Random_train_test.py

# We create image data for train and test purposes with the following inputs
num_train_samples = 1000
num_test_samples = 200
image_width = 128
image_height = 128
channels = 1 # Grayscale image (single channel)
num_classes = 2 # Binary classification (two classes)

# Generate random training and test data for demonstration
X_train = np.random.random((num_train_samples, image_width, image_height, channels))
y_train = np.random.randint(low=0, high=num_classes, size=num_train_samples)
X_test = np.random.random((num_test_samples, image_width, image_height, channels))