Untitled0.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "n6dTOOdqwfEX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40a6b482-3df8-4c21-a145-41319c5ad611"
      },
      "source": [
        "!pip install keras-tuner\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting keras-tuner\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/20/ec/1ef246787174b1e2bb591c95f29d3c1310070cad877824f907faba3dade9/keras-tuner-1.0.2.tar.gz (62kB)\n",
            "\r\u001b[K     |█████▏                          | 10kB 14.9MB/s eta 0:00:01\r\u001b[K     |██████████▍                     | 20kB 14.0MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 30kB 9.8MB/s eta 0:00:01\r\u001b[K     |████████████████████▉           | 40kB 8.4MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 51kB 5.4MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▎| 61kB 5.9MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 71kB 4.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (20.9)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (0.16.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (1.19.5)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (0.8.9)\n",
            "Collecting terminaltables\n",
            "  Downloading https://files.pythonhosted.org/packages/9b/c4/4a21174f32f8a7e1104798c445dacdc1d4df86f2f26722767034e4de4bff/terminaltables-3.1.0.tar.gz\n",
            "Collecting colorama\n",
            "  Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (4.41.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (2.23.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (0.22.2.post1)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->keras-tuner) (2.4.7)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (2020.12.5)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->keras-tuner) (1.0.1)\n",
            "Building wheels for collected packages: keras-tuner, terminaltables\n",
            "  Building wheel for keras-tuner (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-tuner: filename=keras_tuner-1.0.2-cp37-none-any.whl size=78938 sha256=95a44373e2186ae4493fa9994be226432f2ebcb8fb4285a2f1a44da1bf8de9f7\n",
            "  Stored in directory: /root/.cache/pip/wheels/bb/a1/8a/7c3de0efb3707a1701b36ebbfdbc4e67aedf6d4943a1f463d6\n",
            "  Building wheel for terminaltables (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for terminaltables: filename=terminaltables-3.1.0-cp37-none-any.whl size=15356 sha256=467fd452ae8e548e4d91d768c5f1b93b7e69ef1f9ee57f3e7fc5be7ded1caafb\n",
            "  Stored in directory: /root/.cache/pip/wheels/30/6b/50/6c75775b681fb36cdfac7f19799888ef9d8813aff9e379663e\n",
            "Successfully built keras-tuner terminaltables\n",
            "Installing collected packages: terminaltables, colorama, keras-tuner\n",
            "Successfully installed colorama-0.4.4 keras-tuner-1.0.2 terminaltables-3.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0Pfij4Kzc7y"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KwmQJxxz4Z4"
      },
      "source": [
        "fashion_mnist=keras.datasets.fashion_mnist"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Yvoy-3E0OYM"
      },
      "source": [
        "(train_images,train_labels),(test_images,test_lables)=fashion_mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVkZaCzf0kYf"
      },
      "source": [
        "train_images=train_images/255.0\n",
        "test_images=test_images/255.0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V8XQqaSJ1dct",
        "outputId": "c51f8425-1094-4b7e-9173-181cbf596c49"
      },
      "source": [
        "train_images[0].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cxIViZ4-11ZK"
      },
      "source": [
        "train_images=train_images.reshape(len(train_images),28,28,1)\n",
        "test_images=test_images.reshape(len(test_images),28,28,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tK5cHhr82RNR"
      },
      "source": [
        "def build_model(hp):\n",
        "  model=keras.Sequential([\n",
        "                          keras.layers.Conv2D(\n",
        "                              filters=hp.Int('conv_1_filter',min_value=32, max_value=128,step=16),\n",
        "                              kernel_size=hp.Choice('conv_1_kernel',values=[3,5]),\n",
        "                              activation='relu',\n",
        "                              input_shape=(28,28,1)\n",
        "                              \n",
        "                          ),\n",
        "                          keras.layers.Conv2D(\n",
        "                               filters=hp.Int('conv_2_filter',min_value=32, max_value=64,step=16),\n",
        "                              kernel_size=hp.Choice('conv_2_kernel',values=[3,5]),\n",
        "                              activation='relu'\n",
        "                          ),\n",
        "                          keras.layers.Flatten(),\n",
        "                          keras.layers.Dense(\n",
        "                              units=hp.Int('dense_1_units',min_value=32, max_value=128,step=16),\n",
        "                              activation='relu'\n",
        "                          ),\n",
        "                          keras.layers.Dense(10,activation='softmax')\n",
        "  ])\n",
        "  model.compile(optimizer=keras.optimizers.Adam(hp.Choice('learning_rate',values=[1e-2,1e-3])),\n",
        "                loss='sparse_categorical_crossentropy',\n",
        "                metrics=['accuracy'])\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUxboH8V8TKu"
      },
      "source": [
        "from kerastuner import RandomSearch\n",
        "from kerastuner.engine.hyperparameters import HyperParameters"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dzhb_IkH9Kbl"
      },
      "source": [
        "tuner_search=RandomSearch(build_model,objective='val_accuracy',max_trials=5,directory='output',project_name=\"Mnist Fashion\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKH_cDV7-0Bl"
      },
      "source": [
        "tuner_search.search(train_images,train_labels,epochs=3,validation_split=0.1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NPHGmFnNAhey"
      },
      "source": [
        "model=tuner_search.get_best_models(num_models=1)[0]"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FO_tFSXQC9Ti",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6fc24271-ed44-4b72-902a-aac438092ed7"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 24, 24, 96)        2496      \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 22, 22, 48)        41520     \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 23232)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 80)                1858640   \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                810       \n",
            "=================================================================\n",
            "Total params: 1,903,466\n",
            "Trainable params: 1,903,466\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}