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ai_research/labs/tf_keras.md

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+# Lab Guide: Image Recognition with TensorFlow and Keras
+
+## **Objective**
+
+To provide students with hands-on experience in developing, training, and evaluating image recognition models using TensorFlow and Keras.
+
+## **Prerequisites**
+
+1. Basic understanding of Python programming.
+2. Familiarity with machine learning concepts.
+3. Python and necessary libraries installed: TensorFlow and Keras.
+
+## **Lab Outline**
+
+**Introduction to Image Recognition**:
+    - Discussing the basics of image recognition and convolutional neural networks (CNN).
+   
+**Setting Up the Environment**:
+    - Installing TensorFlow and Keras:
+    ```bash
+    pip install tensorflow keras
+    ```
+
+**Image Data Preprocessing**:
+
+    - **Step 1**: Importing Necessary Libraries:
+    ```python
+    import tensorflow as tf
+    from tensorflow.keras import datasets, layers, models
+    ```
+
+    - **Step 2**: Loading and Preprocessing Image Data:
+    ```python
+    (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
+    
+    # Normalize pixel values to be between 0 and 1
+    train_images, test_images = train_images / 255.0, test_images / 255.0
+    ```
+
+**Building a Convolutional Neural Network (CNN)**:
+
+    - **Step 3**: Defining the CNN Architecture:
+    ```python
+    model = models.Sequential([
+        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
+        layers.MaxPooling2D((2, 2)),
+        layers.Conv2D(64, (3, 3), activation='relu'),
+        layers.MaxPooling2D((2, 2)),
+        layers.Conv2D(64, (3, 3), activation='relu')
+    ])
+    ```
+
+    - **Step 4**: Adding Dense Layers:
+    ```python
+    model.add(layers.Flatten())
+    model.add(layers.Dense(64, activation='relu'))
+    model.add(layers.Dense(10))
+    ```
+
+**Compiling and Training the Model**:
+
+    - **Step 5**: Compiling the Model:
+    ```python
+    model.compile(optimizer='adam',
+                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+                  metrics=['accuracy'])
+    ```
+
+    - **Step 6**: Training the Model:
+    ```python
+    history = model.fit(train_images, train_labels, epochs=10, 
+                        validation_data=(test_images, test_labels))
+    ```
+
+**Evaluating the Model**:
+
+    - **Step 7**: Evaluating the Model and Visualizing Results:
+    ```python
+    test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
+    
+    import matplotlib.pyplot as plt
+
+    plt.plot(history.history['accuracy'], label='accuracy')
+    plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
+    plt.xlabel('Epoch')
+    plt.ylabel('Accuracy')
+    plt.ylim([0.5, 1])
+    plt.legend(loc='lower right')
+    plt.show()
+    ```
+
+
+
+## **Resources**
+
+1. [TensorFlow Documentation](https://www.tensorflow.org/api_docs)
+2. [Keras Documentation](https://keras.io/api/)
+