convNet_with_7x7_kernels.ipynb

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      "name": "convNet with 7x7 kernels",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UTfuvCXYqeKf",
        "colab_type": "text"
      },
      "source": [
        "Applying Convolutional neural network on MNIST dataset (In this notebook, CNN is tried out with different number of layers and different architectures with help of 7x7 kernel)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJ4GQPOQqh5m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#import all the libraries for data analysis and plotting\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import plotly as py\n",
        "import plotly.express as px\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.graph_objects as go\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rn_th29ZqiEB",
        "colab_type": "code",
        "outputId": "f301c976-1cea-43c9-8562-8ad25f12fc58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "#import all the libraries for deep learning\n",
        "import tensorflow.keras\n",
        "from tensorflow.keras.layers import Dense,Conv2D,MaxPooling2D,Dropout,Flatten\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.initializers import he_normal,he_uniform,glorot_normal,glorot_uniform,RandomNormal,RandomUniform\n",
        "from keras.utils import np_utils\n",
        "from tensorflow.keras.datasets import mnist\n",
        "import tensorflow.keras.backend as k"
      ],
      "execution_count": 0,
      "outputs": [
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          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
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              "<IPython.core.display.HTML object>"
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          "text": [
            "Using TensorFlow backend.\n"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "as0zGTU4qiCD",
        "colab_type": "code",
        "outputId": "3eb0cc75-2e7b-4ef7-e0bc-74f80d3f8df0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#load the dataset\n",
        "(X_train,y_train),(X_test,y_test) = mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tFg1aTVqiAh",
        "colab_type": "code",
        "outputId": "4f293378-ede4-416b-d5c6-1d9ee415ad9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print(f\"X_train shape --- {X_train.shape}\")\n",
        "print(f\"X_test shape --- {X_test.shape}\")\n",
        "print(f\"y_train shape --- {y_train.shape}\")\n",
        "print(f\"y_test shape --- {y_test.shape}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train shape --- (60000, 28, 28)\n",
            "X_test shape --- (10000, 28, 28)\n",
            "y_train shape --- (60000,)\n",
            "y_test shape --- (10000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8rpdnMiqh8z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img_rows,img_cols = 28,28\n",
        "\n",
        "if k.image_data_format() == 'channels_first':\n",
        "  X_train = X_train.reshape(X_train.shape[0],1,img_rows,img_cols)\n",
        "  X_test = X_test.reshape(X_test.shape[0],1,img_rows,img_cols)\n",
        "  input_shape = (1,img_rows,img_cols)\n",
        "\n",
        "else:\n",
        "  X_train = X_train.reshape(X_train.shape[0],img_rows,img_cols,1)\n",
        "  X_test = X_test.reshape(X_test.shape[0],img_rows,img_cols,1)\n",
        "  input_shape = (img_rows,img_cols,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MiX6wKzTttl7",
        "colab_type": "code",
        "outputId": "d18e2827-3505-4d5a-c978-a929121d945f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "#normalizing the data\n",
        "X_train = X_train.astype('float32')\n",
        "X_test = X_test.astype('float32')\n",
        "\n",
        "X_train = (X_train - X_train.min())/(X_train.max() - X_train.min())\n",
        "X_test = (X_test - X_test.min())/(X_test.max() - X_test.min())\n",
        "\n",
        "print(f\"number of train samples --- {X_train.shape[0]}\")\n",
        "print(f\"number of test samples --- {X_test.shape[0]}\")\n",
        "\n",
        "print(f\"train samples shape --- {X_train.shape}\")\n",
        "print(f\"test samples shape --- {X_test.shape}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of train samples --- 60000\n",
            "number of test samples --- 10000\n",
            "train samples shape --- (60000, 28, 28, 1)\n",
            "test samples shape --- (10000, 28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xxx0P5Hqu7zx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#other important factors\n",
        "batch_size = 128\n",
        "epoch = 20"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RceBAxZxu9Et",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# we have a class number for each image\n",
        "# lets convert this into a 10 dimensional vector\n",
        "# ex: consider an image is 5 convert it into 5 => [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]\n",
        "# this conversion needed for MLPs\n",
        "Y_train = np_utils.to_categorical(y_train,10)\n",
        "Y_test = np_utils.to_categorical(y_test,10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kw8wI7Olu9RT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#plot function to generate loss plots\n",
        "no_of_epochs = list(range(1,21))\n",
        "\n",
        "\n",
        "def loss_plots(n_epochs,train_loss,validation_loss):\n",
        "\n",
        "  fig = go.Figure()\n",
        "  fig.add_trace(go.Scatter(x=n_epochs, y=train_loss,\n",
        "                  mode='lines+markers',name='train_loss'))\n",
        "\n",
        "  fig.add_trace(go.Scatter(x=n_epochs, y=validation_loss,\n",
        "                  mode='lines+markers',name='validation_loss'))\n",
        "\n",
        "  fig.update_layout(title='Train_loss and Validation_loss',xaxis_title='number of epochs',yaxis_title='Categorical Cross Entropy Loss')\n",
        "  fig.show()\n",
        "\n",
        "def accuracy_plots(n_epochs,train_accuracy,validation_accuracy):\n",
        "\n",
        "  fig = go.Figure()\n",
        "  fig.add_trace(go.Scatter(x=n_epochs, y=train_accuracy,\n",
        "                  mode='lines+markers',name='train_accuracy'))\n",
        "\n",
        "  fig.add_trace(go.Scatter(x=n_epochs, y=validation_accuracy,\n",
        "                  mode='lines+markers',name='validation_accuracy'))\n",
        "\n",
        "  fig.update_layout(title='Train_accuracy and Validation_accuracy',xaxis_title='number of epochs',yaxis_title='Accuracy')\n",
        "  fig.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HGM6I-Mxu9Pk",
        "colab_type": "text"
      },
      "source": [
        "## Model -1 \n",
        "Basic Architecture of Convnet Only **3 layers of ConvNets** (Without any BatchNormalization or any complex layers) **Without** **padding and MaxPooling layer and with default stride**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKWMoQjQu9Nw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_1 = Sequential()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fg1lpxy4u9MM",
        "colab_type": "code",
        "outputId": "f5832744-9956-49f3-dd5b-48807ddb8ddf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "model_1.add(Conv2D(256,kernel_size=(7,7),\n",
        "                   activation='relu',\n",
        "                   input_shape=input_shape))\n",
        "model_1.add(Conv2D(128,kernel_size=(7,7),\n",
        "                   activation='relu'))\n",
        "model_1.add(Conv2D(64,kernel_size=(7,7),\n",
        "                   activation='relu'))\n",
        "\n",
        "model_1.add(Flatten())\n",
        "model_1.add(Dense(128,activation='relu'))\n",
        "model_1.add(Dropout(0.25))\n",
        "model_1.add(Dense(10,activation='softmax'))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tF3uKxl7u9JA",
        "colab_type": "code",
        "outputId": "ee06cac4-7448-4242-8735-59d2c8d2e770",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "model_1.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 22, 22, 256)       12800     \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 16, 16, 128)       1605760   \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 10, 10, 64)        401472    \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 6400)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               819328    \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 2,840,650\n",
            "Trainable params: 2,840,650\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LfNhclgWu9DF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_1.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J2xAE4nAx1hs",
        "colab_type": "code",
        "outputId": "886f72c8-efb6-4d2b-8714-8c55333919b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "history = model_1.fit(X_train,Y_train,epochs=epoch,validation_data=(X_test,Y_test),batch_size=batch_size)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 35s 589us/sample - loss: 0.1711 - acc: 0.9464 - val_loss: 0.0470 - val_acc: 0.9850\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 30s 502us/sample - loss: 0.0508 - acc: 0.9847 - val_loss: 0.0327 - val_acc: 0.9904\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 30s 499us/sample - loss: 0.0379 - acc: 0.9886 - val_loss: 0.0298 - val_acc: 0.9917\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 30s 499us/sample - loss: 0.0294 - acc: 0.9916 - val_loss: 0.0288 - val_acc: 0.9920\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 30s 498us/sample - loss: 0.0265 - acc: 0.9916 - val_loss: 0.0259 - val_acc: 0.9926\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 30s 497us/sample - loss: 0.0227 - acc: 0.9933 - val_loss: 0.0265 - val_acc: 0.9922\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 30s 497us/sample - loss: 0.0168 - acc: 0.9946 - val_loss: 0.0245 - val_acc: 0.9937\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 30s 497us/sample - loss: 0.0161 - acc: 0.9949 - val_loss: 0.0265 - val_acc: 0.9933\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 30s 497us/sample - loss: 0.0160 - acc: 0.9952 - val_loss: 0.0311 - val_acc: 0.9924\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 30s 496us/sample - loss: 0.0124 - acc: 0.9965 - val_loss: 0.0410 - val_acc: 0.9900\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 30s 495us/sample - loss: 0.0135 - acc: 0.9958 - val_loss: 0.0345 - val_acc: 0.9919\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 30s 495us/sample - loss: 0.0122 - acc: 0.9962 - val_loss: 0.0339 - val_acc: 0.9911\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 30s 495us/sample - loss: 0.0115 - acc: 0.9964 - val_loss: 0.0302 - val_acc: 0.9938\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 30s 493us/sample - loss: 0.0123 - acc: 0.9963 - val_loss: 0.0478 - val_acc: 0.9909\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 29s 491us/sample - loss: 0.0093 - acc: 0.9972 - val_loss: 0.0360 - val_acc: 0.9938\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 29s 491us/sample - loss: 0.0095 - acc: 0.9974 - val_loss: 0.0403 - val_acc: 0.9921\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 30s 492us/sample - loss: 0.0080 - acc: 0.9975 - val_loss: 0.0376 - val_acc: 0.9934\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 30s 492us/sample - loss: 0.0057 - acc: 0.9985 - val_loss: 0.0420 - val_acc: 0.9909\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 29s 489us/sample - loss: 0.0092 - acc: 0.9975 - val_loss: 0.0382 - val_acc: 0.9916\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 29s 488us/sample - loss: 0.0120 - acc: 0.9968 - val_loss: 0.0418 - val_acc: 0.9914\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LFle_2oqyTvS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = model_1.evaluate(X_test,Y_test,verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P8AgtC9_yrYL",
        "colab_type": "code",
        "outputId": "b9423400-580b-4598-fd96-0b9a3d80953e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(f\"Test score --- {score[0]}\")\n",
        "print(f\"Test accuracy --- {score[1]}\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test score --- 0.041814787835881766\n",
            "Test accuracy --- 0.9914000034332275\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RzEEpgsEy4CP",
        "colab_type": "code",
        "outputId": "23420a82-e853-4449-e3da-e582b4d803dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "validation_loss = history.history['val_loss']\n",
        "train_loss = history.history['loss']\n",
        "train_accuracy = history.history['acc']\n",
        "validation_accuracy = history.history['val_acc']\n",
        "loss_plots(no_of_epochs,train_loss,validation_loss)\n",
        "accuracy_plots(no_of_epochs,train_accuracy,validation_accuracy)"
      ],
      "execution_count": 0,
      "outputs": [
        {
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          "metadata": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MdJmqXrDVCri",
        "colab_type": "text"
      },
      "source": [
        "**Observation**\n",
        "*   There is slight overfit in this model (which can be interpreted from both the plots)\n",
        "*   ideal epochs might be considered as 5 in this architecture because when only at that stage both train loss and test loss are close\n",
        "*   This is very basic architecture without batch normalization and max pooling and padding. Let's see further cases.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dd-g0xclVCmv",
        "colab_type": "text"
      },
      "source": [
        "## Model - 2 \n",
        "Slightly complex Architecture of Convnet Only **3 layers of ConvNets** (With BatchNormalization and MaxPooling and slight complex layers) **Without** **padding and with default stride** 3 layers of ConvNet because of kernel size (as we are not using padding here , so because of max pooling and large kernel size our dimensionality reduces greatly)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Frr-Sry7HEDL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import BatchNormalization"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "MxRqSpFSF-eu",
        "outputId": "c235b568-c007-4919-e5f9-a688adb41e89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "model_2 = Sequential()\n",
        "model_2.add(Conv2D(1024,kernel_size=(7,7),\n",
        "                   activation='relu',\n",
        "                   input_shape=input_shape))\n",
        "model_2.add(Conv2D(512,kernel_size=(7,7),\n",
        "                   activation='relu'))\n",
        "model_2.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model_2.add(Conv2D(256,kernel_size=(7,7),\n",
        "                   activation='relu'))\n",
        "\n",
        "model_2.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "\n",
        "model_2.add(Flatten())\n",
        "model_2.add(Dense(128,activation='relu'))\n",
        "model_2.add(BatchNormalization())\n",
        "model_2.add(Dropout(0.25))\n",
        "model_2.add(Dense(64,activation='relu'))\n",
        "model_2.add(BatchNormalization())\n",
        "model_2.add(Dropout(0.25))\n",
        "model_2.add(Dense(10,activation='softmax'))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "631660d3-afa4-4f76-a2b5-d6b8b7e58d84",
        "id": "rhH-BUXQF-e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "model_2.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 22, 22, 1024)      51200     \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 16, 16, 512)       25690624  \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 8, 8, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 2, 2, 256)         6422784   \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 1, 1, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 64)                256       \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 32,207,178\n",
            "Trainable params: 32,206,794\n",
            "Non-trainable params: 384\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "sVxUl4ZJF-fD",
        "colab": {}
      },
      "source": [
        "model_2.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "f7d52e68-cc0e-47b3-b6d5-1c45dfeb8d33",
        "id": "LNO7M4NKF-fL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "history = model_2.fit(X_train,Y_train,batch_size=batch_size,epochs=epoch,validation_data=(X_test,Y_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 337s 6ms/sample - loss: 0.2276 - acc: 0.9340 - val_loss: 0.0413 - val_acc: 0.9873\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 314s 5ms/sample - loss: 0.0527 - acc: 0.9848 - val_loss: 0.0282 - val_acc: 0.9909\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 314s 5ms/sample - loss: 0.0352 - acc: 0.9896 - val_loss: 0.0351 - val_acc: 0.9899\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 314s 5ms/sample - loss: 0.0268 - acc: 0.9920 - val_loss: 0.0289 - val_acc: 0.9902\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 314s 5ms/sample - loss: 0.0216 - acc: 0.9936 - val_loss: 0.1501 - val_acc: 0.9561\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 312s 5ms/sample - loss: 0.0188 - acc: 0.9944 - val_loss: 0.0364 - val_acc: 0.9887\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 312s 5ms/sample - loss: 0.0141 - acc: 0.9957 - val_loss: 0.0275 - val_acc: 0.9913\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 311s 5ms/sample - loss: 0.0126 - acc: 0.9960 - val_loss: 0.0269 - val_acc: 0.9928\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 309s 5ms/sample - loss: 0.0131 - acc: 0.9958 - val_loss: 0.0328 - val_acc: 0.9902\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 308s 5ms/sample - loss: 0.0106 - acc: 0.9969 - val_loss: 0.0336 - val_acc: 0.9894\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 308s 5ms/sample - loss: 0.0083 - acc: 0.9977 - val_loss: 0.0385 - val_acc: 0.9915\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 308s 5ms/sample - loss: 0.0076 - acc: 0.9976 - val_loss: 0.0240 - val_acc: 0.9933\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 306s 5ms/sample - loss: 0.0062 - acc: 0.9980 - val_loss: 0.0336 - val_acc: 0.9915\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 304s 5ms/sample - loss: 0.0068 - acc: 0.9978 - val_loss: 0.0268 - val_acc: 0.9929\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 304s 5ms/sample - loss: 0.0052 - acc: 0.9984 - val_loss: 0.0315 - val_acc: 0.9918\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 304s 5ms/sample - loss: 0.0049 - acc: 0.9985 - val_loss: 0.0258 - val_acc: 0.9932\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 303s 5ms/sample - loss: 0.0047 - acc: 0.9984 - val_loss: 0.0242 - val_acc: 0.9938\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 300s 5ms/sample - loss: 0.0050 - acc: 0.9985 - val_loss: 0.0277 - val_acc: 0.9932\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 300s 5ms/sample - loss: 0.0040 - acc: 0.9988 - val_loss: 0.0300 - val_acc: 0.9924\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 299s 5ms/sample - loss: 0.0044 - acc: 0.9985 - val_loss: 0.0257 - val_acc: 0.9933\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ed5TZJs0F-fR",
        "colab": {}
      },
      "source": [
        "score = model_2.evaluate(X_test,Y_test,verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "twSYmazXF-fW",
        "outputId": "3fcf2136-ee7e-4f9e-9c96-ceafef138bee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(f\"Test score is {score[0]}\")\n",
        "print(f\"Test accuracy is {score[1]}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test score is 0.02570439639581464\n",
            "Test accuracy is 0.9933000206947327\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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      "metadata": {
        "colab_type": "code",
        "id": "zThufS85F-fb",
        "outputId": "ff83547d-5877-446e-8ef9-f756c94ba9ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "validation_loss = history.history['val_loss']\n",
        "train_loss = history.history['loss']\n",
        "train_accuracy = history.history['acc']\n",
        "validation_accuracy = history.history['val_acc']\n",
        "loss_plots(no_of_epochs,train_loss,validation_loss)\n",
        "accuracy_plots(no_of_epochs,train_accuracy,validation_accuracy)"
      ],
      "execution_count": 0,
      "outputs": [
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          "metadata": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pk3lV-HeVCdU",
        "colab_type": "text"
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      "source": [
        "**Observation**\n",
        "*   Even in this model, there is overfitting condition (which can be interpreted from both the plots)\n",
        "*   As we have seen in the example of Resnet as no.of.layers are increasing the model's performance is getting worse, the better approach would be to use hyperparameter tuning to find accurate number of epochs, moreover there are architecture and kernel size might also be the issue (which are again hyperparameters).\n",
        "*   There is uncertainity in the losses between train and test.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "v17bEgB7DasU"
      },
      "source": [
        "## Model - 3\n",
        "Complex Architecture of Convnet Only **6 layers of ConvNets** (With BatchNormalization and MaxPooling and **With** **padding and default stride**)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "kULpaW-wDasc",
        "colab": {}
      },
      "source": [
        "model_3 = Sequential()\n",
        "model_3.add(Conv2D(512,kernel_size=(7,7),\n",
        "                   activation='relu',\n",
        "                   padding='same',\n",
        "                   input_shape=input_shape))\n",
        "model_3.add(Conv2D(256,kernel_size=(7,7),\n",
        "                   activation='relu',\n",
        "                   padding='same'))\n",
        "model_3.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model_3.add(Conv2D(128,kernel_size=(7,7),\n",
        "                   activation='relu',\n",
        "                   padding='same'))\n",
        "model_3.add(Conv2D(64,kernel_size=(7,7),\n",
        "                   activation='relu',\n",
        "                   padding='same'))\n",
        "model_3.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model_3.add(Conv2D(32,kernel_size=(7,7),\n",
        "                   activation='relu',\n",
        "                   padding='same'))\n",
        "model_3.add(Conv2D(32,kernel_size=(7,7),\n",
        "                   activation='relu',\n",
        "                   padding='same'))\n",
        "model_3.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model_3.add(Flatten())\n",
        "model_3.add(Dense(256,activation='relu'))\n",
        "model_3.add(BatchNormalization())\n",
        "model_3.add(Dropout(0.20))\n",
        "model_3.add(Dense(128,activation='relu'))\n",
        "model_3.add(BatchNormalization())\n",
        "model_3.add(Dropout(0.20))\n",
        "model_3.add(Dense(64,activation='relu'))\n",
        "model_3.add(BatchNormalization())\n",
        "model_3.add(Dropout(0.20))\n",
        "model_3.add(Dense(10,activation='softmax'))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "aae64e25-c5a1-47c7-c1be-951758672a16",
        "id": "N62AitCNDasg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        }
      },
      "source": [
        "model_3.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 28, 28, 512)       25600     \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 28, 28, 256)       6422784   \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 14, 14, 128)       1605760   \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 14, 14, 64)        401472    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 7, 7, 32)          100384    \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 7, 7, 32)          50208     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 3, 3, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 288)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 256)               73984     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 256)               1024      \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 64)                256       \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 8,723,786\n",
            "Trainable params: 8,722,890\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6Mkn_od2Dasm",
        "colab": {}
      },
      "source": [
        "model_3.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "715ad19d-b29f-41e2-9b2e-b6623ac4a282",
        "id": "oZ-XVaLzDast",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "history = model_3.fit(X_train,Y_train,batch_size=batch_size,epochs=epoch,validation_data=(X_test,Y_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 271s 5ms/sample - loss: 0.5598 - acc: 0.8210 - val_loss: 1.6289 - val_acc: 0.4263\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 256s 4ms/sample - loss: 0.0744 - acc: 0.9795 - val_loss: 0.0511 - val_acc: 0.9841\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 256s 4ms/sample - loss: 0.0530 - acc: 0.9849 - val_loss: 0.0593 - val_acc: 0.9816\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 256s 4ms/sample - loss: 0.0404 - acc: 0.9882 - val_loss: 0.0511 - val_acc: 0.9833\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 256s 4ms/sample - loss: 0.0372 - acc: 0.9887 - val_loss: 0.0591 - val_acc: 0.9832\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 255s 4ms/sample - loss: 0.0290 - acc: 0.9917 - val_loss: 0.0678 - val_acc: 0.9801\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 254s 4ms/sample - loss: 0.0259 - acc: 0.9924 - val_loss: 0.0291 - val_acc: 0.9912\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 253s 4ms/sample - loss: 0.0263 - acc: 0.9923 - val_loss: 0.0613 - val_acc: 0.9808\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 252s 4ms/sample - loss: 0.0238 - acc: 0.9932 - val_loss: 0.0344 - val_acc: 0.9910\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 252s 4ms/sample - loss: 0.0204 - acc: 0.9941 - val_loss: 0.0619 - val_acc: 0.9840\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 252s 4ms/sample - loss: 0.0185 - acc: 0.9944 - val_loss: 0.0606 - val_acc: 0.9834\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 251s 4ms/sample - loss: 0.0161 - acc: 0.9951 - val_loss: 0.0355 - val_acc: 0.9914\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 250s 4ms/sample - loss: 0.0154 - acc: 0.9955 - val_loss: 0.0318 - val_acc: 0.9921\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 250s 4ms/sample - loss: 0.0135 - acc: 0.9962 - val_loss: 0.0312 - val_acc: 0.9927\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 249s 4ms/sample - loss: 0.0126 - acc: 0.9965 - val_loss: 0.0447 - val_acc: 0.9887\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 248s 4ms/sample - loss: 0.0122 - acc: 0.9964 - val_loss: 0.0550 - val_acc: 0.9863\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 248s 4ms/sample - loss: 0.0105 - acc: 0.9969 - val_loss: 0.0289 - val_acc: 0.9935\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 247s 4ms/sample - loss: 0.0109 - acc: 0.9969 - val_loss: 0.0326 - val_acc: 0.9915\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 247s 4ms/sample - loss: 0.0097 - acc: 0.9971 - val_loss: 0.0631 - val_acc: 0.9840\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 246s 4ms/sample - loss: 0.0093 - acc: 0.9971 - val_loss: 0.0407 - val_acc: 0.9921\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Hs7xhhnBDasx",
        "colab": {}
      },
      "source": [
        "score = model_3.evaluate(X_test,Y_test,verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "56b27e7e-57ac-455f-c4f7-ff4496f4b29d",
        "id": "ZaDu9efBDas4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(f\"Test score is {score[0]}\")\n",
        "print(f\"Test accuracy is {score[1]}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test score is 0.040735170758265306\n",
            "Test accuracy is 0.9921000003814697\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "8251be9f-ecdc-42de-b110-dd3d44cbd120",
        "id": "HUZMRg8lDas9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "validation_loss = history.history['val_loss']\n",
        "train_loss = history.history['loss']\n",
        "train_accuracy = history.history['acc']\n",
        "validation_accuracy = history.history['val_acc']\n",
        "loss_plots(no_of_epochs,train_loss,validation_loss)\n",
        "accuracy_plots(no_of_epochs,train_accuracy,validation_accuracy)"
      ],
      "execution_count": 0,
      "outputs": [
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          "metadata": {
            "tags": []
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4w442yaLpFE2",
        "colab_type": "text"
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      "source": [
        "**Observation**:\n",
        "\n",
        "\n",
        "By the plots, it is clearly evident that , the model has performed very well|\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTYTOOh7gmaH",
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      "source": [
        "**Final Observation**\n",
        "\n",
        "Among all the architectures complex architecture which we implemented as **model_3 has performed better than other architectures**. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}