Update README.md

README.md

@@ -180,15 +180,41 @@ dataloader = DataLoader(dataset, batch_size=16, shuffle=True)
 import tensorflow as tf
 from datasets import load_dataset
 
-# Load dataset
+TARGET_IMG_SIZE = (224, 224)
+BATCH_SIZE = 16
 dataset = load_dataset("Dataseeds/DataSeeds.AI-Sample-Dataset-DSD", split="train")
 
-# Convert to TensorFlow Dataset
-tf_dataset = dataset.to_tf_dataset(
-    columns=["image", "image_title"],
-    batch_size=16,
-    shuffle=True
+def hf_dataset_generator():
+    for example in dataset:
+        yield example['image'], example['image_title']
+
+def preprocess(image, title):
+    # Resize the image to a fixed size
+    image = tf.image.resize(image, TARGET_IMG_SIZE)
+    image = tf.cast(image, tf.uint8)
+    return image, title
+
+# The output_signature defines the data types and shapes
+tf_dataset = tf.data.Dataset.from_generator(
+    hf_dataset_generator,
+    output_signature=(
+        tf.TensorSpec(shape=(None, None, 3), dtype=tf.uint8),
+        tf.TensorSpec(shape=(), dtype=tf.string),
+    )
+)
+
+# Apply the preprocessing, shuffle, and batch
+tf_dataset = (
+    tf_dataset.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)
+    .shuffle(buffer_size=100)
+    .batch(BATCH_SIZE)
+    .prefetch(tf.data.AUTOTUNE)
 )
+
+print("Dataset is ready.")
+for images, titles in tf_dataset.take(1):
+    print("Image batch shape:", images.shape)
+    print("A title from the batch:", titles.numpy()[0].decode('utf-8'))
 ```
 
 ## Dataset Characterization