Use tf.io.parse_tensor to convert the binary-string back to a tensor.īelow are some examples of how these functions work. The simplest way to handle non-scalar features is to use tf.io.serialize_tensor to convert tensors to binary-strings. Note: To stay simple, this example only uses scalar inputs. Return tf.train.Feature(int64_list=tf.train.Int64List(value=)) """Returns an int64_list from a bool / enum / int / uint.""" Return tf.train.Feature(float_list=tf.train.FloatList(value=)) """Returns a float_list from a float / double.""" Return tf.train.Feature(bytes_list=tf.train.BytesList(value=)) Value = value.numpy() # BytesList won't unpack a string from an EagerTensor. If isinstance(value, type(tf.constant(0))): """Returns a bytes_list from a string / byte.""" Note that each function takes a scalar input value and returns a tf.train.Feature containing one of the three list types above: # The following functions can be used to convert a value to a type compatible In order to convert a standard TensorFlow type to a tf.train.Example-compatible tf.train.Feature, you can use the shortcut functions below. Tf.train.Int64List (the following types can be coerced) Tf.train.FloatList (the following types can be coerced) Tf.train.BytesList (the following types can be coerced) Most other generic types can be coerced into one of these: The tf.train.Feature message type can accept one of the following three types (See the. The tf.train.Example message (or protobuf) is a flexible message type that represents a mapping. proto files, these are often the easiest way to understand a message type. Protocol buffers are a cross-platform, cross-language library for efficient serialization of structured data. The TFRecord format is a simple format for storing a sequence of binary records.
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