fashion_mnist.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "fashion_mnist.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vUkOXTVCCPPR",
        "colab_type": "text"
      },
      "source": [
        "# YAPAY SİNİR AĞLARI İLE FASHION MNIST VERİ SETİNİN MODELLENMESİ"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "juYSIrs0H3kX",
        "colab_type": "text"
      },
      "source": [
        "Fashion-MNIST, Zalando Araştırma Ekibi tarafından geliştirilen ve kıyafetlerden oluşan bir veri setidir .  60.000 resim modeli eğitmek için, 10.000 resim ise eğitilen modelin performansını ölçmek için kullanılmaktadır. Resimler 28*28 piksel boyularındadır."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJG-ZBwLF3rI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "89926c16-8384-4cf1-f026-942378ba000b"
      },
      "source": [
        "from IPython.display import Image\n",
        "Image('fashion.jpeg')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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OwrS+Mnwo8SfEWWD+yfGV5oNmkeyWyt0AWU5ySW689K+dfEn/AATrfxqXGt+KLiZdwZRG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\nFc+hIA61+n/8A+lfin8Zv+Sp+If+v65/9GmgD//Z\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "83zFwXC6HJW_",
        "colab_type": "text"
      },
      "source": [
        "### Tensorflow 2.0 Kurulumu"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noCN_59tB4p6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "outputId": "fc10f327-863c-4d64-dead-ac9814f2729c"
      },
      "source": [
        "!pip install tensorflow-gpu==2.0.0.alpha0"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu==2.0.0.alpha0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1a/66/32cffad095253219d53f6b6c2a436637bbe45ac4e7be0244557210dc3918/tensorflow_gpu-2.0.0a0-cp36-cp36m-manylinux1_x86_64.whl (332.1MB)\n",
            "\u001b[K     |████████████████████████████████| 332.1MB 69kB/s \n",
            "\u001b[?25hCollecting tf-estimator-nightly<1.14.0.dev2019030116,>=1.14.0.dev2019030115 (from tensorflow-gpu==2.0.0.alpha0)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/13/82/f16063b4eed210dc2ab057930ac1da4fbe1e91b7b051a6c8370b401e6ae7/tf_estimator_nightly-1.14.0.dev2019030115-py2.py3-none-any.whl (411kB)\n",
            "\u001b[K     |████████████████████████████████| 419kB 38.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (1.0.8)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (1.15.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (1.1.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (0.1.7)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (0.7.1)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (0.2.2)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (0.33.4)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (1.1.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (3.7.1)\n",
            "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (1.16.4)\n",
            "Collecting tb-nightly<1.14.0a20190302,>=1.14.0a20190301 (from tensorflow-gpu==2.0.0.alpha0)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a9/51/aa1d756644bf4624c03844115e4ac4058eff77acd786b26315f051a4b195/tb_nightly-1.14.0a20190301-py3-none-any.whl (3.0MB)\n",
            "\u001b[K     |████████████████████████████████| 3.0MB 23.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (1.12.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (0.8.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==2.0.0.alpha0) (2.8.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu==2.0.0.alpha0) (41.0.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0.alpha0) (3.1.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0.alpha0) (0.15.4)\n",
            "Installing collected packages: tf-estimator-nightly, tb-nightly, tensorflow-gpu\n",
            "Successfully installed tb-nightly-1.14.0a20190301 tensorflow-gpu-2.0.0a0 tf-estimator-nightly-1.14.0.dev2019030115\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FBAGIGsCaoA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "24675581-176d-4c09-afb2-e74933daf3aa"
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.0.0-alpha0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aId8E3zGIayl",
        "colab_type": "text"
      },
      "source": [
        "Gerekli kütüphanelerin import Edilmesi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcL2PybyCCuD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import datetime\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import fashion_mnist"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nCz0YcF8IgIR",
        "colab_type": "text"
      },
      "source": [
        "FASHION MNIST veri setinin train ve test olarak alınması"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "shpLybPGCXLd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "outputId": "9a5ca19b-fd02-47e6-8416-f0ace878611d"
      },
      "source": [
        "#Loading the Fashion Mnist dataset\n",
        "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NV4Ff6ZFInDx",
        "colab_type": "text"
      },
      "source": [
        "Train ve Test Verilerin Normalize Edilmesi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcNHoVulCeIL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X_train / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_W1RBauChiU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test = X_test / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tIKwt3bTDL9K",
        "colab_type": "text"
      },
      "source": [
        "Yapay Sinir Ağı için Resimlerin Flatten Edilmesi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBisIfnUCj2P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X_train.reshape(-1, 28*28)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1uKfU221Cm7l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "78410df6-6dc2-4334-ed0c-347f06caa5bd"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 784)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XpzfvzlHCpad",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Reshape the testing subset in the same way\n",
        "X_test = X_test.reshape(-1, 28*28)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QGnvzy2KCtKt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "81122b9e-736f-4a8d-dc28-14db0d93caf2"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 784)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nF6v2f2tCy7X",
        "colab_type": "text"
      },
      "source": [
        "### Model 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mCGIJzbcCwGO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWKHRCH8C3Kr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.add(tf.keras.layers.Dense(units=128, activation='relu', input_shape=(784, )))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ngttjSaWC5wC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.add(tf.keras.layers.Dropout(0.2))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Iu-1Po-C8Rp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.add(tf.keras.layers.Dense(units=10, activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPy-2BQpC_Xz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jCoFugsoDB2f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "5675754b-29d8-489c-8f3a-f0feeb8ea793"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 128)               100480    \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 101,770\n",
            "Trainable params: 101,770\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UkhujTCVDENz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "outputId": "1d801c84-41b6-4ac1-a253-4561581fa274"
      },
      "source": [
        "model.fit(X_train, y_train, epochs=5)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 8s 139us/sample - loss: 0.5284 - sparse_categorical_accuracy: 0.8128\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 8s 128us/sample - loss: 0.3995 - sparse_categorical_accuracy: 0.8547\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 8s 128us/sample - loss: 0.3654 - sparse_categorical_accuracy: 0.8666\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 8s 129us/sample - loss: 0.3455 - sparse_categorical_accuracy: 0.8719\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 8s 132us/sample - loss: 0.3314 - sparse_categorical_accuracy: 0.8781\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f00c2fcb588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9Dl-J_HDotg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "341352b6-2dcd-4d51-b8ef-1aa0e660c8fe"
      },
      "source": [
        "test_loss, test_accuracy = model.evaluate(X_test, y_test)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 86us/sample - loss: 0.3497 - sparse_categorical_accuracy: 0.8731\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBDHsjxuD2TO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "beda556e-3143-45f6-a206-5124508eebde"
      },
      "source": [
        "print(\"Test accuracy: {}\".format(test_accuracy))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 0.8730999827384949\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjP1phn4D68_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_json = model.to_json()\n",
        "with open(\"fashion_model.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kmmabfjFD-Nh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save_weights(\"fashion_model.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C2pcuvxnEE7g",
        "colab_type": "text"
      },
      "source": [
        "### Model 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qpGdhPIGECo3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model2 = tf.keras.models.Sequential()\n",
        "model2.add(tf.keras.layers.Dense(units=32, activation='relu', input_shape=(784, )))\n",
        "model2.add(tf.keras.layers.Dropout(0.4))\n",
        "model2.add(tf.keras.layers.Dense(units=64, activation='relu'))\n",
        "model2.add(tf.keras.layers.Dropout(0.6))\n",
        "model2.add(tf.keras.layers.Dense(units=128, activation='relu'))\n",
        "model2.add(tf.keras.layers.Dense(units=10, activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xPElpjWiERZB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model2.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5snon4oEW4E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "dfd3c317-2ddf-46e8-8caf-a24596afd976"
      },
      "source": [
        "model2.summary()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_2 (Dense)              (None, 32)                25120     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 64)                2112      \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 128)               8320      \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 36,842\n",
            "Trainable params: 36,842\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OxNCuDtuEZye",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "9fd114f4-5ce5-444f-b13f-b7d2d8b16694"
      },
      "source": [
        "model2.fit(X_train, y_train, epochs=10)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 10s 164us/sample - loss: 0.9659 - sparse_categorical_accuracy: 0.6315\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 10s 163us/sample - loss: 0.7451 - sparse_categorical_accuracy: 0.7234\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 10s 162us/sample - loss: 0.6942 - sparse_categorical_accuracy: 0.7438\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 10s 162us/sample - loss: 0.6639 - sparse_categorical_accuracy: 0.7590\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 10s 162us/sample - loss: 0.6454 - sparse_categorical_accuracy: 0.7633\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 10s 161us/sample - loss: 0.6357 - sparse_categorical_accuracy: 0.7681\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 10s 162us/sample - loss: 0.6278 - sparse_categorical_accuracy: 0.7726\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 10s 161us/sample - loss: 0.6164 - sparse_categorical_accuracy: 0.7760\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 10s 161us/sample - loss: 0.6071 - sparse_categorical_accuracy: 0.7784\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 10s 162us/sample - loss: 0.6037 - sparse_categorical_accuracy: 0.7793\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f00c02d16a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5Ix9XhHEi9k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fd12d81c-db83-474d-a566-9db371a2994a"
      },
      "source": [
        "test_loss, test_accuracy = model2.evaluate(X_test, y_test)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 89us/sample - loss: 0.6859 - sparse_categorical_accuracy: 0.7465\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FxfT7ITxFEXt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f521acc8-74e1-474c-bb69-c628cded12c9"
      },
      "source": [
        "print(\"Test accuracy: {}\".format(test_accuracy))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 0.7465000152587891\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvkc3IYdFKHx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_json2 = model2.to_json()\n",
        "with open(\"fashion_model2.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHHYApkPFSUn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model2.save_weights(\"fashion_model2.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D0ZzTTg0K8k8",
        "colab_type": "text"
      },
      "source": [
        "### Model 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B_sWaw7fFVTm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model3 = tf.keras.models.Sequential()\n",
        "model3.add(tf.keras.layers.Dense(units=128, activation='relu', input_shape=(784, )))\n",
        "model3.add(tf.keras.layers.Dropout(0.4))\n",
        "model3.add(tf.keras.layers.Dense(units=256, activation='relu'))\n",
        "model3.add(tf.keras.layers.Dropout(0.6))\n",
        "model3.add(tf.keras.layers.Dense(units=128, activation='relu'))\n",
        "model3.add(tf.keras.layers.Dense(units=10, activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X12DCyPmLKd5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model3.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWWDwfJ8LOUj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "d14e0ed9-f560-4e0b-dc9e-06c632a8fb06"
      },
      "source": [
        "model3.summary()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_6 (Dense)              (None, 128)               100480    \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 256)               33024     \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 167,690\n",
            "Trainable params: 167,690\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s11d40m_LR8I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "62de6bbe-8618-47ca-f803-c9163f05922c"
      },
      "source": [
        "model3.fit(X_train, y_train, epochs=10)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 10s 167us/sample - loss: 0.6925 - sparse_categorical_accuracy: 0.7466\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 10s 165us/sample - loss: 0.5058 - sparse_categorical_accuracy: 0.8192\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 10s 165us/sample - loss: 0.4731 - sparse_categorical_accuracy: 0.8298\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 10s 164us/sample - loss: 0.4504 - sparse_categorical_accuracy: 0.8399\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 10s 165us/sample - loss: 0.4314 - sparse_categorical_accuracy: 0.8444\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 10s 165us/sample - loss: 0.4201 - sparse_categorical_accuracy: 0.8485\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 10s 163us/sample - loss: 0.4132 - sparse_categorical_accuracy: 0.8510\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 10s 163us/sample - loss: 0.4006 - sparse_categorical_accuracy: 0.8575\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 10s 165us/sample - loss: 0.3945 - sparse_categorical_accuracy: 0.8568\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 10s 164us/sample - loss: 0.3912 - sparse_categorical_accuracy: 0.8582\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f00a35a07f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0pGUu_KTLYxy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a5ec6ad9-b9b7-4d8d-d684-015b622699ae"
      },
      "source": [
        "test_loss, test_accuracy = model3.evaluate(X_test, y_test)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 96us/sample - loss: 0.3751 - sparse_categorical_accuracy: 0.8684\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xkR60tKpL5mm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2a957737-3ee4-48b8-f4c3-4585ffde9e02"
      },
      "source": [
        "print(\"Test accuracy: {}\".format(test_accuracy))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 0.868399977684021\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BI8YocprL_hN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_json3 = model3.to_json()\n",
        "with open(\"fashion_model3.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zW3X0WmMMFhE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model3.save_weights(\"fashion_model3.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TBRLGDQaMNls",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}