[英]AssertionError while trying to concatenate two models and fit in Keras
我正在尝试开发图像字幕模型。 我指的是这个Github存储库 。 我有三种方法,它们执行以下操作:
由于代码很长,因此我创建了一个Gist来显示方法 。
这是我的图像模型和标题模型的摘要 。
但是然后我运行代码,却收到此错误:
TraceTraceback (most recent call last):
File "trainer.py", line 99, in <module>
model.fit([images, encoded_captions], one_hot_captions, batch_size = 1, epochs = 5)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py", line 950, in fit
batch_size=batch_size)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py", line 671, in _standardize_user_data
self._set_inputs(x)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py", line 575, in _set_inputs
assert len(inputs) == 1
AssertionError
由于错误来自Keras库,所以我不知道如何调试它。 但是当我尝试将它们连接在一起时出了点问题。
我想知道我是否在这里错过了什么
您需要使用output
属性获取模型的output
,然后使用Keras功能API进行连接(通过Concatenate
层或其等效功能接口concatenate
)并创建最终模型:
from keras.models import Model
image_model = get_image_model()
language_model = get_language_model(vocab_size)
merged = concatenate([image_model.output, language_model.output])
x = LSTM(256, return_sequences = False)(merged)
x = Dense(vocab_size)(x)
out = Activation('softmax')(x)
model = Model([image_model.input, language_model.input], out)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
model.fit([images, encoded_captions], one_hot_captions, ...)
就像现在在代码中一样,您还可以为模型创建逻辑定义一个函数:
def get_concatenated_model(image_model, language_model, vocab_size):
merged = concatenate([image_model.output, language_model.output])
x = LSTM(256, return_sequences = False)(merged)
x = Dense(vocab_size)(x)
out = Activation('softmax')(x)
model = Model([image_model.input, language_model.input], out)
return model
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