Neural Network Projects with Python

Word embedding and LSTM layers

With our input preprocessed, we can now turn our attention to model building. As always, we will use the Sequential class in Keras to build our model. Recall that the Sequential class allows us to stack layers on top of one another, making it really easy to build complex models layer by layer.

As always, let's define a new Sequential class:

from keras.models import Sequentialmodel = Sequential()

We can now add the word embedding layer to our model. The word embedding layer can be constructed directly from the keras.layers as follows:

from keras.layers import Embedding

The Embedding class takes the following important arguments:

• input_dim: The input dimensions of the word embedding layer. This should be the ...