ValueError：图形已断开连接：无法获取张量Tensor的值...访问了以下先前的层，没有出现问题：[] [英] ValueError: Graph disconnected: cannot obtain value for tensor Tensor...The following previous layers were accessed without issue: []
问题描述
我一直在尝试使用Keras创建多输入模型，但是出现了错误。这个想法是将文本和相应的主题结合起来，对情绪进行预测。这是代码：
I have been trying to create a multi-input model using Keras, but got errors. The idea is to combine the text and corresonding topics to make predictions for sentiments. Here's the code:
import numpy as np
text = np.random.randint(5000, size=(442702, 200), dtype='int32')
topic = np.random.randint(2, size=(442702, 227), dtype='int32')
sentiment = to_categorical(np.random.randint(5, size=442702), dtype='int32')
from keras.models import Sequential
from keras.layers import Dense, Activation, Embedding, Flatten, GlobalMaxPool1D, Dropout, Conv1D
from keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
from keras.losses import binary_crossentropy
from keras.optimizers import Adam
text_input = Input(shape=(200,), dtype='int32', name='text')
text_encoded = Embedding(input_dim=5000, output_dim=20, input_length=200)(text_input)
text_encoded = Dropout(0.1)(text_encoded)
text_encoded = Conv1D(300, 3, padding='valid', activation='relu', strides=1)(text_encoded)
text_encoded = GlobalMaxPool1D()(text_encoded)
topic_input = Input(shape=(227,), dtype='int32', name='topic')
concatenated = concatenate([text_encoded, topic_input])
sentiment = Dense(5, activation='softmax')(concatenated)
model = Model(inputs=[text_encoded, topic_input], outputs=sentiment)
# summarize layers
print(model.summary())
# plot graph
plot_model(model)
但是，这给了我以下错误：
However, this gives me the below error:
TypeError：列表中的张量传递给'ConcatV2'Op的'values'的类型[float32，int32]全部匹配。
现在，如果我将topic_input的dtype从'int32'更改为'float32'，则会收到另一个错误：
Now if I change dtype of topic_input from 'int32' to 'float32', I got a different error:
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("text_37:0", shape=(?, 200), dtype=int32) at layer "text". The following previous layers were accessed without issue: []
model = Sequential()
model.add(Embedding(5000, 20, input_length=200))
model.add(Dropout(0.1))
model.add(Conv1D(300, 3, padding='valid', activation='relu', strides=1))
model.add(GlobalMaxPool1D())
model.add(Dense(227))
model.add(Activation('sigmoid'))
print(model.summary())
任何指针都受到高度赞赏。
Any pointers are highly appreciated.
推荐答案
Keras功能API的实现几乎没有问题，
There are few issues with your Keras functional API implementation,
-
您应将
串联层用作串联（axis = -1）（[text_encoded，topic_input] ）。
在连接层中，您尝试将 int32 张量和 float32 张量，这是不允许的。您应该做的是，从keras.backend import cast 和 concatenated = Concatenate（axis = -1）（[text_encoded，cast（topic_input，' float32'）]）。
In the concatenate layer you are trying to combine an int32 tensor and a float32 tensor, which is not allowed. What you should do is, from keras.backend import cast and concatenated = Concatenate(axis=-1)([text_encoded, cast(topic_input, 'float32')]).
您遇到了变量冲突，其中有两个情感变量，一个指向 to_categorical 输出，另一个指向最后一个 Dense 层的输出。
You got variable conflicts, there are two sentiment variables, one pointing to a to_categorical output and the other the output of the final Dense layer.
您的模型输入不能是中间张量，例如 text_encoded 。它们应该来自 Input 层。
Your model inputs cannot be intermediate tensors like text_encoded. They should come from Input layers.
您的实现，这是TF 1.13中的代码的有效版本（我不确定这是否正是您想要的）。
To help with your implementation, here's a working version of your code (I am not sure if this is exactly what you wanted though) in TF 1.13.
from keras.utils import to_categorical
text = np.random.randint(5000, size=(442702, 200), dtype='int32')
topic = np.random.randint(2, size=(442702, 227), dtype='int32')
sentiment1 = to_categorical(np.random.randint(5, size=442702), dtype='int32')
from keras.models import Sequential
from keras.layers import Input, Dense, Activation, Embedding, Flatten, GlobalMaxPool1D, Dropout, Conv1D, Concatenate, Lambda
from keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
from keras.losses import binary_crossentropy
from keras.optimizers import Adam
from keras.backend import cast
from keras.models import Model
text_input = Input(shape=(200,), dtype='int32', name='text')
text_encoded = Embedding(input_dim=5000, output_dim=20, input_length=200)(text_input)
text_encoded = Dropout(0.1)(text_encoded)
text_encoded = Conv1D(300, 3, padding='valid', activation='relu', strides=1)(text_encoded)
text_encoded = GlobalMaxPool1D()(text_encoded)
topic_input = Input(shape=(227,), dtype='int32', name='topic')
topic_float = Lambda(lambda x:cast(x, 'float32'), name='Floatconverter')(topic_input)
concatenated = Concatenate(axis=-1)([text_encoded, topic_float])
sentiment = Dense(5, activation='softmax')(concatenated)
model = Model(inputs=[text_input, topic_input], outputs=sentiment)
# summarize layers
print(model.summary())
希望这些帮助。
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