MNIST_lessParameter_10k.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "obvious-villa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dramatic-marks",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "prospective-geography",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train),(x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "exposed-particular",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "verified-variance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa2f3c4f370>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "reduced-aviation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "extensive-paradise",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "scheduled-license",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "civic-owner",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.reshape(60000, 28, 28, 1)\n",
    "x_test  = x_test.reshape(10000, 28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "moderate-crash",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.astype(\"float32\") /255 \n",
    "x_test = x_test.astype(\"float32\") / 255\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "compressed-cuisine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa3046db8e0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "stable-federation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa2f5261790>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "conventional-alaska",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical\n",
    "#one-hot encode target column\n",
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "pediatric-revolution",
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "royal-performer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "essential-kelly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "thousand-breakdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D,Dense,Flatten, MaxPooling2D, BatchNormalization, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "secondary-national",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "declared-labor",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_shape = (28, 28, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "constant-hundred",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Conv2D(32, kernel_size=(3,3), activation=\"relu\", input_shape=input_shape,padding='same')) # parameter calculation filter * kernel size + bias ( bias is equal to the fitlers)\n",
    "#parametes1 = 32 *3 * 3 + 32\n",
    "#print(f\"parameter layer 1 {parametes1}\")\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Conv2D(8, kernel_size=(1*1), activation=\"relu\",padding='same'))\n",
    "model.add(Conv2D(8, kernel_size=(3*3), activation=\"relu\", padding='same'))\n",
    "model.add(Conv2D(4, kernel_size=(3*3), activation=\"relu\", padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.2))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Conv2D(4, kernel_size=(1*1), activation=\"relu\"))\n",
    "model.add(Conv2D(4, kernel_size=(2*2), activation=\"relu\"))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(8, kernel_size=(2*2), activation=\"relu\"))\n",
    "#model.add(Dropout(0.5))\n",
    "\n",
    "#model.add(BatchNormalization())\n",
    " \n",
    "model.add(Flatten())\n",
    "model.add(Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "nasty-washington",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_56\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_312 (Conv2D)          (None, 28, 28, 32)        320       \n",
      "_________________________________________________________________\n",
      "batch_normalization_161 (Bat (None, 28, 28, 32)        128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_106 (MaxPoolin (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_313 (Conv2D)          (None, 14, 14, 8)         264       \n",
      "_________________________________________________________________\n",
      "conv2d_314 (Conv2D)          (None, 14, 14, 8)         5192      \n",
      "_________________________________________________________________\n",
      "conv2d_315 (Conv2D)          (None, 14, 14, 4)         2596      \n",
      "_________________________________________________________________\n",
      "batch_normalization_162 (Bat (None, 14, 14, 4)         16        \n",
      "_________________________________________________________________\n",
      "dropout_77 (Dropout)         (None, 14, 14, 4)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_107 (MaxPoolin (None, 7, 7, 4)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_316 (Conv2D)          (None, 7, 7, 4)           20        \n",
      "_________________________________________________________________\n",
      "conv2d_317 (Conv2D)          (None, 4, 4, 4)           260       \n",
      "_________________________________________________________________\n",
      "batch_normalization_163 (Bat (None, 4, 4, 4)           16        \n",
      "_________________________________________________________________\n",
      "conv2d_318 (Conv2D)          (None, 1, 1, 8)           520       \n",
      "_________________________________________________________________\n",
      "flatten_40 (Flatten)         (None, 8)                 0         \n",
      "_________________________________________________________________\n",
      "dense_40 (Dense)             (None, 10)                90        \n",
      "=================================================================\n",
      "Total params: 9,422\n",
      "Trainable params: 9,342\n",
      "Non-trainable params: 80\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "animated-region",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='RMSprop', loss='categorical_crossentropy', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "killing-guarantee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "1875/1875 [==============================] - 100s 53ms/step - loss: 0.6276 - accuracy: 0.8109 - val_loss: 0.1027 - val_accuracy: 0.9694\n",
      "Epoch 2/30\n",
      "1875/1875 [==============================] - 92s 49ms/step - loss: 0.0897 - accuracy: 0.9734 - val_loss: 0.0637 - val_accuracy: 0.9814\n",
      "Epoch 3/30\n",
      "1875/1875 [==============================] - 88s 47ms/step - loss: 0.0660 - accuracy: 0.9810 - val_loss: 0.0562 - val_accuracy: 0.9814\n",
      "Epoch 4/30\n",
      "1875/1875 [==============================] - 84s 45ms/step - loss: 0.0564 - accuracy: 0.9823 - val_loss: 0.0487 - val_accuracy: 0.9859\n",
      "Epoch 5/30\n",
      "1875/1875 [==============================] - 86s 46ms/step - loss: 0.0477 - accuracy: 0.9861 - val_loss: 0.0493 - val_accuracy: 0.9836\n",
      "Epoch 6/30\n",
      "1875/1875 [==============================] - 92s 49ms/step - loss: 0.0439 - accuracy: 0.9861 - val_loss: 0.0520 - val_accuracy: 0.9841\n",
      "Epoch 7/30\n",
      "1875/1875 [==============================] - 89s 47ms/step - loss: 0.0362 - accuracy: 0.9890 - val_loss: 0.0392 - val_accuracy: 0.9878\n",
      "Epoch 8/30\n",
      "1875/1875 [==============================] - 87s 46ms/step - loss: 0.0357 - accuracy: 0.9887 - val_loss: 0.0425 - val_accuracy: 0.9874\n",
      "Epoch 9/30\n",
      "1875/1875 [==============================] - 77s 41ms/step - loss: 0.0345 - accuracy: 0.9899 - val_loss: 0.0456 - val_accuracy: 0.9858\n",
      "Epoch 10/30\n",
      "1875/1875 [==============================] - 89s 47ms/step - loss: 0.0287 - accuracy: 0.9912 - val_loss: 0.0379 - val_accuracy: 0.9885\n",
      "Epoch 11/30\n",
      "1875/1875 [==============================] - 90s 48ms/step - loss: 0.0276 - accuracy: 0.9917 - val_loss: 0.0371 - val_accuracy: 0.9890\n",
      "Epoch 12/30\n",
      "1875/1875 [==============================] - 97s 51ms/step - loss: 0.0276 - accuracy: 0.9911 - val_loss: 0.0452 - val_accuracy: 0.9864\n",
      "Epoch 13/30\n",
      "1875/1875 [==============================] - 100s 53ms/step - loss: 0.0250 - accuracy: 0.9915 - val_loss: 0.0327 - val_accuracy: 0.9899\n",
      "Epoch 14/30\n",
      "1875/1875 [==============================] - 103s 55ms/step - loss: 0.0220 - accuracy: 0.9932 - val_loss: 0.0480 - val_accuracy: 0.9852\n",
      "Epoch 15/30\n",
      "1875/1875 [==============================] - 87s 46ms/step - loss: 0.0231 - accuracy: 0.9926 - val_loss: 0.0448 - val_accuracy: 0.9875\n",
      "Epoch 16/30\n",
      "1875/1875 [==============================] - 92s 49ms/step - loss: 0.0211 - accuracy: 0.9935 - val_loss: 0.0363 - val_accuracy: 0.9893\n",
      "Epoch 17/30\n",
      "1875/1875 [==============================] - 90s 48ms/step - loss: 0.0212 - accuracy: 0.9929 - val_loss: 0.0330 - val_accuracy: 0.9908\n",
      "Epoch 18/30\n",
      "1875/1875 [==============================] - 84s 45ms/step - loss: 0.0206 - accuracy: 0.9939 - val_loss: 0.0469 - val_accuracy: 0.9867\n",
      "Epoch 19/30\n",
      "1875/1875 [==============================] - 84s 45ms/step - loss: 0.0199 - accuracy: 0.9934 - val_loss: 0.0331 - val_accuracy: 0.9903\n",
      "Epoch 20/30\n",
      "1875/1875 [==============================] - 87s 47ms/step - loss: 0.0182 - accuracy: 0.9943 - val_loss: 0.0330 - val_accuracy: 0.9903\n",
      "Epoch 21/30\n",
      "1875/1875 [==============================] - 81s 43ms/step - loss: 0.0167 - accuracy: 0.9946 - val_loss: 0.0347 - val_accuracy: 0.9902\n",
      "Epoch 22/30\n",
      "1875/1875 [==============================] - 111s 59ms/step - loss: 0.0177 - accuracy: 0.9942 - val_loss: 0.0392 - val_accuracy: 0.9891\n",
      "Epoch 23/30\n",
      "1875/1875 [==============================] - 97s 52ms/step - loss: 0.0180 - accuracy: 0.9938 - val_loss: 0.0565 - val_accuracy: 0.9854\n",
      "Epoch 24/30\n",
      "1875/1875 [==============================] - 93s 50ms/step - loss: 0.0159 - accuracy: 0.9946 - val_loss: 0.0402 - val_accuracy: 0.9887\n",
      "Epoch 25/30\n",
      "1875/1875 [==============================] - 87s 46ms/step - loss: 0.0153 - accuracy: 0.9951 - val_loss: 0.0393 - val_accuracy: 0.9896\n",
      "Epoch 26/30\n",
      "1875/1875 [==============================] - 88s 47ms/step - loss: 0.0154 - accuracy: 0.9951 - val_loss: 0.0403 - val_accuracy: 0.9883\n",
      "Epoch 27/30\n",
      "1875/1875 [==============================] - 91s 49ms/step - loss: 0.0140 - accuracy: 0.9953 - val_loss: 0.0393 - val_accuracy: 0.9899\n",
      "Epoch 28/30\n",
      "1875/1875 [==============================] - 102s 54ms/step - loss: 0.0162 - accuracy: 0.9949 - val_loss: 0.0436 - val_accuracy: 0.9887\n",
      "Epoch 29/30\n",
      "1875/1875 [==============================] - 93s 50ms/step - loss: 0.0145 - accuracy: 0.9953 - val_loss: 0.0419 - val_accuracy: 0.9897\n",
      "Epoch 30/30\n",
      "1875/1875 [==============================] - 88s 47ms/step - loss: 0.0161 - accuracy: 0.9952 - val_loss: 0.0383 - val_accuracy: 0.9910\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fa2b8517a00>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "starting-china",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "individual-shoot",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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