lab_assignment2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "lab assignment2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Konic-NLP/5922-deep-learning/blob/main/lab_assignment2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "check the device and see the type of GPU"
      ],
      "metadata": {
        "id": "KJAf7VBLUTmx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ibadJC8dL4P_",
        "outputId": "9f7ebb8f-d159-433d-bd90-01229fede3ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Feb 14 03:05:43 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   73C    P0    72W / 149W |   2360MiB / 11441MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9glBSnTVZgyI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad14c39c-289c-4499-d43a-1b9647be548a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 3s 0us/step\n",
            "170508288/170498071 [==============================] - 3s 0us/step\n"
          ]
        }
      ],
      "source": [
        "#read the built-in data \n",
        "from keras.datasets import cifar10\n",
        "(trainx,trainy),(testx,testy) = cifar10.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#have a look the dataset\n",
        "import numpy as np\n",
        "print(trainx.shape,trainy.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwI9p1-hdYu8",
        "outputId": "8d2e0284-80f2-462b-e518-d6e0e3452889"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(50000, 32, 32, 3) (50000, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "concanate two parts into a whole\n"
      ],
      "metadata": {
        "id": "F6tQr6MAQGaD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=np.vstack((trainx,testx))\n",
        "Y=np.vstack((trainy,testy))"
      ],
      "metadata": {
        "id": "qIrGPkW9VFvz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#ensure the concanating works"
      ],
      "metadata": {
        "id": "A6yXyx2LQPgB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape,Y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhcoeUwoVfDc",
        "outputId": "557b560d-53c3-4f01-c615-ef29f3e5db01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 32, 32, 3) (60000, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "split the data according to the ratio"
      ],
      "metadata": {
        "id": "eP24GvmoQg9-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "fLR06XhEc9EM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,shuffle=True,random_state=1,stratify=Y)"
      ],
      "metadata": {
        "id": "b_Hj1jM6ZpxB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainx,x_val,trainy,y_val=train_test_split(x_train,y_train,test_size=0.25,shuffle=True,random_state=1,stratify=y_train)\n"
      ],
      "metadata": {
        "id": "s1JKlCRtZ5GL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train.shape,y_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9jvGlVsRPYf",
        "outputId": "2d52d880-23a0-41a3-d5a4-e35ce2d8b96f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(48000, 32, 32, 3) (48000, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "switch the class digit to one-hot"
      ],
      "metadata": {
        "id": "vJHmRAEBQlZr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "trainy = to_categorical(trainy)\n",
        "y_val = to_categorical(y_val)"
      ],
      "metadata": {
        "id": "RBnBh0BFYE0j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train=to_categorical(y_train)"
      ],
      "metadata": {
        "id": "NJexYSssjnM9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "view the split result and ensure everything is ok"
      ],
      "metadata": {
        "id": "ZxvuM_omQt8L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(trainx.shape,x_val.shape,x_test.shape,trainy.shape,y_val.shape,trainy[0].shape,y_val[0].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tyYrvqzPRf1D",
        "outputId": "46dedcee-d29a-4962-c971-c2043c7059a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(36000, 32, 32, 3) (12000, 32, 32, 3) (12000, 32, 32, 3) (36000, 10) (12000, 10) (10,) (10,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Part A: \n",
        "\n",
        "\n",
        "*   no regularization\n",
        "\n",
        "\n",
        "\n",
        "1.   single-convolution layer without regularization\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "heL5n1DPQ2Jl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import Sequential\n",
        "from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D\n",
        "import time\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = \"relu\"))\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "start=time.time()\n",
        "trained_model_base = model.fit(trainx,trainy, epochs = 15, batch_size = 64, validation_data=(x_val,y_val),shuffle=True)\n",
        "end=time.time()\n",
        "runtime=(end-start)/60\n",
        "print(\"running time is %.2f minutes\"%runtime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FZYo8rm3EjYt",
        "outputId": "7cbf44a5-2281-41cc-f09a-cad44938aa54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_10\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_15 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_15 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_9 (Flatten)         (None, 14400)             0         \n",
            "                                                                 \n",
            " dense_18 (Dense)            (None, 128)               1843328   \n",
            "                                                                 \n",
            " dense_19 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,846,410\n",
            "Trainable params: 1,846,410\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "563/563 [==============================] - 6s 10ms/step - loss: 9.8465 - accuracy: 0.3134 - val_loss: 1.6824 - val_accuracy: 0.4047\n",
            "Epoch 2/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 1.4266 - accuracy: 0.5030 - val_loss: 1.5669 - val_accuracy: 0.4585\n",
            "Epoch 3/15\n",
            "563/563 [==============================] - 5s 10ms/step - loss: 1.1378 - accuracy: 0.5993 - val_loss: 1.5109 - val_accuracy: 0.4986\n",
            "Epoch 4/15\n",
            "563/563 [==============================] - 5s 9ms/step - loss: 0.9052 - accuracy: 0.6817 - val_loss: 1.6323 - val_accuracy: 0.5029\n",
            "Epoch 5/15\n",
            "563/563 [==============================] - 5s 10ms/step - loss: 0.6908 - accuracy: 0.7587 - val_loss: 1.8274 - val_accuracy: 0.5098\n",
            "Epoch 6/15\n",
            "563/563 [==============================] - 6s 10ms/step - loss: 0.5337 - accuracy: 0.8169 - val_loss: 1.9941 - val_accuracy: 0.5013\n",
            "Epoch 7/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.3931 - accuracy: 0.8671 - val_loss: 2.5109 - val_accuracy: 0.4970\n",
            "Epoch 8/15\n",
            "563/563 [==============================] - 5s 10ms/step - loss: 0.3249 - accuracy: 0.8924 - val_loss: 2.5624 - val_accuracy: 0.4903\n",
            "Epoch 9/15\n",
            "563/563 [==============================] - 5s 10ms/step - loss: 0.2955 - accuracy: 0.9036 - val_loss: 2.8177 - val_accuracy: 0.5032\n",
            "Epoch 10/15\n",
            "563/563 [==============================] - 5s 10ms/step - loss: 0.2596 - accuracy: 0.9181 - val_loss: 2.9605 - val_accuracy: 0.4811\n",
            "Epoch 11/15\n",
            "563/563 [==============================] - 6s 10ms/step - loss: 0.2311 - accuracy: 0.9255 - val_loss: 3.4531 - val_accuracy: 0.4984\n",
            "Epoch 12/15\n",
            "563/563 [==============================] - 6s 10ms/step - loss: 0.1968 - accuracy: 0.9367 - val_loss: 3.5045 - val_accuracy: 0.4889\n",
            "Epoch 13/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.2038 - accuracy: 0.9374 - val_loss: 3.6183 - val_accuracy: 0.4914\n",
            "Epoch 14/15\n",
            "563/563 [==============================] - 5s 10ms/step - loss: 0.1963 - accuracy: 0.9398 - val_loss: 3.9069 - val_accuracy: 0.4918\n",
            "Epoch 15/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.1707 - accuracy: 0.9470 - val_loss: 4.2939 - val_accuracy: 0.4888\n",
            "running time is 2.38 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"single-CONV without regularization\",trained_model_base)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "suMLCJmgRDp3",
        "outputId": "06e1382d-ff48-4ddb-ab3a-035aa6b15653"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.   2-layers without regularization\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "GaqxH5RZV41Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import regularizers\n",
        "from tensorflow.python.keras.layers.normalization.batch_normalization import BatchNormalization\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\"))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = \"relu\"))\n",
        "\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "start=time.time()\n",
        "trained_2layer = model.fit(trainx,trainy, epochs = 15, batch_size = 64, validation_data=(x_val,y_val),shuffle=True)\n",
        "end=time.time()\n",
        "runtime=(end-start)/60\n",
        "print(\"running time is %.2f minutes\"%runtime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "--HxibF2ko58",
        "outputId": "f1b3ad63-cd41-4499-ef64-45c562006ddb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_16 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_16 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_17 (Conv2D)          (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_17 (MaxPoolin  (None, 6, 6, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_10 (Flatten)        (None, 2304)              0         \n",
            "                                                                 \n",
            " dense_20 (Dense)            (None, 128)               295040    \n",
            "                                                                 \n",
            " dense_21 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 335,050\n",
            "Trainable params: 335,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "563/563 [==============================] - 7s 11ms/step - loss: 2.5006 - accuracy: 0.3388 - val_loss: 1.5293 - val_accuracy: 0.4427\n",
            "Epoch 2/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 1.3923 - accuracy: 0.5047 - val_loss: 1.4262 - val_accuracy: 0.5098\n",
            "Epoch 3/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 1.1876 - accuracy: 0.5849 - val_loss: 1.2789 - val_accuracy: 0.5652\n",
            "Epoch 4/15\n",
            "563/563 [==============================] - 6s 12ms/step - loss: 1.0636 - accuracy: 0.6278 - val_loss: 1.1929 - val_accuracy: 0.5863\n",
            "Epoch 5/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.9459 - accuracy: 0.6677 - val_loss: 1.1644 - val_accuracy: 0.6059\n",
            "Epoch 6/15\n",
            "563/563 [==============================] - 6s 12ms/step - loss: 0.8555 - accuracy: 0.7014 - val_loss: 1.1802 - val_accuracy: 0.6082\n",
            "Epoch 7/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.7509 - accuracy: 0.7385 - val_loss: 1.2212 - val_accuracy: 0.6236\n",
            "Epoch 8/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.6695 - accuracy: 0.7677 - val_loss: 1.2553 - val_accuracy: 0.6227\n",
            "Epoch 9/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.5795 - accuracy: 0.7964 - val_loss: 1.3228 - val_accuracy: 0.6275\n",
            "Epoch 10/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.5143 - accuracy: 0.8221 - val_loss: 1.4932 - val_accuracy: 0.6118\n",
            "Epoch 11/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.4352 - accuracy: 0.8472 - val_loss: 1.6369 - val_accuracy: 0.6203\n",
            "Epoch 12/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.3960 - accuracy: 0.8605 - val_loss: 1.7239 - val_accuracy: 0.6125\n",
            "Epoch 13/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.3701 - accuracy: 0.8727 - val_loss: 1.7395 - val_accuracy: 0.6142\n",
            "Epoch 14/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.3202 - accuracy: 0.8902 - val_loss: 1.9159 - val_accuracy: 0.6136\n",
            "Epoch 15/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.2950 - accuracy: 0.8989 - val_loss: 2.1349 - val_accuracy: 0.6035\n",
            "running time is 1.57 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"2-layer CONV without regularization\",trained_2layer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "zOTB_HxCWHyh",
        "outputId": "1f9db52e-5974-45d6-9443-4b05b9c89c0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3.3-layer without regulairzation"
      ],
      "metadata": {
        "id": "6_7C_-eiaznY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\"))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\"))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = \"relu\"))\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "start=time.time()\n",
        "trained_model_3layer = model.fit(trainx,trainy, epochs = 15, batch_size = 64, validation_data=(x_val,y_val),shuffle=True)\n",
        "end=time.time()\n",
        "runtime=(end-start)/60\n",
        "print(\"running time is %.2f minutes\"%runtime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJyf3JVuleQ4",
        "outputId": "28db9a14-fb63-44f2-c3ee-75eba21daff4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_12\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_18 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_18 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_19 (Conv2D)          (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_19 (MaxPoolin  (None, 6, 6, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_20 (Conv2D)          (None, 4, 4, 64)          36928     \n",
            "                                                                 \n",
            " max_pooling2d_20 (MaxPoolin  (None, 2, 2, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_11 (Flatten)        (None, 256)               0         \n",
            "                                                                 \n",
            " dense_22 (Dense)            (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_23 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 109,834\n",
            "Trainable params: 109,834\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "563/563 [==============================] - 9s 14ms/step - loss: 2.0571 - accuracy: 0.3420 - val_loss: 1.5042 - val_accuracy: 0.4538\n",
            "Epoch 2/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 1.4483 - accuracy: 0.4798 - val_loss: 1.3833 - val_accuracy: 0.4987\n",
            "Epoch 3/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 1.2936 - accuracy: 0.5458 - val_loss: 1.2649 - val_accuracy: 0.5491\n",
            "Epoch 4/15\n",
            "563/563 [==============================] - 8s 13ms/step - loss: 1.1795 - accuracy: 0.5856 - val_loss: 1.1974 - val_accuracy: 0.5816\n",
            "Epoch 5/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 1.1002 - accuracy: 0.6164 - val_loss: 1.2183 - val_accuracy: 0.5679\n",
            "Epoch 6/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 1.0354 - accuracy: 0.6398 - val_loss: 1.1716 - val_accuracy: 0.5898\n",
            "Epoch 7/15\n",
            "563/563 [==============================] - 8s 13ms/step - loss: 0.9796 - accuracy: 0.6570 - val_loss: 1.1143 - val_accuracy: 0.6162\n",
            "Epoch 8/15\n",
            "563/563 [==============================] - 8s 13ms/step - loss: 0.9235 - accuracy: 0.6764 - val_loss: 1.1066 - val_accuracy: 0.6244\n",
            "Epoch 9/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.8703 - accuracy: 0.6956 - val_loss: 1.1414 - val_accuracy: 0.6208\n",
            "Epoch 10/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.8389 - accuracy: 0.7061 - val_loss: 1.0859 - val_accuracy: 0.6368\n",
            "Epoch 11/15\n",
            "563/563 [==============================] - 8s 13ms/step - loss: 0.7959 - accuracy: 0.7201 - val_loss: 1.0929 - val_accuracy: 0.6384\n",
            "Epoch 12/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.7551 - accuracy: 0.7351 - val_loss: 1.0933 - val_accuracy: 0.6440\n",
            "Epoch 13/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.7154 - accuracy: 0.7498 - val_loss: 1.1532 - val_accuracy: 0.6308\n",
            "Epoch 14/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.6831 - accuracy: 0.7618 - val_loss: 1.1607 - val_accuracy: 0.6352\n",
            "Epoch 15/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.6399 - accuracy: 0.7771 - val_loss: 1.2195 - val_accuracy: 0.6339\n",
            "running time is 2.38 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"3layer-COV without regularization\",trained_model_3layer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "8z5vee7oXJ5K",
        "outputId": "17ea7fb9-d57a-4fac-c786-535393d16176"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   with L2 regularization lambda=0.00005\n",
        "4. one convolutional layer with L2 regularization \n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "yKUgb9K5YdV3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import Sequential\n",
        "from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D\n",
        "import time\n",
        "from keras import regularizers\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3),kernel_regularizer=regularizers.l2(0.00005)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = \"relu\",kernel_regularizer=regularizers.l2(0.00005)))\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "start=time.time()\n",
        "trained_model_1layer_L2 = model.fit(trainx,trainy, epochs = 15, batch_size = 64, validation_data=(x_val,y_val),shuffle=True)\n",
        "end=time.time()\n",
        "runtime=(end-start)/60\n",
        "print(\"running time is %.2f minutes\"%runtime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yjhHDVrEIXiI",
        "outputId": "0587266b-9191-4dc9-ff3e-d840bbc2be3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_13\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_21 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_21 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_12 (Flatten)        (None, 14400)             0         \n",
            "                                                                 \n",
            " dense_24 (Dense)            (None, 128)               1843328   \n",
            "                                                                 \n",
            " dense_25 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,846,410\n",
            "Trainable params: 1,846,410\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "563/563 [==============================] - 7s 11ms/step - loss: 9.5260 - accuracy: 0.3312 - val_loss: 1.6122 - val_accuracy: 0.4359\n",
            "Epoch 2/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 1.4259 - accuracy: 0.5082 - val_loss: 1.5682 - val_accuracy: 0.4754\n",
            "Epoch 3/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 1.1616 - accuracy: 0.6054 - val_loss: 1.5475 - val_accuracy: 0.4974\n",
            "Epoch 4/15\n",
            "563/563 [==============================] - 6s 10ms/step - loss: 0.9461 - accuracy: 0.6847 - val_loss: 1.6387 - val_accuracy: 0.5067\n",
            "Epoch 5/15\n",
            "563/563 [==============================] - 5s 10ms/step - loss: 0.7739 - accuracy: 0.7476 - val_loss: 1.7469 - val_accuracy: 0.5173\n",
            "Epoch 6/15\n",
            "563/563 [==============================] - 5s 10ms/step - loss: 0.6238 - accuracy: 0.8092 - val_loss: 1.9700 - val_accuracy: 0.5095\n",
            "Epoch 7/15\n",
            "563/563 [==============================] - 5s 10ms/step - loss: 0.5283 - accuracy: 0.8447 - val_loss: 2.2081 - val_accuracy: 0.5131\n",
            "Epoch 8/15\n",
            "563/563 [==============================] - 5s 10ms/step - loss: 0.4713 - accuracy: 0.8700 - val_loss: 2.4591 - val_accuracy: 0.5079\n",
            "Epoch 9/15\n",
            "563/563 [==============================] - 5s 9ms/step - loss: 0.4329 - accuracy: 0.8874 - val_loss: 2.7479 - val_accuracy: 0.5148\n",
            "Epoch 10/15\n",
            "563/563 [==============================] - 5s 10ms/step - loss: 0.4273 - accuracy: 0.8928 - val_loss: 2.9441 - val_accuracy: 0.5078\n",
            "Epoch 11/15\n",
            "563/563 [==============================] - 5s 10ms/step - loss: 0.4087 - accuracy: 0.9046 - val_loss: 3.3886 - val_accuracy: 0.4859\n",
            "Epoch 12/15\n",
            "563/563 [==============================] - 5s 10ms/step - loss: 0.3776 - accuracy: 0.9198 - val_loss: 3.2866 - val_accuracy: 0.4947\n",
            "Epoch 13/15\n",
            "563/563 [==============================] - 5s 10ms/step - loss: 0.4041 - accuracy: 0.9159 - val_loss: 3.3482 - val_accuracy: 0.5074\n",
            "Epoch 14/15\n",
            "563/563 [==============================] - 5s 9ms/step - loss: 0.3731 - accuracy: 0.9300 - val_loss: 3.5974 - val_accuracy: 0.5151\n",
            "Epoch 15/15\n",
            "563/563 [==============================] - 5s 10ms/step - loss: 0.3700 - accuracy: 0.9336 - val_loss: 3.5245 - val_accuracy: 0.5053\n",
            "running time is 2.39 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"1-CONV layer with regularization\",trained_model_1layer_L2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "-sjCM1KUZi2G",
        "outputId": "a5b8f910-e199-42db-9861-8c2310fe46c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5.2-layer convolutional layers with regularization"
      ],
      "metadata": {
        "id": "EYoEn3YGaHyM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#model 2 one-layer with L2 regularization\n",
        "from keras import regularizers\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3),kernel_regularizer=regularizers.l2(0.00005)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\",kernel_regularizer=regularizers.l2(0.00005)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = \"relu\",kernel_regularizer=regularizers.l2(0.00005)))\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "start=time.time()\n",
        "trained_model_2layer_L2 = model.fit(trainx,trainy, epochs = 15, batch_size = 64, validation_data=(x_val,y_val),shuffle=True)\n",
        "end=time.time()\n",
        "runtime=(end-start)/60\n",
        "print(\"running time is %.2f minutes\"%runtime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H-l5xv4XQs5z",
        "outputId": "6627bf82-e294-49ee-a0d0-0a7e8d39dd7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_14\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_22 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_22 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_23 (Conv2D)          (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_23 (MaxPoolin  (None, 6, 6, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_13 (Flatten)        (None, 2304)              0         \n",
            "                                                                 \n",
            " dense_26 (Dense)            (None, 128)               295040    \n",
            "                                                                 \n",
            " dense_27 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 335,050\n",
            "Trainable params: 335,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "563/563 [==============================] - 8s 12ms/step - loss: 2.8015 - accuracy: 0.2662 - val_loss: 1.7961 - val_accuracy: 0.3531\n",
            "Epoch 2/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 1.6261 - accuracy: 0.4178 - val_loss: 1.5357 - val_accuracy: 0.4497\n",
            "Epoch 3/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 1.4378 - accuracy: 0.4920 - val_loss: 1.3898 - val_accuracy: 0.5063\n",
            "Epoch 4/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 1.2794 - accuracy: 0.5513 - val_loss: 1.3244 - val_accuracy: 0.5379\n",
            "Epoch 5/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 1.1444 - accuracy: 0.6029 - val_loss: 1.2704 - val_accuracy: 0.5744\n",
            "Epoch 6/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 1.0198 - accuracy: 0.6492 - val_loss: 1.2732 - val_accuracy: 0.5833\n",
            "Epoch 7/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.9070 - accuracy: 0.6935 - val_loss: 1.3660 - val_accuracy: 0.5729\n",
            "Epoch 8/15\n",
            "563/563 [==============================] - 6s 12ms/step - loss: 0.8043 - accuracy: 0.7302 - val_loss: 1.3334 - val_accuracy: 0.5905\n",
            "Epoch 9/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.7140 - accuracy: 0.7620 - val_loss: 1.4292 - val_accuracy: 0.5947\n",
            "Epoch 10/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.6353 - accuracy: 0.7927 - val_loss: 1.5483 - val_accuracy: 0.5786\n",
            "Epoch 11/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.5531 - accuracy: 0.8240 - val_loss: 1.5911 - val_accuracy: 0.6019\n",
            "Epoch 12/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.4872 - accuracy: 0.8500 - val_loss: 1.7462 - val_accuracy: 0.5971\n",
            "Epoch 13/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.4431 - accuracy: 0.8671 - val_loss: 1.8140 - val_accuracy: 0.6098\n",
            "Epoch 14/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.4029 - accuracy: 0.8827 - val_loss: 2.0292 - val_accuracy: 0.5901\n",
            "Epoch 15/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.3901 - accuracy: 0.8907 - val_loss: 2.0516 - val_accuracy: 0.6022\n",
            "running time is 1.72 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"2 CONV layers with L2 regularization\",trained_model_2layer_L2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Ouv6zOIGbJ9g",
        "outputId": "8e1ec600-725d-49b9-b6db-00974fc3eada"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "6. 3-layer convolutional layers with regularization"
      ],
      "metadata": {
        "id": "QIEYXPkFccy_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3),kernel_regularizer=regularizers.l2(0.00005)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", kernel_regularizer=regularizers.l2(0.00005)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", kernel_regularizer=regularizers.l2(0.00005)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = \"relu\",kernel_regularizer=regularizers.l2(0.00005)))\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "start=time.time()\n",
        "trained_model_3layer_L2 = model.fit(trainx,trainy, epochs = 15, batch_size = 64, validation_data=(x_val,y_val),shuffle=True)\n",
        "end=time.time()\n",
        "runtime=(end-start)/60\n",
        "print(\"running time is %.2f minutes\"%runtime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6TaurlXnoFTB",
        "outputId": "80ec1b87-1c7d-43e6-fe8f-fedc153854ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_14\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_21 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_21 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_22 (Conv2D)          (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_22 (MaxPoolin  (None, 6, 6, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_23 (Conv2D)          (None, 4, 4, 64)          36928     \n",
            "                                                                 \n",
            " max_pooling2d_23 (MaxPoolin  (None, 2, 2, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_14 (Flatten)        (None, 256)               0         \n",
            "                                                                 \n",
            " dense_28 (Dense)            (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_29 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 109,834\n",
            "Trainable params: 109,834\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "563/563 [==============================] - 9s 14ms/step - loss: 2.3781 - accuracy: 0.3347 - val_loss: 1.5993 - val_accuracy: 0.4148\n",
            "Epoch 2/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 1.4824 - accuracy: 0.4711 - val_loss: 1.4412 - val_accuracy: 0.4767\n",
            "Epoch 3/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 1.3384 - accuracy: 0.5285 - val_loss: 1.4080 - val_accuracy: 0.4967\n",
            "Epoch 4/15\n",
            "563/563 [==============================] - 8s 13ms/step - loss: 1.2447 - accuracy: 0.5638 - val_loss: 1.3086 - val_accuracy: 0.5441\n",
            "Epoch 5/15\n",
            "563/563 [==============================] - 8s 13ms/step - loss: 1.1572 - accuracy: 0.5998 - val_loss: 1.2106 - val_accuracy: 0.5817\n",
            "Epoch 6/15\n",
            "563/563 [==============================] - 8s 13ms/step - loss: 1.0920 - accuracy: 0.6214 - val_loss: 1.1996 - val_accuracy: 0.5932\n",
            "Epoch 7/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 1.0244 - accuracy: 0.6474 - val_loss: 1.1242 - val_accuracy: 0.6153\n",
            "Epoch 8/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.9762 - accuracy: 0.6650 - val_loss: 1.1050 - val_accuracy: 0.6187\n",
            "Epoch 9/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 0.9187 - accuracy: 0.6839 - val_loss: 1.1117 - val_accuracy: 0.6302\n",
            "Epoch 10/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.8718 - accuracy: 0.7000 - val_loss: 1.0866 - val_accuracy: 0.6395\n",
            "Epoch 11/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.8248 - accuracy: 0.7190 - val_loss: 1.2001 - val_accuracy: 0.6128\n",
            "Epoch 12/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.7967 - accuracy: 0.7297 - val_loss: 1.0781 - val_accuracy: 0.6476\n",
            "Epoch 13/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.7562 - accuracy: 0.7446 - val_loss: 1.2016 - val_accuracy: 0.6152\n",
            "Epoch 14/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.7235 - accuracy: 0.7565 - val_loss: 1.1452 - val_accuracy: 0.6450\n",
            "Epoch 15/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.6783 - accuracy: 0.7686 - val_loss: 1.1684 - val_accuracy: 0.6499\n",
            "running time is 2.38 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(trained_model_3layer_L2.history[\"accuracy\"], color =\"green\", label = \"train-accuracy\")\n",
        "\n",
        "# plt.plot(trained_model_base.history[\"val_loss\"], color =\"red\", label = \"base-test\")\n",
        "plt.plot(trained_model_3layer_L2.history[\"val_accuracy\"], color =\"orange\", label = \"val_accuracy\")\n",
        "\n",
        "plt.title(\"the 3-conv layer model with L2 regularazation\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.xlabel(\"Number of epochs\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "kZV7n5TSTHaB",
        "outputId": "6e6408f3-d99d-4ebf-e34a-b476bbe15eb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*  Drop out with doup out rate=0.1\n",
        "\n",
        "7. 1 layer convolutional layer with dropout\n",
        "\n"
      ],
      "metadata": {
        "id": "nz9oT_9Ncpgh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# single convolutional layer with dropout\n",
        "from keras import Sequential\n",
        "from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D\n",
        "import time\n",
        "from tensorflow.python.keras.layers.core import Dropout\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = \"relu\"))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "start=time.time()\n",
        "trained_model_base_dp = model.fit(trainx,trainy, epochs = 15, batch_size = 64, validation_data=(x_val,y_val),shuffle=True)\n",
        "end=time.time()\n",
        "runtime=(end-start)/60\n",
        "print(\"running time is %.2f minutes\"%runtime)"
      ],
      "metadata": {
        "id": "mYagxV3HX7zh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c0b7926-ebee-4998-acb2-b438389a843c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_3 (Conv2D)           (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 15, 15, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " module_wrapper (ModuleWrapp  (None, 15, 15, 64)       0         \n",
            " er)                                                             \n",
            "                                                                 \n",
            " flatten_3 (Flatten)         (None, 14400)             0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 128)               1843328   \n",
            "                                                                 \n",
            " module_wrapper_1 (ModuleWra  (None, 128)              0         \n",
            " pper)                                                           \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,846,410\n",
            "Trainable params: 1,846,410\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "563/563 [==============================] - 7s 11ms/step - loss: 8.0635 - accuracy: 0.2407 - val_loss: 1.9285 - val_accuracy: 0.3149\n",
            "Epoch 2/15\n",
            "563/563 [==============================] - 6s 10ms/step - loss: 1.7202 - accuracy: 0.3709 - val_loss: 1.5882 - val_accuracy: 0.4384\n",
            "Epoch 3/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 1.5193 - accuracy: 0.4584 - val_loss: 1.5167 - val_accuracy: 0.4807\n",
            "Epoch 4/15\n",
            "563/563 [==============================] - 6s 10ms/step - loss: 1.3766 - accuracy: 0.5146 - val_loss: 1.6512 - val_accuracy: 0.4541\n",
            "Epoch 5/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 1.2679 - accuracy: 0.5555 - val_loss: 1.4003 - val_accuracy: 0.5297\n",
            "Epoch 6/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 1.1718 - accuracy: 0.5854 - val_loss: 1.4063 - val_accuracy: 0.5263\n",
            "Epoch 7/15\n",
            "563/563 [==============================] - 6s 10ms/step - loss: 1.0944 - accuracy: 0.6114 - val_loss: 1.4724 - val_accuracy: 0.5155\n",
            "Epoch 8/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 1.0194 - accuracy: 0.6382 - val_loss: 1.5693 - val_accuracy: 0.5114\n",
            "Epoch 9/15\n",
            "563/563 [==============================] - 6s 10ms/step - loss: 0.9696 - accuracy: 0.6579 - val_loss: 1.5052 - val_accuracy: 0.5378\n",
            "Epoch 10/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.9314 - accuracy: 0.6715 - val_loss: 1.4930 - val_accuracy: 0.5361\n",
            "Epoch 11/15\n",
            "563/563 [==============================] - 6s 10ms/step - loss: 0.8666 - accuracy: 0.6966 - val_loss: 1.5400 - val_accuracy: 0.5422\n",
            "Epoch 12/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.8176 - accuracy: 0.7094 - val_loss: 1.5996 - val_accuracy: 0.5438\n",
            "Epoch 13/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.7859 - accuracy: 0.7236 - val_loss: 1.6299 - val_accuracy: 0.5390\n",
            "Epoch 14/15\n",
            "563/563 [==============================] - 6s 10ms/step - loss: 0.7624 - accuracy: 0.7346 - val_loss: 1.7318 - val_accuracy: 0.5197\n",
            "Epoch 15/15\n",
            "563/563 [==============================] - 6s 10ms/step - loss: 0.7340 - accuracy: 0.7425 - val_loss: 1.7959 - val_accuracy: 0.5447\n",
            "running time is 2.38 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"1-CONV layer with dropout\",trained_model_base_dp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "bKAjE7Myc9aL",
        "outputId": "03893c55-7337-42ce-fc43-15e86d5292f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "8. 2-layer with dropout"
      ],
      "metadata": {
        "id": "p6sWFXBze-7D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 2 convolutional layer with dropout\n",
        "from keras import Sequential\n",
        "from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D\n",
        "import time\n",
        "from tensorflow.python.keras.layers.core import Dropout\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\"))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = \"relu\"))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "start=time.time()\n",
        "trained_model_2layer_DP = model.fit(trainx,trainy, epochs = 15, batch_size = 64, validation_data=(x_val,y_val),shuffle=True)\n",
        "end=time.time()\n",
        "runtime=(end-start)/60\n",
        "print(\"running time is %.2f minutes\"%runtime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbEzV2Dci0un",
        "outputId": "8b43a297-0732-444b-a261-df80522d8c3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_4 (Conv2D)           (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPooling  (None, 15, 15, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " module_wrapper_2 (ModuleWra  (None, 15, 15, 64)       0         \n",
            " pper)                                                           \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPooling  (None, 6, 6, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " module_wrapper_3 (ModuleWra  (None, 6, 6, 64)         0         \n",
            " pper)                                                           \n",
            "                                                                 \n",
            " flatten_4 (Flatten)         (None, 2304)              0         \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 128)               295040    \n",
            "                                                                 \n",
            " module_wrapper_4 (ModuleWra  (None, 128)              0         \n",
            " pper)                                                           \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 335,050\n",
            "Trainable params: 335,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "563/563 [==============================] - 8s 13ms/step - loss: 2.7643 - accuracy: 0.2051 - val_loss: 1.8105 - val_accuracy: 0.3327\n",
            "Epoch 2/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 1.6690 - accuracy: 0.3945 - val_loss: 1.4552 - val_accuracy: 0.4755\n",
            "Epoch 3/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 1.4210 - accuracy: 0.4934 - val_loss: 1.3332 - val_accuracy: 0.5299\n",
            "Epoch 4/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 1.2842 - accuracy: 0.5449 - val_loss: 1.2492 - val_accuracy: 0.5628\n",
            "Epoch 5/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 1.1770 - accuracy: 0.5905 - val_loss: 1.1682 - val_accuracy: 0.5963\n",
            "Epoch 6/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 1.0906 - accuracy: 0.6163 - val_loss: 1.1437 - val_accuracy: 0.6053\n",
            "Epoch 7/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 1.0158 - accuracy: 0.6441 - val_loss: 1.1448 - val_accuracy: 0.6007\n",
            "Epoch 8/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.9434 - accuracy: 0.6706 - val_loss: 1.0694 - val_accuracy: 0.6360\n",
            "Epoch 9/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.8836 - accuracy: 0.6904 - val_loss: 1.1071 - val_accuracy: 0.6303\n",
            "Epoch 10/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.8416 - accuracy: 0.7049 - val_loss: 1.1412 - val_accuracy: 0.6217\n",
            "Epoch 11/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.7879 - accuracy: 0.7254 - val_loss: 1.1164 - val_accuracy: 0.6388\n",
            "Epoch 12/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.7560 - accuracy: 0.7339 - val_loss: 1.0794 - val_accuracy: 0.6479\n",
            "Epoch 13/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.7118 - accuracy: 0.7519 - val_loss: 1.1779 - val_accuracy: 0.6299\n",
            "Epoch 14/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.6676 - accuracy: 0.7664 - val_loss: 1.1369 - val_accuracy: 0.6432\n",
            "Epoch 15/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.6394 - accuracy: 0.7772 - val_loss: 1.1771 - val_accuracy: 0.6492\n",
            "running time is 1.72 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"the 2-conv model with Dropout\",trained_model_2layer_DP)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "1t207Cf4egpb",
        "outputId": "ae2e3e7b-d19f-4bf1-b379-9924d883e3a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "9. 3-CONV layer with dropout"
      ],
      "metadata": {
        "id": "8KeXoKjZfH-X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\"))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\"))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = \"relu\"))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "start=time.time()\n",
        "trained_model_3layer_DP = model.fit(trainx,trainy, epochs = 15, batch_size = 64, validation_data=(x_val,y_val),shuffle=True)\n",
        "end=time.time()\n",
        "runtime=(end-start)/60\n",
        "print(\"running time is %.2f minutes\"%runtime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FVCX4qhZpBCM",
        "outputId": "2bbda66e-b628-44d6-9688-dbcbb1a7e8cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_15\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_24 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_24 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_19 (ModuleWr  (None, 15, 15, 64)       0         \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " conv2d_25 (Conv2D)          (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_25 (MaxPoolin  (None, 6, 6, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_20 (ModuleWr  (None, 6, 6, 64)         0         \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " conv2d_26 (Conv2D)          (None, 4, 4, 64)          36928     \n",
            "                                                                 \n",
            " max_pooling2d_26 (MaxPoolin  (None, 2, 2, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_21 (ModuleWr  (None, 2, 2, 64)         0         \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " flatten_15 (Flatten)        (None, 256)               0         \n",
            "                                                                 \n",
            " dense_30 (Dense)            (None, 128)               32896     \n",
            "                                                                 \n",
            " module_wrapper_22 (ModuleWr  (None, 128)              0         \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " dense_31 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 109,834\n",
            "Trainable params: 109,834\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "563/563 [==============================] - 9s 14ms/step - loss: 2.4236 - accuracy: 0.2701 - val_loss: 1.6487 - val_accuracy: 0.4087\n",
            "Epoch 2/15\n",
            "563/563 [==============================] - 8s 13ms/step - loss: 1.6207 - accuracy: 0.4050 - val_loss: 1.4775 - val_accuracy: 0.4670\n",
            "Epoch 3/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 1.4830 - accuracy: 0.4644 - val_loss: 1.4017 - val_accuracy: 0.4939\n",
            "Epoch 4/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 1.3964 - accuracy: 0.5002 - val_loss: 1.2969 - val_accuracy: 0.5420\n",
            "Epoch 5/15\n",
            "563/563 [==============================] - 8s 13ms/step - loss: 1.3221 - accuracy: 0.5314 - val_loss: 1.2175 - val_accuracy: 0.5656\n",
            "Epoch 6/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 1.2735 - accuracy: 0.5496 - val_loss: 1.1830 - val_accuracy: 0.5817\n",
            "Epoch 7/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 1.2154 - accuracy: 0.5695 - val_loss: 1.1382 - val_accuracy: 0.5968\n",
            "Epoch 8/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 1.1766 - accuracy: 0.5856 - val_loss: 1.1967 - val_accuracy: 0.5803\n",
            "Epoch 9/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 1.1256 - accuracy: 0.6021 - val_loss: 1.0686 - val_accuracy: 0.6242\n",
            "Epoch 10/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 1.0923 - accuracy: 0.6166 - val_loss: 1.0793 - val_accuracy: 0.6175\n",
            "Epoch 11/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 1.0554 - accuracy: 0.6307 - val_loss: 1.0183 - val_accuracy: 0.6434\n",
            "Epoch 12/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 1.0235 - accuracy: 0.6396 - val_loss: 1.0503 - val_accuracy: 0.6330\n",
            "Epoch 13/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 1.0021 - accuracy: 0.6482 - val_loss: 1.0673 - val_accuracy: 0.6213\n",
            "Epoch 14/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 0.9902 - accuracy: 0.6524 - val_loss: 1.0003 - val_accuracy: 0.6506\n",
            "Epoch 15/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.9565 - accuracy: 0.6673 - val_loss: 0.9858 - val_accuracy: 0.6622\n",
            "running time is 1.96 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(trained_model_3layer_DP.history[\"accuracy\"], color =\"green\", label = \"train-accuracy\")\n",
        "\n",
        "# plt.plot(trained_model_base.history[\"val_loss\"], color =\"red\", label = \"base-test\")\n",
        "plt.plot(trained_model_3layer_DP.history[\"val_accuracy\"], color =\"orange\", label = \"val_accuracy\")\n",
        "\n",
        "plt.title(\"the 3-conv model with Dropout\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.xlabel(\"Number of epochs\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "K17RLfS2cjq6",
        "outputId": "d98f4137-696e-4acc-cb67-869a8ef2eb1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   batch normalization\n",
        "10. 1-layer CONV with batch normalization\n",
        "\n"
      ],
      "metadata": {
        "id": "IoJDe62BgDM3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#10 1 layer Batch Normalization\n",
        "\n",
        "from keras import regularizers\n",
        "from tensorflow.python.keras.layers.normalization.batch_normalization import BatchNormalization\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = \"relu\"))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "start=time.time()\n",
        "trained_model_BN = model.fit(trainx,trainy, epochs = 15, batch_size = 64, validation_data=(x_val,y_val),shuffle=True)\n",
        "end=time.time()\n",
        "runtime=(end-start)/60\n",
        "print(\"running time is %.2f minutes\"%runtime)"
      ],
      "metadata": {
        "id": "YAgGz-EiY513",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a595288-e241-435a-a443-74aaa1438def"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_15\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_24 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_24 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_9 (ModuleWra  (None, 15, 15, 64)       256       \n",
            " pper)                                                           \n",
            "                                                                 \n",
            " flatten_14 (Flatten)        (None, 14400)             0         \n",
            "                                                                 \n",
            " dense_28 (Dense)            (None, 128)               1843328   \n",
            "                                                                 \n",
            " module_wrapper_10 (ModuleWr  (None, 128)              512       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " dense_29 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,847,178\n",
            "Trainable params: 1,846,794\n",
            "Non-trainable params: 384\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "563/563 [==============================] - 7s 11ms/step - loss: 1.3369 - accuracy: 0.5351 - val_loss: 1.6640 - val_accuracy: 0.4697\n",
            "Epoch 2/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.9266 - accuracy: 0.6784 - val_loss: 1.1650 - val_accuracy: 0.6044\n",
            "Epoch 3/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.7072 - accuracy: 0.7524 - val_loss: 2.1223 - val_accuracy: 0.4776\n",
            "Epoch 4/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.5199 - accuracy: 0.8251 - val_loss: 1.2937 - val_accuracy: 0.6020\n",
            "Epoch 5/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.3665 - accuracy: 0.8784 - val_loss: 1.4495 - val_accuracy: 0.5881\n",
            "Epoch 6/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.2600 - accuracy: 0.9176 - val_loss: 1.5881 - val_accuracy: 0.6100\n",
            "Epoch 7/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.1938 - accuracy: 0.9379 - val_loss: 1.5536 - val_accuracy: 0.6301\n",
            "Epoch 8/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.1467 - accuracy: 0.9540 - val_loss: 1.5976 - val_accuracy: 0.6356\n",
            "Epoch 9/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.1189 - accuracy: 0.9614 - val_loss: 1.7402 - val_accuracy: 0.6060\n",
            "Epoch 10/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.1112 - accuracy: 0.9641 - val_loss: 2.1421 - val_accuracy: 0.5709\n",
            "Epoch 11/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.0903 - accuracy: 0.9714 - val_loss: 2.0365 - val_accuracy: 0.5996\n",
            "Epoch 12/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.0827 - accuracy: 0.9739 - val_loss: 3.0753 - val_accuracy: 0.5437\n",
            "Epoch 13/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.0751 - accuracy: 0.9762 - val_loss: 1.9379 - val_accuracy: 0.6226\n",
            "Epoch 14/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.0708 - accuracy: 0.9765 - val_loss: 2.0928 - val_accuracy: 0.6051\n",
            "Epoch 15/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.0723 - accuracy: 0.9759 - val_loss: 2.5336 - val_accuracy: 0.5864\n",
            "running time is 1.63 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"1layer with Batch_normalization\",trained_model_BN)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "74nVXkydg5AN",
        "outputId": "fa4eddfd-84b6-4c26-b55a-4efa2723bec0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "11. 2-CONV layer with batch normalization"
      ],
      "metadata": {
        "id": "3GoIBP9phtWh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from keras import regularizers\n",
        "from tensorflow.python.keras.layers.normalization.batch_normalization import BatchNormalization\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\"))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = \"relu\"))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "start=time.time()\n",
        "trained_model_2layer_BN = model.fit(trainx,trainy, epochs = 15, batch_size = 64, validation_data=(x_val,y_val),shuffle=True)\n",
        "end=time.time()\n",
        "runtime=(end-start)/60\n",
        "print(\"running time is %.2f minutes\"%runtime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hn0eIv4ij5pa",
        "outputId": "36e3cbbe-b448-4da2-ec16-28add5eefc76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_25 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_25 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_11 (ModuleWr  (None, 15, 15, 64)       256       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " conv2d_26 (Conv2D)          (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_26 (MaxPoolin  (None, 6, 6, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_12 (ModuleWr  (None, 6, 6, 64)         256       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " flatten_15 (Flatten)        (None, 2304)              0         \n",
            "                                                                 \n",
            " dense_30 (Dense)            (None, 128)               295040    \n",
            "                                                                 \n",
            " module_wrapper_13 (ModuleWr  (None, 128)              512       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " dense_31 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 336,074\n",
            "Trainable params: 335,562\n",
            "Non-trainable params: 512\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "563/563 [==============================] - 9s 14ms/step - loss: 1.2971 - accuracy: 0.5480 - val_loss: 1.3158 - val_accuracy: 0.5604\n",
            "Epoch 2/15\n",
            "563/563 [==============================] - 8s 13ms/step - loss: 0.9250 - accuracy: 0.6762 - val_loss: 1.1913 - val_accuracy: 0.5886\n",
            "Epoch 3/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.7550 - accuracy: 0.7358 - val_loss: 0.9453 - val_accuracy: 0.6748\n",
            "Epoch 4/15\n",
            "563/563 [==============================] - 8s 13ms/step - loss: 0.6295 - accuracy: 0.7837 - val_loss: 1.0062 - val_accuracy: 0.6683\n",
            "Epoch 5/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.5041 - accuracy: 0.8268 - val_loss: 1.3658 - val_accuracy: 0.5841\n",
            "Epoch 6/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.4080 - accuracy: 0.8610 - val_loss: 1.2767 - val_accuracy: 0.6175\n",
            "Epoch 7/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.3176 - accuracy: 0.8926 - val_loss: 1.5168 - val_accuracy: 0.5957\n",
            "Epoch 8/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.2470 - accuracy: 0.9191 - val_loss: 1.2134 - val_accuracy: 0.6737\n",
            "Epoch 9/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.1922 - accuracy: 0.9359 - val_loss: 1.2605 - val_accuracy: 0.6577\n",
            "Epoch 10/15\n",
            "563/563 [==============================] - 8s 13ms/step - loss: 0.1551 - accuracy: 0.9501 - val_loss: 1.6759 - val_accuracy: 0.6278\n",
            "Epoch 11/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.1342 - accuracy: 0.9570 - val_loss: 1.4094 - val_accuracy: 0.6870\n",
            "Epoch 12/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.1049 - accuracy: 0.9660 - val_loss: 1.3476 - val_accuracy: 0.6842\n",
            "Epoch 13/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.1022 - accuracy: 0.9659 - val_loss: 1.5805 - val_accuracy: 0.6514\n",
            "Epoch 14/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.0997 - accuracy: 0.9669 - val_loss: 1.9558 - val_accuracy: 0.6116\n",
            "Epoch 15/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.0921 - accuracy: 0.9687 - val_loss: 1.6505 - val_accuracy: 0.6623\n",
            "running time is 2.39 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"2conv with batch_normalization\",trained_model_2layer_BN)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "x5am-sZ8jEiR",
        "outputId": "5f10c2f5-ae38-4fe5-f739-2adf3fc97fec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "12. 3-layer with batch normalization"
      ],
      "metadata": {
        "id": "CBCcnFb7lB-9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.python.keras.layers.normalization.batch_normalization import BatchNormalization\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\"))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\"))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = \"relu\"))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "start=time.time()\n",
        "trained_model_3layer_BN = model.fit(trainx,trainy, epochs = 15, batch_size = 64, validation_data=(x_val,y_val),shuffle=True)\n",
        "end=time.time()\n",
        "runtime=(end-start)/60\n",
        "print(\"running time is %.2f minutes\"%runtime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HhuNrmhFqE8J",
        "outputId": "e515db3b-a77d-42dc-d46e-27ec9c1a1fb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_10 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_10 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_5 (ModuleWra  (None, 15, 15, 64)       256       \n",
            " pper)                                                           \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_11 (MaxPoolin  (None, 6, 6, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_6 (ModuleWra  (None, 6, 6, 64)         256       \n",
            " pper)                                                           \n",
            "                                                                 \n",
            " conv2d_12 (Conv2D)          (None, 4, 4, 64)          36928     \n",
            "                                                                 \n",
            " max_pooling2d_12 (MaxPoolin  (None, 2, 2, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_7 (ModuleWra  (None, 2, 2, 64)         256       \n",
            " pper)                                                           \n",
            "                                                                 \n",
            " flatten_6 (Flatten)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 128)               32896     \n",
            "                                                                 \n",
            " module_wrapper_8 (ModuleWra  (None, 128)              512       \n",
            " pper)                                                           \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 111,114\n",
            "Trainable params: 110,474\n",
            "Non-trainable params: 640\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "563/563 [==============================] - 10s 16ms/step - loss: 1.4209 - accuracy: 0.4974 - val_loss: 1.5951 - val_accuracy: 0.4683\n",
            "Epoch 2/15\n",
            "563/563 [==============================] - 8s 15ms/step - loss: 1.0362 - accuracy: 0.6364 - val_loss: 1.1461 - val_accuracy: 0.5930\n",
            "Epoch 3/15\n",
            "563/563 [==============================] - 8s 15ms/step - loss: 0.8640 - accuracy: 0.6981 - val_loss: 1.0479 - val_accuracy: 0.6379\n",
            "Epoch 4/15\n",
            "563/563 [==============================] - 8s 15ms/step - loss: 0.7648 - accuracy: 0.7319 - val_loss: 1.0726 - val_accuracy: 0.6335\n",
            "Epoch 5/15\n",
            "563/563 [==============================] - 8s 15ms/step - loss: 0.6833 - accuracy: 0.7609 - val_loss: 1.7133 - val_accuracy: 0.4997\n",
            "Epoch 6/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 0.6109 - accuracy: 0.7874 - val_loss: 1.0388 - val_accuracy: 0.6582\n",
            "Epoch 7/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 0.5494 - accuracy: 0.8100 - val_loss: 1.1136 - val_accuracy: 0.6400\n",
            "Epoch 8/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 0.4861 - accuracy: 0.8305 - val_loss: 0.8915 - val_accuracy: 0.7053\n",
            "Epoch 9/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 0.4433 - accuracy: 0.8450 - val_loss: 0.9358 - val_accuracy: 0.7002\n",
            "Epoch 10/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 0.3950 - accuracy: 0.8626 - val_loss: 1.0684 - val_accuracy: 0.6783\n",
            "Epoch 11/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 0.3621 - accuracy: 0.8724 - val_loss: 1.0815 - val_accuracy: 0.6827\n",
            "Epoch 12/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 0.3196 - accuracy: 0.8878 - val_loss: 1.1232 - val_accuracy: 0.6775\n",
            "Epoch 13/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 0.2922 - accuracy: 0.8967 - val_loss: 1.2396 - val_accuracy: 0.6778\n",
            "Epoch 14/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 0.2667 - accuracy: 0.9071 - val_loss: 1.2183 - val_accuracy: 0.6903\n",
            "Epoch 15/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 0.2458 - accuracy: 0.9144 - val_loss: 1.2479 - val_accuracy: 0.6784\n",
            "running time is 2.07 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(trained_model_3layer_BN.history[\"accuracy\"], color =\"green\", label = \"train-accuracy\")\n",
        "\n",
        "# plt.plot(trained_model_base.history[\"val_loss\"], color =\"red\", label = \"base-test\")\n",
        "plt.plot(trained_model_3layer_BN.history[\"val_accuracy\"], color =\"orange\", label = \"val_accuracy\")\n",
        "\n",
        "plt.title(\"the 3-layer CONV model with BatchNormalization\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.xlabel(\"Number of epochs\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "0UsDC7MsfLO-",
        "outputId": "a7629873-2f2e-4cbb-938a-081b17660365"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   best model with 'all' training data\n",
        "\n"
      ],
      "metadata": {
        "id": "OGzeb5LXlbvv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3)))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\"))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, kernel_size = 3, activation = \"relu\"))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = \"relu\"))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "start=time.time()\n",
        "bestmodel = model.fit(x_train,y_train, epochs = 15, batch_size = 64,  shuffle=True)\n",
        "end=time.time()\n",
        "runtime=(end-start)/60\n",
        "print(\"running time is %.2f minutes\"%runtime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMbagXMEq-7L",
        "outputId": "1281375d-0d8f-497a-8909-235d34c68b59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_18\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_30 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_30 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_18 (ModuleWr  (None, 15, 15, 64)       256       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " conv2d_31 (Conv2D)          (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_31 (MaxPoolin  (None, 6, 6, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_19 (ModuleWr  (None, 6, 6, 64)         256       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " conv2d_32 (Conv2D)          (None, 4, 4, 64)          36928     \n",
            "                                                                 \n",
            " max_pooling2d_32 (MaxPoolin  (None, 2, 2, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_20 (ModuleWr  (None, 2, 2, 64)         256       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " flatten_17 (Flatten)        (None, 256)               0         \n",
            "                                                                 \n",
            " dense_34 (Dense)            (None, 128)               32896     \n",
            "                                                                 \n",
            " module_wrapper_21 (ModuleWr  (None, 128)              512       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " dense_35 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 111,114\n",
            "Trainable params: 110,474\n",
            "Non-trainable params: 640\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "750/750 [==============================] - 12s 15ms/step - loss: 1.3511 - accuracy: 0.5217\n",
            "Epoch 2/15\n",
            "750/750 [==============================] - 12s 16ms/step - loss: 0.9698 - accuracy: 0.6588\n",
            "Epoch 3/15\n",
            "750/750 [==============================] - 12s 16ms/step - loss: 0.8210 - accuracy: 0.7134\n",
            "Epoch 4/15\n",
            "750/750 [==============================] - 11s 15ms/step - loss: 0.7236 - accuracy: 0.7469\n",
            "Epoch 5/15\n",
            "750/750 [==============================] - 11s 15ms/step - loss: 0.6433 - accuracy: 0.7770\n",
            "Epoch 6/15\n",
            "750/750 [==============================] - 11s 15ms/step - loss: 0.5823 - accuracy: 0.7961\n",
            "Epoch 7/15\n",
            "750/750 [==============================] - 11s 15ms/step - loss: 0.5249 - accuracy: 0.8150\n",
            "Epoch 8/15\n",
            "750/750 [==============================] - 11s 15ms/step - loss: 0.4764 - accuracy: 0.8335\n",
            "Epoch 9/15\n",
            "750/750 [==============================] - 11s 15ms/step - loss: 0.4315 - accuracy: 0.8477\n",
            "Epoch 10/15\n",
            "750/750 [==============================] - 11s 15ms/step - loss: 0.3946 - accuracy: 0.8621\n",
            "Epoch 11/15\n",
            "750/750 [==============================] - 11s 15ms/step - loss: 0.3644 - accuracy: 0.8730\n",
            "Epoch 12/15\n",
            "750/750 [==============================] - 11s 15ms/step - loss: 0.3273 - accuracy: 0.8858\n",
            "Epoch 13/15\n",
            "750/750 [==============================] - 11s 15ms/step - loss: 0.3036 - accuracy: 0.8927\n",
            "Epoch 14/15\n",
            "750/750 [==============================] - 11s 15ms/step - loss: 0.2767 - accuracy: 0.9011\n",
            "Epoch 15/15\n",
            "750/750 [==============================] - 11s 15ms/step - loss: 0.2567 - accuracy: 0.9085\n",
            "running time is 3.39 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate on the test data \n",
        "y_test=to_categorical(y_test)\n",
        "score=model.evaluate(x_test,y_test,verbose=0)"
      ],
      "metadata": {
        "id": "e7CkQPN5tQ3r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Test loss: %.4f\" % score[0])\n",
        "print(\"Test accuracy: %.2f\" % (score[1] * 100.0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "smBt9DrOtpdP",
        "outputId": "2a18dc67-9d85-445d-dee2-dd78b4fac68a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 1.0475\n",
            "Test accuracy: 72.09\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# define a function to draw the training accuracy and test accuracy\n",
        "from matplotlib import pyplot as plt\n",
        "def drawplot(name,modelfordraw):\n",
        "  plt.plot(modelfordraw.history[\"accuracy\"], color =\"green\", label = \"acc\")\n",
        "\n",
        "  # plt.plot(trained_model_base.history[\"val_loss\"], color =\"red\", label = \"base-test\")\n",
        "  plt.plot(modelfordraw.history[\"val_accuracy\"], color =\"orange\", label = \"val_acc\")\n",
        "\n",
        "  plt.title(name)\n",
        "  plt.ylabel(\"Accracy\")\n",
        "  plt.xlabel(\"Number of epochs\")\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "5rNNR1dgy57K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "part **B**\n",
        "\n",
        "define three functions with three different regularization techniques combined with a two-layers convolutional models\n",
        "\n",
        "1.Batch_nomrlization with 50% data"
      ],
      "metadata": {
        "id": "gj4nVeALvRM8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import Sequential\n",
        "from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "import time\n",
        "def BN(portion=1):\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3)))\n",
        "  model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Conv2D(64, kernel_size = 3, activation = \"relu\"))\n",
        "  model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Flatten())\n",
        "  model.add(Dense(128, activation = \"relu\"))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "  model.summary()\n",
        "  if portion!=1:\n",
        "\n",
        "    partx,_,party,_=train_test_split(trainx,trainy,test_size=1-portion,random_state=1,shuffle=True,stratify=trainy)\n",
        "  else:\n",
        "    partx,party=trainx,trainy\n",
        "  model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "  start=time.time()\n",
        "  trained_model_2layer_BN = model.fit(partx,party, epochs = 15, batch_size = 64, validation_data=(x_val,y_val),shuffle=True)\n",
        "  end=time.time()\n",
        "  runtime=(end-start)/60\n",
        "  print(\"running time is %.2f minutes\"%runtime)\n",
        "  return trained_model_2layer_BN"
      ],
      "metadata": {
        "id": "kh8Y1b_vvZvw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model50=BN(0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZGAd_nskwdPL",
        "outputId": "e10d8b18-dd2e-44cc-8075-3954e66b0ded"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_19\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_36 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_36 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_35 (ModuleWr  (None, 15, 15, 64)       256       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " conv2d_37 (Conv2D)          (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_37 (MaxPoolin  (None, 6, 6, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_36 (ModuleWr  (None, 6, 6, 64)         256       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " flatten_19 (Flatten)        (None, 2304)              0         \n",
            "                                                                 \n",
            " dense_38 (Dense)            (None, 128)               295040    \n",
            "                                                                 \n",
            " module_wrapper_37 (ModuleWr  (None, 128)              512       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " dense_39 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 336,074\n",
            "Trainable params: 335,562\n",
            "Non-trainable params: 512\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "282/282 [==============================] - 6s 17ms/step - loss: 1.4706 - accuracy: 0.4900 - val_loss: 1.6747 - val_accuracy: 0.4650\n",
            "Epoch 2/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 1.0340 - accuracy: 0.6414 - val_loss: 1.3061 - val_accuracy: 0.5474\n",
            "Epoch 3/15\n",
            "282/282 [==============================] - 4s 13ms/step - loss: 0.8300 - accuracy: 0.7131 - val_loss: 1.8268 - val_accuracy: 0.4897\n",
            "Epoch 4/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 0.6640 - accuracy: 0.7721 - val_loss: 1.3492 - val_accuracy: 0.5780\n",
            "Epoch 5/15\n",
            "282/282 [==============================] - 4s 13ms/step - loss: 0.5161 - accuracy: 0.8262 - val_loss: 1.4470 - val_accuracy: 0.5679\n",
            "Epoch 6/15\n",
            "282/282 [==============================] - 4s 13ms/step - loss: 0.3841 - accuracy: 0.8745 - val_loss: 1.4566 - val_accuracy: 0.5680\n",
            "Epoch 7/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 0.2864 - accuracy: 0.9112 - val_loss: 1.3498 - val_accuracy: 0.6204\n",
            "Epoch 8/15\n",
            "282/282 [==============================] - 4s 13ms/step - loss: 0.2105 - accuracy: 0.9369 - val_loss: 1.4527 - val_accuracy: 0.6072\n",
            "Epoch 9/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 0.1525 - accuracy: 0.9552 - val_loss: 1.4072 - val_accuracy: 0.6444\n",
            "Epoch 10/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 0.1252 - accuracy: 0.9634 - val_loss: 1.5071 - val_accuracy: 0.6109\n",
            "Epoch 11/15\n",
            "282/282 [==============================] - 4s 13ms/step - loss: 0.1051 - accuracy: 0.9715 - val_loss: 1.5701 - val_accuracy: 0.6339\n",
            "Epoch 12/15\n",
            "282/282 [==============================] - 5s 16ms/step - loss: 0.0953 - accuracy: 0.9732 - val_loss: 1.7210 - val_accuracy: 0.5991\n",
            "Epoch 13/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 0.0779 - accuracy: 0.9782 - val_loss: 1.5924 - val_accuracy: 0.6355\n",
            "Epoch 14/15\n",
            "282/282 [==============================] - 5s 16ms/step - loss: 0.0651 - accuracy: 0.9811 - val_loss: 1.8511 - val_accuracy: 0.6063\n",
            "Epoch 15/15\n",
            "282/282 [==============================] - 4s 14ms/step - loss: 0.0837 - accuracy: 0.9736 - val_loss: 1.7968 - val_accuracy: 0.6053\n",
            "running time is 1.38 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plt.plot(trained_model_base.history[\"loss\"], color =\"blue\", label = \"base-train\")\n",
        "plt.plot(model50.history[\"accuracy\"], color =\"green\", label = \"acc\")\n",
        "\n",
        "# plt.plot(trained_model_base.history[\"val_loss\"], color =\"red\", label = \"base-test\")\n",
        "plt.plot(model50.history[\"val_accuracy\"], color =\"orange\", label = \"val_acc\")\n",
        "\n",
        "plt.title(\"50%train with 2-OCNV BN\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xlabel(\"Number of epochs\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ElLmdO8kxP3f",
        "outputId": "73cc0c93-0f03-49f8-bbec-d52745a07969"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. 75% data with BN and two-conv layers"
      ],
      "metadata": {
        "id": "Jt7ejrGmv77n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model75=BN(0.75)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ld8ptVlx9c4",
        "outputId": "ac968aeb-db57-465b-8018-1d629d858147"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_20\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_38 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_38 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_38 (ModuleWr  (None, 15, 15, 64)       256       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " conv2d_39 (Conv2D)          (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_39 (MaxPoolin  (None, 6, 6, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_39 (ModuleWr  (None, 6, 6, 64)         256       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " flatten_20 (Flatten)        (None, 2304)              0         \n",
            "                                                                 \n",
            " dense_40 (Dense)            (None, 128)               295040    \n",
            "                                                                 \n",
            " module_wrapper_40 (ModuleWr  (None, 128)              512       \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " dense_41 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 336,074\n",
            "Trainable params: 335,562\n",
            "Non-trainable params: 512\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "422/422 [==============================] - 7s 14ms/step - loss: 1.3790 - accuracy: 0.5214 - val_loss: 1.9096 - val_accuracy: 0.4356\n",
            "Epoch 2/15\n",
            "422/422 [==============================] - 6s 14ms/step - loss: 0.9683 - accuracy: 0.6636 - val_loss: 1.1579 - val_accuracy: 0.5954\n",
            "Epoch 3/15\n",
            "422/422 [==============================] - 6s 13ms/step - loss: 0.7843 - accuracy: 0.7276 - val_loss: 1.4089 - val_accuracy: 0.5467\n",
            "Epoch 4/15\n",
            "422/422 [==============================] - 6s 13ms/step - loss: 0.6342 - accuracy: 0.7813 - val_loss: 1.5667 - val_accuracy: 0.5329\n",
            "Epoch 5/15\n",
            "422/422 [==============================] - 6s 14ms/step - loss: 0.5032 - accuracy: 0.8286 - val_loss: 1.2019 - val_accuracy: 0.6084\n",
            "Epoch 6/15\n",
            "422/422 [==============================] - 6s 14ms/step - loss: 0.3942 - accuracy: 0.8658 - val_loss: 1.5389 - val_accuracy: 0.5722\n",
            "Epoch 7/15\n",
            "422/422 [==============================] - 6s 14ms/step - loss: 0.2927 - accuracy: 0.9044 - val_loss: 1.2602 - val_accuracy: 0.6428\n",
            "Epoch 8/15\n",
            "422/422 [==============================] - 6s 13ms/step - loss: 0.2188 - accuracy: 0.9280 - val_loss: 1.2787 - val_accuracy: 0.6386\n",
            "Epoch 9/15\n",
            "422/422 [==============================] - 8s 18ms/step - loss: 0.1723 - accuracy: 0.9450 - val_loss: 1.5649 - val_accuracy: 0.5982\n",
            "Epoch 10/15\n",
            "422/422 [==============================] - 7s 17ms/step - loss: 0.1368 - accuracy: 0.9574 - val_loss: 1.3598 - val_accuracy: 0.6593\n",
            "Epoch 11/15\n",
            "422/422 [==============================] - 7s 17ms/step - loss: 0.1244 - accuracy: 0.9613 - val_loss: 1.5628 - val_accuracy: 0.6344\n",
            "Epoch 12/15\n",
            "422/422 [==============================] - 6s 14ms/step - loss: 0.1001 - accuracy: 0.9693 - val_loss: 1.4556 - val_accuracy: 0.6728\n",
            "Epoch 13/15\n",
            "422/422 [==============================] - 6s 14ms/step - loss: 0.0890 - accuracy: 0.9727 - val_loss: 1.7723 - val_accuracy: 0.6223\n",
            "Epoch 14/15\n",
            "422/422 [==============================] - 6s 14ms/step - loss: 0.0835 - accuracy: 0.9737 - val_loss: 1.7645 - val_accuracy: 0.6463\n",
            "Epoch 15/15\n",
            "422/422 [==============================] - 6s 15ms/step - loss: 0.0898 - accuracy: 0.9708 - val_loss: 1.7641 - val_accuracy: 0.6347\n",
            "running time is 1.56 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "4Jb5X0N9wDe-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"2CONV+BN with 75%data\",model75)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "pGRPz94zzr2R",
        "outputId": "d80c9820-8a52-4065-c560-b84b2b374b4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BN100=BN()"
      ],
      "metadata": {
        "id": "POKgMiPksejD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "751233a3-3a56-46bc-d3ea-6cfe0c6750a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_5 (Conv2D)           (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPooling  (None, 15, 15, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_6 (Batc  (None, 15, 15, 64)       256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_6 (MaxPooling  (None, 6, 6, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_7 (Batc  (None, 6, 6, 64)         256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 2304)              0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 128)               295040    \n",
            "                                                                 \n",
            " batch_normalization_8 (Batc  (None, 128)              512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 336,074\n",
            "Trainable params: 335,562\n",
            "Non-trainable params: 512\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "563/563 [==============================] - 8s 13ms/step - loss: 1.2967 - accuracy: 0.5477 - val_loss: 1.2645 - val_accuracy: 0.5618\n",
            "Epoch 2/15\n",
            "563/563 [==============================] - 7s 13ms/step - loss: 0.9313 - accuracy: 0.6765 - val_loss: 1.1435 - val_accuracy: 0.6005\n",
            "Epoch 3/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.7553 - accuracy: 0.7369 - val_loss: 1.2248 - val_accuracy: 0.6093\n",
            "Epoch 4/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.6312 - accuracy: 0.7811 - val_loss: 1.0015 - val_accuracy: 0.6564\n",
            "Epoch 5/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.5074 - accuracy: 0.8266 - val_loss: 1.0695 - val_accuracy: 0.6642\n",
            "Epoch 6/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.4005 - accuracy: 0.8648 - val_loss: 1.1093 - val_accuracy: 0.6745\n",
            "Epoch 7/15\n",
            "563/563 [==============================] - 6s 12ms/step - loss: 0.3170 - accuracy: 0.8928 - val_loss: 1.0778 - val_accuracy: 0.6923\n",
            "Epoch 8/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.2478 - accuracy: 0.9169 - val_loss: 1.2039 - val_accuracy: 0.6738\n",
            "Epoch 9/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.1867 - accuracy: 0.9384 - val_loss: 1.3276 - val_accuracy: 0.6609\n",
            "Epoch 10/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.1530 - accuracy: 0.9516 - val_loss: 1.7405 - val_accuracy: 0.5986\n",
            "Epoch 11/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.1381 - accuracy: 0.9561 - val_loss: 1.4030 - val_accuracy: 0.6719\n",
            "Epoch 12/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.1119 - accuracy: 0.9642 - val_loss: 1.3896 - val_accuracy: 0.6881\n",
            "Epoch 13/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.0971 - accuracy: 0.9684 - val_loss: 1.7077 - val_accuracy: 0.6280\n",
            "Epoch 14/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.0954 - accuracy: 0.9676 - val_loss: 1.5784 - val_accuracy: 0.6708\n",
            "Epoch 15/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.0888 - accuracy: 0.9707 - val_loss: 1.5474 - val_accuracy: 0.6862\n",
            "running time is 2.40 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot('2-conv layer with 100%data and Batch normalization',BN100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "yF4MAFOl0qrt",
        "outputId": "e3c30ad9-cbb8-4731-a05e-eca35fa58856"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. dropout with 50% data"
      ],
      "metadata": {
        "id": "yONugIyAwFi_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.python.keras.layers.core import Dropout\n",
        "from keras import Sequential\n",
        "from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D\n",
        "def DP(portion=1):\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3)))\n",
        "  model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "  model.add(Dropout(0.1))\n",
        "  model.add(Conv2D(64, kernel_size = 3, activation = \"relu\"))\n",
        "  model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "  model.add(Dropout(0.1))\n",
        "  model.add(Flatten())\n",
        "  model.add(Dense(128, activation = \"relu\"))\n",
        "  model.add(Dropout(0.1))\n",
        "  model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "  model.summary()\n",
        "  if portion!=1:\n",
        "    partx,_,party,_=train_test_split(trainx,trainy,test_size=1-portion,random_state=1,shuffle=True,stratify=trainy)\n",
        "  else:\n",
        "    partx,party=trainx,trainy\n",
        "  model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "  start=time.time()\n",
        "  trained_model_2layer_DP = model.fit(partx,party, epochs = 15, batch_size = 64, validation_data=(x_val,y_val),shuffle=True)\n",
        "  end=time.time()\n",
        "  runtime=(end-start)/60\n",
        "  print(\"running time is %.2f minutes\"%runtime)\n",
        "  return trained_model_2layer_DP"
      ],
      "metadata": {
        "id": "S-4LuCiRyPZC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DP50=DP(0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XOtGkcxm0AVO",
        "outputId": "9c5da65c-fd10-4f95-dd80-16dd8e32d058"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_21\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_40 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_40 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_41 (ModuleWr  (None, 15, 15, 64)       0         \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " conv2d_41 (Conv2D)          (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_41 (MaxPoolin  (None, 6, 6, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_42 (ModuleWr  (None, 6, 6, 64)         0         \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " flatten_21 (Flatten)        (None, 2304)              0         \n",
            "                                                                 \n",
            " dense_42 (Dense)            (None, 128)               295040    \n",
            "                                                                 \n",
            " module_wrapper_43 (ModuleWr  (None, 128)              0         \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " dense_43 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 335,050\n",
            "Trainable params: 335,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "282/282 [==============================] - 5s 16ms/step - loss: 3.4213 - accuracy: 0.1803 - val_loss: 2.1056 - val_accuracy: 0.2202\n",
            "Epoch 2/15\n",
            "282/282 [==============================] - 4s 14ms/step - loss: 1.9688 - accuracy: 0.2640 - val_loss: 1.8671 - val_accuracy: 0.3128\n",
            "Epoch 3/15\n",
            "282/282 [==============================] - 4s 14ms/step - loss: 1.7947 - accuracy: 0.3364 - val_loss: 1.7073 - val_accuracy: 0.3792\n",
            "Epoch 4/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 1.6502 - accuracy: 0.3972 - val_loss: 1.5714 - val_accuracy: 0.4276\n",
            "Epoch 5/15\n",
            "282/282 [==============================] - 4s 14ms/step - loss: 1.5220 - accuracy: 0.4458 - val_loss: 1.4864 - val_accuracy: 0.4649\n",
            "Epoch 6/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 1.4360 - accuracy: 0.4829 - val_loss: 1.4574 - val_accuracy: 0.4726\n",
            "Epoch 7/15\n",
            "282/282 [==============================] - 4s 13ms/step - loss: 1.3364 - accuracy: 0.5227 - val_loss: 1.3763 - val_accuracy: 0.5142\n",
            "Epoch 8/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 1.2384 - accuracy: 0.5632 - val_loss: 1.3598 - val_accuracy: 0.5110\n",
            "Epoch 9/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 1.1564 - accuracy: 0.5933 - val_loss: 1.3156 - val_accuracy: 0.5434\n",
            "Epoch 10/15\n",
            "282/282 [==============================] - 4s 16ms/step - loss: 1.0586 - accuracy: 0.6263 - val_loss: 1.3638 - val_accuracy: 0.5437\n",
            "Epoch 11/15\n",
            "282/282 [==============================] - 5s 18ms/step - loss: 0.9922 - accuracy: 0.6486 - val_loss: 1.3841 - val_accuracy: 0.5328\n",
            "Epoch 12/15\n",
            "282/282 [==============================] - 5s 17ms/step - loss: 0.9131 - accuracy: 0.6776 - val_loss: 1.2774 - val_accuracy: 0.5752\n",
            "Epoch 13/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 0.8512 - accuracy: 0.6976 - val_loss: 1.3307 - val_accuracy: 0.5732\n",
            "Epoch 14/15\n",
            "282/282 [==============================] - 4s 16ms/step - loss: 0.7688 - accuracy: 0.7259 - val_loss: 1.3879 - val_accuracy: 0.5787\n",
            "Epoch 15/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 0.7564 - accuracy: 0.7346 - val_loss: 1.4366 - val_accuracy: 0.5735\n",
            "running time is 1.38 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"2CONV+DP with 50%data\",DP50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "hcx2r86T0VTg",
        "outputId": "beb29b6a-5bc6-43b8-d7db-11b6e5642765"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "75% data with dropout"
      ],
      "metadata": {
        "id": "jPYKobMjwQt4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DP75=DP(0.75)"
      ],
      "metadata": {
        "id": "vlwXa-Ca0rz-",
        "outputId": "bb896918-078d-486b-cc3e-2dfbe10a7aa7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_22\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_42 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_42 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_44 (ModuleWr  (None, 15, 15, 64)       0         \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " conv2d_43 (Conv2D)          (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_43 (MaxPoolin  (None, 6, 6, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " module_wrapper_45 (ModuleWr  (None, 6, 6, 64)         0         \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " flatten_22 (Flatten)        (None, 2304)              0         \n",
            "                                                                 \n",
            " dense_44 (Dense)            (None, 128)               295040    \n",
            "                                                                 \n",
            " module_wrapper_46 (ModuleWr  (None, 128)              0         \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " dense_45 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 335,050\n",
            "Trainable params: 335,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "422/422 [==============================] - 71s 13ms/step - loss: 3.6858 - accuracy: 0.1148 - val_loss: 2.2902 - val_accuracy: 0.1270\n",
            "Epoch 2/15\n",
            "422/422 [==============================] - 5s 13ms/step - loss: 2.1670 - accuracy: 0.1956 - val_loss: 1.9257 - val_accuracy: 0.3050\n",
            "Epoch 3/15\n",
            "422/422 [==============================] - 5s 13ms/step - loss: 1.7501 - accuracy: 0.3601 - val_loss: 1.6252 - val_accuracy: 0.4160\n",
            "Epoch 4/15\n",
            "422/422 [==============================] - 5s 13ms/step - loss: 1.5231 - accuracy: 0.4486 - val_loss: 1.4063 - val_accuracy: 0.4938\n",
            "Epoch 5/15\n",
            "422/422 [==============================] - 5s 13ms/step - loss: 1.3734 - accuracy: 0.5099 - val_loss: 1.2972 - val_accuracy: 0.5381\n",
            "Epoch 6/15\n",
            "422/422 [==============================] - 5s 11ms/step - loss: 1.2747 - accuracy: 0.5476 - val_loss: 1.2766 - val_accuracy: 0.5500\n",
            "Epoch 7/15\n",
            "422/422 [==============================] - 5s 13ms/step - loss: 1.1727 - accuracy: 0.5848 - val_loss: 1.2006 - val_accuracy: 0.5838\n",
            "Epoch 8/15\n",
            "422/422 [==============================] - 5s 12ms/step - loss: 1.0996 - accuracy: 0.6125 - val_loss: 1.1861 - val_accuracy: 0.5889\n",
            "Epoch 9/15\n",
            "422/422 [==============================] - 5s 11ms/step - loss: 1.0238 - accuracy: 0.6380 - val_loss: 1.1748 - val_accuracy: 0.5918\n",
            "Epoch 10/15\n",
            "422/422 [==============================] - 5s 13ms/step - loss: 0.9652 - accuracy: 0.6589 - val_loss: 1.1626 - val_accuracy: 0.6061\n",
            "Epoch 11/15\n",
            "422/422 [==============================] - 5s 13ms/step - loss: 0.9093 - accuracy: 0.6781 - val_loss: 1.2035 - val_accuracy: 0.5924\n",
            "Epoch 12/15\n",
            "422/422 [==============================] - 5s 12ms/step - loss: 0.8536 - accuracy: 0.6995 - val_loss: 1.1721 - val_accuracy: 0.6144\n",
            "Epoch 13/15\n",
            "422/422 [==============================] - 6s 13ms/step - loss: 0.7998 - accuracy: 0.7199 - val_loss: 1.2418 - val_accuracy: 0.6068\n",
            "Epoch 14/15\n",
            "422/422 [==============================] - 5s 13ms/step - loss: 0.7599 - accuracy: 0.7354 - val_loss: 1.1924 - val_accuracy: 0.6261\n",
            "Epoch 15/15\n",
            "422/422 [==============================] - 6s 13ms/step - loss: 0.7338 - accuracy: 0.7413 - val_loss: 1.2176 - val_accuracy: 0.6211\n",
            "running time is 2.46 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"2CONV+DP with 75% data\",DP75)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ntWNMJCO1W9z",
        "outputId": "02cb6140-be4f-4743-9423-4d95a380a5c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DP100=DP()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gCm7aTbS2ZB8",
        "outputId": "920c285d-a0ef-47e5-8f1e-f38881847087"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_7 (Conv2D)           (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_7 (MaxPooling  (None, 15, 15, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " module_wrapper (ModuleWrapp  (None, 15, 15, 64)       0         \n",
            " er)                                                             \n",
            "                                                                 \n",
            " conv2d_8 (Conv2D)           (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_8 (MaxPooling  (None, 6, 6, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " module_wrapper_1 (ModuleWra  (None, 6, 6, 64)         0         \n",
            " pper)                                                           \n",
            "                                                                 \n",
            " flatten_3 (Flatten)         (None, 2304)              0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 128)               295040    \n",
            "                                                                 \n",
            " module_wrapper_2 (ModuleWra  (None, 128)              0         \n",
            " pper)                                                           \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 335,050\n",
            "Trainable params: 335,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "563/563 [==============================] - 8s 12ms/step - loss: 2.2934 - accuracy: 0.3121 - val_loss: 1.5699 - val_accuracy: 0.4189\n",
            "Epoch 2/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 1.5015 - accuracy: 0.4639 - val_loss: 1.3452 - val_accuracy: 0.5187\n",
            "Epoch 3/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 1.3341 - accuracy: 0.5278 - val_loss: 1.2259 - val_accuracy: 0.5602\n",
            "Epoch 4/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 1.2009 - accuracy: 0.5763 - val_loss: 1.1589 - val_accuracy: 0.5970\n",
            "Epoch 5/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 1.1041 - accuracy: 0.6117 - val_loss: 1.1398 - val_accuracy: 0.5975\n",
            "Epoch 6/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 1.0306 - accuracy: 0.6390 - val_loss: 1.0904 - val_accuracy: 0.6240\n",
            "Epoch 7/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.9815 - accuracy: 0.6571 - val_loss: 1.0809 - val_accuracy: 0.6273\n",
            "Epoch 8/15\n",
            "563/563 [==============================] - 6s 12ms/step - loss: 0.9143 - accuracy: 0.6811 - val_loss: 1.0931 - val_accuracy: 0.6357\n",
            "Epoch 9/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.8684 - accuracy: 0.6961 - val_loss: 1.0523 - val_accuracy: 0.6412\n",
            "Epoch 10/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.8209 - accuracy: 0.7129 - val_loss: 1.0433 - val_accuracy: 0.6480\n",
            "Epoch 11/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.7808 - accuracy: 0.7284 - val_loss: 1.0482 - val_accuracy: 0.6533\n",
            "Epoch 12/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.7286 - accuracy: 0.7444 - val_loss: 1.0396 - val_accuracy: 0.6550\n",
            "Epoch 13/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.7074 - accuracy: 0.7545 - val_loss: 1.0759 - val_accuracy: 0.6530\n",
            "Epoch 14/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.6747 - accuracy: 0.7658 - val_loss: 1.1285 - val_accuracy: 0.6482\n",
            "Epoch 15/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.6471 - accuracy: 0.7755 - val_loss: 1.1369 - val_accuracy: 0.6510\n",
            "running time is 1.61 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"2conv+dropout and 100%data\",DP100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Waenwb6X3QJ0",
        "outputId": "b4c865eb-026a-4a8f-f5e6-599214a34c6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import regularizers\n",
        "from keras import Sequential\n",
        "from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "import time\n",
        "def L2(portion=1):\n",
        "  model = Sequential()\n",
        "  model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", input_shape = (32, 32, 3),kernel_regularizer=regularizers.l2(0.00005)))\n",
        "  model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "  model.add(Conv2D(64, kernel_size = 3, activation = \"relu\", kernel_regularizer=regularizers.l2(0.00005)))\n",
        "  model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "  model.add(Flatten())\n",
        "  model.add(Dense(128, activation = \"relu\",kernel_regularizer=regularizers.l2(0.00005)))\n",
        "  model.add(Dense(10, activation = \"softmax\"))\n",
        "  model.summary()\n",
        "  if portion !=1:\n",
        "    partx,_,party,_=train_test_split(trainx,trainy,test_size=1-portion,random_state=1,shuffle=True,stratify=trainy)\n",
        "  else:\n",
        "    partx,party=trainx,trainy\n",
        "  model.compile(loss = \"categorical_crossentropy\", optimizer= \"adam\", metrics = [\"accuracy\"])\n",
        "  start=time.time()\n",
        "  trained_model_3layer_L2 = model.fit(partx,party, epochs = 15, batch_size = 64, validation_data=(x_val,y_val),shuffle=True)\n",
        "  end=time.time()\n",
        "  runtime=(end-start)/60\n",
        "  print(\"running time is %.2f minutes\"%runtime)\n",
        "  return trained_model_3layer_L2"
      ],
      "metadata": {
        "id": "QnxjqEHg1tqH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "L250=L2(0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VAld6TT32NM5",
        "outputId": "89091116-f1b6-4b3b-a26f-8482a6e69c0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_23\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_44 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_44 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_45 (Conv2D)          (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_45 (MaxPoolin  (None, 6, 6, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_23 (Flatten)        (None, 2304)              0         \n",
            "                                                                 \n",
            " dense_46 (Dense)            (None, 128)               295040    \n",
            "                                                                 \n",
            " dense_47 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 335,050\n",
            "Trainable params: 335,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "282/282 [==============================] - 6s 19ms/step - loss: 3.4613 - accuracy: 0.2783 - val_loss: 1.7914 - val_accuracy: 0.3517\n",
            "Epoch 2/15\n",
            "282/282 [==============================] - 5s 18ms/step - loss: 1.6210 - accuracy: 0.4203 - val_loss: 1.6004 - val_accuracy: 0.4249\n",
            "Epoch 3/15\n",
            "282/282 [==============================] - 5s 16ms/step - loss: 1.4566 - accuracy: 0.4843 - val_loss: 1.5209 - val_accuracy: 0.4674\n",
            "Epoch 4/15\n",
            "282/282 [==============================] - 4s 14ms/step - loss: 1.3108 - accuracy: 0.5369 - val_loss: 1.5360 - val_accuracy: 0.4678\n",
            "Epoch 5/15\n",
            "282/282 [==============================] - 5s 18ms/step - loss: 1.2115 - accuracy: 0.5729 - val_loss: 1.5729 - val_accuracy: 0.4813\n",
            "Epoch 6/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 1.0782 - accuracy: 0.6227 - val_loss: 1.5295 - val_accuracy: 0.4930\n",
            "Epoch 7/15\n",
            "282/282 [==============================] - 4s 16ms/step - loss: 0.9620 - accuracy: 0.6681 - val_loss: 1.4958 - val_accuracy: 0.5174\n",
            "Epoch 8/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 0.8618 - accuracy: 0.7023 - val_loss: 1.6429 - val_accuracy: 0.5236\n",
            "Epoch 9/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 0.7653 - accuracy: 0.7382 - val_loss: 1.7632 - val_accuracy: 0.5174\n",
            "Epoch 10/15\n",
            "282/282 [==============================] - 4s 13ms/step - loss: 0.6894 - accuracy: 0.7678 - val_loss: 1.8982 - val_accuracy: 0.5224\n",
            "Epoch 11/15\n",
            "282/282 [==============================] - 4s 13ms/step - loss: 0.5736 - accuracy: 0.8094 - val_loss: 2.0055 - val_accuracy: 0.5199\n",
            "Epoch 12/15\n",
            "282/282 [==============================] - 4s 14ms/step - loss: 0.5128 - accuracy: 0.8307 - val_loss: 2.0670 - val_accuracy: 0.5238\n",
            "Epoch 13/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 0.4433 - accuracy: 0.8591 - val_loss: 2.2984 - val_accuracy: 0.5123\n",
            "Epoch 14/15\n",
            "282/282 [==============================] - 4s 15ms/step - loss: 0.4291 - accuracy: 0.8613 - val_loss: 2.4660 - val_accuracy: 0.5140\n",
            "Epoch 15/15\n",
            "282/282 [==============================] - 4s 13ms/step - loss: 0.3960 - accuracy: 0.8750 - val_loss: 2.5973 - val_accuracy: 0.5070\n",
            "running time is 1.09 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"2CONV+L2 with 50% data\",L250)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ekUz-sOJ2Upx",
        "outputId": "02d8ac9f-138d-4de9-e4ac-5149a6b81037"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "L275=L2(0.75)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BqT9-55D2QnN",
        "outputId": "b3fb1c64-762d-4cc3-f306-0d5fdf1b337e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_24\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_46 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_46 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_47 (Conv2D)          (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_47 (MaxPoolin  (None, 6, 6, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_24 (Flatten)        (None, 2304)              0         \n",
            "                                                                 \n",
            " dense_48 (Dense)            (None, 128)               295040    \n",
            "                                                                 \n",
            " dense_49 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 335,050\n",
            "Trainable params: 335,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "422/422 [==============================] - 8s 18ms/step - loss: 2.9824 - accuracy: 0.2976 - val_loss: 1.7781 - val_accuracy: 0.3769\n",
            "Epoch 2/15\n",
            "422/422 [==============================] - 6s 14ms/step - loss: 1.5572 - accuracy: 0.4473 - val_loss: 1.5538 - val_accuracy: 0.4608\n",
            "Epoch 3/15\n",
            "422/422 [==============================] - 6s 14ms/step - loss: 1.3900 - accuracy: 0.5164 - val_loss: 1.4262 - val_accuracy: 0.5045\n",
            "Epoch 4/15\n",
            "422/422 [==============================] - 7s 17ms/step - loss: 1.2765 - accuracy: 0.5555 - val_loss: 1.3835 - val_accuracy: 0.5243\n",
            "Epoch 5/15\n",
            "422/422 [==============================] - 7s 15ms/step - loss: 1.1476 - accuracy: 0.6018 - val_loss: 1.3693 - val_accuracy: 0.5398\n",
            "Epoch 6/15\n",
            "422/422 [==============================] - 6s 13ms/step - loss: 1.0339 - accuracy: 0.6455 - val_loss: 1.4424 - val_accuracy: 0.5357\n",
            "Epoch 7/15\n",
            "422/422 [==============================] - 5s 12ms/step - loss: 0.9321 - accuracy: 0.6817 - val_loss: 1.3862 - val_accuracy: 0.5637\n",
            "Epoch 8/15\n",
            "422/422 [==============================] - 5s 12ms/step - loss: 0.8282 - accuracy: 0.7191 - val_loss: 1.4947 - val_accuracy: 0.5567\n",
            "Epoch 9/15\n",
            "422/422 [==============================] - 6s 13ms/step - loss: 0.7146 - accuracy: 0.7602 - val_loss: 1.5937 - val_accuracy: 0.5663\n",
            "Epoch 10/15\n",
            "422/422 [==============================] - 5s 12ms/step - loss: 0.6407 - accuracy: 0.7899 - val_loss: 1.7208 - val_accuracy: 0.5587\n",
            "Epoch 11/15\n",
            "422/422 [==============================] - 5s 12ms/step - loss: 0.5415 - accuracy: 0.8246 - val_loss: 2.0451 - val_accuracy: 0.5420\n",
            "Epoch 12/15\n",
            "422/422 [==============================] - 5s 12ms/step - loss: 0.4950 - accuracy: 0.8427 - val_loss: 1.9900 - val_accuracy: 0.5562\n",
            "Epoch 13/15\n",
            "422/422 [==============================] - 5s 12ms/step - loss: 0.4404 - accuracy: 0.8670 - val_loss: 2.1258 - val_accuracy: 0.5587\n",
            "Epoch 14/15\n",
            "422/422 [==============================] - 6s 13ms/step - loss: 0.4058 - accuracy: 0.8778 - val_loss: 2.2423 - val_accuracy: 0.5571\n",
            "Epoch 15/15\n",
            "422/422 [==============================] - 6s 14ms/step - loss: 0.3735 - accuracy: 0.8895 - val_loss: 2.3641 - val_accuracy: 0.5587\n",
            "running time is 2.38 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"2CONV+l2 with 75%data\",L275)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "MDDgY_kn2tit",
        "outputId": "281a776b-80a3-4f70-8d55-5abd0cf25daf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "L2100=L2()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hsbP-X6_4ErD",
        "outputId": "e4b45ce7-ae71-481e-b90a-80425b3615df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_14 (Conv2D)          (None, 30, 30, 64)        1792      \n",
            "                                                                 \n",
            " max_pooling2d_14 (MaxPoolin  (None, 15, 15, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_15 (Conv2D)          (None, 13, 13, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_15 (MaxPoolin  (None, 6, 6, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_7 (Flatten)         (None, 2304)              0         \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 128)               295040    \n",
            "                                                                 \n",
            " dense_15 (Dense)            (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 335,050\n",
            "Trainable params: 335,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/15\n",
            "563/563 [==============================] - 8s 14ms/step - loss: 2.6479 - accuracy: 0.3153 - val_loss: 1.6052 - val_accuracy: 0.4136\n",
            "Epoch 2/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 1.5395 - accuracy: 0.4495 - val_loss: 1.4537 - val_accuracy: 0.4870\n",
            "Epoch 3/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 1.3368 - accuracy: 0.5303 - val_loss: 1.4337 - val_accuracy: 0.5134\n",
            "Epoch 4/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 1.1804 - accuracy: 0.5907 - val_loss: 1.2843 - val_accuracy: 0.5678\n",
            "Epoch 5/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 1.0504 - accuracy: 0.6417 - val_loss: 1.2139 - val_accuracy: 0.5978\n",
            "Epoch 6/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.9231 - accuracy: 0.6864 - val_loss: 1.1925 - val_accuracy: 0.6153\n",
            "Epoch 7/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.8211 - accuracy: 0.7244 - val_loss: 1.2954 - val_accuracy: 0.6013\n",
            "Epoch 8/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.7197 - accuracy: 0.7602 - val_loss: 1.3120 - val_accuracy: 0.6226\n",
            "Epoch 9/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.6354 - accuracy: 0.7928 - val_loss: 1.3781 - val_accuracy: 0.6219\n",
            "Epoch 10/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.5599 - accuracy: 0.8179 - val_loss: 1.5034 - val_accuracy: 0.6236\n",
            "Epoch 11/15\n",
            "563/563 [==============================] - 6s 12ms/step - loss: 0.4817 - accuracy: 0.8470 - val_loss: 1.6558 - val_accuracy: 0.6077\n",
            "Epoch 12/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.4352 - accuracy: 0.8685 - val_loss: 1.7001 - val_accuracy: 0.6209\n",
            "Epoch 13/15\n",
            "563/563 [==============================] - 6s 11ms/step - loss: 0.4112 - accuracy: 0.8781 - val_loss: 1.8821 - val_accuracy: 0.6138\n",
            "Epoch 14/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.3791 - accuracy: 0.8923 - val_loss: 1.9241 - val_accuracy: 0.6063\n",
            "Epoch 15/15\n",
            "563/563 [==============================] - 7s 12ms/step - loss: 0.3473 - accuracy: 0.9061 - val_loss: 2.2944 - val_accuracy: 0.5894\n",
            "running time is 2.38 minutes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawplot(\"2layer-CONV with L2 and 100%data\",L2100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "LCfeiAAqG6sc",
        "outputId": "e39af85c-d99d-45f7-aff3-3843d800021b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}