MNIST CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 as cv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in d:\\study\\anaconda\\lib\\site-packages (1.19.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = pd.read_csv('D:\\STUDY\\AUTONOMOUS/mnist_train.csv').values\n",
    "test_set = pd.read_csv('D:\\STUDY\\AUTONOMOUS/mnist_test.csv').values    #to convert it to numpy format add values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train_set[:,1:]    #images\n",
    "y_train = train_set[:,:1]    #predication numbers LABELS\n",
    "\n",
    "x_train = np.reshape(x_train,(60000,28,28,1)) #28,28 in 1D\n",
    "\n",
    "x_train = x_train/255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = test_set[:,1:]    #images\n",
    "y_test = test_set[:,:1]    #predication numbers LABELS\n",
    "\n",
    "x_test = np.reshape(x_test,(10000,28,28,1)) #28,28 in 1D\n",
    "\n",
    "x_test = x_test/255."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN Architecture Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Conv2D,Dropout,Flatten,MaxPooling2D  \n",
    "#1d neuron,add up convo reduce size of img,cut down no of weights,\n",
    "#convert multi dimensional img to 1D,pass a filter of size through whole image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(28, kernel_size = (3,3),input_shape = (28,28,1)))  #shape of input passing\n",
    "model.add(Conv2D(28, kernel_size = (3,3)))\n",
    "model.add(MaxPooling2D(pool_size = (2,2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(128,activation = tf.nn.relu))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(10,activation = tf.nn.softmax)) #10 values to predict gives prob distri\n",
    "\n",
    "model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy',metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/7\n",
      "1875/1875 [==============================] - 70s 36ms/step - loss: 0.3017 - accuracy: 0.9073\n",
      "Epoch 2/7\n",
      "1875/1875 [==============================] - 69s 37ms/step - loss: 0.0707 - accuracy: 0.9779\n",
      "Epoch 3/7\n",
      "1875/1875 [==============================] - 68s 36ms/step - loss: 0.0469 - accuracy: 0.9851\n",
      "Epoch 4/7\n",
      "1875/1875 [==============================] - 62s 33ms/step - loss: 0.0385 - accuracy: 0.9869\n",
      "Epoch 5/7\n",
      "1875/1875 [==============================] - 68s 36ms/step - loss: 0.0284 - accuracy: 0.9907\n",
      "Epoch 6/7\n",
      "1875/1875 [==============================] - 72s 38ms/step - loss: 0.0258 - accuracy: 0.9913\n",
      "Epoch 7/7\n",
      "1875/1875 [==============================] - 71s 38ms/step - loss: 0.0242 - accuracy: 0.9921\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1df5c995460>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x = x_train,y = y_train, epochs = 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0110 - accuracy: 0.9963\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.011039462871849537, 0.9962833523750305]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_train,y_train) #accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The image is of: 7\n"
     ]
    }
   ],
   "source": [
    "img = cv.imread('D:\\STUDY\\AUTONOMOUS/seven.png')\n",
    "gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)\n",
    "\n",
    "gray = gray/255.\n",
    "gray = 1.-gray #to invert number and colour\n",
    "\n",
    "gray = cv.resize(gray,(28,28))\n",
    "\n",
    "plt.imshow(gray)\n",
    "plt.show()\n",
    "\n",
    "gray = np.reshape(gray,(1,28,28,1))    #to comapre to mnist \n",
    "\n",
    "print (\"The image is of:\",np.argmax(model.predict(gray)))  #to pass gray img,10 values deduced with DENSE layer\n",
    "result = model.predict(gray)                                  #ARGMAX gives us return which has max value/prob\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.641656e-10\n",
      "3.5200376e-09\n",
      "1.5672933e-08\n",
      "2.0099309e-09\n",
      "1.0819068e-09\n",
      "1.2737325e-10\n",
      "3.2644606e-14\n",
      "1.0\n",
      "2.7078178e-08\n",
      "2.9520905e-08\n"
     ]
    }
   ],
   "source": [
    "for i in result[0]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_img = np.reshape(train_set[0][1:],(28,28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1df3c247d30>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gzetQb6dp5KPhk5I21f7O7fR7V+irLe8bp8sCSXAGHZAEYQeSIOxAEoQdSIKwA0kQdiAJwg4k8X+zhHFo7nUhhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(temp_img) #2 channel hence different color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train,y_train),(x_test,y_test) = tf.keras.datasets.mnist.load_data() #download and resized ready"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# save models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 26, 26, 28)        280       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 24, 24, 28)        7084      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 12, 12, 28)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 4032)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               516224    \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 524,878\n",
      "Trainable params: 524,878\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('CNN_7epochs.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
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