tutorials/01_basics/Sequential_and_Model.ipynb

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        "<a href=\"https://colab.research.google.com/github/hellocybernetics/TensorFlow_Eager_Execution_Tutorials/blob/master/tutorials/01_basics/Sequential_and_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "!pip install tf-nightly-2.0-preview"
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    {
      "metadata": {
        "colab_type": "code",
        "id": "uk1oMHkAyQ2s",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow.keras.layers as L"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "7gRtOWU70Z-q"
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      "cell_type": "markdown",
      "source": [
        "## tf.keras.Sequential\n",
        "\n",
        "`tf.keras.Sequential` recieves a list of layers."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "kcwWxgaiyUiI",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    L.InputLayer([20]),\n",
        "    L.Dense(10),\n",
        "    L.Dense(5),\n",
        "    L.Dense(3)\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "LjvFRCiY0nfj"
      },
      "cell_type": "markdown",
      "source": [
        "### Note\n",
        "'tf.keras.layers' don't need input_dimension, only output_dimension as above. Input_dimension is determined by first execution, so you have to write `model(inputs_data)` code before writing `model.summary()`."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Xm0kVQ4PynHK",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(),\n",
        "              loss=tf.keras.losses.mean_squared_error)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "OIsZnSTvy0sf",
        "outputId": "f8e55075-c3ea-4e45-c0f9-047a31ac31b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        }
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_6 (Dense)              (None, 10)                210       \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 5)                 55        \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 3)                 18        \n",
            "=================================================================\n",
            "Total params: 283\n",
            "Trainable params: 283\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "-IkJwYAl1l5N"
      },
      "cell_type": "markdown",
      "source": [
        "## tf.keras.Model\n",
        "`tf.keras.Model` is used as below. This is like PyTorch `torch.nn.Module` and `chainer.Chain`. When Inheriting `tf.keras.Model`, call method must be implimated.\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "f9cYPCt_zUUJ",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class Model(tf.keras.Model):\n",
        "    def __init__(self):\n",
        "        super(Model, self).__init__()\n",
        "        \n",
        "        self.dense1 = L.Dense(10)\n",
        "        self.dense2 = L.Dense(5)\n",
        "        self.dense3 = L.Dense(3)\n",
        "        \n",
        "    def call(self, x):\n",
        "        \n",
        "        x = self.dense1(x)\n",
        "        x = self.dense2(x)\n",
        "        x = self.dense3(x)\n",
        "        \n",
        "        return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "9Xwy3Mf3zqyb",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = Model()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "rkfLpKsizr7Z",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(),\n",
        "              loss=tf.keras.losses.mean_squared_error)\n",
        "\n",
        "# need execution before get summary tf.keras.Model\n",
        "_ = model(tf.random.normal(shape=[100, 20]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "-7rHSADzzxX_",
        "outputId": "1f8ec6ba-137d-4fb9-e7e1-702ea8b4fef6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        }
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_12 (Dense)             multiple                  210       \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             multiple                  55        \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             multiple                  18        \n",
            "=================================================================\n",
            "Total params: 283\n",
            "Trainable params: 283\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "lvSm_AS_zzQP",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
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}