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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
- Basic understanding of Python programming.
- Familiarity with machine learning concepts.
- 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()
```