#CNN #DeepLearning #Python #Tutorial

Lesson: Building a Convolutional Neural Network (CNN) for Image Classification

This lesson will guide you through building a CNN from scratch using TensorFlow and Keras to classify images from the CIFAR-10 dataset.

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Part 1: Setup and Data Loading

First, we import the necessary libraries and load the CIFAR-10 dataset. This dataset contains 60,000 32x32 color images in 10 classes.

import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
import numpy as np

# Load the CIFAR-10 dataset
(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()

# Check the shape of the data
print("Training data shape:", x_train.shape)
print("Test data shape:", x_test.shape)

#TensorFlow #Keras #DataLoading

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Part 2: Data Exploration and Preprocessing

We need to prepare the data before feeding it to the network. This involves:
• Normalization: Scaling pixel values from the 0-255 range to the 0-1 range.
• One-Hot Encoding: Converting class vectors (integers) to a binary matrix.

Let's also visualize some images to understand our data.

# Define class names for CIFAR-10
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

# Visualize a few images
plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(x_train[i])
    plt.xlabel(class_names[y_train[i][0]])
plt.show()

# Normalize pixel values to be between 0 and 1
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0

# One-hot encode the labels
y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)
y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)

#DataPreprocessing #Normalization #Visualization

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Part 3: Building the CNN Model

Now, we'll construct our CNN model. A common architecture consists of a stack of Conv2D and MaxPooling2D layers, followed by Dense layers for classification.

• Conv2D: Extracts features (like edges, corners) from the input image.
• MaxPooling2D: Reduces the spatial dimensions (downsampling), which helps in making the feature detection more robust.
• Flatten: Converts the 2D feature maps into a 1D vector.
• Dense: A standard fully-connected neural network layer.

model = models.Sequential()

# Convolutional Base
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))

# Flatten and Dense Layers
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax')) # 10 output classes

# Print the model summary
model.summary()

#ModelBuilding #CNN #KerasLayers

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Part 4: Compiling the Model

Before training, we need to configure the learning process. This is done via the compile() method, which requires:
• Optimizer: An algorithm to update the model's weights (e.g., 'adam').
• Loss Function: A function to measure how inaccurate the model is during training (e.g., 'categorical_crossentropy' for multi-class classification).
• Metrics: Used to monitor the training and testing steps (e.g., 'accuracy').

model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

#ModelCompilation #Optimizer #LossFunction

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