Recording tensor flow with numat array

Question

I want to create tensorflow entries to feed my model; so far I am using the following code to store a uint8 numpy array in TFRecord format;

def _int64_feature(value):
  return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))


def _bytes_feature(value):
  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))


def _floats_feature(value):
  return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))


def convert_to_record(name, image, label, map):
    filename = os.path.join(params.TRAINING_RECORDS_DATA_DIR, name + '.' + params.DATA_EXT)

    writer = tf.python_io.TFRecordWriter(filename)

    image_raw = image.tostring()
    map_raw   = map.tostring()
    label_raw = label.tostring()

    example = tf.train.Example(features=tf.train.Features(feature={
        'image_raw': _bytes_feature(image_raw),
        'map_raw': _bytes_feature(map_raw),
        'label_raw': _bytes_feature(label_raw)
    }))        
    writer.write(example.SerializeToString())
    writer.close()

which I read using this code example

features = tf.parse_single_example(example, features={
  'image_raw': tf.FixedLenFeature([], tf.string),
  'map_raw': tf.FixedLenFeature([], tf.string),
  'label_raw': tf.FixedLenFeature([], tf.string),
})

image = tf.decode_raw(features['image_raw'], tf.uint8)
image.set_shape(params.IMAGE_HEIGHT*params.IMAGE_WIDTH*3)
image = tf.reshape(image_, (params.IMAGE_HEIGHT,params.IMAGE_WIDTH,3))

map = tf.decode_raw(features['map_raw'], tf.uint8)
map.set_shape(params.MAP_HEIGHT*params.MAP_WIDTH*params.MAP_DEPTH)
map = tf.reshape(map, (params.MAP_HEIGHT,params.MAP_WIDTH,params.MAP_DEPTH))

label = tf.decode_raw(features['label_raw'], tf.uint8)
label.set_shape(params.NUM_CLASSES)

and it works fine. Now I want to do the same with my "map" array, which is a numat float array, not a uint8, and I could not find examples on how to do this; I tried the _floats_feature function, which works if I pass it a scalar, but not with arrays; with uint8, serialization can be done with the tostring () method;

How can I serialize an numat numat array and how can I read it?

Answer 1

FloatList BytesList . . _float_feature, ..

def _floats_feature(value):
  return tf.train.Feature(float_list=tf.train.FloatList(value=value))

numpy_arr = np.ones((3,)).astype(np.float)
example = tf.train.Example(features=tf.train.Features(feature={"bytes": _floats_feature(numpy_arr)}))
print(example)

features {
  feature {
    key: "bytes"
    value {
      float_list {
        value: 1.0
        value: 1.0
        value: 1.0
      }
    }
  }
}

Answer 2

.

Int64List, BytesList FloatList ( ). .

: , , . , , , _floats_feature float ( , ). , , .

, : float_list=tf.train.FloatList(value=value)

Answer 3

, n- :

numpy_arr = np.ones((3,3)). astype (np.float)

I found that it worked when I used numpy_arr.ravel () as input. But is there a better way to do this?