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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/omarcevi/ML-Projects/blob/main/TF_Distributed_training_sample.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "C0YUAc4pECA7"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Download the Cassava dataset from TensorFlow\n",
        "data, info = tfds.load(name='cassava', as_supervised=True, with_info=True)\n",
        "NUM_CLASSES = info.features['label'].num_classes"
      ],
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        "outputId": "9a340a95-b9a6-417c-8ac7-9be67bba0477"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset 1.26 GiB (download: 1.26 GiB, generated: Unknown size, total: 1.26 GiB) to /root/tensorflow_datasets/cassava/0.1.0...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ],
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            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
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        {
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          "data": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train examples...:   0%|          | 0/5656 [00:00<?, ? examples/s]"
            ],
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/cassava/0.1.0.incompleteWDG3TP/cassava-train.tfrecord*...:   0%|          …"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test examples...:   0%|          | 0/1885 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/cassava/0.1.0.incompleteWDG3TP/cassava-test.tfrecord*...:   0%|          |…"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation examples...:   0%|          | 0/1889 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/cassava/0.1.0.incompleteWDG3TP/cassava-validation.tfrecord*...:   0%|     …"
            ],
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset cassava downloaded and prepared to /root/tensorflow_datasets/cassava/0.1.0. Subsequent calls will reuse this data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add a preprocess_data function to scale the image\n",
        "def preprocess_data(image, label):\n",
        "\timage = tf.image.resize(image, (300, 300))\n",
        "\treturn tf.cast(image, tf.float32) / 255., label"
      ],
      "metadata": {
        "id": "UVYJbLhNEcdo"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the model\n",
        "def create_model():\n",
        "\tbase_model = tf.keras.applications.ResNet50(weights='imagenet', include_top=False)\n",
        "\tx = base_model.output\n",
        "\tx = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
        "\tx = tf.keras.layers.Dense(1016, activation='relu')(x)\n",
        "\tpredictions = tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')(x)\n",
        "\tmodel = tf.keras.Model(inputs=base_model.input, outputs=predictions)\n",
        "\treturn model"
      ],
      "metadata": {
        "id": "YKqAwt0IE7w5"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the strategy object\n",
        "strategy = tf.distribute.MirroredStrategy()\n",
        "\n",
        "# Create model variables within the strategy scope\n",
        "with strategy.scope():\n",
        "\tmodel = create_model()\n",
        "\tmodel.compile(\n",
        "\t\tloss='sparse_categorical_crossentropy',\n",
        "\t\toptimizer=tf.keras.optimizers.Adam(0.0001),\n",
        "\t\tmetrics=['accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6RLc_owE9R8",
        "outputId": "992658a1-3521-49d5-a13b-de720d0444d1"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94765736/94765736 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Change the batch size\n",
        "batch_size = 64 * strategy.num_replicas_in_sync\n",
        "\n",
        "# Map, shuffle, and prefetch data\n",
        "train_data = data['train'].map(preprocess_data)\n",
        "train_data = train_data.shuffle(1000)\n",
        "train_data = train_data.batch(batch_size)\n",
        "train_data = train_data.prefetch(tf.data.experimental.AUTOTUNE)\n",
        "\n",
        "model.fit(train_data, epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NHXYOV66FBE4",
        "outputId": "cf250f11-583d-481b-9003-a1a45d680eff"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "89/89 [==============================] - 173s 1s/step - loss: 0.6311 - accuracy: 0.7751\n",
            "Epoch 2/5\n",
            "89/89 [==============================] - 109s 1s/step - loss: 0.1813 - accuracy: 0.9429\n",
            "Epoch 3/5\n",
            "89/89 [==============================] - 110s 1s/step - loss: 0.0598 - accuracy: 0.9818\n",
            "Epoch 4/5\n",
            "89/89 [==============================] - 110s 1s/step - loss: 0.0418 - accuracy: 0.9878\n",
            "Epoch 5/5\n",
            "89/89 [==============================] - 111s 1s/step - loss: 0.0393 - accuracy: 0.9885\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7ebc0817f550>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bRa7aZlvFq4b"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}