我正在为R中具有1d输入层的CNN开发功能性Keras模型。
当我运行keras_model函数来构建模型时，出现以下错误：
py_call_impl（可调用，dots $ args，dots $ keywords）错误： ValueError：图形已断开连接：无法在“ main_input”层获取张量Tensor（“ main_input_15：0”，shape =（4201，1024），dtype = float32）的值。可以顺利访问以下先前的图层：[]
详细的回溯： 包装中的文件“ /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/keras/legacy/interfaces.py”，第91行 return func（* args，** kwargs） init 中的文件“ /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/keras/engine/network.py”，第93行 self._init_graph_network（* args，** kwargs） _init_graph_network中的文件“ /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/keras/engine/network.py”，第231行 自输入，自输出） 文件“ /Library/Frameworks/Python.framew
我正在附上我的代码，对您的帮助将不胜感激。
main_input = layer_input(shape = c(1024), batch_shape = c(4201,1024), dtype = 'float32', name = 'main_input') %>%
layer_reshape( target_shape = list(1024,1), input_shape = c(1024),dtype = 'float32', batch_input_shape = c(4201, 1024), batch_size = 4201)
conv1 = layer_conv_1d(filters = 64, kernel_size = 10, strides = 5, dtype = 'float32', activation = 'relu' )
max1 = layer_max_pooling_1d(pool_size = 10)
first_conv = main_input %>% conv1%>%max1
conv2 = layer_conv_1d(filters = 32, kernel_size = 5, strides = 3, dtype = 'float32', activation = 'relu' )
max2 = layer_max_pooling_1d(pool_size = 5)
second_conv = first_conv %>% conv2 %>% max2
conc1 = second_conv %>% layer_flatten()
semantic_input = layer_input(shape = c(2074), dtype = 'float32', batch_shape = c(4201,2074), name = 'semantic_input')%>%
layer_reshape(target_shape = list(2074,1), input_shape = c (2074), dtype = 'float32')
conc2 = semantic_input %>% layer_flatten()
output = layer_concatenate(c(conc1, conc2)) %>%
layer_dense( units = 100, activation = 'relu', use_bias = TRUE) %>%
layer_dense(units = 50, activation = 'relu', use_bias = TRUE) %>%
layer_dense(units = 25, activation = 'relu', use_bias = TRUE)%>%
layer_dense(units = 10, activation = 'relu', use_bias = TRUE)%>%
layer_dense(units = 1, activation = 'softmax', name = 'output')
cnn1_model = keras_model(
inputs = c(main_input,semantic_input),
outputs = c(output)
)
当我尝试构建模型时，在代码的最后一行中遇到上述错误。
答案 0 :(得分：0)
经过2天的封锁，我已经知道了！
输入层在定义时都不应重塑形状。重塑可以在下一步中进行，输入层应独立声明。
以下是固定代码：
main_input = layer_input(shape = c(1024), batch_shape = c(4201,1024), dtype = 'float32', name = 'main_input')
main_reshaped = main_input %>% layer_reshape( target_shape = list(1024,1), input_shape = c(1024),dtype = 'float32', batch_input_shape = c(4201, 1024), batch_size = 4201)
conv1 = layer_conv_1d(filters = 64, kernel_size = 10, strides = 5, dtype = 'float32', activation = 'relu' )
max1 = layer_max_pooling_1d(pool_size = 10)
conv2 = layer_conv_1d(filters = 32, kernel_size = 5, strides = 3, dtype = 'float32', activation = 'relu' )
max2 = layer_max_pooling_1d(pool_size = 5)
conv = reshaped %>% conv1%>%max1%>%conv2 %>% max2 %>% layer_flatten()
semantic_input = layer_input(shape = c(2074), dtype = 'float32', batch_shape = c(4201,2074), name = 'semantic_input')
sem_reshaped = semantic_input %>% layer_reshape(target_shape = list(2074,1), input_shape = c (2074), dtype = 'float32')
conc = sem_reshaped %>% layer_flatten()
output = layer_concatenate(c(conv, conc)) %>%
layer_dense( units = 100, activation = 'relu', use_bias = TRUE) %>%
layer_dense(units = 50, activation = 'relu', use_bias = TRUE) %>%
layer_dense(units = 25, activation = 'relu', use_bias = TRUE)%>%
layer_dense(units = 10, activation = 'relu', use_bias = TRUE)%>%
layer_dense(units = 1, activation = 'softmax', name = 'output')
cnn1_model = keras_model(
inputs = c(main_input,semantic_input),
outputs = c(output)
)
所以模型看起来像这样
summary (cnn1_model)
_______________________________________________________________________________________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
===============================================================================================================================================================================
main_input (InputLayer) (4201, 1024) 0
_______________________________________________________________________________________________________________________________________________________________________________
reshape_25 (Reshape) (4201, 1024, 1) 0 main_input[0][0]
_______________________________________________________________________________________________________________________________________________________________________________
conv1d_65 (Conv1D) (4201, 203, 64) 704 reshape_25[0][0]
_______________________________________________________________________________________________________________________________________________________________________________
max_pooling1d_50 (MaxPooling1D) (4201, 20, 64) 0 conv1d_65[6][0]
_______________________________________________________________________________________________________________________________________________________________________________
conv1d_66 (Conv1D) (4201, 6, 32) 10272 max_pooling1d_50[6][0]
_______________________________________________________________________________________________________________________________________________________________________________
semantic_input (InputLayer) (4201, 2074) 0
_______________________________________________________________________________________________________________________________________________________________________________
max_pooling1d_51 (MaxPooling1D) (4201, 1, 32) 0 conv1d_66[5][0]
_______________________________________________________________________________________________________________________________________________________________________________
reshape_26 (Reshape) (4201, 2074, 1) 0 semantic_input[0][0]
_______________________________________________________________________________________________________________________________________________________________________________
flatten_35 (Flatten) (4201, 32) 0 max_pooling1d_51[5][0]
_______________________________________________________________________________________________________________________________________________________________________________
flatten_36 (Flatten) (4201, 2074) 0 reshape_26[0][0]
_______________________________________________________________________________________________________________________________________________________________________________
concatenate_38 (Concatenate) (4201, 2106) 0 flatten_35[0][0]
flatten_36[0][0]
_______________________________________________________________________________________________________________________________________________________________________________
dense_77 (Dense) (4201, 100) 210700 concatenate_38[0][0]
_______________________________________________________________________________________________________________________________________________________________________________
dense_78 (Dense) (4201, 50) 5050 dense_77[0][0]
_______________________________________________________________________________________________________________________________________________________________________________
dense_79 (Dense) (4201, 25) 1275 dense_78[0][0]
_______________________________________________________________________________________________________________________________________________________________________________
dense_80 (Dense) (4201, 10) 260 dense_79[0][0]
_______________________________________________________________________________________________________________________________________________________________________________
output (Dense) (4201, 1) 11 dense_80[0][0]
===============================================================================================================================================================================
Total params: 228,272
Trainable params: 228,272
Non-trainable params: 0
_______________________________________________________________________________________________________________________________________________________________________________