这是我的代码：
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow.keras.applications.vgg16 as vgg16
tf.enable_eager_execution()
def resize_image(image, shape = (224,224)):
target_width = shape[0]
target_height = shape[1]
initial_width = tf.shape(image)[0]
initial_height = tf.shape(image)[1]
im = image
ratio = 0
if(initial_width < initial_height):
ratio = tf.cast(256 / initial_width, tf.float32)
h = tf.cast(initial_height, tf.float32) * ratio
im = tf.image.resize(im, (256, h), method="bicubic")
else:
ratio = tf.cast(256 / initial_height, tf.float32)
w = tf.cast(initial_width, tf.float32) * ratio
im = tf.image.resize(im, (w, 256), method="bicubic")
width = tf.shape(im)[0]
height = tf.shape(im)[1]
startx = width//2 - (target_width//2)
starty = height//2 - (target_height//2)
im = tf.image.crop_to_bounding_box(im, startx, starty, target_width, target_height)
return im
def scale16(image, label):
im = resize_image(image)
im = vgg16.preprocess_input(im)
return (im, label)
data_dir = "/content/"
model = vgg16.VGG16(weights='imagenet', include_top=True)
datasets, info = tfds.load(name="imagenet2012",
with_info=True,
as_supervised=True,
download=False,
data_dir=data_dir, shuffle_files=False
)
train = datasets['train'].map(scale16).batch(2).take(1)
@tf.function
def predict_batch(b, m):
return m.predict_on_batch(b)
sample_imgs = train
for x in sample_imgs:
print("batch prediction", predict_batch(x[0], model))
当我运行不带@ tf.function的代码时，我得到了预期的结果（对该批次的预测）。当我使用@ tf.function运行它时，出现此错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-1-ba7af933ed07> in <module>()
49 sample_imgs = train
50 for x in sample_imgs:
---> 51 print("batch prediction", predict_batch(x[0], model))
7 frames
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/func_graph.py in wrapper(*args, **kwargs)
903 except Exception as e: # pylint:disable=broad-except
904 if hasattr(e, "ag_error_metadata"):
--> 905 raise e.ag_error_metadata.to_exception(e)
906 else:
907 raise
ValueError: in converted code:
<ipython-input-1-ba7af933ed07>:47 predict_batch *
return m.predict_on_batch(b)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py:1155 predict_on_batch
self._make_predict_function()
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py:2179 _make_predict_function
**kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/backend.py:3669 function
return EagerExecutionFunction(inputs, outputs, updates=updates, name=name)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/backend.py:3553 __init__
raise ValueError('Unknown graph. Aborting.')
ValueError: Unknown graph. Aborting.
我正在google Colaboratory上运行代码。为什么会出现此错误？训练步骤通常不应该使用@ tf.function吗？为什么我不能在这种方法中使用模型？