Assignment 3 (Bhargavi Joshi).ipynb

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      "name": "Mushroom_Tensorflow.ipynb",
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      "metadata": {
        "id": "I4ZFBPMRv4bE",
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      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras \n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZRV3hVjXz3D8",
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      "source": [
        "data=pd.read_csv(\"Mushroom.csv\",sep=',',names=[\"class\",\"cap-shape\",\"cap-surface\",\"cap-color\",\"bruises\",\"odor\",\"gill-attachment\",\"gill-spacing\",\"gill-size\",\"gill-color\",\"stalk-shape\",\"stalk-root\",\"stalk-surface-above-ring\",\"stalk-surface-below-ring\",\"stalk-color-above-ring\",\"stalk-color-below-ring\",\"veil-type\",\"veil-color\",\"ring-number\",\"ring-type\",\"spore-print-color\",\"population\",\"habitat\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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          "height": 270
        },
        "outputId": "42fd8632-ee30-4580-9a13-a5c583849fe6"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>class</th>\n",
              "      <th>cap-shape</th>\n",
              "      <th>cap-surface</th>\n",
              "      <th>cap-color</th>\n",
              "      <th>bruises</th>\n",
              "      <th>odor</th>\n",
              "      <th>gill-attachment</th>\n",
              "      <th>gill-spacing</th>\n",
              "      <th>gill-size</th>\n",
              "      <th>gill-color</th>\n",
              "      <th>stalk-shape</th>\n",
              "      <th>stalk-root</th>\n",
              "      <th>stalk-surface-above-ring</th>\n",
              "      <th>stalk-surface-below-ring</th>\n",
              "      <th>stalk-color-above-ring</th>\n",
              "      <th>stalk-color-below-ring</th>\n",
              "      <th>veil-type</th>\n",
              "      <th>veil-color</th>\n",
              "      <th>ring-number</th>\n",
              "      <th>ring-type</th>\n",
              "      <th>spore-print-color</th>\n",
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            "text/plain": [
              "  class cap-shape cap-surface  ... spore-print-color population habitat\n",
              "0     p         x           s  ...                 k          s       u\n",
              "1     e         x           s  ...                 n          n       g\n",
              "2     e         b           s  ...                 n          n       m\n",
              "3     p         x           y  ...                 k          s       u\n",
              "4     e         x           s  ...                 n          a       g\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1on350P55DNJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "outputId": "6cf0c63a-1a3c-43ba-96d1-58beee19df2b"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": null,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8124 entries, 0 to 8123\n",
            "Data columns (total 23 columns):\n",
            " #   Column                    Non-Null Count  Dtype \n",
            "---  ------                    --------------  ----- \n",
            " 0   class                     8124 non-null   object\n",
            " 1   cap-shape                 8124 non-null   object\n",
            " 2   cap-surface               8124 non-null   object\n",
            " 3   cap-color                 8124 non-null   object\n",
            " 4   bruises                   8124 non-null   object\n",
            " 5   odor                      8124 non-null   object\n",
            " 6   gill-attachment           8124 non-null   object\n",
            " 7   gill-spacing              8124 non-null   object\n",
            " 8   gill-size                 8124 non-null   object\n",
            " 9   gill-color                8124 non-null   object\n",
            " 10  stalk-shape               8124 non-null   object\n",
            " 11  stalk-root                8124 non-null   object\n",
            " 12  stalk-surface-above-ring  8124 non-null   object\n",
            " 13  stalk-surface-below-ring  8124 non-null   object\n",
            " 14  stalk-color-above-ring    8124 non-null   object\n",
            " 15  stalk-color-below-ring    8124 non-null   object\n",
            " 16  veil-type                 8124 non-null   object\n",
            " 17  veil-color                8124 non-null   object\n",
            " 18  ring-number               8124 non-null   object\n",
            " 19  ring-type                 8124 non-null   object\n",
            " 20  spore-print-color         8124 non-null   object\n",
            " 21  population                8124 non-null   object\n",
            " 22  habitat                   8124 non-null   object\n",
            "dtypes: object(23)\n",
            "memory usage: 1.4+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6oGNtmVJ9HUR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "d6698177-df03-42c0-e8a4-2fae18ba2c08"
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class                       0\n",
              "cap-shape                   0\n",
              "cap-surface                 0\n",
              "cap-color                   0\n",
              "bruises                     0\n",
              "odor                        0\n",
              "gill-attachment             0\n",
              "gill-spacing                0\n",
              "gill-size                   0\n",
              "gill-color                  0\n",
              "stalk-shape                 0\n",
              "stalk-root                  0\n",
              "stalk-surface-above-ring    0\n",
              "stalk-surface-below-ring    0\n",
              "stalk-color-above-ring      0\n",
              "stalk-color-below-ring      0\n",
              "veil-type                   0\n",
              "veil-color                  0\n",
              "ring-number                 0\n",
              "ring-type                   0\n",
              "spore-print-color           0\n",
              "population                  0\n",
              "habitat                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S5WzqOlU9UTe",
        "colab_type": "text"
      },
      "source": [
        "# **Converting the categorical data to numerical**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuYUcRBG9MDI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels=data[[\"class\",\"cap-shape\",\"cap-surface\",\"cap-color\",\"bruises\",\"odor\",\"gill-attachment\",\"gill-spacing\",\"gill-size\",\"gill-color\",\"stalk-shape\",\"stalk-root\",\"stalk-surface-above-ring\",\"stalk-surface-below-ring\",\"stalk-color-above-ring\",\"stalk-color-below-ring\",\"veil-type\",\"veil-color\",\"ring-number\",\"ring-type\",\"spore-print-color\",\"population\",\"habitat\"]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Ms1DD7z-Ad8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "for label in labels:\n",
        "  encoder=LabelEncoder()\n",
        "  encoder.fit(data[label])\n",
        "  data[label]=encoder.transform(data[label])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NkFEdilgBas3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f0ff6cb6-6531-4878-a9ce-67b5f303f4de"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8124, 23)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRE56-iWTzOE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "1ed0ac82-00a7-4f35-b29a-dcae8471b2de"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "   class  cap-shape  cap-surface  ...  spore-print-color  population  habitat\n",
              "0      1          5            2  ...                  2           3        5\n",
              "1      0          5            2  ...                  3           2        1\n",
              "2      0          0            2  ...                  3           2        3\n",
              "3      1          5            3  ...                  2           3        5\n",
              "4      0          5            2  ...                  3           0        1\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a7DARri6BeMw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=data.drop([\"class\"],axis=1)\n",
        "Y=data[\"class\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USX_kyOSI4ZY",
        "colab_type": "text"
      },
      "source": [
        "# Train Test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p8XXFizFJGdT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "Xtrain_full,X_test,Ytrain_full,Y_test=train_test_split(X,Y,test_size=0.20)\n",
        "X_train,X_valid,Y_train,Y_valid=train_test_split(Xtrain_full,Ytrain_full,test_size=0.20)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xCdXheMrJeLK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "110a3ab9-e65c-468b-bf9c-574671caee6d"
      },
      "source": [
        "Xtrain_full.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6499, 22)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yNZh0lNVp244",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fadff842-6ba8-413f-b2a9-92d44b1db2b5"
      },
      "source": [
        "Ytrain_full.shape\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6499,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMAYRKYCJ5if",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e408a6f6-35fc-405f-aebb-1b0614c39af5"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1625, 22)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PujJ_HOXKASt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9a4ce808-a40d-4e1f-dd4d-c9196963af79"
      },
      "source": [
        "Y_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1625,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OsWRSwD9-KmL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "468e6e76-9b14-42e4-8765-3e2cda53bc4a"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5199, 22)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CW6LoD0DMr62",
        "colab_type": "text"
      },
      "source": [
        "# Creating a Sequential Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jaooGC9nsWu3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = keras.models.Sequential()\n",
        "model.add(keras.layers.Dense(32, activation=\"relu\",input_dim=22,))\n",
        "model.add(keras.layers.Dense(15, activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(20,activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(10, activation=\"sigmoid\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zN8QrGsfOOhV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "d6dcaff9-ecaa-49a8-e58d-03504e2d69d0"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_21 (Dense)             (None, 32)                736       \n",
            "_________________________________________________________________\n",
            "dense_22 (Dense)             (None, 15)                495       \n",
            "_________________________________________________________________\n",
            "dense_23 (Dense)             (None, 20)                320       \n",
            "_________________________________________________________________\n",
            "dense_24 (Dense)             (None, 10)                210       \n",
            "=================================================================\n",
            "Total params: 1,761\n",
            "Trainable params: 1,761\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxzaUFUCPERS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "746f7ba0-219a-4fd0-a559-49f15e22e182"
      },
      "source": [
        "model.layers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tensorflow.python.keras.layers.core.Dense at 0x7f7d0480a8d0>,\n",
              " <tensorflow.python.keras.layers.core.Dense at 0x7f7d054ee668>,\n",
              " <tensorflow.python.keras.layers.core.Dense at 0x7f7d08b0ab00>,\n",
              " <tensorflow.python.keras.layers.core.Dense at 0x7f7d04469ac8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wUr5hyA1PGmI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bcafc0b1-b1d2-40c5-8b91-12ed7abbf5f3"
      },
      "source": [
        "model.layers[0].name\n",
        "model.layers[1].name\n",
        "model.layers[2].name"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'dense_23'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zr2jyb_3POUy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "w,b=model.layers[2].get_weights()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FkrNNlOpPlw4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a036a67c-391f-41ac-9fe7-c04e7ed42769"
      },
      "source": [
        "w.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15, 20)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qyh3sAgfPnkn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5a1e92d1-93f6-4d8e-88d1-038d49dae74f"
      },
      "source": [
        "b.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NEVs3nXVPpMr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#model.compile(loss='loss_categorical_crossentropy',optimizer=\"sgd\",metrics=[\"accuracy\"])\n",
        "model.compile(optimizer='adam',\n",
        "             loss='sparse_categorical_crossentropy',\n",
        "             metrics=['acc']) \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UTwzDlR4QJ6h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4067a0d0-664d-433b-dfd9-c3439486b6a9"
      },
      "source": [
        "fithistory=model.fit(Xtrain_full,Ytrain_full,validation_data=(X_valid,Y_valid),epochs=30)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.9967 - acc: 0.5192 - val_loss: 0.6913 - val_acc: 0.4869\n",
            "Epoch 2/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.5300 - acc: 0.6064 - val_loss: 0.2903 - val_acc: 0.8738\n",
            "Epoch 3/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.2388 - acc: 0.9028 - val_loss: 0.1820 - val_acc: 0.9223\n",
            "Epoch 4/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.1646 - acc: 0.9258 - val_loss: 0.1418 - val_acc: 0.9392\n",
            "Epoch 5/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.1165 - acc: 0.9371 - val_loss: 0.0890 - val_acc: 0.9454\n",
            "Epoch 6/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0853 - acc: 0.9432 - val_loss: 0.0665 - val_acc: 0.9562\n",
            "Epoch 7/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0671 - acc: 0.9475 - val_loss: 0.0559 - val_acc: 0.9562\n",
            "Epoch 8/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0575 - acc: 0.9480 - val_loss: 0.0467 - val_acc: 0.9592\n",
            "Epoch 9/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0537 - acc: 0.9474 - val_loss: 0.0410 - val_acc: 0.9592\n",
            "Epoch 10/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0476 - acc: 0.9485 - val_loss: 0.0423 - val_acc: 0.9585\n",
            "Epoch 11/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0449 - acc: 0.9488 - val_loss: 0.0356 - val_acc: 0.9592\n",
            "Epoch 12/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0430 - acc: 0.9492 - val_loss: 0.0351 - val_acc: 0.9592\n",
            "Epoch 13/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0407 - acc: 0.9492 - val_loss: 0.0379 - val_acc: 0.9585\n",
            "Epoch 14/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0413 - acc: 0.9495 - val_loss: 0.0329 - val_acc: 0.9592\n",
            "Epoch 15/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0412 - acc: 0.9495 - val_loss: 0.0312 - val_acc: 0.9592\n",
            "Epoch 16/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0404 - acc: 0.9488 - val_loss: 0.0414 - val_acc: 0.9615\n",
            "Epoch 17/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0385 - acc: 0.9498 - val_loss: 0.0314 - val_acc: 0.9592\n",
            "Epoch 18/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0370 - acc: 0.9498 - val_loss: 0.0334 - val_acc: 0.9592\n",
            "Epoch 19/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0369 - acc: 0.9498 - val_loss: 0.0302 - val_acc: 0.9592\n",
            "Epoch 20/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0370 - acc: 0.9498 - val_loss: 0.0302 - val_acc: 0.9600\n",
            "Epoch 21/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0364 - acc: 0.9498 - val_loss: 0.0294 - val_acc: 0.9592\n",
            "Epoch 22/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0366 - acc: 0.9500 - val_loss: 0.0297 - val_acc: 0.9600\n",
            "Epoch 23/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0360 - acc: 0.9498 - val_loss: 0.0294 - val_acc: 0.9592\n",
            "Epoch 24/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0361 - acc: 0.9498 - val_loss: 0.0289 - val_acc: 0.9592\n",
            "Epoch 25/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0358 - acc: 0.9500 - val_loss: 0.0299 - val_acc: 0.9592\n",
            "Epoch 26/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0361 - acc: 0.9498 - val_loss: 0.0288 - val_acc: 0.9592\n",
            "Epoch 27/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0356 - acc: 0.9498 - val_loss: 0.0287 - val_acc: 0.9592\n",
            "Epoch 28/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0386 - acc: 0.9491 - val_loss: 0.0293 - val_acc: 0.9592\n",
            "Epoch 29/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0355 - acc: 0.9498 - val_loss: 0.0286 - val_acc: 0.9592\n",
            "Epoch 30/30\n",
            "204/204 [==============================] - 0s 2ms/step - loss: 0.0354 - acc: 0.9498 - val_loss: 0.0286 - val_acc: 0.9592\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "syVjBODotYe1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "outputId": "0fd64a4c-2280-46e5-8a14-d473e58eff5e"
      },
      "source": [
        "fithistory.epoch"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0,\n",
              " 1,\n",
              " 2,\n",
              " 3,\n",
              " 4,\n",
              " 5,\n",
              " 6,\n",
              " 7,\n",
              " 8,\n",
              " 9,\n",
              " 10,\n",
              " 11,\n",
              " 12,\n",
              " 13,\n",
              " 14,\n",
              " 15,\n",
              " 16,\n",
              " 17,\n",
              " 18,\n",
              " 19,\n",
              " 20,\n",
              " 21,\n",
              " 22,\n",
              " 23,\n",
              " 24,\n",
              " 25,\n",
              " 26,\n",
              " 27,\n",
              " 28,\n",
              " 29]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HVSXhu8P9-cv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9e375fe5-21e4-4338-fad0-b6016520ccf3"
      },
      "source": [
        "fithistory.params"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'epochs': 30, 'steps': 204, 'verbose': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yyc1gYrK-GU-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "outputId": "f8f1f892-b297-485b-eb1e-0034600b161a"
      },
      "source": [
        "fithistory.history[\"acc\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.519156813621521,\n",
              " 0.6064009666442871,\n",
              " 0.9027542471885681,\n",
              " 0.9258347153663635,\n",
              " 0.9370672702789307,\n",
              " 0.9432220458984375,\n",
              " 0.9475303888320923,\n",
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              " 0.949222981929779,\n",
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              " 0.9495307207107544,\n",
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              " 0.9498384594917297,\n",
              " 0.9498384594917297,\n",
              " 0.9498384594917297,\n",
              " 0.9498384594917297,\n",
              " 0.9498384594917297,\n",
              " 0.949992299079895,\n",
              " 0.9498384594917297,\n",
              " 0.9498384594917297,\n",
              " 0.949992299079895,\n",
              " 0.9498384594917297,\n",
              " 0.9498384594917297,\n",
              " 0.949069082736969,\n",
              " 0.9498384594917297,\n",
              " 0.9498384594917297]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPXz2Scs-KWE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "eb1cdbbe-b0ea-434c-aa24-7b61c16ec6cf"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(fithistory.history['acc'])\n",
        "plt.plot(fithistory.history['val_acc'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Accuracy', 'Loss'], loc='upper right')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJ1eI4X7BVpX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "outputId": "3c84b0e1-1570-4ee8-f6df-2855acba648d"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "pd.DataFrame(fithistory.history).plot(figsize=(8, 5))\n",
        "plt.grid(True)\n",
        "plt.gca().set_ylim(0, 1) # set the vertical range to [0-1]\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLpvaYXc_PrQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prediction_features=model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "096xkNLGAY2v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "f434e0eb-b4b7-44b3-b52c-849ec597881a"
      },
      "source": [
        "prediction_features"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.9983561e-01, 5.3802417e-08, 2.8398697e-08, ..., 7.6740534e-09,\n",
              "        1.1670991e-09, 2.2119670e-08],\n",
              "       [2.3150902e-12, 9.5815015e-01, 8.3245493e-16, ..., 6.8880905e-16,\n",
              "        6.8130830e-18, 1.6064769e-14],\n",
              "       [1.0000000e+00, 1.9440055e-04, 2.2878700e-08, ..., 5.4711634e-08,\n",
              "        1.1589409e-06, 1.5533089e-08],\n",
              "       ...,\n",
              "       [8.6070591e-18, 9.8434347e-01, 4.4700597e-16, ..., 2.3644507e-16,\n",
              "        4.2153077e-19, 4.2845287e-13],\n",
              "       [9.9572372e-01, 1.1750460e-03, 2.6703816e-08, ..., 2.9051034e-08,\n",
              "        2.2293539e-08, 1.2329333e-08],\n",
              "       [9.9308872e-01, 2.7772925e-08, 8.0075430e-10, ..., 6.7553729e-10,\n",
              "        3.9811130e-11, 2.6354763e-09]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKSJkDPVBegG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8b9c4cd4-d747-4f1d-bcaa-e6592712cb31"
      },
      "source": [
        "a=prediction_features.tolist()\n",
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8qeqs8_BoWN",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "pred=[]\n",
        "for i in a:\n",
        "    if i[0]>i[1]:\n",
        "        pred.append(0)\n",
        "    else:\n",
        "        pred.append(1)\n",
        "        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBQd1Gp9BsVD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
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        "outputId": "36c83807-32be-48d3-a93c-0d99d05bed31"
      },
      "source": [
        "pred"
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      "execution_count": null,
      "outputs": [
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          "execution_count": 150
        }
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      "source": [
        "model.evaluate(X_test,Y_test)"
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      "execution_count": null,
      "outputs": [
        {
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          "text": [
            "51/51 [==============================] - 0s 1ms/step - loss: 0.0288 - acc: 0.9594\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.028804544359445572, 0.9593846201896667]"
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
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        "id": "6TRoOftuH0W0",
        "colab_type": "code",
        "colab": {}
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      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}