Lab09/Lab09_2_LEEJOOYOUNG.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Lab09-2_LEEJOOYOUNG.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9NkQ0270XJ3",
        "outputId": "274fdab3-5788-42a4-f2af-e8d307e1ed52"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import tensorflow as tf\n",
        "\n",
        "tf.random.set_seed(777)  # for reproducibility\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "fJUj1yTs0uzp",
        "outputId": "c3c3e9d0-38f7-49d3-d65b-4add014b16a1"
      },
      "source": [
        "x_data = [[0, 0],\n",
        "          [0, 1],\n",
        "          [1, 0],\n",
        "          [1, 1]]\n",
        "y_data = [[0],\n",
        "          [1],\n",
        "          [1],\n",
        "          [0]]\n",
        "\n",
        "plt.scatter(x_data[0][0],x_data[0][1], c='red' , marker='^')\n",
        "plt.scatter(x_data[3][0],x_data[3][1], c='red' , marker='^')\n",
        "plt.scatter(x_data[1][0],x_data[1][1], c='blue' , marker='^')\n",
        "plt.scatter(x_data[2][0],x_data[2][1], c='blue' , marker='^')\n",
        "\n",
        "plt.xlabel(\"x1\")\n",
        "plt.ylabel(\"x2\")\n",
        "plt.show()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMNe7oFo0y-5"
      },
      "source": [
        "dataset = tf.data.Dataset.from_tensor_slices((x_data, y_data)).batch(len(x_data))\n",
        "\n",
        "def preprocess_data(features, labels):\n",
        "    features = tf.cast(features, tf.float32)\n",
        "    labels = tf.cast(labels, tf.float32)\n",
        "    return features, labels"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n2XWKwiv03k8"
      },
      "source": [
        "W1 = tf.Variable(tf.random.normal((2, 1)), name='weight1')\n",
        "b1 = tf.Variable(tf.random.normal((1,)), name='bias1')\n",
        "\n",
        "W2 = tf.Variable(tf.random.normal((2, 1)), name='weight2')\n",
        "b2 = tf.Variable(tf.random.normal((1,)), name='bias2')\n",
        "\n",
        "W3 = tf.Variable(tf.random.normal((2, 1)), name='weight3')\n",
        "b3 = tf.Variable(tf.random.normal((1,)), name='bias3')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TwZLMtul0-Kp"
      },
      "source": [
        "def neural_net(features):\n",
        "    layer1 = tf.sigmoid(tf.matmul(features, W1) + b1)\n",
        "    layer2 = tf.sigmoid(tf.matmul(features, W2) + b2)\n",
        "    layer3 = tf.concat([layer1, layer2],-1)\n",
        "    layer3 = tf.reshape(layer3, shape = [-1,2])\n",
        "    hypothesis = tf.sigmoid(tf.matmul(layer3, W3) + b3)\n",
        "    return hypothesis\n",
        "\n",
        "def loss_fn(hypothesis, labels):\n",
        "    cost = -tf.reduce_mean(labels * tf.math.log(hypothesis) + (1 - labels) * tf.math.log(1 - hypothesis))\n",
        "    return cost\n",
        "\n",
        "optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)\n",
        "\n",
        "def accuracy_fn(hypothesis, labels):\n",
        "    predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)\n",
        "    accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, labels), dtype=tf.float32))\n",
        "    return accuracy\n",
        "\n",
        "def grad(hypothesis, features, labels):\n",
        "    with tf.GradientTape() as tape:\n",
        "        loss_value = loss_fn(neural_net(features),labels)\n",
        "    return tape.gradient(loss_value, [W1, W2, W3, b1, b2, b3])"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tYGKFup_1BzA",
        "outputId": "cf58fbbf-138a-436b-bf15-47ce511c8bfd"
      },
      "source": [
        "EPOCHS = 50000\n",
        "\n",
        "for step in range(EPOCHS):\n",
        "    for features, labels  in dataset:\n",
        "        features, labels = preprocess_data(features, labels)\n",
        "        grads = grad(neural_net(features), features, labels)\n",
        "        optimizer.apply_gradients(grads_and_vars=zip(grads,[W1, W2, W3, b1, b2, b3]))\n",
        "        if step % 5000 == 0:\n",
        "            print(\"Iter: {}, Loss: {:.4f}\".format(step, loss_fn(neural_net(features),labels)))\n",
        "x_data, y_data = preprocess_data(x_data, y_data)\n",
        "test_acc = accuracy_fn(neural_net(x_data),y_data)\n",
        "print(\"Testset Accuracy: {:.4f}\".format(test_acc))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iter: 0, Loss: 0.8487\n",
            "Iter: 5000, Loss: 0.6847\n",
            "Iter: 10000, Loss: 0.6610\n",
            "Iter: 15000, Loss: 0.6154\n",
            "Iter: 20000, Loss: 0.5722\n",
            "Iter: 25000, Loss: 0.5433\n",
            "Iter: 30000, Loss: 0.5211\n",
            "Iter: 35000, Loss: 0.4911\n",
            "Iter: 40000, Loss: 0.4416\n",
            "Iter: 45000, Loss: 0.3313\n",
            "Testset Accuracy: 1.0000\n"
          ]
        }
      ]
    }
  ]
}