为什么会出现“input_2_1： 0既进又取”的错误？

Question

我试图在我的网络中显示中间层的输出，我使用了以下代码：

from keras import models
layer_outputs = [layer.output for layer in w_extraction.layers[:102]] 
activation_model = models.Model(inputs=w_extraction.input, outputs=layer_outputs)
activations = activation_model.predict([x_test[8000:8001],wt_expand])

但它会产生这种错误。我不知道它为什么会产生这种错误！你能帮我解决这个问题吗

回溯（最近一次呼叫最后一次）：

File"，第1行，在激活=activation_model.predict（[x_test[8000:8001]，wt_expand]）

文件“D:\software\Anaconda3\envs\py36\lib\site packages\keras\engine\training.py”，第1169行，预测步骤=步骤）

文件“D:\software\Anaconda3\envs\py36\lib\site packages\keras\engine\training\u arrays.py”，第294行，在predict\u loop batch\u outs=f（ins\U batch）中

文件“D:\software\Anaconda3\envs\py36\lib\site packages\keras\backend\tensorflow\u backend.py”，第2715行，在call return self中_呼叫（输入）

文件“D:\software\Anaconda3\envs\py36\lib\site packages\keras\backend\tensorflow\u backend.py”，第2671行，在调用会话中）

文件“D:\software\Anaconda3\envs\py36\lib\site packages\keras\backend\tensorflow\u backend.py”，第2623行，位于“make\u callable callable\u fn=session”中_从选项（可调用选项）使可调用

文件"D：\Software\Anaconda3\envs\py36\lib\site-包\tenstorflow\python\Client\session.py"，第1471行，_make_callable_from_options返回Base会话。_Callable（自我、callable_options）

文件“D:\software\Anaconda3\envs\py36\lib\site packages\tensorflow\python\client\session.py”，第1425行，在初始化会话中_会话、选项（ptr、状态）

在退出c_api中的文件"D：\Software\Anaconda3\envs\py36\lib\site-包\tenstorflow\python\frame\errors_impl.py"，第528行。TF_GetCode（self.status.status））

InvalidArgumentError:input_2_1:0同时被馈送和获取。

我的完整代码如下：

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)


add_const = Kr.layers.Lambda(lambda x: x[0] + x[1])
encoded_merged = add_const([encoded,wtm])

#-----------------------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)
#DrO2=Dropout(0.25,name='DrO2')(BNd)

decoded = Conv2D(1, (5, 5), activation='sigmoid', padding='same', name='decoder_output',dilation_rate=(2,2))(BNd) 
#model=Model(inputs=image,outputs=decoded)

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])

#----------------------training the model--------------------------------------
#------------------------------------------------------------------------------
#----------------------Data preparation----------------------------------------

(x_train, _), (x_test, _) = mnist.load_data()
x_validation=x_train[1:10000,:,:]
x_train=x_train[10001:60000,:,:]
#
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_validation = x_validation.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))  # adapt this if using `channels_first` image data format
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))  # adapt this if using `channels_first` image data format
x_validation = np.reshape(x_validation, (len(x_validation), 28, 28, 1))

#---------------------compile and train the model------------------------------
w_extraction.compile(optimizer='adam', loss={'decoder_output':'mse','reconstructed_W':'binary_crossentropy'}, loss_weights={'decoder_output': 0.45, 'reconstructed_W': 1.0},metrics=['mae'])
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=20)
#rlrp = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=20, min_delta=1E-4, verbose=1)
mc = ModelCheckpoint('best_model_5x5F_dil_Los751.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)
history=w_extraction.fit([x_train,w_expand], [x_train,w_expand],
          epochs=200,
          batch_size=16, 
          validation_data=([x_validation,wv_expand], [x_validation,wv_expand]),
          callbacks=[TensorBoard(log_dir='E:concatnatenetwork', histogram_freq=0, write_graph=False),es,mc])

Answer 1

您不能同时提供和获取占位符（即输入层的基础张量）。有关更多信息，请参阅此答案。因此，您必须将w_extraction模型的输入张量从activation_model的输出中排除。一种方法是根据图层名称对其进行过滤：

layer_outputs = [layer.output for layer in w_extraction.layers[:102] if not layer.name.startswith('input')]