sequencial_ANN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "sequencial_ANN.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6ly8801R97h"
      },
      "source": [
        "import numpy as np\n",
        "from random import randint\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5BM7N9h2SQiV"
      },
      "source": [
        "train_labels = []\n",
        "train_samples = []"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2_Bf7ohSQeu"
      },
      "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)\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNpVdD9nSQb4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e9ff10b-51bf-4c0c-b55b-0bcda14461a8"
      },
      "source": [
        "for i in train_samples:\n",
        "    print(i)\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VQRIcypnSQZP"
      },
      "source": [
        "train_labels = np.array(train_labels)\n",
        "train_samples = np.array(train_samples)\n",
        "train_labels, train_samples = shuffle(train_labels, train_samples)\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DFqAPjH5SQWU"
      },
      "source": [
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "scaled_train_samples = scaler.fit_transform(train_samples.reshape(-1,1))"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OwHnGkgLP9--",
        "outputId": "a47bf8be-5338-47af-8ae1-486ff282d3bb"
      },
      "source": [
        "for i in scaled_train_samples:\n",
        "    print(i)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.13793103]\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H1TY8gWZQMA3"
      },
      "source": [
        "create model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4CQVA_NP980"
      },
      "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\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6MFq7odvP96X"
      },
      "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": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4aPprCyPP93f",
        "outputId": "fd235800-eed0-47a5-b4f7-7537daaa620b"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 10,
      "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": "elm7kLD9QlbD"
      },
      "source": [
        "training model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ujEWd-PGP9ff"
      },
      "source": [
        "model.compile(optimizer=Adam(learning_rate=0.0001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9qOW6-hQp29",
        "outputId": "31ca2c36-4538-41d3-d4d7-1b830397253c"
      },
      "source": [
        "model.fit(x=scaled_train_samples, y=train_labels, batch_size=10, epochs=30, shuffle=True, verbose=2)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "210/210 - 1s - loss: 0.6767 - accuracy: 0.5543\n",
            "Epoch 2/30\n",
            "210/210 - 0s - loss: 0.6510 - accuracy: 0.6800\n",
            "Epoch 3/30\n",
            "210/210 - 0s - loss: 0.6203 - accuracy: 0.7495\n",
            "Epoch 4/30\n",
            "210/210 - 0s - loss: 0.5865 - accuracy: 0.7881\n",
            "Epoch 5/30\n",
            "210/210 - 0s - loss: 0.5518 - accuracy: 0.8129\n",
            "Epoch 6/30\n",
            "210/210 - 0s - loss: 0.5164 - accuracy: 0.8324\n",
            "Epoch 7/30\n",
            "210/210 - 0s - loss: 0.4820 - accuracy: 0.8576\n",
            "Epoch 8/30\n",
            "210/210 - 0s - loss: 0.4494 - accuracy: 0.8724\n",
            "Epoch 9/30\n",
            "210/210 - 0s - loss: 0.4197 - accuracy: 0.8819\n",
            "Epoch 10/30\n",
            "210/210 - 0s - loss: 0.3933 - accuracy: 0.9000\n",
            "Epoch 11/30\n",
            "210/210 - 0s - loss: 0.3698 - accuracy: 0.8990\n",
            "Epoch 12/30\n",
            "210/210 - 0s - loss: 0.3496 - accuracy: 0.9057\n",
            "Epoch 13/30\n",
            "210/210 - 0s - loss: 0.3330 - accuracy: 0.9124\n",
            "Epoch 14/30\n",
            "210/210 - 0s - loss: 0.3191 - accuracy: 0.9190\n",
            "Epoch 15/30\n",
            "210/210 - 0s - loss: 0.3078 - accuracy: 0.9195\n",
            "Epoch 16/30\n",
            "210/210 - 0s - loss: 0.2984 - accuracy: 0.9248\n",
            "Epoch 17/30\n",
            "210/210 - 0s - loss: 0.2908 - accuracy: 0.9276\n",
            "Epoch 18/30\n",
            "210/210 - 0s - loss: 0.2843 - accuracy: 0.9290\n",
            "Epoch 19/30\n",
            "210/210 - 0s - loss: 0.2790 - accuracy: 0.9286\n",
            "Epoch 20/30\n",
            "210/210 - 0s - loss: 0.2748 - accuracy: 0.9333\n",
            "Epoch 21/30\n",
            "210/210 - 0s - loss: 0.2712 - accuracy: 0.9348\n",
            "Epoch 22/30\n",
            "210/210 - 0s - loss: 0.2681 - accuracy: 0.9362\n",
            "Epoch 23/30\n",
            "210/210 - 0s - loss: 0.2657 - accuracy: 0.9362\n",
            "Epoch 24/30\n",
            "210/210 - 0s - loss: 0.2634 - accuracy: 0.9348\n",
            "Epoch 25/30\n",
            "210/210 - 0s - loss: 0.2614 - accuracy: 0.9362\n",
            "Epoch 26/30\n",
            "210/210 - 0s - loss: 0.2598 - accuracy: 0.9343\n",
            "Epoch 27/30\n",
            "210/210 - 0s - loss: 0.2584 - accuracy: 0.9376\n",
            "Epoch 28/30\n",
            "210/210 - 0s - loss: 0.2570 - accuracy: 0.9367\n",
            "Epoch 29/30\n",
            "210/210 - 0s - loss: 0.2558 - accuracy: 0.9362\n",
            "Epoch 30/30\n",
            "210/210 - 0s - loss: 0.2548 - accuracy: 0.9362\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f16f88c8dd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtzhwaFIQpzp"
      },
      "source": [
        ""
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iyj_KbFAQpxC"
      },
      "source": [
        ""
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZkXb1GsQpWG"
      },
      "source": [
        ""
      ],
      "execution_count": 12,
      "outputs": []
    }
  ]
}