我正在构建一个带有一个编码器和两个解码器的自编码器模型。我希望编码器部分的输出像U-net一样连接到解码器。但是，当我尝试返回具有四层输出的列表以连接解码器时，出现“图形断开：”错误。有像pytorch这样的Modellist吗？
我不能只连接编码器和解码器。因为我需要做一些编码器输出。并且这个模型有两个解码器。有任何用于此的keras API。
keras：2.2.4 张量流：1.12 python：3.68
编码器部分
def enc_flow(e_dims, ae_dims, lowest_dense_res):
def func(inp):
x0 = downscale(e_dims, 3, 1,False)(inp)
x1 = downscale(e_dims * 2, 3, 1,True)(x0)
x2 = downscale(e_dims * 4, 3, 1,True)(x1)
x3 = downscale(e_dims * 8, 3, 1,True)(x2)
x3 = Dense(lowest_dense_res * lowest_dense_res * ae_dims)(x3)
x3 = Reshape((lowest_dense_res, lowest_dense_res, ae_dims))(x3)
x4 = upscale(ae_dims,True)(x3)
par_list=[x0,x2,x3,x4]
return x4
return func
解码器部分
def dec_flow(output_nc, d_ch_dims, add_residual_blocks=True):
dims = output_nc * d_ch_dims
def ResidualBlock(dim):
def func(inp):
x = Conv2D(dim, kernel_size=3, padding='same')(inp)
x = LeakyReLU(0.2)(x)
x = Conv2D(dim, kernel_size=3, padding='same')(x)
x = Add()([x, inp])
x = LeakyReLU(0.2)(x)
return x
return func
def func(inp): # input
print(type(inp))
x = upscale(dims * 8,True)(inp)
x = ResidualBlock(dims * 8)(x)
# x = Concatenate()([x,par_list[1]])
x = upscale(dims * 4,True)(x)
x = ResidualBlock(dims * 4)(x)
# x = Concatenate()([x,par_list[0]])
x = upscale(dims * 2,True)(x)
x = ResidualBlock(dims * 2)(x)
return Conv2D(output_nc, kernel_size=5, padding='same', activation='sigmoid')(x)
return func
当我尝试时，我会收到错误消息。
Graph disconnected: cannot obtain value for tensor Tensor("
input_1:0", shape=(?, 128, 128, 3),
dtype=float32) at layer "input_1". The following previous layers were
accessed without issue: ['input_2', 'conv2d_10', 'space_attention_5',
'channel_attention_5', 'concatenate_5', 'conv2d_11', 'leaky_re_lu_6',
'pixel_shuffler_2', 'conv2d_12', 'leaky_re_lu_7', 'conv2d_13', 'add_1',
'leaky_re_lu_8', 'conv2d_14', 'space_attention_6', 'channel_attention_6',
'concatenate_6', 'conv2d_15', 'leaky_re_lu_9', 'pixel_shuffler_3',
'conv2d_16']