf5\n",
            "142/142 [==============================] - 11s 39ms/step - loss: 3.5914 - accuracy: 0.1020 - val_loss: 3.7181 - val_accuracy: 0.0417 - lr: 0.0100\n",
            "Epoch 2/200\n",
            "140/142 [============================>.] - ETA: 0s - loss: 3.1818 - accuracy: 0.2004\n",
            "Epoch 2: val_loss improved from 3.71806 to 3.13419, saving model to /content/weight/model.02-3.180875.hdf5\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 3.1809 - accuracy: 0.2009 - val_loss: 3.1342 - val_accuracy: 0.1757 - lr: 0.0100\n",
            "Epoch 3/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.8575 - accuracy: 0.2668\n",
            "Epoch 3: val_loss improved from 3.13419 to 2.98342, saving model to /content/weight/model.03-2.856906.hdf5\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 2.8569 - accuracy: 0.2667 - val_loss: 2.9834 - val_accuracy: 0.2174 - lr: 0.0100\n",
            "Epoch 4/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.6487 - accuracy: 0.3138\n",
            "Epoch 4: val_loss did not improve from 2.98342\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 2.6498 - accuracy: 0.3139 - val_loss: 4.0542 - val_accuracy: 0.0344 - lr: 0.0100\n",
            "Epoch 5/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.5735 - accuracy: 0.3355\n",
            "Epoch 5: val_loss improved from 2.98342 to 2.71079, saving model to /content/weight/model.05-2.574492.hdf5\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 2.5745 - accuracy: 0.3351 - val_loss: 2.7108 - val_accuracy: 0.3080 - lr: 0.0100\n",
            "Epoch 6/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.4793 - accuracy: 0.3568\n",
            "Epoch 6: val_loss did not improve from 2.71079\n",
            "142/142 [==============================] - 4s 28ms/step - loss: 2.4792 - accuracy: 0.3563 - val_loss: 3.2134 - val_accuracy: 0.1486 - lr: 0.0100\n",
            "Epoch 7/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.3470 - accuracy: 0.3879\n",
            "Epoch 7: val_loss did not improve from 2.71079\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 2.3446 - accuracy: 0.3890 - val_loss: 3.9819 - val_accuracy: 0.0489 - lr: 0.0100\n",
            "Epoch 8/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.2676 - accuracy: 0.4149\n",
            "Epoch 8: val_loss improved from 2.71079 to 2.57181, saving model to /content/weight/model.08-2.269977.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 2.2700 - accuracy: 0.4150 - val_loss: 2.5718 - val_accuracy: 0.3424 - lr: 0.0100\n",
            "Epoch 9/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.2166 - accuracy: 0.4300\n",
            "Epoch 9: val_loss did not improve from 2.57181\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 2.2156 - accuracy: 0.4296 - val_loss: 3.9964 - val_accuracy: 0.0543 - lr: 0.0100\n",
            "Epoch 10/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.1621 - accuracy: 0.4415\n",
            "Epoch 10: val_loss did not improve from 2.57181\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 2.1604 - accuracy: 0.4419 - val_loss: 3.3247 - val_accuracy: 0.1775 - lr: 0.0100\n",
            "Epoch 11/200\n",
            "140/142 [============================>.] - ETA: 0s - loss: 2.1132 - accuracy: 0.4652\n",
            "Epoch 11: val_loss improved from 2.57181 to 2.49093, saving model to /content/weight/model.11-2.113063.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 2.1131 - accuracy: 0.4640 - val_loss: 2.4909 - val_accuracy: 0.3641 - lr: 0.0100\n",
            "Epoch 12/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.0343 - accuracy: 0.4801\n",
            "Epoch 12: val_loss did not improve from 2.49093\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 2.0342 - accuracy: 0.4804 - val_loss: 2.5144 - val_accuracy: 0.3460 - lr: 0.0100\n",
            "Epoch 13/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.0009 - accuracy: 0.4965\n",
            "Epoch 13: val_loss did not improve from 2.49093\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 2.0039 - accuracy: 0.4954 - val_loss: 2.5036 - val_accuracy: 0.3841 - lr: 0.0100\n",
            "Epoch 14/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.9481 - accuracy: 0.5235\n",
            "Epoch 14: val_loss did not improve from 2.49093\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.9489 - accuracy: 0.5232 - val_loss: 2.6086 - val_accuracy: 0.3514 - lr: 0.0100\n",
            "Epoch 15/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.9118 - accuracy: 0.5297\n",
            "Epoch 15: val_loss improved from 2.49093 to 2.37531, saving model to /content/weight/model.15-1.911904.hdf5\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.9119 - accuracy: 0.5302 - val_loss: 2.3753 - val_accuracy: 0.3895 - lr: 0.0100\n",
            "Epoch 16/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.8838 - accuracy: 0.5465\n",
            "Epoch 16: val_loss did not improve from 2.37531\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.8856 - accuracy: 0.5461 - val_loss: 2.6853 - val_accuracy: 0.3623 - lr: 0.0100\n",
            "Epoch 17/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.8302 - accuracy: 0.5541\n",
            "Epoch 17: val_loss did not improve from 2.37531\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.8310 - accuracy: 0.5536 - val_loss: 3.7332 - val_accuracy: 0.1250 - lr: 0.0100\n",
            "Epoch 18/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.8011 - accuracy: 0.5638\n",
            "Epoch 18: val_loss did not improve from 2.37531\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.8027 - accuracy: 0.5634 - val_loss: 4.3743 - val_accuracy: 0.0598 - lr: 0.0100\n",
            "Epoch 19/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.7880 - accuracy: 0.5629\n",
            "Epoch 19: val_loss did not improve from 2.37531\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.7867 - accuracy: 0.5634 - val_loss: 2.7617 - val_accuracy: 0.3478 - lr: 0.0100\n",
            "Epoch 20/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.7395 - accuracy: 0.5926\n",
            "Epoch 20: val_loss did not improve from 2.37531\n",
            "\n",
            "Epoch 20: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.\n",
            "142/142 [==============================] - 4s 32ms/step - loss: 1.7400 - accuracy: 0.5925 - val_loss: 2.5123 - val_accuracy: 0.3967 - lr: 0.0100\n",
            "Epoch 21/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.6456 - accuracy: 0.6228\n",
            "Epoch 21: val_loss improved from 2.37531 to 2.35982, saving model to /content/weight/model.21-1.645509.hdf5\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.6455 - accuracy: 0.6225 - val_loss: 2.3598 - val_accuracy: 0.4475 - lr: 0.0020\n",
            "Epoch 22/200\n",
            "140/142 [============================>.] - ETA: 0s - loss: 1.5600 - accuracy: 0.6451\n",
            "Epoch 22: val_loss improved from 2.35982 to 2.32121, saving model to /content/weight/model.22-1.560576.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.5606 - accuracy: 0.6455 - val_loss: 2.3212 - val_accuracy: 0.4620 - lr: 0.0020\n",
            "Epoch 23/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5221 - accuracy: 0.6658\n",
            "Epoch 23: val_loss did not improve from 2.32121\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.5234 - accuracy: 0.6658 - val_loss: 2.4054 - val_accuracy: 0.4312 - lr: 0.0020\n",
            "Epoch 24/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5061 - accuracy: 0.6711\n",
            "Epoch 24: val_loss did not improve from 2.32121\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.5065 - accuracy: 0.6715 - val_loss: 3.7527 - val_accuracy: 0.1322 - lr: 0.0020\n",
            "Epoch 25/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5013 - accuracy: 0.6746\n",
            "Epoch 25: val_loss did not improve from 2.32121\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.4992 - accuracy: 0.6755 - val_loss: 2.3810 - val_accuracy: 0.4475 - lr: 0.0020\n",
            "Epoch 26/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.4718 - accuracy: 0.6866\n",
            "Epoch 26: val_loss did not improve from 2.32121\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.4710 - accuracy: 0.6874 - val_loss: 2.6434 - val_accuracy: 0.3551 - lr: 0.0020\n",
            "Epoch 27/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.4537 - accuracy: 0.6902\n",
            "Epoch 27: val_loss improved from 2.32121 to 2.30149, saving model to /content/weight/model.27-1.454011.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.4540 - accuracy: 0.6905 - val_loss: 2.3015 - val_accuracy: 0.4402 - lr: 0.0020\n",
            "Epoch 28/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.4510 - accuracy: 0.6884\n",
            "Epoch 28: val_loss did not improve from 2.30149\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.4505 - accuracy: 0.6887 - val_loss: 2.4710 - val_accuracy: 0.4185 - lr: 0.0020\n",
            "Epoch 29/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.4373 - accuracy: 0.6968\n",
            "Epoch 29: val_loss did not improve from 2.30149\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.4380 - accuracy: 0.6962 - val_loss: 2.3426 - val_accuracy: 0.4511 - lr: 0.0020\n",
            "Epoch 30/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.4241 - accuracy: 0.6973\n",
            "Epoch 30: val_loss did not improve from 2.30149\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.4254 - accuracy: 0.6967 - val_loss: 2.4729 - val_accuracy: 0.4312 - lr: 0.0020\n",
            "Epoch 31/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.4215 - accuracy: 0.7097\n",
            "Epoch 31: val_loss did not improve from 2.30149\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.4210 - accuracy: 0.7091 - val_loss: 2.5903 - val_accuracy: 0.3859 - lr: 0.0020\n",
            "Epoch 32/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.4231 - accuracy: 0.7145\n",
            "Epoch 32: val_loss did not improve from 2.30149\n",
            "\n",
            "Epoch 32: ReduceLROnPlateau reducing learning rate to 0.0003999999724328518.\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.4222 - accuracy: 0.7143 - val_loss: 2.6705 - val_accuracy: 0.3786 - lr: 0.0020\n",
            "Epoch 33/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3777 - accuracy: 0.7137\n",
            "Epoch 33: val_loss improved from 2.30149 to 2.29922, saving model to /content/weight/model.33-1.378947.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 1.3789 - accuracy: 0.7130 - val_loss: 2.2992 - val_accuracy: 0.4601 - lr: 4.0000e-04\n",
            "Epoch 34/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3701 - accuracy: 0.7221\n",
            "Epoch 34: val_loss improved from 2.29922 to 2.28850, saving model to /content/weight/model.34-1.370333.hdf5\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.3703 - accuracy: 0.7227 - val_loss: 2.2885 - val_accuracy: 0.4583 - lr: 4.0000e-04\n",
            "Epoch 35/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3516 - accuracy: 0.7376\n",
            "Epoch 35: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.3516 - accuracy: 0.7377 - val_loss: 2.2945 - val_accuracy: 0.4656 - lr: 4.0000e-04\n",
            "Epoch 36/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3486 - accuracy: 0.7363\n",
            "Epoch 36: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.3472 - accuracy: 0.7369 - val_loss: 2.3439 - val_accuracy: 0.4420 - lr: 4.0000e-04\n",
            "Epoch 37/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3575 - accuracy: 0.7234\n",
            "Epoch 37: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.3579 - accuracy: 0.7232 - val_loss: 2.3294 - val_accuracy: 0.4565 - lr: 4.0000e-04\n",
            "Epoch 38/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3454 - accuracy: 0.7278\n",
            "Epoch 38: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.3479 - accuracy: 0.7272 - val_loss: 2.2955 - val_accuracy: 0.4728 - lr: 4.0000e-04\n",
            "Epoch 39/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3455 - accuracy: 0.7363\n",
            "Epoch 39: val_loss did not improve from 2.28850\n",
            "\n",
            "Epoch 39: ReduceLROnPlateau reducing learning rate to 7.999999215826393e-05.\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 1.3444 - accuracy: 0.7360 - val_loss: 2.2894 - val_accuracy: 0.4674 - lr: 4.0000e-04\n",
            "Epoch 40/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3407 - accuracy: 0.7411\n",
            "Epoch 40: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.3393 - accuracy: 0.7417 - val_loss: 2.2989 - val_accuracy: 0.4638 - lr: 8.0000e-05\n",
            "Epoch 41/200\n",
            "140/142 [============================>.] - ETA: 0s - loss: 1.3369 - accuracy: 0.7478\n",
            "Epoch 41: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.3389 - accuracy: 0.7475 - val_loss: 2.3186 - val_accuracy: 0.4601 - lr: 8.0000e-05\n",
            "Epoch 42/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3391 - accuracy: 0.7402\n",
            "Epoch 42: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 1.3393 - accuracy: 0.7400 - val_loss: 2.3097 - val_accuracy: 0.4601 - lr: 8.0000e-05\n",
            "Epoch 43/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3291 - accuracy: 0.7434\n",
            "Epoch 43: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.3312 - accuracy: 0.7417 - val_loss: 2.3095 - val_accuracy: 0.4565 - lr: 8.0000e-05\n",
            "Epoch 44/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3268 - accuracy: 0.7509\n",
            "Epoch 44: val_loss did not improve from 2.28850\n",
            "\n",
            "Epoch 44: ReduceLROnPlateau reducing learning rate to 1.599999814061448e-05.\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.3299 - accuracy: 0.7501 - val_loss: 2.2975 - val_accuracy: 0.4638 - lr: 8.0000e-05\n",
            "Epoch 45/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3374 - accuracy: 0.7429\n",
            "Epoch 45: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.3369 - accuracy: 0.7435 - val_loss: 2.3097 - val_accuracy: 0.4547 - lr: 1.6000e-05\n",
            "Epoch 46/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3385 - accuracy: 0.7349\n",
            "Epoch 46: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.3394 - accuracy: 0.7351 - val_loss: 2.3097 - val_accuracy: 0.4583 - lr: 1.6000e-05\n",
            "Epoch 47/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3354 - accuracy: 0.7420\n",
            "Epoch 47: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.3357 - accuracy: 0.7417 - val_loss: 2.3063 - val_accuracy: 0.4583 - lr: 1.6000e-05\n",
            "Epoch 48/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3322 - accuracy: 0.7358\n",
            "Epoch 48: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.3325 - accuracy: 0.7351 - val_loss: 2.3068 - val_accuracy: 0.4565 - lr: 1.6000e-05\n",
            "Epoch 49/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3222 - accuracy: 0.7491\n",
            "Epoch 49: val_loss did not improve from 2.28850\n",
            "\n",
            "Epoch 49: ReduceLROnPlateau reducing learning rate to 3.199999628122896e-06.\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.3241 - accuracy: 0.7488 - val_loss: 2.3081 - val_accuracy: 0.4565 - lr: 1.6000e-05\n",
            "Epoch 50/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3313 - accuracy: 0.7323\n",
            "Epoch 50: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.3312 - accuracy: 0.7329 - val_loss: 2.3062 - val_accuracy: 0.4547 - lr: 3.2000e-06\n",
            "Epoch 51/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3372 - accuracy: 0.7349\n",
            "Epoch 51: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.3370 - accuracy: 0.7347 - val_loss: 2.3052 - val_accuracy: 0.4565 - lr: 3.2000e-06\n",
            "Epoch 52/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3272 - accuracy: 0.7411\n",
            "Epoch 52: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.3268 - accuracy: 0.7413 - val_loss: 2.3056 - val_accuracy: 0.4547 - lr: 3.2000e-06\n",
            "Epoch 53/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3253 - accuracy: 0.7385\n",
            "Epoch 53: val_loss did not improve from 2.28850\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.3257 - accuracy: 0.7386 - val_loss: 2.3058 - val_accuracy: 0.4547 - lr: 3.2000e-06\n",
            "Epoch 54/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3331 - accuracy: 0.7416\n",
            "Epoch 54: val_loss did not improve from 2.28850\n",
            "\n",
            "Epoch 54: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.3305 - accuracy: 0.7426 - val_loss: 2.3051 - val_accuracy: 0.4547 - lr: 3.2000e-06\n",
            "Epoch 54: early stopping\n",
            "Train: 0.765, Test: 0.455\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputShape = (trainx.shape[1], trainx.shape[2], trainx.shape[3])\n",
        "model = build_model(inputShape, 'poisson')\n",
        "\n",
        "earlystopper = EarlyStopping(patience=20, verbose=1)\n",
        "\n",
        "checkpointer = ModelCheckpoint(filepath = '/content/weight/model.{epoch:02d}-{loss:.6f}.hdf5', verbose=1, save_best_only=True, save_weights_only = True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,patience=5, min_lr=0.000001, verbose=1, cooldown=1)\n",
        "\n",
        "filepath= '/content/weight/model.{epoch:02d}-{loss:.6f}.hdf5'\n",
        "\n",
        "history = model.fit(trainx, trainy, batch_size=16, epochs=200, validation_data=(testx, testy), verbose=1,callbacks=[earlystopper, checkpointer, reduce_lr])\n",
        "\n",
        "_, train_acc = model.evaluate(trainx, trainy, verbose=0)\n",
        "_, test_acc = model.evaluate(testx, testy, verbose=0)\n",
        "print('Train: %.3f, Test: %.3f' % (train_acc, test_acc))\n",
        "# plot loss during training\n",
        "pyplot.subplot(211)\n",
        "pyplot.title('Loss')\n",
        "pyplot.plot(history.history['loss'], label='train')\n",
        "pyplot.plot(history.history['val_loss'], label='test')\n",
        "pyplot.legend()\n",
        "# plot accuracy during training\n",
        "pyplot.subplot(212)\n",
        "pyplot.title('Accuracy')\n",
        "pyplot.plot(history.history['accuracy'], label='train')\n",
        "pyplot.plot(history.history['val_accuracy'], label='test')\n",
        "pyplot.legend()\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vVbLH5ja6x2S",
        "outputId": "6bf94012-b7cb-4f5e-8d6f-52b0ca871b74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.SGD.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_15\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_162 (Conv2D)         (None, 252, 252, 3)       30        \n",
            "                                                                 \n",
            " conv2d_163 (Conv2D)         (None, 250, 250, 3)       84        \n",
            "                                                                 \n",
            " conv2d_164 (Conv2D)         (None, 248, 248, 3)       84        \n",
            "                                                                 \n",
            " conv2d_165 (Conv2D)         (None, 246, 246, 3)       84        \n",
            "                                                                 \n",
            " batch_normalization_45 (Ba  (None, 246, 246, 3)       12        \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " max_pooling2d_45 (MaxPooli  (None, 123, 123, 3)       0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_51 (Dropout)        (None, 123, 123, 3)       0         \n",
            "                                                                 \n",
            " conv2d_166 (Conv2D)         (None, 121, 121, 3)       84        \n",
            "                                                                 \n",
            " conv2d_167 (Conv2D)         (None, 119, 119, 3)       84        \n",
            "                                                                 \n",
            " conv2d_168 (Conv2D)         (None, 117, 117, 3)       84        \n",
            "                                                                 \n",
            " conv2d_169 (Conv2D)         (None, 115, 115, 3)       84        \n",
            "                                                                 \n",
            " batch_normalization_46 (Ba  (None, 115, 115, 3)       12        \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " max_pooling2d_46 (MaxPooli  (None, 57, 57, 3)         0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_52 (Dropout)        (None, 57, 57, 3)         0         \n",
            "                                                                 \n",
            " conv2d_170 (Conv2D)         (None, 55, 55, 3)         84        \n",
            "                                                                 \n",
            " conv2d_171 (Conv2D)         (None, 53, 53, 3)         84        \n",
            "                                                                 \n",
            " conv2d_172 (Conv2D)         (None, 51, 51, 3)         84        \n",
            "                                                                 \n",
            " conv2d_173 (Conv2D)         (None, 49, 49, 3)         84        \n",
            "                                                                 \n",
            " batch_normalization_47 (Ba  (None, 49, 49, 3)         12        \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " max_pooling2d_47 (MaxPooli  (None, 24, 24, 3)         0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_53 (Dropout)        (None, 24, 24, 3)         0         \n",
            "                                                                 \n",
            " reshape_6 (Reshape)         (None, 12, 144)           0         \n",
            "                                                                 \n",
            " lstm_15 (LSTM)              (None, 12, 128)           139776    \n",
            "                                                                 \n",
            " lstm_16 (LSTM)              (None, 64)                49408     \n",
            "                                                                 \n",
            " dropout_54 (Dropout)        (None, 64)                0         \n",
            "                                                                 \n",
            " flatten_11 (Flatten)        (None, 64)                0         \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 31)                2015      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 192189 (750.74 KB)\n",
            "Trainable params: 192171 (750.67 KB)\n",
            "Non-trainable params: 18 (72.00 Byte)\n",
            "_________________________________________________________________\n",
            "Epoch 1/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.4643 - accuracy: 0.0452\n",
            "Epoch 1: val_loss improved from inf to 0.38013, saving model to /content/weight/model.01-0.464019.hdf5\n",
            "142/142 [==============================] - 13s 40ms/step - loss: 0.4640 - accuracy: 0.0450 - val_loss: 0.3801 - val_accuracy: 0.0326 - lr: 0.0100\n",
            "Epoch 2/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.3230 - accuracy: 0.0501\n",
            "Epoch 2: val_loss improved from 0.38013 to 0.27573, saving model to /content/weight/model.02-0.322799.hdf5\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 0.3228 - accuracy: 0.0499 - val_loss: 0.2757 - val_accuracy: 0.0471 - lr: 0.0100\n",
            "Epoch 3/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.2433 - accuracy: 0.0700\n",
            "Epoch 3: val_loss improved from 0.27573 to 0.21722, saving model to /content/weight/model.03-0.243201.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.2432 - accuracy: 0.0698 - val_loss: 0.2172 - val_accuracy: 0.0308 - lr: 0.0100\n",
            "Epoch 4/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1989 - accuracy: 0.0864\n",
            "Epoch 4: val_loss improved from 0.21722 to 0.18409, saving model to /content/weight/model.04-0.198824.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1988 - accuracy: 0.0861 - val_loss: 0.1841 - val_accuracy: 0.0652 - lr: 0.0100\n",
            "Epoch 5/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1740 - accuracy: 0.0940\n",
            "Epoch 5: val_loss improved from 0.18409 to 0.16575, saving model to /content/weight/model.05-0.174014.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1740 - accuracy: 0.0945 - val_loss: 0.1658 - val_accuracy: 0.0996 - lr: 0.0100\n",
            "Epoch 6/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1602 - accuracy: 0.1020\n",
            "Epoch 6: val_loss improved from 0.16575 to 0.15558, saving model to /content/weight/model.06-0.160146.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1601 - accuracy: 0.1024 - val_loss: 0.1556 - val_accuracy: 0.1214 - lr: 0.0100\n",
            "Epoch 7/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1524 - accuracy: 0.1210\n",
            "Epoch 7: val_loss improved from 0.15558 to 0.14980, saving model to /content/weight/model.07-0.152399.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1524 - accuracy: 0.1214 - val_loss: 0.1498 - val_accuracy: 0.1377 - lr: 0.0100\n",
            "Epoch 8/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1480 - accuracy: 0.1520\n",
            "Epoch 8: val_loss improved from 0.14980 to 0.14660, saving model to /content/weight/model.08-0.148045.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1480 - accuracy: 0.1514 - val_loss: 0.1466 - val_accuracy: 0.1322 - lr: 0.0100\n",
            "Epoch 9/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1456 - accuracy: 0.1609\n",
            "Epoch 9: val_loss improved from 0.14660 to 0.14477, saving model to /content/weight/model.09-0.145616.hdf5\n",
            "142/142 [==============================] - 5s 34ms/step - loss: 0.1456 - accuracy: 0.1607 - val_loss: 0.1448 - val_accuracy: 0.1322 - lr: 0.0100\n",
            "Epoch 10/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1442 - accuracy: 0.1618\n",
            "Epoch 10: val_loss improved from 0.14477 to 0.14375, saving model to /content/weight/model.10-0.144222.hdf5\n",
            "142/142 [==============================] - 6s 43ms/step - loss: 0.1442 - accuracy: 0.1625 - val_loss: 0.1438 - val_accuracy: 0.1322 - lr: 0.0100\n",
            "Epoch 11/200\n",
            "140/142 [============================>.] - ETA: 0s - loss: 0.1434 - accuracy: 0.1696\n",
            "Epoch 11: val_loss improved from 0.14375 to 0.14316, saving model to /content/weight/model.11-0.143434.hdf5\n",
            "142/142 [==============================] - 5s 34ms/step - loss: 0.1434 - accuracy: 0.1704 - val_loss: 0.1432 - val_accuracy: 0.1413 - lr: 0.0100\n",
            "Epoch 12/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1430 - accuracy: 0.1534\n",
            "Epoch 12: val_loss improved from 0.14316 to 0.14282, saving model to /content/weight/model.12-0.142993.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1430 - accuracy: 0.1545 - val_loss: 0.1428 - val_accuracy: 0.1322 - lr: 0.0100\n",
            "Epoch 13/200\n",
            "140/142 [============================>.] - ETA: 0s - loss: 0.1427 - accuracy: 0.1545\n",
            "Epoch 13: val_loss improved from 0.14282 to 0.14262, saving model to /content/weight/model.13-0.142725.hdf5\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 0.1427 - accuracy: 0.1554 - val_loss: 0.1426 - val_accuracy: 0.1214 - lr: 0.0100\n",
            "Epoch 14/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1425 - accuracy: 0.1587\n",
            "Epoch 14: val_loss improved from 0.14262 to 0.14250, saving model to /content/weight/model.14-0.142548.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1425 - accuracy: 0.1585 - val_loss: 0.1425 - val_accuracy: 0.1159 - lr: 0.0100\n",
            "Epoch 15/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1424 - accuracy: 0.1436\n",
            "Epoch 15: val_loss improved from 0.14250 to 0.14241, saving model to /content/weight/model.15-0.142446.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1424 - accuracy: 0.1430 - val_loss: 0.1424 - val_accuracy: 0.1123 - lr: 0.0100\n",
            "Epoch 16/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1424 - accuracy: 0.1405\n",
            "Epoch 16: val_loss improved from 0.14241 to 0.14238, saving model to /content/weight/model.16-0.142371.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1424 - accuracy: 0.1400 - val_loss: 0.1424 - val_accuracy: 0.1105 - lr: 0.0100\n",
            "Epoch 17/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1423 - accuracy: 0.1405\n",
            "Epoch 17: val_loss improved from 0.14238 to 0.14236, saving model to /content/weight/model.17-0.142300.hdf5\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 0.1423 - accuracy: 0.1408 - val_loss: 0.1424 - val_accuracy: 0.1014 - lr: 0.0100\n",
            "Epoch 18/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1423 - accuracy: 0.1401\n",
            "Epoch 18: val_loss improved from 0.14236 to 0.14225, saving model to /content/weight/model.18-0.142254.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1423 - accuracy: 0.1400 - val_loss: 0.1422 - val_accuracy: 0.0924 - lr: 0.0100\n",
            "Epoch 19/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1422 - accuracy: 0.1259\n",
            "Epoch 19: val_loss did not improve from 0.14225\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1422 - accuracy: 0.1267 - val_loss: 0.1423 - val_accuracy: 0.0870 - lr: 0.0100\n",
            "Epoch 20/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1422 - accuracy: 0.1241\n",
            "Epoch 20: val_loss did not improve from 0.14225\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1422 - accuracy: 0.1241 - val_loss: 0.1425 - val_accuracy: 0.0924 - lr: 0.0100\n",
            "Epoch 21/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1421 - accuracy: 0.1179\n",
            "Epoch 21: val_loss improved from 0.14225 to 0.14213, saving model to /content/weight/model.21-0.142114.hdf5\n",
            "142/142 [==============================] - 5s 38ms/step - loss: 0.1421 - accuracy: 0.1188 - val_loss: 0.1421 - val_accuracy: 0.0707 - lr: 0.0100\n",
            "Epoch 22/200\n",
            "142/142 [==============================] - ETA: 0s - loss: 0.1421 - accuracy: 0.1130\n",
            "Epoch 22: val_loss did not improve from 0.14213\n",
            "142/142 [==============================] - 6s 40ms/step - loss: 0.1421 - accuracy: 0.1130 - val_loss: 0.1425 - val_accuracy: 0.0743 - lr: 0.0100\n",
            "Epoch 23/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1420 - accuracy: 0.1002\n",
            "Epoch 23: val_loss improved from 0.14213 to 0.14209, saving model to /content/weight/model.23-0.142042.hdf5\n",
            "142/142 [==============================] - 5s 34ms/step - loss: 0.1420 - accuracy: 0.1002 - val_loss: 0.1421 - val_accuracy: 0.0725 - lr: 0.0100\n",
            "Epoch 24/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1420 - accuracy: 0.1073\n",
            "Epoch 24: val_loss did not improve from 0.14209\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1420 - accuracy: 0.1073 - val_loss: 0.1424 - val_accuracy: 0.0870 - lr: 0.0100\n",
            "Epoch 25/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1420 - accuracy: 0.1099\n",
            "Epoch 25: val_loss did not improve from 0.14209\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 0.1420 - accuracy: 0.1099 - val_loss: 0.1425 - val_accuracy: 0.0634 - lr: 0.0100\n",
            "Epoch 26/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1419 - accuracy: 0.1024\n",
            "Epoch 26: val_loss did not improve from 0.14209\n",
            "\n",
            "Epoch 26: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1419 - accuracy: 0.1020 - val_loss: 0.1422 - val_accuracy: 0.0761 - lr: 0.0100\n",
            "Epoch 27/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1419 - accuracy: 0.1046\n",
            "Epoch 27: val_loss did not improve from 0.14209\n",
            "142/142 [==============================] - 5s 36ms/step - loss: 0.1419 - accuracy: 0.1042 - val_loss: 0.1421 - val_accuracy: 0.0743 - lr: 0.0020\n",
            "Epoch 28/200\n",
            "140/142 [============================>.] - ETA: 0s - loss: 0.1419 - accuracy: 0.1045\n",
            "Epoch 28: val_loss did not improve from 0.14209\n",
            "142/142 [==============================] - 5s 35ms/step - loss: 0.1419 - accuracy: 0.1033 - val_loss: 0.1421 - val_accuracy: 0.0743 - lr: 0.0020\n",
            "Epoch 29/200\n",
            "140/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.1036\n",
            "Epoch 29: val_loss improved from 0.14209 to 0.14206, saving model to /content/weight/model.29-0.141851.hdf5\n",
            "142/142 [==============================] - 5s 34ms/step - loss: 0.1419 - accuracy: 0.1029 - val_loss: 0.1421 - val_accuracy: 0.0707 - lr: 0.0020\n",
            "Epoch 30/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.0988\n",
            "Epoch 30: val_loss did not improve from 0.14206\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1418 - accuracy: 0.0985 - val_loss: 0.1421 - val_accuracy: 0.0688 - lr: 0.0020\n",
            "Epoch 31/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.0962\n",
            "Epoch 31: val_loss did not improve from 0.14206\n",
            "\n",
            "Epoch 31: ReduceLROnPlateau reducing learning rate to 0.0003999999724328518.\n",
            "142/142 [==============================] - 5s 34ms/step - loss: 0.1418 - accuracy: 0.0958 - val_loss: 0.1422 - val_accuracy: 0.0761 - lr: 0.0020\n",
            "Epoch 32/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.1051\n",
            "Epoch 32: val_loss did not improve from 0.14206\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1418 - accuracy: 0.1046 - val_loss: 0.1421 - val_accuracy: 0.0725 - lr: 4.0000e-04\n",
            "Epoch 33/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.0993\n",
            "Epoch 33: val_loss improved from 0.14206 to 0.14199, saving model to /content/weight/model.33-0.141825.hdf5\n",
            "142/142 [==============================] - 5s 37ms/step - loss: 0.1418 - accuracy: 0.0993 - val_loss: 0.1420 - val_accuracy: 0.0652 - lr: 4.0000e-04\n",
            "Epoch 34/200\n",
            "142/142 [==============================] - ETA: 0s - loss: 0.1419 - accuracy: 0.0980\n",
            "Epoch 34: val_loss improved from 0.14199 to 0.14193, saving model to /content/weight/model.34-0.141850.hdf5\n",
            "142/142 [==============================] - 5s 36ms/step - loss: 0.1419 - accuracy: 0.0980 - val_loss: 0.1419 - val_accuracy: 0.0688 - lr: 4.0000e-04\n",
            "Epoch 35/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.1024\n",
            "Epoch 35: val_loss improved from 0.14193 to 0.14192, saving model to /content/weight/model.35-0.141803.hdf5\n",
            "142/142 [==============================] - 5s 38ms/step - loss: 0.1418 - accuracy: 0.1020 - val_loss: 0.1419 - val_accuracy: 0.0688 - lr: 4.0000e-04\n",
            "Epoch 36/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1419 - accuracy: 0.1020\n",
            "Epoch 36: val_loss improved from 0.14192 to 0.14192, saving model to /content/weight/model.36-0.141860.hdf5\n",
            "142/142 [==============================] - 5s 36ms/step - loss: 0.1419 - accuracy: 0.1015 - val_loss: 0.1419 - val_accuracy: 0.0670 - lr: 4.0000e-04\n",
            "Epoch 37/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.1006\n",
            "Epoch 37: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1418 - accuracy: 0.1007 - val_loss: 0.1420 - val_accuracy: 0.0652 - lr: 4.0000e-04\n",
            "Epoch 38/200\n",
            "142/142 [==============================] - ETA: 0s - loss: 0.1418 - accuracy: 0.0980\n",
            "Epoch 38: val_loss did not improve from 0.14192\n",
            "\n",
            "Epoch 38: ReduceLROnPlateau reducing learning rate to 7.999999215826393e-05.\n",
            "142/142 [==============================] - 5s 36ms/step - loss: 0.1418 - accuracy: 0.0980 - val_loss: 0.1420 - val_accuracy: 0.0688 - lr: 4.0000e-04\n",
            "Epoch 39/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.0988\n",
            "Epoch 39: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1418 - accuracy: 0.0985 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 8.0000e-05\n",
            "Epoch 40/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.1024\n",
            "Epoch 40: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1418 - accuracy: 0.1020 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 8.0000e-05\n",
            "Epoch 41/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.1006\n",
            "Epoch 41: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 0.1418 - accuracy: 0.1002 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 8.0000e-05\n",
            "Epoch 42/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.0993\n",
            "Epoch 42: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 5s 36ms/step - loss: 0.1418 - accuracy: 0.0993 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 8.0000e-05\n",
            "Epoch 43/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.0993\n",
            "Epoch 43: val_loss did not improve from 0.14192\n",
            "\n",
            "Epoch 43: ReduceLROnPlateau reducing learning rate to 1.599999814061448e-05.\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1418 - accuracy: 0.0993 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 8.0000e-05\n",
            "Epoch 44/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.0944\n",
            "Epoch 44: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1418 - accuracy: 0.0945 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 1.6000e-05\n",
            "Epoch 45/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.0997\n",
            "Epoch 45: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 4s 32ms/step - loss: 0.1418 - accuracy: 0.0998 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 1.6000e-05\n",
            "Epoch 46/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.1033\n",
            "Epoch 46: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1418 - accuracy: 0.1033 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 1.6000e-05\n",
            "Epoch 47/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.0997\n",
            "Epoch 47: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 0.1418 - accuracy: 0.0998 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 1.6000e-05\n",
            "Epoch 48/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.0984\n",
            "Epoch 48: val_loss did not improve from 0.14192\n",
            "\n",
            "Epoch 48: ReduceLROnPlateau reducing learning rate to 3.199999628122896e-06.\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1418 - accuracy: 0.0989 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 1.6000e-05\n",
            "Epoch 49/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.0988\n",
            "Epoch 49: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1418 - accuracy: 0.0998 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 3.2000e-06\n",
            "Epoch 50/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.0940\n",
            "Epoch 50: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1418 - accuracy: 0.0936 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 3.2000e-06\n",
            "Epoch 51/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.0993\n",
            "Epoch 51: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1418 - accuracy: 0.0989 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 3.2000e-06\n",
            "Epoch 52/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.1020\n",
            "Epoch 52: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1418 - accuracy: 0.1020 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 3.2000e-06\n",
            "Epoch 53/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.1059\n",
            "Epoch 53: val_loss did not improve from 0.14192\n",
            "\n",
            "Epoch 53: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1418 - accuracy: 0.1064 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 3.2000e-06\n",
            "Epoch 54/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.1046\n",
            "Epoch 54: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1419 - accuracy: 0.1046 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 1.0000e-06\n",
            "Epoch 55/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.1015\n",
            "Epoch 55: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 4s 32ms/step - loss: 0.1418 - accuracy: 0.1011 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 1.0000e-06\n",
            "Epoch 56/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1418 - accuracy: 0.1055\n",
            "Epoch 56: val_loss did not improve from 0.14192\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1418 - accuracy: 0.1051 - val_loss: 0.1420 - val_accuracy: 0.0670 - lr: 1.0000e-06\n",
            "Epoch 56: early stopping\n",
            "Train: 0.087, Test: 0.067\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cc_po_loss(alpha):\n",
        "  def loss_func(ypred, ytrue):\n",
        "    cc = keras.losses.categorical_crossentropy(ypred, ytrue)\n",
        "    po = keras.losses.poisson(ypred, ytrue)\n",
        "    return alpha*cc + (1-alpha)*po\n",
        "  return loss_func\n",
        "\n",
        "custom_loss = cc_po_loss(alpha=0.8)"
      ],
      "metadata": {
        "id": "9kcFPWaXCmfV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputShape = (trainx.shape[1], trainx.shape[2], trainx.shape[3])\n",
        "model = build_model(inputShape, custom_loss)\n",
        "\n",
        "earlystopper = EarlyStopping(patience=20, verbose=1)\n",
        "\n",
        "checkpointer = ModelCheckpoint(filepath = '/content/weight/model.{epoch:02d}-{loss:.6f}.hdf5', verbose=1, save_best_only=True, save_weights_only = True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,patience=5, min_lr=0.000001, verbose=1, cooldown=1)\n",
        "\n",
        "filepath= '/content/weight/model.{epoch:02d}-{loss:.6f}.hdf5'\n",
        "\n",
        "history = model.fit(trainx, trainy, batch_size=16, epochs=200, validation_data=(testx, testy), verbose=1,callbacks=[earlystopper, checkpointer, reduce_lr])\n",
        "\n",
        "_, train_acc = model.evaluate(trainx, trainy, verbose=0)\n",
        "_, test_acc = model.evaluate(testx, testy, verbose=0)\n",
        "print('Train: %.3f, Test: %.3f' % (train_acc, test_acc))\n",
        "# plot loss during training\n",
        "pyplot.subplot(211)\n",
        "pyplot.title('Loss')\n",
        "pyplot.plot(history.history['loss'], label='train')\n",
        "pyplot.plot(history.history['val_loss'], label='test')\n",
        "pyplot.legend()\n",
        "# plot accuracy during training\n",
        "pyplot.subplot(212)\n",
        "pyplot.title('Accuracy')\n",
        "pyplot.plot(history.history['accuracy'], label='train')\n",
        "pyplot.plot(history.history['val_accuracy'], label='test')\n",
        "pyplot.legend()\n",
        "pyplot.show()"
      ],
      "metadata": {
        "id": "9HvHXAg9qXJc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5a421f98-fd0b-47e8-966f-b12810ee82d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.SGD.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_17\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_186 (Conv2D)         (None, 252, 252, 3)       30        \n",
            "                                                                 \n",
            " conv2d_187 (Conv2D)         (None, 250, 250, 3)       84        \n",
            "                                                                 \n",
            " conv2d_188 (Conv2D)         (None, 248, 248, 3)       84        \n",
            "                                                                 \n",
            " conv2d_189 (Conv2D)         (None, 246, 246, 3)       84        \n",
            "                                                                 \n",
            " batch_normalization_51 (Ba  (None, 246, 246, 3)       12        \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " max_pooling2d_51 (MaxPooli  (None, 123, 123, 3)       0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_59 (Dropout)        (None, 123, 123, 3)       0         \n",
            "                                                                 \n",
            " conv2d_190 (Conv2D)         (None, 121, 121, 3)       84        \n",
            "                                                                 \n",
            " conv2d_191 (Conv2D)         (None, 119, 119, 3)       84        \n",
            "                                                                 \n",
            " conv2d_192 (Conv2D)         (None, 117, 117, 3)       84        \n",
            "                                                                 \n",
            " conv2d_193 (Conv2D)         (None, 115, 115, 3)       84        \n",
            "                                                                 \n",
            " batch_normalization_52 (Ba  (None, 115, 115, 3)       12        \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " max_pooling2d_52 (MaxPooli  (None, 57, 57, 3)         0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_60 (Dropout)        (None, 57, 57, 3)         0         \n",
            "                                                                 \n",
            " conv2d_194 (Conv2D)         (None, 55, 55, 3)         84        \n",
            "                                                                 \n",
            " conv2d_195 (Conv2D)         (None, 53, 53, 3)         84        \n",
            "                                                                 \n",
            " conv2d_196 (Conv2D)         (None, 51, 51, 3)         84        \n",
            "                                                                 \n",
            " conv2d_197 (Conv2D)         (None, 49, 49, 3)         84        \n",
            "                                                                 \n",
            " batch_normalization_53 (Ba  (None, 49, 49, 3)         12        \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " max_pooling2d_53 (MaxPooli  (None, 24, 24, 3)         0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_61 (Dropout)        (None, 24, 24, 3)         0         \n",
            "                                                                 \n",
            " reshape_8 (Reshape)         (None, 12, 144)           0         \n",
            "                                                                 \n",
            " lstm_19 (LSTM)              (None, 12, 128)           139776    \n",
            "                                                                 \n",
            " lstm_20 (LSTM)              (None, 64)                49408     \n",
            "                                                                 \n",
            " dropout_62 (Dropout)        (None, 64)                0         \n",
            "                                                                 \n",
            " flatten_13 (Flatten)        (None, 64)                0         \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 31)                2015      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 192189 (750.74 KB)\n",
            "Trainable params: 192171 (750.67 KB)\n",
            "Non-trainable params: 18 (72.00 Byte)\n",
            "_________________________________________________________________\n",
            "Epoch 1/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.9971 - accuracy: 0.0993\n",
            "Epoch 1: val_loss improved from inf to 2.95439, saving model to /content/weight/model.01-2.995330.hdf5\n",
            "142/142 [==============================] - 11s 43ms/step - loss: 2.9953 - accuracy: 0.1002 - val_loss: 2.9544 - val_accuracy: 0.0761 - lr: 0.0100\n",
            "Epoch 2/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.6382 - accuracy: 0.1986\n",
            "Epoch 2: val_loss did not improve from 2.95439\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 2.6375 - accuracy: 0.1991 - val_loss: 3.0545 - val_accuracy: 0.0344 - lr: 0.0100\n",
            "Epoch 3/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.3970 - accuracy: 0.2420\n",
            "Epoch 3: val_loss did not improve from 2.95439\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 2.3947 - accuracy: 0.2428 - val_loss: 3.2557 - val_accuracy: 0.0344 - lr: 0.0100\n",
            "Epoch 4/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.2425 - accuracy: 0.2895\n",
            "Epoch 4: val_loss did not improve from 2.95439\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 2.2415 - accuracy: 0.2901 - val_loss: 2.9789 - val_accuracy: 0.1069 - lr: 0.0100\n",
            "Epoch 5/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.3509 - accuracy: 0.2535\n",
            "Epoch 5: val_loss improved from 2.95439 to 2.36842, saving model to /content/weight/model.05-2.351137.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 2.3511 - accuracy: 0.2530 - val_loss: 2.3684 - val_accuracy: 0.2428 - lr: 0.0100\n",
            "Epoch 6/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.0841 - accuracy: 0.3324\n",
            "Epoch 6: val_loss did not improve from 2.36842\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 2.0848 - accuracy: 0.3320 - val_loss: 2.5956 - val_accuracy: 0.2101 - lr: 0.0100\n",
            "Epoch 7/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.0079 - accuracy: 0.3542\n",
            "Epoch 7: val_loss did not improve from 2.36842\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 2.0094 - accuracy: 0.3532 - val_loss: 3.1863 - val_accuracy: 0.0362 - lr: 0.0100\n",
            "Epoch 8/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.9446 - accuracy: 0.3772\n",
            "Epoch 8: val_loss did not improve from 2.36842\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.9447 - accuracy: 0.3775 - val_loss: 3.1886 - val_accuracy: 0.0344 - lr: 0.0100\n",
            "Epoch 9/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.8794 - accuracy: 0.3918\n",
            "Epoch 9: val_loss did not improve from 2.36842\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.8804 - accuracy: 0.3912 - val_loss: 3.2212 - val_accuracy: 0.0362 - lr: 0.0100\n",
            "Epoch 10/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.8288 - accuracy: 0.4229\n",
            "Epoch 10: val_loss did not improve from 2.36842\n",
            "\n",
            "Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.8277 - accuracy: 0.4230 - val_loss: 3.1598 - val_accuracy: 0.0362 - lr: 0.0100\n",
            "Epoch 11/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.7423 - accuracy: 0.4650\n",
            "Epoch 11: val_loss did not improve from 2.36842\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.7441 - accuracy: 0.4640 - val_loss: 3.2788 - val_accuracy: 0.0290 - lr: 0.0020\n",
            "Epoch 12/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.6819 - accuracy: 0.4885\n",
            "Epoch 12: val_loss did not improve from 2.36842\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.6817 - accuracy: 0.4887 - val_loss: 3.2144 - val_accuracy: 0.0308 - lr: 0.0020\n",
            "Epoch 13/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.6564 - accuracy: 0.5031\n",
            "Epoch 13: val_loss did not improve from 2.36842\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.6551 - accuracy: 0.5038 - val_loss: 2.4014 - val_accuracy: 0.2681 - lr: 0.0020\n",
            "Epoch 14/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.6407 - accuracy: 0.5000\n",
            "Epoch 14: val_loss did not improve from 2.36842\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.6415 - accuracy: 0.4998 - val_loss: 3.2989 - val_accuracy: 0.0290 - lr: 0.0020\n",
            "Epoch 15/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.6230 - accuracy: 0.5062\n",
            "Epoch 15: val_loss did not improve from 2.36842\n",
            "\n",
            "Epoch 15: ReduceLROnPlateau reducing learning rate to 0.0003999999724328518.\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.6227 - accuracy: 0.5060 - val_loss: 3.3039 - val_accuracy: 0.0290 - lr: 0.0020\n",
            "Epoch 16/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5890 - accuracy: 0.5239\n",
            "Epoch 16: val_loss improved from 2.36842 to 2.00640, saving model to /content/weight/model.16-1.590778.hdf5\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.5908 - accuracy: 0.5232 - val_loss: 2.0064 - val_accuracy: 0.3551 - lr: 4.0000e-04\n",
            "Epoch 17/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5852 - accuracy: 0.5213\n",
            "Epoch 17: val_loss did not improve from 2.00640\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.5850 - accuracy: 0.5210 - val_loss: 2.0566 - val_accuracy: 0.3388 - lr: 4.0000e-04\n",
            "Epoch 18/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5762 - accuracy: 0.5266\n",
            "Epoch 18: val_loss improved from 2.00640 to 1.96323, saving model to /content/weight/model.18-1.576352.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.5764 - accuracy: 0.5267 - val_loss: 1.9632 - val_accuracy: 0.3822 - lr: 4.0000e-04\n",
            "Epoch 19/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5782 - accuracy: 0.5363\n",
            "Epoch 19: val_loss improved from 1.96323 to 1.96313, saving model to /content/weight/model.19-1.579142.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.5791 - accuracy: 0.5360 - val_loss: 1.9631 - val_accuracy: 0.3822 - lr: 4.0000e-04\n",
            "Epoch 20/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5734 - accuracy: 0.5319\n",
            "Epoch 20: val_loss did not improve from 1.96313\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.5741 - accuracy: 0.5311 - val_loss: 1.9706 - val_accuracy: 0.3768 - lr: 4.0000e-04\n",
            "Epoch 21/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5755 - accuracy: 0.5341\n",
            "Epoch 21: val_loss did not improve from 1.96313\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.5739 - accuracy: 0.5347 - val_loss: 2.0763 - val_accuracy: 0.3496 - lr: 4.0000e-04\n",
            "Epoch 22/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5702 - accuracy: 0.5368\n",
            "Epoch 22: val_loss did not improve from 1.96313\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.5717 - accuracy: 0.5364 - val_loss: 1.9687 - val_accuracy: 0.3786 - lr: 4.0000e-04\n",
            "Epoch 23/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5729 - accuracy: 0.5403\n",
            "Epoch 23: val_loss improved from 1.96313 to 1.96272, saving model to /content/weight/model.23-1.572430.hdf5\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 1.5724 - accuracy: 0.5395 - val_loss: 1.9627 - val_accuracy: 0.3949 - lr: 4.0000e-04\n",
            "Epoch 24/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5620 - accuracy: 0.5403\n",
            "Epoch 24: val_loss did not improve from 1.96272\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.5619 - accuracy: 0.5404 - val_loss: 2.1739 - val_accuracy: 0.3152 - lr: 4.0000e-04\n",
            "Epoch 25/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5602 - accuracy: 0.5408\n",
            "Epoch 25: val_loss did not improve from 1.96272\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.5601 - accuracy: 0.5408 - val_loss: 1.9746 - val_accuracy: 0.3822 - lr: 4.0000e-04\n",
            "Epoch 26/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5537 - accuracy: 0.5381\n",
            "Epoch 26: val_loss did not improve from 1.96272\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.5530 - accuracy: 0.5382 - val_loss: 1.9874 - val_accuracy: 0.3768 - lr: 4.0000e-04\n",
            "Epoch 27/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5583 - accuracy: 0.5377\n",
            "Epoch 27: val_loss did not improve from 1.96272\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.5589 - accuracy: 0.5373 - val_loss: 1.9750 - val_accuracy: 0.3804 - lr: 4.0000e-04\n",
            "Epoch 28/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5543 - accuracy: 0.5479\n",
            "Epoch 28: val_loss did not improve from 1.96272\n",
            "\n",
            "Epoch 28: ReduceLROnPlateau reducing learning rate to 7.999999215826393e-05.\n",
            "142/142 [==============================] - 5s 36ms/step - loss: 1.5550 - accuracy: 0.5479 - val_loss: 2.0254 - val_accuracy: 0.3659 - lr: 4.0000e-04\n",
            "Epoch 29/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5413 - accuracy: 0.5523\n",
            "Epoch 29: val_loss improved from 1.96272 to 1.95431, saving model to /content/weight/model.29-1.541862.hdf5\n",
            "142/142 [==============================] - 7s 46ms/step - loss: 1.5419 - accuracy: 0.5519 - val_loss: 1.9543 - val_accuracy: 0.3931 - lr: 8.0000e-05\n",
            "Epoch 30/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5500 - accuracy: 0.5381\n",
            "Epoch 30: val_loss improved from 1.95431 to 1.95373, saving model to /content/weight/model.30-1.550501.hdf5\n",
            "142/142 [==============================] - 5s 38ms/step - loss: 1.5505 - accuracy: 0.5382 - val_loss: 1.9537 - val_accuracy: 0.3895 - lr: 8.0000e-05\n",
            "Epoch 31/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5381 - accuracy: 0.5488\n",
            "Epoch 31: val_loss improved from 1.95373 to 1.95263, saving model to /content/weight/model.31-1.537853.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.5379 - accuracy: 0.5492 - val_loss: 1.9526 - val_accuracy: 0.3877 - lr: 8.0000e-05\n",
            "Epoch 32/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5418 - accuracy: 0.5443\n",
            "Epoch 32: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.5423 - accuracy: 0.5439 - val_loss: 1.9603 - val_accuracy: 0.3949 - lr: 8.0000e-05\n",
            "Epoch 33/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5406 - accuracy: 0.5488\n",
            "Epoch 33: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.5417 - accuracy: 0.5479 - val_loss: 1.9550 - val_accuracy: 0.3877 - lr: 8.0000e-05\n",
            "Epoch 34/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5387 - accuracy: 0.5541\n",
            "Epoch 34: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.5383 - accuracy: 0.5545 - val_loss: 1.9559 - val_accuracy: 0.3859 - lr: 8.0000e-05\n",
            "Epoch 35/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5370 - accuracy: 0.5496\n",
            "Epoch 35: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.5401 - accuracy: 0.5488 - val_loss: 1.9559 - val_accuracy: 0.3877 - lr: 8.0000e-05\n",
            "Epoch 36/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5322 - accuracy: 0.5607\n",
            "Epoch 36: val_loss did not improve from 1.95263\n",
            "\n",
            "Epoch 36: ReduceLROnPlateau reducing learning rate to 1.599999814061448e-05.\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.5305 - accuracy: 0.5611 - val_loss: 1.9544 - val_accuracy: 0.3931 - lr: 8.0000e-05\n",
            "Epoch 37/200\n",
            "142/142 [==============================] - ETA: 0s - loss: 1.5307 - accuracy: 0.5563\n",
            "Epoch 37: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 5s 34ms/step - loss: 1.5307 - accuracy: 0.5563 - val_loss: 1.9546 - val_accuracy: 0.3949 - lr: 1.6000e-05\n",
            "Epoch 38/200\n",
            "142/142 [==============================] - ETA: 0s - loss: 1.5329 - accuracy: 0.5479\n",
            "Epoch 38: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 5s 35ms/step - loss: 1.5329 - accuracy: 0.5479 - val_loss: 1.9545 - val_accuracy: 0.3949 - lr: 1.6000e-05\n",
            "Epoch 39/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5250 - accuracy: 0.5581\n",
            "Epoch 39: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 1.5250 - accuracy: 0.5585 - val_loss: 1.9548 - val_accuracy: 0.3949 - lr: 1.6000e-05\n",
            "Epoch 40/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5304 - accuracy: 0.5563\n",
            "Epoch 40: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.5314 - accuracy: 0.5558 - val_loss: 1.9545 - val_accuracy: 0.3931 - lr: 1.6000e-05\n",
            "Epoch 41/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5437 - accuracy: 0.5501\n",
            "Epoch 41: val_loss did not improve from 1.95263\n",
            "\n",
            "Epoch 41: ReduceLROnPlateau reducing learning rate to 3.199999628122896e-06.\n",
            "142/142 [==============================] - 4s 32ms/step - loss: 1.5451 - accuracy: 0.5492 - val_loss: 1.9546 - val_accuracy: 0.3949 - lr: 1.6000e-05\n",
            "Epoch 42/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5412 - accuracy: 0.5474\n",
            "Epoch 42: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.5430 - accuracy: 0.5479 - val_loss: 1.9549 - val_accuracy: 0.3949 - lr: 3.2000e-06\n",
            "Epoch 43/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5345 - accuracy: 0.5474\n",
            "Epoch 43: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.5358 - accuracy: 0.5479 - val_loss: 1.9549 - val_accuracy: 0.3967 - lr: 3.2000e-06\n",
            "Epoch 44/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5474 - accuracy: 0.5412\n",
            "Epoch 44: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.5465 - accuracy: 0.5413 - val_loss: 1.9548 - val_accuracy: 0.3949 - lr: 3.2000e-06\n",
            "Epoch 45/200\n",
            "140/142 [============================>.] - ETA: 0s - loss: 1.5369 - accuracy: 0.5478\n",
            "Epoch 45: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 1.5391 - accuracy: 0.5461 - val_loss: 1.9547 - val_accuracy: 0.3967 - lr: 3.2000e-06\n",
            "Epoch 46/200\n",
            "140/142 [============================>.] - ETA: 0s - loss: 1.5291 - accuracy: 0.5513\n",
            "Epoch 46: val_loss did not improve from 1.95263\n",
            "\n",
            "Epoch 46: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
            "142/142 [==============================] - 5s 35ms/step - loss: 1.5294 - accuracy: 0.5510 - val_loss: 1.9547 - val_accuracy: 0.3949 - lr: 3.2000e-06\n",
            "Epoch 47/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5321 - accuracy: 0.5581\n",
            "Epoch 47: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 6s 40ms/step - loss: 1.5343 - accuracy: 0.5567 - val_loss: 1.9547 - val_accuracy: 0.3949 - lr: 1.0000e-06\n",
            "Epoch 48/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5289 - accuracy: 0.5545\n",
            "Epoch 48: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 5s 38ms/step - loss: 1.5297 - accuracy: 0.5541 - val_loss: 1.9547 - val_accuracy: 0.3949 - lr: 1.0000e-06\n",
            "Epoch 49/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5253 - accuracy: 0.5519\n",
            "Epoch 49: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.5257 - accuracy: 0.5514 - val_loss: 1.9547 - val_accuracy: 0.3949 - lr: 1.0000e-06\n",
            "Epoch 50/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.5335 - accuracy: 0.5434\n",
            "Epoch 50: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.5343 - accuracy: 0.5435 - val_loss: 1.9547 - val_accuracy: 0.3949 - lr: 1.0000e-06\n",
            "Epoch 51/200\n",
            "140/142 [============================>.] - ETA: 0s - loss: 1.5366 - accuracy: 0.5522\n",
            "Epoch 51: val_loss did not improve from 1.95263\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 1.5337 - accuracy: 0.5532 - val_loss: 1.9547 - val_accuracy: 0.3949 - lr: 1.0000e-06\n",
            "Epoch 51: early stopping\n",
            "Train: 0.575, Test: 0.395\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class StoreEpochs(keras.callbacks.Callback):\n",
        "  def __init__(self, epoch:int=0):\n",
        "    super().__init__()\n",
        "    self.my_epoch = keras.backend.variable(epoch)\n",
        "\n",
        "  def on_epoch_begin(self, epoch, logs=None):\n",
        "    keras.backend.set_value(self.my_epoch, epoch)\n",
        "\n",
        "def alpha_update(epoch):\n",
        "  if epoch<100:\n",
        "    return 1.0 - 0.01*epoch\n",
        "  return 0.0\n",
        "\n",
        "def cc_po_varying_mix_loss():\n",
        "  def loss_func(ypred, ytrue):\n",
        "\n",
        "    alpha = alpha_update(epoch = store_epochs.my_epoch)\n",
        "    cc = keras.losses.categorical_crossentropy(ypred, ytrue)\n",
        "    po = keras.losses.poisson(ypred, ytrue)\n",
        "\n",
        "    return alpha*cc + (1-alpha)*po\n",
        "  return loss_func\n",
        "\n",
        "store_epochs = StoreEpochs()\n",
        "custom_loss = cc_po_varying_mix_loss()"
      ],
      "metadata": {
        "id": "JMAVRajWDAkv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputShape = (trainx.shape[1], trainx.shape[2], trainx.shape[3])\n",
        "model = build_model(inputShape, custom_loss)\n",
        "\n",
        "earlystopper = EarlyStopping(patience=20, verbose=1)\n",
        "\n",
        "checkpointer = ModelCheckpoint(filepath = '/content/weight/model.{epoch:02d}-{loss:.6f}.hdf5', verbose=1, save_best_only=True, save_weights_only = True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,patience=5, min_lr=0.000001, verbose=1, cooldown=1)\n",
        "\n",
        "filepath= '/content/weight/model.{epoch:02d}-{loss:.6f}.hdf5'\n",
        "\n",
        "history = model.fit(trainx, trainy, batch_size=16, epochs=200, validation_data=(testx, testy), verbose=1,callbacks=[store_epochs, earlystopper, checkpointer, reduce_lr])\n",
        "\n",
        "_, train_acc = model.evaluate(trainx, trainy, verbose=0)\n",
        "_, test_acc = model.evaluate(testx, testy, verbose=0)\n",
        "print('Train: %.3f, Test: %.3f' % (train_acc, test_acc))\n",
        "# plot loss during training\n",
        "pyplot.subplot(211)\n",
        "pyplot.title('Loss')\n",
        "pyplot.plot(history.history['loss'], label='train')\n",
        "pyplot.plot(history.history['val_loss'], label='test')\n",
        "pyplot.legend()\n",
        "# plot accuracy during training\n",
        "pyplot.subplot(212)\n",
        "pyplot.title('Accuracy')\n",
        "pyplot.plot(history.history['accuracy'], label='train')\n",
        "pyplot.plot(history.history['val_accuracy'], label='test')\n",
        "pyplot.legend()\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uK2Hxd8dEIj8",
        "outputId": "e44aa676-0b7d-4233-dda1-ef373b82244d"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.SGD.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_26\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_294 (Conv2D)         (None, 252, 252, 3)       30        \n",
            "                                                                 \n",
            " conv2d_295 (Conv2D)         (None, 250, 250, 3)       84        \n",
            "                                                                 \n",
            " conv2d_296 (Conv2D)         (None, 248, 248, 3)       84        \n",
            "                                                                 \n",
            " conv2d_297 (Conv2D)         (None, 246, 246, 3)       84        \n",
            "                                                                 \n",
            " batch_normalization_78 (Ba  (None, 246, 246, 3)       12        \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " max_pooling2d_78 (MaxPooli  (None, 123, 123, 3)       0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_95 (Dropout)        (None, 123, 123, 3)       0         \n",
            "                                                                 \n",
            " conv2d_298 (Conv2D)         (None, 121, 121, 3)       84        \n",
            "                                                                 \n",
            " conv2d_299 (Conv2D)         (None, 119, 119, 3)       84        \n",
            "                                                                 \n",
            " conv2d_300 (Conv2D)         (None, 117, 117, 3)       84        \n",
            "                                                                 \n",
            " conv2d_301 (Conv2D)         (None, 115, 115, 3)       84        \n",
            "                                                                 \n",
            " batch_normalization_79 (Ba  (None, 115, 115, 3)       12        \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " max_pooling2d_79 (MaxPooli  (None, 57, 57, 3)         0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_96 (Dropout)        (None, 57, 57, 3)         0         \n",
            "                                                                 \n",
            " conv2d_302 (Conv2D)         (None, 55, 55, 3)         84        \n",
            "                                                                 \n",
            " conv2d_303 (Conv2D)         (None, 53, 53, 3)         84        \n",
            "                                                                 \n",
            " conv2d_304 (Conv2D)         (None, 51, 51, 3)         84        \n",
            "                                                                 \n",
            " conv2d_305 (Conv2D)         (None, 49, 49, 3)         84        \n",
            "                                                                 \n",
            " batch_normalization_80 (Ba  (None, 49, 49, 3)         12        \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " max_pooling2d_80 (MaxPooli  (None, 24, 24, 3)         0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_97 (Dropout)        (None, 24, 24, 3)         0         \n",
            "                                                                 \n",
            " reshape_17 (Reshape)        (None, 12, 144)           0         \n",
            "                                                                 \n",
            " lstm_37 (LSTM)              (None, 12, 128)           139776    \n",
            "                                                                 \n",
            " lstm_38 (LSTM)              (None, 64)                49408     \n",
            "                                                                 \n",
            " dropout_98 (Dropout)        (None, 64)                0         \n",
            "                                                                 \n",
            " flatten_22 (Flatten)        (None, 64)                0         \n",
            "                                                                 \n",
            " dense_21 (Dense)            (None, 31)                2015      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 192189 (750.74 KB)\n",
            "Trainable params: 192171 (750.67 KB)\n",
            "Non-trainable params: 18 (72.00 Byte)\n",
            "_________________________________________________________________\n",
            "Epoch 1/200\n",
            "142/142 [==============================] - ETA: 0s - loss: 3.6399 - accuracy: 0.0865\n",
            "Epoch 1: val_loss improved from inf to 3.96111, saving model to /content/weight/model.01-3.639922.hdf5\n",
            "142/142 [==============================] - 12s 42ms/step - loss: 3.6399 - accuracy: 0.0865 - val_loss: 3.9611 - val_accuracy: 0.0344 - lr: 0.0100\n",
            "Epoch 2/200\n",
            "140/142 [============================>.] - ETA: 0s - loss: 3.1974 - accuracy: 0.1638\n",
            "Epoch 2: val_loss improved from 3.96111 to 3.83143, saving model to /content/weight/model.02-3.198700.hdf5\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 3.1987 - accuracy: 0.1625 - val_loss: 3.8314 - val_accuracy: 0.0344 - lr: 0.0100\n",
            "Epoch 3/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.9291 - accuracy: 0.2238\n",
            "Epoch 3: val_loss improved from 3.83143 to 3.66730, saving model to /content/weight/model.03-2.928807.hdf5\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 2.9288 - accuracy: 0.2238 - val_loss: 3.6673 - val_accuracy: 0.0543 - lr: 0.0100\n",
            "Epoch 4/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.7175 - accuracy: 0.2717\n",
            "Epoch 4: val_loss improved from 3.66730 to 3.17330, saving model to /content/weight/model.04-2.718230.hdf5\n",
            "142/142 [==============================] - 5s 34ms/step - loss: 2.7182 - accuracy: 0.2715 - val_loss: 3.1733 - val_accuracy: 0.1431 - lr: 0.0100\n",
            "Epoch 5/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.5671 - accuracy: 0.3041\n",
            "Epoch 5: val_loss did not improve from 3.17330\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 2.5666 - accuracy: 0.3051 - val_loss: 3.3555 - val_accuracy: 0.1159 - lr: 0.0100\n",
            "Epoch 6/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.4266 - accuracy: 0.3426\n",
            "Epoch 6: val_loss improved from 3.17330 to 2.76258, saving model to /content/weight/model.06-2.423874.hdf5\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 2.4239 - accuracy: 0.3439 - val_loss: 2.7626 - val_accuracy: 0.2554 - lr: 0.0100\n",
            "Epoch 7/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.3145 - accuracy: 0.3630\n",
            "Epoch 7: val_loss did not improve from 2.76258\n",
            "142/142 [==============================] - 5s 34ms/step - loss: 2.3164 - accuracy: 0.3634 - val_loss: 2.9363 - val_accuracy: 0.2301 - lr: 0.0100\n",
            "Epoch 8/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.2134 - accuracy: 0.3998\n",
            "Epoch 8: val_loss did not improve from 2.76258\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 2.2141 - accuracy: 0.4004 - val_loss: 3.4269 - val_accuracy: 0.1286 - lr: 0.0100\n",
            "Epoch 9/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.1290 - accuracy: 0.4149\n",
            "Epoch 9: val_loss did not improve from 2.76258\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 2.1313 - accuracy: 0.4141 - val_loss: 3.5390 - val_accuracy: 0.0833 - lr: 0.0100\n",
            "Epoch 10/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 2.0363 - accuracy: 0.4397\n",
            "Epoch 10: val_loss did not improve from 2.76258\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 2.0357 - accuracy: 0.4402 - val_loss: 4.0268 - val_accuracy: 0.0761 - lr: 0.0100\n",
            "Epoch 11/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.9618 - accuracy: 0.4579\n",
            "Epoch 11: val_loss improved from 2.76258 to 2.44658, saving model to /content/weight/model.11-1.962008.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.9620 - accuracy: 0.4574 - val_loss: 2.4466 - val_accuracy: 0.3098 - lr: 0.0100\n",
            "Epoch 12/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.8809 - accuracy: 0.4925\n",
            "Epoch 12: val_loss did not improve from 2.44658\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.8800 - accuracy: 0.4923 - val_loss: 3.0282 - val_accuracy: 0.1395 - lr: 0.0100\n",
            "Epoch 13/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.8089 - accuracy: 0.5022\n",
            "Epoch 13: val_loss did not improve from 2.44658\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.8104 - accuracy: 0.5020 - val_loss: 2.8101 - val_accuracy: 0.2518 - lr: 0.0100\n",
            "Epoch 14/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.7530 - accuracy: 0.5208\n",
            "Epoch 14: val_loss did not improve from 2.44658\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.7496 - accuracy: 0.5223 - val_loss: 3.2018 - val_accuracy: 0.1159 - lr: 0.0100\n",
            "Epoch 15/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.6976 - accuracy: 0.5341\n",
            "Epoch 15: val_loss did not improve from 2.44658\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.6983 - accuracy: 0.5338 - val_loss: 2.5437 - val_accuracy: 0.3043 - lr: 0.0100\n",
            "Epoch 16/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.6353 - accuracy: 0.5545\n",
            "Epoch 16: val_loss did not improve from 2.44658\n",
            "\n",
            "Epoch 16: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.6374 - accuracy: 0.5532 - val_loss: 3.2953 - val_accuracy: 0.1196 - lr: 0.0100\n",
            "Epoch 17/200\n",
            "140/142 [============================>.] - ETA: 0s - loss: 1.5284 - accuracy: 0.5746\n",
            "Epoch 17: val_loss did not improve from 2.44658\n",
            "142/142 [==============================] - 5s 35ms/step - loss: 1.5309 - accuracy: 0.5726 - val_loss: 3.4400 - val_accuracy: 0.0707 - lr: 0.0020\n",
            "Epoch 18/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.4481 - accuracy: 0.6277\n",
            "Epoch 18: val_loss improved from 2.44658 to 2.07305, saving model to /content/weight/model.18-1.447804.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.4478 - accuracy: 0.6278 - val_loss: 2.0731 - val_accuracy: 0.4149 - lr: 0.0020\n",
            "Epoch 19/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.4021 - accuracy: 0.6325\n",
            "Epoch 19: val_loss did not improve from 2.07305\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.4031 - accuracy: 0.6322 - val_loss: 2.2946 - val_accuracy: 0.3243 - lr: 0.0020\n",
            "Epoch 20/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3784 - accuracy: 0.6423\n",
            "Epoch 20: val_loss did not improve from 2.07305\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.3775 - accuracy: 0.6428 - val_loss: 3.1781 - val_accuracy: 0.1051 - lr: 0.0020\n",
            "Epoch 21/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3486 - accuracy: 0.6498\n",
            "Epoch 21: val_loss did not improve from 2.07305\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.3476 - accuracy: 0.6503 - val_loss: 2.0912 - val_accuracy: 0.3696 - lr: 0.0020\n",
            "Epoch 22/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.3227 - accuracy: 0.6476\n",
            "Epoch 22: val_loss improved from 2.07305 to 2.02895, saving model to /content/weight/model.22-1.322932.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.3229 - accuracy: 0.6472 - val_loss: 2.0289 - val_accuracy: 0.3804 - lr: 0.0020\n",
            "Epoch 23/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.2893 - accuracy: 0.6693\n",
            "Epoch 23: val_loss did not improve from 2.02895\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 1.2904 - accuracy: 0.6689 - val_loss: 3.2798 - val_accuracy: 0.0797 - lr: 0.0020\n",
            "Epoch 24/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.2641 - accuracy: 0.6645\n",
            "Epoch 24: val_loss did not improve from 2.02895\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.2638 - accuracy: 0.6645 - val_loss: 3.2008 - val_accuracy: 0.0960 - lr: 0.0020\n",
            "Epoch 25/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.2484 - accuracy: 0.6764\n",
            "Epoch 25: val_loss improved from 2.02895 to 1.86422, saving model to /content/weight/model.25-1.248115.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.2481 - accuracy: 0.6768 - val_loss: 1.8642 - val_accuracy: 0.4239 - lr: 0.0020\n",
            "Epoch 26/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.2258 - accuracy: 0.6773\n",
            "Epoch 26: val_loss did not improve from 1.86422\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 1.2238 - accuracy: 0.6786 - val_loss: 2.0483 - val_accuracy: 0.3514 - lr: 0.0020\n",
            "Epoch 27/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.2129 - accuracy: 0.6631\n",
            "Epoch 27: val_loss did not improve from 1.86422\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.2123 - accuracy: 0.6636 - val_loss: 2.4661 - val_accuracy: 0.2011 - lr: 0.0020\n",
            "Epoch 28/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.1728 - accuracy: 0.6964\n",
            "Epoch 28: val_loss did not improve from 1.86422\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.1736 - accuracy: 0.6962 - val_loss: 1.9262 - val_accuracy: 0.3841 - lr: 0.0020\n",
            "Epoch 29/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.1550 - accuracy: 0.6959\n",
            "Epoch 29: val_loss did not improve from 1.86422\n",
            "142/142 [==============================] - 5s 34ms/step - loss: 1.1549 - accuracy: 0.6962 - val_loss: 3.0372 - val_accuracy: 0.0942 - lr: 0.0020\n",
            "Epoch 30/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.1449 - accuracy: 0.6946\n",
            "Epoch 30: val_loss improved from 1.86422 to 1.82270, saving model to /content/weight/model.30-1.144974.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.1450 - accuracy: 0.6945 - val_loss: 1.8227 - val_accuracy: 0.4203 - lr: 0.0020\n",
            "Epoch 31/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.1161 - accuracy: 0.7132\n",
            "Epoch 31: val_loss did not improve from 1.82270\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 1.1157 - accuracy: 0.7130 - val_loss: 1.9184 - val_accuracy: 0.3895 - lr: 0.0020\n",
            "Epoch 32/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.1002 - accuracy: 0.7083\n",
            "Epoch 32: val_loss did not improve from 1.82270\n",
            "142/142 [==============================] - 4s 32ms/step - loss: 1.0992 - accuracy: 0.7082 - val_loss: 2.0594 - val_accuracy: 0.2808 - lr: 0.0020\n",
            "Epoch 33/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.0839 - accuracy: 0.7017\n",
            "Epoch 33: val_loss did not improve from 1.82270\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.0849 - accuracy: 0.7020 - val_loss: 2.7070 - val_accuracy: 0.1178 - lr: 0.0020\n",
            "Epoch 34/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.0602 - accuracy: 0.7199\n",
            "Epoch 34: val_loss improved from 1.82270 to 1.68270, saving model to /content/weight/model.34-1.059557.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.0596 - accuracy: 0.7205 - val_loss: 1.6827 - val_accuracy: 0.4493 - lr: 0.0020\n",
            "Epoch 35/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.0442 - accuracy: 0.7141\n",
            "Epoch 35: val_loss did not improve from 1.68270\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 1.0453 - accuracy: 0.7135 - val_loss: 2.8677 - val_accuracy: 0.0779 - lr: 0.0020\n",
            "Epoch 36/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.0276 - accuracy: 0.7207\n",
            "Epoch 36: val_loss did not improve from 1.68270\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.0285 - accuracy: 0.7210 - val_loss: 2.3197 - val_accuracy: 0.1504 - lr: 0.0020\n",
            "Epoch 37/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 1.0212 - accuracy: 0.7172\n",
            "Epoch 37: val_loss improved from 1.68270 to 1.67867, saving model to /content/weight/model.37-1.021675.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 1.0217 - accuracy: 0.7174 - val_loss: 1.6787 - val_accuracy: 0.4221 - lr: 0.0020\n",
            "Epoch 38/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.9974 - accuracy: 0.7243\n",
            "Epoch 38: val_loss did not improve from 1.67867\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.9968 - accuracy: 0.7245 - val_loss: 1.9743 - val_accuracy: 0.2862 - lr: 0.0020\n",
            "Epoch 39/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.9707 - accuracy: 0.7380\n",
            "Epoch 39: val_loss improved from 1.67867 to 1.59502, saving model to /content/weight/model.39-0.970688.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.9707 - accuracy: 0.7386 - val_loss: 1.5950 - val_accuracy: 0.4402 - lr: 0.0020\n",
            "Epoch 40/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.9627 - accuracy: 0.7371\n",
            "Epoch 40: val_loss did not improve from 1.59502\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.9608 - accuracy: 0.7382 - val_loss: 1.7716 - val_accuracy: 0.3424 - lr: 0.0020\n",
            "Epoch 41/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.9438 - accuracy: 0.7473\n",
            "Epoch 41: val_loss did not improve from 1.59502\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.9436 - accuracy: 0.7475 - val_loss: 1.6899 - val_accuracy: 0.4130 - lr: 0.0020\n",
            "Epoch 42/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.9231 - accuracy: 0.7496\n",
            "Epoch 42: val_loss did not improve from 1.59502\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.9234 - accuracy: 0.7488 - val_loss: 2.1858 - val_accuracy: 0.1395 - lr: 0.0020\n",
            "Epoch 43/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.9113 - accuracy: 0.7420\n",
            "Epoch 43: val_loss improved from 1.59502 to 1.48850, saving model to /content/weight/model.43-0.911549.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.9115 - accuracy: 0.7417 - val_loss: 1.4885 - val_accuracy: 0.4493 - lr: 0.0020\n",
            "Epoch 44/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.8981 - accuracy: 0.7478\n",
            "Epoch 44: val_loss did not improve from 1.48850\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.8992 - accuracy: 0.7470 - val_loss: 1.5293 - val_accuracy: 0.4149 - lr: 0.0020\n",
            "Epoch 45/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.8802 - accuracy: 0.7487\n",
            "Epoch 45: val_loss did not improve from 1.48850\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.8803 - accuracy: 0.7483 - val_loss: 1.6172 - val_accuracy: 0.3623 - lr: 0.0020\n",
            "Epoch 46/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.8602 - accuracy: 0.7580\n",
            "Epoch 46: val_loss did not improve from 1.48850\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.8593 - accuracy: 0.7585 - val_loss: 1.5230 - val_accuracy: 0.3913 - lr: 0.0020\n",
            "Epoch 47/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.8582 - accuracy: 0.7509\n",
            "Epoch 47: val_loss improved from 1.48850 to 1.47627, saving model to /content/weight/model.47-0.857184.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.8572 - accuracy: 0.7514 - val_loss: 1.4763 - val_accuracy: 0.4203 - lr: 0.0020\n",
            "Epoch 48/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.8359 - accuracy: 0.7606\n",
            "Epoch 48: val_loss did not improve from 1.47627\n",
            "142/142 [==============================] - 5s 34ms/step - loss: 0.8362 - accuracy: 0.7603 - val_loss: 1.7170 - val_accuracy: 0.2790 - lr: 0.0020\n",
            "Epoch 49/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.8183 - accuracy: 0.7566\n",
            "Epoch 49: val_loss improved from 1.47627 to 1.46524, saving model to /content/weight/model.49-0.817854.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.8179 - accuracy: 0.7567 - val_loss: 1.4652 - val_accuracy: 0.3949 - lr: 0.0020\n",
            "Epoch 50/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.8134 - accuracy: 0.7606\n",
            "Epoch 50: val_loss improved from 1.46524 to 1.26728, saving model to /content/weight/model.50-0.812447.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.8124 - accuracy: 0.7611 - val_loss: 1.2673 - val_accuracy: 0.5036 - lr: 0.0020\n",
            "Epoch 51/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.7874 - accuracy: 0.7691\n",
            "Epoch 51: val_loss did not improve from 1.26728\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.7880 - accuracy: 0.7678 - val_loss: 1.3591 - val_accuracy: 0.4330 - lr: 0.0020\n",
            "Epoch 52/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.7792 - accuracy: 0.7699\n",
            "Epoch 52: val_loss did not improve from 1.26728\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.7794 - accuracy: 0.7695 - val_loss: 1.6235 - val_accuracy: 0.2609 - lr: 0.0020\n",
            "Epoch 53/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.7629 - accuracy: 0.7664\n",
            "Epoch 53: val_loss did not improve from 1.26728\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 0.7627 - accuracy: 0.7660 - val_loss: 1.2816 - val_accuracy: 0.4493 - lr: 0.0020\n",
            "Epoch 54/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.7575 - accuracy: 0.7620\n",
            "Epoch 54: val_loss improved from 1.26728 to 1.25502, saving model to /content/weight/model.54-0.756840.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.7568 - accuracy: 0.7629 - val_loss: 1.2550 - val_accuracy: 0.4547 - lr: 0.0020\n",
            "Epoch 55/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.7398 - accuracy: 0.7713\n",
            "Epoch 55: val_loss did not improve from 1.25502\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.7400 - accuracy: 0.7713 - val_loss: 1.3490 - val_accuracy: 0.3822 - lr: 0.0020\n",
            "Epoch 56/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.7260 - accuracy: 0.7686\n",
            "Epoch 56: val_loss did not improve from 1.25502\n",
            "142/142 [==============================] - 4s 29ms/step - loss: 0.7256 - accuracy: 0.7691 - val_loss: 1.8243 - val_accuracy: 0.1341 - lr: 0.0020\n",
            "Epoch 57/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.7165 - accuracy: 0.7828\n",
            "Epoch 57: val_loss did not improve from 1.25502\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 0.7167 - accuracy: 0.7828 - val_loss: 1.3036 - val_accuracy: 0.3822 - lr: 0.0020\n",
            "Epoch 58/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.7031 - accuracy: 0.7815\n",
            "Epoch 58: val_loss improved from 1.25502 to 1.19611, saving model to /content/weight/model.58-0.702909.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.7029 - accuracy: 0.7810 - val_loss: 1.1961 - val_accuracy: 0.4221 - lr: 0.0020\n",
            "Epoch 59/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.6911 - accuracy: 0.7810\n",
            "Epoch 59: val_loss improved from 1.19611 to 1.15818, saving model to /content/weight/model.59-0.690937.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.6909 - accuracy: 0.7815 - val_loss: 1.1582 - val_accuracy: 0.4402 - lr: 0.0020\n",
            "Epoch 60/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.6770 - accuracy: 0.7788\n",
            "Epoch 60: val_loss improved from 1.15818 to 1.05496, saving model to /content/weight/model.60-0.677321.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.6773 - accuracy: 0.7784 - val_loss: 1.0550 - val_accuracy: 0.5072 - lr: 0.0020\n",
            "Epoch 61/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.6612 - accuracy: 0.7926\n",
            "Epoch 61: val_loss did not improve from 1.05496\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.6627 - accuracy: 0.7912 - val_loss: 1.0839 - val_accuracy: 0.4674 - lr: 0.0020\n",
            "Epoch 62/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.6456 - accuracy: 0.7943\n",
            "Epoch 62: val_loss did not improve from 1.05496\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.6458 - accuracy: 0.7947 - val_loss: 1.0850 - val_accuracy: 0.4457 - lr: 0.0020\n",
            "Epoch 63/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.6374 - accuracy: 0.7895\n",
            "Epoch 63: val_loss did not improve from 1.05496\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.6367 - accuracy: 0.7903 - val_loss: 1.3412 - val_accuracy: 0.2373 - lr: 0.0020\n",
            "Epoch 64/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.6265 - accuracy: 0.7877\n",
            "Epoch 64: val_loss improved from 1.05496 to 1.03676, saving model to /content/weight/model.64-0.626457.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.6265 - accuracy: 0.7881 - val_loss: 1.0368 - val_accuracy: 0.4529 - lr: 0.0020\n",
            "Epoch 65/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.6129 - accuracy: 0.7872\n",
            "Epoch 65: val_loss did not improve from 1.03676\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.6138 - accuracy: 0.7859 - val_loss: 1.2774 - val_accuracy: 0.2554 - lr: 0.0020\n",
            "Epoch 66/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.6023 - accuracy: 0.7899\n",
            "Epoch 66: val_loss improved from 1.03676 to 1.01976, saving model to /content/weight/model.66-0.603267.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.6033 - accuracy: 0.7890 - val_loss: 1.0198 - val_accuracy: 0.4275 - lr: 0.0020\n",
            "Epoch 67/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.5886 - accuracy: 0.7988\n",
            "Epoch 67: val_loss improved from 1.01976 to 0.97346, saving model to /content/weight/model.67-0.588760.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.5888 - accuracy: 0.7987 - val_loss: 0.9735 - val_accuracy: 0.4511 - lr: 0.0020\n",
            "Epoch 68/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.5768 - accuracy: 0.7930\n",
            "Epoch 68: val_loss did not improve from 0.97346\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.5772 - accuracy: 0.7921 - val_loss: 1.0087 - val_accuracy: 0.3841 - lr: 0.0020\n",
            "Epoch 69/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.5665 - accuracy: 0.7934\n",
            "Epoch 69: val_loss did not improve from 0.97346\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.5671 - accuracy: 0.7921 - val_loss: 1.2877 - val_accuracy: 0.1739 - lr: 0.0020\n",
            "Epoch 70/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.5585 - accuracy: 0.7899\n",
            "Epoch 70: val_loss improved from 0.97346 to 0.88561, saving model to /content/weight/model.70-0.559038.hdf5\n",
            "142/142 [==============================] - 4s 32ms/step - loss: 0.5590 - accuracy: 0.7890 - val_loss: 0.8856 - val_accuracy: 0.4656 - lr: 0.0020\n",
            "Epoch 71/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.5402 - accuracy: 0.8023\n",
            "Epoch 71: val_loss did not improve from 0.88561\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.5402 - accuracy: 0.8018 - val_loss: 1.0051 - val_accuracy: 0.3207 - lr: 0.0020\n",
            "Epoch 72/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.5317 - accuracy: 0.8019\n",
            "Epoch 72: val_loss did not improve from 0.88561\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.5336 - accuracy: 0.8000 - val_loss: 1.0356 - val_accuracy: 0.2790 - lr: 0.0020\n",
            "Epoch 73/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.5204 - accuracy: 0.7961\n",
            "Epoch 73: val_loss did not improve from 0.88561\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.5212 - accuracy: 0.7951 - val_loss: 1.0368 - val_accuracy: 0.2446 - lr: 0.0020\n",
            "Epoch 74/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.5092 - accuracy: 0.7983\n",
            "Epoch 74: val_loss did not improve from 0.88561\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.5087 - accuracy: 0.7987 - val_loss: 1.1384 - val_accuracy: 0.1576 - lr: 0.0020\n",
            "Epoch 75/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.4976 - accuracy: 0.7961\n",
            "Epoch 75: val_loss improved from 0.88561 to 0.77004, saving model to /content/weight/model.75-0.497518.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.4975 - accuracy: 0.7960 - val_loss: 0.7700 - val_accuracy: 0.4620 - lr: 0.0020\n",
            "Epoch 76/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.4867 - accuracy: 0.8010\n",
            "Epoch 76: val_loss did not improve from 0.77004\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.4871 - accuracy: 0.8004 - val_loss: 0.7930 - val_accuracy: 0.4112 - lr: 0.0020\n",
            "Epoch 77/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.4743 - accuracy: 0.8036\n",
            "Epoch 77: val_loss did not improve from 0.77004\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.4747 - accuracy: 0.8035 - val_loss: 0.8922 - val_accuracy: 0.2627 - lr: 0.0020\n",
            "Epoch 78/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.4652 - accuracy: 0.7974\n",
            "Epoch 78: val_loss improved from 0.77004 to 0.70386, saving model to /content/weight/model.78-0.465634.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.4656 - accuracy: 0.7965 - val_loss: 0.7039 - val_accuracy: 0.4710 - lr: 0.0020\n",
            "Epoch 79/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.4516 - accuracy: 0.7961\n",
            "Epoch 79: val_loss did not improve from 0.70386\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.4516 - accuracy: 0.7960 - val_loss: 0.7063 - val_accuracy: 0.4330 - lr: 0.0020\n",
            "Epoch 80/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.4374 - accuracy: 0.8067\n",
            "Epoch 80: val_loss did not improve from 0.70386\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.4371 - accuracy: 0.8075 - val_loss: 0.7808 - val_accuracy: 0.2917 - lr: 0.0020\n",
            "Epoch 81/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.4276 - accuracy: 0.7970\n",
            "Epoch 81: val_loss improved from 0.70386 to 0.65785, saving model to /content/weight/model.81-0.427279.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.4273 - accuracy: 0.7974 - val_loss: 0.6578 - val_accuracy: 0.4529 - lr: 0.0020\n",
            "Epoch 82/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.4169 - accuracy: 0.7943\n",
            "Epoch 82: val_loss did not improve from 0.65785\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.4167 - accuracy: 0.7951 - val_loss: 0.7260 - val_accuracy: 0.2862 - lr: 0.0020\n",
            "Epoch 83/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.4052 - accuracy: 0.7957\n",
            "Epoch 83: val_loss improved from 0.65785 to 0.61318, saving model to /content/weight/model.83-0.405135.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.4051 - accuracy: 0.7951 - val_loss: 0.6132 - val_accuracy: 0.4420 - lr: 0.0020\n",
            "Epoch 84/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.3919 - accuracy: 0.7952\n",
            "Epoch 84: val_loss did not improve from 0.61318\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.3918 - accuracy: 0.7956 - val_loss: 0.6905 - val_accuracy: 0.2645 - lr: 0.0020\n",
            "Epoch 85/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.3774 - accuracy: 0.8112\n",
            "Epoch 85: val_loss improved from 0.61318 to 0.56708, saving model to /content/weight/model.85-0.377629.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.3776 - accuracy: 0.8106 - val_loss: 0.5671 - val_accuracy: 0.4348 - lr: 0.0020\n",
            "Epoch 86/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.3671 - accuracy: 0.8041\n",
            "Epoch 86: val_loss improved from 0.56708 to 0.53542, saving model to /content/weight/model.86-0.367041.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.3670 - accuracy: 0.8040 - val_loss: 0.5354 - val_accuracy: 0.4583 - lr: 0.0020\n",
            "Epoch 87/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.3550 - accuracy: 0.8059\n",
            "Epoch 87: val_loss improved from 0.53542 to 0.50878, saving model to /content/weight/model.87-0.354776.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.3548 - accuracy: 0.8062 - val_loss: 0.5088 - val_accuracy: 0.4511 - lr: 0.0020\n",
            "Epoch 88/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.3417 - accuracy: 0.8054\n",
            "Epoch 88: val_loss improved from 0.50878 to 0.48751, saving model to /content/weight/model.88-0.341586.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.3416 - accuracy: 0.8062 - val_loss: 0.4875 - val_accuracy: 0.4620 - lr: 0.0020\n",
            "Epoch 89/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.3286 - accuracy: 0.8138\n",
            "Epoch 89: val_loss improved from 0.48751 to 0.46656, saving model to /content/weight/model.89-0.328601.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.3286 - accuracy: 0.8141 - val_loss: 0.4666 - val_accuracy: 0.4583 - lr: 0.0020\n",
            "Epoch 90/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.3168 - accuracy: 0.8010\n",
            "Epoch 90: val_loss improved from 0.46656 to 0.44029, saving model to /content/weight/model.90-0.316684.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.3167 - accuracy: 0.8013 - val_loss: 0.4403 - val_accuracy: 0.4565 - lr: 0.0020\n",
            "Epoch 91/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.3035 - accuracy: 0.8107\n",
            "Epoch 91: val_loss improved from 0.44029 to 0.41477, saving model to /content/weight/model.91-0.303614.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.3036 - accuracy: 0.8102 - val_loss: 0.4148 - val_accuracy: 0.4565 - lr: 0.0020\n",
            "Epoch 92/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.2887 - accuracy: 0.8001\n",
            "Epoch 92: val_loss improved from 0.41477 to 0.39599, saving model to /content/weight/model.92-0.288674.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.2887 - accuracy: 0.7996 - val_loss: 0.3960 - val_accuracy: 0.4475 - lr: 0.0020\n",
            "Epoch 93/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.2769 - accuracy: 0.7983\n",
            "Epoch 93: val_loss did not improve from 0.39599\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.2768 - accuracy: 0.7987 - val_loss: 0.4069 - val_accuracy: 0.3225 - lr: 0.0020\n",
            "Epoch 94/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.2620 - accuracy: 0.8098\n",
            "Epoch 94: val_loss improved from 0.39599 to 0.34748, saving model to /content/weight/model.94-0.261961.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.2620 - accuracy: 0.8102 - val_loss: 0.3475 - val_accuracy: 0.4620 - lr: 0.0020\n",
            "Epoch 95/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.2468 - accuracy: 0.8107\n",
            "Epoch 95: val_loss improved from 0.34748 to 0.32176, saving model to /content/weight/model.95-0.246805.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.2468 - accuracy: 0.8102 - val_loss: 0.3218 - val_accuracy: 0.4547 - lr: 0.0020\n",
            "Epoch 96/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.2331 - accuracy: 0.7943\n",
            "Epoch 96: val_loss improved from 0.32176 to 0.29624, saving model to /content/weight/model.96-0.233056.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.2331 - accuracy: 0.7951 - val_loss: 0.2962 - val_accuracy: 0.4511 - lr: 0.0020\n",
            "Epoch 97/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.2174 - accuracy: 0.8041\n",
            "Epoch 97: val_loss improved from 0.29624 to 0.27228, saving model to /content/weight/model.97-0.217266.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.2173 - accuracy: 0.8044 - val_loss: 0.2723 - val_accuracy: 0.4583 - lr: 0.0020\n",
            "Epoch 98/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.2016 - accuracy: 0.7974\n",
            "Epoch 98: val_loss improved from 0.27228 to 0.24729, saving model to /content/weight/model.98-0.201591.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.2016 - accuracy: 0.7974 - val_loss: 0.2473 - val_accuracy: 0.4601 - lr: 0.0020\n",
            "Epoch 99/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1852 - accuracy: 0.7965\n",
            "Epoch 99: val_loss improved from 0.24729 to 0.22283, saving model to /content/weight/model.99-0.185214.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1852 - accuracy: 0.7965 - val_loss: 0.2228 - val_accuracy: 0.4547 - lr: 0.0020\n",
            "Epoch 100/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1681 - accuracy: 0.8041\n",
            "Epoch 100: val_loss improved from 0.22283 to 0.19763, saving model to /content/weight/model.100-0.168017.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1680 - accuracy: 0.8044 - val_loss: 0.1976 - val_accuracy: 0.4493 - lr: 0.0020\n",
            "Epoch 101/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1504 - accuracy: 0.7996\n",
            "Epoch 101: val_loss improved from 0.19763 to 0.17131, saving model to /content/weight/model.101-0.150432.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1504 - accuracy: 0.7987 - val_loss: 0.1713 - val_accuracy: 0.4547 - lr: 0.0020\n",
            "Epoch 102/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1471 - accuracy: 0.7912\n",
            "Epoch 102: val_loss improved from 0.17131 to 0.16762, saving model to /content/weight/model.102-0.147055.hdf5\n",
            "142/142 [==============================] - 5s 35ms/step - loss: 0.1471 - accuracy: 0.7916 - val_loss: 0.1676 - val_accuracy: 0.4511 - lr: 0.0020\n",
            "Epoch 103/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1440 - accuracy: 0.7988\n",
            "Epoch 103: val_loss improved from 0.16762 to 0.16416, saving model to /content/weight/model.103-0.143978.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1440 - accuracy: 0.7991 - val_loss: 0.1642 - val_accuracy: 0.4493 - lr: 0.0020\n",
            "Epoch 104/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1412 - accuracy: 0.7970\n",
            "Epoch 104: val_loss improved from 0.16416 to 0.16119, saving model to /content/weight/model.104-0.141239.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1412 - accuracy: 0.7978 - val_loss: 0.1612 - val_accuracy: 0.4475 - lr: 0.0020\n",
            "Epoch 105/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1391 - accuracy: 0.7957\n",
            "Epoch 105: val_loss improved from 0.16119 to 0.15851, saving model to /content/weight/model.105-0.139135.hdf5\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 0.1391 - accuracy: 0.7956 - val_loss: 0.1585 - val_accuracy: 0.4511 - lr: 0.0020\n",
            "Epoch 106/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1372 - accuracy: 0.7917\n",
            "Epoch 106: val_loss improved from 0.15851 to 0.15606, saving model to /content/weight/model.106-0.137278.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1373 - accuracy: 0.7907 - val_loss: 0.1561 - val_accuracy: 0.4438 - lr: 0.0020\n",
            "Epoch 107/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1355 - accuracy: 0.7930\n",
            "Epoch 107: val_loss improved from 0.15606 to 0.15385, saving model to /content/weight/model.107-0.135531.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1355 - accuracy: 0.7934 - val_loss: 0.1538 - val_accuracy: 0.4511 - lr: 0.0020\n",
            "Epoch 108/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1340 - accuracy: 0.7961\n",
            "Epoch 108: val_loss improved from 0.15385 to 0.15202, saving model to /content/weight/model.108-0.134022.hdf5\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 0.1340 - accuracy: 0.7956 - val_loss: 0.1520 - val_accuracy: 0.4547 - lr: 0.0020\n",
            "Epoch 109/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1329 - accuracy: 0.7877\n",
            "Epoch 109: val_loss improved from 0.15202 to 0.15087, saving model to /content/weight/model.109-0.132899.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1329 - accuracy: 0.7872 - val_loss: 0.1509 - val_accuracy: 0.4457 - lr: 0.0020\n",
            "Epoch 110/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1317 - accuracy: 0.7868\n",
            "Epoch 110: val_loss improved from 0.15087 to 0.14925, saving model to /content/weight/model.110-0.131760.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1318 - accuracy: 0.7854 - val_loss: 0.1492 - val_accuracy: 0.4511 - lr: 0.0020\n",
            "Epoch 111/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1308 - accuracy: 0.7992\n",
            "Epoch 111: val_loss improved from 0.14925 to 0.14783, saving model to /content/weight/model.111-0.130748.hdf5\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 0.1307 - accuracy: 0.8000 - val_loss: 0.1478 - val_accuracy: 0.4493 - lr: 0.0020\n",
            "Epoch 112/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1300 - accuracy: 0.7908\n",
            "Epoch 112: val_loss improved from 0.14783 to 0.14677, saving model to /content/weight/model.112-0.130049.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1300 - accuracy: 0.7903 - val_loss: 0.1468 - val_accuracy: 0.4529 - lr: 0.0020\n",
            "Epoch 113/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1293 - accuracy: 0.7877\n",
            "Epoch 113: val_loss improved from 0.14677 to 0.14590, saving model to /content/weight/model.113-0.129342.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1293 - accuracy: 0.7876 - val_loss: 0.1459 - val_accuracy: 0.4493 - lr: 0.0020\n",
            "Epoch 114/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1289 - accuracy: 0.7868\n",
            "Epoch 114: val_loss improved from 0.14590 to 0.14501, saving model to /content/weight/model.114-0.128898.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1289 - accuracy: 0.7863 - val_loss: 0.1450 - val_accuracy: 0.4547 - lr: 0.0020\n",
            "Epoch 115/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1281 - accuracy: 0.7917\n",
            "Epoch 115: val_loss improved from 0.14501 to 0.14436, saving model to /content/weight/model.115-0.128116.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1281 - accuracy: 0.7916 - val_loss: 0.1444 - val_accuracy: 0.4438 - lr: 0.0020\n",
            "Epoch 116/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1278 - accuracy: 0.7832\n",
            "Epoch 116: val_loss improved from 0.14436 to 0.14351, saving model to /content/weight/model.116-0.127759.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1278 - accuracy: 0.7832 - val_loss: 0.1435 - val_accuracy: 0.4457 - lr: 0.0020\n",
            "Epoch 117/200\n",
            "142/142 [==============================] - ETA: 0s - loss: 0.1271 - accuracy: 0.7934\n",
            "Epoch 117: val_loss improved from 0.14351 to 0.14268, saving model to /content/weight/model.117-0.127067.hdf5\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 0.1271 - accuracy: 0.7934 - val_loss: 0.1427 - val_accuracy: 0.4493 - lr: 0.0020\n",
            "Epoch 118/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1271 - accuracy: 0.7779\n",
            "Epoch 118: val_loss improved from 0.14268 to 0.14208, saving model to /content/weight/model.118-0.127109.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1271 - accuracy: 0.7770 - val_loss: 0.1421 - val_accuracy: 0.4493 - lr: 0.0020\n",
            "Epoch 119/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1267 - accuracy: 0.7863\n",
            "Epoch 119: val_loss improved from 0.14208 to 0.14178, saving model to /content/weight/model.119-0.126739.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1267 - accuracy: 0.7868 - val_loss: 0.1418 - val_accuracy: 0.4457 - lr: 0.0020\n",
            "Epoch 120/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1263 - accuracy: 0.7832\n",
            "Epoch 120: val_loss improved from 0.14178 to 0.14115, saving model to /content/weight/model.120-0.126337.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1263 - accuracy: 0.7823 - val_loss: 0.1411 - val_accuracy: 0.4511 - lr: 0.0020\n",
            "Epoch 121/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1263 - accuracy: 0.7748\n",
            "Epoch 121: val_loss improved from 0.14115 to 0.14070, saving model to /content/weight/model.121-0.126276.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1263 - accuracy: 0.7757 - val_loss: 0.1407 - val_accuracy: 0.4511 - lr: 0.0020\n",
            "Epoch 122/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1262 - accuracy: 0.7660\n",
            "Epoch 122: val_loss improved from 0.14070 to 0.14007, saving model to /content/weight/model.122-0.126207.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1262 - accuracy: 0.7664 - val_loss: 0.1401 - val_accuracy: 0.4583 - lr: 0.0020\n",
            "Epoch 123/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1259 - accuracy: 0.7686\n",
            "Epoch 123: val_loss improved from 0.14007 to 0.13984, saving model to /content/weight/model.123-0.125894.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1259 - accuracy: 0.7687 - val_loss: 0.1398 - val_accuracy: 0.4583 - lr: 0.0020\n",
            "Epoch 124/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1257 - accuracy: 0.7708\n",
            "Epoch 124: val_loss improved from 0.13984 to 0.13968, saving model to /content/weight/model.124-0.125713.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1257 - accuracy: 0.7709 - val_loss: 0.1397 - val_accuracy: 0.4493 - lr: 0.0020\n",
            "Epoch 125/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1255 - accuracy: 0.7668\n",
            "Epoch 125: val_loss improved from 0.13968 to 0.13928, saving model to /content/weight/model.125-0.125512.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1255 - accuracy: 0.7669 - val_loss: 0.1393 - val_accuracy: 0.4457 - lr: 0.0020\n",
            "Epoch 126/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1253 - accuracy: 0.7744\n",
            "Epoch 126: val_loss improved from 0.13928 to 0.13893, saving model to /content/weight/model.126-0.125273.hdf5\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 0.1253 - accuracy: 0.7744 - val_loss: 0.1389 - val_accuracy: 0.4457 - lr: 0.0020\n",
            "Epoch 127/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1252 - accuracy: 0.7677\n",
            "Epoch 127: val_loss improved from 0.13893 to 0.13870, saving model to /content/weight/model.127-0.125233.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1252 - accuracy: 0.7669 - val_loss: 0.1387 - val_accuracy: 0.4493 - lr: 0.0020\n",
            "Epoch 128/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1252 - accuracy: 0.7651\n",
            "Epoch 128: val_loss improved from 0.13870 to 0.13854, saving model to /content/weight/model.128-0.125213.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1252 - accuracy: 0.7656 - val_loss: 0.1385 - val_accuracy: 0.4493 - lr: 0.0020\n",
            "Epoch 129/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1250 - accuracy: 0.7637\n",
            "Epoch 129: val_loss improved from 0.13854 to 0.13841, saving model to /content/weight/model.129-0.125059.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1251 - accuracy: 0.7634 - val_loss: 0.1384 - val_accuracy: 0.4402 - lr: 0.0020\n",
            "Epoch 130/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1251 - accuracy: 0.7606\n",
            "Epoch 130: val_loss improved from 0.13841 to 0.13818, saving model to /content/weight/model.130-0.125100.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1251 - accuracy: 0.7603 - val_loss: 0.1382 - val_accuracy: 0.4457 - lr: 0.0020\n",
            "Epoch 131/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1249 - accuracy: 0.7611\n",
            "Epoch 131: val_loss improved from 0.13818 to 0.13790, saving model to /content/weight/model.131-0.124899.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1249 - accuracy: 0.7611 - val_loss: 0.1379 - val_accuracy: 0.4475 - lr: 0.0020\n",
            "Epoch 132/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1250 - accuracy: 0.7593\n",
            "Epoch 132: val_loss improved from 0.13790 to 0.13783, saving model to /content/weight/model.132-0.125001.hdf5\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 0.1250 - accuracy: 0.7598 - val_loss: 0.1378 - val_accuracy: 0.4475 - lr: 0.0020\n",
            "Epoch 133/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1249 - accuracy: 0.7655\n",
            "Epoch 133: val_loss improved from 0.13783 to 0.13759, saving model to /content/weight/model.133-0.124858.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1249 - accuracy: 0.7656 - val_loss: 0.1376 - val_accuracy: 0.4457 - lr: 0.0020\n",
            "Epoch 134/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1247 - accuracy: 0.7660\n",
            "Epoch 134: val_loss did not improve from 0.13759\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1247 - accuracy: 0.7660 - val_loss: 0.1376 - val_accuracy: 0.4384 - lr: 0.0020\n",
            "Epoch 135/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1246 - accuracy: 0.7646\n",
            "Epoch 135: val_loss improved from 0.13759 to 0.13738, saving model to /content/weight/model.135-0.124572.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1246 - accuracy: 0.7647 - val_loss: 0.1374 - val_accuracy: 0.4457 - lr: 0.0020\n",
            "Epoch 136/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1246 - accuracy: 0.7589\n",
            "Epoch 136: val_loss improved from 0.13738 to 0.13730, saving model to /content/weight/model.136-0.124566.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1246 - accuracy: 0.7594 - val_loss: 0.1373 - val_accuracy: 0.4402 - lr: 0.0020\n",
            "Epoch 137/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1247 - accuracy: 0.7491\n",
            "Epoch 137: val_loss improved from 0.13730 to 0.13712, saving model to /content/weight/model.137-0.124725.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1247 - accuracy: 0.7501 - val_loss: 0.1371 - val_accuracy: 0.4438 - lr: 0.0020\n",
            "Epoch 138/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1245 - accuracy: 0.7571\n",
            "Epoch 138: val_loss did not improve from 0.13712\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1245 - accuracy: 0.7567 - val_loss: 0.1372 - val_accuracy: 0.4438 - lr: 0.0020\n",
            "Epoch 139/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1245 - accuracy: 0.7575\n",
            "Epoch 139: val_loss improved from 0.13712 to 0.13700, saving model to /content/weight/model.139-0.124558.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1246 - accuracy: 0.7572 - val_loss: 0.1370 - val_accuracy: 0.4457 - lr: 0.0020\n",
            "Epoch 140/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1244 - accuracy: 0.7549\n",
            "Epoch 140: val_loss improved from 0.13700 to 0.13699, saving model to /content/weight/model.140-0.124396.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1244 - accuracy: 0.7550 - val_loss: 0.1370 - val_accuracy: 0.4384 - lr: 0.0020\n",
            "Epoch 141/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1247 - accuracy: 0.7535\n",
            "Epoch 141: val_loss improved from 0.13699 to 0.13696, saving model to /content/weight/model.141-0.124649.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1246 - accuracy: 0.7545 - val_loss: 0.1370 - val_accuracy: 0.4438 - lr: 0.0020\n",
            "Epoch 142/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1244 - accuracy: 0.7522\n",
            "Epoch 142: val_loss improved from 0.13696 to 0.13670, saving model to /content/weight/model.142-0.124368.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1244 - accuracy: 0.7519 - val_loss: 0.1367 - val_accuracy: 0.4420 - lr: 0.0020\n",
            "Epoch 143/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1245 - accuracy: 0.7589\n",
            "Epoch 143: val_loss improved from 0.13670 to 0.13668, saving model to /content/weight/model.143-0.124440.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1244 - accuracy: 0.7598 - val_loss: 0.1367 - val_accuracy: 0.4420 - lr: 0.0020\n",
            "Epoch 144/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1244 - accuracy: 0.7522\n",
            "Epoch 144: val_loss improved from 0.13668 to 0.13665, saving model to /content/weight/model.144-0.124403.hdf5\n",
            "142/142 [==============================] - 5s 33ms/step - loss: 0.1244 - accuracy: 0.7519 - val_loss: 0.1366 - val_accuracy: 0.4384 - lr: 0.0020\n",
            "Epoch 145/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1245 - accuracy: 0.7438\n",
            "Epoch 145: val_loss did not improve from 0.13665\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1245 - accuracy: 0.7439 - val_loss: 0.1367 - val_accuracy: 0.4384 - lr: 0.0020\n",
            "Epoch 146/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1245 - accuracy: 0.7518\n",
            "Epoch 146: val_loss improved from 0.13665 to 0.13648, saving model to /content/weight/model.146-0.124608.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1246 - accuracy: 0.7506 - val_loss: 0.1365 - val_accuracy: 0.4457 - lr: 0.0020\n",
            "Epoch 147/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1244 - accuracy: 0.7482\n",
            "Epoch 147: val_loss improved from 0.13648 to 0.13639, saving model to /content/weight/model.147-0.124437.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1244 - accuracy: 0.7483 - val_loss: 0.1364 - val_accuracy: 0.4438 - lr: 0.0020\n",
            "Epoch 148/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1244 - accuracy: 0.7442\n",
            "Epoch 148: val_loss did not improve from 0.13639\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1245 - accuracy: 0.7439 - val_loss: 0.1364 - val_accuracy: 0.4420 - lr: 0.0020\n",
            "Epoch 149/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.7544\n",
            "Epoch 149: val_loss improved from 0.13639 to 0.13625, saving model to /content/weight/model.149-0.124200.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1242 - accuracy: 0.7541 - val_loss: 0.1363 - val_accuracy: 0.4457 - lr: 0.0020\n",
            "Epoch 150/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1244 - accuracy: 0.7491\n",
            "Epoch 150: val_loss did not improve from 0.13625\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1244 - accuracy: 0.7497 - val_loss: 0.1363 - val_accuracy: 0.4438 - lr: 0.0020\n",
            "Epoch 151/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1244 - accuracy: 0.7491\n",
            "Epoch 151: val_loss did not improve from 0.13625\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1244 - accuracy: 0.7488 - val_loss: 0.1363 - val_accuracy: 0.4420 - lr: 0.0020\n",
            "Epoch 152/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1243 - accuracy: 0.7456\n",
            "Epoch 152: val_loss improved from 0.13625 to 0.13618, saving model to /content/weight/model.152-0.124294.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1243 - accuracy: 0.7448 - val_loss: 0.1362 - val_accuracy: 0.4475 - lr: 0.0020\n",
            "Epoch 153/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.7447\n",
            "Epoch 153: val_loss improved from 0.13618 to 0.13611, saving model to /content/weight/model.153-0.124215.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1242 - accuracy: 0.7453 - val_loss: 0.1361 - val_accuracy: 0.4475 - lr: 0.0020\n",
            "Epoch 154/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.7460\n",
            "Epoch 154: val_loss improved from 0.13611 to 0.13605, saving model to /content/weight/model.154-0.124248.hdf5\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1242 - accuracy: 0.7453 - val_loss: 0.1360 - val_accuracy: 0.4457 - lr: 0.0020\n",
            "Epoch 155/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.7535\n",
            "Epoch 155: val_loss improved from 0.13605 to 0.13601, saving model to /content/weight/model.155-0.124164.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1242 - accuracy: 0.7536 - val_loss: 0.1360 - val_accuracy: 0.4511 - lr: 0.0020\n",
            "Epoch 156/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1243 - accuracy: 0.7398\n",
            "Epoch 156: val_loss improved from 0.13601 to 0.13599, saving model to /content/weight/model.156-0.124280.hdf5\n",
            "142/142 [==============================] - 5s 34ms/step - loss: 0.1243 - accuracy: 0.7404 - val_loss: 0.1360 - val_accuracy: 0.4493 - lr: 0.0020\n",
            "Epoch 157/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1244 - accuracy: 0.7389\n",
            "Epoch 157: val_loss did not improve from 0.13599\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1243 - accuracy: 0.7395 - val_loss: 0.1362 - val_accuracy: 0.4384 - lr: 0.0020\n",
            "Epoch 158/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.7318\n",
            "Epoch 158: val_loss improved from 0.13599 to 0.13595, saving model to /content/weight/model.158-0.124172.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1242 - accuracy: 0.7325 - val_loss: 0.1360 - val_accuracy: 0.4457 - lr: 0.0020\n",
            "Epoch 159/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1241 - accuracy: 0.7416\n",
            "Epoch 159: val_loss did not improve from 0.13595\n",
            "142/142 [==============================] - 5s 34ms/step - loss: 0.1241 - accuracy: 0.7422 - val_loss: 0.1362 - val_accuracy: 0.4384 - lr: 0.0020\n",
            "Epoch 160/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.7371\n",
            "Epoch 160: val_loss did not improve from 0.13595\n",
            "\n",
            "Epoch 160: ReduceLROnPlateau reducing learning rate to 0.0003999999724328518.\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1242 - accuracy: 0.7373 - val_loss: 0.1360 - val_accuracy: 0.4457 - lr: 0.0020\n",
            "Epoch 161/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.7340\n",
            "Epoch 161: val_loss improved from 0.13595 to 0.13592, saving model to /content/weight/model.161-0.124163.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1242 - accuracy: 0.7333 - val_loss: 0.1359 - val_accuracy: 0.4366 - lr: 4.0000e-04\n",
            "Epoch 162/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1241 - accuracy: 0.7442\n",
            "Epoch 162: val_loss improved from 0.13592 to 0.13592, saving model to /content/weight/model.162-0.124128.hdf5\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1241 - accuracy: 0.7439 - val_loss: 0.1359 - val_accuracy: 0.4402 - lr: 4.0000e-04\n",
            "Epoch 163/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.7402\n",
            "Epoch 163: val_loss improved from 0.13592 to 0.13587, saving model to /content/weight/model.163-0.124208.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1242 - accuracy: 0.7391 - val_loss: 0.1359 - val_accuracy: 0.4438 - lr: 4.0000e-04\n",
            "Epoch 164/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1241 - accuracy: 0.7385\n",
            "Epoch 164: val_loss did not improve from 0.13587\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1241 - accuracy: 0.7382 - val_loss: 0.1359 - val_accuracy: 0.4402 - lr: 4.0000e-04\n",
            "Epoch 165/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1241 - accuracy: 0.7380\n",
            "Epoch 165: val_loss did not improve from 0.13587\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1241 - accuracy: 0.7386 - val_loss: 0.1359 - val_accuracy: 0.4384 - lr: 4.0000e-04\n",
            "Epoch 166/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1240 - accuracy: 0.7456\n",
            "Epoch 166: val_loss improved from 0.13587 to 0.13584, saving model to /content/weight/model.166-0.124022.hdf5\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1240 - accuracy: 0.7461 - val_loss: 0.1358 - val_accuracy: 0.4402 - lr: 4.0000e-04\n",
            "Epoch 167/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1241 - accuracy: 0.7447\n",
            "Epoch 167: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1242 - accuracy: 0.7439 - val_loss: 0.1359 - val_accuracy: 0.4402 - lr: 4.0000e-04\n",
            "Epoch 168/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1241 - accuracy: 0.7447\n",
            "Epoch 168: val_loss did not improve from 0.13584\n",
            "\n",
            "Epoch 168: ReduceLROnPlateau reducing learning rate to 7.999999215826393e-05.\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1241 - accuracy: 0.7439 - val_loss: 0.1359 - val_accuracy: 0.4384 - lr: 4.0000e-04\n",
            "Epoch 169/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1241 - accuracy: 0.7473\n",
            "Epoch 169: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1241 - accuracy: 0.7479 - val_loss: 0.1358 - val_accuracy: 0.4420 - lr: 8.0000e-05\n",
            "Epoch 170/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1240 - accuracy: 0.7425\n",
            "Epoch 170: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1240 - accuracy: 0.7408 - val_loss: 0.1359 - val_accuracy: 0.4384 - lr: 8.0000e-05\n",
            "Epoch 171/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.7465\n",
            "Epoch 171: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1241 - accuracy: 0.7470 - val_loss: 0.1359 - val_accuracy: 0.4384 - lr: 8.0000e-05\n",
            "Epoch 172/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.7425\n",
            "Epoch 172: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1242 - accuracy: 0.7435 - val_loss: 0.1359 - val_accuracy: 0.4402 - lr: 8.0000e-05\n",
            "Epoch 173/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1241 - accuracy: 0.7363\n",
            "Epoch 173: val_loss did not improve from 0.13584\n",
            "\n",
            "Epoch 173: ReduceLROnPlateau reducing learning rate to 1.599999814061448e-05.\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1241 - accuracy: 0.7369 - val_loss: 0.1359 - val_accuracy: 0.4384 - lr: 8.0000e-05\n",
            "Epoch 174/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1240 - accuracy: 0.7354\n",
            "Epoch 174: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1240 - accuracy: 0.7355 - val_loss: 0.1359 - val_accuracy: 0.4384 - lr: 1.6000e-05\n",
            "Epoch 175/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1241 - accuracy: 0.7394\n",
            "Epoch 175: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1241 - accuracy: 0.7386 - val_loss: 0.1359 - val_accuracy: 0.4420 - lr: 1.6000e-05\n",
            "Epoch 176/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1240 - accuracy: 0.7389\n",
            "Epoch 176: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1240 - accuracy: 0.7395 - val_loss: 0.1359 - val_accuracy: 0.4366 - lr: 1.6000e-05\n",
            "Epoch 177/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1241 - accuracy: 0.7385\n",
            "Epoch 177: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1241 - accuracy: 0.7391 - val_loss: 0.1359 - val_accuracy: 0.4366 - lr: 1.6000e-05\n",
            "Epoch 178/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1240 - accuracy: 0.7460\n",
            "Epoch 178: val_loss did not improve from 0.13584\n",
            "\n",
            "Epoch 178: ReduceLROnPlateau reducing learning rate to 3.199999628122896e-06.\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1240 - accuracy: 0.7461 - val_loss: 0.1359 - val_accuracy: 0.4384 - lr: 1.6000e-05\n",
            "Epoch 179/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1240 - accuracy: 0.7442\n",
            "Epoch 179: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1240 - accuracy: 0.7439 - val_loss: 0.1359 - val_accuracy: 0.4384 - lr: 3.2000e-06\n",
            "Epoch 180/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1240 - accuracy: 0.7429\n",
            "Epoch 180: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1240 - accuracy: 0.7422 - val_loss: 0.1359 - val_accuracy: 0.4384 - lr: 3.2000e-06\n",
            "Epoch 181/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1243 - accuracy: 0.7411\n",
            "Epoch 181: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1243 - accuracy: 0.7404 - val_loss: 0.1359 - val_accuracy: 0.4402 - lr: 3.2000e-06\n",
            "Epoch 182/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.7389\n",
            "Epoch 182: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1242 - accuracy: 0.7386 - val_loss: 0.1360 - val_accuracy: 0.4384 - lr: 3.2000e-06\n",
            "Epoch 183/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1241 - accuracy: 0.7411\n",
            "Epoch 183: val_loss did not improve from 0.13584\n",
            "\n",
            "Epoch 183: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
            "142/142 [==============================] - 5s 32ms/step - loss: 0.1241 - accuracy: 0.7400 - val_loss: 0.1359 - val_accuracy: 0.4384 - lr: 3.2000e-06\n",
            "Epoch 184/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1241 - accuracy: 0.7402\n",
            "Epoch 184: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 4s 31ms/step - loss: 0.1241 - accuracy: 0.7404 - val_loss: 0.1359 - val_accuracy: 0.4384 - lr: 1.0000e-06\n",
            "Epoch 185/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.7340\n",
            "Epoch 185: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 4s 30ms/step - loss: 0.1242 - accuracy: 0.7347 - val_loss: 0.1359 - val_accuracy: 0.4384 - lr: 1.0000e-06\n",
            "Epoch 186/200\n",
            "141/142 [============================>.] - ETA: 0s - loss: 0.1242 - accuracy: 0.7371\n",
            "Epoch 186: val_loss did not improve from 0.13584\n",
            "142/142 [==============================] - 5s 34ms/step - loss: 0.1241 - accuracy: 0.7373 - val_loss: 0.1359 - val_accuracy: 0.4384 - lr: 1.0000e-06\n",
            "Epoch 186: early stopping\n",
            "Train: 0.666, Test: 0.438\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3VITO6-VEZYI"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}