我在这里附加了代码。它可以完全学习而没有任何错误，但是当我想在lambda层之后显示层的输出时，会产生此错误。
InvalidArgumentError：必须输入占位符张量的值 'input_78'具有dtype浮点型，形状为[？，28,28,1] [[{{node input_78}} = Placeholderdtype = DT_FLOAT，shape = [?, 28,28,1]， _device =“ / job：localhost / replica：0 / task：0 / device：GPU：0”]] [[{{node lambda_35 / add / _2359}} = _Recvclient_terminated = false， recv_device =“ / job：localhost /副本：0 / task：0 / device：CPU：0”， send_device =“ / job：localhost /副本：0 / task：0 / device：GPU：0”， send_device_incarnation = 1，tensor_name =“ edge_50_lambda_35 / add”， tensor_type = DT_FLOAT， _device =“ / job：localhost /副本：0 /任务：0 /设备：CPU：0”]]
我在未添加w_expanded作为fit函数的输入之前实现了此代码，并且显示了无错误的输出，但是使用此代码，它产生了上述错误。我不明白为什么它显示此错误？请帮我。我真的需要解决方案。
from keras.layers import Input, Concatenate, GaussianNoise,Dropout
from keras.layers import Conv2D
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
import numpy as np
import pylab as pl
import matplotlib.cm as cm
#-----------------building w train---------------------------------------------
w_main = np.random.randint(2,size=(1,4,4,1))
w_main=w_main.astype(np.float32)
w_expand=np.zeros((1,28,28,1),dtype='float32')
w_expand[:,0:4,0:4]=w_main
w_expand.reshape(1,28,28,1)
w_expand=np.repeat(w_expand,49999,0)
#-----------------building w test---------------------------------------------
w_test = np.random.randint(2,size=(1,4,4,1))
w_test=w_test.astype(np.float32)
wt_expand=np.zeros((1,28,28,1),dtype='float32')
wt_expand[:,0:4,0:4]=w_test
wt_expand.reshape(1,28,28,1)
wt_expand=np.repeat(wt_expand,9999,0)
#-----------------------encoder------------------------------------------------
#------------------------------------------------------------------------------
wtm=Input((28,28,1))
image = Input((28, 28, 1))
conv1 = Conv2D(16, (3, 3), activation='relu', padding='same', name='convl1e')(image)
conv2 = Conv2D(8, (3, 3), activation='relu', padding='same', name='convl2e')(conv1)
conv3 = Conv2D(8, (3, 3), activation='relu', padding='same', name='convl3e')(conv2)
DrO=Dropout(0.25)(conv3)
encoded = Conv2D(1, (3, 3), activation='relu', padding='same',name='reconstructed_I')(conv3)
#-----------------------adding w---------------------------------------
#add_const = Kr.layers.Lambda(lambda x: x + Kr.backend.constant(w_expand))
add_const = Kr.layers.Lambda(lambda x: x + wtm)
encoded_merged = add_const(encoded)
encoder=Model(inputs=image, outputs=encoded_merged)
encoder.summary()
#-----------------------decoder------------------------------------------------
#------------------------------------------------------------------------------
#encoded_merged = Input((28, 28, 2))
deconv1 = Conv2D(8, (3, 3), activation='relu', padding='same', name='convl1d')(encoded_merged)
deconv2 = Conv2D(8, (3, 3), activation='relu', padding='same', name='convl2d')(deconv1)
deconv3 = Conv2D(16, (3, 3), activation='relu',padding='same', name='convl3d')(deconv2)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same', name='decoder_output')(deconv3)
#decoder=Model(inputs=encoded_merged, outputs=decoded)
#decoder.summary()
model=Model(inputs=image,outputs=decoded)
#----------------------w extraction------------------------------------
convw1 = Conv2D(8, (3,3), activation='relu', padding='same', name='conl1w')(decoded)
convw2 = Conv2D(4, (3, 3), activation='relu', padding='same', name='convl2w')(convw1)
convw3 = Conv2D(2, (3, 3), activation='relu', padding='same', name='conl3w')(convw2)
pred_w = Conv2D(1, (3, 3), activation='relu', padding='same', name='reconstructed_W')(convw3)
# reconsider activation (is W positive?)
# should be filter=1 to match W
watermark_extraction=Model(inputs=[image,wtm],outputs=[decoded,pred_w])
#----------------------training the model--------------------------------------
#------------------------------------------------------------------------------
#----------------------Data preparesion----------------------------------------
(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------------------------------
# is accuracy sensible metric for this model?
watermark_extraction.compile(optimizer='adadelta', loss={'decoder_output':'mse','reconstructed_W':'mse'}, metrics=['mae'])
watermark_extraction.fit([x_train,w_expand], [x_train,w_expand],
epochs=4,
batch_size=128,
validation_data=([x_validation,wt_expand], [x_validation,wt_expand]),
callbacks=[TensorBoard(log_dir='C:/tmp/autoencoder', histogram_freq=0, write_graph=False)])
model.summary()
#model.fit([images, w], [images, w], batch_size=64, epochs=5)
#--------------------visuallize the output layers------------------------------
inputs = [K.learning_phase()] + watermark_extraction.inputs
_convout1_f = K.function(inputs, [watermark_extraction.layers[5].output])
def convout1_f(X):
# The [0] is to disable the training phase flag
return _convout1_f([0] + [X])
# utility functions
i = 4600
# Visualize the first layer of convolutions on an input image
X = x_test[i:i+1]
# Visualize weights
W = model.layers[1].get_weights()[0][:,:,0,:]
w1=W.reshape(16,3,3)
W = np.squeeze(w1)
print("W shape : ", W.shape)
for i in range(0,16):
plt.subplot(4,4,i+1)
plt.imshow(w1[i,:,:], interpolation='nearest',cmap='gray')
plt.show()
W = model.layers[2].get_weights()[0][:,:,0,:]
w2=W.reshape(8,3,3)
W = np.squeeze(w2)
print("W shape : ", W.shape)
for i in range(0,8):
plt.subplot(4,4,i+1)
plt.imshow(w2[i,:,:], interpolation='nearest',cmap='gray')
plt.show()
# Visualize convolution result (after activation)
C1 = convout1_f(X)
C1 = np.squeeze(C1)
print("C1 shape : ", C1.shape)
for i in range(0,C1.shape[2]):
plt.subplot(4,4,i+1)
plt.imshow(C1[:,:,i], interpolation='nearest',cmap='gray')
plt.show()