import tensorflow as tf# Define the input and output sizesinput_size = None None 3output_size = 720 1280 3# Define the network architecturedef build_model # Define the input layer inputs = tfker

Sure, here's an example of how you can modify the code to train the model on image files in folders "train" and "test":

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Define the input and output sizes
input_size = (None, None, 3)
output_size = (720, 1280, 3)

# Define the network architecture
def build_model():
    # Define the input layer
    inputs = tf.keras.layers.Input(shape=input_size)

    # Convolutional Layers
    x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
    x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
    x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
    x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)

    # Upsampling Layers
    x = tf.keras.layers.UpSampling2D(size=(2, 2))(x)

    # Resampling Layers
    x = tf.keras.layers.Conv2D(3, (3, 3), activation='linear', padding='same')(x)  # This layer performs the Lanczos resampling or bicubic interpolation.

    # Reconstruction Layers
    x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
    x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
    x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
    x = tf.keras.layers.Conv2D(3, (3, 3), activation='linear', padding='same')(x)  # This layer enhances the image quality.

    # Output Layer
    outputs = tf.keras.layers.experimental.preprocessing.Resizing(
    height=output_size[0], 
    width=output_size[1], 
    interpolation='bicubic'
)(x)

    # Create the model
    model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
    return model

# Build the model
model = build_model()

# Define data generators for training and validation
train_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
    'train',
    target_size=(720, 1280),
    batch_size=32,
    class_mode=None)

test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
    'test',
    target_size=(720, 1280),
    batch_size=32,
    class_mode=None)

# Compile the model
model.compile(optimizer='adam', loss=tf.keras.losses.MeanSquaredError())

# Train the model
model.fit(train_generator, epochs=10, validation_data=test_generator)

# Save the trained model
model.save('superresolution_model.h5')