AttributeError: 'Tensor' object has no attribute 'compile'

Question

When I tried to run this:

p0 = Sequential()
p0.add(Embedding(vocabulary_size1, 50, weights=[embedding_matrix_passage], 
input_length=50, trainable=False))
p0.add(LSTM(64))
p0.add(Dense(256,name='FC1'))
p0.add(Activation('relu'))
p0.add(Dropout(0.5))
p0.add(Dense(50,name='out_layer'))
p0.add(Activation('sigmoid'))

q0 = Sequential()
q0.add(Embedding(vocabulary_size2,50,weights=embedding_matrix_query], 
input_length=50, trainable=False))
q0.add(LSTM(64))
q0.add(Dense(256,name='FC1'))
q0.add(Activation('relu'))
q0.add(Dropout(0.5))
q0.add(Dense(50,name='out_layer'))
q0.add(Activation('sigmoid'))

model = concatenate([p0.output, q0.output])
model = Dense(10)(model)
model = Activation('softmax')(model)
model.compile(loss='categorical_crossentropy',optimizer='rmsprop', metrics= 
['accuracy'])

it's giving me this error:

AttributeError             
--->  model.compile(loss='categorical_crossentropy',optimizer='rmsprop', metrics=['accuracy'])

As you define it, model is indeed a tensor, and not a model; you should switch all of your pipeline to the functional API - see How to “Merge” Sequential models in Keras 2.0? — desertnaut
– desertnaut, Commented Dec 15, 2018 at 21:11
Yes, your whole code makes little sense, you should use the functional API. — Dr. Snoopy
– Dr. Snoopy, Commented Dec 15, 2018 at 21:34

today · Accepted Answer · 2018-12-16 03:44:26Z

1

As mentioned in the comments you need to use Keras Functional API to create models with branches, multiple inputs/outputs. However, there is no need to do this for all of your code, just for the last part:

concat = concatenate([p0.output, q0.output])
x = Dense(10)(concat)
out = Activation('softmax')(x)

model = Model([p0.input, q0.input], out)

model.compile(loss='categorical_crossentropy',optimizer='rmsprop', metrics=['accuracy'])

answered Dec 16, 2018 at 3:44

today

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3 Comments

desertnaut Over a year ago

Interesting... So, we can use p0.input and q0.input in such a manner even if p0 and q0 are sequential models?

today Over a year ago

@desertnaut Sure, they are valid attributes because the corresponding models have been defined and also they are (placeholder) tensors after all.

desertnaut Over a year ago

A well-deserved upvote - seems you got both myself & Matias off-guard ;)

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