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      "source": [
        "<a href=\"https://colab.research.google.com/github/gmihaila/machine_learning_things/blob/master/learning_tensorflow/tf2_0_text_binary_classification_word_embed.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "### Regular Classification [word embeedings]"
      ]
    },
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        {
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            "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "27jF_okvy53u",
        "colab_type": "text"
      },
      "source": [
        "### Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "44oVj91SohKO",
        "colab_type": "code",
        "outputId": "12d501bc-318e-4671-85c2-f4274867e7e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "import os\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "import tensorflow as tf\n",
        "# tf.debugging.set_log_device_placement(True)\n",
        "import tensorflow_datasets as tfds\n",
        "import tensorflow_hub as hub\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "import time\n",
        "print(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))\n",
        "\n",
        "BUFFER_SIZE = 1000\n",
        "BATCH = 1000\n",
        "EPOCHS = 20\n",
        "MAX_LEN = 500"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n",
            "Num GPUs Available:  0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BrE1BFRhOo1G",
        "colab_type": "text"
      },
      "source": [
        "### Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ok5p7RDOp38",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plt_history(history_dict, \n",
        "                acc_key='accuracy', val_acc_key='val_accuracy', \n",
        "                loss_key='loss', val_loss_key='val_loss'):\n",
        "  acc = history_dict[acc_key]\n",
        "  val_acc = history_dict[val_acc_key]\n",
        "  loss = history_dict[loss_key]\n",
        "  val_loss = history_dict[val_loss_key]\n",
        "  epochs = range(1, len(acc) + 1)\n",
        "  plt.subplot(1, 2, 1)\n",
        "  # \"bo\" is for \"blue dot\"\n",
        "  plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "  # b is for \"solid blue line\"\n",
        "  plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "  plt.title('Training and validation loss')\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.legend()\n",
        "  plt.grid()\n",
        "  plt.tight_layout()\n",
        "  plt.xticks(epochs)\n",
        "\n",
        "\n",
        "  plt.subplot(1, 2, 2)\n",
        "  plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "  plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "  plt.title('Training and validation accuracy')\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.legend(loc='lower right')\n",
        "  plt.grid()\n",
        "  plt.tight_layout()\n",
        "  plt.xticks(epochs)\n",
        "  fig = plt.gcf()\n",
        "  fig.set_size_inches(15, 5)\n",
        "  plt.show()\n",
        "  return"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zj7V2qJ6HbYk",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZCkSSrSAHjfu",
        "colab_type": "text"
      },
      "source": [
        "#### Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzFl5rtWHf6p",
        "colab_type": "code",
        "outputId": "902cd66c-7f27-4526-abb0-1e872c191ae6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552,
          "referenced_widgets": [
            "13623cecaf7c4f28ae4f8a1697794fbf",
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            "5849f6f6bb2a4efe891755b5e4d2f73b",
            "3a01c487e61344b3900b226acaa7ff22",
            "2c78d58af9d44569baf021c6e38b203c",
            "2c1d6ea07e784de4b6032df644a9feb6",
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            "bd2dccd66e0f462498046d0fc8515e7d",
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            "8499dda8a1b0425cae092a02bd02003f",
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            "6e395cd43a8f44278bb9c1361ee72f1b",
            "f8ec1afab6a34f6ba29d1189728f8e31",
            "2acedc2f7aa54cf9b4a1a713392e9eda",
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            "72d8550f218d4276b25659badab408ab",
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            "f2fec66e56b74316b2e4354de4928559",
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            "853b59d41484456fa5b17c499a886fc3",
            "37cf26d226484220bb0857d6d50f80d6",
            "93e9b6bea16544f7a4216bbd086427d2",
            "4cacd55b0fa944d19798a39791b1a8de",
            "205e7329c4b948d4a39b10f37d778221",
            "912e5f91dc1b4f38b642fb3993c4eca9",
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            "de130aff946e41c98e0bc9473ccb0621",
            "6acf637a8a8248de8dd24b3f267bc5e8",
            "860faae14a6345978c3c1b208ea6b344",
            "e8545939ae24410fb1bb340b1c57c48f",
            "3c4dbb5552cb4b0da490d0064d8148f2",
            "7ba0c85b07fd42b190ad671790a0586a",
            "06b24e78287641fa8e7bd2be75834848",
            "700a26ea8bed42238c71d1570b5b6f4e",
            "d1f17492f0da42969724afc049e87846",
            "28da8429358b4107963685d73e3e50ce",
            "f3cab18ceb66405fae17d0b606547298",
            "4aa9bc2764e643d4bf9f3fb716a33833"
          ]
        }
      },
      "source": [
        "# Use the version pre-encoded with an ~8k vocabulary.\n",
        "# Return the train/test datasets as a tuple.\n",
        "# Return (example, label) pairs from the dataset (instead of a dictionary).\n",
        "# Also return the `info` structure. \n",
        "\n",
        "(train_data, test_data), info = tfds.load('imdb_reviews/subwords8k', \n",
        "                                          split = (tfds.Split.TRAIN, tfds.Split.TEST),\n",
        "                                          as_supervised=True,\n",
        "                                          with_info=True)\n",
        "\n",
        "text_encoder = info.features['text'].encoder\n",
        "print ('Vocabulary size: {}'.format(text_encoder.vocab_size))\n",
        "\n",
        "# batch padding\n",
        "train_batches = (train_data.shuffle(BUFFER_SIZE).padded_batch(BATCH, train_data.output_shapes))\n",
        "test_batches = (test_data.padded_batch(BATCH, train_data.output_shapes))\n",
        "\n",
        "# sample\n",
        "encodings, labels = next(iter(train_batches))\n",
        "\n",
        "print(\"Text:                  {}\".format(text_encoder.decode(encodings[0])))\n",
        "print(\"Encoding sample:       {}\".format(encodings[0][:10]))\n",
        "print(\"Label:                 {}\".format(labels[0]))\n",
        "print(\"Encoding batch shape:  {}\".format(encodings.shape))\n",
        "print(\"Labels batch shape:    {}\".format(labels.shape))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset imdb_reviews (80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "13623cecaf7c4f28ae4f8a1697794fbf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Completed...', max=1, style=ProgressStyl…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Size...', max=1, style=ProgressStyle(des…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2c1d6ea07e784de4b6032df644a9feb6",
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              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0.incompleteVLV110/imdb_reviews-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(IntProgress(value=0, max=25000), HTML(value='')))"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e38e7aca5c1845b6b4ebfde21d6f69b5",
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              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0.incompleteVLV110/imdb_reviews-test.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(IntProgress(value=0, max=25000), HTML(value='')))"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4cacd55b0fa944d19798a39791b1a8de",
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              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0.incompleteVLV110/imdb_reviews-unsupervised.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3c4dbb5552cb4b0da490d0064d8148f2",
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              "version_major": 2
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            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=50000), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r\u001b[1mDataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n",
            "Vocabulary size: 8185\n",
            "WARNING:tensorflow:From <ipython-input-4-0487de85325e>:11: DatasetV1.output_shapes (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.data.get_output_shapes(dataset)`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-4-0487de85325e>:11: DatasetV1.output_shapes (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.data.get_output_shapes(dataset)`.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Text:                  I guess I was attracted to this film both because of the sound of the story and the leading actor, so I gave it a chance, from director Gregor Jordan (Buffalo Soldiers). Basically Ned Kelly (Heath Ledger) is set up by the police, especially Superintendent Francis Hare (Geoffrey Rush), he is forced to go on the run forming a gang and go against them to clear his own and his family's names. That's really all I can say about the story, as I wasn't paying the fullest attention to be honest. Also starring Orlando Bloom as Joseph Byrne, Naomi Watts as Julia Cook, Laurence Kinlan as Dan Kelly, Philip Barantini as Steve Hart, Joel Edgerton as Aaron Sherritt, Kiri Paramore as Constable Fitzpatrick, Kerry Condon as Kate Kelly, Emily Browning as Grace Kelly and Rachel Griffiths as Susan Scott. Ledger makes a pretty good performance, for what it's worth, and the film does have it's eye-catching moments, particularly with a gun battle towards the end, but I can't say I enjoyed it as I didn't look at it all. Okay!\n",
            "Encoding sample:       [  12  818   12   18 6381   36    7   14   32  289]\n",
            "Label:                 0\n",
            "Encoding batch shape:  (1000, 1639)\n",
            "Labels batch shape:    (1000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GPb81vrgHxsX",
        "colab_type": "text"
      },
      "source": [
        "#### Sequential Model + regular train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ls-2WEi3HyNJ",
        "colab_type": "code",
        "outputId": "0ca7c942-1e39-46e9-9d78-0a4fc08c2af6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Embedding(text_encoder.vocab_size, 16))\n",
        "model.add(tf.keras.layers.GlobalAveragePooling1D())\n",
        "model.add(tf.keras.layers.Dense(16, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "# use test as valid \n",
        "history = model.fit(train_batches,\n",
        "                    epochs=EPOCHS,\n",
        "                    validation_data=test_batches,\n",
        "                    verbose=1)\n",
        "\n",
        "results = model.evaluate(test_batches, verbose=2)\n",
        "\n",
        "loss, accuracy = model.evaluate(test_batches)\n",
        "\n",
        "print(\"Loss: \", loss)\n",
        "print(\"Accuracy: \", accuracy)\n",
        "\n",
        "plt_history(history_dict=history.history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, None, 16)          130960    \n",
            "_________________________________________________________________\n",
            "global_average_pooling1d (Gl (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 131,249\n",
            "Trainable params: 131,249\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/20\n",
            "25/25 [==============================] - 11s 447ms/step - loss: 0.6930 - accuracy: 0.5144 - val_loss: 0.6928 - val_accuracy: 0.5163\n",
            "Epoch 2/20\n",
            "25/25 [==============================] - 10s 411ms/step - loss: 0.6925 - accuracy: 0.5469 - val_loss: 0.6922 - val_accuracy: 0.5319\n",
            "Epoch 3/20\n",
            "25/25 [==============================] - 11s 441ms/step - loss: 0.6917 - accuracy: 0.5905 - val_loss: 0.6911 - val_accuracy: 0.5503\n",
            "Epoch 4/20\n",
            "25/25 [==============================] - 10s 420ms/step - loss: 0.6901 - accuracy: 0.6051 - val_loss: 0.6892 - val_accuracy: 0.5696\n",
            "Epoch 5/20\n",
            "25/25 [==============================] - 11s 426ms/step - loss: 0.6875 - accuracy: 0.6324 - val_loss: 0.6862 - val_accuracy: 0.6242\n",
            "Epoch 6/20\n",
            "25/25 [==============================] - 11s 431ms/step - loss: 0.6836 - accuracy: 0.6688 - val_loss: 0.6815 - val_accuracy: 0.7044\n",
            "Epoch 7/20\n",
            "25/25 [==============================] - 10s 419ms/step - loss: 0.6775 - accuracy: 0.6894 - val_loss: 0.6748 - val_accuracy: 0.7307\n",
            "Epoch 8/20\n",
            "25/25 [==============================] - 10s 418ms/step - loss: 0.6696 - accuracy: 0.7123 - val_loss: 0.6656 - val_accuracy: 0.7193\n",
            "Epoch 9/20\n",
            "25/25 [==============================] - 11s 435ms/step - loss: 0.6586 - accuracy: 0.7382 - val_loss: 0.6539 - val_accuracy: 0.7318\n",
            "Epoch 10/20\n",
            "25/25 [==============================] - 11s 442ms/step - loss: 0.6445 - accuracy: 0.7548 - val_loss: 0.6395 - val_accuracy: 0.7458\n",
            "Epoch 11/20\n",
            "25/25 [==============================] - 10s 417ms/step - loss: 0.6288 - accuracy: 0.7632 - val_loss: 0.6235 - val_accuracy: 0.7535\n",
            "Epoch 12/20\n",
            "25/25 [==============================] - 11s 427ms/step - loss: 0.6097 - accuracy: 0.7736 - val_loss: 0.6059 - val_accuracy: 0.7568\n",
            "Epoch 13/20\n",
            "25/25 [==============================] - 10s 415ms/step - loss: 0.5877 - accuracy: 0.7816 - val_loss: 0.5875 - val_accuracy: 0.7688\n",
            "Epoch 14/20\n",
            "25/25 [==============================] - 11s 423ms/step - loss: 0.5679 - accuracy: 0.7904 - val_loss: 0.5696 - val_accuracy: 0.7678\n",
            "Epoch 15/20\n",
            "25/25 [==============================] - 11s 429ms/step - loss: 0.5458 - accuracy: 0.8023 - val_loss: 0.5508 - val_accuracy: 0.7855\n",
            "Epoch 16/20\n",
            "25/25 [==============================] - 10s 416ms/step - loss: 0.5272 - accuracy: 0.8136 - val_loss: 0.5329 - val_accuracy: 0.7962\n",
            "Epoch 17/20\n",
            "25/25 [==============================] - 11s 421ms/step - loss: 0.5069 - accuracy: 0.8233 - val_loss: 0.5158 - val_accuracy: 0.8031\n",
            "Epoch 18/20\n",
            "25/25 [==============================] - 11s 433ms/step - loss: 0.4900 - accuracy: 0.8310 - val_loss: 0.4995 - val_accuracy: 0.8137\n",
            "Epoch 19/20\n",
            "25/25 [==============================] - 11s 426ms/step - loss: 0.4684 - accuracy: 0.8416 - val_loss: 0.4839 - val_accuracy: 0.8206\n",
            "Epoch 20/20\n",
            "25/25 [==============================] - 10s 420ms/step - loss: 0.4484 - accuracy: 0.8508 - val_loss: 0.4693 - val_accuracy: 0.8277\n",
            "     25/Unknown - 4s 162ms/step - loss: 0.4693 - accuracy: 0.8277Loss:  0.46926016569137574\n",
            "Accuracy:  0.82768\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HFi3gL3+B7t3hhBMgUaUudO9KUlqpUvCHP8CXX4a7tBs2hF+qk04Kv3j7ovlo\nRURERKSoJWORJoAlS+Cll+rSvTvUqBG+Vz/xREhe//nP0BV63jy46y44+eTEJbGgRFaKidKlw3Q9\n334Ljz4armZ17hzmo43c7NqL5qMVERERkUQYOTIUZYpW1EWaALZtg+nTw/SERx0Fhx0GDz10BPPn\nh+/Fr78Oa9bAlCnwf/+X+EQ7mhJZKVYOOCD80i1cGKrTfvklnHhiKBD16ad7bluSJ5AWERERkcRJ\nVJEmgEWL4B//YNdd127dwt3Whg3D9+enn/6QhQvhoYfC+Ndkne5diawUS+XKwbXXwnffwd/+Bu+/\nD23ahG7IWVNrpsLYBImd7GNTJTnp5yQiIiVFenoYdzpjxiyWLo1fErtlC7z2GlxzDTRuHB4DB8KC\nBWFmkGnTYO3acPf12mvh0EO3pkStGSWyUqxVqgSDB8PixTB0aPgFbdEiFIo65JCc90mmLhMSG+XK\nlWPNmjVKkpKcu7NmzRrKJeulXxEREZK/xop7SFIfeijcba1ZM1QVfvxxOOIIGD069F5cuBBGjQpF\nUrN3c04FqlosJUK1anDbbeHq0z33hO4Uv/4aBqhnZu7eLhFjEyT+6tevz4oVK1i9evUe67dt2xaT\npEntxK6dcuXKUb9+/UIfU0REJB6yaqyE4Wm7a6xA0XQLzs327fDBBzV57rlw93XJkrD+yCPh8stD\nQnvKKVC+fOJijDUlslKi1KoVEtnrroM774RHHsl6xUlLM+68M7F/hCQ+DjjgABo1arTX+oyMDFq3\nbl3o9tVO0bQjIiKSaLGc/zUWfvwxjG995BH48ccWVKgQCp4OGhTutObw9afYUNdiKZEOOSR0q/ju\nu1DtGIxKlcKYARERERGRnCRLjZVPPoF+/cKQuOHD4Zhj4G9/m8fatfDKK3DllcU7iQUlslLCNWgQ\n5p+9++55/PJLmNR58OBQilxEREREJFoi53/dsQNeeCF0ET7mGJg8OXRrnj8fpk6F449fS9my8Y8j\nWSiRFQHatVvLl1/CJZfA3XdD69bw4YeJjkpEREREkkki5n9dvx7uuw8OPxzOOw++/z4sr1gRehge\ncUT8jp3MlMiKRFStCo89Fiobb94c5p+98UbdnRURERGRoCjnf12wAK6+GurXD2NeGzQId2QXLYLr\nrw/FTEsyJbIi2Zx+OnzxBVx6Kfz97+Hu7Acf5L1PspdhFxEREZHYiOf8r+7wxhvQvXuoOPzYY3D+\n+WFM7KxZcO65ULp07I6XypTIiuSgatVwdW369FCJrn37cCVs69a9t80qw75sGbjvLsOuZFZEREQk\neSTzjYctW+DRR6FZs3BTZZlGUn0AACAASURBVO7cMHXk8uXw5JPhxorsSYmsSB66dg13ZwcMCGMR\nWrWC99/fc5u8yrCLiJREZtbNzOab2SIzG5zD62lmNtPMPjWzeWZ2ZtRrf43sN9/MTi/ayEWkuErG\nGw/u8NVXMHbsYdSvH+Z7LV8+FCJdtgyGDoXatRMXX7JTIiuyD1WqhPm53nwzTDZ90knwl7/sTl6T\npQy7iEgyMLPSwBjgDKAp0NvMmmbb7BZgkru3BnoBD0f2bRpZbgZ0Ax6OtCciUijJcuNhw4YwzvWy\ny0Kl4+bN4T//OZTOneGdd2DOnDA1ZEmqPry/yiQ6AJFU0aVLuDt7441w//1hjq5x48IfoWXL9t6+\nKMqwi4gkoXbAIndfDGBmE4FzgK+jtnGgSuR5VWBV5Pk5wER33w4sMbNFkfb2UalARCRvibrx4A6f\nfRaKib72WujZt2NHuFFy2mkwbBhUrfoBf/jDifENpBjSHVmRAqhcGR55BN5+G377Lczj1aRJ6AYS\nLd5l2EVEklg94Puo5RWRddGGA33NbAUwDRhYgH1FRAqsKOd/XbsWJk6Eiy+GunWhTRu4+WbYtCnc\nEJk9G37+GZ5/Hvr3h4MO+jX2QZQAuiMrsh86dw53Z2+6CR5+OIxfqFIFfvrJSUszRo6MTxl2EZFi\nojfwpLvfZ2YnAM+YWfOCNGBmA4ABALVr1yYjI6NQAW3atKnQbagdtaN2kredvn0P5t57j2T79t2j\nFcqW3UHfvvPJyPipUPHs2AELFlTmo49q8NFHNfj22yrs3GlUqfIbbduupV+/tbRrt44aNULCumMH\nvPdebN5XTvEUt3Zyo0RWZD9VqgRjxoSS6JdeGsqw9+69nKefbqCy6CJSkq0EDo1arh9ZF+1SwhhY\n3P0DMysH1MrnvkT2GwuMBWjbtq137NixUEFnZGRQ2DbUjtpRO8nbTseOoRfdkCGwfHnWjYfSpKc3\nJQznL5gNG+Duu79h2bImTJ8Oa9aAGRx7LNx6K3TrBsceewClS9cG8q7YlAyfTzK3kxslsiKF1KkT\nzJsXCkCNHduAtWthwgSoXj3RkYmIJMTHQGMza0RIQnsBfbJtsxw4FXjSzJoA5YDVwBRggpndD9QF\nGgMfFVXgIlK8paeHR0bGrP1OsBYtglGjQp2UzZubcPDBcOaZIXHt2hVq1YptzJK7uCayZtYNeAgo\nDTzu7nflsM0FhLEyDnzu7n0i63cAX0Q2W+7uPeIZq0hhVKoU5v6qXHk+o0YdSbt28PLL0LTgF/hE\nRFKau2ea2dXAdML5f5y7f2VmI4A57j4F+AvwmJldRzj/X+zuDnxlZpMIhaEygavcfUdi3omISOAO\nGRnwwAPw6qtQpgz07g3HHTeXyy8/hlKqOpQQcfvY81N+38waA38F2rt7M+DPUS9vdfdWkYeSWEkJ\nZ531AzNnwsaNcPzxMGVKoiMSESl67j7N3Y9w98PdfWRk3dBIEou7f+3u7d396Mh5/o2ofUdG9jvS\n3V9L1HsQkeQxfjw0bAidO3egYcOim/t12zZ44glo1SrUR/ngA7jlljBbxVNPQdOmG5XEJlA8P/pd\n5ffd/Vcgq/x+tMuAMe6+DsDd92+ktUgSad8+zAF25JFwzjlw++2wc2eioxIRERFJPePHw4ABIXl0\nN5YtC8vxTGZ//BGGD4cGDeCSS8L3uMcfD1P1jBgBhxwSv2NL/sWza3FOJfSPy7bNEQBm9h6h+9Fw\nd3898lo5M5tD6Fp0l7u/lP0Asa5YCMlXpUvtpG47d9xRivvuO4KhQ+vw5pur+etfv6V8+fz1kEvm\n96V21E6i2hERkZJnyBDYsmXPdVu2hPWxniHis8/goYdCrZNff4Xu3eG668LdWLPYHksKL9HFnsoQ\nCjl0JFQmnG1mLdx9PdDA3Vea2WHADDP7wt2/i9451hULIfmqdKmd1G6na1d48EEYNOggbrzxIF56\nCQ4/PHHxqB21k8rtiIhIybN8ecHWF9SOHTB1ahj/mpEBFSrAZZfBNdfAEUfE5hgSH/HsWpyfEvor\ngCnu/pu7LwEWEBJb3H1l5N/FQAbQOo6xisSFWbiSN306rFwZSrK/9Vbu2ydqDIiIiIhIMkpLK9j6\n/Nq4MVQfzhoK9t13cM89sGIF/OMfSmJTQTwT2V3l983sQEL5/eylb14i3I3FzGoRuhovNrPqZlY2\nan17QgVDkZTUpQt8/DHUqwennx6u+rnvuU0ixoCIiIiIJLORI8Nd0mgVKoT1++OHH+Dhhw+nfn24\n9lo4+GD4z39g8WK44QZNn5hK4pbIunsmkFV+/xtgUlb5fTPLqkI8HVhjZl8DM4Eb3H0N0ASYY2af\nR9bf5e5KZCWlHX54qHbXsydcfz306wdbt+5+Pa8xICIiIiKpJhY9zdLTYezYUHjJzGnQICzvz/jY\nKVOgeXOYPLk+Z54J//0vvP8+XHBBmFJHUktcf2TuPg2Ylm3d0KjnDlwfeURv8z7QIp6xiSRCpUrw\n3HPhKuLQofDNN/Dii1C/fvzHgIiIiIgUlayeZuEi/e6eZlDwJDQ9PTwyMmbtV82Fbdvgxhth9Gho\n3RoeeOAjLrooew1aSTWa+UikiJUqBbfeCi+/DPPnQ9u28N578RsDIiIiIlLUkqWn2bffwvHHhyT2\n2mtD77i0tK373lGSnhJZkQTp0SN0aalcGTp1glNPje0YEBEREZFESXRPM3cYNw6OOSYU3HzllTCT\nRNmyRXN8iT8lsiIJ1LQpfPRRmJ9s3Dg44YRwB7awY0BEREREEimRPc02bIA+feDSS+G44+Dzz+Gs\ns+J/XClaSmRFEqx69TB/2Y03wttvh2IGzz//PkuXKokVERGR1BTrasP59eGHYRxsVk2SN9+EunXj\ne0xJDCWyIkmgdGm4+26YMCFM03P11W1YtCjRUYmIiEhJlGzVhvNj587wXeqkk8Lz2bPh5pvDdywp\nnpTIiiSR3r1h1izYvLkMJ54Ic+cmOiIREREpSWI5r316OixdCjNmzIprT7P//Q9OPx0GDw7THH72\nGZx4YnyOJclDiaxIkmnXDkaP/oTy5aFjR3jrrURHJCIiIiVFslQbzq/XX4eWLcMMEGPHwqRJUK1a\noqOSoqBEViQJpaVt5f33Q7eeM88Mf5RFRERE4i3R1Ybz69dfYdAgOOMMqF0b5syByy4Ds0RHJkVF\niaxIkqpXL4zvOO446NULxoxJdEQiIiJS3KXCvPaLFoWuw/fdB1deGWaAaNo00VFJUVMiK5LEqleH\nN96As8+Gq6+GW28N86KJiIiIxEOiqg3n17PPhqrE330HL7wQLvSXL5/oqCQRyiQ6ABHJW/nyMHky\nXH453HEH/PgjPPwwlNFvr4iIiMRYVkGmIUNg+XInLc0YOXL/CjXNmhW+u3z4YXuqVIGKFfd+VKiQ\n8/qctvvb347ijTdCZeLx45PrLrEUPX0VFkkBZcrAY49BnTrhiujq1fDvf0O5comOTERERIqb9PTw\nyMiYRceOHQu0rztkZMBtt4VEtk4d6NhxNbVq1WXLFti8OTzWrYMVK3Yvb968d5Gp7EqVqs3QoaGH\nmi7oi/4LiKQIs3BVs3ZtuPbaUGb+5Zdzrsw3fnzWldQOpKWx31dSRUREJHUk8vzvDm+/HRLYd9+F\nQw6Bhx4KBZg+/HABHTvW3WcbO3fC1q17JrdZCe7mzfDTT3O45JJji+DdSCpQIiuSYgYOhIMOgosu\nglNOCWXn60adG7LmfwtXNXfP/wZKZkVERIqrRJ3/3UM9jxEj4P33Q7HKf/wDLr204D3HSpXa3ZU4\nJxkZmwsfsBQbKvYkkoJ69YKpU2HxYmjfHhYs2P1aqs3/JiIiIoVX1Od/d5g2DU44Abp1g++/DzU8\nvvsOrrpKw58k/pTIiqSo004LY1A2bQrJ7Mcfh/WpMv+biIiIxE5Rnf/d4dVXw/SA3bvD//4Hjz4a\npsS54gooWza2xxPJjRJZkRTWti289x5UqgSdOsGbb6bG/G8iIiISW/E+/7uH2hxt24ZpAX/+GR5/\nPPQKGzAADjwwNscRyS8lsiIp7ogjQjJ7+OHhymj37sk9/5uIiIjEXrzmf925M8zX2ro19OwJGzbA\nE0/A/PlhHKwSWEkUJbIixUDduqHE/QknhPEp550HDRqAmdOgAYwdq0JPIiIiyWr8eGjYEDp37kDD\nhmG5oNLTw/k+Vuf/nTshI+MgWrUK3yu2bIGnnoJvv4WLL4YDDti/dkViRVWLRYqJatVg+nTo0wee\neQZuvhm6dJlFp04dEx2aiIiI5CKW1YYLOv/r9u2wbFko0LR48d7/btnSjCOPhGefDYUmS5cu6LsT\niR8lsiLFSLly8NxzcOWVcOedMG/eEXToEMrZi4iISPLJq9pwYXtTucO6dbknqt9/H7bJUr58GKp0\n2GGhqGSVKl9x663NlMBKUlIiK1LMlC4N//wn1KoFd95Zl8sug8ceUzIrIkXHzLoBDwGlgcfd/a5s\nrz8AdIosVgAOdvdqkdd2AF9EXlvu7j2KJmqRxIhlteH58+Gll+C115qycWNIWDds2HObOnVCotqh\nQ/g3K3E9/HCoXRvMdm+bkbFaSawkLSWyIsWQGdxxB6xYsZRx4xqyc2eoLKiTkYjEm5mVBsYApwEr\ngI/NbIq7f521jbtfF7X9QKB1VBNb3b1VUcUrkmhpaaF7b07r98UdPvkEXnwxFGT65puwvl69SrRo\nEWpnRCerhx0GFSvGNn6RRFEiK1JMmcGf/rSUww5ryPDh4WT3r38pmRWRuGsHLHL3xQBmNhE4B/g6\nl+17A8OKKDaRpDNyZPQY2SCvasOZmfDuuyF5femlcOe2dGk45ZQwj2vPnvDddx/la4ysSCpTIitS\nzA0bFpLaYcNCBcInnlAyKyJxVQ/4Pmp5BXBcThuaWQOgETAjanU5M5sDZAJ3uftL8QpUJBlkjYMd\nMgSWL3fS0oyRI/ccH7ttG7z1Vkhep0wJc7iWLQtdu8Lw4WFe11q1dm//3XdF+hZEEkKJrEgJMHRo\nGCN7660hmX3qKSWzIpIUegHPu/uOqHUN3H2lmR0GzDCzL9x9r6/lZjYAGABQu3ZtMjIyChXIpk2b\nCt2G2lE7+6tePXjyydBOpUqVAJg6tTT//W9N3nmnFh99VIOtW8tQsWImxx+/hpNP/pl27dZSvnz4\n1fnyy9jGo3bUTjK1kxslsiIlxC23hGR2yJCQzD79NJTRXwARib2VwKFRy/Uj63LSC7gqeoW7r4z8\nu9jMMgjjZ/dKZN19LDAWoG3btl7YbpQZGRkx6YqpdtROYbzwwnssXNieF18Md2B/+y0UYLrwQjj3\nXOjcuQwHHlgbqF0k8agdtZMM7eRGX2NFSpCbbw7J7F//GpLZZ59VMisiMfcx0NjMGhES2F5An+wb\nmdlRQHXgg6h11YEt7r7dzGoB7YF7iiRqkQT69lu48UZ49dUTcYdGjeCaa0Lyevzx6kUlkhN9hRUp\nYQYPDifEG28Myez48XDAAYmOSkSKC3fPNLOrgemE6XfGuftXZjYCmOPuUyKb9gImukfPYkkT4FEz\n2wmUIoyRza1IlEjKW7cObrsNxowJ1YT79l3GX/7SkJYt95wGR0T2pkRWpAS64YZwZ3bQoFDNeMIE\nJbMiEjvuPg2Ylm3d0GzLw3PY732gRVyDE0kCmZnw6KOhhsX69XDZZXD77fDVV0s5+uiGiQ5PJCUo\nkRUpof7yl5DMXn99uDM7caKSWRERkXh780247jr46ivo1AkefBBatkx0VCKpp1SiAxCRxLnuunAC\nfeEF+OMf4ddfEx2RiIhI6hg/Hho2hM6dO9CwYVjOzcKF0KNHmDJn69Ywlc7bbyuJFdlfSmRFSrhr\nr4VRo8IJ9YIL9kxmC3KCFhERKUnGj4cBA2DZMnA3li0Ly9nPlevXh15QzZpBRgbcfTd8/TX07Klx\nsCKFoURWRBg4EP7xD3j5ZTj/fNi+Pf8naBERkZJoyBDYsmXPdVu2hPUAO3aEcbCNG8MDD8BFF8GC\nBaHYYtmyRR+vSHGjMbIiAsBVV4Urw1ddBeedB198kfsJOj09MTGKiIgki+XLc18/Y0YYvjNvHpxy\nShjG07p10cYnUtwpkRWRXa68MhSAuuKK3LfJ7cQtIiJSkqSlhV5L2ZUrB6eeGobmPPdcuDisLsQi\nsaeuxSKyh8svD12hcpOWVnSxiIiIJKuRI6FChb3X79wZXvvmmzBcR0msSHwokRWRvQwYAP37772+\nQoVwchYRESnp0tPh4YehRg0AB+Dkk2HxYrj55nBnVkTiR4msiOToscfCBO2Bk5YGY8dqfKyIiCTO\njh1w003wr3814qefEhvHM8/AiBGwdi00b/4LH38Ms2dD3bqJi0ukJFEiKyK5GjsWxo0DMI4+OkzP\nIyIikgjucPXVcM898OyzDWjQIBQoXLKk6GLYuRMmTYLmzUMV4ipV4NVXYdSoT2nbtujiEBElsiKy\nD3/6E1x77QJeeSXcjc3MTHREIiJSEt16K/zzn2H6mqee+pD09NB7qHFj6Ns3VNuPF/cwRV3r1vDH\nP4bCiM8/D3PnQvfuGgcrkghxTWTNrJuZzTezRWY2OJdtLjCzr83sKzObELW+n5ktjDz6xTNOEclb\nz56ruO++UH3xkkvCFWkREZGi8sADoUZD//5w112QlraVxx8P41GvvRZeeglatoSzz4b33ovdcd3h\n9dehXTvo2TNMQzd+fJhW57zzQkIrIokRt18/MysNjAHOAJoCvc2sabZtGgN/Bdq7ezPgz5H1NYBh\nwHFAO2CYmVWPV6wism/XXw+33x7GBF1xRTi5i4iIxNvTT4dz0HnnhTuy0Xc/69eH++4LU8Pddht8\n8AGcdFIoujRtWuHOVRkZoZ0zzoDVq8NQm2++gT59oHTpQr8tESmkeF5HagcscvfF7v4rMBE4J9s2\nlwFj3H0dgLtnDds/HXjT3ddGXnsT6BbHWEUkH265JVRiHDsW/vxnJbMiIhJfU6aEnkCnnhruhOaW\nQNaoAUOHhnldH3oo/Nu9Oxx9NEyYULBhMR98AF26QKdOYfztww/DggVhqE2ZMrF5XyJSePFMZOsB\n30ctr4isi3YEcISZvWdm/zWzbgXYV0QS4I47QhI7ahT89a9KZkVEJD4yMkKRwTZt4MUXoWzZfe9T\nsSJccw189x08+WRIYNPT4Ygj4JFHYOvW3PfNGu964olhvO0DD8CiRaEX0oEHxupdiUisJPq6Uhmg\nMdARqA/MNrMW+d3ZzAYAAwBq165NRkZGoQPatGmT2lE7amcf7fToAYsXN+buu+vx449L6NdvWULj\nUTslox0RKTk++SScaw47DF57DSpXLtj+BxwA/frBhRfCK6/A3/4GV14Zuh//+c8hOa1aNWz7xRcw\nbFhIlqtXD9tefTVUqhT79yUisRPPRHYlcGjUcv3IumgrgA/d/TdgiZktICS2KwnJbfS+GdkP4O5j\ngbEAbdu29Y4dO2bfpMAyMjJQO2pH7ey7nQ4dQnevJ59sRNOmjbjhhsTGo3aKfzsiUjIsWADduoWk\n8o03oGbN/W+rVCk455yQFM+aFQpF/fWvIVm9/HKYM6cJM2eGRHn48JDkZiW4IpLc4pnIfgw0NrNG\nhMS0F9An2zYvAb2BJ8ysFqGr8WLgO+DOqAJPXQlFoUQkSZQqBf/6F2zbFqZCKF8+XMEWERHZX99/\nD6edFp6/+WYo5hQLZtCxY3h8+mlIaO+9Fw48sBaDB8OgQWGcrYikjrglsu6eaWZXA9OB0sA4d//K\nzEYAc9x9SuS1rmb2NbADuMHd1wCY2e2EZBhghLuvjVesIrJ/SpcOVYy3bYOBA6FcuTA1goiISEH9\n/DN07Qrr1oXxsUccEZ/jtG4N//kPrFwJc+d+QI8eJ8XnQCISV3EdI+vu04Bp2dYNjXruwPWRR/Z9\nxwHj4hmfiBTeAQeELwQ9e8KAAeHObHp6oqMSEZFUsnFjmOZm6dIwb2ubNvE/Zr16sHBhAcoZi0hS\n0TTOIlJoZcvCCy+ELlv9+sHkyYmOSEREUsX27eFi6KefwqRJoQZDvI0fDw0bQufOHWjYMCyLSGpR\nIisiMVG+fJjv77jjoFcvmDo10RGJiEiyy8yEPn1gxgwYNw7OPjv+xxw/PvQgWrYM3I1ly8KyklmR\n1KJEVkRiplIlmDYNWrWC886Dt95KdEQiIpKs3EPl4BdeCHO2XnRR0Rx3yBDYsmXPdVu2hPUikjqU\nyIpITFWtCtOnw5FHhukOZs9OdEQiIpKMbropVL+/5ZYw7U1RWb68YOtFJDkpkRWRmKtRI0yb0KAB\ndO8eJqDXWCSR1GJmA6OmwROJqXvugb//Ha68EkaMKNpjp6UVbL2IJCclsiISFwcfDG+/DRUqhEnm\nNRZJJOXUBj42s0lm1s3MLNEBSfHw+OPhbmyvXjB6dJjjtSiNHBnOTdEqVAjrRSR1KJEVkbipWzdM\nz5OdxiKJJD93vwVoDPwLuBhYaGZ3mtnhCQ1MUtqsWbX4v/+Dbt3gqaegVAK+iaanw9ixodeQmdOg\nQVjW1HEiqUWJrIjE1apVOa/XWCSR5BeZ7/1/kUcmUB143szuSWhgkpLeegtGjmzK8cfD88/DgQcm\nLpb09DBn7YwZs1i6VEmsSCpSIisicaWxSCKpycyuNbO5wD3Ae0ALd78COAY4bx/7djOz+Wa2yMwG\n5/D6A2b2WeSxwMzWR73Wz8wWRh79Yvy2JEEyM0OyWL/+Fl59FSpWTHREIpLqlMiKSFzlNBbJDAYO\nTEw8IpJvNYDfu/vp7v6cu/8G4O47gbNy28nMSgNjgDOApkBvM2savY27X+furdy9FTAaeCGybw1g\nGHAc0A4YpoJTxcP778NPP0G/fsuorp+oiMSAElkRiavsY5EOOSTMN/vgg7B4caKjE5E8vAaszVow\nsypmdhyAu3+Tx37tgEXuvtjdfwUmAufksX1v4N+R56cDb7r7WndfB7wJdCvEe5Ak8coroWZC27Zr\n972xiEg+KJEVkbiLHou0ahW8804o+NSpU1gvIknpEWBT1PKmyLp9qQd8H7W8IrJuL2bWAGgEzCjo\nvpJapkwJf/MrVtyR6FBEpJgok+gARKTkOfroUPSjc+fwxSYjI9yxFZGkYpFiT0DoUmxmsf7e0At4\n3t0LnN2Y2QBgAEDt2rXJyMgoVCCbNm0qdBtqJ2fLl5dnwYLj6NZtQVLEo3bUjtpJrXZyo0RWRBKi\ndeuQzJ56akhoMzLg0EMTHZWIRFlsZtew+y7slUB+BgSsBKJ/m+tH1uWkF3BVtn07Zts3I6cd3X0s\nMBagbdu23rFjx5w2y7eMjAwK24baydm994Z/r7/+CJYsWZXweNSO2lE7qdVObtS1WEQS5phj4I03\n4OefQzK7MrevuiKSCJcDJxKSyxWEAkwD8rHfx0BjM2tkZgcSktUp2Tcys6MI0/l8ELV6OtDVzKpH\nijx1jayTFDZlSuiJo543IhJLSmRFJKHatYPp0+HHH0My+8MPiY5IRADc/Sd37+XuB7t7bXfv4+4/\n5WO/TOBqQgL6DTDJ3b8ysxFm1iNq017AxGzdl9cCtxOS4Y+BEZF1kqJ+/hneew969Nj3tiIiBZGv\nrsVmdjiwwt23m1lHoCXwtLuvz3tPEZF9O/54eO01OP303d2Ma9dOdFQiJZuZlQMuBZoB5bLWu/sl\n+9rX3acB07KtG5pteXgu+44DxhU8YklG06bBzp1KZEUk9vJ7R3YysMPMfkcYj3IoMCFuUYlIidO+\nffjCs3x5SGZ/2ud9HxGJs2eAOoQpcWYRxqtuTGhEknJeeQUOOQTatIlNe+PHQ8OG0LlzBxo2DMsi\nUjLlN5HdGekqdC4w2t1vAA6JX1giUhKdcgpMnQpLlkCXLqFLmogkzO/c/VZgs7s/BXQnjJMVyZft\n2+H11+Hss6FUDAazjR8PAwbAsmXgbixbFpaVzIqUTPn9s/KbmfUG+gGvRtYdEJ+QRKQk69gxXMFf\nuDAks2vWJDoikRLrt8i/682sOVAVODiB8UiKyciATZti1614yJAwB3m0LVvCehEpefKbyP4JOAEY\n6e5LzKwRocuRiEjMnXoqvPwyfPstnHYarFuX6IhESqSxkcrBtxCqDn8N3J3YkCSVvPIKVKgQhovE\nwvLlBVsvIsVbvhJZd//a3a9x939HTmqV3V0nMxGJm65d4cUX4auvwvP1Ki0nUmTMrBTwi7uvc/fZ\n7n5YpHrxo4mOTVKDe5h257TToHz52LSZllaw9SJSvOUrkTWzDDOrYmY1gE+Ax8zs/viGJiIl3Rln\nwOTJ8PnnoaLxhg2JjkikZHD3ncCNiY5DUtfnn8P338e2WvHIkeEOb7QKFcJ6ESl58tu1uKq7/wL8\nnjDtznFAl/iFJSISnHUWPPccfPJJSGw3qmaqSFF5y8wGmdmhZlYj65HooCQ1TJkCZtC9e+zaTE+H\nsWOhQQMwcxo0CMvp6bE7hoikjvwmsmXM7BDgAnYXexIRKRLnnAP/+Q98+CEcdBB06qRpF0SKwB+B\nq4DZwNzIY05CI5KU8corcNxxsZ8TPD0dli6FGTNmsXSpkliRkqxMPrcbAUwH3nP3j83sMGBh/MIS\nEdnT1q1QpkyYzgF2T7sA+iIjEg/u3ijRMUhqWrkS5syBO+9MdCQiUpzlK5F19+eA56KWFwPnxSso\nEZHshgyBX3/dc13WtAtKZEViz8wuymm9uz9d1LFIank10ncvluNjRUSyy1cia2b1gdFA+8iqd4Br\n3X1FvAITEYmW2/QKy5YVbRwiJcixUc/LAacSCj4qkZU8TZkChx0GTZsmOhIRKc7y27X4CWAC8IfI\nct/IutPiEZSISHZpaTknrZUqhWkezIo+JpHizN0HRi+bWTVgYoLCkRSxeTO8/TZcfrn+LotIfOW3\n2NNB7v6Eu2dGHk8CB8UxLhGRPeQ07UKZMrBpE9xwQ0hmRSSuNgMaNyt5evPNUMtA3YpFJN7ye0d2\njZn1Bf4dWe4NrIlP+BLdVgAAIABJREFUSCIie8saBztkCCxf7qSlGXfcAR99BPfdB1Wrwq23JjZG\nkeLEzF4Bsi4RlQKaApMSF5GkgilTwt/jk09OdCQiUtzlN5G9hDBG9gHCSe194OI4xSQikqP09PDI\nyJhFx44dAejTB375BYYOhSpV4NprExujSDFyb9TzTGCZamNIXnbsCIWezjwTDjgg0dGISHGXr67F\n7r7M3Xu4+0HufrC790RVi0UkCZQqBY8/Dr//Pfz5z/DEE4mOSKTYWA586O6z3P09Qu+shokNSZLZ\nRx/B6tVw9tl7vzZ+PDRsCJ07ax5wEYmN/I6Rzcn1MYtCRKQQypSBCROga1fo3x8mT050RCLFwnPA\nzqjlHURNxSeS3f+3d+fhUZXn/8ffNxFEFgFBUdn1iwvIIiJgUQmIAq2Au2L6VVREbcW9FX+4FUu/\nLhWp1apIsS4RtEUtVBQRCWrdABUUUKEaNhEVEElBZLl/fzyHOIQEkllyEvi8rmuuzHnmnDv3wGTO\n3HOeZeLE8H7cu/f27bm5Yd3vxYvB/ad1wFXMikgqUilkNRediFQYe+8Nzz0HXbrAgAEwZUrcGYlU\nenu5e+HqzdH9ajHmIxXcxIlw4olQr9727cOGhXW/E21bB1xEJFmpFLKaI1REKpSaNeHFF6F1azj9\ndHjzzbgzEqnUvjGzwrlnzaw/8G2M+UgF9p//wPz5xc9WXNI64CW1i4iUxk4LWTNbZ2bfF3NbBxxc\nTjmKiJRa3brhamzTpvCLX8D778edkUildTnw/8xsiZktAW4ELos5J6mgJk0KP4sbH9u0afHHlNQu\nIlIaOy1k3b22u+9bzK22u5d2xmMRkXJ1wAFhLcO6daFXL1iwIO6MRCofd/+Pu3chLLvTyt1/5u6L\n4s5LKqaJE0NvmEMO2fGx4tYBr1EjtIuIJCuVrsUiIhVWkybw6quQlQUnnwz5+XFnJFK5mNkfzKyu\nuxe4e4GZ1TOz38edl1Q8a9bA668X360YwrJpo0dDs2Zg5jRrFra3rQ8uIpIMFbIisttq2TJcmV2/\nHnr2hBUr4s5IpFLp4+7fbdtw9zXAz2PMRyqol18Oa8gW1614m5yc8IXia6/NID9fRayIpE6FrIjs\n1tq0gZdegq++CsvzrF4dd0YilUaWme29bcPM9gH23sn+soeaODEM6ejUKe5MRGRPokJWRHZ7nTuH\niUgWLoQ+fWDdurgzEqkUcoFpZnaJmQ0CpgKPx5yTVDCbNoUvC089NQzlEBEpLxktZM2st5l9amaL\nzGxoMY8PNLNvzOzD6DYo4bEtCe0TM5mniOz+uneHv/8dZs8O47g2bIg7I5GKzd3vAn4PHAkcDkwB\nmsWalFQ4b7wBa9eWPD5WRCRTMlbImlkW8CDQhzDj4QAza1XMrs+4e/voNiahfUNCu94eRSRlffvC\nE0/AjBlwzjmwebPFnZJIRbeSsG782UAPQHOAy3YmToS99w7zEIiIlKdMLqHTCVjk7p8DmNl4oD8w\nP4O/U0Rkp84/P3Qtvvxy+O9/jyQ7G/bSYmIihczsMGBAdPsWeAYwd+8ea2JS4biHQrZnT6hZM+5s\nRGRPk8muxY2ApQnby6K2os40s7lm9g8za5LQXt3MZpnZO2Z2WgbzFJE9zGWXwT33wPTpBzBwYJht\nU0QKfUK4+nqqux/v7n8GyvRXsquhRdE+55jZfDObZ2ZPJ7RraFElMW8efPGFuhWLSDzivg4xCRjn\n7hvN7DLCJBI9oseauftyMzsEeM3MPnL3/yQebGaDgcEADRs2JC8vL+WECgoKFEdxFGcPiNOxI1xw\nQUOeeOJIvv76K2688ZOkJyqpSM9rd44j5eYM4Dxgupm9DIwHSt0PP2Fo0cmEL7FnmtlEd5+fsE9L\n4Cagq7uvMbMDEkJscPf2aXgekmGTJoWfp54abx4ismfKZCG7HEi8wto4aivk7qsSNscAdyc8tjz6\n+bmZ5QFHA/8pcvxoYDRAx44dPTs7O+Wk8/LyUBzFUZzdO05uLgwbBkuWOHXrwtSpB3LQQQcydmxy\ns25WlOe1u8eR8uHuLwAvmFlNwpCga4ADzOwh4Hl3f2UXIUoztOhS4MFobVrc/es0Pw0pBxMnhi8F\nDz447kxEZE+Uya7FM4GWZtbCzKoRvt3drouQmR2UsNmPaBIJM6u3be06M2sAdEVja0UkDXJzYfBg\nWLwY3I3vvoOqVcMkUIMGwdatcWcoUjG4+3/d/Wl370v4MvoD4MZSHFqaoUWHAYeZ2b+jIUS9Ex7T\n0KJKYOVKePdddSsWkfhk7Iqsu282sysJ0/VnAWPdfZ6ZDQdmuftE4Coz6wdsBlYDA6PDjwQeMbOt\nhGL7zsQuSSIiyRo2DNav375t0yaoUwf+9jeoUgUefTT8FJEgunJa2AsqDfYCWgLZhCL5dTNr4+7f\nUYqhRZD+4UUVrSt9RY8zefKBuB/BQQfNJC/vv7HnoziKozi7b5ySZHSMrLtPBiYXabs14f5NhDEy\nRY97C2iTydxEZM+0ZEnx7d9/D7feCsOHhyL2kUdUzIokaZdDiwhXad91903AF2b2GaGwnVmaoUXR\n42kdXlTRutJX9DijRkGTJnDJJcdiZVjJrKI/L8VRHMWpeHFKoo9pIrJHadq05Pbbb4ebb4YxY+CK\nK9TNWCRJuxxaBLxAuBq7bQjRYcDnGlpUOWzYAK+8EroVl6WIFRFJp7hnLRYRKVcjRoQxsondi2vU\nCO1m4Yrs1q3whz+EK7J/+Ys+qImURSmHFk0BTjGz+YSlfX7j7qvM7GdoaFGFN21aKGY1PlZE4qRC\nVkT2KDk54ee2WYubNjVGjPip3Qx+//uwtuxdd4Vi9oEHVMyKlEUphhY5cF10S9xHQ4sqgUmToHZt\n6NYt7kxEZE+mQlZE9jg5OeGWlzej2LEbZvB//xeuzN5zTyhm779fxayIyNatoZDt1Qv23jvubERk\nT6ZCVkSkGGbhiuzWrXDvvWF92fvuUzErInu22bNhxQp1KxaR+KmQFREpgVm4Irt1609F7MiRKmZF\nZM81cWLopfLzn8ediYjs6VTIiojshFm4Irt1a1huokoV+OMfVcyKyJ5p0iQ4/nioXz/uTERkT6dC\nVkRkF8zCFdktW8IV2SpV4O67VcyKyJ5l8WKYMyf0VBERiZsKWRGRUjALEz5t3RquyGZlhQmhVMyK\nyO4sN3fbLO/dqFs3tGl8rIhUBCpkRURKySwsxeP+09I8I0bEnZWISGbk5iauu22sWRPeB2fOhMMO\nizs7EdnTqZAVESmDbcXsli3himyVKnDSSXFnJSKSfsOGbStif+Ie2retvS0iEhcVsiIiZVSlCjz0\nUOhmPGIELFnSnOxsdTMWkd3LkiVlaxcRKU8qZEVEklClCjzySLg68de/NufggzVmVkR2L02bhgme\nimsXEYlblbgTEBGprKpUgdGjoV+/5dx1F1x7bShsRUR2ByNGQI0a27fVqKG5AUSkYtAVWRGRFFSp\nAtdcs5BDDmnEqFHwww/wl7+EdhGRymzbONjrr4eVK6FBg7CetsbHikhFoEJWRCRJictSNGkCffuG\n7sYbN8KYMWGJHhGRyiwnBz74AO6/fyuff16F2rXjzkhEJFAhKyKShKLLUixZAt98A2ecAX/7Wyhm\nn3gC9tK7rIhUYu7wz39C+/bfUbv2fnGnIyJSSJ3fRESSUNyyFBs2wOzZcOedMG4cnHsu/PhjPPmJ\niKTD1KmwaBGcdNLKuFMREdmOClkRkSTsbFmKG28M48ieey5cof3hh/LNTUQkXUaOhIMOgh49vo47\nFRGR7aiQFRFJQknLT2xrv/rqsNbsiy9C//47Xr0VEanoPv4YpkyBK6+EqlU1JbuIVCwqZEVEklCa\nZSkuvxzGjg1d837xCygoKN8cRURSMWoU7LMPXHZZ3JmIiOxIhayISBJycsIass2agZnTrFnYLros\nxUUXwVNPwRtvQK9esHZtPPmKiJTFypXhveuii6B+/bizERHZkQpZEZEk5eRAfj689toM8vNLXlvx\n/PPhmWfgvfegZ09Yvbo8sxQRKbuHHgqT1V19ddyZiIgUT4WsiEg5OPPMMPnT3LnQo0dYqkdEpCLa\nsAEefDCsjX3YYXFnIyJSPBWyIiLlpG9fmDQJPv0UsrPhq6/izkhEZEdPPQXffgvXXRd3JiIiJVMh\nKyJSjk45BV56CRYvhm7dYNmyuDMSEfnJ1q1w333QoQOceGLc2YiIlEyFrIhIOcvODktarFgRPijm\n58edkYhIMGUKLFgQrsaaxZ2NiEjJVMiKiMSga1eYNg3WrAnF7KJFcWckIgIjR0KjRnD22XFnIiKy\ncypkRURicuyxMH16mFjlxBNh8eIauz5IRKQUcnOheXPo0aMbzZuH7V2ZOxdefRWGDIFq1TKdoYhI\nalTIiojEqH17yMsL49KuuaY9778fd0YiUtnl5sLgwWEsvruxeHHY3lUxe999ULNm2FdEpKJTISsi\nErPWreH112HvvbeSnR0KWxGRZA0bBuvXb9+2fn1oL8mKFaHQvfhiqFcvs/mJiKSDClkRkQrgsMPg\nz3/+gCZNoHdveOGFuDMSkcpqyZKytUNYN3bzZrj66szkJCKSbipkRUQqiP3338jrr8PRR8OZZ8LY\nsXFnJJIcM+ttZp+a2SIzG1rCPueY2Xwzm2dmTye0X2hmC6PbheWX9e6jadOyta9fDw89BKedBoce\nmrm8RETSSYWsiEgFUr9+mGzl5JPhkkvg7rvjzkikbMwsC3gQ6AO0AgaYWasi+7QEbgK6untr4Jqo\nfT/gNqAz0Am4zczU0bWMRoyAGkXmjqtRI7QX54knYPXqsOSOiEhloUJWRKSCqVkTJk6Ec8+FG2+E\n3/4W3OPOSqTUOgGL3P1zd/8RGA/0L7LPpcCD7r4GwN2/jtp7AVPdfXX02FSgdznlvdvIyYHRo6FZ\nMzBzmjUL2zk5O+67dWuY5OnYY8OyYCIilcVecScgIiI7qlYtTLxSvz7ccw+sWgWPPAJ76V1bKr5G\nwNKE7WWEK6yJDgMws38DWcDt7v5yCcc2Ku6XmNlgYDBAw4YNyUtxlrSCgoKUY1SkOI0awd/+FuLU\nqlULKH4iubffrs9nn7XhllvmM2PG1zvukKZ8FEdxFEdx0k0fiUREYpabG2YTXbKkG02bhu5/OTmQ\nlQUPPAANGsDw4aHr37hxUL163BmLpGwvoCWQDTQGXjezNmUJ4O6jgdEAHTt29Ozs7JQSysvLI9UY\nlTHO8OHQpAnccksrqlZtVeJ+le15KY7iKM7uE6ck6losIhKjXa33aAa/+x3cf3+YybhPH/j++3hz\nFtmF5UCThO3GUVuiZcBEd9/k7l8AnxEK29IcK2nywQcwfXqYqbhq1bizEREpGxWyIiIxKu16j0OG\nhOL2zTehe3f4uuQegCJxmwm0NLMWZlYNOA+YWGSfFwhXYzGzBoSuxp8DU4BTzKxeNMnTKVGbZMB9\n90GtWjBoUNyZiIiUnQpZEZEYlWW9x/PPD5NALVgAxx8P+fkZTU0kKe6+GbiSUIAuAJ5193lmNtzM\n+kW7TQFWmdl8YDrwG3df5e6rgTsIxfBMYHjUtsfIzYXmzaFHj240b/5T74x0W748DFUYNAjq1MnM\n7xARySSNkRURiVHTpqFbcXHtxenTJyzP84tfhBlGX3kFWrfObI4iZeXuk4HJRdpuTbjvwHXRreix\nY4E9chXlbUMNQi+Nn4YaQPEzDqfigQfCjMVXXZXeuCIi5UVXZEVEYlTW9R4BfvYzeP31sCTPCSfA\n229nNkcRKR+lHWqQqoICePhhOOMMaNEivbFFRMpLRgtZM+ttZp+a2SIzG1rM4wPN7Bsz+zC6DUp4\n7EIzWxjdLsxkniIicSnLeo+J2rSBf/8b9tsPevaEKRpFKFLplWWoQSoefxy++w6u2+F6uIhI5ZGx\nQtbMsoAHgT5AK2CAmRU3r/sz7t4+uo2Jjt0PuI2w7lwn4LZo0gcRkd1OTk4Y7/raazPIzy99F8IW\nLUIx27Il9O0L48dnMksRybSShhSU1J6MLVtg1Cjo0gWOOy59cUVEylsmr8h2Aha5++fu/iMwHuhf\nymN7AVPdfbW7rwGmAr0zlKeISKXVsCHMmBE+kJ5/PvzlL3FnJCLJSmaoQVn961+waJGuxopI5ZfJ\nQrYRsDRhe1nUVtSZZjbXzP5hZtvWjivtsSIie7w6deDll8NV2V//Gh5/vBnucWclImWV7FCDshg5\nMsQ//fT0xRQRiUPcsxZPAsa5+0Yzuwx4HOhR2oPNbDAwGKBhw4bk5eWlnFBBQYHiKI7iKE6ljHPV\nVcaPPx7G3/7Wgm+++ZJrr11IVlbyFW1FeV4ie5KcnHDLy5tBdnZ2WmPPmhUmirvvPtgr7k+AIiIp\nyuTb2HKgScJ246itkLuvStgcA9ydcGx2kWPziv4Cdx8NjAbo2LGjp+MNPy8vLy0nDsVRHMVRnDji\n9OgBF1ywmKeeaob7wTzzDNSqFV8+6YwjIqkZORL23RcuvjjuTEREUpfJrsUzgZZm1sLMqgHnARMT\ndzCzgxI2+xEWToewUPopZlYvmuTplKhNRER2wgwuueQLHn44dDfu3h1Wrow7KxGJ29Kl8OyzcOml\noZgVEansMlbIuvtm4EpCAboAeNbd55nZcDPrF+12lZnNM7M5wFXAwOjY1cAdhGJ4JjA8ahMRkVK4\n7DL45z9h/vwwEdRnn8WdkYjE6c9/Dj+HDIk3DxGRdMnoCAl3nwxMLtJ2a8L9m4CbSjh2LDA2k/mJ\niOzOTj0Vpk8PP3/2M5g4MfwUkT3LunVh0qizzgoTPYmI7A4y2bVYRERi1qkTvP021KsHJ50Ezz8f\nd0YiUt4eewzWrtWSOyKye1EhKyKymzv0UHjrLWjfHs48Ex54IO6MRKS8bNkCo0ZB167hiy0Rkd2F\nClkRkd1Ebi40bw49enSjefOwvc3++8O0adCvXxgj99vfwtatcWUqIuXl3/9uwBdf6GqsiOx+VMiK\niOwGcnNh8GBYvBjcjcWLw3ZiMVujBkyYAL/6FdxzT1ircuPG+HIWkcz7+9+bcMgh0L9/3JmIiKSX\nClkRkd3AsGGwfv32bevXh/ZEWVmha/Fdd8H48dCrF3z3XfnlKSLl59134eOP63DNNeFvX0Rkd6JC\nVkRkN7BkSenbzULX4tzcMHb2+ONLPl5EKq9774WaNTdz0UVxZyIikn4qZEVEdgNNm5atHeD882HK\nFFi6NKw1O2dOZnITkfL1449w5ZXw97/Daactp1atuDMSEUk/FbIiIruBESPCGNhENWqE9p3p3h3e\nfDNcpT3hBHj11czlKCKZ99VXYamtBx+EG26Aiy7KjzslEZGMUCErIrIbyMmB0aOhWTMwc5o1C9s5\nObs+tk0beOedcGyfPvDkk5nPV0TS7+234Zhj4P33Ydy4MKlbVpbHnZaISEaokBUR2U3k5EB+Prz2\n2gzy80tXxG7TuHG4MnvCCXDBBfCHP4Dr869IpTF6NHTrBtWrh4L2vPPizkhEJLNUyIqICAB16sDL\nL4cCeNgwGDnyMDZtijsrEdmZH36ASy+Fyy6Dnj1h1ixo2zburEREMk+FrIiIFKpWDZ54Am66Cf71\nr4Pp1QtWrYo7KxEpzrJl4SrsmDHhy6dJk6BevbizEhEpH3vFnYCIiFQsVaps61q8gJEjj6RzZ5g4\nEVq1ijszEdlmxgw45xzYsAGefx5OOy3ujEREypeuyIqISLF69VpJXh4UFECXLvDii3FnJCLu8Kc/\nhZmJ69WD995TESsieyYVsiIiUqLjjoOZM6FlS+jbF+6+W5NAicRl/Xr43/+Fa64Jf4/vvQdHHBF3\nViIi8VAhKyIiO9WkCbzxBpx9Ntx4I1x4YZhgRkTKzxdfQNeu8PTTcMcdMGEC7Ltv3FmJiMRHY2RF\nRGSXatSA8ePhqKPg1lvhs8/CuLyDDoo7M5Hd39SpYTmdrVtDF/8+feLOSEQkfroiKyIipWIGt9wC\n//gHfPQRHHsszJ4dd1Yiuy93uOsu6N0bGjUKS+uoiBURCVTIiohImZx5Jvz735CVBSecAM88E3dG\nUtGYWW8z+9TMFpnZ0GIeH2hm35jZh9FtUMJjWxLaJ5Zv5hXHhg1ZnHMODB0auvW//TYcemjcWYmI\nVBzqWiwiImXWvn2YBOqMM0KXx3nz4Pbbw9I9smczsyzgQeBkYBkw08wmuvv8Irs+4+5XFhNig7u3\nz3SeFdnChXDFFR1YuhT++Ee47rrQI0JERH6ijxwiIpKUAw6AadPg4ovD5DNnnRWW6pE9Xidgkbt/\n7u4/AuOB/jHnlHG5udC8OfTo0Y3mzcN2Mp57Djp2hDVrqvHKK3D99SpiRUSKoyuyIiKStL33hjFj\noE2b8IG7a1eYOBGaNYs7M4lRI2BpwvYyoHMx+51pZicCnwHXuvu2Y6qb2SxgM3Cnu79Q3C8xs8HA\nYICGDRuSl5eXUtIFBQVJx3j11QP44x8PZ+PGLMBYvBguuWQLCxZ8Ss+eX5cqxqZNxiOPHMqECY05\n4ojv+c1vZpKVlUWKTyul56U4iqM4ilMR4pREhayIiKTELKxreeSRcO65YRKo556D44+POzOpwCYB\n49x9o5ldBjwO9Igea+buy83sEOA1M/vI3f9TNIC7jwZGA3Ts2NGzs7NTSigvL49kYwwcCBs3bt+2\ncWMWTz3Vit//vtUuj8/PD387770X/pbuumtf3norK+l8EqXyvBRHcXaXOJs2bWLZsmX8EK0dV6dO\nHapXr55yPoqTvjjVq1encePGVK1atdTxVciKiEha9OoF774LfftCjx7w0ENwySVxZyUxWA40Sdhu\nHLUVcvdVCZtjgLsTHlse/fzczPKAo4EdCtmKZMmSsrUnmjQprM28ZUtYG/aMM9Kbm4jAsmXLqF27\nNs2bN8fMWLduHbVr1045ruKkJ467s2rVKpYtW0aLFi1KHV9jZEVEZDupjPU7/PBQzGZnw6BB4erS\nli0a4LeHmQm0NLMWZlYNOA/YbvZhM0tcgbgfsCBqr2dme0f3GwBdgaKTRFU4TZuWrR1g0yb47W+h\nXz9o0QLef19FrEim/PDDD9SvXx/TgPMKycyoX79+4RXz0lIhKyIihXJzYfBgWLwY3MNYv8GDy1bM\n1qsHkyfD1VfDn/4EQ4e24bvvMpezVCzuvhm4EphCKFCfdfd5ZjbczPpFu11lZvPMbA5wFTAwaj8S\nmBW1TyeMka3wheyIEVCjxvZtNWqE9uIsWxa+7LnnHvjVr8JyVlpaRySzVMRWbMn8/6iQFRGRQsOG\nwfr127etXx/ay2KvvWDUKHj0UVi7tipZWenLUSo+d5/s7oe5+6HuPiJqu9XdJ0b3b3L31u7ezt27\nu/snUftb7t4mam/j7n+N83mUVk4OjB4dJjkzc5o1C9s5OTvu+/LLYfmquXNh3Dh48EFIw1A0EanA\nVq1aRfv27Wnfvj0HHnggjRo1Ktz+8ccfSxXjoosu4tNPP93pPqNHjyY32SnTKyGNkRURkUKpjPUr\nzqBB0KLFbGrXzk46J5HKICcn3PLyZhQ7Gc3mzWGt5REjwizff/976IovIhVPbm74AnfJkjBEYMSI\n4r+YKq369evz4YcfAnD77bdTq1YtbrjhhsLHN27ciLvj7lQpYUH2xx57bJe/Z/DgwWkZ21pZ6Iqs\niIgUSmas367oaqzs6VasgJ49w4fhQYPCOHIVsSIV0/ZDbEhqiE1pLVq0iFatWnHJJZfQunVrVqxY\nweDBg+nYsSOtW7dm+PDhhfsef/zxfPjhh2zevJm6desydOhQ2rVrx3HHHcfXX4dlvoYPH86oUaMK\n9x86dCidOnXi8MMP56233gLgv//9L2eeeSatWrXirLPOomPHjoVFdqLbbruNY489lqOOOorLL78c\ndwfgs88+o0ePHrRr144OHTqQn58PwB/+8AfatGlDu3btGFbWblxJUiErIiKFyjrWT0R2btq00JV4\n5kx4/PHQ3X6ffeLOSkRKkq4hNqX1ySef8Otf/5r58+fTqFEj7rzzTmbNmsWcOXOYOnUq8+fvOE3A\n2rVr6datG3PmzOG4445j7NixxcZ2d9577z3uueeewqL4z3/+MwceeCDz58/nlltu4YMPPij22Kuv\nvpqZM2fy0UcfsXbtWl5++WUABgwYwLXXXsucOXN46623OOCAA5g0aRIvvfQS7733HnPmzOH6669P\n07/OzqmQFRGRQmUZ6yciJduyBX73Ozj5ZKhfPxSyF1wQd1YisivpHmKzK4ceeigdOnQo3B43bhwd\nOnSgQ4cOLFiwoNhCdp999qFPnz4AHHPMMYVXRYs6I5oKPXGfN998k/POOw+Adu3a0bp162KPnTZt\nGp06daJdu3bMmDGDefPmsWbNGr799lv69u0LhLVfa9SowauvvsrFF1/MPtG3dPvtt1/Z/yGSoDGy\nIiKynV2N9RORnfv66/A39Oqr8L//G9ZUrlkz7qxEpDSaNg3diYtrz4SaCW8OCxcu5E9/+hPvvfce\ndevW5Ze//GWxS9JUq1at8H5WVhabN28uNvbee++9y32Ks379eq688kref/99GjVqxM0331zmpXHK\ng67IioiIiKTJnDl1aN8e3nwTxowJ3YlVxIpUHnEOsfn++++pXbs2++67LytWrGDKlClp/x1du3bl\n2WefBeCjjz4q9orvhg0bqFKlCg0aNGDdunVMmDABgHr16rH//vszadIkIKzPu379ek4++WTGjh3L\nhg0bAFi9enXa8y6OrsiKiIiIpGjLFrj7brj55vYcemhYZqdt27izEpGy2jaUJp2zFpdWhw4daNWq\nFUcccQTNmjWja9euaf8dQ4YM4YILLqBVq1aFtzp16my3T/369bnwwgtp1aoVBx10EJ07dy58LDc3\nl8suu4xhw4ZRrVo1JkyYwKmnnsqcOXPo2LEjVatWpW/fvtxxxx1pz70oFbIiIiIiKZgzBy69NIyD\n7d79G1544QBjfhDsAAAV4ElEQVT23TfurEQkWduG2GTC7bffXnj/f/7nf/jwww9Zt24dAGbGk08+\nWexxb775ZuH97777rvD+eeedVzjm9dZbby1cfidx/wMPPJBFixYBYVzr008/TfXq1Vm4cCGnnHIK\nTZo02eH33Xnnndx55507tB9++OHk5eXt0D5s2LBym614GxWyIiIiIkn44Qe4445wJXa//WD8eDjg\ngPnsu+8BcacmIlKsgoICTjrpJDZv3oy788gjj7DXXpWzJKycWYuIiIjEaMaMsLbkZ5/BwIFw772h\nmC3mQoWISIVRt25dZs+eHXcaaaHJnkRERERK6bvv4LLLIDsbNm2CqVPhscdCESsiIuVHhayIiIhI\nKTz/PLRqFWYjvuEG+Phj6Nkz7qxERPZM6losIiIishNffglDhsBzz0G7djBpEhxzTNxZiYjs2XRF\nVkRERKQY7vDoo+Eq7OTJcOedYWZiFbEiIvFTISsiIiJSxMKF0KNHmNDp6KNh7ly48UaoWjXuzESk\nsunevTtTpkzZrm3UqFFcccUVOz2uVq1aAHz55ZecddZZxe6TnZ3NrFmzdhpn1KhRrF+/vnD75z//\n+XZL+FRWGS1kzay3mX1qZovMbOhO9jvTzNzMOkbbzc1sg5l9GN0ezmSeIiIiIhAmcLrzTmjTBj74\nIIyHfe01aNky7sxEpLIaMGAA48eP365t/PjxDBgwoFTHH3zwwfzjH/9I+vcXLWQnT55M3bp1k45X\nUWSskDWzLOBBoA/QChhgZq2K2a82cDXwbpGH/uPu7aPb5ZnKU0RERARg1iw49li46SY49VRYsAAu\nuQTM4s5MRCqzs846ixdffJEff/wRgPz8fL788ktOOOEECgoK6Nu3Lx06dKBNmzb885//3OH4/Px8\njjrqKAA2bNjAeeedx5FHHsnpp5/Ohg0bCve79tpr6dixI61bt+a2224D4P777+fLL7+ke/fudO/e\nHYDmzZvz7bffAjBy5EiOOuoojjrqKEaNGgXA4sWLOfLII7n00ktp3bo1p5xyyna/Z5tJkybRuXNn\njj76aHr27MnKlSuBsFbtRRddRJcuXWjbti0TJkwA4OWXX6ZDhw60a9eOk046KeV/10xO9tQJWOTu\nnwOY2XigPzC/yH53AHcBv8lgLiIiUs5yc2HYMFiypBtNm8KIEZCTE3dWItvLzQ2F69Kl3QCoWzdM\n6nT66TEnJiIZcc01MHv2PmRlpR5ry5YQp317iGrAYu2333506tSJl156if79+zN+/HjOOecczIzq\n1auTm5tLo0aN+Pbbb+nSpQv9+vXDSvgG7aGHHqJGjRosWLCAuXPn0qFDh8LHbrnlFpo1a8aWLVs4\n6aSTmDt3LldddRUjR45k+vTpNGjQYLtYs2fP5rHHHuPdd9/F3encuTPdunWjatWqLFy4kHHjxvHo\no49yzjnnMGHCBH75y19ud/zxxx/PO++8g5kxZswY7r77bu69917uuOMO6tSpwzvvvEPt2rVZs2YN\n33zzDZdeeimvv/46LVq0YPXq1cn/w0cy2bW4EbA0YXtZ1FbIzDoATdz9xWKOb2FmH5jZDDM7IYN5\niohImuXmhrGFixeDu7F4cdjOzY07M5Gf5ObCoEGwdClA+NC4cSMk9MATEUmLxO7Fid2K3Z3f/e53\ntG3blp49e7J8+fLCK5vFef311wsLyrZt29K2bdvCx55//nk6dOjA0Ucfzbx585g/v+j1w+29+eab\nnH766dSsWZNatWpxxhln8MYbbwDQokUL2rdvD8AxxxxDfn7+DscvW7aMXr160aZNG+655x7mzZsH\nwKuvvsqvf/3rwv3q1avHO++8w4knnkiLFi2AUNynKrbld8ysCjASGFjMwyuApu6+ysyOAV4ws9bu\n/n2RGIOBwQANGzYkLy8v5bwKCgoUR3EUR3EUJ8U411/fhfXrq2/Xtn49XH/9DzRq9E7KuYmkw7Bh\n8MMP27dt2BDa1XtAZPc0ahSsW7eB2rVrpxyrLHH69+/Ptddey/vvv8/69es5Jpr+PDc3l1WrVjF7\n9myqVq1K8+bN+aHoG1MpfPHFF9x///3Mnj2bevXqMXDgwKTibLP33nsX3s/Kyiq2a/GQIUO47rrr\n6NevH3l5edx+++1J/75kZLKQXQ40SdhuHLVtUxs4CsiLLp0fCEw0s37uPgvYCODus83sP8BhwHZT\ncrn7aGA0QMeOHT07OzvlpPPy8lAcxVEcxVGc1OJ8/XVJ7dXTkptIOixZUrZ2EZFk1apVi+7du3Px\nxRdvN8nT2rVradCgAVWrVmX69OksXrx4p3FOPPFEnn76aXr06MHHH3/M3LlzAfj++++pWbMmderU\nYeXKlbz00kuF59vatWuzbt26HboWn3DCCQwcOJChQ4fi7jz//PM8+eSTpX5Oa9eupVGj0OH28ccf\nL2w/+eSTefDBB7njjjsAWLNmDV26dOFXv/oVX3zxRWHX4lSvymaya/FMoKWZtTCzasB5wMRtD7r7\nWndv4O7N3b058A7Qz91nmdn+0WRRmNkhQEvg8wzmKiIiadS0adnaReKg16mIlKcBAwYwZ86c7QrZ\nnJwcPvjgA9q0acMTTzzBEUccsdMYV1xxBQUFBRx55JHceuuthVd227VrR9u2bTniiCM4//zz6dq1\na+ExgwcPpnfv3oWTPW3ToUMHBg4cSKdOnejcuTODBg3i6KOPLvXzuf322zn77LM55phjtiuSb775\nZtasWUPnzp1p164d06dPZ//992f06NGcccYZtGvXjnPPPbfUv6ckGbsi6+6bzexKYAqQBYx193lm\nNhyY5e4Td3L4icBwM9sEbAUud/fURwSLiEi5GDEijIlNHGtYo0ZoF6ko9DoVkfJ02mmn4e7btTVo\n0IBp06YV20W5oKAACLMMf/zxxwDss88+Oyzls83DDz9cbJwhQ4YwZMiQwu3E8a7XXXcd11133Xb7\nN2vWrPD3Adxwww3F/r7+/fvTv3//Hdpr1arF448/zrp167bLp0+fPvTp06fYWMnI6BhZd58MTC7S\ndmsJ+2Yn3J8ATMhkbiIikjnbxheGWYudpk1NsxZLhaPXqYhI5ZXJrsUiIrIHy8mB/Hx47bUZ5Oer\nOJCKSa9TEZHKSYWsiIiIiIiIVCoqZEVEREREZLdWdGyqVCzJ/P+okBURERERkd1W9erVWbVqlYrZ\nCsrdWbVqFdWrV9/1zgkyOtmTiIiI7HnMrDfwJ8KqBWPc/c4ijw8E7uGn9eUfcPcx0WMXAjdH7b93\n98cREUlB48aNWbZsGd988w0AP/zwQ5mLpuIoTvriVK9encaNG5cpvgpZERERSZtoHfgHgZOBZcBM\nM5vo7vOL7PqMu19Z5Nj9gNuAjoADs6Nj15RD6iKym6patSotWrQo3M7LyyvTeqklUZzyiVMSdS0W\nERGRdOoELHL3z939R2A8sONCg8XrBUx199VR8ToV6J2hPEVEpBLTFVkRERFJp0bA0oTtZUDnYvY7\n08xOBD4DrnX3pSUc26i4X2Jmg4HBAA0bNiQvLy+lpAsKClKOoTiKoziKozjpj1MSFbIiIiJS3iYB\n49x9o5ldBjwO9ChLAHcfDYwG6Nixo2dnZ6eUUF5eHqnGUBzFURzFUZz0xynJblPIzp49+1szW5yG\nUA2AbxVHcRRHcRSnQsVploY8pHwsB5okbDfmp0mdAHD3VQmbY4C7E47NLnJs3q5+YZo+A1Sk17vi\nKI7iKI7iBCWf/91dt4QbMEtxFEdxFEdxKl4c3SrHjfAl+edAC6AaMAdoXWSfgxLunw68E93fD/gC\nqBfdvgD2K6e8K9TrXXEUR3EUR3F2ftttrsiKiIhI/Nx9s5ldCUwhLL8z1t3nmdlwwoeaicBVZtYP\n2AysBgZGx642szuAmVG44e6+utyfhIiIVHgqZEVERCSt3H0yMLlI260J928Cbirh2LHA2IwmKCIi\nlZ6W39nRaMVRHMVRHMWpkHFEMqmivd4VR3EUR3EUZycs6r8sIiIiIiIiUinoiqyIiIiIiIhUKipk\nI2Y21sy+NrOPU4jRxMymm9l8M5tnZlcnGae6mb1nZnOiOL9LNqcoXpaZfWBm/0ohRr6ZfWRmH5rZ\nrBTi1DWzf5jZJ2a2wMyOSyLG4VEe227fm9k1SeZzbfRv/LGZjTOz6knGuTqKMa+suRT32jOz/cxs\nqpktjH7WSzLO2VFOW82sYwr53BP9n801s+fNrG6Sce6IYnxoZq+Y2cHJxEl47HozczNrkGQ+t5vZ\n8oTX0s+TzcfMhkT/RvPM7O6Sjt9FPs8k5JJvZh8mGae9mb2z7e/VzDolGaedmb0d/e1PMrN9SxGn\n2PfBZF7TIuVB5/9dxqgw5/8oTlo+A+j8X+o4Ov/vIh+d/wuPKf/zfyanRK5MN+BEoAPwcQoxDgI6\nRPdrA58BrZKIY0Ct6H5V4F2gSwp5XQc8DfwrhRj5QIM0/Ds/DgyK7lcD6qYYLwv4CmiWxLGNCEs7\n7BNtPwsMTCLOUcDHQA3CBGqvAv+TymuPsKbi0Oj+UOCuJOMcCRxOWIexYwr5nALsFd2/K4V89k24\nfxXwcDJxovYmhFlRF5fmtVlCPrcDN5Tx/7u4ON2j//e9o+0Dkn1eCY/fC9yaZD6vAH2i+z8H8pKM\nMxPoFt2/GLijFHGKfR9M5jWtm27lcdvV32IpY+j8v+s4aT3/R3GS+gyAzv9liaPz/87j6Pz/0zHl\nfv7XFdmIu79OWAIglRgr3P396P46YAHhzbKscdzdC6LNqtEtqcHMZtYY+AVhwflYmVkdwh/LXwHc\n/Ud3/y7FsCcB/3H3xUkevxewj5ntRTgRfZlEjCOBd919vbtvBmYAZ5T24BJee/0JJ32in6clE8fd\nF7j7p6XNZSdxXomeG8A7QOMk43yfsFmTUryud/K3eR/w29LE2EWcMikhzhXAne6+Mdrn61TyMTMD\nzgHGJRnHgW3fntahFK/rEuIcBrwe3Z8KnFmKOCW9D5b5NS1SHnT+z7wMnf8htc8AOv+XLo7O/zuP\no/P/T3HK/fyvQjZDzKw5cDTh29Rkjs+KuhV8DUx196TiAKMIf+xbkzx+GwdeMbPZZjY4yRgtgG+A\nx6KuTmPMrGaKeZ1HKf7Yi+Puy4E/AkuAFcBad38liVAfAyeYWX0zq0H4BqxJMjklaOjuK6L7XwEN\nU4yXThcDLyV7sJmNMLOlQA5w6672LyFGf2C5u89JNo8EV0bdncam0N3lMMJr4F0zm2Fmx6aY0wnA\nSndfmOTx1wD3RP/Of6SEZU5KYR7hBARwNmV8XRd5H6zIr2mRtNH5v1iZOP9Dkp8BdP5Pms7/O9L5\nvxjldf5XIZsBZlYLmABcU+QbqFJz9y3u3p7wzVcnMzsqiTxOBb5299nJ5FDE8e7eAegD/NrMTkwi\nxl6ErgsPufvRwH8JXQySYmbVgH7A35M8vh7hj7QFcDBQ08x+WdY47r6A0N3mFeBl4ENgSzI5lRDf\nSfIb+XQzs2HAZiA32RjuPszdm0QxrkwihxrA/yPJk2ARDwGHAu0JH2buTTLOXsB+QBfgN8Cz0beq\nyRpAkl/QRK4Aro3+na8lugqShIuBX5nZbEI3oR9Le+DO3gcr0mtaJJ10/i9RWs//kNpnAJ3/y07n\n/xLp/F9EeZ7/VcimmZlVJfzn5br7c6nGi7reTAd6J3F4V6CfmeUD44EeZvZUknksj35+DTwP7HLw\neDGWAcsSvl3+B+HElqw+wPvuvjLJ43sCX7j7N+6+CXgO+Fkygdz9r+5+jLufCKwhjAtIxUozOwgg\n+rnLriqZZmYDgVOBnOiNKFW5lKKrSjEOJXz4mBO9thsD75vZgWUN5O4row+NW4FHSe51DeG1/VzU\nLfA9whWQXU5AUZyom9sZwDNJ5gJwIeH1DOFDXlLPy90/cfdT3P0Ywon1P6U5roT3wQr3mhZJJ53/\ndyrd539I7TOAzv9loPP/Tun8n6C8z/8qZNMo+gbmr8ACdx+ZQpz9LZoVzsz2AU4GPilrHHe/yd0b\nu3tzQveb19y9zN84mllNM6u97T5h4H+ZZ3d096+ApWZ2eNR0EjC/rHESpPqt1RKgi5nViP7vTiL0\n5y8zMzsg+tmU8Cb0dAp5AUwkvBkR/fxnivFSYma9CV3U+rn7+hTitEzY7E9yr+uP3P0Ad28evbaX\nESYX+CqJfA5K2DydJF7XkRcIEz5gZocRJjL5NslYPYFP3H1ZksdDGBPTLbrfA0iqi1LC67oKcDPw\ncCmOKel9sEK9pkXSSef/XeaT7vM/pPYZQOf/UtL5f5d0/v/pmPI//3uaZo2q7DfCm+EKYBPhD+OS\nJGIcT7hcPpfQveRD4OdJxGkLfBDF+ZhSzFxWipjZJDlrIXAIMCe6zQOGpZBHe2BW9NxeAOolGacm\nsAqok+K/y+8Ib6YfA08SzTqXRJw3CCflOcBJqb72gPrANMIb0KvAfknGOT26vxFYCUxJMs4iYGnC\n67o0sw0WF2dC9G89F5gENEomTpHH8yndrIXF5fMk8FGUz0TgoCTjVAOeip7b+0CPZJ8X8Dfg8hRf\nP8cDs6PX47vAMUnGuZpwdeEz4E7AShGn2PfBZF7TuulWHrddvceUMobO/7uOlZbzfxQr5c8A6Pxf\n2jg6/+88js7/P8Up9/O/Rb9YREREREREpFJQ12IRERERERGpVFTIioiIiIiISKWiQlZEREREREQq\nFRWyIiIiIiIiUqmokBUREREREZFKRYWsSAzMbIuZfZhwG5rG2M3NLNn10ERERCRDdP4XSZ+94k5A\nZA+1wd3bx52EiIiIlCud/0XSRFdkRSoQM8s3s7vN7CMze8/M/idqb25mr5nZXDObZmZNo/aGZva8\nmc2Jbj+LQmWZ2aNmNs/MXjGzfaL9rzKz+VGc8TE9TREREUmg879I2amQFYnHPkW6Fp2b8Nhad28D\nPACMitr+DDzu7m2BXOD+qP1+YIa7twM6APOi9pbAg+7eGvgOODNqHwocHcW5PFNPTkRERIql879I\nmpi7x52DyB7HzArcvVYx7flAD3f/3MyqAl+5e30z+xY4yN03Re0r3L2BmX0DNHb3jQkxmgNT3b1l\ntH0jUNXdf29mLwMFwAvAC+5ekOGnKiIiIhGd/0XSR1dkRSoeL+F+WWxMuL+Fn8bD/wJ4kPDt7Uwz\n0zh5ERGRikHnf5EyUCErUvGcm/Dz7ej+W8B50f0c4I3o/jTgCgAzyzKzOiUFNbMqQBN3nw7cCNQB\ndvhWWERERGKh879IGejbGJF47GNmHyZsv+zu26bgr2dmcwnfqg6I2oYAj5nZb4BvgIui9quB0WZ2\nCeGb1yuAFSX8zizgqehkZ8D97v5d2p6RiIiI7IrO/yJpojGyIhVINEamo7t/G3cuIiIiUj50/hcp\nO3UtFhERERERkUpFV2RFRERERESkUtEVWREREREREalUVMiKiIiIiIhIpaJCVkRERERERCoVFbIi\nIiIiIiJSqaiQFRERERERkUpFhayIiIiIiIhUKv8f0ZYK63wJs84AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vn17XbWeIxGk",
        "colab_type": "text"
      },
      "source": [
        "#### Sequential Model + gradient tape\n",
        "\n",
        "\n",
        "**Notes:**\n",
        "\n",
        "* Apply with logits if we apply optimizer to values coming form softmax\n",
        "* if sequence varies, use funcion signature and specify exact tensor shape and dtype\n",
        "* Specify Global Scope variables that will be used in functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S5VYo9hiIz4U",
        "colab_type": "code",
        "outputId": "d82be317-27db-494f-e9ee-37932835b964",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        }
      },
      "source": [
        "class Classification(tf.keras.Model):\n",
        "  def __init__(self, vocab_size, emb_size):\n",
        "    super(Classification, self).__init__()\n",
        "    self.emb_layer = tf.keras.layers.Embedding(vocab_size, emb_size)\n",
        "    self.avg_layer = tf.keras.layers.GlobalAveragePooling1D()\n",
        "    self.l1 = tf.keras.layers.Dense(units=16, activation=tf.nn.relu)\n",
        "    self.l2 = tf.keras.layers.Dense(units=1, activation=tf.nn.sigmoid)\n",
        "    return\n",
        "\n",
        "  def call(self, input_batch):\n",
        "    intermediate_value = self.emb_layer(input_batch)\n",
        "    intermediate_value = self.avg_layer(intermediate_value)\n",
        "    intermediate_value = self.l1(intermediate_value)\n",
        "    output_batch = self.l2(intermediate_value)\n",
        "    return output_batch\n",
        "\n",
        "\n",
        "def loss_func(target, logits):\n",
        "  bce = tf.losses.BinaryCrossentropy(from_logits=False)\n",
        "  return bce(y_true=target, y_pred=logits)\n",
        "\n",
        "# need signature becuase each batch different shape\n",
        "@tf.function(input_signature=[\n",
        "    tf.TensorSpec(shape=[None, None], dtype=tf.int64),\n",
        "    tf.TensorSpec(shape=[None,], dtype=tf.int64)])\n",
        "def train_step(input_batch, label_batch):\n",
        "  # use global scope variables\n",
        "  global model, model_optimizer\n",
        "  loss = 0\n",
        "  with tf.GradientTape() as tape:\n",
        "    preds_batch = model(input_batch)\n",
        "    loss = loss_func(label_batch, preds_batch)\n",
        "\n",
        "  variables = model.variables\n",
        "  gradients = tape.gradient(loss, variables)\n",
        "  model_optimizer.apply_gradients(zip(gradients, variables))\n",
        "  return loss\n",
        "\n",
        "history_dict = {'accuracy':[],\n",
        "                'val_accuracy':[], \n",
        "                'loss':[], \n",
        "                'val_loss':[]}\n",
        "\n",
        "# global scope variables\n",
        "model = Classification(vocab_size=text_encoder.vocab_size, emb_size=16)\n",
        "model_optimizer = tf.optimizers.Adam()\n",
        "model_metrics = tf.metrics.Accuracy()\n",
        "\n",
        "EPOCHS = 20\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "  batch_loss = 0\n",
        "  batch_val_loss = 0\n",
        "\n",
        "  batch_acc = 0\n",
        "  batch_val_acc = 0\n",
        "\n",
        "  for batch, (text_batch, label_batch) in enumerate(train_batches, start=1):\n",
        "    batch_loss += train_step(text_batch, label_batch)\n",
        "\n",
        "    preds_batch = model(text_batch)\n",
        "    preds_batch = tf.keras.backend.round(preds_batch)\n",
        "    batch_acc += model_metrics(label_batch, preds_batch) \n",
        "\n",
        "  batch_loss = tf.get_static_value(batch_loss) / batch\n",
        "  history_dict['loss'].append(batch_loss)\n",
        "\n",
        "  batch_acc = tf.get_static_value(batch_acc) / batch\n",
        "  history_dict['accuracy'].append(batch_acc)\n",
        "  \n",
        "\n",
        "  for batch, (text_batch, label_batch) in enumerate(test_batches, start=1):\n",
        "    preds_batch = model(text_batch)\n",
        "    batch_val_loss += loss_func(label_batch, preds_batch)\n",
        "\n",
        "    preds_batch = tf.keras.backend.round(preds_batch)\n",
        "    batch_val_acc += model_metrics(label_batch, preds_batch) \n",
        "\n",
        "  \n",
        "  batch_val_loss = tf.get_static_value(batch_val_loss) / batch\n",
        "  history_dict['val_loss'].append(batch_val_loss)\n",
        "\n",
        "  batch_val_acc =tf.get_static_value(batch_val_acc) / batch\n",
        "  history_dict['val_accuracy'].append(batch_val_acc)\n",
        "\n",
        "  print('Epoch: %s/%s\\tloss: %.4f\\tacc: %.4f\\tval_loss: %.4f\\tval_acc: %.4f'%(epoch+1, EPOCHS, batch_loss, batch_acc, batch_val_loss, batch_val_acc))\n",
        "\n",
        "plt_history(history_dict)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1/20\tloss: 0.6931\tacc: 0.5015\tval_loss: 0.6931\tval_acc: 0.5026\n",
            "Epoch: 2/20\tloss: 0.6930\tacc: 0.5055\tval_loss: 0.6929\tval_acc: 0.5194\n",
            "Epoch: 3/20\tloss: 0.6927\tacc: 0.5312\tval_loss: 0.6924\tval_acc: 0.5338\n",
            "Epoch: 4/20\tloss: 0.6920\tacc: 0.5382\tval_loss: 0.6914\tval_acc: 0.5454\n",
            "Epoch: 5/20\tloss: 0.6905\tacc: 0.5527\tval_loss: 0.6896\tval_acc: 0.5563\n",
            "Epoch: 6/20\tloss: 0.6880\tacc: 0.5615\tval_loss: 0.6864\tval_acc: 0.5661\n",
            "Epoch: 7/20\tloss: 0.6836\tacc: 0.5719\tval_loss: 0.6813\tval_acc: 0.5785\n",
            "Epoch: 8/20\tloss: 0.6769\tacc: 0.5854\tval_loss: 0.6737\tval_acc: 0.5921\n",
            "Epoch: 9/20\tloss: 0.6676\tacc: 0.5991\tval_loss: 0.6631\tval_acc: 0.6058\n",
            "Epoch: 10/20\tloss: 0.6544\tacc: 0.6123\tval_loss: 0.6498\tval_acc: 0.6183\n",
            "Epoch: 11/20\tloss: 0.6389\tacc: 0.6239\tval_loss: 0.6340\tval_acc: 0.6293\n",
            "Epoch: 12/20\tloss: 0.6196\tacc: 0.6346\tval_loss: 0.6161\tval_acc: 0.6398\n",
            "Epoch: 13/20\tloss: 0.6000\tacc: 0.6448\tval_loss: 0.5976\tval_acc: 0.6493\n",
            "Epoch: 14/20\tloss: 0.5783\tacc: 0.6538\tval_loss: 0.5786\tval_acc: 0.6582\n",
            "Epoch: 15/20\tloss: 0.5617\tacc: 0.6623\tval_loss: 0.5598\tval_acc: 0.6665\n",
            "Epoch: 16/20\tloss: 0.5382\tacc: 0.6705\tval_loss: 0.5417\tval_acc: 0.6744\n",
            "Epoch: 17/20\tloss: 0.5175\tacc: 0.6782\tval_loss: 0.5238\tval_acc: 0.6820\n",
            "Epoch: 18/20\tloss: 0.4962\tacc: 0.6858\tval_loss: 0.5068\tval_acc: 0.6893\n",
            "Epoch: 19/20\tloss: 0.4771\tacc: 0.6929\tval_loss: 0.4905\tval_acc: 0.6964\n",
            "Epoch: 20/20\tloss: 0.4605\tacc: 0.6998\tval_loss: 0.4751\tval_acc: 0.7031\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hhx7iiSeeoEKFChx11FE8++yz8XvDREREpFRwh/ffh7/8BU46qS5xGPszV2W+kF26NPf1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\nIiIiIiKSVlTIioiIiIiISFr5f5eq5+feBiG5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l2ckMqF_IxOG",
        "colab_type": "text"
      },
      "source": [
        "#### Separate Models + gradient tape"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "c0900f74-b314-4ab3-9301-e9d198d1b27a",
        "id": "BaVG9GW-8hQU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        }
      },
      "source": [
        "class Embed(tf.keras.Model):\n",
        "  def __init__(self, vocab_size, emb_size):\n",
        "    super(Embed, self).__init__()\n",
        "    self.emb_layer = tf.keras.layers.Embedding(vocab_size, emb_size)\n",
        "    self.avg_layer = tf.keras.layers.GlobalAveragePooling1D()\n",
        "    return\n",
        "\n",
        "  def call(self, input_batch):\n",
        "    output_batch = self.emb_layer(input_batch)\n",
        "    output_batch = self.avg_layer(intermediate_value)\n",
        "    return output_batch\n",
        "\n",
        "class Dens(tf.keras.Model):\n",
        "  def __init__(self):\n",
        "    super(Dens, self).__init__()\n",
        "    self.l1 = tf.keras.layers.Dense(units=16, activation=tf.nn.relu)\n",
        "    self.l2 = tf.keras.layers.Dense(units=1, activation=tf.nn.sigmoid)\n",
        "    return\n",
        "\n",
        "  def call(self, input_batch):\n",
        "    intermediate_value = self.l1(input_batch)\n",
        "    output_batch = self.l2(intermediate_value)\n",
        "    return output_batch\n",
        "\n",
        "\n",
        "def loss_func(target, logits):\n",
        "  bce = tf.losses.BinaryCrossentropy(from_logits=False)\n",
        "  return bce(y_true=target, y_pred=logits)\n",
        "\n",
        "\n",
        "# need signature becuase each batch different shape\n",
        "@tf.function(input_signature=[\n",
        "    tf.TensorSpec(shape=[None, None], dtype=tf.int64),\n",
        "    tf.TensorSpec(shape=[None,], dtype=tf.int64)])\n",
        "def train_step(input_batch, lables_batch):\n",
        "  loss = 0\n",
        "  # use global scope variables\n",
        "  global model_embed, model_dens, model_optimizer\n",
        "  with tf.GradientTape() as tape:\n",
        "    preds_batch = model_embed(input_batch)\n",
        "    preds_batch = model_dens(preds_batch)\n",
        "\n",
        "    loss = loss_func(lables_batch, preds_batch)\n",
        "\n",
        "  variables = model_embed.variables + model_dens.variables\n",
        "  gradients = tape.gradient(loss, variables)\n",
        "  model_optimizer.apply_gradients(zip(gradients, variables))\n",
        "  return loss\n",
        "\n",
        "\n",
        "\n",
        "# global scope variables\n",
        "model_embed = Classification(vocab_size=text_encoder.vocab_size, emb_size=16)\n",
        "model_dens = Dens()\n",
        "model_optimizer = tf.optimizers.Adam()\n",
        "model_metrics = tf.metrics.Accuracy()\n",
        "\n",
        "history_dict = {'accuracy':[],\n",
        "                'val_accuracy':[], \n",
        "                'loss':[], \n",
        "                'val_loss':[]}\n",
        "\n",
        "EPOCHS = 20\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "  batch_loss = 0\n",
        "  batch_val_loss = 0\n",
        "\n",
        "  batch_acc = 0\n",
        "  batch_val_acc = 0\n",
        "\n",
        "  for batch, (text_batch, label_batch) in enumerate(train_batches, start=1):\n",
        "    batch_loss += train_step(text_batch, label_batch)\n",
        "\n",
        "    preds_batch = model_embed(text_batch)\n",
        "    preds_batch = model_dens(preds_batch)\n",
        "\n",
        "    preds_batch = tf.keras.backend.round(preds_batch)\n",
        "    batch_acc += model_metrics(label_batch, preds_batch) \n",
        "\n",
        "  batch_loss = tf.get_static_value(batch_loss) / batch\n",
        "  history_dict['loss'].append(batch_loss)\n",
        "\n",
        "  batch_acc = tf.get_static_value(batch_acc) / batch\n",
        "  history_dict['accuracy'].append(batch_acc)\n",
        "\n",
        "  for batch, (text_batch, label_batch) in enumerate(test_batches, start=1):\n",
        "    preds_batch = model_embed(text_batch)\n",
        "    preds_batch = model_dens(preds_batch)\n",
        "\n",
        "    batch_val_loss += loss_func(label_batch, preds_batch)\n",
        "\n",
        "    preds_batch = tf.keras.backend.round(preds_batch)\n",
        "    batch_val_acc += model_metrics(label_batch, preds_batch) \n",
        "\n",
        "  \n",
        "  batch_val_loss = tf.get_static_value(batch_val_loss) / batch\n",
        "  history_dict['val_loss'].append(batch_val_loss)\n",
        "\n",
        "  batch_val_acc =tf.get_static_value(batch_val_acc) / batch\n",
        "  history_dict['val_accuracy'].append(batch_val_acc)\n",
        "\n",
        "  print('Epoch: %s/%s\\tloss: %.4f\\tacc: %.4f\\tval_loss: %.4f\\tval_acc: %.4f'%(epoch+1, EPOCHS, batch_loss, batch_acc, batch_val_loss, batch_val_acc))\n",
        "    \n",
        "plt_history(history_dict)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1/20\tloss: 0.6936\tacc: 0.4994\tval_loss: 0.6931\tval_acc: 0.5005\n",
            "Epoch: 2/20\tloss: 0.6932\tacc: 0.4999\tval_loss: 0.6931\tval_acc: 0.4998\n",
            "Epoch: 3/20\tloss: 0.6932\tacc: 0.5000\tval_loss: 0.6931\tval_acc: 0.5000\n",
            "Epoch: 4/20\tloss: 0.6931\tacc: 0.5007\tval_loss: 0.6931\tval_acc: 0.5011\n",
            "Epoch: 5/20\tloss: 0.6931\tacc: 0.5009\tval_loss: 0.6931\tval_acc: 0.5012\n",
            "Epoch: 6/20\tloss: 0.6931\tacc: 0.5011\tval_loss: 0.6931\tval_acc: 0.5014\n",
            "Epoch: 7/20\tloss: 0.6931\tacc: 0.5026\tval_loss: 0.6931\tval_acc: 0.5032\n",
            "Epoch: 8/20\tloss: 0.6930\tacc: 0.5044\tval_loss: 0.6930\tval_acc: 0.5054\n",
            "Epoch: 9/20\tloss: 0.6930\tacc: 0.5065\tval_loss: 0.6929\tval_acc: 0.5075\n",
            "Epoch: 10/20\tloss: 0.6928\tacc: 0.5083\tval_loss: 0.6927\tval_acc: 0.5092\n",
            "Epoch: 11/20\tloss: 0.6925\tacc: 0.5103\tval_loss: 0.6924\tval_acc: 0.5110\n",
            "Epoch: 12/20\tloss: 0.6920\tacc: 0.5121\tval_loss: 0.6916\tval_acc: 0.5133\n",
            "Epoch: 13/20\tloss: 0.6907\tacc: 0.5145\tval_loss: 0.6898\tval_acc: 0.5159\n",
            "Epoch: 14/20\tloss: 0.6878\tacc: 0.5180\tval_loss: 0.6858\tval_acc: 0.5208\n",
            "Epoch: 15/20\tloss: 0.6820\tacc: 0.5243\tval_loss: 0.6777\tval_acc: 0.5287\n",
            "Epoch: 16/20\tloss: 0.6702\tacc: 0.5328\tval_loss: 0.6634\tval_acc: 0.5373\n",
            "Epoch: 17/20\tloss: 0.6524\tacc: 0.5422\tval_loss: 0.6433\tval_acc: 0.5472\n",
            "Epoch: 18/20\tloss: 0.6274\tacc: 0.5523\tval_loss: 0.6185\tval_acc: 0.5576\n",
            "Epoch: 19/20\tloss: 0.5993\tacc: 0.5628\tval_loss: 0.5902\tval_acc: 0.5682\n",
            "Epoch: 20/20\tloss: 0.5657\tacc: 0.5735\tval_loss: 0.5589\tval_acc: 0.5789\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WERGRXOYOl1wCu+wCF1+cvE+HDrVrzwUqZEVEamnPPcOU9c8+G5bcEREREclV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\nXDObC9wAvAx0c/ezgAOAX8a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HX+E3v/k+3bp9yX77vbVVEyPm4udqiHFSUSErItLA9e8P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\nZEVERERERKRc+f8Bu03dNO7sGGgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}