Merge pull request #17970 from SamuelMarks:keras.layers.rnn-defaults-to

PiperOrigin-RevId: 526073662

keras/layers/rnn/base_conv_lstm.py

@@ -45,9 +45,10 @@ class ConvLSTMCell(DropoutRNNCellMixin, base_layer.BaseRandomLayer):
         up/down of the input such that output has the same height/width
         dimension as the input.
       data_format: A string, one of `channels_last` (default) or
-        `channels_first`.  It defaults to the `image_data_format` value found in
-        your Keras config file at `~/.keras/keras.json`. If you never set it,
-        then it will be "channels_last".
+        `channels_first`. When unspecified, uses
+        `image_data_format` value found in your Keras config file at
+        `~/.keras/keras.json` (if exists) else 'channels_last'.
+        Defaults to 'channels_last'.
       dilation_rate: An integer or tuple/list of n integers, specifying the
         dilation rate to use for dilated convolution. Currently, specifying any
         `dilation_rate` value != 1 is incompatible with specifying any `strides`
@@ -383,9 +384,10 @@ class ConvLSTM(ConvRNN):
         `(batch, time, ..., channels)`
         while `channels_first` corresponds to
         inputs with shape `(batch, time, channels, ...)`.
-        It defaults to the `image_data_format` value found in your
-        Keras config file at `~/.keras/keras.json`.
-        If you never set it, then it will be "channels_last".
+        When unspecified, uses
+        `image_data_format` value found in your Keras config file at
+         `~/.keras/keras.json` (if exists) else 'channels_last'.
+        Defaults to 'channels_last'.
       dilation_rate: An integer or tuple/list of n integers, specifying
         the dilation rate to use for dilated convolution.
         Currently, specifying any `dilation_rate` value != 1 is

keras/layers/rnn/conv_lstm1d.py

@@ -44,9 +44,10 @@ class ConvLSTM1D(ConvLSTM):
         `channels_first`.  The ordering of the dimensions in the inputs.
         `channels_last` corresponds to inputs with shape `(batch, time, ...,
         channels)` while `channels_first` corresponds to inputs with shape
-        `(batch, time, channels, ...)`. It defaults to the `image_data_format`
-        value found in your Keras config file at `~/.keras/keras.json`. If you
-        never set it, then it will be "channels_last".
+        `(batch, time, channels, ...)`. When unspecified, uses
+        `image_data_format` value found in your Keras config file at
+        `~/.keras/keras.json` (if exists) else 'channels_last'.
+        Defaults to 'channels_last'.
       dilation_rate: An integer or tuple/list of n integers, specifying the
         dilation rate to use for dilated convolution. Currently, specifying any
         `dilation_rate` value != 1 is incompatible with specifying any `strides`

keras/layers/rnn/conv_lstm2d.py

@@ -44,9 +44,10 @@ class ConvLSTM2D(ConvLSTM):
         `channels_first`.  The ordering of the dimensions in the inputs.
         `channels_last` corresponds to inputs with shape `(batch, time, ...,
         channels)` while `channels_first` corresponds to inputs with shape
-        `(batch, time, channels, ...)`. It defaults to the `image_data_format`
-        value found in your Keras config file at `~/.keras/keras.json`. If you
-        never set it, then it will be "channels_last".
+        `(batch, time, channels, ...)`. When unspecified, uses
+        `image_data_format` value found in your Keras config file at
+        `~/.keras/keras.json` (if exists) else 'channels_last'.
+        Defaults to 'channels_last'.
       dilation_rate: An integer or tuple/list of n integers, specifying the
         dilation rate to use for dilated convolution. Currently, specifying any
         `dilation_rate` value != 1 is incompatible with specifying any `strides`

keras/layers/rnn/conv_lstm3d.py

@@ -44,9 +44,10 @@ class ConvLSTM3D(ConvLSTM):
         `channels_first`. The ordering of the dimensions in the inputs.
         `channels_last` corresponds to inputs with shape `(batch, time, ...,
         channels)` while `channels_first` corresponds to inputs with shape
-        `(batch, time, channels, ...)`. It defaults to the `image_data_format`
-        value found in your Keras config file at `~/.keras/keras.json`. If you
-        never set it, then it will be "channels_last".
+        `(batch, time, channels, ...)`. When unspecified, uses
+        `image_data_format` value found in your Keras config file at
+        `~/.keras/keras.json` (if exists) else 'channels_last'.
+        Defaults to 'channels_last'.
       dilation_rate: An integer or tuple/list of n integers, specifying the
         dilation rate to use for dilated convolution. Currently, specifying any
         `dilation_rate` value != 1 is incompatible with specifying any `strides`

keras/layers/rnn/gru.py

@@ -507,17 +507,17 @@ class GRU(DropoutRNNCellMixin, RNN, base_layer.BaseRandomLayer):
     Call arguments:
       inputs: A 3D tensor, with shape `[batch, timesteps, feature]`.
       mask: Binary tensor of shape `[samples, timesteps]` indicating whether
-        a given timestep should be masked  (optional, defaults to `None`).
+        a given timestep should be masked  (optional).
         An individual `True` entry indicates that the corresponding timestep
         should be utilized, while a `False` entry indicates that the
-        corresponding timestep should be ignored.
+        corresponding timestep should be ignored. Defaults to `None`.
       training: Python boolean indicating whether the layer should behave in
         training mode or in inference mode. This argument is passed to the cell
         when calling it. This is only relevant if `dropout` or
-        `recurrent_dropout` is used  (optional, defaults to `None`).
+        `recurrent_dropout` is used  (optional). Defaults to `None`.
       initial_state: List of initial state tensors to be passed to the first
-        call of the cell  (optional, defaults to `None` which causes creation
-        of zero-filled initial state tensors).
+        call of the cell  (optional, `None` causes creation
+        of zero-filled initial state tensors). Defaults to `None`.
     """
 
     def __init__(

keras/layers/rnn/legacy_cells.py

@@ -186,7 +186,8 @@ def __call__(self, inputs, state, scope=None):
             `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise,
             if `self.state_size` is a tuple of integers, this should be a tuple
             with shapes `[batch_size, s] for s in self.state_size`.
-          scope: VariableScope for the created subgraph; defaults to class name.
+          scope: VariableScope for the created subgraph; None uses class name.
+            Defaults to `None`.
 
         Returns:
           A pair containing:

keras/layers/rnn/lstm.py

@@ -480,17 +480,17 @@ class LSTM(DropoutRNNCellMixin, RNN, base_layer.BaseRandomLayer):
     Call arguments:
       inputs: A 3D tensor with shape `[batch, timesteps, feature]`.
       mask: Binary tensor of shape `[batch, timesteps]` indicating whether
-        a given timestep should be masked (optional, defaults to `None`).
+        a given timestep should be masked (optional).
         An individual `True` entry indicates that the corresponding timestep
         should be utilized, while a `False` entry indicates that the
-        corresponding timestep should be ignored.
+        corresponding timestep should be ignored. Defaults to `None`.
       training: Python boolean indicating whether the layer should behave in
         training mode or in inference mode. This argument is passed to the cell
         when calling it. This is only relevant if `dropout` or
-        `recurrent_dropout` is used (optional, defaults to `None`).
+        `recurrent_dropout` is used (optional). Defaults to `None`.
       initial_state: List of initial state tensors to be passed to the first
-        call of the cell (optional, defaults to `None` which causes creation
-        of zero-filled initial state tensors).
+        call of the cell (optional, `None` causes creation
+        of zero-filled initial state tensors). Defaults to `None`.
     """
 
     def __init__(