MNIST dataset 3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "06a70af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "721d6f39",
   "metadata": {},
   "outputs": [],
   "source": [
    " class myCallback(tf.keras.callbacks.Callback):\n",
    "        def on_epoch_end(self,epoch,logs={}):\n",
    "            if(logs.get('accuracy') > 0.99):\n",
    "                print(\"\\n Reached 99% accuracy so cancelling training\")\n",
    "                self.model.stop_training = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ef9296c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = tf.keras.datasets.mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a986f45a",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train),(x_test,y_test) = mnist.load_data()\n",
    "x_train,x_test = x_train/255, x_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7330310f",
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = myCallback()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9019292d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28,28)),\n",
    "    tf.keras.layers.Dense(512,activation=tf.nn.relu),\n",
    "    tf.keras.layers.Dense(10,activation=tf.nn.softmax)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "02ef7b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "             loss='sparse_categorical_crossentropy',\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40aa12b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.2015 - accuracy: 0.9401\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0804 - accuracy: 0.9756\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0515 - accuracy: 0.9842\n",
      "Epoch 4/10\n",
      "1304/1875 [===================>..........] - ETA: 3s - loss: 0.0362 - accuracy: 0.9885"
     ]
    }
   ],
   "source": [
    "model.fit(x_train,y_train, epochs=10,callbacks=[callbacks])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "61a250b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 7s 3ms/step - loss: 0.2004 - accuracy: 0.9414\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0826 - accuracy: 0.9750\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0545 - accuracy: 0.9829\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0374 - accuracy: 0.9878\n",
      "Epoch 5/10\n",
      "1867/1875 [============================>.] - ETA: 0s - loss: 0.0268 - accuracy: 0.9917\n",
      "Reached 99% accuracy so cancelling training!\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0267 - accuracy: 0.9917\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1b088899ca0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "class myCallback(tf.keras.callbacks.Callback):\n",
    "  def on_epoch_end(self, epoch, logs={}):\n",
    "    if(logs.get('accuracy') > 0.99):\n",
    "      print(\"\\nReached 99% accuracy so cancelling training!\")\n",
    "      self.model.stop_training = True\n",
    "\n",
    "mnist = tf.keras.datasets.mnist\n",
    "\n",
    "(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "\n",
    "callbacks = myCallback()\n",
    "\n",
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "  tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
    "  tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
    "])\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train, epochs=10, callbacks=[callbacks])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d30217b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}