这两种在keras中建立模型的方法有什么区别？

Question

I am new to Keras and after going through a few tutorials i started building a model and found these two styles of implementations. However i am getting an error in the first one and second one works fine. Can someone explain the difference between the two?

First Method:

visible = Embedding(QsVocabSize, 1024, input_length=max_length_inp, mask_zero=True)
encoder = LSTM(100,activation='relu')(visible)

Second Method:

model = Sequential()
model.add(Embedding(QsVocabSize, 1024, input_length=max_length_inp, mask_zero=True))
model.add(LSTM(100,activation ='relu'))

This is the error I get:

ValueError: Layer lstm_59 was called with an input that isn't a symbolic tensor. Received type: <class 'keras.layers.embeddings.Embedding'>. Full input: [<keras.layers.embeddings.Embedding object at 0x00000207BC7DBCC0>]. All inputs to the layer should be tensors.

Answer 1

They're two ways of creating DL models in Keras. The first code snippet follows functional style. This style is used for creating complex models like multi-input/output, shared layers etc.

https://keras.io/getting-started/functional-api-guide/

The second code snippet is Sequential style. Simple models can be created which involves just stacking of layers.

https://keras.io/getting-started/sequential-model-guide/

If you read the functional API guide, you'll notice the following point:

'A layer instance is callable (on a tensor), and it returns a tensor'

Now the error you're seeing would make sense. This line only creates the layer and doesn't invoke it by passing a tensor.

visible = Embedding(QsVocabSize, 1024, input_length=max_length_inp, mask_zero=True)

Subsequently, passing this Embedding object to LSTM layer throws an error as it is expecting a Tensor.

This is an example from the functional API guide. Notice the output tensors getting passed from one layer to another.

main_input = Input(shape=(100,), dtype='int32', name='main_input')
x = Embedding(output_dim=512, input_dim=10000, input_length=100)(main_input)
lstm_out = LSTM(32)(x)