问题描述
Env：Tensorflow2.3.0 python3.6
我正在尝试为训练过程自定义图层以进行图像增强。这是我的代码：
class RandomLight(layers.Layer):
def __init__(self,factor=0.2):
super(RandomLight,self).__init__()
self.factor = factor
def call(self,input,training=None):
return tf.cond(training,lambda: tf.clip_by_value(tf.image.random_brightness(input,self.factor),1),lambda: input)
以及当我要将其放入网络时：
import tensorflow.keras as keras
import tensorflow.keras.layers as layers
from tensorflow.keras.applications import VGG16
inputs = keras.Input(shape=(224,224,3))
vgg16 = VGG16(include_top=False,weights='imagenet',input_shape=(224,3))
data_augmentation = keras.Sequential(
[
layers.experimental.preprocessing.Randomrotation(0.25),layers.experimental.preprocessing.RandomFlip(),RandomLight()
])
i1 = data_augmentation(inputs)
bn = layers.Batchnormalization()(i1)
x = vgg16(bn)
flat_out = layers.Flatten()(x)
h1 = layers.Dense(1024,activation='relu',name='fc1')(flat_out)
h2 = layers.Dropout(0.5)(h1)
h3 = layers.Dense(32,name='fc2')(h2)
h4 = layers.Dropout(0.5)(h3)
new_out = layers.Dense(1,activation='sigmoid',name='prediction')(h4)
vgg_ft = keras.Model(inputs,new_out)
错误似乎与“ training = None”有关
ValueError Traceback (most recent call last)
<ipython-input-290-966a2fabc71b> in <module>()
----> 1 inputs = data_augmentation(inputs)
2 inputs = randomLight(inputs)
3 bn = layers.Batchnormalization()(inputs)
4 x = vgg16(bn)
5 flat_out = layers.Flatten()(x)
F:\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__(self,*args,**kwargs)
924 if _in_functional_construction_mode(self,inputs,args,kwargs,input_list):
925 return self._functional_construction_call(inputs,--> 926 input_list)
927
928 # Maintains info about the `Layer.call` stack.
F:\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _functional_construction_call(self,input_list)
1115 try:
1116 with ops.enable_auto_cast_variables(self._compute_dtype_object):
-> 1117 outputs = call_fn(cast_inputs,**kwargs)
1118
1119 except errors.OperatorNotAllowedInGraphError as e:
F:\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\autograph\impl\api.py in wrapper(*args,**kwargs)
256 except Exception as e: # pylint:disable=broad-except
257 if hasattr(e,'ag_error_Metadata'):
--> 258 raise e.ag_error_Metadata.to_exception(e)
259 else:
260 raise
ValueError: in user code:
<ipython-input-278-87ec004f05b3>:11 call *
lambda: input)
F:\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper **
return target(*args,**kwargs)
F:\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\ops\control_flow_ops.py:1396 cond_for_tf_v2
return cond(pred,true_fn=true_fn,false_fn=false_fn,strict=True,name=name)
F:\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args,**kwargs)
F:\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\util\deprecation.py:507 new_func
return func(*args,**kwargs)
F:\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\ops\control_flow_ops.py:1180 cond
return cond_v2.cond_v2(pred,true_fn,false_fn,name)
F:\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\ops\cond_v2.py:74 cond_v2
pred = ops.convert_to_tensor(pred)
F:\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\framework\ops.py:1499 convert_to_tensor
ret = conversion_func(value,dtype=dtype,name=name,as_ref=as_ref)
F:\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\framework\constant_op.py:338 _constant_tensor_conversion_function
return constant(v,name=name)
F:\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\framework\constant_op.py:264 constant
allow_broadcast=True)
F:\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\framework\constant_op.py:282 _constant_impl
allow_broadcast=allow_broadcast))
F:\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\framework\tensor_util.py:444 make_tensor_proto
raise ValueError("None values not supported.")
ValueError: None values not supported.
我也尝试过training=False，但这也不起作用。
看来Sequential()在我的自定义图层上效果很好，但是如何以我的格式使用
解决方法
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