Data augmentation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "41999284",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "00945e34",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=keras.Sequential(\n",
    "[\n",
    "    layers.Conv2D(32,(3,3),activation=\"relu\",padding=\"same\",input_shape=(28,28,1)),\n",
    "    layers.MaxPooling2D(pool_size=(2,2)),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(10,activation=\"softmax\"),\n",
    "])\n",
    "\n",
    "#model2=keras.Sequential(\n",
    "#[\n",
    "#    layers.Conv2D(32,(3,3),activation=\"relu\",padding=\"same\",input_shape=(28,28,1)),\n",
    "#    layers.MaxPooling2D(pool_size=(2,2)),\n",
    "#    layers.Flatten(),\n",
    "#   layers.Dense(10,activation=\"softmax\"),\n",
    "#])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "93f2affc",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train,y_train),(x_test,y_test)=keras.datasets.mnist.load_data()\n",
    "\n",
    "#sacale the images to the [0,1]range\n",
    "x_train=x_train.astype(\"float32\")/255\n",
    "x_test=x_test.astype(\"float32\")/255\n",
    "\n",
    "#Add a channel dimension to the images\n",
    "x_train=np.expand_dims(x_train,-1)\n",
    "x_test=np.expand_dims(x_test,-1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8bb1e3d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_val=x_train[:50000],x_train[50000:]\n",
    "y_train,y_val=y_train[:50000],y_train[50000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f11c1b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen=ImageDataGenerator(\n",
    "    rotation_range=10,\n",
    "    zoom_range=0.1,\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1,\n",
    "    shear_range=0.1,\n",
    "    horizontal_flip=False,\n",
    "    vertical_flip=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "26c6c8bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model without agumentation\n",
    "#model1.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "#history1=model.fit(x_train,y_train,batch_size=32,epochs=5,validation_data=(x_val,y_val))\n",
    "\n",
    "#with data agumentation\n",
    "#model2.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "#history2=model.fit(datagen.flow(x_train,y_train,batch_size=32),epochs=5,validation_data=(x_val,y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7e1aa95d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1563/1563 [==============================] - 7s 4ms/step - loss: 0.2388 - accuracy: 0.9321 - val_loss: 0.1082 - val_accuracy: 0.9726\n",
      "Epoch 2/5\n",
      "1563/1563 [==============================] - 7s 4ms/step - loss: 0.0897 - accuracy: 0.9735 - val_loss: 0.0783 - val_accuracy: 0.9783\n",
      "Epoch 3/5\n",
      "1563/1563 [==============================] - 7s 4ms/step - loss: 0.0689 - accuracy: 0.9795 - val_loss: 0.0705 - val_accuracy: 0.9794\n",
      "Epoch 4/5\n",
      "1563/1563 [==============================] - 7s 4ms/step - loss: 0.0550 - accuracy: 0.9831 - val_loss: 0.0665 - val_accuracy: 0.9809\n",
      "Epoch 5/5\n",
      "1563/1563 [==============================] - 7s 4ms/step - loss: 0.0460 - accuracy: 0.9861 - val_loss: 0.0696 - val_accuracy: 0.9809\n",
      "Epoch 1/5\n",
      "1563/1563 [==============================] - 9s 5ms/step - loss: 0.3165 - accuracy: 0.9054 - val_loss: 0.1208 - val_accuracy: 0.9669\n",
      "Epoch 2/5\n",
      "1563/1563 [==============================] - 8s 5ms/step - loss: 0.2143 - accuracy: 0.9357 - val_loss: 0.0984 - val_accuracy: 0.9716\n",
      "Epoch 3/5\n",
      "1563/1563 [==============================] - 8s 5ms/step - loss: 0.1876 - accuracy: 0.9439 - val_loss: 0.1424 - val_accuracy: 0.9597\n",
      "Epoch 4/5\n",
      "1563/1563 [==============================] - 8s 5ms/step - loss: 0.1774 - accuracy: 0.9463 - val_loss: 0.1358 - val_accuracy: 0.9621\n",
      "Epoch 5/5\n",
      "1563/1563 [==============================] - 8s 5ms/step - loss: 0.1640 - accuracy: 0.9515 - val_loss: 0.0951 - val_accuracy: 0.9727\n"
     ]
    }
   ],
   "source": [
    "#model without agumentation\n",
    "model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "history1=model.fit(x_train,y_train,batch_size=32,epochs=5,validation_data=(x_val,y_val))\n",
    "\n",
    "#with data agumentation\n",
    "model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "history2=model.fit(datagen.flow(x_train,y_train,batch_size=32),epochs=5,validation_data=(x_val,y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b246be86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 2.3055 - accuracy: 0.0768\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[2.3055238723754883, 0.07680000364780426]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model1.evaluate(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "135e5f6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 2.3239 - accuracy: 0.0747\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[2.3239121437072754, 0.0746999979019165]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model2.evaluate(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7ee1d800",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.0838 - accuracy: 0.9741\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.08375035971403122, 0.9740999937057495]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "77f18b6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(history1.history['loss'],label='Training Loss')\n",
    "plt.plot(history1.history['val_loss'],label='validation Loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "20cb92b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history2.history['accuracy'],label='Training accuracy')\n",
    "plt.plot(history2.history['val_accuracy'],label='validation accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d608ef45",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}