tf.io.serialize_tensor

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Transforms a Tensor into a serialized TensorProto proto.

tf.io.serialize_tensor( tensor, name=None ) 

This operation transforms data in a tf.Tensor into a tf.Tensor of type tf.string containing the data in a binary string in little-endian format. This operation can transform scalar data and linear arrays, but it is most useful in converting multidimensional arrays into a format accepted by binary storage formats such as a TFRecord or tf.train.Example.

See also:

• tf.io.parse_tensor: inverse operation of tf.io.serialize_tensor that transforms a scalar string containing a serialized Tensor in little-endian format into a Tensor of a specified type.
• tf.ensure_shape: parse_tensor cannot statically determine the shape of the parsed tensor. Use tf.ensure_shape to set the static shape when running under a tf.function
• .SerializeToString, serializes a proto to a binary-string

Example of serializing scalar data:

t = tf.constant(1) tf.io.serialize_tensor(t) <tf.Tensor: shape=(), dtype=string, numpy=b'\x08...\x00'>

Example of storing non-scalar data into a tf.train.Example:

t1 = [[1, 2]] t2 = [[7, 8]] nonscalar = tf.concat([t1, t2], 0) nonscalar <tf.Tensor: shape=(2, 2), dtype=int32, numpy= array([[1, 2],  [7, 8]], dtype=int32)>

Serialize the data using tf.io.serialize_tensor.

serialized_nonscalar = tf.io.serialize_tensor(nonscalar) serialized_nonscalar <tf.Tensor: shape=(), dtype=string, numpy=b'\x08...\x00'>

Store the data in a tf.train.Feature.

feature_of_bytes = tf.train.Feature(  bytes_list=tf.train.BytesList(value=[serialized_nonscalar.numpy()])) feature_of_bytes bytes_list {  value: "\010...\000" }

Put the tf.train.Feature message into a tf.train.Example.

features_for_example = {  'feature0': feature_of_bytes } example_proto = tf.train.Example(  features=tf.train.Features(feature=features_for_example)) example_proto features {  feature {  key: "feature0"  value {  bytes_list {  value: "\010...\000"  }  }  } }