python - 如何使用 lambda 层包装函数？

Question

我想在自动编码器中的一个层上做一些处理，然后将其发送到下一层，但我不能使用 Keras 中的预定义函数，比如 add ，...我认为我应该使用 lambda 和函数。我的自动编码器的输出是一个名为编码器的形状为 (1,28,28,1) 的张量，我有一个名为 wtm 的形状为 (1,4,4,1) 的输入张量。现在，我想考虑编码器中的 7x7 块，并分别将每个 7x7 块的中间值与一个 wtm 值相加（每个编码器块具有一个 wtm 值）我编写了两个函数来执行此操作，但它产生了这个错误：

TypeError：“张量”对象不支持项目分配

我是 python 和 Keras 的初学者，我正在寻找原因，但不幸的是，我不明白为什么会发生这种情况，我该怎么办？请指导我如何编写我的 lambda 层？我在这里附上了代码。我可以用 lambda 做一些简单的事情

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

但是如果 wtm 有不同的形状，层上有编码或复杂的东西，我不知道该怎么办？

from keras.layers import Input, Concatenate, GaussianNoise,Dropout,BatchNormalization,MaxPool2D,AveragePooling2D
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---------------------------------------------
    def grid_w(args):
    Enc, W = args
#    Ex,Ey,Ez=Enc.shape

#    Wx,Wy,Wz=W.shape
    Enc=tf.reshape(Enc,[28,28])
    W=tf.reshape(W,[4,4])
    Enc[3::7, 3::7] += W
    Enc=tf.reshape(Enc,[1,28,28,1])
    W=tf.reshape(W,[1,4,4,1])
#    Enc[:, 3::7, 3::7]=K.sum(W,axis=1)

    return Enc

    def grid_w_output_shape(shapes):
        shape1, shape2 = shapes
        return (shape1[0], 1)
    wt_random=np.random.randint(2, size=(49999,4,4))
    w_expand=wt_random.astype(np.float32)
    wv_random=np.random.randint(2, size=(9999,4,4))
    wv_expand=wv_random.astype(np.float32)
    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)
    w_test=w_test.reshape((1,4,4,1))
    #-----------------------encoder------------------------------------------------
    #------------------------------------------------------------------------------
    wtm=Input((4,4,1))
    image = Input((28, 28, 1))
    conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1e')(image)
    conv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2e')(conv1)
    conv3 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl3e')(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')(BN)

    #-----------------------adding w---------------------------------------
    encoded_merged=Kr.layers.Lambda(grid_w, output_shape=grid_w_output_shape)([encoded, wtm])

    #-----------------------decoder------------------------------------------------
    #------------------------------------------------------------------------------

    deconv1 = Conv2D(64, (5, 5), activation='elu', padding='same', name='convl1d')(encoded_merged)
    deconv2 = Conv2D(64, (5, 5), activation='elu', padding='same', name='convl2d')(deconv1)
    deconv3 = Conv2D(64, (5, 5), activation='elu',padding='same', name='convl3d')(deconv2)
    deconv4 = Conv2D(64, (5, 5), activation='elu',padding='same', name='convl4d')(deconv3)
    BNd=BatchNormalization()(deconv3)  
    decoded = Conv2D(1, (5, 5), activation='sigmoid', padding='same', name='decoder_output')(BNd) 

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

    decoded_noise = GaussianNoise(0.5)(decoded)

    #----------------------w extraction------------------------------------
    convw1 = Conv2D(64, (3,3), activation='relu', name='conl1w')(decoded_noise)
    convw2 = Conv2D(64, (3, 3), activation='relu', name='convl2w')(convw1)
    Avw1=AveragePooling2D(pool_size=(2,2))
    convw3 = Conv2D(64, (3, 3), activation='relu', padding='same', name='conl3w')(convw2)
    convw4 = Conv2D(64, (3, 3), activation='relu', padding='same', name='conl4w')(convw3)
    Avw2=AveragePooling2D(pool_size=(2,2))
    convw5 = Conv2D(64, (3, 3), activation='relu', name='conl5w')(convw4)
    convw6 = Conv2D(64, (3, 3), activation='relu', padding='same', name='conl6w')(convw5)
    BNed=BatchNormalization()(convw6)
    #DrO3=Dropout(0.25, name='DrO3')(BNed)
    pred_w = Conv2D(1, (1, 1), activation='sigmoid', padding='same', name='reconstructed_W')(BNed)  
    watermark_extraction=Model(inputs=[image,wtm],outputs=[decoded,pred_w])

    watermark_extraction.summary()

标签： pythontensorflowlambdakeraskeras-layer