03_M02L02_Lab_Transfer_Learning_Implementation__M.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab: Transfer Learning Implementation \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Estimated time needed:  30 minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab, you will learn to implement transfer learning using a pre-trained model in Keras.\n",
    "\n",
    "#### Learning objectives\n",
    "\n",
    "By the end of this lab, you will:\n",
    "\n",
    " - Import necessary libraries and load the dataset.\n",
    " - Load a pre-trained model, VGG16, excluding the top layers.\n",
    " - Add new layers on top of the base model and compile the model.\n",
    " - Train the model on the new dataset.\n",
    " - Unfreeze some of the layers of the pre-trained model and fine-tune them.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step-by-Step Guide: \n",
    "\n",
    "#### Step 1: Setup the Environment \n",
    "\n",
    "Before we start, make sure to import the required libraries: TensorFlow and Keras. Keras is included within TensorFlow as `tensorflow.keras`. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
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     "text": [
      "2024-11-28 12:12:10.138265: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-11-28 12:12:10.140608: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-11-28 12:12:10.148940: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-11-28 12:12:10.161679: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-11-28 12:12:10.190770: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-11-28 12:12:10.190844: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-11-28 12:12:10.209423: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-11-28 12:12:11.718205: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow==2.16.2 matplotlib==3.9.1\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.applications import VGG16\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Explanation:\n",
    "- `tensorflow` is the main library for machine learning in Python.\n",
    "- `Sequential` is used to create a model with a linear stack of layers.\n",
    "- `Dense` and `Flatten` are types of layers that we will use in our model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Load Pre-trained Model \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "\u001b[1m58889256/58889256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
     ]
    }
   ],
   "source": [
    "# Load the VGG16 model pre-trained on ImageNet\n",
    "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
    "\n",
    "# Freeze the base model layers\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Create and Compile the Model \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new model and add the base model and new layers\n",
    "model = Sequential([\n",
    "    base_model,\n",
    "    Flatten(),\n",
    "    Dense(256, activation='relu'),\n",
    "    Dense(1, activation='sigmoid')  # Change to the number of classes you have\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Create Placeholder Images**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample images created in 'sample_data/'\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "# Create directories if they don't exist\n",
    "os.makedirs('sample_data/class_a', exist_ok=True)\n",
    "os.makedirs('sample_data/class_b', exist_ok=True)\n",
    "\n",
    "# Create 10 sample images for each class\n",
    "for i in range(10):\n",
    "    # Create a blank white image for class_a\n",
    "    img = Image.fromarray(np.ones((224, 224, 3), dtype=np.uint8) * 255)\n",
    "    img.save(f\"sample_data/class_a/img_{i}.jpg\")\n",
    "\n",
    "    # Create a blank black image for class_b\n",
    "    img = Image.fromarray(np.zeros((224, 224, 3), dtype=np.uint8))\n",
    "    img.save(f\"sample_data/class_b/img_{i}.jpg\")\n",
    "\n",
    "print(\"Sample images created in 'sample_data/'\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: Train the Model \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20 images belonging to 2 classes.\n",
      "Found 20 images belonging to 2 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 10s/step - accuracy: 0.5000 - loss: 0.7384\n",
      "Epoch 2/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 4.9954\n",
      "Epoch 3/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 6s/step - accuracy: 0.5000 - loss: 2.0971\n",
      "Epoch 4/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 0.6119\n",
      "Epoch 5/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 6s/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Epoch 6/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Epoch 7/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Epoch 8/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Epoch 9/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Epoch 10/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 0.6931\n"
     ]
    }
   ],
   "source": [
    "# Load and preprocess the dataset\n",
    "train_datagen = ImageDataGenerator(rescale=1./255)\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    'sample_data',\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='binary'\n",
    ")\n",
    "\n",
    "# Verify if the generator has loaded images correctly\n",
    "print(f\"Found {train_generator.samples} images belonging to {train_generator.num_classes} classes.\")\n",
    "\n",
    "# Train the model\n",
    "if train_generator.samples > 0:\n",
    "    model.fit(train_generator, epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5: Fine-Tune the Model \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 10s/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Epoch 2/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Epoch 3/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Epoch 4/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Epoch 5/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Epoch 6/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Epoch 7/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Epoch 8/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Epoch 9/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Epoch 10/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x7fe84c722450>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unfreeze the top layers of the base model \n",
    "\n",
    "for layer in base_model.layers[-4:]:\n",
    "    layer.trainable = True \n",
    "\n",
    "# Compile the model again \n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) \n",
    "\n",
    "# Train the model again \n",
    "model.fit(train_generator, epochs=10) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 1: Visualize Training and Validation Loss\n",
    "\n",
    "**Objective:** Plot the training and validation loss to observe the learning process of the model.\n",
    "\n",
    "**Instructions:**\n",
    "1. Modify the training code to include validation data.\n",
    "2. Plot the training and validation loss for each epoch.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 16 images belonging to 2 classes.\n",
      "Found 4 images belonging to 2 classes.\n",
      "Epoch 1/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 2/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 3/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 4/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 5/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 6/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 7/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 8/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 9/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 9s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 10/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Modify data generator to include validation data\n",
    "train_datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    'sample_data',\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='binary',\n",
    "    subset='training'\n",
    ")\n",
    "\n",
    "validation_generator = train_datagen.flow_from_directory(\n",
    "    'sample_data',\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='binary',\n",
    "    subset='validation'\n",
    ")\n",
    "\n",
    "# Train the model with validation data\n",
    "history = model.fit(train_generator, epochs=10, validation_data=validation_generator)\n",
    "\n",
    "# Plot training and validation loss\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Click here for solution</summary> </br>\n",
    "\n",
    "```python\n",
    "# Modify data generator to include validation data\n",
    "train_datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    'sample_data',\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='binary',\n",
    "    subset='training'\n",
    ")\n",
    "\n",
    "validation_generator = train_datagen.flow_from_directory(\n",
    "    'sample_data',\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='binary',\n",
    "    subset='validation'\n",
    ")\n",
    "\n",
    "# Train the model with validation data\n",
    "history = model.fit(train_generator, epochs=10, validation_data=validation_generator)\n",
    "\n",
    "# Plot training and validation loss\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 2: Experiment with Different Optimizers\n",
    "\n",
    "**Objective:** Experiment with different optimizers and observe their impact on model performance.\n",
    "\n",
    "**Instructions:**\n",
    "1. Change the optimizer from `adam` to `sgd` and `rmsprop`.\n",
    "2. Retrain the model with each optimizer and compare the accuracy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 2/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 3/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 4/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 5/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 6/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 7/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 8/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 9/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 10/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 9s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 2/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 3/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 4/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 5/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 6/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 7/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 8/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 9/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
      "Epoch 10/10\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.6931\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow.keras.models import clone_model\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Function to reset the model weights\n",
    "def reset_model(model):\n",
    "    # Clone the model to reset weights\n",
    "    model_clone = clone_model(model)\n",
    "    model_clone.set_weights(model.get_weights())\n",
    "    return model_clone\n",
    "\n",
    "# Prepare to reset the model for each optimizer test\n",
    "initial_model = reset_model(model)  # Assume 'model' is the initial compiled model\n",
    "\n",
    "# Experiment with SGD optimizer\n",
    "sgd_model = reset_model(initial_model)  # Reset model\n",
    "sgd_model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "history_sgd = sgd_model.fit(train_generator, epochs=10, validation_data=validation_generator)\n",
    "\n",
    "# Plot training and validation accuracy for SGD\n",
    "plt.plot(history_sgd.history['accuracy'], label='Training Accuracy SGD')\n",
    "plt.plot(history_sgd.history['val_accuracy'], label='Validation Accuracy SGD')\n",
    "plt.title('Training and Validation Accuracy with SGD')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Experiment with RMSprop optimizer\n",
    "rmsprop_model = reset_model(initial_model)  # Reset model\n",
    "rmsprop_model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "history_rmsprop = rmsprop_model.fit(train_generator, epochs=10, validation_data=validation_generator)\n",
    "\n",
    "# Plot training and validation accuracy for RMSprop\n",
    "plt.plot(history_rmsprop.history['accuracy'], label='Training Accuracy RMSprop')\n",
    "plt.plot(history_rmsprop.history['val_accuracy'], label='Validation Accuracy RMSprop')\n",
    "plt.title('Training and Validation Accuracy with RMSprop')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Click here for solution</summary> </br>\n",
    "\n",
    "```python\n",
    "from tensorflow.keras.models import clone_model\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Function to reset the model weights\n",
    "def reset_model(model):\n",
    "    # Clone the model to reset weights\n",
    "    model_clone = clone_model(model)\n",
    "    model_clone.set_weights(model.get_weights())\n",
    "    return model_clone\n",
    "\n",
    "# Prepare to reset the model for each optimizer test\n",
    "initial_model = reset_model(model)  # Assume 'model' is the initial compiled model\n",
    "\n",
    "# Experiment with SGD optimizer\n",
    "sgd_model = reset_model(initial_model)  # Reset model\n",
    "sgd_model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "history_sgd = sgd_model.fit(train_generator, epochs=10, validation_data=validation_generator)\n",
    "\n",
    "# Plot training and validation accuracy for SGD\n",
    "plt.plot(history_sgd.history['accuracy'], label='Training Accuracy SGD')\n",
    "plt.plot(history_sgd.history['val_accuracy'], label='Validation Accuracy SGD')\n",
    "plt.title('Training and Validation Accuracy with SGD')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Experiment with RMSprop optimizer\n",
    "rmsprop_model = reset_model(initial_model)  # Reset model\n",
    "rmsprop_model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "history_rmsprop = rmsprop_model.fit(train_generator, epochs=10, validation_data=validation_generator)\n",
    "\n",
    "# Plot training and validation accuracy for RMSprop\n",
    "plt.plot(history_rmsprop.history['accuracy'], label='Training Accuracy RMSprop')\n",
    "plt.plot(history_rmsprop.history['val_accuracy'], label='Validation Accuracy RMSprop')\n",
    "plt.title('Training and Validation Accuracy with RMSprop')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 3: Evaluate the Model on a Test Set\n",
    "\n",
    "**Objective:** Evaluate the fine-tuned model on an unseen test set to assess its generalization performance.\n",
    "\n",
    "**Instructions:**\n",
    "1. Load a separate test set.\n",
    "2. Evaluate the model on this test set and report the accuracy and loss.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20 images belonging to 2 classes.\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7s/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Test Accuracy: 50.00%\n",
      "Test Loss: 0.6931\n"
     ]
    }
   ],
   "source": [
    "# Load and preprocess the test dataset\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    'sample_data',\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='binary'\n",
    ")\n",
    "\n",
    "# Evaluate the fine-tuned model on the test set\n",
    "test_loss, test_accuracy = model.evaluate(test_generator)\n",
    "print(f\"Test Accuracy: {test_accuracy * 100:.2f}%\")\n",
    "print(f\"Test Loss: {test_loss:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Click here for solution</summary> </br>\n",
    "\n",
    "```python\n",
    "# Load and preprocess the test dataset\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    'sample_data',\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='binary'\n",
    ")\n",
    "\n",
    "# Evaluate the fine-tuned model on the test set\n",
    "test_loss, test_accuracy = model.evaluate(test_generator)\n",
    "print(f'Test Accuracy: {test_accuracy * 100:.2f}%')\n",
    "print(f'Test Loss: {test_loss:.4f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "By completing these exercises, students will:\n",
    "\n",
    "1. Visualize the training and validation loss to gain insights into the training process.\n",
    "2. Experiment with different optimizers to understand their impact on model performance.\n",
    "3. Evaluate the fine-tuned model on an unseen test set to assess its generalization capability.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "\n",
    "Congratulations! In this lab, you have successfully implemented transfer learning using a pre-trained model in Keras. This lab exercise demonstrated how to train and fine-tune the model by unfreezing some of the layers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © IBM Corporation. All rights reserved.\n"
   ]
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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