Python/ml/Iris_Classifier_using_Keras.ipynb

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    "colab": {
      "name": "Iris_Classifier_using_Keras.ipynb",
      "provenance": [],
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      "name": "python3",
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      "cell_type": "markdown",
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      "source": [
        "### Step 1: Importing important libraries. *Numpy:* Linear Algebra and manipulation of data. *Keras:* High-level API built on top of Tensorflow. *SKLearn:* Machine Learning libraries that holds datasets, models and other useful functions."
      ]
    },
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        "outputId": "8e85f91c-cb96-4f60-805f-97078555675a"
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      "source": [
        "import numpy as np\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.optimizers import Adam\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
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        },
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              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
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      "source": [
        "## *Problem statement: Given the dimensions of petal and sepal, can we predict the species of Iris?*\n",
        "### Classification Problem: 3 Classes = Setosa, Versicolor and Virginica. Total number of samples: 150 (50 in each class). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "![alt text](https://thegoodpython.com/assets/images/iris-species.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CROK4l5lXXex",
        "colab_type": "text"
      },
      "source": [
        "### Step 2: Loading and visualizing the data.* Seaborn:* Data Visualization library built on top of Matplotlib\n",
        "### **Class 0:** Setosa **Class 1:** Versicolor **Class 2:** Virginica"
      ]
    },
    {
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        },
        "outputId": "0c704f9d-c71f-41ac-80b3-fabc0b330099"
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      "source": [
        "import seaborn as sb\n",
        "import matplotlib.pyplot as plt\n",
        "iris = load_iris()\n",
        "iris_x = iris.data\n",
        "iris_y = iris.target\n",
        "#(Sepal Length, Sepal Width, Petal Length, Petal Width)in centimeters\n",
        "print(\"Example of iris_x = {}\".format(iris_x[0]))\n",
        "#(Target Class)\n",
        "print(\"Example of iris_y = {}\".format(iris_y[0]))\n",
        "\n",
        "sb.pairplot(sb.load_dataset(\"iris\"),hue='species')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Example of iris_x = [5.1 3.5 1.4 0.2]\n",
            "Example of iris_y = 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
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              "<seaborn.axisgrid.PairGrid at 0x7f303c9fa390>"
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Ac9F8nKd+C6/mxapa/R2OwP3nTpjLnA1z/MsrLlpKYdaIvv3DUpek6xX6t2YT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\nv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w9U3I99hnPQ8ethrCtjxHO0iubM4ne0yjTxXCVcMtJBwmF1mB7jVq4SY5vP/mo8T4mU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\nUsXy4xoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 804.75x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBaiiXn9XW5Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "75ee3266-eb12-4661-a324-d1880f0bc3b1"
      },
      "source": [
        "#Converting class vector from row vector to column vector\n",
        "print(iris_y.shape)\n",
        "iris_y = iris_y.reshape(-1,1)\n",
        "print(iris_y.shape)\n",
        "print(iris_x.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(150,)\n",
            "(150, 1)\n",
            "(150, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ug7PifdEbQV0",
        "colab_type": "text"
      },
      "source": [
        "### Step 3: Building the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9EdeayEbE_W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 898
        },
        "outputId": "59fff2b8-d26a-4d14-8024-7e8eb1f72625"
      },
      "source": [
        "#encoding the classes\n",
        "iris_ye = OneHotEncoder(sparse=False).fit_transform(iris_y)\n",
        "train_x,test_x,train_y,test_y = train_test_split(iris_x,iris_ye,test_size=0.2)\n",
        "\n",
        "iris_m = Sequential()\n",
        "iris_m.add(Dense(4,input_shape=(4,),activation='relu',name='Layer'))\n",
        "iris_m.add(Dense(5,activation='relu',name='HiddenLayer'))\n",
        "iris_m.add(Dense(3,activation='softmax',name='Output'))\n",
        "\n",
        "iris_m.compile(optimizer=Adam(lr=0.001),loss='categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "iris_m.summary()\n",
        "\n",
        "keras.utils.plot_model(iris_m)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "Layer (Dense)                (None, 4)                 20        \n",
            "_________________________________________________________________\n",
            "HiddenLayer (Dense)          (None, 5)                 25        \n",
            "_________________________________________________________________\n",
            "Output (Dense)               (None, 3)                 18        \n",
            "=================================================================\n",
            "Total params: 63\n",
            "Trainable params: 63\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
            "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
            "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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UVMReX45qjVbNzc3JyclVVVW9vb1Xr15taGgIDg62rytCjfWN1WiN5H78+PHjOp1uqHd0\n/PhxdjG9Xj9x4kRPT8+YmJh9+/YBgE6na2xstPYzZ86crVu3Dui8p6dHr9drNBqBQKBUKqOiom7c\nuJGZmcmOJKhWqw8dOsQuefLkSZlMZr2vt1VcXBwUFETTtIeHBzuaMHsDPn/+/LS0tLa2tmeuMScn\nh6ZpAAgICKitrc3Ly2Nz7OfnV1NTU19fHxoa6uXlxefzp06dmpqayj5PGLSr4TcmmffjxP1+z9Gj\nR2NjY11TVURExL59+/z9/V2wLmLFxMQAQEFBAdeF/A/3OY+PEPucBQCuX78uFouf81ASy93ue55J\nr9dv2LCBYZjExMRDhw5xXQ4a3HOXS5qm2V8UzcnJmTlzJtfloME9d+fx9PR0i8XS2NhoexuOSPPc\n5RKNC5hLRCLMJSIR5hKRCHOJSIS5RCTCXCISYS4RiTCXiESYS0QizCUiEeYSkQhziUhE6Ofcjh49\nynUJz4umpiZfX1+uqxiI0FzGxsZyXcJzhMCfeibu+z3jF0VR+fn5b7/9NteFuAO8vkQkwlwiEmEu\nEYkwl4hEmEtEIswlIhHmEpEIc4lIhLlEJMJcIhJhLhGJMJeIRJhLRCLMJSIR5hKRCHOJSIS5RCTC\nXCISYS4RiTCXiESYS0QizCUiEeYSkQhziUiEuUQkwlwiEmEuEYkwl4hEmEtEIswlIhHmEpEIc4lI\nhLlEJCJ0HOtxIS8vr7293XbOiRMnfvrpJ+tkQkLC5MmTXV6XO8BxrO2XlJSUl5cnEonYSYZhKIpi\n/93X16dQKFpaWoRCIXcFjmN4HrffihUrAKDnP3p7e63/5vF4K1aswFDaDY+X9uvv758yZYrBYBi0\n9cKFCwsWLHBxSW4Dj5f24/F48fHxHh4eTzdNmTIlNDTU9SW5DcylQ1asWNHb2ztgplAoXLNmjfVa\nE9kBz+OO0mq1tvfgrGvXrv385z/npB73gMdLR61Zs2bA/Y1Wq8VQOghz6aj4+Hiz2WydFAqFiYmJ\nHNbjHvA87gRBQUE//vijdUvW1NQEBARwW9J4h8dLJ1izZg2fzwcAiqLmzJmDoXQc5tIJVq5cabFY\nAIDP5//2t7/luhx3gLl0gqlTp4aGhlIU1d/fHxMTw3U57gBz6RyrV69mGOYXv/jF1KlTua7FLTAO\n4Lp2RLT8/Hy7o+Xo59w2bdoUEhLilLcx3n388cdJSUkTJkzguhAixMbGOvJyR3MZEhLy9ttvO9iJ\newgNDfX19eW6ClI4mEu8vnQaDKUTYS4RiTCXiESYS0QizCUiEeYSkQhziUiEuUQkwlwiEmEuEYkw\nl4hEmEtEIswlIhHmEpFobHN57NgxrVZLURRFUWq1+sCBA+z8tWvXenl5URQlFArnzJnT2NgIACdP\nnlQoFCUlJU/3s27dOplMRlHUtWvXRttqR6kqlSo+Pt7ursaIbYUsDw8Pb2/vsLCwPXv2DBjxcNxz\n8PPqI/lMsk6nUygUA2ZevHgRADZu3GidU1paKpfLi4uLB+3k8OHDAHD16lU7Wkdu0FKJYq2wv7+/\nvb39q6++SkhIoChqypQpV65c4bq6/xphNoZC0LisERERRqOR6yrGDYqiPD09w8LCwsLCIiIiYmNj\nIyIiampqFAoF16U5wbi5vhx+GKrnfJCq6OjohIQEg8Hw6aefcl2Lc5CSywsXLmg0Goqi9u3bx85h\nGGbPnj0vvviiSCRSKBTvv/++7fLDt1oslh07dmg0GolEEhQUlJ+fDwC5ublSqZSm6RMnToSHh8vl\ncl9fX/YCYOTOnz8/c+ZMhUIhFotnz559+vRpAFi3bh17wafT6a5evQoAiYmJNE0rFIri4uKh6vno\no49ompbJZAaDYfPmzT4+PtXV1adOnZLL5RkZGaPdgAkJCQBQVlbmyBb4+uuv58+fT9O0XC6fPXt2\nR0fHUF2NORdcQ4zw+vLOnTsAkJ2dzU6mpqZSFPXxxx+3t7ebTKacnBywuYIcvnXLli0ikaiwsLC9\nvX3btm08Ho+99kpNTQWAiooKo9FoMBgWLVoklUp7e3uHL9VWQUHBzp07Hz582NbWFhwcPGnSJHZ+\nVFQUn8+/e/eudcmVK1dar5WHr2fjxo3Z2dmRkZE3b94sLS2VyWRpaWmj2pgMw7AZUqvVdm+Bzs5O\nuVyemZnZ3d3d0tISGRnZ2to6TFfDG2E2hny53a8c+bp1Ot1Q/yuGyqXJZKJpesmSJdZW2zub4Vu7\nu7tpmo6Li2ObTCaTSCRKSUlh/rNXuru72SY2zbdv37YtdeT3Pbt27QIAg8HAMEx5eTkApKens01G\nozEgIKCvr29U9YzEMBWyV5x2b4Eff/wRAEpLS237HKar4TmYSxedx4c6Xg7l9u3bJpPptddes6O1\nurraZDLNmjWLnZRIJCqVqqqq6ukl2aF+bUdjGxV2eEF2BJhXX311+vTpn332GbtLjhw5EhcXxw5a\nNPJ6HNHV1cUwjFwuH9UabbeAVqv19vaOj4/fuXNnfX09u4Brin8aKdeXAzQ1NQGAUqm0o7WrqwsA\ntm/fbn3O19DQYDKZnFLYF198ERYWplQqRSLRBx98YJ1PUVRycnJdXV1FRQUAfP7552vXrnVBPVY1\nNTUAMGPGDLvXKJFIzpw5s3DhwoyMDK1WGxcX193d7Zrin0ZoLsViMQD09PTY0crmNSsra+SH5+Gd\nO3cuKysLABobG5cvX65SqS5fvmw0GjMzM20XS0hIEIvF+/fvr66ulsvlfn5+Y1TPoE6dOgUA4eHh\njqwxMDCwpKSkublZr9fn5+fv3bvXNcU/jdBczpo1i8fjff3113a0qtVqsVjsyN9+Bvj222+lUikA\n/PDDD2azOSUlRavVisXiAQ+nvLy8YmNji4qK9u7du379+rGr52ktLS1ZWVm+vr7vvPOO3Wtsbm6u\nrKwEAKVSuXv37pdffrmystIFxQ+K0FwqlcqoqKjCwsIDBw50dHRcv349Ly9vhK1isTgxMfHw4cO5\nubkdHR0Wi6WpqenevXt2lGE2m+/fv3/27Fk2lxqNBgDKy8ufPHly69aty5cvD1h+w4YNPT09paWl\nS5cuta+esrKyZz4nYhims7Ozv7+fYZjW1tb8/PwFCxbw+fyioiL2+tK+LdDc3JycnFxVVdXb23v1\n6tWGhobg4GAnbszRsfuOiRnBPdfx48etN+N+fn4HDx5k569fv97LywsAhELhK6+80tjYmJ2drVKp\nAICm6WXLljEM8/jx43Xr1k2aNGnChAkLFy7csWMHAPj6+n7//ffPbO3p6dHr9RqNRiAQsCG+ceNG\nTk4OTdMAEBAQUFtbm5eXx+5FPz+/mpoa21Kfdvz4cbZyvV4/ceJET0/PmJgY9lGrTqdrbGy0vuU5\nc+Zs3bp1wHYYtJ7MzEyJRAIAarX60KFD7JInT56UyWTW+3pbxcXFQUFBNE17eHjweDz4z5985s+f\nn5aW1tbW9sw1Dr8F6uvrQ0NDvby8+Hz+1KlTU1NT2ecJg3bleDae8XK7X+n4ut3Sm2++WVdXx3UV\n3HMwG4Sex8cX65Om69evi8Vif39/butxAwR9bmP80uv1GzZsYBgmMTHx0KFDXJfjDjCXTkDT9IwZ\nM3x8fHJycmbOnMl1Oe4Az+NOkJ6ebrFYGhsbbW/DkSMwl4hEmEtEIswlIhHmEpEIc4lIhLlEJMJc\nIhJhLhGJMJeIRJhLRCLMJSIR5hKRCHOJiOTgZ5IRGgpn47m5aKiacSI2NhZ/jd1WaGio3a+l8LDn\nLBRF5efn46+xOwVeXyISYS4RiTCXiESYS0QizCUiEeYSkQhziUiEuUQkwlwiEmEuEYkwl4hEmEtE\nIswlIhHmEpEIc4lIhLlEJMJcIhJhLhGJMJeIRJhLRCLMJSIR5hKRCHOJSIS5RCTCXCISYS4RiTCX\niESYS0QizCUiEeYSkQhziUiEuUQkcmi84OdcQ0ODxWKxnXP//v26ujrr5JQpUyQSicvrcgc4XrD9\nwsPDT506NVSrQCBoaWmZNGmSK0tyG3get19cXBxFUYM28Xi8JUuWYCjthrm0X2RkpFAoHKp19erV\nrizGzWAu7SeTyX79618PGk2hULh06VLXl+Q2MJcOWbVqVV9f34CZAoFg+fLlEyZM4KQk94C5dEhE\nRIRUKh0w02KxrFq1ipN63Abm0iEikSg6OtrDw8N25oQJE371q19xVZJ7wFw6auXKlb29vdZJoVAY\nFxc3IKlotPD5paP6+/snT5784MED65yvvvoqLCyMu4rcAR4vHcXj8VauXGk9QCqVykWLFnFbkhvA\nXDrBihUr2FO5h4fHmjVr+Hw+1xWNe3gedwKGYfz8/O7cuQMAV65cmTt3LtcVjXt4vHQCiqLWrFkD\nAH5+fhhKp+D+80SffPLJxYsXua7CUR0dHQAglUpjYmK4rsUJCgoKuC2A++PlxYsXL126xHUVjpLL\n5QqFwtfXl+tCHNXU1FRYWMh1FQQcLwEgODiY8/+gjjt9+vTrr7/OdRWOOnr0aGxsLNdVEHC8dBtu\nEEpyYC4RiTCXiESYS0QizCUiEeYSkQhziUiEuUQkwlwiEmEuEYkwl4hEmEtEIswlIhHmEpFonOWy\nvLw8OjparVaLRKIJEyYEBgb+8Y9/bGho4LquZzh27JhWq6VseHh4eHt7h4WF7dmzp729nesCiTOe\ncvnhhx8uWbJELpeXlJQYjcbm5uZPPvnk/PnzQUFBZ86c4bq64URFRdXV1el0OoVCwTBMf3+/wWA4\nevSov7+/Xq8PDAz85ptvuK6RMAzXoqOjo6Ojn7lYUVERAPzud78bMP/x48fTp0+fNGnSgwcPRrI6\nk8kUEhJiT6EOd2XNpa2CggIej+ft7f3o0SOnVOWg/Px8ElIxbo6Xe/fuBYDt27cPmD9hwoT33nuv\nra1t//79I+nnwIEDBoPBKSU5pavo6OiEhASDwfDpp586pSo3wfV/jBEdL7u6ugQCgUajGbT1+++/\nB4DXXnuNYZh3331XKBROnjyZbUpJSaFpGgBaW1sZhtm4caN1AAKdTvfXv/5VJBIplcqkpCSVSiUS\niUJCQi5dusS+dlRdMQxTVlYmk8nS09OHeheDHi8Zhjl37hwA/PKXv2Qn+/r6/vSnP6nVarFYPHv2\n7CNHjjAMk5OTQ9O0RCIpKip64403ZDKZj4/Pv/71L2snZ8+enTdvnkQikclks2bNMhqNQ3U1PEKO\nl9xXMJJc3rx5EwDmzp07aOv9+/cBwN/fn51ctWqVNUwMw+zZs8caJoZhoqKi2BixkpKSpFJpZWXl\nkydPbty4MW/ePJlM1tjYaEdXpaWlMpksLS1tqHcxVC7Z71Kq1Wp2csuWLSKRqLCwsL29fdu2bTwe\n78qVKwzDpKamAkBFRYXRaDQYDIsWLZJKpb29vQzDdHZ2yuXyzMzM7u7ulpaWyMhItsihuhoGIbkc\nH+fxzs5OAJDL5YO2enp6AsDjx4/t61wgELz00ksikWjmzJm5ubmPHz8+ePCgHf1ERER0dHT86U9/\nGu0LZTIZRVFs/U+ePMnNzV2+fHlUVJSnp+f27duFQqFtPaGhoXK5XKlUxsXFdXV1NTY2AkB9fX1H\nR0dgYKBYLJ48efKxY8d+9rOfPbMrko2PXMpkMgB49OjRoK0PHz6EoVM7KnPnzqVpuqqqyvGuRq6r\nq4thGLb+6upqk8k0a9YstkkikahUqkHrYa8izGYzAGi1Wm9v7/j4+J07d9bX17MLjLwrAo2PXPr5\n+QmFQvZ8/bSWlhYACAgIcMq6RCJRa2urU7oaoZqaGgCYMWMGAHR1dQHA9u3brU86GxoaTCbT8D1I\nJJIzZ84sXLgwIyNDq9XGxcV1d3fb1xUhxkcuxWLxokWL7t69+9NPPz3deuHCBXDS12TNZvOjR49c\nPDwB+2Mr4eHhAKBUKgEgKyvL9mJrJOORBAYGlpSUNDc36/X6/Pz8vXv32t0VCcZHLgHgww8/BIC0\ntLQB8zs6OrKysry9vd955x12jkAgYM9udjh79izDMMHBwY53NUItLS1ZWVm+vr5s/ey987Vr10bV\nSXNzc2VlJQAolcrdu3e//PLLlZWV9nVFiHGTyyVLluzevfsf//hHQkLC9+L8V14AAAJMSURBVN9/\n/+TJk46Oji+//HLx4sXt7e2FhYUKhYJdctq0aQ8fPiwqKjKbza2trQP+Sjlx4sTm5ub6+vrHjx+z\nmevv729vb+/r67t+/fqmTZs0Gk1CQoIdXZWVlcnl8oyMjGHeBcMwnZ2d/f39DMO0trbm5+cvWLCA\nz+cXFRWx15disTgxMfHw4cO5ubkdHR0Wi6WpqenevXvDb5zm5ubk5OSqqqre3t6rV682NDQEBwfb\n1xUpXHfrP4QR/r2HdfHixZUrV2o0Gg8PD6lUOmvWrM2bNzc1Ndku09bWtnjxYrFY7O/v/+67777/\n/vsAMG3aNPbpz3fffefn5yeRSBYuXNjS0pKUlCQUCn18fAQCgVwuf+utt2pra+3r6uTJk0M9vywu\nLg4KCqJp2sPDg8fjAQBFUZ6envPnz09LS2tra7NduKenR6/XazQagUCgVCqjoqJu3LjBPr8EgICA\ngNra2ry8PDbHfn5+NTU19fX1oaGhXl5efD5/6tSpqampfX19Q3U1/BYm5DkR9+NfsgOgcTU+UXJy\nckFBQVtbGydrJxA7PhHnqRg35/GxM+C3RxEJMJeIRM91Lrdt23bw4EGj0ejv70/CoI/IiojxL7my\na9euXbt2cV0FGsRzfbxExMJcIhJhLhGJMJeIRJhLRCLMJSIR5hKRCHOJSIS5RCTCXCISYS4RiTCX\niESYS0QiIj5PdOnSJff42W430NTUxHUJACTkMiQkhOsS0H/5+vpGR0dzXQVw//0ehJ6G15eIRJhL\nRCLMJSIR5hKR6P8BzuI6D4/aYyQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wes7CwKpdJWM",
        "colab_type": "text"
      },
      "source": [
        "### Step 4: Training the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySgEFWf_cq2S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ae51e62f-604e-418c-c907-3c4121f8c9ac"
      },
      "source": [
        "history = iris_m.fit(train_x,train_y,verbose=1,batch_size=5,epochs=500)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "Epoch 1/500\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "120/120 [==============================] - 1s 8ms/step - loss: 1.8603 - acc: 0.3417\n",
            "Epoch 2/500\n",
            "120/120 [==============================] - 0s 259us/step - loss: 1.4386 - acc: 0.3417\n",
            "Epoch 3/500\n",
            "120/120 [==============================] - 0s 245us/step - loss: 1.1582 - acc: 0.3417\n",
            "Epoch 4/500\n",
            "120/120 [==============================] - 0s 242us/step - loss: 0.9870 - acc: 0.3500\n",
            "Epoch 5/500\n",
            "120/120 [==============================] - 0s 225us/step - loss: 0.8838 - acc: 0.4417\n",
            "Epoch 6/500\n",
            "120/120 [==============================] - 0s 354us/step - loss: 0.8310 - acc: 0.6500\n",
            "Epoch 7/500\n",
            "120/120 [==============================] - 0s 288us/step - loss: 0.8051 - acc: 0.6917\n",
            "Epoch 8/500\n",
            "120/120 [==============================] - 0s 231us/step - loss: 0.7906 - acc: 0.6917\n",
            "Epoch 9/500\n",
            "120/120 [==============================] - 0s 267us/step - loss: 0.7779 - acc: 0.6917\n",
            "Epoch 10/500\n",
            "120/120 [==============================] - 0s 261us/step - loss: 0.7647 - acc: 0.6917\n",
            "Epoch 11/500\n",
            "120/120 [==============================] - 0s 270us/step - loss: 0.7497 - acc: 0.6917\n",
            "Epoch 12/500\n",
            "120/120 [==============================] - 0s 249us/step - loss: 0.7328 - acc: 0.6917\n",
            "Epoch 13/500\n",
            "120/120 [==============================] - 0s 253us/step - loss: 0.7145 - acc: 0.6917\n",
            "Epoch 14/500\n",
            "120/120 [==============================] - 0s 268us/step - loss: 0.6946 - acc: 0.6917\n",
            "Epoch 15/500\n",
            "120/120 [==============================] - 0s 300us/step - loss: 0.6758 - acc: 0.6917\n",
            "Epoch 16/500\n",
            "120/120 [==============================] - 0s 251us/step - loss: 0.6563 - acc: 0.6917\n",
            "Epoch 17/500\n",
            "120/120 [==============================] - 0s 294us/step - loss: 0.6374 - acc: 0.6917\n",
            "Epoch 18/500\n",
            "120/120 [==============================] - 0s 246us/step - loss: 0.6195 - acc: 0.6917\n",
            "Epoch 19/500\n",
            "120/120 [==============================] - 0s 239us/step - loss: 0.6018 - acc: 0.6917\n",
            "Epoch 20/500\n",
            "120/120 [==============================] - 0s 246us/step - loss: 0.5860 - acc: 0.6917\n",
            "Epoch 21/500\n",
            "120/120 [==============================] - 0s 265us/step - loss: 0.5708 - acc: 0.6917\n",
            "Epoch 22/500\n",
            "120/120 [==============================] - 0s 266us/step - loss: 0.5571 - acc: 0.6917\n",
            "Epoch 23/500\n",
            "120/120 [==============================] - 0s 250us/step - loss: 0.5445 - acc: 0.6917\n",
            "Epoch 24/500\n",
            "120/120 [==============================] - 0s 264us/step - loss: 0.5335 - acc: 0.6917\n",
            "Epoch 25/500\n",
            "120/120 [==============================] - 0s 258us/step - loss: 0.5227 - acc: 0.6917\n",
            "Epoch 26/500\n",
            "120/120 [==============================] - 0s 271us/step - loss: 0.5140 - acc: 0.6917\n",
            "Epoch 27/500\n",
            "120/120 [==============================] - 0s 315us/step - loss: 0.5054 - acc: 0.6917\n",
            "Epoch 28/500\n",
            "120/120 [==============================] - 0s 245us/step - loss: 0.4981 - acc: 0.6917\n",
            "Epoch 29/500\n",
            "120/120 [==============================] - 0s 270us/step - loss: 0.4914 - acc: 0.6917\n",
            "Epoch 30/500\n",
            "120/120 [==============================] - 0s 250us/step - loss: 0.4852 - acc: 0.6917\n",
            "Epoch 31/500\n",
            "120/120 [==============================] - 0s 247us/step - loss: 0.4807 - acc: 0.6917\n",
            "Epoch 32/500\n",
            "120/120 [==============================] - 0s 279us/step - loss: 0.4742 - acc: 0.6917\n",
            "Epoch 33/500\n",
            "120/120 [==============================] - 0s 260us/step - loss: 0.4691 - acc: 0.6917\n",
            "Epoch 34/500\n",
            "120/120 [==============================] - 0s 282us/step - loss: 0.4651 - acc: 0.6917\n",
            "Epoch 35/500\n",
            "120/120 [==============================] - 0s 293us/step - loss: 0.4604 - acc: 0.6917\n",
            "Epoch 36/500\n",
            "120/120 [==============================] - 0s 270us/step - loss: 0.4583 - acc: 0.6917\n",
            "Epoch 37/500\n",
            "120/120 [==============================] - 0s 254us/step - loss: 0.4528 - acc: 0.7083\n",
            "Epoch 38/500\n",
            "120/120 [==============================] - 0s 239us/step - loss: 0.4489 - acc: 0.7083\n",
            "Epoch 39/500\n",
            "120/120 [==============================] - 0s 289us/step - loss: 0.4452 - acc: 0.7083\n",
            "Epoch 40/500\n",
            "120/120 [==============================] - 0s 235us/step - loss: 0.4400 - acc: 0.7083\n",
            "Epoch 41/500\n",
            "120/120 [==============================] - 0s 271us/step - loss: 0.4361 - acc: 0.7167\n",
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            "Epoch 330/500\n",
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            "Epoch 335/500\n",
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            "Epoch 336/500\n",
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            "Epoch 340/500\n",
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            "Epoch 348/500\n",
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            "Epoch 350/500\n",
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            "Epoch 351/500\n",
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            "Epoch 352/500\n",
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            "Epoch 353/500\n",
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            "Epoch 354/500\n",
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            "Epoch 355/500\n",
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            "Epoch 356/500\n",
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            "Epoch 358/500\n",
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            "Epoch 359/500\n",
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            "Epoch 360/500\n",
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            "Epoch 361/500\n",
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            "Epoch 362/500\n",
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            "Epoch 367/500\n",
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            "Epoch 374/500\n",
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            "Epoch 379/500\n",
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            "Epoch 381/500\n",
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            "Epoch 382/500\n",
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            "Epoch 383/500\n",
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            "Epoch 385/500\n",
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            "Epoch 386/500\n",
            "120/120 [==============================] - 0s 288us/step - loss: 0.0778 - acc: 0.9750\n",
            "Epoch 387/500\n",
            "120/120 [==============================] - 0s 257us/step - loss: 0.0798 - acc: 0.9750\n",
            "Epoch 388/500\n",
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            "Epoch 389/500\n",
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            "Epoch 390/500\n",
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            "Epoch 391/500\n",
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            "Epoch 392/500\n",
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            "Epoch 393/500\n",
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            "Epoch 394/500\n",
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            "Epoch 395/500\n",
            "120/120 [==============================] - 0s 251us/step - loss: 0.0724 - acc: 0.9750\n",
            "Epoch 396/500\n",
            "120/120 [==============================] - 0s 240us/step - loss: 0.0741 - acc: 0.9667\n",
            "Epoch 397/500\n",
            "120/120 [==============================] - 0s 259us/step - loss: 0.0726 - acc: 0.9750\n",
            "Epoch 398/500\n",
            "120/120 [==============================] - 0s 252us/step - loss: 0.0738 - acc: 0.9833\n",
            "Epoch 399/500\n",
            "120/120 [==============================] - 0s 238us/step - loss: 0.0742 - acc: 0.9667\n",
            "Epoch 400/500\n",
            "120/120 [==============================] - 0s 245us/step - loss: 0.0713 - acc: 0.9667\n",
            "Epoch 401/500\n",
            "120/120 [==============================] - 0s 247us/step - loss: 0.0831 - acc: 0.9750\n",
            "Epoch 402/500\n",
            "120/120 [==============================] - 0s 248us/step - loss: 0.0725 - acc: 0.9750\n",
            "Epoch 403/500\n",
            "120/120 [==============================] - 0s 239us/step - loss: 0.0711 - acc: 0.9750\n",
            "Epoch 404/500\n",
            "120/120 [==============================] - 0s 291us/step - loss: 0.0712 - acc: 0.9750\n",
            "Epoch 405/500\n",
            "120/120 [==============================] - 0s 220us/step - loss: 0.0784 - acc: 0.9833\n",
            "Epoch 406/500\n",
            "120/120 [==============================] - 0s 281us/step - loss: 0.0754 - acc: 0.9750\n",
            "Epoch 407/500\n",
            "120/120 [==============================] - 0s 336us/step - loss: 0.0719 - acc: 0.9750\n",
            "Epoch 408/500\n",
            "120/120 [==============================] - 0s 255us/step - loss: 0.0717 - acc: 0.9750\n",
            "Epoch 409/500\n",
            "120/120 [==============================] - 0s 258us/step - loss: 0.0724 - acc: 0.9750\n",
            "Epoch 410/500\n",
            "120/120 [==============================] - 0s 246us/step - loss: 0.0723 - acc: 0.9833\n",
            "Epoch 411/500\n",
            "120/120 [==============================] - 0s 258us/step - loss: 0.0744 - acc: 0.9667\n",
            "Epoch 412/500\n",
            "120/120 [==============================] - 0s 242us/step - loss: 0.0703 - acc: 0.9750\n",
            "Epoch 413/500\n",
            "120/120 [==============================] - 0s 274us/step - loss: 0.0761 - acc: 0.9750\n",
            "Epoch 414/500\n",
            "120/120 [==============================] - 0s 275us/step - loss: 0.0703 - acc: 0.9750\n",
            "Epoch 415/500\n",
            "120/120 [==============================] - 0s 264us/step - loss: 0.0721 - acc: 0.9750\n",
            "Epoch 416/500\n",
            "120/120 [==============================] - 0s 291us/step - loss: 0.0735 - acc: 0.9750\n",
            "Epoch 417/500\n",
            "120/120 [==============================] - 0s 221us/step - loss: 0.0712 - acc: 0.9750\n",
            "Epoch 418/500\n",
            "120/120 [==============================] - 0s 228us/step - loss: 0.0721 - acc: 0.9667\n",
            "Epoch 419/500\n",
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            "Epoch 420/500\n",
            "120/120 [==============================] - 0s 242us/step - loss: 0.0705 - acc: 0.9750\n",
            "Epoch 421/500\n",
            "120/120 [==============================] - 0s 273us/step - loss: 0.0706 - acc: 0.9750\n",
            "Epoch 422/500\n",
            "120/120 [==============================] - 0s 292us/step - loss: 0.0755 - acc: 0.9667\n",
            "Epoch 423/500\n",
            "120/120 [==============================] - 0s 231us/step - loss: 0.0735 - acc: 0.9833\n",
            "Epoch 424/500\n",
            "120/120 [==============================] - 0s 239us/step - loss: 0.0729 - acc: 0.9750\n",
            "Epoch 425/500\n",
            "120/120 [==============================] - 0s 231us/step - loss: 0.0710 - acc: 0.9833\n",
            "Epoch 426/500\n",
            "120/120 [==============================] - 0s 273us/step - loss: 0.0719 - acc: 0.9667\n",
            "Epoch 427/500\n",
            "120/120 [==============================] - 0s 292us/step - loss: 0.0724 - acc: 0.9750\n",
            "Epoch 428/500\n",
            "120/120 [==============================] - 0s 307us/step - loss: 0.0732 - acc: 0.9750\n",
            "Epoch 429/500\n",
            "120/120 [==============================] - 0s 254us/step - loss: 0.0749 - acc: 0.9750\n",
            "Epoch 430/500\n",
            "120/120 [==============================] - 0s 255us/step - loss: 0.0718 - acc: 0.9750\n",
            "Epoch 431/500\n",
            "120/120 [==============================] - 0s 248us/step - loss: 0.0715 - acc: 0.9750\n",
            "Epoch 432/500\n",
            "120/120 [==============================] - 0s 229us/step - loss: 0.0773 - acc: 0.9833\n",
            "Epoch 433/500\n",
            "120/120 [==============================] - 0s 217us/step - loss: 0.0707 - acc: 0.9750\n",
            "Epoch 434/500\n",
            "120/120 [==============================] - 0s 249us/step - loss: 0.0702 - acc: 0.9667\n",
            "Epoch 435/500\n",
            "120/120 [==============================] - 0s 263us/step - loss: 0.0729 - acc: 0.9750\n",
            "Epoch 436/500\n",
            "120/120 [==============================] - 0s 240us/step - loss: 0.0720 - acc: 0.9833\n",
            "Epoch 437/500\n",
            "120/120 [==============================] - 0s 264us/step - loss: 0.0702 - acc: 0.9833\n",
            "Epoch 438/500\n",
            "120/120 [==============================] - 0s 264us/step - loss: 0.0717 - acc: 0.9750\n",
            "Epoch 439/500\n",
            "120/120 [==============================] - 0s 262us/step - loss: 0.0762 - acc: 0.9667\n",
            "Epoch 440/500\n",
            "120/120 [==============================] - 0s 245us/step - loss: 0.0724 - acc: 0.9750\n",
            "Epoch 441/500\n",
            "120/120 [==============================] - 0s 265us/step - loss: 0.0714 - acc: 0.9833\n",
            "Epoch 442/500\n",
            "120/120 [==============================] - 0s 227us/step - loss: 0.0712 - acc: 0.9833\n",
            "Epoch 443/500\n",
            "120/120 [==============================] - 0s 235us/step - loss: 0.0707 - acc: 0.9750\n",
            "Epoch 444/500\n",
            "120/120 [==============================] - 0s 241us/step - loss: 0.0729 - acc: 0.9750\n",
            "Epoch 445/500\n",
            "120/120 [==============================] - 0s 230us/step - loss: 0.0765 - acc: 0.9667\n",
            "Epoch 446/500\n",
            "120/120 [==============================] - 0s 248us/step - loss: 0.0693 - acc: 0.9750\n",
            "Epoch 447/500\n",
            "120/120 [==============================] - 0s 246us/step - loss: 0.0702 - acc: 0.9750\n",
            "Epoch 448/500\n",
            "120/120 [==============================] - 0s 232us/step - loss: 0.0695 - acc: 0.9750\n",
            "Epoch 449/500\n",
            "120/120 [==============================] - 0s 251us/step - loss: 0.0712 - acc: 0.9750\n",
            "Epoch 450/500\n",
            "120/120 [==============================] - 0s 238us/step - loss: 0.0724 - acc: 0.9750\n",
            "Epoch 451/500\n",
            "120/120 [==============================] - 0s 238us/step - loss: 0.0700 - acc: 0.9833\n",
            "Epoch 452/500\n",
            "120/120 [==============================] - 0s 235us/step - loss: 0.0712 - acc: 0.9750\n",
            "Epoch 453/500\n",
            "120/120 [==============================] - 0s 226us/step - loss: 0.0707 - acc: 0.9750\n",
            "Epoch 454/500\n",
            "120/120 [==============================] - 0s 237us/step - loss: 0.0695 - acc: 0.9750\n",
            "Epoch 455/500\n",
            "120/120 [==============================] - 0s 232us/step - loss: 0.0705 - acc: 0.9750\n",
            "Epoch 456/500\n",
            "120/120 [==============================] - 0s 225us/step - loss: 0.0697 - acc: 0.9750\n",
            "Epoch 457/500\n",
            "120/120 [==============================] - 0s 240us/step - loss: 0.0736 - acc: 0.9750\n",
            "Epoch 458/500\n",
            "120/120 [==============================] - 0s 252us/step - loss: 0.0694 - acc: 0.9750\n",
            "Epoch 459/500\n",
            "120/120 [==============================] - 0s 223us/step - loss: 0.0705 - acc: 0.9833\n",
            "Epoch 460/500\n",
            "120/120 [==============================] - 0s 239us/step - loss: 0.0755 - acc: 0.9667\n",
            "Epoch 461/500\n",
            "120/120 [==============================] - 0s 226us/step - loss: 0.0779 - acc: 0.9667\n",
            "Epoch 462/500\n",
            "120/120 [==============================] - 0s 256us/step - loss: 0.0687 - acc: 0.9750\n",
            "Epoch 463/500\n",
            "120/120 [==============================] - 0s 226us/step - loss: 0.0815 - acc: 0.9667\n",
            "Epoch 464/500\n",
            "120/120 [==============================] - 0s 225us/step - loss: 0.0857 - acc: 0.9667\n",
            "Epoch 465/500\n",
            "120/120 [==============================] - 0s 257us/step - loss: 0.0707 - acc: 0.9833\n",
            "Epoch 466/500\n",
            "120/120 [==============================] - 0s 276us/step - loss: 0.0689 - acc: 0.9750\n",
            "Epoch 467/500\n",
            "120/120 [==============================] - 0s 236us/step - loss: 0.0768 - acc: 0.9667\n",
            "Epoch 468/500\n",
            "120/120 [==============================] - 0s 255us/step - loss: 0.0730 - acc: 0.9833\n",
            "Epoch 469/500\n",
            "120/120 [==============================] - 0s 253us/step - loss: 0.0747 - acc: 0.9750\n",
            "Epoch 470/500\n",
            "120/120 [==============================] - 0s 302us/step - loss: 0.0734 - acc: 0.9750\n",
            "Epoch 471/500\n",
            "120/120 [==============================] - 0s 304us/step - loss: 0.0724 - acc: 0.9750\n",
            "Epoch 472/500\n",
            "120/120 [==============================] - 0s 241us/step - loss: 0.0750 - acc: 0.9667\n",
            "Epoch 473/500\n",
            "120/120 [==============================] - 0s 277us/step - loss: 0.0698 - acc: 0.9750\n",
            "Epoch 474/500\n",
            "120/120 [==============================] - 0s 250us/step - loss: 0.0692 - acc: 0.9833\n",
            "Epoch 475/500\n",
            "120/120 [==============================] - 0s 231us/step - loss: 0.0691 - acc: 0.9750\n",
            "Epoch 476/500\n",
            "120/120 [==============================] - 0s 248us/step - loss: 0.0698 - acc: 0.9750\n",
            "Epoch 477/500\n",
            "120/120 [==============================] - 0s 258us/step - loss: 0.0694 - acc: 0.9750\n",
            "Epoch 478/500\n",
            "120/120 [==============================] - 0s 245us/step - loss: 0.0691 - acc: 0.9750\n",
            "Epoch 479/500\n",
            "120/120 [==============================] - 0s 237us/step - loss: 0.0708 - acc: 0.9750\n",
            "Epoch 480/500\n",
            "120/120 [==============================] - 0s 239us/step - loss: 0.0697 - acc: 0.9750\n",
            "Epoch 481/500\n",
            "120/120 [==============================] - 0s 256us/step - loss: 0.0713 - acc: 0.9750\n",
            "Epoch 482/500\n",
            "120/120 [==============================] - 0s 245us/step - loss: 0.0693 - acc: 0.9750\n",
            "Epoch 483/500\n",
            "120/120 [==============================] - 0s 284us/step - loss: 0.0693 - acc: 0.9750\n",
            "Epoch 484/500\n",
            "120/120 [==============================] - 0s 233us/step - loss: 0.0719 - acc: 0.9667\n",
            "Epoch 485/500\n",
            "120/120 [==============================] - 0s 243us/step - loss: 0.0694 - acc: 0.9750\n",
            "Epoch 486/500\n",
            "120/120 [==============================] - 0s 256us/step - loss: 0.0712 - acc: 0.9750\n",
            "Epoch 487/500\n",
            "120/120 [==============================] - 0s 225us/step - loss: 0.0688 - acc: 0.9750\n",
            "Epoch 488/500\n",
            "120/120 [==============================] - 0s 262us/step - loss: 0.0717 - acc: 0.9750\n",
            "Epoch 489/500\n",
            "120/120 [==============================] - 0s 221us/step - loss: 0.0699 - acc: 0.9750\n",
            "Epoch 490/500\n",
            "120/120 [==============================] - 0s 237us/step - loss: 0.0756 - acc: 0.9750\n",
            "Epoch 491/500\n",
            "120/120 [==============================] - 0s 250us/step - loss: 0.0759 - acc: 0.9583\n",
            "Epoch 492/500\n",
            "120/120 [==============================] - 0s 240us/step - loss: 0.0683 - acc: 0.9750\n",
            "Epoch 493/500\n",
            "120/120 [==============================] - 0s 235us/step - loss: 0.0723 - acc: 0.9750\n",
            "Epoch 494/500\n",
            "120/120 [==============================] - 0s 238us/step - loss: 0.0679 - acc: 0.9750\n",
            "Epoch 495/500\n",
            "120/120 [==============================] - 0s 235us/step - loss: 0.0689 - acc: 0.9750\n",
            "Epoch 496/500\n",
            "120/120 [==============================] - 0s 235us/step - loss: 0.0700 - acc: 0.9750\n",
            "Epoch 497/500\n",
            "120/120 [==============================] - 0s 255us/step - loss: 0.0685 - acc: 0.9750\n",
            "Epoch 498/500\n",
            "120/120 [==============================] - 0s 247us/step - loss: 0.0715 - acc: 0.9750\n",
            "Epoch 499/500\n",
            "120/120 [==============================] - 0s 257us/step - loss: 0.0672 - acc: 0.9750\n",
            "Epoch 500/500\n",
            "120/120 [==============================] - 0s 230us/step - loss: 0.0710 - acc: 0.9833\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOIMRjZGfCTd",
        "colab_type": "text"
      },
      "source": [
        "Step 5: Evaluating the model on unseen data (Test set)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EsGD5_uddcmI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9fba40d9-1cf7-4ea4-adfb-149f97c4b34a"
      },
      "source": [
        "results = iris_m.evaluate(test_x,test_y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r30/30 [==============================] - 0s 992us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S0o0ho7ZfMTz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "b142de2a-b517-4ee0-a350-743dc03da0ac"
      },
      "source": [
        "print('Test Loss = {}'.format(results[0]))\n",
        "print('Test Accuracy = {}'.format(results[1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Loss = 0.018710220232605934\n",
            "Test Accuracy = 1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "saf8aYsMfX44",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "38927702-7b6f-4b5d-af6a-c6d3548a4370"
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "pred_y = iris_m.predict(test_x)\n",
        "pred_yl = np.argmax(pred_y,axis=1)\n",
        "test_yl = np.argmax(test_y,axis=1)\n",
        "print(classification_report(test_yl,pred_yl))\n",
        "print(confusion_matrix(test_yl,pred_yl))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00         9\n",
            "           1       1.00      1.00      1.00        13\n",
            "           2       1.00      1.00      1.00         8\n",
            "\n",
            "    accuracy                           1.00        30\n",
            "   macro avg       1.00      1.00      1.00        30\n",
            "weighted avg       1.00      1.00      1.00        30\n",
            "\n",
            "[[ 9  0  0]\n",
            " [ 0 13  0]\n",
            " [ 0  0  8]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8IB09mgf6dQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "087ab149-74bd-44f7-9b7a-b2725ac2e8ed"
      },
      "source": [
        "plt.xlim(0,500)\n",
        "plt.ylim(0.0,1.0)\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('Model Metrics')\n",
        "plt.ylabel('Accuracy/Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['ACC', 'LOSS'], loc='upper left')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bG8trr71G7969KS4u5o9//CM/+clPiIyMJDo6uqZU8dlnn3HLLbcQEWHvJ3n8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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qgaJ1u0chY4d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}