use channels last by default

bob/learn/tensorflow/network/SimpleCNN.py

 import tensorflow as tf
-from ..utils import get_available_gpus, to_channels_first
 
 
 def architecture(input_layer, mode=tf.estimator.ModeKeys.TRAIN,
-                 kernerl_size=(3, 3), n_classes=2):
-    data_format = 'channels_last'
-    if len(get_available_gpus()) != 0:
-        # When running on GPU, transpose the data from channels_last (NHWC) to
-        # channels_first (NCHW) to improve performance. See
-        # https://www.tensorflow.org/performance/performance_guide#data_formats
-        input_layer = to_channels_first('input_layer')
-        data_format = 'channels_first'
+                 kernerl_size=(3, 3), n_classes=2,
+                 data_format='channels_last'):
 
     # Keep track of all the endpoints
     endpoints = {}
@@ -78,15 +71,20 @@ def architecture(input_layer, mode=tf.estimator.ModeKeys.TRAIN,
 
 def model_fn(features, labels, mode, params=None, config=None):
     """Model function for CNN."""
+    data = features['data']
+    keys = features['key']
+
     params = params or {}
     learning_rate = params.get('learning_rate', 1e-5)
-    kernerl_size = params.get('kernerl_size', (3, 3))
-    n_classes = params.get('n_classes', 2)
 
-    data = features['data']
-    keys = features['keys']
-    logits, _ = architecture(
-        data, mode, kernerl_size=kernerl_size, n_classes=n_classes)
+    arch_kwargs = {
+        'kernerl_size': params.get('kernerl_size', None),
+        'n_classes': params.get('n_classes', None),
+        'data_format': params.get('data_format', None),
+    }
+    arch_kwargs = {k: v for k, v in arch_kwargs.items() if v is not None}
+
+    logits, _ = architecture(data, mode, **arch_kwargs)
 
     predictions = {
         # Generate predictions (for PREDICT and EVAL mode)