[DOC] docstring for SimpleRNNClassifier

sktime/classification/deep_learning/rnn.py

@@ -19,6 +19,36 @@
 
 class SimpleRNNClassifier(BaseDeepClassifier):
     """Simple recurrent neural network.
+
+    Parameters
+    ----------
+    should inherited fields be listed here?
+    n_epochs       : int, default = 2000
+        the number of epochs to train the model
+    batch_size      : int, default = 16
+        the number of samples per gradient update.
+    kernel_size     : int, default = 7
+        the length of the 1D convolution window
+    avg_pool_size   : int, default = 3
+        size of the average pooling windows
+    n_conv_layers   : int, default = 2
+        the number of convolutional plus average pooling layers
+    filter_sizes    : array of shape (n_conv_layers) default = [6, 12]
+    random_state    : int or None, default=None
+                      Seed for random number generation.
+    verbose         : boolean, default = False
+                      whether to output extra information
+    loss            : string, default="mean_squared_error"
+        fit parameter for the keras model
+    optimizer       : keras.optimizer, default=keras.optimizers.Adam(),
+    metrics         : list of strings, default=["accuracy"],
+    activation      : string or a tf callable, default="sigmoid"
+                      Activation function used in the output linear layer.
+                      List of available activation functions: https://keras.io/api/layers/activations/
+    use_bias        : boolean, default = True
+                      whether the layer uses a bias vector.
+    optimizer       : keras.optimizers object, default = Adam(lr=0.01)
+                      specify the optimizer and the learning rate to be used.
 
     References
     ----------

sktime/regression/deep_learning/rnn.py

@@ -20,6 +20,36 @@
 class SimpleRNNRegressor(BaseDeepRegressor):
     """
     Simple recurrent neural network.
+
+    Parameters
+    ----------
+    should inherited fields be listed here?
+    n_epochs       : int, default = 2000
+        the number of epochs to train the model
+    batch_size      : int, default = 16
+        the number of samples per gradient update.
+    kernel_size     : int, default = 7
+        the length of the 1D convolution window
+    avg_pool_size   : int, default = 3
+        size of the average pooling windows
+    n_conv_layers   : int, default = 2
+        the number of convolutional plus average pooling layers
+    filter_sizes    : array of shape (n_conv_layers) default = [6, 12]
+    random_state    : int or None, default=None
+                      Seed for random number generation.
+    verbose         : boolean, default = False
+                      whether to output extra information
+    loss            : string, default="mean_squared_error"
+                      fit parameter for the keras model
+    optimizer       : keras.optimizer, default=keras.optimizers.Adam(),
+    metrics         : list of strings, default=["accuracy"],
+    activation      : string or a tf callable, default="sigmoid"
+                      Activation function used in the output linear layer.
+                      List of available activation functions:https://keras.io/api/layers/activations/
+    use_bias        : boolean, default = True
+                      whether the layer uses a bias vector.
+    optimizer       : keras.optimizers object, default = Adam(lr=0.01)
+                      specify the optimizer and the learning rate to be used.
 
     References
     ----------