TransferLearning_FeatureExtraction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AthiFiz/deepLearning/blob/main/TransferLearning_FeatureExtraction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vKr_OlTNh_kZ"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OUitSrpsi4sp"
      },
      "outputs": [],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BKcaOv69kZkj"
      },
      "outputs": [],
      "source": [
        "import zipfile\n",
        "\n",
        "!wget https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_10_percent.zip\n",
        "\n",
        "zip_ref = zipfile.ZipFile(\"10_food_classes_10_percent.zip\", \"r\")\n",
        "zip_ref.extractall()\n",
        "zip_ref.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WDseBBAkkyz1"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "for dirpath, dirnames, filenames in os.walk(\"10_food_classes_10_percent\"):\n",
        "  print(f\"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'.\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install neptune-client"
      ],
      "metadata": {
        "id": "UwOstmgEWQth"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LNXku9uClm-n"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "IMAGE_SHAPE = (224,224)\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "train_dir = \"10_food_classes_10_percent/train\"\n",
        "test_dir = \"10_food_classes_10_percent/test\"\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale=1/255)\n",
        "test_datagen = ImageDataGenerator(rescale=1/255)\n",
        "\n",
        "train_data_10_percent = train_datagen.flow_from_directory(train_dir,\n",
        "                                            target_size=IMAGE_SHAPE,\n",
        "                                            batch_size=BATCH_SIZE,\n",
        "                                            class_mode=\"categorical\")\n",
        "\n",
        "test_data = test_datagen.flow_from_directory(test_dir,\n",
        "                                             target_size=IMAGE_SHAPE)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import neptune.new as neptune\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "from tensorflow.keras import layers"
      ],
      "metadata": {
        "id": "nwZL1w3ZgBgK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_model(model_url, num_classes=10):\n",
        "  feature_extractor_layer = hub.KerasLayer(model_url,\n",
        "                                           trainable=False,\n",
        "                                           name=\"feature_extraction_layer\",\n",
        "                                           input_shape=IMAGE_SHAPE+(3,))\n",
        "\n",
        "  model = tf.keras.Sequential([\n",
        "      feature_extractor_layer,\n",
        "      layers.Dense(num_classes, activation=\"softmax\", name=\"output_layer\")\n",
        "  ])\n",
        "\n",
        "  return model"
      ],
      "metadata": {
        "id": "aJI_cQTGknWQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "resnet_url = \"https://www.kaggle.com/models/google/resnet-v2/frameworks/TensorFlow2/variations/50-feature-vector/versions/2\"\n",
        "efficientnet_url = \"https://www.kaggle.com/models/google/efficientnet-v2/frameworks/TensorFlow2/variations/imagenet21k-b0-feature-vector/versions/1\""
      ],
      "metadata": {
        "id": "K_C27keiksbN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class NeptuneCallback(tf.keras.callbacks.Callback):\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        logs = logs or {}\n",
        "        run[\"training/accuracy\"].log(logs.get(\"accuracy\"), step=epoch)\n",
        "        run[\"training/loss\"].log(logs.get(\"loss\"), step=epoch)\n",
        "        run[\"validation/val_accuracy\"].log(logs.get(\"val_accuracy\"), step=epoch)\n",
        "        run[\"validation/val_loss\"].log(logs.get(\"val_loss\"), step=epoch)"
      ],
      "metadata": {
        "id": "Ny_BbpFWkkPb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "run = neptune.init_run(\n",
        "    project=\"athif.ai/TransferLearning\",\n",
        "    api_token=\"eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vYXBwLm5lcHR1bmUuYWkiLCJhcGlfdXJsIjoiaHR0cHM6Ly9hcHAubmVwdHVuZS5haSIsImFwaV9rZXkiOiI1MWM3MTc4ZC04NTMxLTRiMzItODY4Ny1mNDkxY2RmYTFiOTUifQ==\")\n",
        "run['sys/tags'].add('ResNet-Run')\n",
        "\n",
        "resnet_model = create_model(resnet_url, train_data_10_percent.num_classes)\n",
        "\n",
        "resnet_model.compile(loss=\"categorical_crossentropy\",\n",
        "                     optimizer = tf.keras.optimizers.Adam(),\n",
        "                     metrics=[\"accuracy\"])\n",
        "\n",
        "resnet_history = resnet_model.fit(train_data_10_percent,\n",
        "                                  epochs=5,\n",
        "                                  steps_per_epoch=len(train_data_10_percent),\n",
        "                                  validation_data=test_data,\n",
        "                                  validation_steps=len(test_data),\n",
        "                                  callbacks=[NeptuneCallback()])\n",
        "run.stop()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MLnE-ZBif_ng",
        "outputId": "4d7888ae-a146-456f-d7b1-4cbfe7454818"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://app.neptune.ai/athif.ai/TransferLearning/e/TRAN-5\n",
            "Epoch 1/5\n",
            "24/24 [==============================] - 22s 598ms/step - loss: 1.9151 - accuracy: 0.3680 - val_loss: 1.1847 - val_accuracy: 0.6364\n",
            "Epoch 2/5\n",
            "24/24 [==============================] - 13s 545ms/step - loss: 0.8983 - accuracy: 0.7440 - val_loss: 0.8497 - val_accuracy: 0.7336\n",
            "Epoch 3/5\n",
            "24/24 [==============================] - 11s 449ms/step - loss: 0.6144 - accuracy: 0.8253 - val_loss: 0.7410 - val_accuracy: 0.7704\n",
            "Epoch 4/5\n",
            "24/24 [==============================] - 11s 476ms/step - loss: 0.4655 - accuracy: 0.8827 - val_loss: 0.6824 - val_accuracy: 0.7844\n",
            "Epoch 5/5\n",
            "24/24 [==============================] - 11s 480ms/step - loss: 0.3714 - accuracy: 0.9267 - val_loss: 0.6825 - val_accuracy: 0.7796\n",
            "Shutting down background jobs, please wait a moment...\n",
            "Done!\n",
            "All 0 operations synced, thanks for waiting!\n",
            "Explore the metadata in the Neptune app:\n",
            "https://app.neptune.ai/athif.ai/TransferLearning/e/TRAN-5/metadata\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "run = neptune.init_run(\n",
        "    project=\"athif.ai/TransferLearning\",\n",
        "    api_token=\"eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vYXBwLm5lcHR1bmUuYWkiLCJhcGlfdXJsIjoiaHR0cHM6Ly9hcHAubmVwdHVuZS5haSIsImFwaV9rZXkiOiI1MWM3MTc4ZC04NTMxLTRiMzItODY4Ny1mNDkxY2RmYTFiOTUifQ==\")\n",
        "run['sys/tags'].add('EfficientNet-Run')\n",
        "\n",
        "efficientnet_url = create_model(efficientnet_url, train_data_10_percent.num_classes)\n",
        "\n",
        "resnet_model.compile(loss=\"categorical_crossentropy\",\n",
        "                     optimizer = tf.keras.optimizers.Adam(),\n",
        "                     metrics=[\"accuracy\"])\n",
        "\n",
        "resnet_history = resnet_model.fit(train_data_10_percent,\n",
        "                                  epochs=5,\n",
        "                                  steps_per_epoch=len(train_data_10_percent),\n",
        "                                  validation_data=test_data,\n",
        "                                  validation_steps=len(test_data),\n",
        "                                  callbacks=[NeptuneCallback()])\n",
        "\n",
        "run.stop()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tV9axbAhi07R",
        "outputId": "b351ac47-65cf-46e1-843b-c9277fffe219"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://app.neptune.ai/athif.ai/TransferLearning/e/TRAN-6\n",
            "Epoch 1/5\n",
            "24/24 [==============================] - 14s 500ms/step - loss: 0.3438 - accuracy: 0.9107 - val_loss: 0.6514 - val_accuracy: 0.7852\n",
            "Epoch 2/5\n",
            "24/24 [==============================] - 11s 448ms/step - loss: 0.2325 - accuracy: 0.9573 - val_loss: 0.6253 - val_accuracy: 0.7896\n",
            "Epoch 3/5\n",
            "24/24 [==============================] - 11s 490ms/step - loss: 0.1712 - accuracy: 0.9760 - val_loss: 0.6294 - val_accuracy: 0.7872\n",
            "Epoch 4/5\n",
            "24/24 [==============================] - 12s 496ms/step - loss: 0.1373 - accuracy: 0.9867 - val_loss: 0.6361 - val_accuracy: 0.7940\n",
            "Epoch 5/5\n",
            "24/24 [==============================] - 13s 544ms/step - loss: 0.1108 - accuracy: 0.9920 - val_loss: 0.6282 - val_accuracy: 0.7916\n",
            "Shutting down background jobs, please wait a moment...\n",
            "Done!\n",
            "Waiting for the remaining 4 operations to synchronize with Neptune. Do not kill this process.\n",
            "All 4 operations synced, thanks for waiting!\n",
            "Explore the metadata in the Neptune app:\n",
            "https://app.neptune.ai/athif.ai/TransferLearning/e/TRAN-6/metadata\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OfkZZVI6AMbK"
      },
      "outputs": [],
      "source": [
        "# If you wanted to, you could really turn this into a helper function to load in with a helper.py script...\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot the validation and training data separately\n",
        "def plot_loss_curves(history):\n",
        "  \"\"\"\n",
        "  Returns separate loss curves for training and validation metrics.\n",
        "  \"\"\"\n",
        "  loss = history.history['loss']\n",
        "  val_loss = history.history['val_loss']\n",
        "\n",
        "  accuracy = history.history['accuracy']\n",
        "  val_accuracy = history.history['val_accuracy']\n",
        "\n",
        "  epochs = range(len(history.history['loss']))\n",
        "\n",
        "  # Plot loss\n",
        "  plt.plot(epochs, loss, label='training_loss')\n",
        "  plt.plot(epochs, val_loss, label='val_loss')\n",
        "  plt.title('Loss')\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.legend()\n",
        "\n",
        "  # Plot accuracy\n",
        "  plt.figure()\n",
        "  plt.plot(epochs, accuracy, label='training_accuracy')\n",
        "  plt.plot(epochs, val_accuracy, label='val_accuracy')\n",
        "  plt.title('Accuracy')\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.legend();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "r8TFugBuANO7",
        "outputId": "71d36784-6c9e-4097-918e-ef8d0387bba6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_loss_curves(resnet_history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mtcXvJjCA9NB",
        "outputId": "3dd79e1b-6e20-4cc3-ab0a-bc8f02cfe246"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " feature_extraction_layer (  (None, 2048)              23564800  \n",
            " KerasLayer)                                                     \n",
            "                                                                 \n",
            " output_layer (Dense)        (None, 10)                20490     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 23585290 (89.97 MB)\n",
            "Trainable params: 20490 (80.04 KB)\n",
            "Non-trainable params: 23564800 (89.89 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Resnet summary\n",
        "resnet_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uqgg3oXaBE3w",
        "outputId": "ef3fc515-167a-44de-eae8-9e6b1751f258"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving TensorBoard log files to: tensorflow_hub/efficientnetB0/20231117-134859\n",
            "Epoch 1/5\n",
            "24/24 [==============================] - 20s 508ms/step - loss: 1.6614 - accuracy: 0.4867 - val_loss: 0.6325 - val_accuracy: 0.7940\n",
            "Epoch 2/5\n",
            "24/24 [==============================] - 9s 386ms/step - loss: 0.6165 - accuracy: 0.8107 - val_loss: 0.4960 - val_accuracy: 0.8360\n",
            "Epoch 3/5\n",
            "24/24 [==============================] - 10s 435ms/step - loss: 0.4061 - accuracy: 0.8720 - val_loss: 0.4034 - val_accuracy: 0.8656\n",
            "Epoch 4/5\n",
            "24/24 [==============================] - 10s 445ms/step - loss: 0.2731 - accuracy: 0.9147 - val_loss: 0.3848 - val_accuracy: 0.8736\n",
            "Epoch 5/5\n",
            "24/24 [==============================] - 9s 389ms/step - loss: 0.1959 - accuracy: 0.9533 - val_loss: 0.3586 - val_accuracy: 0.8812\n"
          ]
        }
      ],
      "source": [
        "# Create model\n",
        "efficientnet_model = create_model(model_url=efficientnet_url, # use EfficientNetB0 TensorFlow Hub URL\n",
        "                                  num_classes=train_data_10_percent.num_classes)\n",
        "\n",
        "# Compile EfficientNet model\n",
        "efficientnet_model.compile(loss='categorical_crossentropy',\n",
        "                           optimizer=tf.keras.optimizers.Adam(),\n",
        "                           metrics=['accuracy'])\n",
        "\n",
        "# Fit EfficientNet model\n",
        "efficientnet_history = efficientnet_model.fit(train_data_10_percent, # only use 10% of training data\n",
        "                                              epochs=5, # train for 5 epochs\n",
        "                                              steps_per_epoch=len(train_data_10_percent),\n",
        "                                              validation_data=test_data,\n",
        "                                              validation_steps=len(test_data),\n",
        "                                              callbacks=[create_tensorboard_callback(dir_name=\"tensorflow_hub\",\n",
        "                                                                                     # Track logs under different experiment name\n",
        "                                                                                     experiment_name=\"efficientnetB0\")])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "n7fsSwE6B7aJ",
        "outputId": "fdd3af7f-07bc-4ddb-a9fd-28ef41f9d9e5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_loss_curves(efficientnet_history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQBsT_6rCBO7",
        "outputId": "b589aff2-7c94-4a8c-81cc-00bdf0349094"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " feature_extraction_layer (  (None, 1280)              5919312   \n",
            " KerasLayer)                                                     \n",
            "                                                                 \n",
            " output_layer (Dense)        (None, 10)                12810     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5932122 (22.63 MB)\n",
            "Trainable params: 12810 (50.04 KB)\n",
            "Non-trainable params: 5919312 (22.58 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "efficientnet_model.summary()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyODfggWLmdRBMimDegccfF7",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}