Functional_API_keras_use_cases.ipynb

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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mrgBBFPXFSC-"
      },
      "outputs": [],
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = keras.Input(shape=(3,), name=\"my_input\")\n",
        "features = layers.Dense(64, activation=\"relu\")(inputs)\n",
        "outputs = layers.Dense(10, activation=\"softmax\")(features)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)"
      ],
      "metadata": {
        "id": "H7GOd8w7GhiI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nvDVLZ8-Gixa",
        "outputId": "5cb18bcc-9f6f-4f5f-825f-fd1b2f312586"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " my_input (InputLayer)       [(None, 3)]               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                256       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                650       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 906 (3.54 KB)\n",
            "Trainable params: 906 (3.54 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Problem Statement\n",
        "\n",
        "Let’s say you’re building a system to rank customer support tickets by priority and route them to the appropriate department. Your model has three inputs:\n",
        "\n",
        " The title of the ticket (text input)\n",
        "\n",
        " The text body of the ticket (text input)\n",
        "\n",
        " Any tags added by the user (categorical input, assumed here to be one-hot\n",
        "encoded)\n",
        "\n",
        "We can encode the text inputs as arrays of ones and zeros of size vocabulary_size.\n",
        "\n",
        "Your model also has two outputs:\n",
        "\n",
        " The priority score of the ticket, a scalar between 0 and 1 (sigmoid output)\n",
        "\n",
        " The department that should handle the ticket (a softmax over the set of departments)"
      ],
      "metadata": {
        "id": "zoUELo20JnFb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vocabulary_size = 10000\n",
        "num_tags = 100\n",
        "num_departments = 4\n",
        "\n",
        "title = keras.Input(shape=(vocabulary_size,), name=\"title\")\n",
        "text_body = keras.Input(shape=(vocabulary_size,), name=\"text_body\")\n",
        "tags = keras.Input(shape=(num_tags,), name=\"tags\")\n",
        "\n",
        "features = layers.Concatenate()([title, text_body, tags]) # Combine input features into a single tensor, features, by concatenating them.\n",
        "features = layers.Dense(64, activation=\"relu\")(features) # Apply an intermediate layer to recombine input features into richer representations.\n",
        "\n",
        "priority = layers.Dense(1, activation=\"sigmoid\", name=\"priority\")(features)\n",
        "department = layers.Dense(num_departments, activation=\"softmax\", name=\"department\")(features)\n",
        "\n",
        "model = keras.Model(inputs=[title, text_body, tags], outputs=[priority, department])"
      ],
      "metadata": {
        "id": "sRmat3-oJ82q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yw0u92OzKu6I",
        "outputId": "4dc0d68a-fb13-41ec-99d7-e2b7e540b4ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " title (InputLayer)          [(None, 10000)]              0         []                            \n",
            "                                                                                                  \n",
            " text_body (InputLayer)      [(None, 10000)]              0         []                            \n",
            "                                                                                                  \n",
            " tags (InputLayer)           [(None, 100)]                0         []                            \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)   (None, 20100)                0         ['title[0][0]',               \n",
            "                                                                     'text_body[0][0]',           \n",
            "                                                                     'tags[0][0]']                \n",
            "                                                                                                  \n",
            " dense_2 (Dense)             (None, 64)                   1286464   ['concatenate[0][0]']         \n",
            "                                                                                                  \n",
            " priority (Dense)            (None, 1)                    65        ['dense_2[0][0]']             \n",
            "                                                                                                  \n",
            " department (Dense)          (None, 4)                    260       ['dense_2[0][0]']             \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 1286789 (4.91 MB)\n",
            "Trainable params: 1286789 (4.91 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "num_samples = 1280\n",
        " # Dummy input data\n",
        "title_data = np.random.randint(0, 2, size=(num_samples, vocabulary_size))\n",
        "text_body_data = np.random.randint(0, 2, size=(num_samples, vocabulary_size))\n",
        "tags_data = np.random.randint(0, 2, size=(num_samples, num_tags))\n",
        " # Dummy output data\n",
        "priority_data = np.random.random(size=(num_samples, 1))\n",
        "department_data = np.random.randint(0, 2, size=(num_samples, num_departments))"
      ],
      "metadata": {
        "id": "bmza-8o6LUWE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=\"rmsprop\",\n",
        " loss=[\"mean_squared_error\", \"categorical_crossentropy\"],\n",
        " metrics=[[\"mean_absolute_error\"], [\"accuracy\"]])"
      ],
      "metadata": {
        "id": "HEl0vczOLmVj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit([title_data, text_body_data, tags_data], [priority_data, department_data], epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AqyTbEs1LpBe",
        "outputId": "aea083ab-da04-4193-a06a-c0893c21eb88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "40/40 [==============================] - 6s 5ms/step - loss: 28.6975 - priority_loss: 0.3358 - department_loss: 28.3617 - priority_mean_absolute_error: 0.5006 - department_accuracy: 0.2539\n",
            "Epoch 2/10\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 32.5789 - priority_loss: 0.3415 - department_loss: 32.2374 - priority_mean_absolute_error: 0.5057 - department_accuracy: 0.2328\n",
            "Epoch 3/10\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 38.7817 - priority_loss: 0.3415 - department_loss: 38.4402 - priority_mean_absolute_error: 0.5057 - department_accuracy: 0.2867\n",
            "Epoch 4/10\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 43.2647 - priority_loss: 0.3415 - department_loss: 42.9232 - priority_mean_absolute_error: 0.5057 - department_accuracy: 0.2914\n",
            "Epoch 5/10\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 56.5413 - priority_loss: 0.3415 - department_loss: 56.1998 - priority_mean_absolute_error: 0.5057 - department_accuracy: 0.2508\n",
            "Epoch 6/10\n",
            "40/40 [==============================] - 0s 6ms/step - loss: 67.8589 - priority_loss: 0.3415 - department_loss: 67.5174 - priority_mean_absolute_error: 0.5057 - department_accuracy: 0.2820\n",
            "Epoch 7/10\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 79.6577 - priority_loss: 0.3415 - department_loss: 79.3162 - priority_mean_absolute_error: 0.5057 - department_accuracy: 0.2508\n",
            "Epoch 8/10\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 87.2907 - priority_loss: 0.3415 - department_loss: 86.9492 - priority_mean_absolute_error: 0.5057 - department_accuracy: 0.2680\n",
            "Epoch 9/10\n",
            "40/40 [==============================] - 0s 9ms/step - loss: 99.2851 - priority_loss: 0.3415 - department_loss: 98.9436 - priority_mean_absolute_error: 0.5057 - department_accuracy: 0.2508\n",
            "Epoch 10/10\n",
            "40/40 [==============================] - 0s 9ms/step - loss: 108.5816 - priority_loss: 0.3415 - department_loss: 108.2401 - priority_mean_absolute_error: 0.5057 - department_accuracy: 0.2711\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e73d1662d40>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate([title_data, text_body_data, tags_data],\n",
        " [priority_data, department_data])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vzdzrPmlL3Lf",
        "outputId": "de7a98e5-140b-43da-b5f4-055697a65199"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40/40 [==============================] - 0s 4ms/step - loss: 113.3758 - priority_loss: 0.3415 - department_loss: 113.0343 - priority_mean_absolute_error: 0.5057 - department_accuracy: 0.2820\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[113.37581634521484,\n",
              " 0.34151870012283325,\n",
              " 113.0342788696289,\n",
              " 0.505698561668396,\n",
              " 0.28203123807907104]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "priority_preds, department_preds = model.predict(\n",
        " [title_data, text_body_data, tags_data])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bxc7xGA2L8wJ",
        "outputId": "803445c0-82a6-4428-fc0d-5d692c265b22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40/40 [==============================] - 0s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "priority_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n8RyMgcFL-sa",
        "outputId": "23dbdb4f-a612-4d6d-fbdb-8d05f7905579"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.],\n",
              "       [0.],\n",
              "       [0.],\n",
              "       ...,\n",
              "       [0.],\n",
              "       [0.],\n",
              "       [0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If you don’t want to rely on input order (for instance, because you have many inputs\n",
        "or outputs), you can also leverage the names you gave to the Input objects and the\n",
        "output layers, and pass data via dictionaries."
      ],
      "metadata": {
        "id": "uPuMlfXcMXe2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=\"rmsprop\", loss={\"priority\": \"mean_squared_error\", \"department\": \"categorical_crossentropy\"}, metrics={\"priority\": [\"mean_absolute_error\"], \"department\": [\"accuracy\"]})\n",
        "\n",
        "model.fit({\"title\": title_data, \"text_body\": text_body_data, \"tags\": tags_data}, {\"priority\": priority_data, \"department\": department_data}, epochs=1)\n",
        "\n",
        "model.evaluate({\"title\": title_data, \"text_body\": text_body_data, \"tags\": tags_data}, {\"priority\": priority_data, \"department\": department_data})\n",
        "\n",
        "priority_preds, department_preds = model.predict({\"title\": title_data, \"text_body\": text_body_data, \"tags\": tags_data})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cakL0ypMYnC",
        "outputId": "96812ad6-bc80-4306-f0d2-5f007e9ac7d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40/40 [==============================] - 1s 8ms/step - loss: 152.7341 - priority_loss: 0.3415 - department_loss: 152.3926 - priority_mean_absolute_error: 0.5057 - department_accuracy: 0.2727\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 174.6814 - priority_loss: 0.3415 - department_loss: 174.3399 - priority_mean_absolute_error: 0.5057 - department_accuracy: 0.2680\n",
            "40/40 [==============================] - 0s 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can plot a Functional model as a graph with the plot_model() utility"
      ],
      "metadata": {
        "id": "3w_WOBkYNClA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "keras.utils.plot_model(model, \"ticket_classifier.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "_4ParFWaNDBY",
        "outputId": "f64f778f-c8fc-47db-9519-ac41481ef8d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can add to this plot the input and output shapes of each layer in the model, which\n",
        "can be helpful during debugging"
      ],
      "metadata": {
        "id": "JXB5SMKkNu1Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "keras.utils.plot_model(\n",
        " model, \"ticket_classifier_with_shape_info.png\", show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "Ykj-U41DNvmx",
        "outputId": "ec69d904-0505-43a3-ffea-4fc19b582640"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The “None” in the tensor shapes represents the batch size: this model allows batches\n",
        "of any size."
      ],
      "metadata": {
        "id": "TNVL9jAnOBT2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Access to layer connectivity also means that you can inspect and reuse individual\n",
        "nodes (layer calls) in the graph. The model.layers model property provides the list\n",
        "of layers that make up the model, and for each layer you can query layer.input and\n",
        "layer.output."
      ],
      "metadata": {
        "id": "tv0GpagkOx_C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.layers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wnk0SiBQOyzy",
        "outputId": "de38f82b-cfb9-4b93-a3ce-f68911b76be7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.src.engine.input_layer.InputLayer at 0x7e73e7541930>,\n",
              " <keras.src.engine.input_layer.InputLayer at 0x7e73e7543ca0>,\n",
              " <keras.src.engine.input_layer.InputLayer at 0x7e73e7deff40>,\n",
              " <keras.src.layers.merging.concatenate.Concatenate at 0x7e73e7543040>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7e73e75431f0>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7e73e75403d0>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7e73e7540ca0>]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " model.layers[3].input"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-_RnjMX4O5EM",
        "outputId": "8cb625ee-6634-4058-c55b-7b8a1fd2dbe6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<KerasTensor: shape=(None, 10000) dtype=float32 (created by layer 'title')>,\n",
              " <KerasTensor: shape=(None, 10000) dtype=float32 (created by layer 'text_body')>,\n",
              " <KerasTensor: shape=(None, 100) dtype=float32 (created by layer 'tags')>]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.layers[3].output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oKOIOFbbO9_j",
        "outputId": "43fd3237-5364-4d44-dff7-efd2641ae602"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<KerasTensor: shape=(None, 20100) dtype=float32 (created by layer 'concatenate')>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This enables you to do feature extraction, creating models that reuse intermediate features from another model."
      ],
      "metadata": {
        "id": "_GPi4xzXPQGO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let’s say you want to add another output to the previous model—you want to estimate how long a given issue ticket will take to resolve, a kind of difficulty rating. You\n",
        "could do this via a classification layer over three categories: “quick,” “medium,” and\n",
        "“difficult.” You don’t need to recreate and retrain a model from scratch. You can start\n",
        "from the intermediate features of your previous model, since you have access to them,\n",
        "like this.\n"
      ],
      "metadata": {
        "id": "jHsdhWKlPaw0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features = model.layers[4].output\n",
        "print(features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N0WRbrbsPQxr",
        "outputId": "fcd43967-148b-4670-ea2b-453d9f5a1832"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KerasTensor(type_spec=TensorSpec(shape=(None, 64), dtype=tf.float32, name=None), name='dense_2/Relu:0', description=\"created by layer 'dense_2'\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "difficulty = layers.Dense(3, activation=\"softmax\", name=\"difficulty\")(features)\n",
        "new_model = keras.Model(\n",
        " inputs=[title, text_body, tags],\n",
        " outputs=[priority, department, difficulty])"
      ],
      "metadata": {
        "id": "_suEuDP3P6LY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "keras.utils.plot_model(\n",
        " new_model, \"updated_ticket_classifier.png\", show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "quG2vOjsP_4P",
        "outputId": "eeb810f9-7745-46fd-ea95-feb3ffc6b234"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    }
  ]
}