Repository: keras-team/keras

Issue:
SeparableConv1D has incorrect documentation listing width and height as input dimensions.
In documentation for SeparableConv1D data_format parameter explanation includes an extra dimension (width or height) 
![image](https://user-images.githubusercontent.com/7511567/43028531-fc815d36-8c45-11e8-9d26-747c67c05d72.png)
)
This only makes sense for Conv2D layers and is contrary to the corresponding doc from TensorFlow:
![image](https://user-images.githubusercontent.com/7511567/43001926-aa976834-8bec-11e8-99db-b613a6d3ef85.png)

SeparableConv1D has incorrect documentation listing width and height as input dimensions.
In documentation for SeparableConv1D data_format parameter explanation includes an extra dimension (width or height) 
![image](https://user-images.githubusercontent.com/7511567/43028531-fc815d36-8c45-11e8-9d26-747c67c05d72.png)
)
This only makes sense for Conv2D layers and is contrary to the corresponding doc from TensorFlow:
![image](https://user-images.githubusercontent.com/7511567/43001926-aa976834-8bec-11e8-99db-b613a6d3ef85.png)



Discussion:

Resolving patch:
diff --git a/keras/layers/convolutional.py b/keras/layers/convolutional.py
--- a/keras/layers/convolutional.py
+++ b/keras/layers/convolutional.py
@@ -271,10 +271,10 @@ class Conv1D(_Conv):
             one of `"channels_last"` (default) or `"channels_first"`.
             The ordering of the dimensions in the inputs.
             `"channels_last"` corresponds to inputs with shape
-            `(batch, length, channels)`
+            `(batch, steps, channels)`
             (default format for temporal data in Keras)
             while `"channels_first"` corresponds to inputs
-            with shape `(batch, channels, length)`.
+            with shape `(batch, channels, steps)`.
         dilation_rate: an integer or tuple/list of a single integer, specifying
             the dilation rate to use for dilated convolution.
             Currently, specifying any `dilation_rate` value != 1 is
@@ -302,10 +302,10 @@ class Conv1D(_Conv):
             (see [constraints](../constraints.md)).
 
     # Input shape
-        3D tensor with shape: `(batch_size, steps, input_dim)`
+        3D tensor with shape: `(batch, steps, channels)`
 
     # Output shape
-        3D tensor with shape: `(batch_size, new_steps, filters)`
+        3D tensor with shape: `(batch, new_steps, filters)`
         `steps` value might have changed due to padding or strides.
     """
 
@@ -429,18 +429,18 @@ class Conv2D(_Conv):
 
     # Input shape
         4D tensor with shape:
-        `(samples, channels, rows, cols)`
+        `(batch, channels, rows, cols)`
         if `data_format` is `"channels_first"`
         or 4D tensor with shape:
-        `(samples, rows, cols, channels)`
+        `(batch, rows, cols, channels)`
         if `data_format` is `"channels_last"`.
 
     # Output shape
         4D tensor with shape:
-        `(samples, filters, new_rows, new_cols)`
+        `(batch, filters, new_rows, new_cols)`
         if `data_format` is `"channels_first"`
         or 4D tensor with shape:
-        `(samples, new_rows, new_cols, filters)`
+        `(batch, new_rows, new_cols, filters)`
         if `data_format` is `"channels_last"`.
         `rows` and `cols` values might have changed due to padding.
     """
@@ -557,18 +557,18 @@ class Conv3D(_Conv):
 
     # Input shape
         5D tensor with shape:
-        `(samples, channels, conv_dim1, conv_dim2, conv_dim3)`
+        `(batch, channels, conv_dim1, conv_dim2, conv_dim3)`
         if `data_format` is `"channels_first"`
         or 5D tensor with shape:
-        `(samples, conv_dim1, conv_dim2, conv_dim3, channels)`
+        `(batch, conv_dim1, conv_dim2, conv_dim3, channels)`
         if `data_format` is `"channels_last"`.
 
     # Output shape
         5D tensor with shape:
-        `(samples, filters, new_conv_dim1, new_conv_dim2, new_conv_dim3)`
+        `(batch, filters, new_conv_dim1, new_conv_dim2, new_conv_dim3)`
         if `data_format` is `"channels_first"`
         or 5D tensor with shape:
-        `(samples, new_conv_dim1, new_conv_dim2, new_conv_dim3, filters)`
+        `(batch, new_conv_dim1, new_conv_dim2, new_conv_dim3, filters)`
         if `data_format` is `"channels_last"`.
         `new_conv_dim1`, `new_conv_dim2` and `new_conv_dim3` values might have changed due to padding.
     """
@@ -1411,9 +1411,9 @@ class SeparableConv1D(_SeparableConv):
             one of `"channels_last"` or `"channels_first"`.
             The ordering of the dimensions in the inputs.
             `"channels_last"` corresponds to inputs with shape
-            `(batch, height, width, channels)` while `"channels_first"`
+            `(batch, steps, channels)` while `"channels_first"`
             corresponds to inputs with shape
-            `(batch, channels, height, width)`.
+            `(batch, channels, steps)`.
             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".
@@ -1720,18 +1720,18 @@ class DepthwiseConv2D(Conv2D):
 
     # Input shape
         4D tensor with shape:
-        `[batch, channels, rows, cols]`
+        `(batch, channels, rows, cols)`
         if `data_format` is `"channels_first"`
         or 4D tensor with shape:
-        `[batch, rows, cols, channels]`
+        `(batch, rows, cols, channels)`
         if `data_format` is `"channels_last"`.
 
     # Output shape
         4D tensor with shape:
-        `[batch, filters, new_rows, new_cols]`
+        `(batch, filters, new_rows, new_cols)`
         if `data_format` is `"channels_first"`
         or 4D tensor with shape:
-        `[batch, new_rows, new_cols, filters]`
+        `(batch, new_rows, new_cols, filters)`
         if `data_format` is `"channels_last"`.
         `rows` and `cols` values might have changed due to padding.
     """


Test patch: