MNIST_CNN_Omkar.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3wF5wszaj97Y"
   },
   "source": [
    "# MNIST using Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nnrWf3PCEzXL"
   },
   "source": [
    "Run the statement to import the TensorFlow library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "0trJmd6DjqBZ"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7NAbSZiaoJ4z"
   },
   "source": [
    "Run the statement to load and prepare the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) and to convert the samples from integers to floating-point numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "executionInfo": {
     "elapsed": 2887,
     "status": "ok",
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    "outputId": "1340781f-c5e6-4f7f-88a0-6e1ff220044b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 6s 0us/step\n"
     ]
    }
   ],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.reshape(x_train,(60000,28,28,1))\n",
    "x_test = np.reshape(x_test,(10000,28,28,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BPZ68wASog_I"
   },
   "source": [
    "Build the model with a single layer and relu activation function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "h3IKyzTCDNGo"
   },
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28,28,1)),\n",
    "  tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "  tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "  tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "  tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),\n",
    "  tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "  tf.keras.layers.Flatten(),\n",
    "  tf.keras.layers.Dense(128, activation='relu'),\n",
    "  tf.keras.layers.Dense(10)\n",
    "])\n",
    "# try to add convolutions and change the no of layers and no of nodes/neurons\n",
    "# to add a new layer\n",
    "# tf.keras.layers.Dense(10, activation='relu') \n",
    "# to add convolutions \n",
    "# tf.keras.layers.Conv2D(28, (3, 3), activation='relu', input_shape=(28, 28)) \n",
    "# tf.keras.layers.MaxPooling2D((2, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_18 (Conv2D)           (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_19 (Conv2D)           (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_20 (Conv2D)           (None, 3, 3, 128)         73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling (None, 1, 1, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_6 (Flatten)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 110,474\n",
      "Trainable params: 110,474\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hQyugpgRIyrA"
   },
   "source": [
    "Choosing our loss function, in this case crossentropy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "RSkzdv8MD0tT"
   },
   "outputs": [],
   "source": [
    "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "9foNKHzTD2Vo"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=loss_fn,\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ix4mEL65on-w"
   },
   "source": [
    "Adjusting the model parameters to minimize the loss of our model: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
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    "executionInfo": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1500/1500 [==============================] - 24s 16ms/step - loss: 0.2127 - accuracy: 0.9340 - val_loss: 0.0897 - val_accuracy: 0.9746\n",
      "Epoch 2/5\n",
      "1500/1500 [==============================] - 26s 17ms/step - loss: 0.0704 - accuracy: 0.9787 - val_loss: 0.0662 - val_accuracy: 0.9799\n",
      "Epoch 3/5\n",
      "1500/1500 [==============================] - 26s 17ms/step - loss: 0.0519 - accuracy: 0.9834 - val_loss: 0.0639 - val_accuracy: 0.9812\n",
      "Epoch 4/5\n",
      "1500/1500 [==============================] - 26s 17ms/step - loss: 0.0387 - accuracy: 0.9883 - val_loss: 0.0511 - val_accuracy: 0.9843\n",
      "Epoch 5/5\n",
      "1500/1500 [==============================] - 26s 18ms/step - loss: 0.0308 - accuracy: 0.9901 - val_loss: 0.0566 - val_accuracy: 0.9836\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2a7b57d5610>"
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     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, epochs=5, validation_split = 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4mDAAPFqVVgn"
   },
   "source": [
    "Checking the model's performance on the Validation-set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "executionInfo": {
     "elapsed": 1651,
     "status": "ok",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 1s - loss: 0.0568 - accuracy: 0.9825\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.05680268630385399, 0.9825000166893005]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test,  y_test, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T4JfEh7kvx6m"
   },
   "source": [
    "Try to improve the accuracy to 97% by changing the number of layers and/or adding convolutions."
   ]
  }
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