如何解决这个错误：当我想填充张量时，“Tensor”对象不可调用？

Question

当我想用另一个张量的值填充张量时，会产生以下错误：

Traceback (most recent call last):

File "", line 80, in wtm_Fill=wfill(temp(0,0))

TypeError: 'Tensor' object is not callable

wtm=Input((28,28,1))
image = Input((28, 28, 1))
conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1e',dilation_rate=(2,2))(image)
conv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2e',dilation_rate=(2,2))(conv1)
conv3 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl3e',dilation_rate=(2,2))(conv2)
#conv3 = Conv2D(8, (3, 3), activation='relu', padding='same', name='convl3e', kernel_initializer='Orthogonal',bias_initializer='glorot_uniform')(conv2)
BN=BatchNormalization()(conv3)
#DrO1=Dropout(0.25,name='Dro1')(BN)
encoded =  Conv2D(1, (5, 5), activation='relu', padding='same',name='encoded_I',dilation_rate=(2,2))(BN)

#-----------------------adding w---------------------------------------

temp=tf.reshape(wtm,(28,28))
wfill=Kr.layers.Lambda(lambda x:tf.fill([28,28],x))
wtm_Fill=wfill(temp(0,0))
add_const = Kr.layers.Lambda(lambda x: x[0] + x[1])
encoded_merged = add_const([encoded,wtm])

我需要这样的东西： wtm=

^{pr2}$

wtm（0,0）=0，所以我要生成这个 新的形状张量（28,28,1）

0 0 0 ... 0
.
. 0 0 ... 0
0 0 0 ... 0

from keras.layers import Input, Concatenate, GaussianNoise,Dropout,BatchNormalization
from keras.layers import Conv2D, AtrousConv2D
from keras.models import Model
from keras.datasets import mnist
from keras.callbacks import TensorBoard
from keras import backend as K
from keras import layers
import matplotlib.pyplot as plt
import tensorflow as tf
import keras as Kr
from keras.optimizers import SGD,RMSprop,Adam
from keras.callbacks import ReduceLROnPlateau
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
import numpy as np
import pylab as pl
import matplotlib.cm as cm
import keract
from matplotlib import pyplot
from keras import optimizers
from keras import regularizers

from tensorflow.python.keras.layers import Lambda;
#-----------------building w train---------------------------------------------
w_expand=np.zeros((49999,28,28),dtype='float32')
wv_expand=np.zeros((9999,28,28),dtype='float32')
wt_random=np.random.randint(2, size=(49999,4,4))
wt_random=wt_random.astype(np.float32)
wv_random=np.random.randint(2, size=(9999,4,4))
wv_random=wv_random.astype(np.float32)
w_expand[:,:4,:4]=wt_random
wv_expand[:,:4,:4]=wv_random
x,y,z=w_expand.shape
w_expand=w_expand.reshape((x,y,z,1))
x,y,z=wv_expand.shape
wv_expand=wv_expand.reshape((x,y,z,1))

#-----------------building w test---------------------------------------------
w_test = np.random.randint(2,size=(1,4,4))
w_test=w_test.astype(np.float32)
wt_expand=np.zeros((1,28,28),dtype='float32')
wt_expand[:,0:4,0:4]=w_test
wt_expand=wt_expand.reshape((1,28,28,1))

#-----------------------encoder------------------------------------------------
#------------------------------------------------------------------------------
wtm=Input((28,28,1))
image = Input((28, 28, 1))
conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1e',dilation_rate=(2,2))(image)
conv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2e',dilation_rate=(2,2))(conv1)
conv3 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl3e',dilation_rate=(2,2))(conv2)
BN=BatchNormalization()(conv3)
encoded =  Conv2D(1, (5, 5), activation='relu', padding='same',name='encoded_I',dilation_rate=(2,2))(BN)


temp=tf.reshape(wtm,(28,28))
wfill=Kr.layers.Lambda(lambda x:tf.fill([28,28],x))
wtm_Fill=wfill(temp[0,0])
add_const = Kr.layers.Lambda(lambda x: x[0] + x[1])
encoded_merged = add_const([encoded,wtm_Fill])

#wfill=Kr.layers.Lambda(lambda x:tf.fill([28,28],x))
#value=wtm[0][0][0]
#x=tf.fill((28,28,1),value)
#add_const = Kr.layers.Lambda(lambda x: x[0] + x[1])
#encoded_merged = add_const([encoded,x])
#encoder=Model(inputs=[image,wtm], outputs= encoded_merged ,name='encoder')
#encoder.summary()

#-----------------------decoder------------------------------------------------
#------------------------------------------------------------------------------
deconv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1d',dilation_rate=(2,2))(encoded_merged)
deconv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2d',dilation_rate=(2,2))(deconv1)
deconv3 = Conv2D(64, (5, 5), activation='relu',padding='same', name='convl3d',dilation_rate=(2,2))(deconv2)
deconv4 = Conv2D(64, (5, 5), activation='relu',padding='same', name='convl4d',dilation_rate=(2,2))(deconv3)
BNd=BatchNormalization()(deconv3)

decoded = Conv2D(1, (5, 5), activation='sigmoid', padding='same', name='decoder_output',dilation_rate=(2,2))(BNd) 

model=Model(inputs=[image,wtm],outputs=decoded)

decoded_noise = GaussianNoise(0.5)(decoded)

#----------------------w extraction------------------------------------
convw1 = Conv2D(64, (3,3), activation='relu', padding='same', name='conl1w',dilation_rate=(2,2))(decoded_noise)
convw2 = Conv2D(64, (3, 3), activation='relu', padding='same', name='convl2w',dilation_rate=(2,2))(convw1)
convw3 = Conv2D(64, (3, 3), activation='relu', padding='same', name='conl3w',dilation_rate=(2,2))(convw2)
convw4 = Conv2D(64, (3, 3), activation='relu', padding='same', name='conl4w',dilation_rate=(2,2))(convw3)
convw5 = Conv2D(64, (3, 3), activation='relu', padding='same', name='conl5w',dilation_rate=(2,2))(convw4)
convw6 = Conv2D(64, (3, 3), activation='relu', padding='same', name='conl6w',dilation_rate=(2,2))(convw5)
pred_w = Conv2D(1, (1, 1), activation='sigmoid', padding='same', name='reconstructed_W',dilation_rate=(2,2))(convw6)  
w_extraction=Model(inputs=[image,wtm],outputs=[decoded,pred_w])

w_extraction.summary()

这是我的新代码，但在实现后会产生以下错误：

Traceback (most recent call last):

File "", line 106, in model=Model(inputs=[image,wtm],outputs=decoded)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper return func(*args, **kwargs)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\keras\engine\network.py", line 93, in init self._init_graph_network(*args, **kwargs)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\keras\engine\network.py", line 231, in _init_graph_network self.inputs, self.outputs)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\keras\engine\network.py", line 1366, in _map_graph_network tensor_index=tensor_index)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\keras\engine\network.py", line 1353, in build_map node_index, tensor_index)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\keras\engine\network.py", line 1353, in build_map node_index, tensor_index)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\keras\engine\network.py", line 1353, in build_map node_index, tensor_index)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\keras\engine\network.py", line 1353, in build_map node_index, tensor_index)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\keras\engine\network.py", line 1353, in build_map node_index, tensor_index)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\keras\engine\network.py", line 1353, in build_map node_index, tensor_index)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\keras\engine\network.py", line 1353, in build_map node_index, tensor_index)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\keras\engine\network.py", line 1325, in build_map node = layer._inbound_nodes[node_index]

AttributeError: 'NoneType' object has no attribute '_inbound_nodes'

Answer 1

要产生这样的张量，您需要： 假设张量形状为（28,28,1）

value = tensor[0][0][0]
wtm = tf.fill((10,10,1), value)

这将输出填充了值的形状（10,10,1）的张量