Sequential_ANN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Sequential_ANN.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/souvikg123/SOUVIK/blob/master/Sequential_ANN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VY7Tn7qOgqub"
      },
      "source": [
        "import numpy as np\n",
        "from random import randint\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qzOKpIVBgxxe"
      },
      "source": [
        "train_labels = []\n",
        "train_samples = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8FY0vLz-g2zw"
      },
      "source": [
        "Example data: . An experimental drug was tested on individuals from ages 13 to 100 in a clinical trial . The trial had 2100 participants. Half were under 65 years old, half were 65 years or older. . Around 95% of patients 65 0r older experienced side effects. . Around 95% of patients under 65 experienced no side effects."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZtYVBL1g_mX"
      },
      "source": [
        "for i in range(50):\n",
        "    # The ~5% of younger individuals who did experience side effects\n",
        "    random_younger = randint(13,64)\n",
        "    train_samples.append(random_younger)\n",
        "    train_labels.append(1)\n",
        "    \n",
        "    # The ~5% of older individuals who did not experience side effects\n",
        "    random_older = randint(65,100)\n",
        "    train_samples.append(random_older)\n",
        "    train_labels.append(0)\n",
        "\n",
        "for i in range(1000):\n",
        "    # The ~95% of younger individuals who did not experience side effects\n",
        "    random_younger = randint(13,64)\n",
        "    train_samples.append(random_younger)\n",
        "    train_labels.append(0)\n",
        "    \n",
        "    # The ~95% of older individuals who did experience side effects\n",
        "    random_older = randint(65,100)\n",
        "    train_samples.append(random_older)\n",
        "    train_labels.append(1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "woKn7YxQhDuk",
        "outputId": "00614d3a-1d54-4f19-a445-a0a4525e494a"
      },
      "source": [
        "for i in train_samples:\n",
        "    print(i)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZXfiQk1OhN-7",
        "outputId": "d713ca11-1a47-43b1-f544-9c9aa423f007"
      },
      "source": [
        "for i in train_labels:\n",
        "    print(i)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G5U069-nhddA"
      },
      "source": [
        "train_labels = np.array(train_labels)\n",
        "train_samples = np.array(train_samples)\n",
        "train_labels, train_samples = shuffle(train_labels, train_samples)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6qHGgfIOhtn9"
      },
      "source": [
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "scaled_train_samples = scaler.fit_transform(train_samples.reshape(-1,1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Xag6QAAhxVz",
        "outputId": "fb8da80a-9d05-48c6-a70d-7a0c95528edb"
      },
      "source": [
        "for i in scaled_train_samples:\n",
        "    print(i)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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            "[0.02298851]\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nrNp3JN9iGxB"
      },
      "source": [
        "# Creating Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ekz2tcJiNhW"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Activation, Dense\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.losses import categorical_crossentropy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BEI4dexYiYRD"
      },
      "source": [
        "model = Sequential([\n",
        "    Dense(units=16, input_shape=(1,), activation='relu'),\n",
        "    Dense(units=32, activation='relu'),\n",
        "    Dense(units=2, activation='softmax')\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gYD0txiYia-_",
        "outputId": "f36a1251-3f35-4acb-fa7e-fda81a46066a"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 16)                32        \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 32)                544       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 2)                 66        \n",
            "=================================================================\n",
            "Total params: 642\n",
            "Trainable params: 642\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zqtYfDyjig0w"
      },
      "source": [
        "# Training Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ou19ytbPiio8"
      },
      "source": [
        "model.compile(optimizer=Adam(learning_rate=0.0001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6t7pOOzwilfn",
        "outputId": "acfb5da4-ac62-4d2d-eaed-4be5216ed06b"
      },
      "source": [
        "model.fit(x=scaled_train_samples, y=train_labels, batch_size=10, epochs=30, shuffle=True, verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "210/210 - 1s - loss: 0.6224 - accuracy: 0.5662\n",
            "Epoch 2/30\n",
            "210/210 - 0s - loss: 0.5898 - accuracy: 0.6662\n",
            "Epoch 3/30\n",
            "210/210 - 0s - loss: 0.5562 - accuracy: 0.7310\n",
            "Epoch 4/30\n",
            "210/210 - 0s - loss: 0.5227 - accuracy: 0.7833\n",
            "Epoch 5/30\n",
            "210/210 - 0s - loss: 0.4892 - accuracy: 0.8205\n",
            "Epoch 6/30\n",
            "210/210 - 0s - loss: 0.4566 - accuracy: 0.8457\n",
            "Epoch 7/30\n",
            "210/210 - 0s - loss: 0.4268 - accuracy: 0.8690\n",
            "Epoch 8/30\n",
            "210/210 - 0s - loss: 0.4003 - accuracy: 0.8829\n",
            "Epoch 9/30\n",
            "210/210 - 0s - loss: 0.3769 - accuracy: 0.8881\n",
            "Epoch 10/30\n",
            "210/210 - 0s - loss: 0.3571 - accuracy: 0.8948\n",
            "Epoch 11/30\n",
            "210/210 - 0s - loss: 0.3406 - accuracy: 0.9048\n",
            "Epoch 12/30\n",
            "210/210 - 0s - loss: 0.3269 - accuracy: 0.9076\n",
            "Epoch 13/30\n",
            "210/210 - 0s - loss: 0.3156 - accuracy: 0.9105\n",
            "Epoch 14/30\n",
            "210/210 - 0s - loss: 0.3066 - accuracy: 0.9148\n",
            "Epoch 15/30\n",
            "210/210 - 0s - loss: 0.2991 - accuracy: 0.9152\n",
            "Epoch 16/30\n",
            "210/210 - 0s - loss: 0.2927 - accuracy: 0.9186\n",
            "Epoch 17/30\n",
            "210/210 - 0s - loss: 0.2875 - accuracy: 0.9186\n",
            "Epoch 18/30\n",
            "210/210 - 0s - loss: 0.2830 - accuracy: 0.9271\n",
            "Epoch 19/30\n",
            "210/210 - 0s - loss: 0.2796 - accuracy: 0.9205\n",
            "Epoch 20/30\n",
            "210/210 - 0s - loss: 0.2764 - accuracy: 0.9233\n",
            "Epoch 21/30\n",
            "210/210 - 0s - loss: 0.2737 - accuracy: 0.9300\n",
            "Epoch 22/30\n",
            "210/210 - 0s - loss: 0.2714 - accuracy: 0.9238\n",
            "Epoch 23/30\n",
            "210/210 - 0s - loss: 0.2691 - accuracy: 0.9281\n",
            "Epoch 24/30\n",
            "210/210 - 0s - loss: 0.2672 - accuracy: 0.9262\n",
            "Epoch 25/30\n",
            "210/210 - 0s - loss: 0.2655 - accuracy: 0.9267\n",
            "Epoch 26/30\n",
            "210/210 - 0s - loss: 0.2640 - accuracy: 0.9305\n",
            "Epoch 27/30\n",
            "210/210 - 0s - loss: 0.2626 - accuracy: 0.9281\n",
            "Epoch 28/30\n",
            "210/210 - 0s - loss: 0.2614 - accuracy: 0.9329\n",
            "Epoch 29/30\n",
            "210/210 - 0s - loss: 0.2604 - accuracy: 0.9324\n",
            "Epoch 30/30\n",
            "210/210 - 0s - loss: 0.2595 - accuracy: 0.9329\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fbc9a630110>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    }
  ]
}