意外找到 BatchNormalization 类型的实例。期望一个符号张量实例
Unexpectedly found an instance of type of BatchNormalization. Expected a symbolic tensor instance
我在 Keras 中实现残差网络时出错。下面是给我错误的代码（错误来自函数定义最后一步的第一行）：
加载包：
import numpy as np
from keras import layers
from keras.layers import Input, Add, Concatenate, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D
from keras.models import Model, load_model
from keras.preprocessing import image
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import preprocess_input
import pydot
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
from resnets_utils import *
from keras.initializers import glorot_uniform
import scipy.misc
from matplotlib.pyplot import imshow
%matplotlib inline
import keras.backend as K
K.set_image_data_format('channels_last')
K.set_learning_phase(1)
定义函数：（这是 "final step" 的第一行给我的错误）
def identity_block(X, f, filters, stage, block):
"""
Implementation of the identity block as defined in Figure 4
Arguments:
X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
f -- integer, specifying the shape of the middle CONV's window for the main path
filters -- python list of integers, defining the number of filters in the CONV layers of the main path
stage -- integer, used to name the layers, depending on their position in the network
block -- string/character, used to name the layers, depending on their position in the network
Returns:
X -- output of the identity block, tensor of shape (n_H, n_W, n_C)
"""
# defining name basis
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
# Save the input value. You'll need this later to add back to the main path.
X_shortcut = X
# First component of main path
X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)
X = Activation('relu')(X)
# Second component of main path
X = Conv2D(filters=F2, kernel_size=(f,f),strides=(1,1),padding='same',name=conv_name_base+'2b',kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3,name=bn_name_base+'2b')(X)
X = Activation('relu')(X)
# Third component of main path
X = Conv2D(filters=F3,kernel_size=(1,1),strides=(1,1),padding='valid',name=conv_name_base+'2c',kernel_initializer=glorot_uniform(seed=0))(X)
print(f'before BatchNormalization: X={X}');X = BatchNormalization(axis=3,name=bn_name_base+'2c');print(f'after BatchNormalization: X={X}');
# Final step: Add shortcut value to main path, and pass it through a RELU activation
X = Add()([X_shortcut,X])
X = Activation('relu')(X)
### END CODE HERE ###
return X
Call/test 上面的函数：
tf.reset_default_graph()
with tf.Session() as test:
np.random.seed(1)
A_prev = tf.placeholder("float", [3, 4, 4, 6])
X = np.random.randn(3, 4, 4, 6)
A = identity_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')
test.run(tf.global_variables_initializer())
out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})
print("out = " + str(out[0][1][1][0]))
下面是打印信息和错误信息：
BatchNormalization 之前：X=Tensor("res1a_branch2c/BiasAdd:0", shape=(3, 4, 4, 6), dtype=float32)
BatchNormalization 之后：X= <keras.layers.normalization.BatchNormalization object at 0x7f169c6d9668>
ValueError: Unexpectedly found an instance of type `<class 'keras.layers.normalization.BatchNormalization'>`. Expected a symbolic tensor instance.
下面是完整的日志（以备不时之需）
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/keras/engine/topology.py in assert_input_compatibility(self, inputs)
424 try:
--> 425 K.is_keras_tensor(x)
426 except ValueError:
/opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in is_keras_tensor(x)
399 tf.SparseTensor)):
--> 400 raise ValueError('Unexpectedly found an instance of type `' + str(type(x)) + '`. '
401 'Expected a symbolic tensor instance.')
ValueError: Unexpectedly found an instance of type `<class 'keras.layers.normalization.BatchNormalization'>`. Expected a symbolic tensor instance.
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-6-b3d1050f50dc> in <module>()
5 A_prev = tf.placeholder("float", [3, 4, 4, 6])
6 X = np.random.randn(3, 4, 4, 6)
----> 7 A = identity_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')
8 test.run(tf.global_variables_initializer())
9 out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})
<ipython-input-5-013941ce79d6> in identity_block(X, f, filters, stage, block)
43
44 # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)
---> 45 X = Add()([X_shortcut,X])
46 X = Activation('relu')(X)
47
/opt/conda/lib/python3.6/site-packages/keras/engine/topology.py in __call__(self, inputs, **kwargs)
556 # Raise exceptions in case the input is not compatible
557 # with the input_spec specified in the layer constructor.
--> 558 self.assert_input_compatibility(inputs)
559
560 # Collect input shapes to build layer.
/opt/conda/lib/python3.6/site-packages/keras/engine/topology.py in assert_input_compatibility(self, inputs)
429 'Received type: ' +
430 str(type(x)) + '. Full input: ' +
--> 431 str(inputs) + '. All inputs to the layer '
432 'should be tensors.')
433
ValueError: Layer add_1 was called with an input that isn't a symbolic tensor. Received type: <class 'keras.layers.normalization.BatchNormalization'>. Full input: [<tf.Tensor 'Placeholder:0' shape=(3, 4, 4, 6) dtype=float32>, <keras.layers.normalization.BatchNormalization object at 0x7f169c6d9668>]. All inputs to the layer should be tensors.
我想我在函数定义的最后一步遗漏了一些东西，但我不知道为什么会出错。这里有 Keras 专家可以帮助我吗？
永远记得将张量传递到层中：
print(f'before BatchNormalization: X={X}');
#X = BatchNormalization(axis=3,name=bn_name_base+'2c') # <--- INCORRECT
X = BatchNormalization(axis=3,name=bn_name_base+'2c')(X) # <--- CORRECT
print(f'after BatchNormalization: X={X}');
'CORRECT' 和 'INCORRECT' 之间的区别是，后者产生一个 层 - 而前者 将该层评估为张量 当喂 X.
此外，您的 identity_block() 缺少 return，这将引发另一个错误；添加：
return X。最后，F1, F2, F3 既未在函数内定义也未作为参数传递 - 您可能不会将其视为错误，因为它们是在函数 外部 定义的 - 例如在您的本地命名空间中。
我在 Keras 中实现残差网络时出错。下面是给我错误的代码（错误来自函数定义最后一步的第一行）：
加载包：
import numpy as np
from keras import layers
from keras.layers import Input, Add, Concatenate, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D
from keras.models import Model, load_model
from keras.preprocessing import image
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import preprocess_input
import pydot
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
from resnets_utils import *
from keras.initializers import glorot_uniform
import scipy.misc
from matplotlib.pyplot import imshow
%matplotlib inline
import keras.backend as K
K.set_image_data_format('channels_last')
K.set_learning_phase(1)
定义函数：（这是 "final step" 的第一行给我的错误）
def identity_block(X, f, filters, stage, block):
"""
Implementation of the identity block as defined in Figure 4
Arguments:
X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
f -- integer, specifying the shape of the middle CONV's window for the main path
filters -- python list of integers, defining the number of filters in the CONV layers of the main path
stage -- integer, used to name the layers, depending on their position in the network
block -- string/character, used to name the layers, depending on their position in the network
Returns:
X -- output of the identity block, tensor of shape (n_H, n_W, n_C)
"""
# defining name basis
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
# Save the input value. You'll need this later to add back to the main path.
X_shortcut = X
# First component of main path
X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)
X = Activation('relu')(X)
# Second component of main path
X = Conv2D(filters=F2, kernel_size=(f,f),strides=(1,1),padding='same',name=conv_name_base+'2b',kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3,name=bn_name_base+'2b')(X)
X = Activation('relu')(X)
# Third component of main path
X = Conv2D(filters=F3,kernel_size=(1,1),strides=(1,1),padding='valid',name=conv_name_base+'2c',kernel_initializer=glorot_uniform(seed=0))(X)
print(f'before BatchNormalization: X={X}');X = BatchNormalization(axis=3,name=bn_name_base+'2c');print(f'after BatchNormalization: X={X}');
# Final step: Add shortcut value to main path, and pass it through a RELU activation
X = Add()([X_shortcut,X])
X = Activation('relu')(X)
### END CODE HERE ###
return X
Call/test 上面的函数：
tf.reset_default_graph()
with tf.Session() as test:
np.random.seed(1)
A_prev = tf.placeholder("float", [3, 4, 4, 6])
X = np.random.randn(3, 4, 4, 6)
A = identity_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')
test.run(tf.global_variables_initializer())
out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})
print("out = " + str(out[0][1][1][0]))
下面是打印信息和错误信息：
BatchNormalization 之前：X=Tensor("res1a_branch2c/BiasAdd:0", shape=(3, 4, 4, 6), dtype=float32)
BatchNormalization 之后：X= <keras.layers.normalization.BatchNormalization object at 0x7f169c6d9668>
ValueError: Unexpectedly found an instance of type `<class 'keras.layers.normalization.BatchNormalization'>`. Expected a symbolic tensor instance.
下面是完整的日志（以备不时之需）
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/keras/engine/topology.py in assert_input_compatibility(self, inputs)
424 try:
--> 425 K.is_keras_tensor(x)
426 except ValueError:
/opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in is_keras_tensor(x)
399 tf.SparseTensor)):
--> 400 raise ValueError('Unexpectedly found an instance of type `' + str(type(x)) + '`. '
401 'Expected a symbolic tensor instance.')
ValueError: Unexpectedly found an instance of type `<class 'keras.layers.normalization.BatchNormalization'>`. Expected a symbolic tensor instance.
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-6-b3d1050f50dc> in <module>()
5 A_prev = tf.placeholder("float", [3, 4, 4, 6])
6 X = np.random.randn(3, 4, 4, 6)
----> 7 A = identity_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')
8 test.run(tf.global_variables_initializer())
9 out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})
<ipython-input-5-013941ce79d6> in identity_block(X, f, filters, stage, block)
43
44 # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)
---> 45 X = Add()([X_shortcut,X])
46 X = Activation('relu')(X)
47
/opt/conda/lib/python3.6/site-packages/keras/engine/topology.py in __call__(self, inputs, **kwargs)
556 # Raise exceptions in case the input is not compatible
557 # with the input_spec specified in the layer constructor.
--> 558 self.assert_input_compatibility(inputs)
559
560 # Collect input shapes to build layer.
/opt/conda/lib/python3.6/site-packages/keras/engine/topology.py in assert_input_compatibility(self, inputs)
429 'Received type: ' +
430 str(type(x)) + '. Full input: ' +
--> 431 str(inputs) + '. All inputs to the layer '
432 'should be tensors.')
433
ValueError: Layer add_1 was called with an input that isn't a symbolic tensor. Received type: <class 'keras.layers.normalization.BatchNormalization'>. Full input: [<tf.Tensor 'Placeholder:0' shape=(3, 4, 4, 6) dtype=float32>, <keras.layers.normalization.BatchNormalization object at 0x7f169c6d9668>]. All inputs to the layer should be tensors.
我想我在函数定义的最后一步遗漏了一些东西，但我不知道为什么会出错。这里有 Keras 专家可以帮助我吗？
永远记得将张量传递到层中：
print(f'before BatchNormalization: X={X}');
#X = BatchNormalization(axis=3,name=bn_name_base+'2c') # <--- INCORRECT
X = BatchNormalization(axis=3,name=bn_name_base+'2c')(X) # <--- CORRECT
print(f'after BatchNormalization: X={X}');
'CORRECT' 和 'INCORRECT' 之间的区别是，后者产生一个 层 - 而前者 将该层评估为张量 当喂 X.
此外，您的 identity_block() 缺少 return，这将引发另一个错误；添加：
return X。最后，F1, F2, F3 既未在函数内定义也未作为参数传递 - 您可能不会将其视为错误，因为它们是在函数 外部 定义的 - 例如在您的本地命名空间中。