Create README.md

README.md

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+---
+size_categories:
+- 1M<n<10M
+---
+
+E621 TFRecords to train classifiers and other stuff with my codebases.
+
+TFRecord serialization/deserialization code:
+
+```python
+NUM_CLASSES = 8783
+
+
+# Function to convert value to bytes_list
+def _bytes_feature(value):
+    if isinstance(value, type(tf.constant(0))):
+        value = value.numpy()
+    elif isinstance(value, str):
+        value = value.encode()
+    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
+
+
+# Function to convert bool/enum/int/uint to int64_list
+def _int64_feature(value):
+    int64_list = tf.train.Int64List(value=tf.reshape(value, (-1,)))
+    return tf.train.Feature(int64_list=int64_list)
+
+
+# Function to create a tf.train.Example message
+def serialize_example(image_id, image_bytes, label_indexes, tag_string):
+    feature = {
+        "image_id": _int64_feature(image_id),
+        "image_bytes": _bytes_feature(image_bytes),
+        "label_indexes": _int64_feature(label_indexes),
+        "tag_string": _bytes_feature(tag_string),
+    }
+    example_proto = tf.train.Example(features=tf.train.Features(feature=feature))
+    return example_proto.SerializeToString()
+
+
+# Function to deserialize a single tf.train.Example message
+def deserialize_example(example_proto):
+    feature_description = {
+        "image_id": tf.io.FixedLenFeature([], tf.int64),
+        "image_bytes": tf.io.FixedLenFeature([], tf.string),
+        "label_indexes": tf.io.VarLenFeature(tf.int64),
+        "tag_string": tf.io.FixedLenFeature([], tf.string),
+    }
+
+    # Parse the input 'tf.train.Example' proto using the dictionary above.
+    parsed_example = tf.io.parse_single_example(example_proto, feature_description)
+    image_tensor = tf.io.decode_jpeg(parsed_example["image_bytes"], channels=3)
+
+    # We only stored label indexes in the TFRecords to save space
+    # Emulate MultiLabelBinarizer to get a tensor of 0s and 1s
+    label_indexes = tf.sparse.to_dense(
+        parsed_example["label_indexes"],
+        default_value=0,
+    )
+    one_hots = tf.one_hot(label_indexes, NUM_CLASSES)
+    labels = tf.reduce_max(one_hots, axis=0)
+    labels = tf.cast(labels, tf.float32)
+
+    sample = {
+        "image_ids": parsed_example["image_id"],
+        "images": image_tensor,
+        "labels": labels,
+        "tags": parsed_example["tag_string"],
+    }
+    return sample
+```