improve docs and make train flag match eval flag

research/slim/nets/mobilenet_v1.md

@@ -96,13 +96,13 @@ $ bazel build -c opt --config=cuda mobilenet_v1_{eval,train}
 Train:
 
 ```
-$ ./bazel-bin/mobilenet_v1_train
+$ ./bazel-bin/mobilenet_v1_train --dataset_dir "path/to/dataset" --checkpoint_dir "path/to/checkpoints"
 ```
 
 Eval:
 
 ```
-$ ./bazel-bin/mobilenet_v1_eval
+$ ./bazel-bin/mobilenet_v1_eval --dataset_dir "path/to/dataset" --checkpoint_dir "path/to/checkpoints"
 ```
 
 #### Quantized Training and Eval
@@ -110,19 +110,20 @@ $ ./bazel-bin/mobilenet_v1_eval
 Train from preexisting float checkpoint:
 
 ```
-$ ./bazel-bin/mobilenet_v1_train --quantize=True --fine_tune_checkpoint=checkpoint-name
+$ ./bazel-bin/mobilenet_v1_train --dataset_dir "path/to/dataset" --checkpoint_dir "path/to/checkpoints" \
+  --quantize=True --fine_tune_checkpoint=float/checkpoint/path
 ```
 
 Train from scratch:
 
 ```
-$ ./bazel-bin/mobilenet_v1_train --quantize=True
+$ ./bazel-bin/mobilenet_v1_train --dataset_dir "path/to/dataset" --checkpoint_dir "path/to/checkpoints" --quantize=True
 ```
 
 Eval:
 
 ```
-$ ./bazel-bin/mobilenet_v1_eval --quantize=True
+$ ./bazel-bin/mobilenet_v1_eval --dataset_dir "path/to/dataset" --checkpoint_dir "path/to/checkpoints" --quantize=True
 ```
 
 The resulting float and quantized models can be run on-device via [TensorFlow Lite](https://www.tensorflow.org/mobile/tflite/).

research/slim/nets/mobilenet_v1_train.py

@@ -40,7 +40,8 @@ flags.DEFINE_float('depth_multiplier', 1.0, 'Depth multiplier for mobilenet')
 flags.DEFINE_bool('quantize', False, 'Quantize training')
 flags.DEFINE_string('fine_tune_checkpoint', '',
                     'Checkpoint from which to start finetuning.')
-flags.DEFINE_string('logdir', '', 'Directory for writing training event logs')
+flags.DEFINE_string('checkpoint_dir', '',
+                    'Directory for writing training checkpoints and logs')
 flags.DEFINE_string('dataset_dir', '', 'Location of dataset')
 flags.DEFINE_integer('log_every_n_steps', 100, 'Number of steps per log')
 flags.DEFINE_integer('save_summaries_secs', 100,
@@ -191,7 +192,7 @@ def train_model():
   with g.as_default():
     slim.learning.train(
         train_tensor,
-        FLAGS.logdir,
+        FLAGS.checkpoint_dir,
         is_chief=(FLAGS.task == 0),
         master=FLAGS.master,
         log_every_n_steps=FLAGS.log_every_n_steps,