我正在尝试使用 TensorFlow 实现 https://github.com/ts-kim/RevIN 的 ReVin 功能。但是，我得到了相同的“超出范围”错误。我打印了一些标志，但是，我想在反向传播中出现了这个错误：
XXXXXXXXXX
[[[0.328842312 0.5]]
[[0.32185629 0.5]]
[[0.316616774 0.50000006]]
...
[[0.291666657 0.50000006]]
[[0.277694613 0.50000006]]
[[0.274700612 0.5]]]
YYYYYYYYY
[[[0.328842312 0.5]]
[[0.32185629 0.5]]
[[0.316616774 0.50000006]]
...
[[0.291666657 0.50000006]]
[[0.277694613 0.50000006]]
[[0.274700612 0.5]]]
AAAAAAA
[[[0.328842312 0.5]]
[[0.32185629 0.5]]
[[0.316616774 0.50000006]]
...
[[0.291666657 0.50000006]]
[[0.277694613 0.50000006]]
[[0.274700612 0.5]]]
BBBBBBB
[[[0.0613426 0.341575056]]
[[0.0573474802 0.341575056]]
[[0.0628619641 0.341575056]]
...
[[0.0749944896 0.341575056]]
[[0.0566082373 0.341575056]]
[[0.0575896166 0.341575056]]]
CCCCCCC
DDDDDDD
[[[0.328842312 0.5]]
[[0.32185629 0.5]]
[[0.316616774 0.50000006]]
...
[[0.291666657 0.50000006]]
[[0.277694613 0.50000006]]
[[0.274700612 0.5]]]
InaccessibleTensorError Traceback (most recent call last)
<ipython-input-112-d0481f2f6b0e> in <cell line: 1>()
----> 1 history = model1.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=1000, callbacks=[cp,es], verbose=2)
1 frames
/usr/lib/python3.10/contextlib.py in __exit__(self, typ, value, traceback)
140 if typ is None:
141 try:
--> 142 next(self.gen)
143 except StopIteration:
144 return False
InaccessibleTensorError: Exception encountered when calling layer 'rev_in_13' (type RevIN).
<tf.Tensor 'Mean:0' shape=(None, 1, 2) dtype=float32> is out of scope and cannot be used here. Use return values, explicit Python locals or TensorFlow collections to access it.
Please see https://www.tensorflow.org/guide/function#all_outputs_of_a_tffunction_must_be_return_values for more information.
(.....)
call_output = layer_call(*args, **kwargs)
File "/content/RevIN.py", line 27, in call
mean, stdev = self._get_statistics(x)
File "/content/RevIN.py", line 58, in _get_statistics
mean = tf.reduce_mean(x, axis=[i for i in range(1, len(x.shape) - 1)], keepdims=True)
The tensor <tf.Tensor 'Mean:0' shape=(None, 1, 2) dtype=float32> cannot be accessed from FuncGraph(name=model_9_layer_call_and_return_conditional_losses, id=134545682327840), because it was defined in FuncGraph(name=rev_in_13_layer_call_and_return_conditional_losses, id=134545682318048), which is out of scope.
Call arguments received by layer 'rev_in_13' (type RevIN):
• args=('tf.Tensor(shape=(None, 2), dtype=float32)',)
• kwargs={'mode': "'denorm'"}
代码：
from RevIN import RevIN
%load_ext autoreload
%autoreload 2
#tf.enable_eager_execution()
revin_layer = RevIN(2)
x=Input(shape=(12,2))
model=revin_layer(x,mode="norm")
model2=LSTM(32,return_sequences=False)(model)
output_layer=Dense(2)(model2)
output_layer1=revin_layer(output_layer,mode="denorm")
model1 = Model(inputs=x, outputs=output_layer1)
我称之为典型的编译和拟合：
history = model1.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=1000, callbacks=[cp,es], verbose=2)
Revin函数中的代码为：
import tensorflow as tf
from tensorflow.keras import layers
import tensorflow.keras.backend as K
class RevIN(tf.keras.layers.Layer):
def __init__(self, num_features, eps=1e-5, affine=True, **kwargs):
"""
:param num_features: the number of features or channels
:param eps: a value added for numerical stability
:param affine: if True, RevIN has learnable affine parameters
"""
super(RevIN, self).__init__(**kwargs)
self.num_features = num_features
self.eps = eps
self.affine = affine
if self.affine:
print("XXXXXXXXXXX")
self._init_params()
else:
print("YYYYYYYYYYY")
self.affine_weight = None
self.affine_bias = None
def call(self, x, mode):
x = tf.convert_to_tensor(x, dtype=self.dtype) # Ensure the input tensor is converted to the correct dtype
if mode == 'norm':
mean, stdev = self._get_statistics(x)
self.mean=mean
self.stdev=stdev
x = self._normalize(x, self.mean, self.stdev)
elif mode == 'denorm':
x = self._denormalize(x,self.mean,self.stdev)
else:
raise NotImplementedError
return x
def _init_params(self):
# initialize RevIN params: (C,)
self.affine_weight = self.add_weight(name='affine_weight',
shape=(self.num_features,),
initializer='ones',
trainable=True)
self.affine_bias = self.add_weight(name='affine_bias',
shape=(self.num_features,),
initializer='zeros',
trainable=True)
#self.mean=tf.Variable([],trainable=False)
#self.stdev=tf.Variable(None,trainable=False)
# def _get_statistics(self, x):
# mean = tf.stop_gradient(tf.reduce_mean(x, axis=[i for i in range(1, len(x.shape) - 1)], keepdims=True))
# stdev = tf.stop_gradient(tf.math.reduce_std(x, axis=[i for i in range(1, len(x.shape) - 1)], keepdims=True) + self.eps)
# self.mean=mean
# self.stdev=stdev
# return mean, stdev
def _get_statistics(self, x):
mean = tf.reduce_mean(x, axis=[i for i in range(1, len(x.shape) - 1)], keepdims=True)
stdev = tf.math.reduce_std(x, axis=[i for i in range(1, len(x.shape) - 1)], keepdims=True) + self.eps
#self.mean = K.stop_gradient(mean)
#self.stdev = K.stop_gradient(stdev)
return mean, stdev
def _normalize(self, x, mean, stdev):
tf.print("XXXXXXXXXX")
tf.print(self.mean)
tf.print("YYYYYYYYY")
tf.print(mean)
x = x - mean
x = x / stdev
if self.affine:
x = x * self.affine_weight
x = x + self.affine_bias
return x
def _denormalize(self, x,mean,stdev ):
tf.print("AAAAAAA")
tf.print(self.mean)
tf.print("BBBBBBB")
tf.print(self.stdev)
if self.affine:
x = x - self.affine_bias
x = x / (self.affine_weight + self.eps*self.eps)
#mean, stdev = self._get_statistics(x)
tf.print("CCCCCCC")
#tf.print(mean)
tf.print("DDDDDDD")
tf.print(self.mean)
x = x * stdev
x = x + mean
return x
我不知道如何使用 self.mean...总是出现超出范围的错误...:///