Question

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，对情感进行预测。这是代码：

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：

TypeError: Tensors in list passed to 'values' of 'ConcatV2' Op have types [float32, int32] that don't all match.

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：

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: []

另一方面，该模型的一部分可以与顺序API一起很好地工作。

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。

Answer 1

Keras功能API的实现几乎没有问题，

您应将Concatenate层用作Concatenate(axis=-1)([text_encoded, topic_input])。
在连接层中，您尝试组合int32张量和float32张量，这是不允许的。您应该做的是from keras.backend import cast和concatenated = Concatenate(axis=-1)([text_encoded, cast(topic_input, 'float32')])。
您遇到了变量冲突，有两个sentiment变量，一个指向to_categorical输出，另一个指向最后Dense层的输出。
您的模型输入不能是text_encoded之类的中间张量。它们应来自Input层。

为帮助您实现，这是TF 1.13中的代码的工作版本（我不确定这是否正是您想要的）。

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。

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：[]

1 个答案: