我正在尝试使用以下代码:
import tensorflow as tf
from keras.layers import Input, Dense
from keras.models import Model, Sequential
from keras.layers import Conv2D, Concatenate
from keras.utils.vis_utils import plot_model
if __name__ == '__main__':
imgRows = imgCols = 28
print ("ImgRow and imgCols " , imgRows, imgCols)
inputLayer = Input(shape=( 1,28,28))
conv1 = Conv2D(64,(3,3),strides=1, padding="same", activation='relu') (inputLayer)
#Residual 1
skip = Conv2D(128, (1,1), strides=1, padding="same", activation='relu') (conv1)
conv1 = Conv2D(128, (3,3), strides=3, padding="same", activation='relu') (skip)
conv1 = Conv2D(128, (3,3), strides=3, padding="same", activation='relu') (conv1)
r1= Concatenate([skip, conv1])
#residual 2
conv1 = Conv2D(128, (3,3), strides=3, padding="same", activation='relu') (r1)
conv1 = Conv2D(128, (3,3), strides=3, padding="same", activation='relu') (conv1)
conv1= Concatenate([r1, conv1])
# Residual 3
skip = Conv2D(256, (1,1), strides=1, padding="same", activation='relu') (conv1)
conv1 = Conv2D(256, (3,3), strides=3, padding="same", activation='relu') (conv1)
conv1 = Conv2D(256, (3,3), strides=3, padding="same", activation='relu') (conv1)
conv1= Concatenate([skip, conv1])
out = Conv2D(1, (1,1), strides=1, padding="same", activation='sigmoid') (conv1)
#model = Sequential()
#model.add (inputLayer)
#model.add ( conv1)
model = Model(input=inputLayer, output=conv1)
model.compile(optimizer=Nadam(lr=1e-5), loss="mean_square_error")
plot_model (model, to_file="./keestu_model.png", show_shapes=True)
我收到以下错误:
错误消息是:
ValueError: Layer conv2d_5 was called with an input that isn't a
symbolic tensor. Received type: <class 'keras.layers.merge.Concatenate'>.
Full input: [<keras.layers.merge.Concatenate object at 0x7fd543841590>].
All inputs to the layer should be tensors.
问题?:
错误消息对我来说非常清楚,第 5 层期望其输入为张量对象而不是连接对象。但我该如何解决它?
最佳答案
这是因为 Concatenate
是一个具有两个 API 版本的图层类:
Concatenate()([tensor1, tensor2])
创建一个新的 concatenate 实例并应用于给定的张量。这是标准的函数式 API 风格。concatenate([tensor1, tensor2])
将实现相同的效果,但会为您创建一个隐式实例。来自 documentation :keras.layers.concatenate(inputs, axis=-1): Functional interface to the Concatenate layer.
顺便说一句merge layers为了方便起见,有这个双接口(interface)。
关于python - 无法在 keras 中连接两个输入层。,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/52233812/