Open henryliangt opened 3 years ago
PIL.Image.Open('').split()
PIL.Image.merge('RGB', (r,g,b))
PIL.Image.resize(139, 139)
PIL.Image.tobytes()
features = {'image':'', 'label':''}
tf.train.Feature(bytes_list=tf.train.BytesList(value=[]))
tf.train.Feature(int64_list=tf.train.BytesList(value=[]))
tf.features = tf.train.Features(feature=features)
tf.example = tf.train.Example(features = tf.features)
tf.example.SerializedToString()
glob2.glob()
tf.placeholder(tf.float32.shape)
np.random.choice(range(len([xx])), size=())
tf.io.FixedLenFeature(() , dtype=tf.int64, default = 0)
tf.io.imageLenFeature(() , dtype=tf.string, default = '')
tf.io.parse_single_example(serial)
tf.cast(image, tf.float32)/255
tf.reshape(Image, (139, 139, 3))
tf.one_hot
tf.image.resize
PIL.Image.Open('').split()
PIL.Image.merge('RGB', (r,g,b))
PIL.Image.resize(139, 139)
PIL.Image.tobytes()
features = {'image':'', 'label':''}
tf.train.Feature(bytes_list=tf.train.BytesList(value=[]))
tf.train.Feature(int64_list=tf.train.BytesList(value=[]))
tf.features = tf.train.Features(feature=features)
tf.example = tf.train.Example(features = tf.features)
tf.example.SerializedToString()
glob2.glob()
tf.placeholder(tf.float32.shape)
np.random.choice(range(len([xx])), size=())
tf.io.FixedLenFeature(() , dtype=tf.int64, default = 0)
tf.io.imageLenFeature(() , dtype=tf.string, default = '')
tf.io.parse_single_example(serial)
tf.cast(image, tf.float32)/255
tf.reshape(Image, (139, 139, 3))
tf.one_hot
tf.image.resize