def read_and_decode(filename, one_hot=True, n_class=None, is_train=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
# img = tf.cast(img, tf.float32) * (1. / 255)
label = tf.cast(features['label'], tf.int32)
if one_hot and n_class:
label = tf.one_hot(label, n_class)
return img, label
def read_and_decode(filename,
w,
h,
one_hot=True,
n_class=None,
is_train=None,
bResize=False,
origImgW=0,
origImgH=0):
""" Return tensor to read from TFRecord """
# files = tf.train.match_filenames_once(filename)
files = filename
# print(files)
filename_queue = tf.train.string_input_producer(files)
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),
'depth': tf.FixedLenFeature([], tf.int64),
'image_raw': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64)
})
# You can do more image distortion here for training data
img = tf.decode_raw(features['image_raw'], tf.uint8)
img = tf.reshape(img, [origImgW, origImgH, 3])
if bResize:
img = tf.image.resize_images(img, (w, h), method=0)
img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
# img = tf.cast(img, tf.float32) * (1. / 255)
label = features['label']
# label = tf.cast(label, tf.float32)
if one_hot and n_class:
label = tf.one_hot(label, n_class)
return img, label
def binarize(x, sz=num_alphabet):
from keras.backend import tf
return tf.to_float(tf.one_hot(x, sz, on_value=1, off_value=0, axis=-1))