如何解决ValueError：形状必须相等，但等级必须为1和0将形状1与其他形状合并表示“损失/增加”
我正在尝试使用张量流创建变体自动编码器。我已按照keras网站（https://keras.io/guides/making_new_layers_and_models_via_subclassing/）遵循了所有步骤 但是我做了些小改动。
annealing_weight = tf.keras.backend.variable(0.01)
test = VariationalAutoEncoder(annealing_weight,[8,8,128],input_shape=(None,256,1))
test.compile('adam',loss=None)
test.summary()
test.train_on_batch(np.random.randn(32,1),None)
我能够编译网络并获得摘要。一切似乎正常。 但是，当我尝试批量训练以查看网络是否正常运行时，收到以下错误消息。问题似乎出在错误功能上。
我希望有人能帮助我。谢谢！
WARNING:tensorflow:AutoGraph could not transform <bound method ConvolutionalBlock.call of <__main__.ConvolutionalBlock object at 0x000000001D5DC408>> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug,set the verbosity to 10 (on Linux,`export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: Unable to locate the source code of <bound method ConvolutionalBlock.call of <__main__.ConvolutionalBlock object at 0x000000001D5DC408>>. Note that functions defined in certain environments,like the interactive Python shell do not expose their source code. If that is the case,you should to define them in a .py source file. If you are certain the code is graph-compatible,wrap the call using @tf.autograph.do_not_convert. Original error: could not get source code
WARNING:tensorflow:Output output_1 missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to output_1.
Traceback (most recent call last):
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\framework\ops.py",line 1619,in _create_c_op
c_op = c_api.TF_FinishOperation(op_desc)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shapes must be equal rank,but are 1 and 0
From merging shape 1 with other shapes. for 'loss_1/AddN' (op: 'AddN') with input shapes: [?],[?],[],[].
During handling of the above exception,another exception occurred:
Traceback (most recent call last):
File "<input>",line 10,in <module>
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\keras\engine\training.py",line 1078,in train_on_batch
standalone=True)
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py",line 416,in train_on_batch
extract_tensors_from_dataset=True)
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\keras\engine\training.py",line 2360,in _standardize_user_data
self._compile_from_inputs(all_inputs,y_input,x,y)
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\keras\engine\training.py",line 2618,in _compile_from_inputs
experimental_run_tf_function=self._experimental_run_tf_function)
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\training\tracking\base.py",line 457,in _method_wrapper
result = method(self,*args,**kwargs)
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\keras\engine\training.py",line 446,in compile
self._compile_weights_loss_and_weighted_metrics()
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\training\tracking\base.py",line 1592,in _compile_weights_loss_and_weighted_metrics
self.total_loss = self._prepare_total_loss(masks)
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\keras\engine\training.py",line 1701,in _prepare_total_loss
math_ops.add_n(custom_losses))
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\util\dispatch.py",line 180,in wrapper
return target(*args,**kwargs)
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\ops\math_ops.py",line 3053,in add_n
return gen_math_ops.add_n(inputs,name=name)
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\ops\gen_math_ops.py",line 420,in add_n
"AddN",inputs=inputs,name=name)
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\framework\op_def_library.py",line 742,in _apply_op_helper
attrs=attr_protos,op_def=op_def)
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\framework\func_graph.py",line 595,in _create_op_internal
compute_device)
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\framework\ops.py",line 3322,in _create_op_internal
op_def=op_def)
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\framework\ops.py",line 1786,in __init__
control_input_ops)
File "C:\Users\user\.conda\envs\Thesis\lib\site-packages\tensorflow_core\python\framework\ops.py",line 1622,in _create_c_op
raise ValueError(str(e))
ValueError: Shapes must be equal rank,[].
代码如下所示。
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers as tfl
class ConvolutionalBlock(tfl.Layer):
def __init__(self,filters,name,deconv=False,**kwargs):
self.conv_layer = tfl.Conv2DTranspose if deconv else tfl.Conv2D
self.conv_layer = self.conv_layer(filters,kernel_size=3,padding='same',kernel_initializer='he_normal',kernel_regularizer=tf.keras.regularizers.l2(0.0001),strides=2,name='conv')
self.batch_norm = tfl.BatchNormalization(name='de_ennorm')
self.relu = tfl.ReLU(name='en_relu')# + str(index))
super(ConvolutionalBlock,self).__init__(name=name,**kwargs)
def call(self,inputs,**kwargs):
outputs = self.conv_layer(inputs)
outputs = self.batch_norm(outputs)
outputs = self.relu(outputs)
return outputs
class Sampling(tfl.Layer):
def __init__(self,**kwargs):
super(Sampling,self).__init__(name='reparameterization_trick',training=None,mask=None,**kwargs):
x_mean,x_variance = inputs
return x_mean + tf.keras.backend.exp(0.5 * x_variance) * \
tf.keras.backend.random_normal(shape=(32,128),mean=0.,stddev=1.0)
class Encoder(tfl.Layer):
def __init__(self,**kwargs):
super(Encoder,self).__init__(name='Encoder',**kwargs)
self.convs = [
ConvolutionalBlock(8,'conv1'),ConvolutionalBlock(16,'conv2'),ConvolutionalBlock(32,'conv3'),ConvolutionalBlock(64,'conv4'),ConvolutionalBlock(128,'conv5')
]
self.features = tfl.GlobalAveragePooling2D(name='globaverpool')
self.denserepresentation = tfl.Dense(128,activation='relu',name='Dense1')
self.x_mean = tfl.Dense(128,name='meanvector')
self.x_variance = tfl.Dense(128,name='variancevector')
self.sampling = Sampling()
def call(self,**kwargs):
outputs = inputs
print(outputs)
for layer in self.convs:
outputs = layer(outputs)
print(outputs)
outputs = self.features(outputs)
print(outputs)
dense_output = self.denserepresentation(outputs)
print(dense_output)
x_mean = self.x_mean(dense_output)
x_variance = self.x_variance(dense_output)
output = self.sampling((x_mean,x_variance))
return output,x_mean,x_variance
class Decoder(tfl.Layer):
def __init__(self,dense_reshape,**kwargs):
super(Decoder,self).__init__(name='Decoder',**kwargs)
self.denserepresentation = tfl.Dense(np.prod(dense_reshape),name='dense2')
self.reshaped = tfl.Reshape(dense_reshape,name='reshape')
self.deconvs=[
ConvolutionalBlock(128,'conv1',deconv=True),'conv2','conv3','conv4',ConvolutionalBlock(8,'conv5',deconv=True)
]
self.output_layer = tfl.Conv2D(filters=1,activation='sigmoid',# check this
padding='same',name='decodedconv',kernel_initializer='he_normal')
def call(self,mask=None):
outputs = inputs
outputs = self.denserepresentation(outputs)
outputs = self.reshaped(outputs)
for layer in self.deconvs:
outputs = layer(outputs)
outputs = self.output_layer(outputs)
return outputs
class VariationalAutoEncoder(tf.keras.Model):
def __init__(self,annealing_weight,input_shape,**kwargs):
super(VariationalAutoEncoder,self).__init__(**kwargs)
self.annealing_weight = annealing_weight # for KL-loss
self.encoder = Encoder()
self.decoder = Decoder(dense_reshape)
self.build(input_shape)
def call(self,mask=None):
dense_output,x_variance = self.encoder(inputs)
output = self.decoder(dense_output)
kl_loss = - self.annealing_weight * tf.reduce_mean(
x_variance - tf.keras.backend.square(x_mean)
- tf.keras.backend.exp(x_variance) + 1,axis=-1)
self.add_loss(lambda: kl_loss)
return output
解决方法
如错误消息所示，tf.math.add_n函数的输入张量具有不同的等级。下面，我重新创建了您的错误-
重现错误的代码-
%tensorflow_version 1.x
import tensorflow as tf
a = tf.constant([[3,5],[4,8]])
b = tf.constant([[[1,6]],[[2,9]]])
tf.math.add_n([a,b,a])
输出-
TensorFlow 1.x selected.
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/tensorflow-1.15.2/python3.6/tensorflow_core/python/framework/ops.py in _create_c_op(graph,node_def,inputs,control_inputs)
1606 try:
-> 1607 c_op = c_api.TF_FinishOperation(op_desc)
1608 except errors.InvalidArgumentError as e:
InvalidArgumentError: Shapes must be equal rank,but are 3 and 2
From merging shape 1 with other shapes. for 'AddN' (op: 'AddN') with input shapes: [2,2],[2,1,2].
During handling of the above exception,another exception occurred:
ValueError Traceback (most recent call last)
9 frames
/tensorflow-1.15.2/python3.6/tensorflow_core/python/framework/ops.py in _create_c_op(graph,control_inputs)
1608 except errors.InvalidArgumentError as e:
1609 # Convert to ValueError for backwards compatibility.
-> 1610 raise ValueError(str(e))
1611
1612 return c_op
ValueError: Shapes must be equal rank,2].
注意-错误消息的措辞在 tensorflow 2.x
中有所不同要解决此错误，请将相同秩的张量传递给tf.math.add_n函数。
固定代码-
%tensorflow_version 1.x
import tensorflow as tf
a = tf.constant([[3,8]])
b = tf.constant([[1,6],9]])
tf.math.add_n([a,a])
输出-
<tf.Tensor 'AddN_1:0' shape=(2,2) dtype=int32>
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