Generative AI - Implementing ResNet for Image Classification Tasks from Scratch

Implementing ResNet for Image Classification Tasks from Scratch

Here, we will apply a streamlined version of ResNet to picture categorization jobs. In order to construct and train our model, we will be use TensorFlow and Keras. To begin, I will walk you through each step by step.

Step 1: Import Libraries

Importing the required libraries should be our first step.

We import the Keras and TensorFlow modules here. For the purpose of training and testing our ResNet model, we additionally import the CIFAR-10 dataset.

Step 2: Load and Preprocess the Data

The CIFAR-10 dataset is then loaded and preprocessed.

Prior to training the model, we standardize the picture pixel values to the interval [0, 1] and transform the labels into one-hot encoding.

Step 3: Define the Residual Block

As the fundamental unit of ResNet, we create a function to specify a residual block.

Two convolutional layers are generated by this algorithm as a residual block. It adjusts the shortcut link by adding a convolutional layer if the input and output dimensions are different.

Step 4: Build the ResNet Model

We next use the residual blocks to create the ResNet model.

def build_resnet(input_shape, num_classes):
    inputs = layers.Input(shape=input_shape)
    x = layers.Conv2D(64, kernel_size=7, strides=2, padding='same')(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.ReLU()(x)
    x = layers.MaxPooling2D(pool_size=3, strides=2, padding='same')(x)

    x = residual_block(x, 64)
    x = residual_block(x, 64)
    
    x = residual_block(x, 128, stride=2)
    x = residual_block(x, 128)
    
    x = residual_block(x, 256, stride=2)
    x = residual_block(x, 256)
    
    x = residual_block(x, 512, stride=2)
    x = residual_block(x, 512)

    x = layers.GlobalAveragePooling2D()(x)
    outputs = layers.Dense(num_classes, activation='softmax')(x)

    model = models.Model(inputs, outputs)
    return model

To generate a ResNet model, use this function. A max-pooling layer comes after a convolutional layer at the beginning. Afterward, it reduces the spatial dimensions and increases the number of filters by stacking many leftover blocks. Lastly, classification, employs a thick layer with a softmax activation and global average pooling.

Step 5: Compile and Train the Model

A suitable optimizer, loss function, and metrics are included during model compilation. Our next step is to teach it using the CIFAR-10 dataset.

# Define the model
model = build_resnet((32, 32, 3), 10)

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

# Train the model
history = model.fit(x_train, y_train, epochs=20, batch_size=64, validation_data=(x_test, y_test))

In this case, we use the categorical cross-entropy loss function and the Adam optimizer. We use a 64-batch size to train the model for 20 iterations, validating it on the test set after each iteration.

Step 6: Evaluate the Model

We conclude by running the trained model through its paces on the test data set.

# Evaluate the model
test_loss, test_accuracy = model.evaluate(x_test, y_test)
print(f"Test accuracy: {test_accuracy:.4f}")

The model's ability to generalize is evaluated here by testing it on new data.

Once you've followed these instructions, you'll have built a ResNet model for image classification tasks from the ground up. Deep residual networks are very useful for a variety of image identification problems; this technique will help you learn how to build and train one.