我试图显示网络中中间层的输出，并且使用了以下代码：
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])
但是会产生此错误。我不知道为什么会产生这个错误！ coudl，请帮助我解决这个问题
回溯（最近通话最近一次）：
文件“”，第1行，在 激活次数= activation_model.predict（[x_test [8000：8001]，wt_expand]）
文件 “ D：\ software \ Anaconda3 \ envs \ py36 \ lib \ site-packages \ keras \ engine \ training.py”， 1169行，在预测中 步骤=步骤）
文件 “ D：\ software \ Anaconda3 \ envs \ py36 \ lib \ site-packages \ keras \ engine \ training_arrays.py”， 第294行，位于predict_loop中 batch_outs = f（ins_batch）
文件 “ D：\ software \ Anaconda3 \ envs \ py36 \ lib \ site-packages \ keras \ backend \ tensorflow_backend.py”， 第2715行，在致电中 返回self._call（inputs）
文件 “ D：\ software \ Anaconda3 \ envs \ py36 \ lib \ site-packages \ keras \ backend \ tensorflow_backend.py”， _call中的第2671行 会话）
文件 “ D：\ software \ Anaconda3 \ envs \ py36 \ lib \ site-packages \ keras \ backend \ tensorflow_backend.py”， 第2623行，在_make_callable中 callable_fn =会话。_make_callable_from_options（callable_opts）
文件 “ D：\ software \ Anaconda3 \ envs \ py36 \ lib \ site-packages \ tensorflow \ python \ client \ session.py”， 第1471行，在_make_callable_from_options中 返回BaseSession._Callable（self，callable_options）
文件 “ D：\ software \ Anaconda3 \ envs \ py36 \ lib \ site-packages \ tensorflow \ python \ client \ session.py”， 第1425行，在 init 中 session._session，options_ptr，状态）
文件 “ D：\ software \ Anaconda3 \ envs \ py36 \ lib \ site-packages \ tensorflow \ python \ framework \ errors_impl.py”， 第528行，位于退出中 c_api.TF_GetCode（self.status.status））
InvalidArgumentError：输入_2_1：0被送入和获取。
我的完整代码在这里：
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])
答案 0 :(得分：0)
您不能同时提供和获取占位符（即Input层的基础张量）。有关更多信息，请参见this answer。因此，必须从w_extraction的输出中排除activation_model模型的输入张量。一种方法是根据图层名称过滤它们：
layer_outputs = [layer.output for layer in w_extraction.layers[:102] if not layer.name.startswith('input')]