Clarify docs and fix linter

DeepSpeech.py

@@ -446,10 +446,12 @@ def train():
 
     # Building the graph
     optimizer = create_optimizer()
-    
+
     # Enable mixed precision training
     if FLAGS.automatic_mixed_precision:
-        optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(optimizer)
+        log_info('Enabling automatic mixed precision training.')
+        optimizer = tfv1.train.experimental.enable_mixed_precision_graph_rewrite(optimizer)
+
     gradients, loss, non_finite_files = get_tower_results(iterator, optimizer, dropout_rates)
 
     # Average tower gradients across GPUs

util/flags.py

@@ -80,8 +80,8 @@ def create_flags():
     f.DEFINE_boolean('use_cudnn_rnn', False, 'use CuDNN RNN backend for training on GPU. Note that checkpoints created with this flag can only be used with CuDNN RNN, i.e. fine tuning on a CPU device will not work')
     f.DEFINE_string('cudnn_checkpoint', '', 'path to a checkpoint created using --use_cudnn_rnn. Specifying this flag allows one to convert a CuDNN RNN checkpoint to a checkpoint capable of running on a CPU graph.')
 
-    f.DEFINE_boolean('automatic_mixed_precision', False, 'whether to allow automatic mixed precision training')
-    
+    f.DEFINE_boolean('automatic_mixed_precision', False, 'whether to allow automatic mixed precision training. USE OF THIS FLAG IS UNSUPPORTED. Checkpoints created with automatic mixed precision training will not be usable without mixed precision.')
+
     # Sample limits
 
     f.DEFINE_integer('limit_train', 0, 'maximum number of elements to use from train set - 0 means no limit')