Use NCW/NWC for conv1d data format in TF backend.

keras/backend/tensorflow_backend.py

@@ -3325,12 +3325,12 @@ def _preprocess_conv1d_input(x, data_format):
     """
     if dtype(x) == 'float64':
         x = tf.cast(x, 'float32')
-    tf_data_format = 'NHWC'  # to pass TF Conv2dNative operations
+    tf_data_format = 'NWC'  # to pass TF Conv2dNative operations
     if data_format == 'channels_first':
         if not _has_nchw_support():
             x = tf.transpose(x, (0, 2, 1))  # NCW -> NWC
         else:
-            tf_data_format = 'NCHW'
+            tf_data_format = 'NCW'
     return x, tf_data_format
 
 
@@ -3441,7 +3441,7 @@ def conv1d(x, kernel, strides=1, padding='valid',
         padding=padding,
         data_format=tf_data_format)
 
-    if data_format == 'channels_first' and tf_data_format in {'NWC', 'NHWC'}:
+    if data_format == 'channels_first' and tf_data_format == 'NWC':
         x = tf.transpose(x, (0, 2, 1))  # NWC -> NCW
     return x
 
@@ -3571,6 +3571,10 @@ def separable_conv1d(x, depthwise_kernel, pointwise_kernel, strides=1,
         dilation_rate = (dilation_rate,)
 
     x, tf_data_format = _preprocess_conv1d_input(x, data_format)
+    if tf_data_format == 'NWC':
+        tf_data_format = 'NHWC'
+    else:
+        tf_data_format = 'NCHW'
     padding = _preprocess_padding(padding)
     if tf_data_format == 'NHWC':
         spatial_start_dim = 1