python类parse_single_example()的实例源码

train.py 文件源码 项目:neuroimage-tensorflow 作者: corticometrics 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def read_and_decode(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(serialized_example,features={
        'image_raw': tf.FixedLenFeature([], tf.string),
        'label_raw': tf.FixedLenFeature([], tf.string)})
    image  = tf.cast(tf.decode_raw(features['image_raw'], tf.int16), tf.float32)
    labels = tf.decode_raw(features['label_raw'], tf.int16)

    #PW 2017/03/03: Zero-center data here?
    image.set_shape([IMG_DIM*IMG_DIM*IMG_DIM])
    image  = tf.reshape(image, [IMG_DIM,IMG_DIM,IMG_DIM,1])

    labels.set_shape([IMG_DIM*IMG_DIM*IMG_DIM])
    labels  = tf.reshape(image, [IMG_DIM,IMG_DIM,IMG_DIM])

    # Dimensions (X, Y, Z, channles)
    return image, labels
distribute_cake.py 文件源码 项目:deepcake 作者: ericyue 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def read_and_decode(filename_queue):
  reader = tf.TFRecordReader()
  _, serialized_example = reader.read(filename_queue)
  features = tf.parse_single_example(
      serialized_example,
      features={
          "label": tf.FixedLenFeature([], tf.float32),
          "categorical_features": tf.FixedLenFeature([CATEGORICAL_FEATURES_SIZE], tf.string),
          "continuous_features": tf.FixedLenFeature([CONTINUOUS_FEATURES_SIZE], tf.float32),
      })
  label = features["label"]
  continuous_features = features["continuous_features"]
  categorical_features = tf.cast(tf.string_to_hash_bucket(features["categorical_features"], BUCKET_SIZE), tf.float32)
  return label, tf.concat(0, [continuous_features, categorical_features])


# Read serialized examples from filename queue
inputs_test.py 文件源码 项目:tensorflow_fasttext 作者: apcode 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def test_parse_spec():
    fc = FeatureColumns(
        True,
        False,
        VOCAB_FILE,
        VOCAB_SIZE,
        10,
        10,
        1000,
        10)
    parse_spec = tf.feature_column.make_parse_example_spec(fc)
    print parse_spec
    reader = tf.python_io.tf_record_iterator(INPUT_FILE)
    sess = tf.Session()
    for record in reader:
        example = tf.parse_single_example(
            record,
            parse_spec)
        print sess.run(example)
        break
vfn_train.py 文件源码 项目:view-finding-network 作者: yiling-chen 项目源码 文件源码 阅读 33 收藏 0 点赞 0 评论 0
def read_and_decode(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(
        serialized_example,
      # Defaults are not specified since both keys are required.
        features={
            'image_raw': tf.FixedLenFeature([], tf.string),
        })

    image = tf.decode_raw(features['image_raw'], tf.uint8)
    image = tf.reshape(image, [227, 227, 6])

  # Convert from [0, 255] -> [-0.5, 0.5] floats.
    image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
    return tf.split(image, 2, 2) # 3rd dimension two parts
vfn_train.py 文件源码 项目:view-finding-network 作者: yiling-chen 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def read_and_decode_aug(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(
        serialized_example,
      # Defaults are not specified since both keys are required.
        features={
            'image_raw': tf.FixedLenFeature([], tf.string),
        })

    image = tf.decode_raw(features['image_raw'], tf.uint8)
    image = tf.image.random_flip_left_right(tf.reshape(image, [227, 227, 6]))
  # Convert from [0, 255] -> [-0.5, 0.5] floats.
    image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
    image = tf.image.random_brightness(image, 0.01)
    image = tf.image.random_contrast(image, 0.95, 1.05)
    return tf.split(image, 2, 2) # 3rd dimension two parts
09_tfrecord_example.py 文件源码 项目:deeplearning 作者: fanfanfeng 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def read_from_tfrecord(filenames):
    tfrecord_file_queue = tf.train.string_input_producer(filenames,name='queue')
    reader = tf.TFRecordReader()
    _,tfrecord_serialized = reader.read(tfrecord_file_queue)

    tfrecord_features = tf.parse_single_example(tfrecord_serialized,features={
        'label':tf.FixedLenFeature([],tf.int64),
        'shape':tf.FixedLenFeature([],tf.string),
        'image':tf.FixedLenFeature([],tf.string),
    },name='features')


    image = tf.decode_raw(tfrecord_features['image'],tf.uint8)
    shape = tf.decode_raw(tfrecord_features['shape'],tf.int32)

    image = tf.reshape(image,shape)
    label = tfrecord_features['label']
    return label,shape,image
CNN_for_tfrecords.py 文件源码 项目:gong_an_pictures 作者: oukohou 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def read_decode_tfrecords(records_path, num_epochs=1020, batch_size=Flags.batch_size, num_threads=2):
    if gfile.IsDirectory(records_path):
        records_path = [os.path.join(records_path, i) for i in os.listdir(records_path)]
    else:
        records_path = [records_path]
    records_path_queue = tf.train.string_input_producer(records_path, seed=123,
                                                        num_epochs=num_epochs,
                                                        name="string_input_producer")
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(records_path_queue, name="serialized_example")
    features = tf.parse_single_example(serialized=serialized_example,
                                       features={"img_raw": tf.FixedLenFeature([], tf.string),
                                                 "label": tf.FixedLenFeature([], tf.int64),
                                                 "height": tf.FixedLenFeature([], tf.int64),
                                                 "width": tf.FixedLenFeature([], tf.int64),
                                                 "depth": tf.FixedLenFeature([], tf.int64)},
                                       name="parse_single_example")
    image = tf.decode_raw(features["img_raw"], tf.uint8, name="decode_raw")
    image.set_shape([height * width * 3])
    image = tf.cast(image, tf.float32) * (1.0 / 255) - 0.5
    label = tf.cast(features["label"], tf.int32)
    images, labels = tf.train.shuffle_batch([image, label], batch_size=batch_size, num_threads=num_threads,
                                            name="shuffle_bath", capacity=1020, min_after_dequeue=64)
    print("images' shape is :", str(images.shape))
    return images, labels
test_CNN_no_refactor.py 文件源码 项目:gong_an_pictures 作者: oukohou 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def read_decode_tfrecords(records_path, num_epochs=1, batch_size=Flags.batch_size, num_threads=1):
    if gfile.IsDirectory(records_path):
        records_path = [os.path.join(records_path, i) for i in os.listdir(records_path)]
    else:
        records_path = [records_path]
    records_path_queue = tf.train.string_input_producer(records_path, seed=123,
                                                        num_epochs=None,
                                                        name="string_input_producer")
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(records_path_queue, name="serialized_example")
    features = tf.parse_single_example(serialized=serialized_example,
                                       features={"img_raw": tf.FixedLenFeature([], tf.string),
                                                 "label": tf.FixedLenFeature([], tf.int64),
                                                 "height": tf.FixedLenFeature([], tf.int64),
                                                 "width": tf.FixedLenFeature([], tf.int64),
                                                 "depth": tf.FixedLenFeature([], tf.int64)},
                                       name="parse_single_example")
    image = tf.decode_raw(features["img_raw"], tf.uint8, name="decode_raw")
    image.set_shape([IMAGE_PIXELS])
    image = tf.cast(image, tf.float32) * (1.0 / 255) - 0.5
    label = tf.cast(features["label"], tf.int32)
    images, labels = tf.train.shuffle_batch([image, label], batch_size=batch_size, num_threads=num_threads,
                                            name="shuffle_bath", capacity=1020, min_after_dequeue=50)
    return images, labels
test_CNN_with_checkpoints.py 文件源码 项目:gong_an_pictures 作者: oukohou 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def read_decode_tfrecords(records_path, num_epochs=1020, batch_size=Flags.batch_size, num_threads=2):
    if gfile.IsDirectory(records_path):
        records_path = [os.path.join(records_path, i) for i in os.listdir(records_path)]
    else:
        records_path = [records_path]
    records_path_queue = tf.train.string_input_producer(records_path, seed=123,
                                                        # num_epochs=num_epochs,
                                                        name="string_input_producer")
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(records_path_queue, name="serialized_example")
    features = tf.parse_single_example(serialized=serialized_example,
                                       features={"img_raw": tf.FixedLenFeature([], tf.string),
                                                 "label": tf.FixedLenFeature([], tf.int64),
                                                 "height": tf.FixedLenFeature([], tf.int64),
                                                 "width": tf.FixedLenFeature([], tf.int64),
                                                 "depth": tf.FixedLenFeature([], tf.int64)},
                                       name="parse_single_example")
    image = tf.decode_raw(features["img_raw"], tf.uint8, name="decode_raw")
    image.set_shape([IMAGE_PIXELS])
    image = tf.cast(image, tf.float32) * (1.0 / 255) - 0.5
    label = tf.cast(features["label"], tf.int32)
    # images, labels = tf.train.shuffle_batch([image, label], batch_size=batch_size, num_threads=num_threads,
    #                                         name="shuffle_bath", capacity=1020, min_after_dequeue=64)
    return image, label
tutorial_tfrecord.py 文件源码 项目:tensorlayer-chinese 作者: shorxp 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def read_and_decode(filename):
    # generate a queue with a given file name
    filename_queue = tf.train.string_input_producer([filename])
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)     # return the file and the name of file
    features = tf.parse_single_example(serialized_example,  # see parse_single_sequence_example for sequence example
                                       features={
                                           'label': tf.FixedLenFeature([], tf.int64),
                                           'img_raw' : tf.FixedLenFeature([], tf.string),
                                       })
    # You can do more image distortion here for training data
    img = tf.decode_raw(features['img_raw'], tf.uint8)
    img = tf.reshape(img, [224, 224, 3])
    # img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
    label = tf.cast(features['label'], tf.int32)
    return img, label
tutorial_tfrecord.py 文件源码 项目:tensorlayer-chinese 作者: shorxp 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def read_and_decode(filename):
    # generate a queue with a given file name
    filename_queue = tf.train.string_input_producer([filename])
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)     # return the file and the name of file
    features = tf.parse_single_example(serialized_example,  # see parse_single_sequence_example for sequence example
                                       features={
                                           'label': tf.FixedLenFeature([], tf.int64),
                                           'img_raw' : tf.FixedLenFeature([], tf.string),
                                       })
    # You can do more image distortion here for training data
    img = tf.decode_raw(features['img_raw'], tf.uint8)
    img = tf.reshape(img, [224, 224, 3])
    # img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
    label = tf.cast(features['label'], tf.int32)
    return img, label
ocr_utils.py 文件源码 项目:video_subtitle_extract 作者: thewintersun 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def read_and_decode(filename_queue):
  reader = tf.TFRecordReader()
  _, serialized_example = reader.read(filename_queue)
  features = tf.parse_single_example(
      serialized_example,
      features={
          'height': tf.FixedLenFeature([], tf.int64),
          'width': tf.FixedLenFeature([], tf.int64),
          'image_raw': tf.FixedLenFeature([], tf.string),
          'label': tf.VarLenFeature(tf.int64),
      })

  image = tf.decode_raw(features['image_raw'], tf.uint8)
  image = tf.reshape(image, [730, 38])

  image = tf.cast(image, tf.float32) * (1. / 255) - 0.5

  label = tf.cast(features['label'], tf.int32)

  return image, label
ocr_utils.py 文件源码 项目:video_subtitle_extract 作者: thewintersun 项目源码 文件源码 阅读 36 收藏 0 点赞 0 评论 0
def read_and_decode(filename_queue):
  reader = tf.TFRecordReader()
  _, serialized_example = reader.read(filename_queue)
  features = tf.parse_single_example(
      serialized_example,
      features={
          'height': tf.FixedLenFeature([], tf.int64),
          'width': tf.FixedLenFeature([], tf.int64),
          'image_raw': tf.FixedLenFeature([], tf.string),
          'label': tf.VarLenFeature(tf.int64),
      })

  image = tf.decode_raw(features['image_raw'], tf.uint8)
  image = tf.reshape(image, [730, 38])

  image = tf.cast(image, tf.float32) * (1. / 255) - 0.5

  label = tf.cast(features['label'], tf.int32)

  return image, label
batch_inputs.py 文件源码 项目:sample-cnn 作者: tae-jun 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def _read_example(filename_queue, n_labels=50, n_samples=59049):
  reader = tf.TFRecordReader()
  _, serialized_example = reader.read(filename_queue)
  features = tf.parse_single_example(
    serialized_example,
    features={
      'raw_labels': tf.FixedLenFeature([], tf.string),
      'raw_segment': tf.FixedLenFeature([], tf.string)
    })

  segment = tf.decode_raw(features['raw_segment'], tf.float32)
  segment.set_shape([n_samples])

  labels = tf.decode_raw(features['raw_labels'], tf.uint8)
  labels.set_shape([n_labels])
  labels = tf.cast(labels, tf.float32)

  return segment, labels
tfrecord_model_test_mnist.py 文件源码 项目:sample-cnn 作者: tae-jun 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def read_and_decode(filename, one_hot=True, n_classes=None):
  """ Return tensor to read from TFRecord """
  filename_queue = tf.train.string_input_producer([filename])
  reader = tf.TFRecordReader()
  _, serialized_example = reader.read(filename_queue)
  features = tf.parse_single_example(serialized_example,
                                     features={
                                       'label': tf.FixedLenFeature([],
                                                                   tf.int64),
                                       'image_raw': tf.FixedLenFeature([],
                                                                       tf.string),
                                     })
  # You can do more image distortion here for training data
  img = tf.decode_raw(features['image_raw'], tf.uint8)
  img.set_shape([28 * 28])
  img = tf.reshape(img, [28, 28, 1])
  img = tf.cast(img, tf.float32) * (1. / 255) - 0.5

  label = tf.cast(features['label'], tf.int32)
  if one_hot and n_classes:
    label = tf.one_hot(label, n_classes)

  return img, label
data.py 文件源码 项目:facescore 作者: nanpian 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def parse_example_proto(example_serialized):
  # Dense features in Example proto.
  feature_map = {
      'image/encoded': tf.FixedLenFeature([], dtype=tf.string,
                                          default_value=''),
      'image/filename': tf.FixedLenFeature([], dtype=tf.string,
                                          default_value=''),

      'image/class/label': tf.FixedLenFeature([1], dtype=tf.int64,
                                              default_value=-1),
      'image/class/text': tf.FixedLenFeature([], dtype=tf.string,
                                             default_value=''),
      'image/height': tf.FixedLenFeature([1], dtype=tf.int64,
                                         default_value=-1),
      'image/width': tf.FixedLenFeature([1], dtype=tf.int64,
                                         default_value=-1),

  }

  features = tf.parse_single_example(example_serialized, feature_map)
  label = tf.cast(features['image/class/label'], dtype=tf.int32)
  return features['image/encoded'], label, features['image/filename']
input.py 文件源码 项目:IllustrationGAN 作者: tdrussell 项目源码 文件源码 阅读 33 收藏 0 点赞 0 评论 0
def read_and_decode2(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(
        serialized_example,
        features={
            'file_bytes': tf.FixedLenFeature([], tf.string),
        })

    # decode the png image
    image = tf.image.decode_png(features['file_bytes'], channels=3)

    # Convert to float image
    image = tf.cast(image, tf.float32)

    image.set_shape((IMAGE_SIZE, IMAGE_SIZE, CHANNELS))

    # convert to grayscale if needed
    if CHANNELS == 1:
        image = tf.reduce_mean(image, reduction_indices=[2], keep_dims=True)

    # normalize
    image = image * (2. / 255) - 1

    return image
02_tfrecord_example.py 文件源码 项目:tf_oreilly 作者: chiphuyen 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def read_from_tfrecord(filenames):
    tfrecord_file_queue = tf.train.string_input_producer(filenames, name='queue')
    reader = tf.TFRecordReader()
    _, tfrecord_serialized = reader.read(tfrecord_file_queue)

    # label and image are stored as bytes but could be stored as 
    # int64 or float64 values in a serialized tf.Example protobuf.
    tfrecord_features = tf.parse_single_example(tfrecord_serialized,
                        features={
                            'label': tf.FixedLenFeature([], tf.int64),
                            'shape': tf.FixedLenFeature([], tf.string),
                            'image': tf.FixedLenFeature([], tf.string),
                        }, name='features')
    # image was saved as uint8, so we have to decode as uint8.
    image = tf.decode_raw(tfrecord_features['image'], tf.uint8)
    shape = tf.decode_raw(tfrecord_features['shape'], tf.int32)
    # the image tensor is flattened out, so we have to reconstruct the shape
    image = tf.reshape(image, shape)
    label = tfrecord_features['label']
    return label, shape, image
loadLargeData.py 文件源码 项目:tensorflow-DDT 作者: wangchao66 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def read_and_decode(filename):
    filename_queue = tf.train.string_input_producer([filename])

    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(serialized_example,
                                       features={
                                           'label': tf.FixedLenFeature([], tf.int64),
                                           'img_raw': tf.FixedLenFeature([], tf.string),
                                       })

    img = tf.decode_raw(features['img_raw'], tf.uint8)
    img = tf.reshape(img, [28, 28, 3])
    img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
    label = tf.cast(features['label'], tf.int32)

    return img, label
read_rec.py 文件源码 项目:Stock-Predict-RNN 作者: daiab 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def read_and_decode(record_file):
    print(record_file)
    # read_and_decode_test(record_file)
    data_queue = tf.train.input_producer([record_file], capacity=1e5, name="string_input_producer")
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(data_queue)
    features = tf.parse_single_example(
        serialized_example,
        features={'label': tf.FixedLenFeature([], tf.int64),
                  'target': tf.FixedLenFeature([], tf.float32),
                  'data': tf.FixedLenFeature([cfg.time_step * 4], tf.float32)})
    data_raw = features['data']
    label = features['label']
    target = features['target']
    data = tf.reshape(data_raw, [cfg.time_step, 4])
    data.set_shape([cfg.time_step, 4])
    if cfg.is_training:
        data_batch, label_batch, target_batch = tf.train.batch([data, label, target],
                                                     batch_size=cfg.batch_size,
                                                     capacity=cfg.batch_size * 50,
                                                     num_threads=4)
        return data_batch, label_batch, target_batch
    else:
        return tf.expand_dims(data, 0), tf.expand_dims(label, 0), tf.expand_dims(target, 0)


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