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