keras_apis_seq_func_subc.ipynb

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      "cell_type": "markdown",
      "source": [
        "Keras APIs three layers of abstraction: sequential , functional, subclassing (in order of simplicity).\n",
        "\n",
        "The simplicty of the sequential abstraction comes at a cost, it is basically a Python list limiting it to simple stacks of layers. The function API is a graph-like model architecture, and perhaps provides the best balance between simplicity & flexibility; this may be why it is the most commonly used API. The subclassing API involves building everything from scratch, and should be used when a model architecture cannot be formulated as a graph. All three layers are compatible with one another.\n",
        "\n",
        "The sequential API:"
      ],
      "metadata": {
        "id": "pI6zCwfgjOQs"
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    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "#Instantiating a model with two dense layers, with the final being a softmax classification head.\n",
        "model = keras.Sequential([\n",
        "    layers.Dense(64, activation = \"relu\"),\n",
        "    layers.Dense(10, activation = \"softmax\")\n",
        "])\n",
        "\n"
      ],
      "metadata": {
        "id": "bjk_D9qLjiDQ"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Alternative method is to instantiate a model and add each layer:\n",
        "model = keras.Sequential()\n",
        "model.add(layers.Dense(64, activation = \"relu\"))\n",
        "model.add(layers.Dense(10, activation = \"softmax\"))"
      ],
      "metadata": {
        "id": "4nOf-duOjvcH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The layers are not built (create weights) instantly - because the shape of the layers is input-dependent and at the time of instantiating the model we have no input data, so do not know the shape the layers should be. Which is another way of saying the model has no weights, we could check that by calling model.weights.\n",
        "\n",
        "Calling our model for the first time will build its weights:"
      ],
      "metadata": {
        "id": "Jb6DgDTGjx3k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.build(input_shape = (None, 3)) #None signals that batch size could be anything, 3 signals the expected shape of the inputs\n",
        "#Call this once model is built to get the model's summary - layers, output sahpes, # of parameters - total & trainable/non-trainable\n"
      ],
      "metadata": {
        "id": "O2R8SMvkj4E3"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.weights\n",
        "\n"
      ],
      "metadata": {
        "id": "2gPVi9_pj7jM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " model.summary() "
      ],
      "metadata": {
        "id": "-MsGZhPcj8i2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The model summary will show that each layer has a name like sequential_1, we could have named each layer with the name = \"foo_bar\" argument when creating each sequential layer.\n",
        "\n",
        "When we make changes to a model it's helpful to print a summary to check our changes have occurred as we expected. However, currently the model will not build until it's called on data (reminder that we need to know the input dims). One way to get around this is to set the input dimensions in advance, which we can do with model.add(keras.Input(shape = (3,))), after calling model = keras.Sequential(), we can then call model.summary() as and when we please."
      ],
      "metadata": {
        "id": "UYoYBQ8TkFjS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Functional API:\n",
        "\n",
        "When we want models that can deal with multiple inputs and provide multiple outputs, or to customize our model/deal with bespoke problems etc. we need to turn to the functional API. "
      ],
      "metadata": {
        "id": "Xu_cVRc3kOkz"
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    },
    {
      "cell_type": "code",
      "source": [
        "inputs = keras.Input(shape = (3,), name = \"my_input\")\n",
        "\n",
        "inputs.shape\n",
        "inputs.dtype\n",
        "\n",
        "features = layers.Dense(64, activation = \"relu\")(inputs)\n",
        "\n",
        "features.shape\n",
        "\n",
        "outputs = layers.Dense(10, activation = \"softmax\")(features)\n",
        "model = keras.Model(inputs = inputs, outputs = outputs)\n",
        "\n",
        "model.summary()\n"
      ],
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      "execution_count": 9,
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          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " my_input (InputLayer)       [(None, 3)]               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 64)                256       \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 10)                650       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 906\n",
            "Trainable params: 906\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this case inputs is a symbolic tensor; it carries information about what type of dimensions our model should expect but it contains no data. We can think of it as setting the constraints of our model, or the rules of the game - if we try to feed our model something that does not fit these dimensions then the model will halt. \n",
        "\n",
        "Chollet says 'it stands for future tensors of data'.\n",
        "\n",
        "However, this model is simple and could be achived with the sequential API. The functional API shines when dealing with a graph-like structure, i.e. it's able to deal with multiple inputs and do different things with each one.\n",
        "\n",
        "Let's say if building a text classification model for customer support tickets - a model could take three inputs:\n",
        "* Title of the ticket (text input)\n",
        "* Text body of the ticket (text input)\n",
        "* Tags added by the user (categorical input that should be one-hot encoded)\n",
        "\n",
        "One way of encoding the texts would be to create binary arrays of size_vocabulary.\n",
        "\n",
        "The model will produce two outputs:\n",
        "* Priority score of the ticket (sigmoid/logistic output)\n",
        "* Department that should handle the ticket (softmax over the set of departments)\n",
        "\n",
        "Building the model with the functional API:"
      ],
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        "id": "E5aM4qjtkbXC"
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    {
      "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])\n",
        "features = layers.Dense(64, activation = \"relu\")(features)\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],\n",
        "                   outputs = [priority,department])\n"
      ],
      "metadata": {
        "id": "J1vyVykRkVot"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AnjQUtmpkeU-",
        "outputId": "cd370e6f-dae9-4b9a-c02a-5344de1bd6ef"
      },
      "execution_count": 12,
      "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_1 (Concatenate)    (None, 20100)        0           ['title[0][0]',                  \n",
            "                                                                  'text_body[0][0]',              \n",
            "                                                                  'tags[0][0]']                   \n",
            "                                                                                                  \n",
            " dense_5 (Dense)                (None, 64)           1286464     ['concatenate_1[0][0]']          \n",
            "                                                                                                  \n",
            " priority (Dense)               (None, 1)            65          ['dense_5[0][0]']                \n",
            "                                                                                                  \n",
            " department (Dense)             (None, 4)            260         ['dense_5[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 1,286,789\n",
            "Trainable params: 1,286,789\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Above we set parameters of inputs - vocabulary size, number of tags and number of departments.\n",
        "\n",
        "Then we set the shape of each input and name them respectively.\n",
        "\n",
        "We create features by concatenating our inputs, then create a 64-deep Dense layer with a relu activation.\n",
        "\n",
        "We feed our features into a sigmoid classification head to get our priority score (between 0 & 1), and we feed those same features into a softmax classification head to extract which department (a probability distribution - i.e. the score of all 4 adds up to 1).\n",
        "\n",
        "The next step would be to train our model, there are multiple methods - calling fit with lists of input and output data, or training a model with dicts of input and target arrays. Calling with lists relies on input order, so we can use dicts to call with names.\n"
      ],
      "metadata": {
        "id": "aHdH5vc0kwvD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Training model by providiung lists of input & target arrays\n",
        "import numpy as np\n",
        "num_samples = 1280\n",
        "\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",
        "\n",
        "# Dummy target data.\n",
        "priority_data = np.random.random(size=(num_samples, 1))\n",
        "department_data = np.random.randint(0, 2, size=(num_samples, num_departments))\n",
        "model.compile(optimizer='adam',\n",
        "              loss=['mean_squared_error', 'categorical_crossentropy'],\n",
        "              metrics=[['mean_absolute_error'], ['accuracy']])\n",
        "model.fit([title_data, text_body_data, tags_data],\n",
        "          [priority_data, department_data],\n",
        "          epochs=1)\n",
        "model.evaluate([title_data, text_body_data, tags_data],\n",
        "               [priority_data, department_data])\n",
        "priority_preds, department_preds = model.predict([title_data, text_body_data, tags_data])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ljXrwznCkzig",
        "outputId": "90f2880d-0a96-4f8f-f179-5bc1d5645799"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40/40 [==============================] - 1s 5ms/step - loss: 14.7308 - priority_loss: 0.3299 - department_loss: 14.4009 - priority_mean_absolute_error: 0.5016 - department_accuracy: 0.2578\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 3.7903 - priority_loss: 0.3299 - department_loss: 3.4604 - priority_mean_absolute_error: 0.5016 - department_accuracy: 0.1906\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Training model by providing dicts of input and target arrays\n",
        "model.compile(optimizer = \"rmsprop\",\n",
        "             loss = {\"priority\":\"mean_squared_error\", \"department\": \"categorical_crossentropy\"},\n",
        "             metrics = {\"priority\": [\"mean_absolute_error\"], \"department\":[\"accuracy\"]})\n",
        "\n",
        "model.fit({\"title\": title_data, \"text_body\": text_body_data, \"tags\": tags_data},\n",
        "         {\"priority\": priority_data, \"department\": department_data}, epochs = 1)\n",
        "\n",
        "\n",
        "model.evaluate({\"title\": title_data, \"text_body\": text_body_data,\n",
        "               \"tags\": tags_data},\n",
        "              {\"priority\": priority_data, \"department\": department_data})\n",
        "\n",
        "priority_preds, department_preds = model.predict(\n",
        "{\"title\": title_data, \"text_body\": text_body_data, \"tags\": tags_data})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "OXfU-QdEk3IV",
        "outputId": "6cde6986-4579-4d91-bfff-8e2237f75e51"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40/40 [==============================] - 1s 6ms/step - loss: 41.1419 - priority_loss: 0.3299 - department_loss: 40.8120 - priority_mean_absolute_error: 0.5016 - department_accuracy: 0.2633\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 7.8149 - priority_loss: 0.3299 - department_loss: 7.4851 - priority_mean_absolute_error: 0.5016 - department_accuracy: 0.1516\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "keras.utils.plot_model(model, \"ticket_classifier.png\")"
      ],
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          "height": 369
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        "id": "oRtTx9BlmF6s",
        "outputId": "da09ab0c-b437-4e9b-8d2e-b7b971e3ac7a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "keras.utils.plot_model(model, \"ticket_classifier_with_shape_info.png\", show_shapes = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "kKTYosfMmLD1",
        "outputId": "04a1bbee-34e9-46d1-87a7-72720f7038f9"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature extraction - using intermediate layers/representations to feed into another model. For example, if we wanted to add another classification head to fulfil a different ask, we could use the layers of our pre-trained model rather than training from scratch."
      ],
      "metadata": {
        "id": "3IJAohaRmbFS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.layers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gH56aJN-mcfE",
        "outputId": "e569775e-6570-42f6-f0fc-ec88b7fa2457"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.engine.input_layer.InputLayer at 0x7f77fd369710>,\n",
              " <keras.engine.input_layer.InputLayer at 0x7f77fd3690d0>,\n",
              " <keras.engine.input_layer.InputLayer at 0x7f77fd35be50>,\n",
              " <keras.layers.merge.Concatenate at 0x7f77fd300a10>,\n",
              " <keras.layers.core.dense.Dense at 0x7f77fd357b10>,\n",
              " <keras.layers.core.dense.Dense at 0x7f77fd30a090>,\n",
              " <keras.layers.core.dense.Dense at 0x7f77fd30af50>]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.layers[3].input"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3YEjsEgmcnR",
        "outputId": "ea66afae-5c89-4615-d902-6e6bcf913292"
      },
      "execution_count": 26,
      "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": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.layers[3].output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-zJXu8Xvmcpf",
        "outputId": "59552abb-685f-4420-c0d0-c8b81916a2b6"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<KerasTensor: shape=(None, 20100) dtype=float32 (created by layer 'concatenate_1')>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = model.layers[4].output\n",
        "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": "FNTto6ramjGG"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "        keras.utils.plot_model(model, \"updated_ticket_classifier.png\", show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "p_R3ZngvmkNp",
        "outputId": "2d8c600d-02a9-4734-a2eb-e86863d7f9ce"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After the functional API we're left with the sequential API, which necesitates building our model from scratch - or creating a new Python object - so we can't rely on some of the handy features of the functional/sequential APIs, like plotting the model, the forward pass etc.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "There are some similarities however - weights will not be created until model is built, "
      ],
      "metadata": {
        "id": "JHGZbE4cnJyS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomerTicketModel(keras.Model):\n",
        "    def __init__(self, num_departments):\n",
        "        super(CustomerTicketModel, self).__init__()\n",
        "        self.concat_layer = layers.Concatenate()\n",
        "        self.mixing_layer = layers.Dense(64, activation='relu')\n",
        "        self.priority_scorer = layers.Dense(1, activation='sigmoid')\n",
        "        self.department_classifier = layers.Dense(\n",
        "          num_departments, activation='softmax')\n",
        "    def call(self, inputs):\n",
        "        title = inputs['title']\n",
        "        text_body = inputs['text_body']\n",
        "        tags = inputs['tags']\n",
        "        features = self.concat_layer([title, text_body, tags])\n",
        "        features = self.mixing_layer(features)\n",
        "        priority = self.priority_scorer(features)\n",
        "        department = self.department_classifier(features)\n",
        "        return priority, department"
      ],
      "metadata": {
        "id": "ZShSkOK1nbA9"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We define a Class, making sure to call the superconstructor, define sublayers in the constructor and define the forward pass inside the call() method. We can then instantiate models for our priority and department classifiers."
      ],
      "metadata": {
        "id": "AT7eZQ8tn2MF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = CustomerTicketModel(num_departments=4)\n",
        "\n",
        "priority, department = model(\n",
        "  {'title': title_data, 'text_body': text_body_data, 'tags': tags_data})"
      ],
      "metadata": {
        "id": "dX3pc7CKnZ8r"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A model subclass can be compiled and trained similarly to the sequential and functional APIs:"
      ],
      "metadata": {
        "id": "uqV7e5HnoIBc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=['mean_squared_error', 'categorical_crossentropy'],\n",
        "              metrics=[['mean_absolute_error'], ['accuracy']])\n",
        "\n",
        "model.fit({'title': title_data, 'text_body': text_body_data, 'tags': tags_data},\n",
        "          [priority_data, department_data],\n",
        "          epochs=1)\n",
        "\n",
        "model.evaluate({'title': title_data, 'text_body': text_body_data, 'tags': tags_data},                 [priority_data, department_data])\n",
        "\n",
        "priority_preds, department_preds = model.predict(\n",
        "    {'title': title_data, 'text_body': text_body_data, 'tags': tags_data})"
      ],
      "metadata": {
        "id": "8pUAlZqUmmdY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The structure of what you pass as the loss and metrics must match exactly what gets returned by call() — since we returned a list of two elements, so should loss and metrics be lists of two elements.\n",
        "The structure of the input data must match exactly what is expected by the call() method, and the structure of the target data must match exactly what gets returned by the call() method. Here, the input data must be a dict with 3 keys ('title', 'text_body', and 'tags') and the target data must be a list of two elements."
      ],
      "metadata": {
        "id": "GgiisaEGod9w"
      }
    }
  ]
}