kerasTuner.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPrItiRhLRQ1BOS/Jil/2d8",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ashutoshsinha25/computer-vision/blob/main/kerasTuner.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Ce2ULoFDvjk7"
      },
      "outputs": [],
      "source": [
        "# importing necessary libraries \n",
        "\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns \n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras.models import Sequential \n",
        "from tensorflow.keras.layers import Dense , Dropout , Flatten \n",
        "from tensorflow.keras.datasets import fashion_mnist"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pre-requisite = python -- 3.5 and tensorflow -- 2.0+\n",
        "\n",
        "print(tf.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Du1J7U8v37x",
        "outputId": "0828be36-81da-4cc4-ca68-4cc91c10cd40"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.9.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the data \n",
        "\n",
        "(X_train , y_train) , (X_test , y_test) = fashion_mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-AC74k_SwBw4",
        "outputId": "e633e5bb-4e19-4c7a-8c5f-374d4be42f5b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "29515/29515 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26421880/26421880 [==============================] - 2s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "5148/5148 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4422102/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualizing the data \n",
        "plt.figure(figsize = (12 , 12))\n",
        "\n",
        "for i in range(25):\n",
        "  # define subplot \n",
        "  plt.subplot( 5 , 5 , i + 1)\n",
        "\n",
        "  # plt pixel data \n",
        "  plt.imshow(X_train[i] , cmap = plt.get_cmap('gray'))\n",
        "\n",
        "# show the figure \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 702
        },
        "id": "5R4psv2QwI_9",
        "outputId": "560940ec-ff44-4f0b-ac8f-ce14f555f8b1"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# normalize the image \n",
        "\n",
        "X_train = X_train/255.\n",
        "X_test = X_test/255."
      ],
      "metadata": {
        "id": "F2Ox5E9qwhkD"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# building the model \n",
        "\n",
        "model = Sequential([\n",
        "    # flatten the images \n",
        "    Flatten(input_shape = (28 , 28)),\n",
        "    # adding the first hidden layer\n",
        "    Dense(256 , activation = 'relu'),\n",
        "    # adding the second hidden layer\n",
        "    Dense(128 , activation = 'relu'),\n",
        "    # adding the third hidden layer\n",
        "    Dense(64 , activation = 'relu'),\n",
        "    # adding the output layer\n",
        "    Dense(10 , activation = 'softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "bWRTQ8bOw2Rq"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compile the model \n",
        "\n",
        "model.compile(loss = 'sparse_categorical_crossentropy' ,\n",
        "              optimizer = 'adam' ,\n",
        "              metrics = ['accuracy'])\n"
      ],
      "metadata": {
        "id": "GNJv9AZZxSUy"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fitting the model \n",
        "\n",
        "model.fit(X_train , y_train , epochs = 20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rUw7UI3NxkRb",
        "outputId": "a2ddd5ba-665d-47b9-ccf6-05b08c536795"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1875/1875 [==============================] - 9s 3ms/step - loss: 0.4863 - accuracy: 0.8235\n",
            "Epoch 2/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3656 - accuracy: 0.8653\n",
            "Epoch 3/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3292 - accuracy: 0.8787\n",
            "Epoch 4/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3060 - accuracy: 0.8874\n",
            "Epoch 5/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2910 - accuracy: 0.8908\n",
            "Epoch 6/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2735 - accuracy: 0.8982\n",
            "Epoch 7/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2613 - accuracy: 0.9016\n",
            "Epoch 8/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2511 - accuracy: 0.9049\n",
            "Epoch 9/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2404 - accuracy: 0.9078\n",
            "Epoch 10/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2338 - accuracy: 0.9113\n",
            "Epoch 11/20\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2261 - accuracy: 0.9132\n",
            "Epoch 12/20\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2178 - accuracy: 0.9173\n",
            "Epoch 13/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2116 - accuracy: 0.9199\n",
            "Epoch 14/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2035 - accuracy: 0.9225\n",
            "Epoch 15/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1964 - accuracy: 0.9250\n",
            "Epoch 16/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1899 - accuracy: 0.9272\n",
            "Epoch 17/20\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1869 - accuracy: 0.9279\n",
            "Epoch 18/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1844 - accuracy: 0.9306\n",
            "Epoch 19/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1743 - accuracy: 0.9326\n",
            "Epoch 20/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1696 - accuracy: 0.9355\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7efdc06edb50>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the model \n",
        "\n",
        "model.evaluate(X_test , y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BecYXU5Gxn7a",
        "outputId": "a944c273-b44b-4ac9-8352-43da3b8e9dc4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 2s 5ms/step - loss: 0.3941 - accuracy: 0.8846\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.39406248927116394, 0.8845999836921692]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " '''\n",
        " Now lets tue the following things in the model : \n",
        " \n",
        "\n",
        " 1. number of hidden layers \n",
        " 2. number of neurons \n",
        " 3. learning rate \n",
        " 4. activation function\n",
        " '''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "PcVgiyxKypUs",
        "outputId": "823cae97-cfb1-4c49-a8ec-314e08ed9a22"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nNow lets tue the following things in the model : \\n\\n\\n1. number of hidden layers \\n2. number of neurons \\n3. learning rate \\n4. activation function\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# install keras tuner\n",
        "\n",
        "!pip install keras-tuner"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ngZ6yG_zCC8",
        "outputId": "0feafc0c-86cf-40ad-b460-0e6f58c0d0ac"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting keras-tuner\n",
            "  Downloading keras_tuner-1.1.3-py3-none-any.whl (135 kB)\n",
            "\u001b[K     |████████████████████████████████| 135 kB 36.7 MB/s \n",
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            "Collecting kt-legacy\n",
            "  Downloading kt_legacy-1.0.4-py3-none-any.whl (9.6 kB)\n",
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            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (57.4.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (4.4.2)\n",
            "Collecting jedi>=0.10\n",
            "  Downloading jedi-0.18.1-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.6 MB 32.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.7/dist-packages (from jedi>=0.10->ipython->keras-tuner) (0.8.3)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython->keras-tuner) (1.15.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython->keras-tuner) (0.2.5)\n",
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            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard->keras-tuner) (4.1.1)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->keras-tuner) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->keras-tuner) (3.2.2)\n",
            "Installing collected packages: jedi, kt-legacy, keras-tuner\n",
            "Successfully installed jedi-0.18.1 keras-tuner-1.1.3 kt-legacy-1.0.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# installing the required libraires \n",
        "\n",
        "from tensorflow import keras \n",
        "from keras_tuner import RandomSearch"
      ],
      "metadata": {
        "id": "VthBikBgzGQT"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# funtion to build the model \n",
        "\n",
        "def build_model(hp):\n",
        "  # hp --> hyper parameter\n",
        "  model = Sequential()\n",
        "  model.add(Flatten(input_shape = (28 , 28)))\n",
        "\n",
        "  # provide range for neurons \n",
        "  model.add(Dense(units = hp.Int('num_of_neurons' , min_value = 32 , max_value = 512 , step = 32) , activation = 'relu'))\n",
        "\n",
        "  # output layer \n",
        "  model.add(Dense(10 , activation = 'softmax'))\n",
        "\n",
        "  # compile the model \n",
        "  model.compile(optimizer = keras.optimizers.Adam(hp.Choice('learning_rate' , values = [1e-2 , 1e-3 , 1e-4])) ,\n",
        "                loss = 'sparse_categorical_crossentropy' ,\n",
        "                metrics = ['accuracy'])\n",
        "  return model\n"
      ],
      "metadata": {
        "id": "weYRTSzMzQaH"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# using random search\n",
        "\n",
        "tuner = RandomSearch(build_model ,\n",
        "                     objective = 'val_accuracy' ,\n",
        "                     max_trials = 5 ,\n",
        "                     executions_per_trial = 3 ,\n",
        "                     directory = 'tuner1' ,\n",
        "                     project_name = 'Clothing'\n",
        "                     ) \n",
        "\n",
        "# this will run for 5 * 3 = 15 times with 20 epochs "
      ],
      "metadata": {
        "id": "0U5vmFwczwbJ"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# this will tells us how many hyperparameters we are training \n",
        "\n",
        "tuner.search_space_summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SlTXB3lv0sZl",
        "outputId": "192688b9-e6d8-438d-85c7-7c06ee55997a"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Search space summary\n",
            "Default search space size: 2\n",
            "num_of_neurons (Int)\n",
            "{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 512, 'step': 32, 'sampling': None}\n",
            "learning_rate (Choice)\n",
            "{'default': 0.01, 'conditions': [], 'values': [0.01, 0.001, 0.0001], 'ordered': True}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fitting the tuner \n",
        "\n",
        "tuner.search(X_train , y_train  , epochs = 20 , validation_data = (X_test , y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2baGqWMG1BMJ",
        "outputId": "284b0d1e-6f94-4143-985c-525a93354bf4"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 5 Complete [00h 06m 32s]\n",
            "val_accuracy: 0.8922000130017599\n",
            "\n",
            "Best val_accuracy So Far: 0.8922000130017599\n",
            "Total elapsed time: 00h 30m 04s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# parameters selected by kerasTuner \n",
        "\n",
        "tuner.results_summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NEPezp_F8IvO",
        "outputId": "f10f3b60-8fb1-4608-9b2c-39b7cc61545f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results summary\n",
            "Results in tuner1/Clothing\n",
            "Showing 10 best trials\n",
            "<keras_tuner.engine.objective.Objective object at 0x7efd24ef0e50>\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_of_neurons: 256\n",
            "learning_rate: 0.001\n",
            "Score: 0.8922000130017599\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_of_neurons: 352\n",
            "learning_rate: 0.001\n",
            "Score: 0.8918999830881754\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_of_neurons: 352\n",
            "learning_rate: 0.0001\n",
            "Score: 0.8863333264986674\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_of_neurons: 288\n",
            "learning_rate: 0.0001\n",
            "Score: 0.8839333256085714\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_of_neurons: 256\n",
            "learning_rate: 0.01\n",
            "Score: 0.8602333466211954\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "now lets tune soome more parameters :\n",
        "\n",
        "we will provide the range for number of layers between 2 to 20 \n",
        "'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "fcHa-ZOr8N6K",
        "outputId": "feaa0711-c4f1-450c-877a-74598dc61cce"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nnow lets tune soome more parameters :\\n\\nwe will provide the range for number of layers between 2 to 20 \\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_model(hp):\n",
        "  # hp --> hyper parameter\n",
        "  model = Sequential()\n",
        "  model.add(Flatten(input_shape = (28 , 28)))\n",
        "\n",
        "  # provide range for neurons \n",
        "  for i in range(hp.Int('num_of_layers' , 2 , 20)):\n",
        "    # providing range for neuron in each layer\n",
        "    model.add(Dense(units = hp.Int('num_of_neurons' + str(i) , min_value = 32 , max_value = 512 , step = 32) , activation = 'relu'))\n",
        "\n",
        "  # output layer \n",
        "  model.add(Dense(10 , activation = 'softmax'))\n",
        "\n",
        "  # compile the model \n",
        "  model.compile(optimizer = keras.optimizers.Adam(hp.Choice('learning_rate' , values = [1e-2 , 1e-3 , 1e-4])) ,\n",
        "                loss = 'sparse_categorical_crossentropy' ,\n",
        "                metrics = ['accuracy'])\n",
        "  return model"
      ],
      "metadata": {
        "id": "V8-VYAMh86lA"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# setting up random search \n",
        "tuner = RandomSearch(build_model ,\n",
        "                     objective = 'val_accuracy' ,\n",
        "                     max_trials = 5 ,\n",
        "                     executions_per_trial = 3 ,\n",
        "                     directory = 'project' ,\n",
        "                     project_name = 'Clothing'\n",
        "                     ) "
      ],
      "metadata": {
        "id": "kvbGp1y29UWY"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tuner.search_space_summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bYDsap3-9l-Y",
        "outputId": "bfeb09a7-54cb-4b07-c677-5ef0553ad3e1"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Search space summary\n",
            "Default search space size: 4\n",
            "num_of_layers (Int)\n",
            "{'default': None, 'conditions': [], 'min_value': 2, 'max_value': 20, 'step': 1, 'sampling': None}\n",
            "num_of_neurons0 (Int)\n",
            "{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 512, 'step': 32, 'sampling': None}\n",
            "num_of_neurons1 (Int)\n",
            "{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 512, 'step': 32, 'sampling': None}\n",
            "learning_rate (Choice)\n",
            "{'default': 0.01, 'conditions': [], 'values': [0.01, 0.001, 0.0001], 'ordered': True}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fitting the model \n",
        "\n",
        "tuner.search(X_train , y_train , epochs = 20 , validation_data = (X_test , y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a8o2Su759qEg",
        "outputId": "e05c5506-aff8-4531-cbcf-2ea8acf07936"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 5 Complete [00h 07m 04s]\n",
            "val_accuracy: 0.34763333449761075\n",
            "\n",
            "Best val_accuracy So Far: 0.8936333258946737\n",
            "Total elapsed time: 00h 35m 42s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# hyper parameters selected by tuner \n",
        "\n",
        "tuner.results_summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lRFiPm3FHmie",
        "outputId": "88fcc805-4173-4881-ca9f-62380e6c4dd6"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results summary\n",
            "Results in project/Clothing\n",
            "Showing 10 best trials\n",
            "<keras_tuner.engine.objective.Objective object at 0x7efdd2c0dd90>\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_of_layers: 6\n",
            "num_of_neurons0: 448\n",
            "num_of_neurons1: 320\n",
            "learning_rate: 0.001\n",
            "num_of_neurons2: 192\n",
            "num_of_neurons3: 288\n",
            "num_of_neurons4: 448\n",
            "num_of_neurons5: 224\n",
            "num_of_neurons6: 480\n",
            "num_of_neurons7: 480\n",
            "num_of_neurons8: 224\n",
            "Score: 0.8936333258946737\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_of_layers: 9\n",
            "num_of_neurons0: 160\n",
            "num_of_neurons1: 288\n",
            "learning_rate: 0.001\n",
            "num_of_neurons2: 288\n",
            "num_of_neurons3: 320\n",
            "num_of_neurons4: 288\n",
            "num_of_neurons5: 512\n",
            "num_of_neurons6: 32\n",
            "num_of_neurons7: 32\n",
            "num_of_neurons8: 32\n",
            "Score: 0.8858000040054321\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_of_layers: 6\n",
            "num_of_neurons0: 64\n",
            "num_of_neurons1: 384\n",
            "learning_rate: 0.0001\n",
            "num_of_neurons2: 32\n",
            "num_of_neurons3: 32\n",
            "num_of_neurons4: 32\n",
            "num_of_neurons5: 32\n",
            "Score: 0.8779666622479757\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_of_layers: 9\n",
            "num_of_neurons0: 352\n",
            "num_of_neurons1: 160\n",
            "learning_rate: 0.01\n",
            "num_of_neurons2: 352\n",
            "num_of_neurons3: 160\n",
            "num_of_neurons4: 320\n",
            "num_of_neurons5: 224\n",
            "num_of_neurons6: 32\n",
            "num_of_neurons7: 320\n",
            "num_of_neurons8: 128\n",
            "num_of_neurons9: 224\n",
            "num_of_neurons10: 128\n",
            "num_of_neurons11: 448\n",
            "num_of_neurons12: 192\n",
            "num_of_neurons13: 384\n",
            "num_of_neurons14: 288\n",
            "num_of_neurons15: 448\n",
            "Score: 0.34763333449761075\n",
            "Trial summary\n",
            "Hyperparameters:\n",
            "num_of_layers: 16\n",
            "num_of_neurons0: 448\n",
            "num_of_neurons1: 512\n",
            "learning_rate: 0.01\n",
            "num_of_neurons2: 64\n",
            "num_of_neurons3: 448\n",
            "num_of_neurons4: 160\n",
            "num_of_neurons5: 64\n",
            "num_of_neurons6: 480\n",
            "num_of_neurons7: 352\n",
            "num_of_neurons8: 352\n",
            "num_of_neurons9: 32\n",
            "num_of_neurons10: 32\n",
            "num_of_neurons11: 32\n",
            "num_of_neurons12: 32\n",
            "num_of_neurons13: 32\n",
            "num_of_neurons14: 32\n",
            "num_of_neurons15: 32\n",
            "Score: 0.10000000149011612\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "R0y22pOVHmaT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}