Children vs. Adults Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "426a99f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "import tensorflow as tf\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
    "from keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint\n",
    "from keras.models import Sequential, load_model\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "57e2f039",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 680 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(rescale=1/255.,\n",
    "                                  horizontal_flip=True,\n",
    "                                  shear_range=0.2,\n",
    "                                  height_shift_range=0.2,\n",
    "                                  width_shift_range=0.2)\n",
    "\n",
    "train = train_datagen.flow_from_directory('train/',\n",
    "                                         batch_size=32,\n",
    "                                         target_size=(128,128),\n",
    "                                         class_mode='binary',\n",
    "                                         shuffle=True,\n",
    "                                         interpolation='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bda9fac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 120 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "test_datagen = ImageDataGenerator(rescale=1/255.)\n",
    "\n",
    "test = test_datagen.flow_from_directory('test/',\n",
    "                                        batch_size=32,\n",
    "                                        target_size=(128,128),\n",
    "                                        class_mode='binary',\n",
    "                                        shuffle=True,\n",
    "                                        interpolation='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f365ac47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_9\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_40 (Conv2D)          (None, 126, 126, 32)      896       \n",
      "                                                                 \n",
      " max_pooling2d_37 (MaxPoolin  (None, 63, 63, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_41 (Conv2D)          (None, 61, 61, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_38 (MaxPoolin  (None, 30, 30, 64)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_42 (Conv2D)          (None, 28, 28, 128)       73856     \n",
      "                                                                 \n",
      " max_pooling2d_39 (MaxPoolin  (None, 14, 14, 128)      0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_6 (Flatten)         (None, 25088)             0         \n",
      "                                                                 \n",
      " dense_18 (Dense)            (None, 64)                1605696   \n",
      "                                                                 \n",
      " dense_19 (Dense)            (None, 128)               8320      \n",
      "                                                                 \n",
      " dense_20 (Dense)            (None, 1)                 129       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,707,393\n",
      "Trainable params: 1,707,393\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn = Sequential()\n",
    "\n",
    "cnn.add(Conv2D(filters=32,kernel_size=(3,3),activation='relu',input_shape=(128,128,3)))\n",
    "cnn.add(MaxPooling2D(pool_size=(2,2)))\n",
    "\n",
    "cnn.add(Conv2D(filters=64,kernel_size=(3,3),activation='relu'))\n",
    "cnn.add(MaxPooling2D(pool_size=(2,2)))\n",
    "\n",
    "cnn.add(Conv2D(filters=128,kernel_size=(3,3),activation='relu'))\n",
    "cnn.add(MaxPooling2D(pool_size=(2,2)))\n",
    "\n",
    "# cnn.add(Conv2D(filters=256,kernel_size=(3,3),activation='relu'))\n",
    "# cnn.add(MaxPooling2D(pool_size=(2,2)))\n",
    "# cnn.add(BatchNormalization())\n",
    "# cnn.add(Dropout(0.3))\n",
    "\n",
    "cnn.add(Flatten())\n",
    "\n",
    "cnn.add(Dense(units=64,activation='relu'))\n",
    "cnn.add(Dense(units=128,activation='relu'))\n",
    "cnn.add(Dense(units=1,activation='sigmoid'))\n",
    "cnn.compile(loss='binary_crossentropy',optimizer='adam',metrics='accuracy')\n",
    "cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d223d94d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1992 - accuracy: 0.9412\n",
      "Epoch 1: val_accuracy improved from -inf to 0.71667, saving model to checkpoint\\\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: checkpoint\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: checkpoint\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 13s 580ms/step - loss: 0.1992 - accuracy: 0.9412 - val_loss: 1.0887 - val_accuracy: 0.7167 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1821 - accuracy: 0.9279\n",
      "Epoch 2: val_accuracy improved from 0.71667 to 0.75833, saving model to checkpoint\\\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: checkpoint\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: checkpoint\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 14s 628ms/step - loss: 0.1821 - accuracy: 0.9279 - val_loss: 1.0866 - val_accuracy: 0.7583 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1747 - accuracy: 0.9235\n",
      "Epoch 3: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 12s 549ms/step - loss: 0.1747 - accuracy: 0.9235 - val_loss: 1.1148 - val_accuracy: 0.7167 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1892 - accuracy: 0.9147\n",
      "Epoch 4: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 499ms/step - loss: 0.1892 - accuracy: 0.9147 - val_loss: 1.1259 - val_accuracy: 0.7417 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1576 - accuracy: 0.9397\n",
      "Epoch 5: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 486ms/step - loss: 0.1576 - accuracy: 0.9397 - val_loss: 1.1656 - val_accuracy: 0.6833 - lr: 0.0010\n",
      "Epoch 6/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1812 - accuracy: 0.9353\n",
      "Epoch 6: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 496ms/step - loss: 0.1812 - accuracy: 0.9353 - val_loss: 1.1994 - val_accuracy: 0.7000 - lr: 0.0010\n",
      "Epoch 7/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1989 - accuracy: 0.9147\n",
      "Epoch 7: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 496ms/step - loss: 0.1989 - accuracy: 0.9147 - val_loss: 1.4081 - val_accuracy: 0.6917 - lr: 0.0010\n",
      "Epoch 8/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1677 - accuracy: 0.9250\n",
      "Epoch 8: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 493ms/step - loss: 0.1677 - accuracy: 0.9250 - val_loss: 1.2381 - val_accuracy: 0.7083 - lr: 0.0010\n",
      "Epoch 9/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1692 - accuracy: 0.9250\n",
      "Epoch 9: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 495ms/step - loss: 0.1692 - accuracy: 0.9250 - val_loss: 1.2158 - val_accuracy: 0.7250 - lr: 0.0010\n",
      "Epoch 10/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1781 - accuracy: 0.9294\n",
      "Epoch 10: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 498ms/step - loss: 0.1781 - accuracy: 0.9294 - val_loss: 1.3789 - val_accuracy: 0.6833 - lr: 0.0010\n",
      "Epoch 11/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1761 - accuracy: 0.9191\n",
      "Epoch 11: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 494ms/step - loss: 0.1761 - accuracy: 0.9191 - val_loss: 1.2315 - val_accuracy: 0.6833 - lr: 0.0010\n",
      "Epoch 12/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1780 - accuracy: 0.9368\n",
      "Epoch 12: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 497ms/step - loss: 0.1780 - accuracy: 0.9368 - val_loss: 1.4837 - val_accuracy: 0.6667 - lr: 0.0010\n",
      "Epoch 13/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.2037 - accuracy: 0.9162\n",
      "Epoch 13: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 507ms/step - loss: 0.2037 - accuracy: 0.9162 - val_loss: 1.0553 - val_accuracy: 0.7167 - lr: 0.0010\n",
      "Epoch 14/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1470 - accuracy: 0.9426\n",
      "Epoch 14: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 12s 528ms/step - loss: 0.1470 - accuracy: 0.9426 - val_loss: 1.1675 - val_accuracy: 0.6917 - lr: 0.0010\n",
      "Epoch 15/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1875 - accuracy: 0.9368\n",
      "Epoch 15: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 12s 528ms/step - loss: 0.1875 - accuracy: 0.9368 - val_loss: 1.1510 - val_accuracy: 0.7583 - lr: 0.0010\n",
      "Epoch 16/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1434 - accuracy: 0.9412\n",
      "Epoch 16: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 512ms/step - loss: 0.1434 - accuracy: 0.9412 - val_loss: 0.9901 - val_accuracy: 0.7500 - lr: 0.0010\n",
      "Epoch 17/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1819 - accuracy: 0.9382\n",
      "Epoch 17: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 492ms/step - loss: 0.1819 - accuracy: 0.9382 - val_loss: 1.4011 - val_accuracy: 0.7167 - lr: 0.0010\n",
      "Epoch 18/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1437 - accuracy: 0.9544\n",
      "Epoch 18: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 494ms/step - loss: 0.1437 - accuracy: 0.9544 - val_loss: 1.3070 - val_accuracy: 0.7333 - lr: 0.0010\n",
      "Epoch 19/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1723 - accuracy: 0.9368\n",
      "Epoch 19: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 494ms/step - loss: 0.1723 - accuracy: 0.9368 - val_loss: 1.4127 - val_accuracy: 0.6667 - lr: 0.0010\n",
      "Epoch 20/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1366 - accuracy: 0.9441\n",
      "Epoch 20: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 493ms/step - loss: 0.1366 - accuracy: 0.9441 - val_loss: 1.3709 - val_accuracy: 0.7500 - lr: 0.0010\n",
      "Epoch 21/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1660 - accuracy: 0.9338\n",
      "Epoch 21: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 489ms/step - loss: 0.1660 - accuracy: 0.9338 - val_loss: 1.1356 - val_accuracy: 0.6750 - lr: 0.0010\n",
      "Epoch 22/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1404 - accuracy: 0.9441\n",
      "Epoch 22: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 520ms/step - loss: 0.1404 - accuracy: 0.9441 - val_loss: 1.2305 - val_accuracy: 0.7417 - lr: 0.0010\n",
      "Epoch 23/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1188 - accuracy: 0.9588\n",
      "Epoch 23: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 495ms/step - loss: 0.1188 - accuracy: 0.9588 - val_loss: 1.2460 - val_accuracy: 0.7583 - lr: 0.0010\n",
      "Epoch 24/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1842 - accuracy: 0.9235\n",
      "Epoch 24: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 493ms/step - loss: 0.1842 - accuracy: 0.9235 - val_loss: 1.1125 - val_accuracy: 0.7417 - lr: 0.0010\n",
      "Epoch 25/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1482 - accuracy: 0.9471\n",
      "Epoch 25: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 496ms/step - loss: 0.1482 - accuracy: 0.9471 - val_loss: 1.2243 - val_accuracy: 0.7083 - lr: 0.0010\n",
      "Epoch 26/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1630 - accuracy: 0.9309\n",
      "Epoch 26: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 493ms/step - loss: 0.1630 - accuracy: 0.9309 - val_loss: 1.2787 - val_accuracy: 0.7250 - lr: 0.0010\n",
      "Epoch 27/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1305 - accuracy: 0.9441\n",
      "Epoch 27: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 488ms/step - loss: 0.1305 - accuracy: 0.9441 - val_loss: 1.5794 - val_accuracy: 0.7167 - lr: 0.0010\n",
      "Epoch 28/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1614 - accuracy: 0.9397\n",
      "Epoch 28: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 497ms/step - loss: 0.1614 - accuracy: 0.9397 - val_loss: 1.3808 - val_accuracy: 0.7000 - lr: 0.0010\n",
      "Epoch 29/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1846 - accuracy: 0.9279\n",
      "Epoch 29: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 495ms/step - loss: 0.1846 - accuracy: 0.9279 - val_loss: 1.5124 - val_accuracy: 0.6750 - lr: 0.0010\n",
      "Epoch 30/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1171 - accuracy: 0.9515\n",
      "Epoch 30: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 491ms/step - loss: 0.1171 - accuracy: 0.9515 - val_loss: 1.4443 - val_accuracy: 0.7000 - lr: 0.0010\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 31/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1071 - accuracy: 0.9588\n",
      "Epoch 31: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 491ms/step - loss: 0.1071 - accuracy: 0.9588 - val_loss: 1.5923 - val_accuracy: 0.7000 - lr: 0.0010\n",
      "Epoch 32/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1308 - accuracy: 0.9500\n",
      "Epoch 32: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 498ms/step - loss: 0.1308 - accuracy: 0.9500 - val_loss: 1.6055 - val_accuracy: 0.7083 - lr: 0.0010\n",
      "Epoch 33/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1326 - accuracy: 0.9471\n",
      "Epoch 33: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 491ms/step - loss: 0.1326 - accuracy: 0.9471 - val_loss: 1.5535 - val_accuracy: 0.7167 - lr: 0.0010\n",
      "Epoch 34/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1295 - accuracy: 0.9412\n",
      "Epoch 34: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 493ms/step - loss: 0.1295 - accuracy: 0.9412 - val_loss: 1.4082 - val_accuracy: 0.7083 - lr: 0.0010\n",
      "Epoch 35/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1153 - accuracy: 0.9529\n",
      "Epoch 35: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 494ms/step - loss: 0.1153 - accuracy: 0.9529 - val_loss: 1.4967 - val_accuracy: 0.6917 - lr: 0.0010\n",
      "Epoch 36/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0977 - accuracy: 0.9662\n",
      "Epoch 36: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 502ms/step - loss: 0.0977 - accuracy: 0.9662 - val_loss: 1.5044 - val_accuracy: 0.6917 - lr: 0.0010\n",
      "Epoch 37/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1672 - accuracy: 0.9382\n",
      "Epoch 37: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 497ms/step - loss: 0.1672 - accuracy: 0.9382 - val_loss: 1.6065 - val_accuracy: 0.7000 - lr: 0.0010\n",
      "Epoch 38/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0999 - accuracy: 0.9662\n",
      "Epoch 38: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 496ms/step - loss: 0.0999 - accuracy: 0.9662 - val_loss: 1.6482 - val_accuracy: 0.7167 - lr: 0.0010\n",
      "Epoch 39/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1092 - accuracy: 0.9574\n",
      "Epoch 39: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 494ms/step - loss: 0.1092 - accuracy: 0.9574 - val_loss: 1.8745 - val_accuracy: 0.6917 - lr: 0.0010\n",
      "Epoch 40/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1016 - accuracy: 0.9574\n",
      "Epoch 40: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 495ms/step - loss: 0.1016 - accuracy: 0.9574 - val_loss: 1.4054 - val_accuracy: 0.7167 - lr: 0.0010\n",
      "Epoch 41/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1212 - accuracy: 0.9574\n",
      "Epoch 41: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 495ms/step - loss: 0.1212 - accuracy: 0.9574 - val_loss: 1.7960 - val_accuracy: 0.7000 - lr: 0.0010\n",
      "Epoch 42/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1954 - accuracy: 0.9279\n",
      "Epoch 42: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 495ms/step - loss: 0.1954 - accuracy: 0.9279 - val_loss: 1.3883 - val_accuracy: 0.6917 - lr: 0.0010\n",
      "Epoch 43/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1409 - accuracy: 0.9338\n",
      "Epoch 43: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 492ms/step - loss: 0.1409 - accuracy: 0.9338 - val_loss: 1.2351 - val_accuracy: 0.7250 - lr: 0.0010\n",
      "Epoch 44/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1432 - accuracy: 0.9456\n",
      "Epoch 44: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 511ms/step - loss: 0.1432 - accuracy: 0.9456 - val_loss: 1.3087 - val_accuracy: 0.7250 - lr: 0.0010\n",
      "Epoch 45/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1205 - accuracy: 0.9544\n",
      "Epoch 45: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 494ms/step - loss: 0.1205 - accuracy: 0.9544 - val_loss: 1.3517 - val_accuracy: 0.7167 - lr: 0.0010\n",
      "Epoch 46/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1074 - accuracy: 0.9529\n",
      "Epoch 46: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 492ms/step - loss: 0.1074 - accuracy: 0.9529 - val_loss: 1.4972 - val_accuracy: 0.7500 - lr: 0.0010\n",
      "Epoch 47/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0936 - accuracy: 0.9662\n",
      "Epoch 47: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 501ms/step - loss: 0.0936 - accuracy: 0.9662 - val_loss: 1.8566 - val_accuracy: 0.7333 - lr: 0.0010\n",
      "Epoch 48/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1004 - accuracy: 0.9588\n",
      "Epoch 48: val_accuracy did not improve from 0.75833\n",
      "22/22 [==============================] - 11s 491ms/step - loss: 0.1004 - accuracy: 0.9588 - val_loss: 1.6499 - val_accuracy: 0.7583 - lr: 0.0010\n",
      "Epoch 49/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0985 - accuracy: 0.9662\n",
      "Epoch 49: val_accuracy improved from 0.75833 to 0.76667, saving model to checkpoint\\\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: checkpoint\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: checkpoint\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 14s 631ms/step - loss: 0.0985 - accuracy: 0.9662 - val_loss: 1.8068 - val_accuracy: 0.7667 - lr: 0.0010\n",
      "Epoch 50/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1952 - accuracy: 0.9324\n",
      "Epoch 50: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 16s 699ms/step - loss: 0.1952 - accuracy: 0.9324 - val_loss: 1.9694 - val_accuracy: 0.6917 - lr: 0.0010\n",
      "Epoch 51/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.2410 - accuracy: 0.9074\n",
      "Epoch 51: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 14s 643ms/step - loss: 0.2410 - accuracy: 0.9074 - val_loss: 1.8615 - val_accuracy: 0.6583 - lr: 0.0010\n",
      "Epoch 52/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.2186 - accuracy: 0.9176\n",
      "Epoch 52: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 607ms/step - loss: 0.2186 - accuracy: 0.9176 - val_loss: 1.2763 - val_accuracy: 0.7083 - lr: 0.0010\n",
      "Epoch 53/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1189 - accuracy: 0.9559\n",
      "Epoch 53: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 16s 716ms/step - loss: 0.1189 - accuracy: 0.9559 - val_loss: 1.3145 - val_accuracy: 0.7083 - lr: 0.0010\n",
      "Epoch 54/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1376 - accuracy: 0.9529\n",
      "Epoch 54: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 16s 704ms/step - loss: 0.1376 - accuracy: 0.9529 - val_loss: 1.1640 - val_accuracy: 0.7500 - lr: 0.0010\n",
      "Epoch 55/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0875 - accuracy: 0.9706\n",
      "Epoch 55: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 15s 664ms/step - loss: 0.0875 - accuracy: 0.9706 - val_loss: 1.3241 - val_accuracy: 0.7583 - lr: 0.0010\n",
      "Epoch 56/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0960 - accuracy: 0.9574\n",
      "Epoch 56: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 14s 625ms/step - loss: 0.0960 - accuracy: 0.9574 - val_loss: 1.3116 - val_accuracy: 0.7667 - lr: 0.0010\n",
      "Epoch 57/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1097 - accuracy: 0.9544\n",
      "Epoch 57: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 611ms/step - loss: 0.1097 - accuracy: 0.9544 - val_loss: 1.2014 - val_accuracy: 0.7417 - lr: 0.0010\n",
      "Epoch 58/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0936 - accuracy: 0.9706\n",
      "Epoch 58: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 582ms/step - loss: 0.0936 - accuracy: 0.9706 - val_loss: 1.1065 - val_accuracy: 0.7500 - lr: 0.0010\n",
      "Epoch 59/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0764 - accuracy: 0.9735\n",
      "Epoch 59: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 14s 641ms/step - loss: 0.0764 - accuracy: 0.9735 - val_loss: 1.3119 - val_accuracy: 0.7500 - lr: 0.0010\n",
      "Epoch 60/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0941 - accuracy: 0.9647\n",
      "Epoch 60: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 586ms/step - loss: 0.0941 - accuracy: 0.9647 - val_loss: 1.3379 - val_accuracy: 0.7417 - lr: 0.0010\n",
      "Epoch 61/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0765 - accuracy: 0.9721\n",
      "Epoch 61: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 616ms/step - loss: 0.0765 - accuracy: 0.9721 - val_loss: 1.5880 - val_accuracy: 0.7583 - lr: 0.0010\n",
      "Epoch 62/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0938 - accuracy: 0.9676\n",
      "Epoch 62: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 589ms/step - loss: 0.0938 - accuracy: 0.9676 - val_loss: 1.3359 - val_accuracy: 0.7667 - lr: 0.0010\n",
      "Epoch 63/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1555 - accuracy: 0.9485\n",
      "Epoch 63: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 14s 605ms/step - loss: 0.1555 - accuracy: 0.9485 - val_loss: 1.2702 - val_accuracy: 0.7333 - lr: 0.0010\n",
      "Epoch 64/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0999 - accuracy: 0.9603\n",
      "Epoch 64: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 15s 700ms/step - loss: 0.0999 - accuracy: 0.9603 - val_loss: 1.3669 - val_accuracy: 0.7333 - lr: 0.0010\n",
      "Epoch 65/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0842 - accuracy: 0.9662\n",
      "Epoch 65: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 15s 658ms/step - loss: 0.0842 - accuracy: 0.9662 - val_loss: 1.4897 - val_accuracy: 0.7333 - lr: 0.0010\n",
      "Epoch 66/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0990 - accuracy: 0.9559\n",
      "Epoch 66: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 15s 666ms/step - loss: 0.0990 - accuracy: 0.9559 - val_loss: 1.8203 - val_accuracy: 0.6667 - lr: 0.0010\n",
      "Epoch 67/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0793 - accuracy: 0.9676\n",
      "Epoch 67: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 603ms/step - loss: 0.0793 - accuracy: 0.9676 - val_loss: 1.4020 - val_accuracy: 0.7167 - lr: 0.0010\n",
      "Epoch 68/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0598 - accuracy: 0.9765\n",
      "Epoch 68: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 598ms/step - loss: 0.0598 - accuracy: 0.9765 - val_loss: 1.4738 - val_accuracy: 0.7333 - lr: 0.0010\n",
      "Epoch 69/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0772 - accuracy: 0.9735\n",
      "Epoch 69: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 588ms/step - loss: 0.0772 - accuracy: 0.9735 - val_loss: 1.5356 - val_accuracy: 0.6917 - lr: 0.0010\n",
      "Epoch 70/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1225 - accuracy: 0.9529\n",
      "Epoch 70: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 590ms/step - loss: 0.1225 - accuracy: 0.9529 - val_loss: 1.4335 - val_accuracy: 0.6750 - lr: 0.0010\n",
      "Epoch 71/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0945 - accuracy: 0.9676\n",
      "Epoch 71: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 14s 635ms/step - loss: 0.0945 - accuracy: 0.9676 - val_loss: 1.4123 - val_accuracy: 0.7250 - lr: 0.0010\n",
      "Epoch 72/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0762 - accuracy: 0.9691\n",
      "Epoch 72: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 609ms/step - loss: 0.0762 - accuracy: 0.9691 - val_loss: 1.6387 - val_accuracy: 0.7333 - lr: 0.0010\n",
      "Epoch 73/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0826 - accuracy: 0.9676\n",
      "Epoch 73: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 578ms/step - loss: 0.0826 - accuracy: 0.9676 - val_loss: 1.6077 - val_accuracy: 0.7667 - lr: 0.0010\n",
      "Epoch 74/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1193 - accuracy: 0.9588\n",
      "Epoch 74: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 607ms/step - loss: 0.1193 - accuracy: 0.9588 - val_loss: 1.2984 - val_accuracy: 0.7083 - lr: 0.0010\n",
      "Epoch 75/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0886 - accuracy: 0.9632\n",
      "Epoch 75: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 613ms/step - loss: 0.0886 - accuracy: 0.9632 - val_loss: 1.2573 - val_accuracy: 0.7083 - lr: 0.0010\n",
      "Epoch 76/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0622 - accuracy: 0.9721\n",
      "Epoch 76: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 588ms/step - loss: 0.0622 - accuracy: 0.9721 - val_loss: 1.6324 - val_accuracy: 0.6833 - lr: 0.0010\n",
      "Epoch 77/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0834 - accuracy: 0.9765\n",
      "Epoch 77: val_accuracy did not improve from 0.76667\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 13s 598ms/step - loss: 0.0834 - accuracy: 0.9765 - val_loss: 1.3646 - val_accuracy: 0.7083 - lr: 0.0010\n",
      "Epoch 78/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0922 - accuracy: 0.9574\n",
      "Epoch 78: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 19s 850ms/step - loss: 0.0922 - accuracy: 0.9574 - val_loss: 0.9776 - val_accuracy: 0.7583 - lr: 0.0010\n",
      "Epoch 79/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0983 - accuracy: 0.9603\n",
      "Epoch 79: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 12s 539ms/step - loss: 0.0983 - accuracy: 0.9603 - val_loss: 1.6934 - val_accuracy: 0.7583 - lr: 0.0010\n",
      "Epoch 80/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1392 - accuracy: 0.9397\n",
      "Epoch 80: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 597ms/step - loss: 0.1392 - accuracy: 0.9397 - val_loss: 1.4292 - val_accuracy: 0.7333 - lr: 0.0010\n",
      "Epoch 81/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0848 - accuracy: 0.9721\n",
      "Epoch 81: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 575ms/step - loss: 0.0848 - accuracy: 0.9721 - val_loss: 1.6277 - val_accuracy: 0.7333 - lr: 0.0010\n",
      "Epoch 82/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1241 - accuracy: 0.9515\n",
      "Epoch 82: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 578ms/step - loss: 0.1241 - accuracy: 0.9515 - val_loss: 1.6829 - val_accuracy: 0.7000 - lr: 0.0010\n",
      "Epoch 83/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0821 - accuracy: 0.9706\n",
      "Epoch 83: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 14s 649ms/step - loss: 0.0821 - accuracy: 0.9706 - val_loss: 1.5377 - val_accuracy: 0.7250 - lr: 0.0010\n",
      "Epoch 84/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0941 - accuracy: 0.9676\n",
      "Epoch 84: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 593ms/step - loss: 0.0941 - accuracy: 0.9676 - val_loss: 1.4886 - val_accuracy: 0.7417 - lr: 0.0010\n",
      "Epoch 85/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0757 - accuracy: 0.9691\n",
      "Epoch 85: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 601ms/step - loss: 0.0757 - accuracy: 0.9691 - val_loss: 1.3700 - val_accuracy: 0.7167 - lr: 0.0010\n",
      "Epoch 86/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1101 - accuracy: 0.9618\n",
      "Epoch 86: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 576ms/step - loss: 0.1101 - accuracy: 0.9618 - val_loss: 1.2956 - val_accuracy: 0.7250 - lr: 0.0010\n",
      "Epoch 87/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0867 - accuracy: 0.9662\n",
      "Epoch 87: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 618ms/step - loss: 0.0867 - accuracy: 0.9662 - val_loss: 1.5057 - val_accuracy: 0.7417 - lr: 0.0010\n",
      "Epoch 88/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0629 - accuracy: 0.9706\n",
      "Epoch 88: val_accuracy did not improve from 0.76667\n",
      "22/22 [==============================] - 13s 583ms/step - loss: 0.0629 - accuracy: 0.9706 - val_loss: 1.3840 - val_accuracy: 0.7583 - lr: 0.0010\n",
      "Epoch 89/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0570 - accuracy: 0.9809\n",
      "Epoch 89: val_accuracy improved from 0.76667 to 0.77500, saving model to checkpoint\\\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: checkpoint\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: checkpoint\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 16s 710ms/step - loss: 0.0570 - accuracy: 0.9809 - val_loss: 1.3526 - val_accuracy: 0.7750 - lr: 0.0010\n",
      "Epoch 90/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1127 - accuracy: 0.9559\n",
      "Epoch 90: val_accuracy did not improve from 0.77500\n",
      "22/22 [==============================] - 14s 601ms/step - loss: 0.1127 - accuracy: 0.9559 - val_loss: 1.5631 - val_accuracy: 0.6833 - lr: 0.0010\n",
      "Epoch 91/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1088 - accuracy: 0.9647\n",
      "Epoch 91: val_accuracy did not improve from 0.77500\n",
      "22/22 [==============================] - 13s 582ms/step - loss: 0.1088 - accuracy: 0.9647 - val_loss: 1.4574 - val_accuracy: 0.7500 - lr: 0.0010\n",
      "Epoch 92/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0504 - accuracy: 0.9853\n",
      "Epoch 92: val_accuracy did not improve from 0.77500\n",
      "22/22 [==============================] - 13s 591ms/step - loss: 0.0504 - accuracy: 0.9853 - val_loss: 1.4006 - val_accuracy: 0.7333 - lr: 0.0010\n",
      "Epoch 93/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0734 - accuracy: 0.9765\n",
      "Epoch 93: val_accuracy did not improve from 0.77500\n",
      "22/22 [==============================] - 13s 592ms/step - loss: 0.0734 - accuracy: 0.9765 - val_loss: 1.5106 - val_accuracy: 0.7333 - lr: 0.0010\n",
      "Epoch 94/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0665 - accuracy: 0.9779\n",
      "Epoch 94: val_accuracy did not improve from 0.77500\n",
      "22/22 [==============================] - 13s 590ms/step - loss: 0.0665 - accuracy: 0.9779 - val_loss: 1.5209 - val_accuracy: 0.7250 - lr: 0.0010\n",
      "Epoch 95/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0895 - accuracy: 0.9676\n",
      "Epoch 95: val_accuracy did not improve from 0.77500\n",
      "22/22 [==============================] - 13s 583ms/step - loss: 0.0895 - accuracy: 0.9676 - val_loss: 1.4647 - val_accuracy: 0.7083 - lr: 0.0010\n",
      "Epoch 96/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0906 - accuracy: 0.9647\n",
      "Epoch 96: val_accuracy did not improve from 0.77500\n",
      "22/22 [==============================] - 14s 625ms/step - loss: 0.0906 - accuracy: 0.9647 - val_loss: 1.4316 - val_accuracy: 0.7500 - lr: 0.0010\n",
      "Epoch 97/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1173 - accuracy: 0.9544\n",
      "Epoch 97: val_accuracy did not improve from 0.77500\n",
      "22/22 [==============================] - 13s 603ms/step - loss: 0.1173 - accuracy: 0.9544 - val_loss: 1.7665 - val_accuracy: 0.6833 - lr: 0.0010\n",
      "Epoch 98/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0520 - accuracy: 0.9794\n",
      "Epoch 98: val_accuracy did not improve from 0.77500\n",
      "22/22 [==============================] - 13s 598ms/step - loss: 0.0520 - accuracy: 0.9794 - val_loss: 1.6674 - val_accuracy: 0.7083 - lr: 0.0010\n",
      "Epoch 99/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0622 - accuracy: 0.9735 \n",
      "Epoch 99: val_accuracy did not improve from 0.77500\n",
      "22/22 [==============================] - 550s 26s/step - loss: 0.0622 - accuracy: 0.9735 - val_loss: 1.7670 - val_accuracy: 0.7333 - lr: 0.0010\n",
      "Epoch 100/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0427 - accuracy: 0.9853\n",
      "Epoch 100: val_accuracy did not improve from 0.77500\n",
      "22/22 [==============================] - 14s 616ms/step - loss: 0.0427 - accuracy: 0.9853 - val_loss: 1.6975 - val_accuracy: 0.7417 - lr: 0.0010\n"
     ]
    }
   ],
   "source": [
    "reduce_lr = ReduceLROnPlateau(monitor='val_accuracy',patience=5,mode='max',verbose=1,min_lr=0.001,factor=0.1)\n",
    "model_checkpoint = ModelCheckpoint('checkpoint/',monitor='val_accuracy',mode='max',save_best_only=True,verbose=1)\n",
    "early_stopping = EarlyStopping(monitor='val_accuracy',mode='max',verbose=1,patience=90)\n",
    "\n",
    "r = cnn.fit(train,\n",
    "           epochs=100,\n",
    "           batch_size=32,\n",
    "           validation_data=test,\n",
    "           callbacks=[reduce_lr,model_checkpoint,early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d68ad704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(r.history['loss'],'r',label='train loss')\n",
    "plt.plot(r.history['val_loss'],'b',label='test loss')\n",
    "plt.xlabel('No. of Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Loss Graph')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "69d0b128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(r.history['accuracy'],'r',label='train accuracy')\n",
    "plt.plot(r.history['val_accuracy'],'b',label='test accuracy')\n",
    "plt.xlabel('No. of Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracy Graph')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0ddea09d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.engine.sequential.Sequential at 0x1dd3fd6b3d0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn.save('children_adult_classifier.h5')\n",
    "model = load_model('children_adult_classifier.h5')\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4a652b87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 1s 113ms/step - loss: 1.6975 - accuracy: 0.7417\n",
      "Test Loss: 1.6975151300430298\n",
      "Test Accuracy: 0.7416666746139526\n"
     ]
    }
   ],
   "source": [
    "loss, acc = cnn.evaluate(test)\n",
    "print(\"Test Loss:\",loss)\n",
    "print(\"Test Accuracy:\",acc)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}