无法理解：值错误：图断开连接：无法获取张量张量的值

Question

我编写了一个类似于以下代码的体系结构：https://keras.io/guides/functional_api/#manipulate-复图拓扑

visual_features_input = keras.Input(
shape=(1000,), name="Visual-Input-FM", dtype='float') 
et_features_input = keras.Input(
shape=(12,), name="ET-input", dtype='float') 
sentence_encoding_input = keras.Input(
shape=(784,), name="Sentence-Input-Encoding", dtype='float') 

et_features = layers.Dense(units = 12, name = 'et_features')(et_features_input)
visual_features = layers.Dense(units = 100, name = 'visual_features')(visual_features_input)
sentence_features = layers.Dense(units = 60, name = 'sentence_features')(sentence_encoding_input)
x = layers.concatenate([sentence_features, visual_features, et_features], name = 'hybrid-concatenation')
score_pred = layers.Dense(units = 1, name = "score")(x)
group_pred = layers.Dense(units = 5, name="group")(x)

# Instantiate an end-to-end model predicting both score and group
hybrid_model = keras.Model(
inputs=[sentence_features, visual_features, et_features],
outputs=[group_pred]
# outputs=[group_pred, score_pred],
)

但我得到了错误：

ValueError: Graph disconnected: cannot obtain value for tensor Tensor("Sentence-Input-Encoding_2:0", shape=(None, 784), dtype=float32) at layer "sentence_features". The following previous layers were accessed without issue: []

知道为什么吗？

Answer 1

在构建模型时，请注意正确定义输入层

它们是inputs=[sentence_encoding_input, visual_features_input, et_features_input]而不是inputs=[sentence_features, visual_features, et_features]

这是全型号

from tensorflow import keras
from tensorflow.keras import layers
visual_features_input = keras.Input(
shape=(1000,), name="Visual-Input-FM", dtype='float') 
et_features_input = keras.Input(
shape=(12,), name="ET-input", dtype='float') 
sentence_encoding_input = keras.Input(
shape=(784,), name="Sentence-Input-Encoding", dtype='float') 
et_features = layers.Dense(units = 12, name = 'et_features')(et_features_input)
visual_features = layers.Dense(units = 100, name = 'visual_features')(visual_features_input)
sentence_features = layers.Dense(units = 60, name = 'sentence_features')(sentence_encoding_input)
x = layers.concatenate([sentence_features, visual_features, et_features], name = 'hybrid-concatenation')
score_pred = layers.Dense(units = 1, name = "score")(x)
group_pred = layers.Dense(units = 5, name="group")(x)
# Instantiate an end-to-end model predicting both score and group
hybrid_model = keras.Model(
inputs=[sentence_encoding_input, visual_features_input, et_features_input],
outputs=[group_pred]
# outputs=[group_pred, score_pred],
)
hybrid_model.summary()