Next, we instantiate a sequential model and add the following layers:
- A simple RNN
- A dense layer with one output
Following are the steps of a simple RNN:
- The next code listing shows model creation and compilation:
model = Sequential() model.add(SimpleRNN(n_neurons, batch_input_shape=(n_batch, X.shape[1], X.shape[2]), stateful=True)) model.add(Dense(1))
- Then, we compile the model using model. compile(..) with loss and optimizers, shown as follows:
model.compile(loss='mean_squared_error', optimizer='adam')
We are using mean squared error (MSE) as the loss function and Adam as an optimizer. MSE is a loss function that uses a sum of squared difference between the predicted value and actual value divided b ...
