Deep Learning for Natural Language Processing by Karthiek Reddy Bokka

Author:Karthiek Reddy Bokka
Language: eng
Format: epub
Publisher: Packt Publishing
Published: 2019-06-06T16:00:00+00:00

Note

If you start building a network with an RNN layer, input_shape must be specified.

After a model is built, model.summary() can be used to see the shapes of each layer and the total number of parameters.

Exercise 23: Building an RNN Model to Show the Stability of Parameters over Time

Let's build a simple RNN model to show that the parameters do not change with timesteps. Note that while mentioning the input_shape argument, batch_size need not be mentioned unless needed. It is needed for a stateful network, which we will discuss next. batch_size is mentioned while training the model with the fit() or fit_generator() functions.

The following steps will help you with the solution:

Import the necessary Python packages. We will be using Sequential, SimpleRNN, and Dense.from keras.models import Sequential

from keras.layers import SimpleRNN, Dense

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