tf2_0_linear_classification.ipynb

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        "<a href=\"https://colab.research.google.com/github/Himalaypatel75/Tenserflow-Keras/blob/main/tf2_0_linear_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n",
            "2.15.0\n"
          ]
        }
      ],
      "source": [
        "# Install TensorFlow\n",
        "# !pip install -q tensorflow-gpu==2.0.0-beta1\n",
        "\n",
        "try:\n",
        "  %tensorflow_version 2.x  # Colab only.\n",
        "except Exception:\n",
        "  pass\n",
        "\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load in the data\n",
        "from sklearn.datasets import load_breast_cancer"
      ],
      "metadata": {
        "id": "o7XbECypuIpN"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# load the data\n",
        "data = load_breast_cancer()"
      ],
      "metadata": {
        "id": "PtEHbFufuLHU"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check the type of 'data'\n",
        "type(data)"
      ],
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          "height": 187
        },
        "id": "9UEaJYZPuM1U",
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      "execution_count": 4,
      "outputs": [
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          "data": {
            "text/plain": [
              "sklearn.utils._bunch.Bunch"
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>sklearn.utils._bunch.Bunch</b><br/>def __init__(**kwargs)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/sklearn/utils/_bunch.py</a>Container object exposing keys as attributes.\n",
              "\n",
              "Bunch objects are sometimes used as an output for functions and methods.\n",
              "They extend dictionaries by enabling values to be accessed by key,\n",
              "`bunch[&quot;value_key&quot;]`, or by an attribute, `bunch.value_key`.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "&gt;&gt;&gt; from sklearn.utils import Bunch\n",
              "&gt;&gt;&gt; b = Bunch(a=1, b=2)\n",
              "&gt;&gt;&gt; b[&#x27;b&#x27;]\n",
              "2\n",
              "&gt;&gt;&gt; b.b\n",
              "2\n",
              "&gt;&gt;&gt; b.a = 3\n",
              "&gt;&gt;&gt; b[&#x27;a&#x27;]\n",
              "3\n",
              "&gt;&gt;&gt; b.c = 6\n",
              "&gt;&gt;&gt; b[&#x27;c&#x27;]\n",
              "6</pre>\n",
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          "execution_count": 4
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      ]
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    {
      "cell_type": "code",
      "source": [
        "# note: it is a Bunch object\n",
        "# this basically acts like a dictionary where you can treat the keys like attributes\n",
        "data.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-fOLeYGDuOLi",
        "outputId": "bddaabb6-7c23-4a3e-b959-29dcebe1bd52"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 'data' (the attribute) means the input data\n",
        "data.data.shape\n",
        "# it has 569 samples, 30 features"
      ],
      "metadata": {
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        },
        "id": "WtAlEggMuR38",
        "outputId": "b57ab5bc-ec42-4f82-a67b-9b715b2aae5a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 'targets'\n",
        "data.target\n",
        "# note how the targets are just 0s and 1s\n",
        "# normally, when you have K targets, they are labeled 0..K-1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "z9x-tSbpuS08",
        "outputId": "3c295d41-06d1-478f-b20a-5569b8488bc7"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
              "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
              "       1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
              "       0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
              "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
              "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
              "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
              "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
              "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
              "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
              "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
              "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# their meaning is not lost\n",
        "data.target_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tojK0_d2uZlL",
        "outputId": "e19ef70c-c015-4384-b93b-0c676d105886"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['malignant', 'benign'], dtype='<U9')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# there are also 569 corresponding targets\n",
        "data.target.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7J6BGryubIi",
        "outputId": "5c32c3d6-7b98-4a59-f8fd-c03020d39b9f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569,)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# you can also determine the meaning of each feature\n",
        "data.feature_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tgWCHyG3udIf",
        "outputId": "ccdbac7e-715b-4667-a7d8-3df973944427"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
              "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
              "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
              "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
              "       'smoothness error', 'compactness error', 'concavity error',\n",
              "       'concave points error', 'symmetry error',\n",
              "       'fractal dimension error', 'worst radius', 'worst texture',\n",
              "       'worst perimeter', 'worst area', 'worst smoothness',\n",
              "       'worst compactness', 'worst concavity', 'worst concave points',\n",
              "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# normally we would put all of our imports at the top\n",
        "# but this lets us tell a story\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "# split the data into train and test sets\n",
        "# this lets us simulate how our model will perform in the future\n",
        "X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.33)\n",
        "N, D = X_train.shape"
      ],
      "metadata": {
        "id": "JRU306M4ueH5"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale the data\n",
        "# you'll learn why scaling is needed in a later course\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "66dGUw3gug85"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now all the fun Tensorflow stuff\n",
        "# Build the model\n",
        "\n",
        "# model = tf.keras.models.Sequential([\n",
        "#   tf.keras.layers.Input(shape=(D,)),\n",
        "#   tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "# ])\n",
        "\n",
        "Alternatively, you can do:\n",
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Dense(1, input_shape=(D,), activation='sigmoid'))\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "\n",
        "# Train the model\n",
        "r = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100)\n",
        "\n",
        "\n",
        "# Evaluate the model - evaluate() returns loss and accuracy\n",
        "print(\"Train score:\", model.evaluate(X_train, y_train))\n",
        "print(\"Test score:\", model.evaluate(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "mKZcmF1tujCe",
        "outputId": "ebf82a6f-00ac-4a91-f5bb-ce2acc578912"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "invalid syntax (<ipython-input-25-d39973d78bc0>, line 9)",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-25-d39973d78bc0>\"\u001b[0;36m, line \u001b[0;32m9\u001b[0m\n\u001b[0;31m    Alternatively, you can do:\u001b[0m\n\u001b[0m                       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot what's returned by model.fit()\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(r.history['loss'], label='loss')\n",
        "plt.plot(r.history['val_loss'], label='val_loss')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "BKuRiVESunik",
        "outputId": "71be3dbe-35ea-4955-85ff-1f38d262683e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79f2b54cb970>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the accuracy too\n",
        "plt.plot(r.history['accuracy'], label='acc')\n",
        "plt.plot(r.history['val_accuracy'], label='val_acc')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "MEDWP3y-uqOC",
        "outputId": "2c4cf86a-d209-445d-e7a6-8e67d5f523e1"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79f2b14c6680>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Part 2: Making Predictions"
      ],
      "metadata": {
        "id": "cWQ3s5nuut7G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This goes with the lecture \"Making Predictions\""
      ],
      "metadata": {
        "id": "V4k8z7Mruw_k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions\n",
        "P = model.predict(X_test)\n",
        "print(P) # they are outputs of the sigmoid, interpreted as probabilities p(y = 1 | x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "laYf2TbPuvAY",
        "outputId": "7c0e73ff-d64b-43a3-dcf7-fe8ef04c06c5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6/6 [==============================] - 0s 3ms/step\n",
            "[[9.65620577e-01]\n",
            " [9.89092052e-01]\n",
            " [2.12066947e-03]\n",
            " [2.61336041e-04]\n",
            " [9.14006829e-01]\n",
            " [8.91220212e-01]\n",
            " [9.29919243e-01]\n",
            " [9.02056396e-01]\n",
            " [9.31385517e-01]\n",
            " [9.98061836e-01]\n",
            " [9.82400060e-01]\n",
            " [3.43271583e-01]\n",
            " [9.63116646e-01]\n",
            " [9.66494679e-01]\n",
            " [4.37385440e-02]\n",
            " [1.88149267e-03]\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "np.round(P)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DEop29GLvACf",
        "outputId": "f2b548b9-036b-4d72-b434-30bd2d993ba5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Round to get the actual predictions\n",
        "# Note: has to be flattened since the targets are size (N,) while the predictions are size (N,1)\n",
        "import numpy as np\n",
        "P = np.round(P).flatten()\n",
        "print(P)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PQSO6M89u639",
        "outputId": "aa64908b-a93f-427d-c66e-88aac9795cde"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 0. 0. 1. 0. 0. 1. 1. 0. 1. 1.\n",
            " 1. 1. 0. 1. 1. 1. 0. 1. 0. 0. 0. 1. 0. 1. 1. 1. 1. 0. 0. 0. 1. 0. 1. 1.\n",
            " 1. 0. 1. 0. 1. 0. 1. 1. 1. 1. 1. 0. 0. 0. 1. 0. 0. 0. 0. 1. 1. 1. 0. 1.\n",
            " 1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 0. 1. 0. 1. 1. 1. 1. 1. 1. 0. 0. 1. 0. 1.\n",
            " 1. 1. 1. 1. 1. 1. 0. 0. 0. 1. 0. 0. 1. 0. 1. 1. 1. 1. 0. 1. 1. 1. 0. 1.\n",
            " 0. 0. 0. 0. 1. 1. 0. 1. 1. 1. 1. 0. 1. 0. 1. 1. 1. 1. 1. 0. 0. 0. 1. 1.\n",
            " 1. 0. 0. 1. 1. 1. 1. 1. 0. 0. 1. 0. 0. 1. 1. 1. 1. 1. 0. 1. 1. 1. 0. 1.\n",
            " 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 1. 0. 1. 1. 0. 1. 1. 1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the accuracy, compare it to evaluate() output\n",
        "print(\"Manually calculated accuracy:\", np.mean(P == y_test))\n",
        "print(\"Evaluate output:\", model.evaluate(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4kyqo3WNvYZK",
        "outputId": "683fb021-b9e3-485f-b19f-91c15fae89c5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Manually calculated accuracy: 0.9521276595744681\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.1160 - accuracy: 0.9521\n",
            "Evaluate output: [0.11601962149143219, 0.9521276354789734]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Part 3: Saving and Loading a Model\n",
        "\n",
        "This goes with the lecture \"Saving and Loading a Model\""
      ],
      "metadata": {
        "id": "2TSOQ7Mp2mnQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's now save our model to a file\n",
        "model.save('linearclassifier.h5')"
      ],
      "metadata": {
        "id": "26q7YmcVvZQ-"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check that the model file exists\n",
        "!ls -lh"
      ],
      "metadata": {
        "id": "1XWlNUNX2xmv",
        "outputId": "d83dee23-7ba7-4137-a6ae-81a0b9a12ec4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 24K\n",
            "-rw-r--r-- 1 root root  19K Jun 14 13:09 linearclassifier.h5\n",
            "drwxr-xr-x 1 root root 4.0K Jun 12 18:22 sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's load the model and confirm that it still works\n",
        "# Note: there is a bug in Keras where load/save only works if you DON'T use the Input() layer explicitly\n",
        "# So, make sure you define the model with ONLY Dense(1, input_shape=(D,))\n",
        "# At least, until the bug is fixed\n",
        "# https://github.com/keras-team/keras/issues/10417\n",
        "model = tf.keras.models.load_model('linearclassifier.h5')\n",
        "print(model.layers)\n",
        "model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "id": "kVNt3S5w2zbW",
        "outputId": "0ea9a447-88d6-4d6e-8bf2-a52cd821abe3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[<keras.src.layers.core.dense.Dense object at 0x79f2a3485630>]\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1160 - accuracy: 0.9521\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.11601962149143219, 0.9521276354789734]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Download the file - requires Chrome (at this point)\n",
        "from google.colab import files\n",
        "files.download('linearclassifier.h5')"
      ],
      "metadata": {
        "id": "0uXjTRp22_x8",
        "outputId": "6eb6cc58-6520-4c52-aa29-327e9a770474",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_52de58ce-ff6b-4ac3-9d3a-2478ae0a6c5a\", \"linearclassifier.h5\", 19208)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}