我有一个自动编码器，我想向编码器输出添加随机图像，然后将其发送到解码器部分。我将代码放在这里：
from keras.layers import Input, Concatenate, GaussianNoise
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
import matplotlib.pyplot as plt
#-----------------------encoder------------------------------------------------
#------------------------------------------------------------------------------
def make_encoder():
image = Input((28, 28, 1))
x = Conv2D(8, (5, 5), activation='relu', padding='same')(image)
x = Conv2D(4, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(2, (3, 3), activation='relu', padding='same')(x)
encoded = Conv2D(1, (3, 3), activation='relu', padding='same')(x)
return Model(inputs=image, outputs=encoded)
encoder = make_encoder()
encoder.summary()
#-----------------------decoder------------------------------------------------
#------------------------------------------------------------------------------
def make_decoder():
encoded_merged = Input((28, 28, 2))
x = Conv2D(2, (5, 5), activation='relu', padding='same')(encoded_merged)
x = Conv2D(4, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu',padding='same')(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same', name='decoder_output')(x)
return Model(inputs=encoded_merged, outputs=decoded)
decoder = make_decoder()
decoder.summary()
#-------------------w-predictor------------------------------------------------
#------------------------------------------------------------------------------
def make_w_predictor():
decoded_noise = Input((28, 28, 1))
x = Conv2D(8, (5, 5), activation='relu', padding='same')(decoded_noise)
x = Conv2D(4, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(2, (3, 3), activation='relu', padding='same')(x)
pred_w = Conv2D(1, (3, 3), activation='relu', padding='same', name='extracted_watermark')(x)
# reconsider activation (is W positive?)
# should be filter=1 to match W
return Model(inputs=decoded_noise, outputs=pred_w)
w_predictor = make_w_predictor()
w_predictor.summary()
#-------------------------put together-----------------------------------------
#------------------------------------------------------------------------------
def put_together(encoder, decoder):
image = Input((28, 28, 1))
w = Input((28, 28, 1))
encoded = encoder(image)
# encoded_merged = Concatenate(axis=3)([encoded, w])
encoded_add = encoded+ w
# decoded = decoder(encoded_merged)
decoded = decoder(encoded_add)
# decoded_noise = GaussianNoise(0.5)(decoded)
# pred_w = w_predictor(decoded_noise)
return Model(inputs=[image, w], outputs=[decoded])
model = put_together(encoder, decoder)
model.summary()
#----------------------training the model--------------------------------------
#------------------------------------------------------------------------------
import numpy as np
# dummy data
(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))
#images = np.random.random((1000, 28, 28, 1))
#w = np.random.randint(2,size=(1,4, 4, 1))
#in the folloing line we want to produce a watermark that the corner of image has random values 0 or 1 and
#other part are 0
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)
wt=np.repeat(w_expand,49999,0)
wv = np.repeat(w_expand,9999,0)
wte = np.repeat(w_expand,10000,0)
# is accuracy sensible metric for this model?
model.compile(optimizer='adadelta', loss=['mse'], metrics=['mae'])
model.fit([x_train, wt], [x_train],
epochs=5,
batch_size=64,
validation_data=([x_validation,wv], [x_validation]),
callbacks=[TensorBoard(log_dir='C:/tmp/autoencoder', histogram_freq=0, write_graph=False)])
但是会产生此错误：
ValueError：输入通道数与相应的不匹配 过滤器的尺寸，1！= 2
我不知道哪个部分会产生此错误！我还想可视化图层的输出，但是由于它们是作为函数实现的，所以我不知道该怎么做？请帮助我解决这些问题。